Preferable adsorption of phosphate using lanthanum-incorporated porous zeolite: Characteristics and mechanism

Science.gov (United States)

He, Yinhai; Lin, Hai; Dong, Yingbo; Wang, Liang

2017-12-01

The adsorbent, where lanthanum oxide was incorporated onto porous zeolite (La-Z), of preferable adsorption towards phosphate was prepared by hydrothermal synthesis. Based on pH effect results, La-Z would effectively sequestrate phosphate over wider pH range of 3.0-7.0, alkaline conditions were unfavorable for phosphate. The adsorption of phosphate was not significantly influenced by ionic strength and by coexisting anions of chloride, nitrate and sulfate but bicarbonate showed slightly greater negative effects, indicating La-Z possessed highly selectivity to phosphate. Adsorption of phosphate could be well fitted by pseudo-second-order model and the process was mainly controlled by intra-particle diffusion. Equilibrium adsorption demonstrated that Langmuir model was more suitable than Freundlich model for description phosphate adsorption and the adsorption capacity was 17.2 mg P g-1, which exhibited 95% utilization of incorporated La. Over 95% phosphate was eliminated in real effluent treatment when the dose was 2 g L-1. The underlying mechanism for phosphate capture was probed with Zeta potential and X-ray photoelectron spectroscope analysis, and the formation of La-P inner-sphere complexation was testified to be the dominant pathway. All the results suggested that the porous zeolite-supported lanthanum oxide can serve as a promising adsorbent for phosphate removal in realistic application.

Investigating preferences for color-shape combinations with gaze driven optimization method based on evolutionary algorithms.

Science.gov (United States)

Holmes, Tim; Zanker, Johannes M

2013-01-01

Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioral measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA), which has been demonstrated as a tool to identify aesthetic preferences (Holmes and Zanker, 2012). In the present study, the GDEA was used to investigate the preferred combination of color and shape which have been promoted in the Bauhaus arts school. We used the same three shapes (square, circle, triangle) used by Kandinsky (1923), with the three color palette from the original experiment (A), an extended seven color palette (B), and eight different shape orientation (C). Participants were instructed to look for their preferred circle, triangle or square in displays with eight stimuli of different shapes, colors and rotations, in an attempt to test for a strong preference for red squares, yellow triangles and blue circles in such an unbiased experimental design and with an extended set of possible combinations. We Tested six participants extensively on the different conditions and found consistent preferences for color-shape combinations for individuals, but little evidence at the group level for clear color/shape preference consistent with Kandinsky's claims, apart from some weak link between yellow and triangles. Our findings suggest substantial inter-individual differences in the presence of stable individual associations of color and shapes, but also that these associations are robust within a single individual. These individual differences go some way toward challenging the claims of the universal preference for color/shape combinations proposed by Kandinsky, but also indicate that a much larger sample size would be needed to confidently reject that hypothesis. Moreover, these experiments highlight the

Investigating preferences for colour-shape combinations with gaze driven optimization method based on evolutionary algorithms.

Directory of Open Access Journals (Sweden)

Tim eHolmes

2013-12-01

Full Text Available Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioural measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA, which has been used as a tool to identify aesthetic preferences (Holmes & Zanker, 2012. In the present study, the GDEA was used to investigate the preferred combination of colour and shape which have been promoted in the Bauhaus arts school. We used the same 3 shapes (square, circle, triangle used by Kandinsky (1923, with the 3 colour palette from the original experiment (A, an extended 7 colour palette (B, and 8 different shape orientation (C. Participants were instructed to look for their preferred circle, triangle or square in displays with 8 stimuli of different shapes, colours and rotations, in an attempt to test for a strong preference for red squares, yellow triangles and blue circles in such an unbiased experimental design and with an extended set of possible combinations. We Tested 6 participants extensively on the different conditions and found consistent preferences for individuals, but little evidence at the group level for preference consistent with Kandinsky’s claims, apart from some weak link between yellow and triangles. Our findings suggest substantial inter-individual differences in the presence of stable individual associations of colour and shapes, but also that these associations are robust within a single individual. These individual differences go some way towards challenging the claims of the universal preference for colour/shape combinations proposed by Kandinsky, but also indicate that a much larger sample size would be needed to confidently reject that hypothesis. Moreover, these experiments highlight the vast potential of the GDEA in experimental aesthetics

Freeing Space for NASA: Incorporating a Lossless Compression Algorithm into NASA's FOSS System

Science.gov (United States)

Fiechtner, Kaitlyn; Parker, Allen

2011-01-01

NASA's Fiber Optic Strain Sensing (FOSS) system can gather and store up to 1,536,000 bytes (1.46 megabytes) per second. Since the FOSS system typically acquires hours - or even days - of data, the system can gather hundreds of gigabytes of data for a given test event. To store such large quantities of data more effectively, NASA is modifying a Lempel-Ziv-Oberhumer (LZO) lossless data compression program to compress data as it is being acquired in real time. After proving that the algorithm is capable of compressing the data from the FOSS system, the LZO program will be modified and incorporated into the FOSS system. Implementing an LZO compression algorithm will instantly free up memory space without compromising any data obtained. With the availability of memory space, the FOSS system can be used more efficiently on test specimens, such as Unmanned Aerial Vehicles (UAVs) that can be in flight for days. By integrating the compression algorithm, the FOSS system can continue gathering data, even on longer flights.

Incorporating stakeholders' preferences for ex ante evaluation of energy and climate policy interactions. Development of a Multi Criteria Analysis weighting methodology

International Nuclear Information System (INIS)

Grafakos, S.; Zevgolis, D.; Oikonomou, V.

2008-03-01

Evaluation of energy and climate policy interactions is a complex issue which has not been addressed systematically. Multi Criteria Decision Analysis (MCDA) evaluation processes have been applied widely to different policy and decision cases as they have the ability to cope with high complexity, by structuring and analyzing the policy problem in a transparent and systematic way. Criteria weights elicitation techniques are developed within the framework of MCDA to integrate stakeholders' preferential information in the decision making and evaluation process. There are variant methods to determine criteria weights which can be used in various ways for different policy evaluation purposes. During decision making, policy makers and relevant stakeholders implicitly or explicitly express their relative importance between the evaluation criteria by assigning weighting factors to them. More particular, climate change policy problems lack a simple, transparent and structured way to incorporate stakeholders' views and values. In order to incorporate stakeholders' weighting preferences into an ex ante evaluation of climate change and energy policy instruments interaction, an integrative constructive weighting methodology has been developed. This paper presents the main characteristics of evaluation of energy and climate policy interactions, the reasoning behind the development of the weighting tool, its main theoretical and functional characteristics and the results of its application to obtain and incorporate stakeholders' preferences on energy and climate change policy evaluation criteria. The weighting method that has been elaborated and applied to derive stakeholders' preferences for criteria weights is a combination of pair wise comparisons and ratio importance weighting methods. Initially introduces the stakeholders to the evaluation process through a warming up holistic approach for ranking the criteria and then requires them to express their ratio relative importance

A dynamic model of the marriage market-part 1: matching algorithm based on age preference and availability.

Science.gov (United States)

Matthews, A P; Garenne, M L

2013-09-01

The matching algorithm in a dynamic marriage market model is described in this first of two companion papers. Iterative Proportional Fitting is used to find a marriage function (an age distribution of new marriages for both sexes), in a stable reference population, that is consistent with the one-sex age distributions of new marriages, and includes age preference. The one-sex age distributions (which are the marginals of the two-sex distribution) are based on the Picrate model, and age preference on a normal distribution, both of which may be adjusted by choice of parameter values. For a population that is perturbed from the reference state, the total number of new marriages is found as the harmonic mean of target totals for men and women obtained by applying reference population marriage rates to the perturbed population. The marriage function uses the age preference function, assumed to be the same for the reference and the perturbed populations, to distribute the total number of new marriages. The marriage function also has an availability factor that varies as the population changes with time, where availability depends on the supply of unmarried men and women. To simplify exposition, only first marriage is treated, and the algorithm is illustrated by application to Zambia. In the second paper, remarriage and dissolution are included. Copyright © 2013 Elsevier Inc. All rights reserved.

An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks

Directory of Open Access Journals (Sweden)

Vala Ali Rohani

2014-01-01

Full Text Available Abundance of information in recent years has become a serious challenge for web users. Recommender systems (RSs have been often utilized to alleviate this issue. RSs prune large information spaces to recommend the most relevant items to users by considering their preferences. Nonetheless, in situations where users or items have few opinions, the recommendations cannot be made properly. This notable shortcoming in practical RSs is called cold-start problem. In the present study, we propose a novel approach to address this problem by incorporating social networking features. Coined as enhanced content-based algorithm using social networking (ECSN, the proposed algorithm considers the submitted ratings of faculty mates and friends besides user’s own preferences. The effectiveness of ECSN algorithm was evaluated by implementing it in MyExpert, a newly designed academic social network (ASN for academics in Malaysia. Real feedbacks from live interactions of MyExpert users with the recommended items are recorded for 12 consecutive weeks in which four different algorithms, namely, random, collaborative, content-based, and ECSN were applied every three weeks. The empirical results show significant performance of ECSN in mitigating the cold-start problem besides improving the prediction accuracy of recommendations when compared with other studied recommender algorithms.

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

Science.gov (United States)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Community detection using preference networks

Science.gov (United States)

Tasgin, Mursel; Bingol, Haluk O.

2018-04-01

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

36 CFR 13.320 - Preference to Cook Inlet Region, Incorporated.

Science.gov (United States)

2010-07-01

... Region, Incorporated. 13.320 Section 13.320 Parks, Forests, and Public Property NATIONAL PARK SERVICE... to Cook Inlet Region, Incorporated. (a) The Cook Inlet Region, Incorporated (CIRI), in cooperation with village corporations within the Cook Inlet region when appropriate, will have a right of first...

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

Incorporating a Wheeled Vehicle Model in a New Monocular Visual Odometry Algorithm for Dynamic Outdoor Environments

Science.gov (United States)

Jiang, Yanhua; Xiong, Guangming; Chen, Huiyan; Lee, Dah-Jye

2014-01-01

This paper presents a monocular visual odometry algorithm that incorporates a wheeled vehicle model for ground vehicles. The main innovation of this algorithm is to use the single-track bicycle model to interpret the relationship between the yaw rate and side slip angle, which are the two most important parameters that describe the motion of a wheeled vehicle. Additionally, the pitch angle is also considered since the planar-motion hypothesis often fails due to the dynamic characteristics of wheel suspensions and tires in real-world environments. Linearization is used to calculate a closed-form solution of the motion parameters that works as a hypothesis generator in a RAndom SAmple Consensus (RANSAC) scheme to reduce the complexity in solving equations involving trigonometric. All inliers found are used to refine the winner solution through minimizing the reprojection error. Finally, the algorithm is applied to real-time on-board visual localization applications. Its performance is evaluated by comparing against the state-of-the-art monocular visual odometry methods using both synthetic data and publicly available datasets over several kilometers in dynamic outdoor environments. PMID:25256109

Incorporating a Wheeled Vehicle Model in a New Monocular Visual Odometry Algorithm for Dynamic Outdoor Environments

Directory of Open Access Journals (Sweden)

Yanhua Jiang

2014-09-01

Full Text Available This paper presents a monocular visual odometry algorithm that incorporates a wheeled vehicle model for ground vehicles. The main innovation of this algorithm is to use the single-track bicycle model to interpret the relationship between the yaw rate and side slip angle, which are the two most important parameters that describe the motion of a wheeled vehicle. Additionally, the pitch angle is also considered since the planar-motion hypothesis often fails due to the dynamic characteristics of wheel suspensions and tires in real-world environments. Linearization is used to calculate a closed-form solution of the motion parameters that works as a hypothesis generator in a RAndom SAmple Consensus (RANSAC scheme to reduce the complexity in solving equations involving trigonometric. All inliers found are used to refine the winner solution through minimizing the reprojection error. Finally, the algorithm is applied to real-time on-board visual localization applications. Its performance is evaluated by comparing against the state-of-the-art monocular visual odometry methods using both synthetic data and publicly available datasets over several kilometers in dynamic outdoor environments.

Analysis of Individual Preferences for Tuning Noise-Reduction Algorithms

NARCIS (Netherlands)

Houben, Rolph; Dijkstra, Tjeerd M. H.; Dreschler, Wouter A.

2012-01-01

There is little research on user preference for different settings of noise reduction, especially for individual users. We therefore measured individual preferences for pairs of audio streams differing in the trade-off between noise reduction and speech distortion. A logistic probability model was

Food Culture, Preferences and Ethics in Dysphagia Management.

Science.gov (United States)

Kenny, Belinda

2015-11-01

Adults with dysphagia experience difficulties swallowing food and fluids with potentially harmful health and psychosocial consequences. Speech pathologists who manage patients with dysphagia are frequently required to address ethical issues when patients' food culture and/ or preferences are inconsistent with recommended diets. These issues incorporate complex links between food, identity and social participation. A composite case has been developed to reflect ethical issues identified by practising speech pathologists for the purposes of illustrating ethical concerns in dysphagia management. The case examines a speech pathologist's role in supporting patient autonomy when patients and carers express different goals and values. The case presents a 68-year-old man of Australian/Italian heritage with severe swallowing impairment and strong values attached to food preferences. The case is examined through application of the dysphagia algorithm, a tool for shared decision-making when patients refuse dietary modifications. Case analysis revealed the benefits and challenges of shared decision-making processes in dysphagia management. Four health professional skills and attributes were identified as synonymous with shared decision making: communication, imagination, courage and reflection. © 2015 John Wiley & Sons Ltd.

Incorporating patient preference into the management of infertility in women with polycystic ovary syndrome

Directory of Open Access Journals (Sweden)

Okoroafor UC

2012-05-01

Full Text Available Ugochi C Okoroafor, Emily S JungheimDepartment of Obstetrics and Gynecology, Washington University, St Louis, MO, USAAbstract: Polycystic ovary syndrome (PCOS is a heterogeneous condition characterized by anovulation, hyperandrogenism, and polycystic ovaries. Because of the heterogeneous nature of PCOS, women affected by the condition often require a customized approach for ovulation induction when trying to conceive. Treating symptoms of PCOS in overweight and obese women should always incorporate lifestyle changes with the goal of weight-loss, as many women with PCOS will ovulate after losing 5%–10% of their body weight. On the other hand, other factors must be considered including the woman’s age, age-related decline in fertility, and previous treatments she may have already tried. Fortunately, multiple options for ovulation induction exist for women with PCOS. This paper reviews specific ovulation induction options available for women with PCOS, the benefits and efficacy of these options, and the related side effects and risks women can anticipate with the various options that may affect treatment adherence. The paper also reviews the recommended evidence-based strategies for treating PCOS-related infertility that allow for incorporation of the patient’s preference. Finally, it briefly reviews emerging data and ongoing studies regarding newer agents that have shown great promise as first-line agents for the treatment of infertility in women with PCOS.Keywords: polycystic ovary syndrome, anovulation, clomiphene citrate, letrozole, metformin, obesity

Incorporating outcome uncertainty and prior outcome beliefs in stated preferences

DEFF Research Database (Denmark)

Lundhede, Thomas; Jacobsen, Jette Bredahl; Hanley, Nick

2015-01-01

Stated preference studies tell respondents that policies create environmental changes with varying levels of uncertainty. However, respondents may include their own a priori assessments of uncertainty when making choices among policy options. Using a choice experiment eliciting respondents......’ preferences for conservation policies under climate change, we find that higher outcome uncertainty reduces utility. When accounting for endogeneity, we find that prior beliefs play a significant role in this cost of uncertainty. Thus, merely stating “objective” levels of outcome uncertainty...

Optimization of the test intervals of a nuclear safety system by genetic algorithms, solution clustering and fuzzy preference assignment

International Nuclear Information System (INIS)

Zio, E.; Bazzo, R.

2010-01-01

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into 'families'. On the basis of the decision maker's preferences, each family is then synthetically represented by a 'head of the family' solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions

Bridging Ground Validation and Algorithms: Using Scattering and Integral Tables to Incorporate Observed DSD Correlations into Satellite Algorithms

Science.gov (United States)

Williams, C. R.

2012-12-01

The NASA Global Precipitation Mission (GPM) raindrop size distribution (DSD) Working Group is composed of NASA PMM Science Team Members and is charged to "investigate the correlations between DSD parameters using Ground Validation (GV) data sets that support, or guide, the assumptions used in satellite retrieval algorithms." Correlations between DSD parameters can be used to constrain the unknowns and reduce the degrees-of-freedom in under-constrained satellite algorithms. Over the past two years, the GPM DSD Working Group has analyzed GV data and has found correlations between the mass-weighted mean raindrop diameter (Dm) and the mass distribution standard deviation (Sm) that follows a power-law relationship. This Dm-Sm power-law relationship appears to be robust and has been observed in surface disdrometer and vertically pointing radar observations. One benefit of a Dm-Sm power-law relationship is that a three parameter DSD can be modeled with just two parameters: Dm and Nw that determines the DSD amplitude. In order to incorporate observed DSD correlations into satellite algorithms, the GPM DSD Working Group is developing scattering and integral tables that can be used by satellite algorithms. Scattering tables describe the interaction of electromagnetic waves on individual particles to generate cross sections of backscattering, extinction, and scattering. Scattering tables are independent of the distribution of particles. Integral tables combine scattering table outputs with DSD parameters and DSD correlations to generate integrated normalized reflectivity, attenuation, scattering, emission, and asymmetry coefficients. Integral tables contain both frequency dependent scattering properties and cloud microphysics. The GPM DSD Working Group has developed scattering tables for raindrops at both Dual Precipitation Radar (DPR) frequencies and at all GMI radiometer frequencies less than 100 GHz. Scattering tables include Mie and T-matrix scattering with H- and V

Hesitant Probabilistic Multiplicative Preference Relations in Group Decision Making

Directory of Open Access Journals (Sweden)

Zia Bashir

2018-03-01

Full Text Available The preference of one alternative over another is a useful way to express the opinion of the decision-maker. In the process of group decision-making, preference relations are used in preference modeling of the alternatives under given criteria. The probability is an important tool to deal with uncertainty and, in many scenarios of decision-making problems, the probabilities of different events affect the decision-making process directly. In order to deal with this issue, the hesitant probabilistic multiplicative preference relation (HPMPR is defined in this paper. Furthermore, consistency of the HPMPR and consensus among decision makers are studied here. In this respect, many algorithms are developed to achieve consistency of HPMPRs, reasonable consensus between decision-makers and a final algorithm is proposed comprehending all other algorithms, presenting a complete decision support model for group decision-making. Lastly, we present a case study with complete illustration of the proposed model and discuss the effects of probabilities on decision-making validating the importance of the introduction of probability in hesitant multiplicative preference relations.

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Science.gov (United States)

Gómez-Oliva, Andrea; Molina, Germán

2018-01-01

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising. PMID:29562590

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario.

Science.gov (United States)

Alvarado-Uribe, Joanna; Gómez-Oliva, Andrea; Barrera-Animas, Ari Yair; Molina, Germán; Gonzalez-Mendoza, Miguel; Parra-Meroño, María Concepción; Jara, Antonio J

2018-03-17

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs' context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs' categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs' categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs' categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.

Workflow of the Grover algorithm simulation incorporating CUDA and GPGPU

Science.gov (United States)

Lu, Xiangwen; Yuan, Jiabin; Zhang, Weiwei

2013-09-01

The Grover quantum search algorithm, one of only a few representative quantum algorithms, can speed up many classical algorithms that use search heuristics. No true quantum computer has yet been developed. For the present, simulation is one effective means of verifying the search algorithm. In this work, we focus on the simulation workflow using a compute unified device architecture (CUDA). Two simulation workflow schemes are proposed. These schemes combine the characteristics of the Grover algorithm and the parallelism of general-purpose computing on graphics processing units (GPGPU). We also analyzed the optimization of memory space and memory access from this perspective. We implemented four programs on CUDA to evaluate the performance of schemes and optimization. Through experimentation, we analyzed the organization of threads suited to Grover algorithm simulations, compared the storage costs of the four programs, and validated the effectiveness of optimization. Experimental results also showed that the distinguished program on CUDA outperformed the serial program of libquantum on a CPU with a speedup of up to 23 times (12 times on average), depending on the scale of the simulation.

Preferences in Data Production Planning

Science.gov (United States)

Golden, Keith; Brafman, Ronen; Pang, Wanlin

2005-01-01

This paper discusses the data production problem, which consists of transforming a set of (initial) input data into a set of (goal) output data. There are typically many choices among input data and processing algorithms, each leading to significantly different end products. To discriminate among these choices, the planner supports an input language that provides a number of constructs for specifying user preferences over data (and plan) properties. We discuss these preference constructs, how we handle them to guide search, and additional challenges in the area of preference management that this important application domain offers.

Fluid-structure-coupling algorithm

International Nuclear Information System (INIS)

McMaster, W.H.; Gong, E.Y.; Landram, C.S.; Quinones, D.F.

1980-01-01

A fluid-structure-interaction algorithm has been developed and incorporated into the two dimensional code PELE-IC. This code combines an Eulerian incompressible fluid algorithm with a Lagrangian finite element shell algorithm and incorporates the treatment of complex free surfaces. The fluid structure, and coupling algorithms have been verified by the calculation of solved problems from the literature and from air and steam blowdown experiments. The code has been used to calculate loads and structural response from air blowdown and the oscillatory condensation of steam bubbles in water suppression pools typical of boiling water reactors. The techniques developed here have been extended to three dimensions and implemented in the computer code PELE-3D

Fluid structure coupling algorithm

International Nuclear Information System (INIS)

McMaster, W.H.; Gong, E.Y.; Landram, C.S.; Quinones, D.F.

1980-01-01

A fluid-structure-interaction algorithm has been developed and incorporated into the two-dimensional code PELE-IC. This code combines an Eulerian incompressible fluid algorithm with a Lagrangian finite element shell algorithm and incorporates the treatment of complex free surfaces. The fluid structure and coupling algorithms have been verified by the calculation of solved problems from the literature and from air and steam blowdown experiments. The code has been used to calculate loads and structural response from air blowdown and the oscillatory condensation of steam bubbles in water suppression pools typical of boiling water reactors. The techniques developed have been extended to three dimensions and implemented in the computer code PELE-3D

Performance of Jet Algorithms in CMS

CERN Document Server

CMS Collaboration

The CMS Combined Software and Analysis Challenge 2007 (CSA07) is well underway and expected to produce a wealth of physics analyses to be applied to the first incoming detector data in 2008. The JetMET group of CMS supports four different jet clustering algorithms for the CSA07 Monte Carlo samples, with two different parameterizations each: \\fastkt, \\siscone, \\midpoint, and \\itcone. We present several studies comparing the performance of these algorithms using QCD dijet and \\ttbar Monte Carlo samples. We specifically observe that the \\siscone algorithm performs equal to or better than the \\midpoint algorithm in all presented studies and propose that \\siscone be adopted as the preferred cone-based jet clustering algorithm in future CMS physics analyses, as it is preferred by theorists for its infrared- and collinear-safety to all orders of perturbative QCD. We furthermore encourage the use of the \\fastkt algorithm which is found to perform as good as any other algorithm under study, features dramatically reduc...

Student Preferences for Instructional Methods in an Accounting Curriculum

Science.gov (United States)

Abeysekera, Indra

2015-01-01

Student preferences among instructional methods are largely unexplored across the accounting curriculum. The algorithmic rigor of courses and the societal culture can influence these preferences. This study explored students' preferences of instructional methods for learning in six courses of the accounting curriculum that differ in algorithmic…

A systematic review of stated preference studies reporting public preferences for healthcare priority setting.

Science.gov (United States)

Whitty, Jennifer A; Lancsar, Emily; Rixon, Kylie; Golenko, Xanthe; Ratcliffe, Julie

2014-01-01

There is current interest in incorporating weights based on public preferences for health and healthcare into priority-setting decisions. The aim of this systematic review was to explore the extent to which public preferences and trade-offs for priority-setting criteria have been quantified, and to describe the study contexts and preference elicitation methods employed. A systematic review was performed in April 2013 to identify empirical studies eliciting the stated preferences of the public for the provision of healthcare in a priority-setting context. Studies are described in terms of (i) the stated preference approaches used, (ii) the priority-setting levels and contexts, and (iii) the criteria identified as important and their relative importance. Thirty-nine studies applying 40 elicitation methods reported in 41 papers met the inclusion criteria. The discrete choice experiment method was most commonly applied (n = 18, 45.0 %), but other approaches, including contingent valuation and the person trade-off, were also used. Studies prioritised health systems (n = 4, 10.2 %), policies/programmes/services/interventions (n = 16, 41.0 %), or patient groups (n = 19, 48.7 %). Studies generally confirmed the importance of a wide range of process, non-health and patient-related characteristics in priority setting in selected contexts, alongside health outcomes. However, inconsistencies were observed for the relative importance of some prioritisation criteria, suggesting context and/or elicitation approach matter. Overall, findings suggest caution in directly incorporating public preferences as weights for priority setting unless the methods used to elicit the weights can be shown to be appropriate and robust in the priority-setting context.

Compensatory versus noncompensatory models for predicting consumer preferences

Directory of Open Access Journals (Sweden)

Anja Dieckmann

2009-04-01

Full Text Available Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser and Orlin, 2007; Kohli and Jedidi, 2007 to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.

Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning

International Nuclear Information System (INIS)

Aubry, Jean-Francois; Beaulieu, Frederic; Sevigny, Caroline; Beaulieu, Luc; Tremblay, Daniel

2006-01-01

Inverse planning in external beam radiotherapy often requires a scalar objective function that incorporates importance factors to mimic the planner's preferences between conflicting objectives. Defining those importance factors is not straightforward, and frequently leads to an iterative process in which the importance factors become variables of the optimization problem. In order to avoid this drawback of inverse planning, optimization using algorithms more suited to multiobjective optimization, such as evolutionary algorithms, has been suggested. However, much inverse planning software, including one based on simulated annealing developed at our institution, does not include multiobjective-oriented algorithms. This work investigates the performance of a modified simulated annealing algorithm used to drive aperture-based intensity-modulated radiotherapy inverse planning software in a multiobjective optimization framework. For a few test cases involving gastric cancer patients, the use of this new algorithm leads to an increase in optimization speed of a little more than a factor of 2 over a conventional simulated annealing algorithm, while giving a close approximation of the solutions produced by a standard simulated annealing. A simple graphical user interface designed to facilitate the decision-making process that follows an optimization is also presented

Evaluating Prognostics Performance for Algorithms Incorporating Uncertainty Estimates

Data.gov (United States)

National Aeronautics and Space Administration — Uncertainty Representation and Management (URM) are an integral part of the prognostic system development.1As capabilities of prediction algorithms evolve, research...

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

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Joanna Alvarado-Uribe

2018-03-01

Full Text Available Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS, and Social Networking Sites (SNS have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.

Melodic algorithms for pulse oximetry to allow audible discrimination of abnormal systolic blood pressures.

Science.gov (United States)

Chima, Ranjit S; Ortega, Rafael; Connor, Christopher W

2014-12-01

An anesthesiologist must remain vigilant of the patient's clinical status, incorporating many independent physiological measurements. Oxygen saturation and heart rate are represented by continuous audible tones generated by the pulse oximeter, a mandated monitoring device. Other important clinical parameters--notably blood pressure--lack any audible representation beyond arbitrarily-configured threshold alarms. Attempts to introduce further continuous audible tones have apparently foundered; the complexity and interaction of these tones have exceeded the ability of clinicians to interpret them. Instead, we manipulate the tonal and rhythmic structure of the accepted pulse oximeter tone pattern melodically. Three melodic algorithms were developed to apply tonal and rhythmic variations to the continuous pulse oximeter tone, dependent on the systolic blood pressure. The algorithms distort the original audible pattern minimally, to facilitate comprehension of both the underlying pattern and the applied variations. A panel of anesthesia practitioners (attending anesthesiologists, residents and nurse anesthetists) assessed these algorithms in characterizing perturbations in cardiopulmonary status. Twelve scenarios, incorporating combinations of oxygen desaturation, bradycardia, tachycardia, hypotension and hypertension, were tested. A rhythmic variation in which additional auditory information was conveyed only at halftime intervals, with every other "beat" of the pulse oximeter, was strongly favored. The respondents also strongly favored the use of musical chords over single tones. Given three algorithms of tones embedded in the pulse oximeter signal, anesthesiologists preferred a melodic tone to signal a significant change in blood pressure.

Feature Reduction Based on Genetic Algorithm and Hybrid Model for Opinion Mining

Directory of Open Access Journals (Sweden)

P. Kalaivani

2015-01-01

Full Text Available With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naïve Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar’s test to compare the level of significance of the classifiers.

An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach

Directory of Open Access Journals (Sweden)

Ibrahim Delibalta

2017-01-01

Full Text Available We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios.

Monte Carlo algorithms with absorbing Markov chains: Fast local algorithms for slow dynamics

International Nuclear Information System (INIS)

Novotny, M.A.

1995-01-01

A class of Monte Carlo algorithms which incorporate absorbing Markov chains is presented. In a particular limit, the lowest order of these algorithms reduces to the n-fold way algorithm. These algorithms are applied to study the escape from the metastable state in the two-dimensional square-lattice nearest-neighbor Ising ferromagnet in an unfavorable applied field, and the agreement with theoretical predictions is very good. It is demonstrated that the higher-order algorithms can be many orders of magnitude faster than either the traditional Monte Carlo or n-fold way algorithms

Toward human-centered algorithm design

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Eric PS Baumer

2017-07-01

Full Text Available As algorithms pervade numerous facets of daily life, they are incorporated into systems for increasingly diverse purposes. These systems’ results are often interpreted differently by the designers who created them than by the lay persons who interact with them. This paper offers a proposal for human-centered algorithm design, which incorporates human and social interpretations into the design process for algorithmically based systems. It articulates three specific strategies for doing so: theoretical, participatory, and speculative. Drawing on the author’s work designing and deploying multiple related systems, the paper provides a detailed example of using a theoretical approach. It also discusses findings pertinent to participatory and speculative design approaches. The paper addresses both strengths and challenges for each strategy in helping to center the process of designing algorithmically based systems around humans.

Internal versus external preference analysis : an exploratory study on end-user evaluation

NARCIS (Netherlands)

Kleef, van E.; Trijp, van H.C.M.; Luning, P.A.

2006-01-01

Internal and external preference analysis emphasise fundamentally different perspectives on the same data. We extend the literature on comparisons between internal and external preference analysis by incorporating the perspective of the end user of the preference analysis results. From a conceptual

Radiologists' preferences for digital mammographic display. The International Digital Mammography Development Group.

Science.gov (United States)

Pisano, E D; Cole, E B; Major, S; Zong, S; Hemminger, B M; Muller, K E; Johnston, R E; Walsh, R; Conant, E; Fajardo, L L; Feig, S A; Nishikawa, R M; Yaffe, M J; Williams, M B; Aylward, S R

2000-09-01

To determine the preferences of radiologists among eight different image processing algorithms applied to digital mammograms obtained for screening and diagnostic imaging tasks. Twenty-eight images representing histologically proved masses or calcifications were obtained by using three clinically available digital mammographic units. Images were processed and printed on film by using manual intensity windowing, histogram-based intensity windowing, mixture model intensity windowing, peripheral equalization, multiscale image contrast amplification (MUSICA), contrast-limited adaptive histogram equalization, Trex processing, and unsharp masking. Twelve radiologists compared the processed digital images with screen-film mammograms obtained in the same patient for breast cancer screening and breast lesion diagnosis. For the screening task, screen-film mammograms were preferred to all digital presentations, but the acceptability of images processed with Trex and MUSICA algorithms were not significantly different. All printed digital images were preferred to screen-film radiographs in the diagnosis of masses; mammograms processed with unsharp masking were significantly preferred. For the diagnosis of calcifications, no processed digital mammogram was preferred to screen-film mammograms. When digital mammograms were preferred to screen-film mammograms, radiologists selected different digital processing algorithms for each of three mammographic reading tasks and for different lesion types. Soft-copy display will eventually allow radiologists to select among these options more easily.

A novel application of PageRank and user preference algorithms for assessing the relative performance of track athletes in competition.

Science.gov (United States)

Beggs, Clive B; Shepherd, Simon J; Emmonds, Stacey; Jones, Ben

2017-01-01

Ranking enables coaches, sporting authorities, and pundits to determine the relative performance of individual athletes and teams in comparison to their peers. While ranking is relatively straightforward in sports that employ traditional leagues, it is more difficult in sports where competition is fragmented (e.g. athletics, boxing, etc.), with not all competitors competing against each other. In such situations, complex points systems are often employed to rank athletes. However, these systems have the inherent weakness that they frequently rely on subjective assessments in order to gauge the calibre of the competitors involved. Here we show how two Internet derived algorithms, the PageRank (PR) and user preference (UP) algorithms, when utilised with a simple 'who beat who' matrix, can be used to accurately rank track athletes, avoiding the need for subjective assessment. We applied the PR and UP algorithms to the 2015 IAAF Diamond League men's 100m competition and compared their performance with the Keener, Colley and Massey ranking algorithms. The top five places computed by the PR and UP algorithms, and the Diamond League '2016' points system were all identical, with the Kendall's tau distance between the PR standings and '2016' points system standings being just 15, indicating that only 5.9% of pairs differed in their order between these two lists. By comparison, the UP and '2016' standings displayed a less strong relationship, with a tau distance of 95, indicating that 37.6% of the pairs differed in their order. When compared with the standings produced using the Keener, Colley and Massey algorithms, the PR standings appeared to be closest to the Keener standings (tau distance = 67, 26.5% pair order disagreement), whereas the UP standings were more similar to the Colley and Massey standings, with the tau distances between these ranking lists being only 48 (19.0% pair order disagreement) and 59 (23.3% pair order disagreement) respectively. In particular, the

A novel application of PageRank and user preference algorithms for assessing the relative performance of track athletes in competition.

Directory of Open Access Journals (Sweden)

Clive B Beggs

Full Text Available Ranking enables coaches, sporting authorities, and pundits to determine the relative performance of individual athletes and teams in comparison to their peers. While ranking is relatively straightforward in sports that employ traditional leagues, it is more difficult in sports where competition is fragmented (e.g. athletics, boxing, etc., with not all competitors competing against each other. In such situations, complex points systems are often employed to rank athletes. However, these systems have the inherent weakness that they frequently rely on subjective assessments in order to gauge the calibre of the competitors involved. Here we show how two Internet derived algorithms, the PageRank (PR and user preference (UP algorithms, when utilised with a simple 'who beat who' matrix, can be used to accurately rank track athletes, avoiding the need for subjective assessment. We applied the PR and UP algorithms to the 2015 IAAF Diamond League men's 100m competition and compared their performance with the Keener, Colley and Massey ranking algorithms. The top five places computed by the PR and UP algorithms, and the Diamond League '2016' points system were all identical, with the Kendall's tau distance between the PR standings and '2016' points system standings being just 15, indicating that only 5.9% of pairs differed in their order between these two lists. By comparison, the UP and '2016' standings displayed a less strong relationship, with a tau distance of 95, indicating that 37.6% of the pairs differed in their order. When compared with the standings produced using the Keener, Colley and Massey algorithms, the PR standings appeared to be closest to the Keener standings (tau distance = 67, 26.5% pair order disagreement, whereas the UP standings were more similar to the Colley and Massey standings, with the tau distances between these ranking lists being only 48 (19.0% pair order disagreement and 59 (23.3% pair order disagreement respectively. In

Dopant Adsorption and Incorporation at Irradiated GaN Surfaces

Science.gov (United States)

Sun, Qiang; Selloni, Annabella; Myers, Thomas; Doolittle, W. Alan

2006-03-01

Mg and O are two of the common dopants in GaN, but, in spite of extensive investigation, the atomic scale understanding of their adsorption and incorporation is still incomplete. In particular, high-energy electron irradiation, such as occurring during RHEED, has been reported to have an important effect on the incorporation of these impurities, but no study has addressed the detailed mechanisms of this effect yet. Here we use DFT calculations to study the adsorption and incorporation of Mg and O at the Ga- and N-polar GaN surfaces under various Ga, Mg and O coverage conditions as well as in presence of light or electron beam-induced electronic excitation. We find that the adsorption and incorporation of the two impurities have opposite surface polarity dependence: substitutional Mg prefers to incorporate at the GaN(0001) surface, while O prefers to adsorb and incorporate at the N-polar surface. In addition, our results indicate that in presence of light irradiation the tendency of Mg to surface-segregate is reduced. The O adsorption energy on the N-polar surface is also significantly reduced, consistent with the experimental observation of a much smaller concentration of oxygen in the irradiated samples.

Approximate dynamic programming approaches for appointment scheduling with patient preferences.

Science.gov (United States)

Li, Xin; Wang, Jin; Fung, Richard Y K

2018-04-01

During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparing Online Algorithms for Bin Packing Problems

DEFF Research Database (Denmark)

Epstein, Leah; Favrholdt, Lene Monrad; Kohrt, Jens Svalgaard

2012-01-01

The relative worst-order ratio is a measure of the quality of online algorithms. In contrast to the competitive ratio, this measure compares two online algorithms directly instead of using an intermediate comparison with an optimal offline algorithm. In this paper, we apply the relative worst-ord......-order ratio to online algorithms for several common variants of the bin packing problem. We mainly consider pairs of algorithms that are not distinguished by the competitive ratio and show that the relative worst-order ratio prefers the intuitively better algorithm of each pair....

Cascade-robustness optimization of coupling preference in interconnected networks

International Nuclear Information System (INIS)

Zhang, Xue-Jun; Xu, Guo-Qiang; Zhu, Yan-Bo; Xia, Yong-Xiang

2016-01-01

Highlights: • A specific memetic algorithm was proposed to optimize coupling links. • A small toy model was investigated to examine the underlying mechanism. • The MA optimized strategy exhibits a moderate assortative pattern. • A novel coupling coefficient index was proposed to quantify coupling preference. - Abstract: Recently, the robustness of interconnected networks has attracted extensive attentions, one of which is to investigate the influence of coupling preference. In this paper, the memetic algorithm (MA) is employed to optimize the coupling links of interconnected networks. Afterwards, a comparison is made between MA optimized coupling strategy and traditional assortative, disassortative and random coupling preferences. It is found that the MA optimized coupling strategy with a moderate assortative value shows an outstanding performance against cascading failures on both synthetic scale-free interconnected networks and real-world networks. We then provide an explanation for this phenomenon from a micro-scope point of view and propose a coupling coefficient index to quantify the coupling preference. Our work is helpful for the design of robust interconnected networks.

Patients’ preferences for selection of endpoints in cardiovascular clinical trials

Directory of Open Access Journals (Sweden)

Robert D. Chow

2014-02-01

Full Text Available Background: To reduce the duration and overall costs of cardiovascular trials, use of the combined endpoints in trial design has become commonplace. Though this methodology may serve the needs of investigators and trial sponsors, the preferences of patients or potential trial subjects in the trial design process has not been studied. Objective: To determine the preferences of patients in the design of cardiovascular trials. Design: Participants were surveyed in a pilot study regarding preferences among various single endpoints commonly used in cardiovascular trials, preference for single vs. composite endpoints, and the likelihood of compliance with a heart medication if patients similar to them participated in the trial design process. Participants: One hundred adult English-speaking patients, 38% male, from a primary care ambulatory practice located in an urban setting. Key results: Among single endpoints, participants rated heart attack as significantly more important than death from other causes (4.53 vs. 3.69, p=0.004 on a scale of 1–6. Death from heart disease was rated as significantly more important than chest pain (4.73 vs. 2.47, p<0.001, angioplasty/PCI/CABG (4.73 vs. 2.43, p<0.001, and stroke (4.73 vs. 2.43, p<0.001. Participants also expressed a slight preference for combined endpoints over single endpoint (43% vs. 57%, incorporation of the opinions of the study patient population into the design of trials (48% vs. 41% for researchers, and a greater likelihood of medication compliance if patient preferences were considered during trial design (67% indicated a significant to major effect. Conclusions: Patients are able to make judgments and express preferences regarding trial design. They prefer that the opinions of the study population rather than the general population be incorporated into the design of the study. This novel approach to study design would not only incorporate patient preferences into medical decision making, but

Linear Time Local Approximation Algorithm for Maximum Stable Marriage

Directory of Open Access Journals (Sweden)

Zoltán Király

2013-08-01

Full Text Available We consider a two-sided market under incomplete preference lists with ties, where the goal is to find a maximum size stable matching. The problem is APX-hard, and a 3/2-approximation was given by McDermid [1]. This algorithm has a non-linear running time, and, more importantly needs global knowledge of all preference lists. We present a very natural, economically reasonable, local, linear time algorithm with the same ratio, using some ideas of Paluch [2]. In this algorithm every person make decisions using only their own list, and some information asked from members of these lists (as in the case of the famous algorithm of Gale and Shapley. Some consequences to the Hospitals/Residents problem are also discussed.

Measuring Consumer Preferences Using Conjoint Poker

NARCIS (Netherlands)

O. Toubia; M.G. de Jong (Martijn); D. Stieger; J.H. Fuller (John)

2012-01-01

textabstractWe develop and test an incentive-compatible Conjoint Poker (CP) game. The preference data collected in the context of this game are comparable to incentive-compatible choice-based conjoint (CBC) analysis data. We develop a statistical efficiency measure and an algorithm to construct

Preference Learning and Ranking by Pairwise Comparison

Science.gov (United States)

Fürnkranz, Johannes; Hüllermeier, Eyke

This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.

Valence framing of political preferences and resistance to persuasion

Directory of Open Access Journals (Sweden)

Žeželj Iris

2007-01-01

Full Text Available This study tested the "valence framing effect": an assumption that negatively conceptualized attitudes (as opposing the non-preferred alternative are more resistant to later persuasion attempts. In the experiment we created choice between two political candidates and experimental subjects were led to conceptualize their political preferences in one of two possible ways: either as supporting the preferred candidate or as opposing the non-preferred candidate. The data indicate that negative preferences show less overall change when exposed to counterarguments. This finding can be incorporated in two theoretical frameworks: dual process theories of attitude change (Elaboration likelihood model and descriptive decision making theories (Prospect theory. Results are discussed for their implications for the efficacy of political communication. .

Verification-Based Interval-Passing Algorithm for Compressed Sensing

OpenAIRE

Wu, Xiaofu; Yang, Zhen

2013-01-01

We propose a verification-based Interval-Passing (IP) algorithm for iteratively reconstruction of nonnegative sparse signals using parity check matrices of low-density parity check (LDPC) codes as measurement matrices. The proposed algorithm can be considered as an improved IP algorithm by further incorporation of the mechanism of verification algorithm. It is proved that the proposed algorithm performs always better than either the IP algorithm or the verification algorithm. Simulation resul...

Where to go on your next trip? Optimizing travel destinations based on user preferences

NARCIS (Netherlands)

Kiseleva, Y.; Mueller, M.J.I.; Bernardi, L.; Davis, C.; Kovacek, I.; Einarsen, M.S.; Kamps, J.; Tuzhilin, A.; Hiemstra, D.

2015-01-01

Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and

Where to go on your next trip?: Optimizing travel destinations based on user preferences

NARCIS (Netherlands)

Kiseleva, Y.; Mueller, M.J.I.; Bernardi, L.; Davis, C.; Kovacek, I.; Einarsen, M.S.; Kamps, J.; Tuzhilin, A.; Hiemstra, D.

2015-01-01

Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and

An Online Scheduling Algorithm with Advance Reservation for Large-Scale Data Transfers

Energy Technology Data Exchange (ETDEWEB)

Balman, Mehmet; Kosar, Tevfik

2010-05-20

Scientific applications and experimental facilities generate massive data sets that need to be transferred to remote collaborating sites for sharing, processing, and long term storage. In order to support increasingly data-intensive science, next generation research networks have been deployed to provide high-speed on-demand data access between collaborating institutions. In this paper, we present a practical model for online data scheduling in which data movement operations are scheduled in advance for end-to-end high performance transfers. In our model, data scheduler interacts with reservation managers and data transfer nodes in order to reserve available bandwidth to guarantee completion of jobs that are accepted and confirmed to satisfy preferred time constraint given by the user. Our methodology improves current systems by allowing researchers and higher level meta-schedulers to use data placement as a service where theycan plan ahead and reserve the scheduler time in advance for their data movement operations. We have implemented our algorithm and examined possible techniques for incorporation into current reservation frameworks. Performance measurements confirm that the proposed algorithm is efficient and scalable.

Evaluating progressive-rendering algorithms in appearance design tasks.

Science.gov (United States)

Jiawei Ou; Karlik, Ondrej; Křivánek, Jaroslav; Pellacini, Fabio

2013-01-01

Progressive rendering is becoming a popular alternative to precomputational approaches to appearance design. However, progressive algorithms create images exhibiting visual artifacts at early stages. A user study investigated these artifacts' effects on user performance in appearance design tasks. Novice and expert subjects performed lighting and material editing tasks with four algorithms: random path tracing, quasirandom path tracing, progressive photon mapping, and virtual-point-light rendering. Both the novices and experts strongly preferred path tracing to progressive photon mapping and virtual-point-light rendering. None of the participants preferred random path tracing to quasirandom path tracing or vice versa; the same situation held between progressive photon mapping and virtual-point-light rendering. The user workflow didn’t differ significantly with the four algorithms. The Web Extras include a video showing how four progressive-rendering algorithms converged (at http://youtu.be/ck-Gevl1e9s), the source code used, and other supplementary materials.

Motion Cueing Algorithm Development: Human-Centered Linear and Nonlinear Approaches

Science.gov (United States)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.

2005-01-01

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. Prior research identified viable features from two algorithms: the nonlinear "adaptive algorithm", and the "optimal algorithm" that incorporates human vestibular models. A novel approach to motion cueing, the "nonlinear algorithm" is introduced that combines features from both approaches. This algorithm is formulated by optimal control, and incorporates a new integrated perception model that includes both visual and vestibular sensation and the interaction between the stimuli. Using a time-varying control law, the matrix Riccati equation is updated in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. The neurocomputing approach was crucial in that the number of presentations of an input vector could be reduced to meet the real time requirement without degrading the quality of the motion cues.

Effects of Style, Tempo, and Performing Medium on Children's Music Preference.

Science.gov (United States)

LeBlanc, Albert

1981-01-01

Fifth-graders listened to a tape incorporating fast and slow vocal and instrumental excerpts within the generic styles of rock/pop, country, older jazz, newer jazz, art music, and band music. A preference hierarchy emerged favoring the popular styles. Across pooled styles, faster tempos and instrumentals were slightly preferred. (Author/SJL)

Incorporating Learning Style and Personality Preferences into an Oral Communication Course Syllabus

Science.gov (United States)

Hadas, Michael

2011-01-01

Individual difference factors of personality typology and learning style preference and their effect on second language acquisition have been the focus of several prominent SLA theorists over the past twenty-five years. However, few articles have demonstrated how individual learner difference research can be applied within a classroom by second…

Rhombicuboctahedron unit cell based scaffolds for bone regeneration: geometry optimization with a mechanobiology - driven algorithm.

Science.gov (United States)

Boccaccio, Antonio; Fiorentino, Michele; Uva, Antonio E; Laghetti, Luca N; Monno, Giuseppe

2018-02-01

In a context more and more oriented towards customized medical solutions, we propose a mechanobiology-driven algorithm to determine the optimal geometry of scaffolds for bone regeneration that is the most suited to specific boundary and loading conditions. In spite of the huge number of articles investigating different unit cells for porous biomaterials, no studies are reported in the literature that optimize the geometric parameters of such unit cells based on mechanobiological criteria. Parametric finite element models of scaffolds with rhombicuboctahedron unit cell were developed and incorporated into an optimization algorithm that combines them with a computational mechanobiological model. The algorithm perturbs iteratively the geometry of the unit cell until the best scaffold geometry is identified, i.e. the geometry that allows to maximize the formation of bone. Performances of scaffolds with rhombicuboctahedron unit cell were compared with those of other scaffolds with hexahedron unit cells. We found that scaffolds with rhombicuboctahedron unit cell are particularly suited for supporting medium-low loads, while, for higher loads, scaffolds with hexahedron unit cells are preferable. The proposed algorithm can guide the orthopaedic/surgeon in the choice of the best scaffold to be implanted in a patient-specific anatomic region. Copyright © 2017 Elsevier B.V. All rights reserved.

Weighted Description Logics Preference Formulas for Multiattribute Negotiation

Science.gov (United States)

Ragone, Azzurra; di Noia, Tommaso; Donini, Francesco M.; di Sciascio, Eugenio; Wellman, Michael P.

We propose a framework to compute the utility of an agreement w.r.t a preference set in a negotiation process. In particular, we refer to preferences expressed as weighted formulas in a decidable fragment of First-order Logic and agreements expressed as a formula. We ground our framework in Description Logics (DL) endowed with disjunction, to be compliant with Semantic Web technologies. A logic based approach to preference representation allows, when a background knowledge base is exploited, to relax the often unrealistic assumption of additive independence among attributes. We provide suitable definitions of the problem and present algorithms to compute utility in our setting. We also validate our approach through an experimental evaluation.

Genetic local search algorithm for optimization design of diffractive optical elements.

Science.gov (United States)

Zhou, G; Chen, Y; Wang, Z; Song, H

1999-07-10

We propose a genetic local search algorithm (GLSA) for the optimization design of diffractive optical elements (DOE's). This hybrid algorithm incorporates advantages of both genetic algorithm (GA) and local search techniques. It appears better able to locate the global minimum compared with a canonical GA. Sample cases investigated here include the optimization design of binary-phase Dammann gratings, continuous surface-relief grating array generators, and a uniform top-hat focal plane intensity profile generator. Two GLSA's whose incorporated local search techniques are the hill-climbing method and the simulated annealing algorithm are investigated. Numerical experimental results demonstrate that the proposed algorithm is highly efficient and robust. DOE's that have high diffraction efficiency and excellent uniformity can be achieved by use of the algorithm we propose.

Minkowski metrics in creating universal ranking algorithms

Directory of Open Access Journals (Sweden)

Andrzej Ameljańczyk

2014-06-01

Full Text Available The paper presents a general procedure for creating the rankings of a set of objects, while the relation of preference based on any ranking function. The analysis was possible to use the ranking functions began by showing the fundamental drawbacks of commonly used functions in the form of a weighted sum. As a special case of the ranking procedure in the space of a relation, the procedure based on the notion of an ideal element and generalized Minkowski distance from the element was proposed. This procedure, presented as universal ranking algorithm, eliminates most of the disadvantages of ranking functions in the form of a weighted sum.[b]Keywords[/b]: ranking functions, preference relation, ranking clusters, categories, ideal point, universal ranking algorithm

Residue preference mapping of ligand fragments in the Protein Data Bank.

Science.gov (United States)

Wang, Lirong; Xie, Zhaojun; Wipf, Peter; Xie, Xiang-Qun

2011-04-25

The interaction between small molecules and proteins is one of the major concerns for structure-based drug design because the principles of protein-ligand interactions and molecular recognition are not thoroughly understood. Fortunately, the analysis of protein-ligand complexes in the Protein Data Bank (PDB) enables unprecedented possibilities for new insights. Herein, we applied molecule-fragmentation algorithms to split the ligands extracted from PDB crystal structures into small fragments. Subsequently, we have developed a ligand fragment and residue preference mapping (LigFrag-RPM) algorithm to map the profiles of the interactions between these fragments and the 20 proteinogenic amino acid residues. A total of 4032 fragments were generated from 71 798 PDB ligands by a ring cleavage (RC) algorithm. Among these ligand fragments, 315 unique fragments were characterized with the corresponding fragment-residue interaction profiles by counting residues close to these fragments. The interaction profiles revealed that these fragments have specific preferences for certain types of residues. The applications of these interaction profiles were also explored and evaluated in case studies, showing great potential for the study of protein-ligand interactions and drug design. Our studies demonstrated that the fragment-residue interaction profiles generated from the PDB ligand fragments can be used to detect whether these fragments are in their favorable or unfavorable environments. The algorithm for a ligand fragment and residue preference mapping (LigFrag-RPM) developed here also has the potential to guide lead chemistry modifications as well as binding residues predictions.

Tractable Pareto Optimization of Temporal Preferences

Science.gov (United States)

Morris, Robert; Morris, Paul; Khatib, Lina; Venable, Brent

2003-01-01

This paper focuses on temporal constraint problems where the objective is to optimize a set of local preferences for when events occur. In previous work, a subclass of these problems has been formalized as a generalization of Temporal CSPs, and a tractable strategy for optimization has been proposed, where global optimality is defined as maximizing the minimum of the component preference values. This criterion for optimality, which we call 'Weakest Link Optimization' (WLO), is known to have limited practical usefulness because solutions are compared only on the basis of their worst value; thus, there is no requirement to improve the other values. To address this limitation, we introduce a new algorithm that re-applies WLO iteratively in a way that leads to improvement of all the values. We show the value of this strategy by proving that, with suitable preference functions, the resulting solutions are Pareto Optimal.

A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry

Directory of Open Access Journals (Sweden)

Alexis D. J. Makin

2016-03-01

Full Text Available Empirical work has shown that people like visual symmetry. We used a gaze-driven evolutionary algorithm technique to answer three questions about symmetry preference. First, do people automatically evaluate symmetry without explicit instruction? Second, is perfect symmetry the best stimulus, or do people prefer a degree of imperfection? Third, does initial preference for symmetry diminish after familiarity sets in? Stimuli were generated as phenotypes from an algorithmic genotype, with genes for symmetry (coded as deviation from a symmetrical template, deviation–symmetry, DS gene and orientation (0° to 90°, orientation, ORI gene. An eye tracker identified phenotypes that were good at attracting and retaining the gaze of the observer. Resulting fitness scores determined the genotypes that passed to the next generation. We recorded changes to the distribution of DS and ORI genes over 20 generations. When participants looked for symmetry, there was an increase in high-symmetry genes. When participants looked for the patterns they preferred, there was a smaller increase in symmetry, indicating that people tolerated some imperfection. Conversely, there was no increase in symmetry during free viewing, and no effect of familiarity or orientation. This work demonstrates the viability of the evolutionary algorithm approach as a quantitative measure of aesthetic preference.

Subcubic Control Flow Analysis Algorithms

DEFF Research Database (Denmark)

Midtgaard, Jan; Van Horn, David

We give the first direct subcubic algorithm for performing control flow analysis of higher-order functional programs. Despite the long held belief that inclusion-based flow analysis could not surpass the ``cubic bottleneck, '' we apply known set compression techniques to obtain an algorithm...... that runs in time O(n^3/log n) on a unit cost random-access memory model machine. Moreover, we refine the initial flow analysis into two more precise analyses incorporating notions of reachability. We give subcubic algorithms for these more precise analyses and relate them to an existing analysis from...

Incorporating Duration Information in Activity Recognition

Science.gov (United States)

Chaurasia, Priyanka; Scotney, Bryan; McClean, Sally; Zhang, Shuai; Nugent, Chris

Activity recognition has become a key issue in smart home environments. The problem involves learning high level activities from low level sensor data. Activity recognition can depend on several variables; one such variable is duration of engagement with sensorised items or duration of intervals between sensor activations that can provide useful information about personal behaviour. In this paper a probabilistic learning algorithm is proposed that incorporates episode, time and duration information to determine inhabitant identity and the activity being undertaken from low level sensor data. Our results verify that incorporating duration information consistently improves the accuracy.

Multi-objective engineering design using preferences

Science.gov (United States)

Sanchis, J.; Martinez, M.; Blasco, X.

2008-03-01

System design is a complex task when design parameters have to satisy a number of specifications and objectives which often conflict with those of others. This challenging problem is called multi-objective optimization (MOO). The most common approximation consists in optimizing a single cost index with a weighted sum of objectives. However, once weights are chosen the solution does not guarantee the best compromise among specifications, because there is an infinite number of solutions. A new approach can be stated, based on the designer's experience regarding the required specifications and the associated problems. This valuable information can be translated into preferences for design objectives, and will lead the search process to the best solution in terms of these preferences. This article presents a new method, which enumerates these a priori objective preferences. As a result, a single objective is built automatically and no weight selection need be performed. Problems occuring because of the multimodal nature of the generated single cost index are managed with genetic algorithms (GAs).

A low-complexity feed-forward I/Q imbalance compensation algorithm

NARCIS (Netherlands)

Moseley, N.A.; Slump, Cornelis H.

2006-01-01

This paper presents a low-complexity adaptive feed- forward I/Q imbalance compensation algorithm. The feed-forward so- lution has guaranteed stability. Due to its blind nature the algorithm is easily incorporated into an existing receiver design. The algorithm uses three estimators to obtain the

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

Science.gov (United States)

Salem, Salem Ibrahim; Higa, Hiroto; Kim, Hyungjun; Kobayashi, Hiroshi; Oki, Kazuo; Oki, Taikan

2017-07-31

Numerous algorithms have been proposed to retrieve chlorophyll- a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m -3 , 16.25 mg·m -3 , and 19.05 mg·m -3 , respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll- a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the

Gender differences regarding preferences for specific heterosexual practices.

Science.gov (United States)

Purnine, D M; Carey, M P; Jorgensen, R S

1994-01-01

Few investigations of sexual attitudes have restricted their focus to individuals' preferences for specific behaviors within a heterosexual relationship. None have examined gender differences in a broad and multidimensional array of such behavioral particulars. As part of an effort to develop a measure of preferred scripts in heterosexual couples, 258 men and women reported how much they agreed or disagreed with 74 statements of preference. A reduced and factor analyzed questionnaire included 38 items and was administered to a second sample (N = 228). Results offer qualified support that, compared to women, men are more erotophilic and show a stronger preference for incorporating erotic materials as well as drugs and alcohol into sexual relations with their partner. These results were more robust in the second sample, in which almost half of the subjects were tested in same-sex groups. Across both samples, women showed stronger preferences for activities reflecting romanticism. No gender differences were evident in sexual conventionality or in preference regarding the general use of contraceptives. However, results suggest that both sexes respond more favorably to a partner-focused or unspecified contraceptive method than to a self-focused method.

Algorithm for programming function generators

International Nuclear Information System (INIS)

Bozoki, E.

1981-01-01

The present paper deals with a mathematical problem, encountered when driving a fully programmable μ-processor controlled function generator. An algorithm is presented to approximate a desired function by a set of straight segments in such a way that additional restrictions (hardware imposed) are also satisfied. A computer program which incorporates this algorithm and automatically generates the necessary input for the function generator for a broad class of desired functions is also described

A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems

Directory of Open Access Journals (Sweden)

Ning Dong

2014-01-01

functions are executed, and comparisons with five state-of-the-art algorithms are made. The results illustrate that the proposed algorithm is competitive with and in some cases superior to the compared ones in terms of the quality, efficiency, and the robustness of the obtained results.

Handling risk attitudes for preference learning and intelligent decision support

DEFF Research Database (Denmark)

Franco de los Ríos, Camilo; Hougaard, Jens Leth; Nielsen, Kurt

2015-01-01

Intelligent decision support should allow integrating human knowledge with efficient algorithms for making interpretable and useful recommendations on real world decision problems. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled...

Physicians' preferences for asthma guidelines implementation.

Science.gov (United States)

Kang, Min-Koo; Kim, Byung-Keun; Kim, Tae-Wan; Kim, Sae-Hoon; Kang, Hye-Ryun; Park, Heung-Woo; Chang, Yoon-Seok; Kim, Sun-Sin; Min, Kyung-Up; Kim, You-Young; Cho, Sang-Heon

2010-10-01

Patient care based on asthma guidelines is cost-effective and leads to improved treatment outcomes. However, ineffective implementation strategies interfere with the use of these recommendations in clinical practice. This study investigated physicians' preferences for asthma guidelines, including content, supporting evidence, learning strategies, format, and placement in the clinical workplace. We obtained information through a questionnaire survey. The questionnaire was distributed to physicians attending continuing medical education courses and sent to other physicians by airmail, e-mail, and facsimile. A total of 183 physicians responded (male to female ratio, 2.3:1; mean age, 40.4±9.9 years); 89.9% of respondents were internists or pediatricians, and 51.7% were primary care physicians. Physicians preferred information that described asthma medications, classified the disease according to severity and level of control, and provided methods of evaluation/treatment/monitoring and management of acute exacerbation. The most effective strategies for encouraging the use of the guidelines were through continuing medical education and discussions with colleagues. Physicians required supporting evidence in the form of randomized controlled trials and expert consensus. They preferred that the guidelines be presented as algorithms or flow charts/flow diagrams on plastic sheets, pocket cards, or in electronic medical records. This study identified the items of the asthma guidelines preferred by physicians in Korea. Asthma guidelines with physicians' preferences would encourage their implementation in clinical practice.

Algorithms for optimal dyadic decision trees

Energy Technology Data Exchange (ETDEWEB)

Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory

2009-01-01

A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

Synthesis of Greedy Algorithms Using Dominance Relations

Science.gov (United States)

Nedunuri, Srinivas; Smith, Douglas R.; Cook, William R.

2010-01-01

Greedy algorithms exploit problem structure and constraints to achieve linear-time performance. Yet there is still no completely satisfactory way of constructing greedy algorithms. For example, the Greedy Algorithm of Edmonds depends upon translating a problem into an algebraic structure called a matroid, but the existence of such a translation can be as hard to determine as the existence of a greedy algorithm itself. An alternative characterization of greedy algorithms is in terms of dominance relations, a well-known algorithmic technique used to prune search spaces. We demonstrate a process by which dominance relations can be methodically derived for a number of greedy algorithms, including activity selection, and prefix-free codes. By incorporating our approach into an existing framework for algorithm synthesis, we demonstrate that it could be the basis for an effective engineering method for greedy algorithms. We also compare our approach with other characterizations of greedy algorithms.

Sorting on STAR. [CDC computer algorithm timing comparison

Science.gov (United States)

Stone, H. S.

1978-01-01

Timing comparisons are given for three sorting algorithms written for the CDC STAR computer. One algorithm is Hoare's (1962) Quicksort, which is the fastest or nearly the fastest sorting algorithm for most computers. A second algorithm is a vector version of Quicksort that takes advantage of the STAR's vector operations. The third algorithm is an adaptation of Batcher's (1968) sorting algorithm, which makes especially good use of vector operations but has a complexity of N(log N)-squared as compared with a complexity of N log N for the Quicksort algorithms. In spite of its worse complexity, Batcher's sorting algorithm is competitive with the serial version of Quicksort for vectors up to the largest that can be treated by STAR. Vector Quicksort outperforms the other two algorithms and is generally preferred. These results indicate that unusual instruction sets can introduce biases in program execution time that counter results predicted by worst-case asymptotic complexity analysis.

Analysing Vote Counting Algorithms Via Logic - And its Application to the CADE Election Scheme

DEFF Research Database (Denmark)

Schürmann, Carsten; Beckert, Bernhard; Gore, Rejeev

2013-01-01

We present a method for using first-order logic to specify the semantics of preferences as used in common vote counting algorithms. We also present a corresponding system that uses Celf linear-logic programs to describe voting algorithms and which generates explicit examples when the algorithm de...

Stability, Optimality and Manipulation in Matching Problems with Weighted Preferences

Directory of Open Access Journals (Sweden)

Maria Silvia Pini

2013-11-01

Full Text Available The stable matching problem (also known as the stable marriage problem is a well-known problem of matching men to women, so that no man and woman, who are not married to each other, both prefer each other. Such a problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools or, more generally, to any two-sided market. In the classical stable marriage problem, both men and women express a strict preference order over the members of the other sex, in a qualitative way. Here, we consider stable marriage problems with weighted preferences: each man (resp., woman provides a score for each woman (resp., man. Such problems are more expressive than the classical stable marriage problems. Moreover, in some real-life situations, it is more natural to express scores (to model, for example, profits or costs rather than a qualitative preference ordering. In this context, we define new notions of stability and optimality, and we provide algorithms to find marriages that are stable and/or optimal according to these notions. While expressivity greatly increases by adopting weighted preferences, we show that, in most cases, the desired solutions can be found by adapting existing algorithms for the classical stable marriage problem. We also consider the manipulability properties of the procedures that return such stable marriages. While we know that all procedures are manipulable by modifying the preference lists or by truncating them, here, we consider if manipulation can occur also by just modifying the weights while preserving the ordering and avoiding truncation. It turns out that, by adding weights, in some cases, we may increase the possibility of manipulating, and this cannot be avoided by any reasonable restriction on the weights.

Making Invasion models useful for decision makers; incorporating uncertainty, knowledge gaps, and decision-making preferences

Science.gov (United States)

Denys Yemshanov; Frank H Koch; Mark Ducey

2015-01-01

Uncertainty is inherent in model-based forecasts of ecological invasions. In this chapter, we explore how the perceptions of that uncertainty can be incorporated into the pest risk assessment process. Uncertainty changes a decision maker’s perceptions of risk; therefore, the direct incorporation of uncertainty may provide a more appropriate depiction of risk. Our...

Orthodontic appliance preferences of children and adolescents.

Science.gov (United States)

Walton, Daniel K; Fields, Henry W; Johnston, William M; Rosenstiel, Stephen F; Firestone, Allen R; Christensen, James C

2010-12-01

Although attractiveness and acceptability of orthodontic appliances have been rated by adults for themselves and for adolescents, children and adolescents have not provided any substantial data. The objective of this study was to evaluate preferences and acceptability of orthodontic appliances in children and adolescents. Images of orthodontic appliances previously captured and standardized were selected and incorporated into a computer-based survey. Additional images of shaped brackets and colored elastomeric ties, as well as discolored clear elastomeric ties, were captured and incorporated onto existing survey images with Photoshop (Adobe, San Jose, Calif). The survey displayed 12 orthodontic appliance variations to 139 children in 3 age groups: 9 to 11 years (n = 45), 12 to 14 years (n = 49), and 15 to 17 years (n = 45). The subjects rated each image for attractiveness and acceptability. All images were displayed and rated twice to assess rater reliability. Overall reliability ratings were r = 0.74 for attractiveness and k = 0.66 for acceptability. There were significant differences in bracket attractiveness and acceptability in each age group. The highest-rated appliances were clear aligners, twin brackets with colored ties, and shaped brackets with and without colored ties. Colored elastomeric ties improved attractiveness significantly over brackets without colored ties for children in the 12-to-14 year group. There was a tendency for older subjects to rate clear orthodontic appliances higher than did younger subjects. Ceramic brackets with discolored ties tended to be rated lower than ceramic brackets with new ties and scored lowest in acceptability and attractiveness in all age groups. Girls rated shaped brackets significantly higher than did boys. Children's preferences for orthodontic appliances differ by age and sex. Child and adolescent preferences differ from adult preferences. Copyright © 2010 American Association of Orthodontists. Published by Mosby

Subjective preference evaluation of sound fields by performing singers

Science.gov (United States)

Noson, Dennis

2003-08-01

A model of the auditory process is proposed for performing singers, which incorporates the added signal from bone conduction, as well as the psychological distance for subjective preference of the performer from the acoustic sound field of the stage. The explanatory power of previous scientific studies of vocal stage acoustics has been limited by a lack of an underlying theory of performer preference. Ando's theory, using the autocorrelation function (ACF) for parametrizing temporal factors, was applied to interpretation of singer sound field preference determined by the pair comparison method. Melisma style singing (no lyrics) was shown to increase the preferred delay time of reflections from a mean of 14 ms with lyrics to 23 ms without (pThesis advisor: Yoichi Ando Copies of this thesis are available from the author by inquiry at BRC Acoustics, 1741 First Avenue South, Seattle, WA 98134 USA. E-mail address: dnoson@brcacoustics.com

Fitting PAC spectra with a hybrid algorithm

Energy Technology Data Exchange (ETDEWEB)

Alves, M. A., E-mail: mauro@sepn.org [Instituto de Aeronautica e Espaco (Brazil); Carbonari, A. W., E-mail: carbonar@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (Brazil)

2008-01-15

A hybrid algorithm (HA) that blends features of genetic algorithms (GA) and simulated annealing (SA) was implemented for simultaneous fits of perturbed angular correlation (PAC) spectra. The main characteristic of the HA is the incorporation of a selection criterion based on SA into the basic structure of GA. The results obtained with the HA compare favorably with fits performed with conventional methods.

Feasibility of preference-driven radiotherapy dose treatment planning to support shared decision making in anal cancer

DEFF Research Database (Denmark)

Rønde, Heidi S; Wee, Leonard; Pløen, John

2017-01-01

PURPOSE/OBJECTIVE: Chemo-radiotherapy is an established primary curative treatment for anal cancer, but clinically equal rationale for different target doses exists. If joint preferences (physician and patient) are used to determine acceptable tradeoffs in radiotherapy treatment planning, multipl...... that preference-informed dose planning is feasible for clinical studies utilizing shared decision making....... dose plans must be simultaneously explored. We quantified the degree to which different toxicity priorities might be incorporated into treatment plan selection, to elucidate the feasible decision space for shared decision making in anal cancer radiotherapy. MATERIAL AND METHODS: Retrospective plans.......7%-points; (0.3; 30.6); p decision space available in anal cancer radiotherapy to incorporate preferences, although tradeoffs are highly patient-dependent. This study demonstrates...

Elementary functions algorithms and implementation

CERN Document Server

Muller, Jean-Michel

2016-01-01

This textbook presents the concepts and tools necessary to understand, build, and implement algorithms for computing elementary functions (e.g., logarithms, exponentials, and the trigonometric functions). Both hardware- and software-oriented algorithms are included, along with issues related to accurate floating-point implementation. This third edition has been updated and expanded to incorporate the most recent advances in the field, new elementary function algorithms, and function software. After a preliminary chapter that briefly introduces some fundamental concepts of computer arithmetic, such as floating-point arithmetic and redundant number systems, the text is divided into three main parts. Part I considers the computation of elementary functions using algorithms based on polynomial or rational approximations and using table-based methods; the final chapter in this section deals with basic principles of multiple-precision arithmetic. Part II is devoted to a presentation of “shift-and-add” algorithm...

Economic dispatch using chaotic bat algorithm

International Nuclear Information System (INIS)

Adarsh, B.R.; Raghunathan, T.; Jayabarathi, T.; Yang, Xin-She

2016-01-01

This paper presents the application of a new metaheuristic optimization algorithm, the chaotic bat algorithm for solving the economic dispatch problem involving a number of equality and inequality constraints such as power balance, prohibited operating zones and ramp rate limits. Transmission losses and multiple fuel options are also considered for some problems. The chaotic bat algorithm, a variant of the basic bat algorithm, is obtained by incorporating chaotic sequences to enhance its performance. Five different example problems comprising 6, 13, 20, 40 and 160 generating units are solved to demonstrate the effectiveness of the algorithm. The algorithm requires little tuning by the user, and the results obtained show that it either outperforms or compares favorably with several existing techniques reported in literature. - Highlights: • The chaotic bat algorithm, a new metaheuristic optimization algorithm has been used. • The problem solved – the economic dispatch problem – is nonlinear, discontinuous. • It has number of equality and inequality constraints. • The algorithm has been demonstrated to be applicable on high dimensional problems.

High-dynamic range imaging techniques based on both color-separation algorithms used in conventional graphic arts and the human visual perception modeling

Science.gov (United States)

Lo, Mei-Chun; Hsieh, Tsung-Hsien; Perng, Ruey-Kuen; Chen, Jiong-Qiao

2010-01-01

The aim of this research is to derive illuminant-independent type of HDR imaging modules which can optimally multispectrally reconstruct of every color concerned in high-dynamic-range of original images for preferable cross-media color reproduction applications. Each module, based on either of broadband and multispectral approach, would be incorporated models of perceptual HDR tone-mapping, device characterization. In this study, an xvYCC format of HDR digital camera was used to capture HDR scene images for test. A tone-mapping module was derived based on a multiscale representation of the human visual system and used equations similar to a photoreceptor adaptation equation, proposed by Michaelis-Menten. Additionally, an adaptive bilateral type of gamut mapping algorithm, using approach of a multiple conversing-points (previously derived), was incorporated with or without adaptive Un-sharp Masking (USM) to carry out the optimization of HDR image rendering. An LCD with standard color space of Adobe RGB (D65) was used as a soft-proofing platform to display/represent HDR original RGB images, and also evaluate both renditionquality and prediction-performance of modules derived. Also, another LCD with standard color space of sRGB was used to test gamut-mapping algorithms, used to be integrated with tone-mapping module derived.

An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

Science.gov (United States)

Yook, Sunhyun; Nam, Kyoung Won; Kim, Heepyung; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young

2015-04-01

In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Efficient Implementation Algorithms for Homogenized Energy Models

National Research Council Canada - National Science Library

Braun, Thomas R; Smith, Ralph C

2005-01-01

... for real-time control implementation. In this paper, we develop algorithms employing lookup tables which permit the high speed implementation of formulations which incorporate relaxation mechanisms and electromechanical coupling...

Prioritising health service innovation investments using public preferences: a discrete choice experiment.

Science.gov (United States)

Erdem, Seda; Thompson, Carl

2014-08-28

Prioritising scarce resources for investment in innovation by publically funded health systems is unavoidable. Many healthcare systems wish to foster transparency and accountability in the decisions they make by incorporating the public in decision-making processes. This paper presents a unique conceptual approach exploring the public's preferences for health service innovations by viewing healthcare innovations as 'bundles' of characteristics. This decompositional approach allows policy-makers to compare numerous competing health service innovations without repeatedly administering surveys for specific innovation choices. A Discrete Choice Experiment (DCE) was used to elicit preferences. Individuals chose from presented innovation options that they believe the UK National Health Service (NHS) should invest the most in. Innovations differed according to: (i) target population; (ii) target age; (iii) implementation time; (iv) uncertainty associated with their likely effects; (v) potential health benefits; and, (vi) cost to a taxpayer. This approach fosters multidimensional decision-making, rather than imposing a single decision criterion (e.g., cost, target age) in prioritisation. Choice data was then analysed using scale-adjusted Latent Class models to investigate variability in preferences and scale and valuations amongst respondents. Three latent classes with considerable heterogeneity in the preferences were present. Each latent class is composed of two consumer subgroups varying in the level of certainty in their choices. All groups preferred scientifically proven innovations, those with potential health benefits that cost less. There were, however, some important differences in their preferences for innovation investment choices: Class-1 (54%) prefers innovations benefitting adults and young people and does not prefer innovations targeting people with 'drug addiction' and 'obesity'. Class- 2 (34%) prefers innovations targeting 'cancer' patients only and has

The Role of Work-Integrated Learning in Student Preferences of Instructional Methods in an Accounting Curriculum

Science.gov (United States)

Abeysekera, Indra

2015-01-01

The role of work-integrated learning in student preferences of instructional methods is largely unexplored across the accounting curriculum. This study conducted six experiments to explore student preferences of instructional methods for learning, in six courses of the accounting curriculum that differed in algorithmic rigor, in the context of a…

Algorithmic Mechanism Design of Evolutionary Computation.

Science.gov (United States)

Pei, Yan

2015-01-01

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

Incorporating fan control into air-conditioning systems to improve energy efficiency and transient response

International Nuclear Information System (INIS)

Yeh, T.-J.; Chen, Yun-Jih; Hwang, Wei-Yang; Lin, Jin-Long

2009-01-01

Modern air-conditioners frequently incorporate variable-speed compressors and variable-opening expansion valves with feedback control to improve performance and power efficiency. Because making the fan speeds adjustable adds flexibility to the control design and thus can lead to further improvements in performance and efficiency, this paper proposes two control algorithms, respectively, incorporating the outdoor fan and the indoor fan as the additional control inputs for air-conditioning systems. Both of the control algorithms are designed based on a low-order, linear model obtained from system identification. The first algorithm, which modulates the outdoor fan speed, can reduce the steady state power consumption if the temperature difference between the condenser and the outdoor environment is controlled properly. The second algorithm, which adds one more degree of freedom to control by modulating the indoor fan speed, can improve the transient response because actuator saturations become less likely to occur. The two control algorithms are implemented on a split-type residential air-conditioner and their respective performance is validated experimentally.

Music-Based iPad App Preferences of Young Children

Science.gov (United States)

Burton, Suzanne L.; Pearsall, Aimee

2016-01-01

Music-based technology is frequently included in early childhood classrooms as an attempt to incorporate music education in the curriculum. However, there is a lack of research that addresses the educational benefits of music-based tablet applications (apps) for young children. Researchers in this study explored the preferences of 4-year-old…

Finding the Most Preferred Decision-Making Unit in Data Envelopment Analysis

Directory of Open Access Journals (Sweden)

Shirin Mohammadi

2016-01-01

Full Text Available Data envelopment analysis (DEA evaluates the efficiency of the transformation of a decision-making unit’s (DMU’s inputs into its outputs. Finding the benchmarks of a DMU is one of the important purposes of DEA. The benchmarks of a DMU in DEA are obtained by solving some linear programming models. Currently, the obtained benchmarks are just found by using the information of the data of inputs and outputs without considering the decision-maker’s preferences. If the preferences of the decision-maker are available, it is very important to obtain the most preferred DMU as a benchmark of the under-assessment DMU. In this regard, we present an algorithm to find the most preferred DMU based on the utility function of decision-maker’s preferences by exploring some properties on that. The proposed method is constructed based on the projection of the gradient of the utility function on the production possibility set’s frontier.

Engineering local optimality in quantum Monte Carlo algorithms

Science.gov (United States)

Pollet, Lode; Van Houcke, Kris; Rombouts, Stefan M. A.

2007-08-01

Quantum Monte Carlo algorithms based on a world-line representation such as the worm algorithm and the directed loop algorithm are among the most powerful numerical techniques for the simulation of non-frustrated spin models and of bosonic models. Both algorithms work in the grand-canonical ensemble and can have a winding number larger than zero. However, they retain a lot of intrinsic degrees of freedom which can be used to optimize the algorithm. We let us guide by the rigorous statements on the globally optimal form of Markov chain Monte Carlo simulations in order to devise a locally optimal formulation of the worm algorithm while incorporating ideas from the directed loop algorithm. We provide numerical examples for the soft-core Bose-Hubbard model and various spin- S models.

Counseling women with early pregnancy failure: utilizing evidence, preserving preference.

Science.gov (United States)

Wallace, Robin R; Goodman, Suzan; Freedman, Lori R; Dalton, Vanessa K; Harris, Lisa H

2010-12-01

To apply principles of shared decision-making to EPF management counseling. To present a patient treatment priority checklist developed from review of available literature on patient priorities for EPF management. Review of evidence for patient preferences; personal, emotional, physical and clinical factors that may influence patient priorities for EPF management; and the clinical factors, resources, and provider bias that may influence current practice. Women have strong and diverse preferences for EPF management and report higher satisfaction when treated according to these preferences. However, estimates of actual treatment patterns suggest that current practice does not reflect the evidence for safety and acceptability of all options, or patient preferences. Multiple practice barriers and biases exist that may be influencing provider counseling about options for EPF management. Choosing management for EPF is a preference-sensitive decision. A patient-centered approach to EPF management should incorporate counseling about all treatment options. Providers can integrate a counseling model into EPF management practice that utilizes principles of shared decision-making and an organized method for eliciting patient preferences, priorities, and concerns about treatment options. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Writing on the board as students' preferred teaching modality in a physiology course.

Science.gov (United States)

Armour, Chris; Schneid, Stephen D; Brandl, Katharina

2016-06-01

The introduction of PowerPoint presentation software has generated a paradigm shift in the delivery of lectures. PowerPoint has now almost entirely replaced chalkboard or whiteboard teaching at the undergraduate and graduate levels. This study investigated whether undergraduate biology students preferred to have lectures delivered by PowerPoint or written on the board as well as the reasons behind their preference. Two upper-division physiology courses were surveyed over a period of 7 yr. A total of 1,905 students (86.7%) indicated they preferred lectures delivered by "writing on the board" compared to 291 students (13.3%) who preferred PowerPoint. Common themes drawn from explanations reported by students in favor of writing on the board included: 1) more appropriate pace, 2) facilitation of note taking, and 3) greater alertness and attention. Common themes in favor of PowerPoint included 1) increased convenience, 2) focus on listening, and 3) more accurate and readable notes. Based on the students' very strong preference for writing on the board and the themes supporting that preference, we recommend that instructors incorporate elements of the writing on the board delivery style into whatever teaching modality is used. If instructors plan to use PowerPoint, the presentation should be paced, constructed, and delivered to provide the benefits of lectures written on the board. The advantages of writing on the board can be also incorporated into instruction intended to occur outside the classroom, such as animated narrated videos as part of the flipped classroom approach. Copyright © 2016 The American Physiological Society.

Incorporating a Constrained Optimization Algorithm into Remote- Sensing/Precision Agriculture Methodology

Science.gov (United States)

Morgenthaler, George; Khatib, Nader; Kim, Byoungsoo

application of costly treatments. This model will incorporate information from remote sensing, in-situ weather sources, soil measurements, crop models, and tacit farmer knowledge of the relative productivity of the selected control regions of the farm to provide incremental advice throughout the growing season on water and chemical treatments. Genetic and meta-heuristic algorithms will be used to solve the constrained optimization problem that possesses complex constraints and a non-linear objective function. *

Incorporating a constrained optimization algorithm into remote sensing/precision agriculture methodology

Science.gov (United States)

Moreenthaler, George W.; Khatib, Nader; Kim, Byoungsoo

2003-08-01

Optimization Algorithm" to further improve these processes will be presented. The objective function of the model will used to maximize the farmer's profit via increasing yields while decreasing environmental damage and decreasing applications of costly treatments. This model will incorporate information from Remote Sensing, from in-situ weather sources, from soil history, and from tacit farmer knowledge of the relative productivity of selected "Management Zones" of the farm, to provide incremental advice throughout the growing season on the optimum usage of water and chemical treatments.

Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.

Science.gov (United States)

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran

Energy Technology Data Exchange (ETDEWEB)

Azadeh, A; Seraj, O [Department of Industrial Engineering and Research Institute of Energy Management and Planning, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, P.O. Box 11365-4563 (Iran); Saberi, M [Department of Industrial Engineering, University of Tafresh (Iran); Institute for Digital Ecosystems and Business Intelligence, Curtin University of Technology, Perth (Australia)

2010-06-15

This study presents an integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression (FR) or time series and the integrated algorithm could be an ideal substitute for such cases. At First, preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred Auto Regression Moving Average (ARMA) model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, the preferred model from fuzzy regression and time series model is selected by the Granger-Newbold. Also, the impact of data preprocessing on the fuzzy regression performance is considered. Monthly electricity consumption of Iran from March 1994 to January 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with other intelligent tools such as Genetic Algorithm (GA) and Artificial Neural Network (ANN). (author)

Gender identity rather than sexual orientation impacts on facial preferences.

Science.gov (United States)

Ciocca, Giacomo; Limoncin, Erika; Cellerino, Alessandro; Fisher, Alessandra D; Gravina, Giovanni Luca; Carosa, Eleonora; Mollaioli, Daniele; Valenzano, Dario R; Mennucci, Andrea; Bandini, Elisa; Di Stasi, Savino M; Maggi, Mario; Lenzi, Andrea; Jannini, Emmanuele A

2014-10-01

Differences in facial preferences between heterosexual men and women are well documented. It is still a matter of debate, however, how variations in sexual identity/sexual orientation may modify the facial preferences. This study aims to investigate the facial preferences of male-to-female (MtF) individuals with gender dysphoria (GD) and the influence of short-term/long-term relationships on facial preference, in comparison with healthy subjects. Eighteen untreated MtF subjects, 30 heterosexual males, 64 heterosexual females, and 42 homosexual males from university students/staff, at gay events, and in Gender Clinics were shown a composite male or female face. The sexual dimorphism of these pictures was stressed or reduced in a continuous fashion through an open-source morphing program with a sequence of 21 pictures of the same face warped from a feminized to a masculinized shape. An open-source morphing program (gtkmorph) based on the X-Morph algorithm. MtF GD subjects and heterosexual females showed the same pattern of preferences: a clear preference for less dimorphic (more feminized) faces for both short- and long-term relationships. Conversely, both heterosexual and homosexual men selected significantly much more dimorphic faces, showing a preference for hyperfeminized and hypermasculinized faces, respectively. These data show that the facial preferences of MtF GD individuals mirror those of the sex congruent with their gender identity. Conversely, heterosexual males trace the facial preferences of homosexual men, indicating that changes in sexual orientation do not substantially affect preference for the most attractive faces. © 2014 International Society for Sexual Medicine.

Capacity Constrained Routing Algorithms for Evacuation Route Planning

National Research Council Canada - National Science Library

Lu, Qingsong; George, Betsy; Shekhar, Shashi

2006-01-01

.... In this paper, we propose a new approach, namely a capacity constrained routing planner which models capacity as a time series and generalizes shortest path algorithms to incorporate capacity constraints...

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem

Directory of Open Access Journals (Sweden)

Hao Yin

2014-01-01

Full Text Available For SLA-aware service composition problem (SSC, an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.

Quantum Algorithms for Compositional Natural Language Processing

Directory of Open Access Journals (Sweden)

William Zeng

2016-08-01

Full Text Available We propose a new application of quantum computing to the field of natural language processing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In (Coecke, Sadrzadeh and Clark, 2010, the authors introduce such a model (the CSC model based on tensor product composition. While this algorithm has many advantages, its implementation is hampered by the large classical computational resources that it requires. In this work we show how computational shortcomings of the CSC approach could be resolved using quantum computation (possibly in addition to existing techniques for dimension reduction. We address the value of quantum RAM (Giovannetti,2008 for this model and extend an algorithm from Wiebe, Braun and Lloyd (2012 into a quantum algorithm to categorize sentences in CSC. Our new algorithm demonstrates a quadratic speedup over classical methods under certain conditions.

A case study of a multiobjective recombinative genetic algorithm with coevolutionary sharing

NARCIS (Netherlands)

Neef, R.M.; Thierens, D.; Arciszewski, H.F.R.

1999-01-01

We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination,

A fuzzy set preference model for market share analysis

Science.gov (United States)

Turksen, I. B.; Willson, Ian A.

1992-01-01

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share

IMPLEMENTATION OF OBJECT TRACKING ALGORITHMS ON THE BASIS OF CUDA TECHNOLOGY

Directory of Open Access Journals (Sweden)

B. A. Zalesky

2014-01-01

Full Text Available A fast version of correlation algorithm to track objects on video-sequences made by a nonstabilized camcorder is presented. The algorithm is based on comparison of local correlations of the object image and regions of video-frames. The algorithm is implemented in programming technology CUDA. Application of CUDA allowed to attain real time execution of the algorithm. To improve its precision and stability, a robust version of the Kalman filter has been incorporated into the flowchart. Tests showed applicability of the algorithm to practical object tracking.

An Intuitive Dominant Test Algorithm of CP-nets Applied on Wireless Sensor Network

Directory of Open Access Journals (Sweden)

Liu Zhaowei

2014-07-01

Full Text Available A wireless sensor network is of spatially distributed with autonomous sensors, just like a multi-Agent system with single Agent. Conditional Preference networks is a qualitative tool for representing ceteris paribus (all other things being equal preference statements, it has been a research hotspot in artificial intelligence recently. But the algorithm and complexity of strong dominant test with respect to binary-valued structure CP-nets have not been solved, and few researchers address the application to other domain. In this paper, strong dominant test and application of CP-nets are studied in detail. Firstly, by constructing induced graph of CP-nets and studying its properties, we make a conclusion that the problem of strong dominant test on binary-valued CP-nets is single source shortest path problem essentially, so strong dominant test problem can be solved by improved Dijkstra’s algorithm. Secondly, we apply the algorithm above mentioned to the completeness of wireless sensor network, and design a completeness judging algorithm based on strong dominant test. Thirdly, we apply the algorithm on wireless sensor network to solve routing problem. In the end, we point out some interesting work in the future.

Global dynamics of a PDE model for aedes aegypti mosquitoe incorporating female sexual preference

KAUST Repository

Parshad, Rana; Agusto, Folashade B.

2011-01-01

In this paper we study the long time dynamics of a reaction diffusion system, describing the spread of Aedes aegypti mosquitoes, which are the primary cause of dengue infection. The system incorporates a control attempt via the sterile insect

Public Governance Quality and Tax Compliance Behavior in Nigeria: The Moderating Role of Financial Condition and Risk Preference

Directory of Open Access Journals (Sweden)

James O. Alabede

2011-06-01

public governance quality on tax compliance behavior of individual taxpayers as well as the moderating effect of financial condition and risk preference on tax compliance and its determinants. This study extended tax compliance model to incorporate public governance quality and moderating effects of financial condition and risk preference.

Radiation Shielding Materials and Containers Incorporating Same

Energy Technology Data Exchange (ETDEWEB)

Mirsky, Steven M.; Krill, Stephen J.; and Murray, Alexander P.

2005-11-01

An improved radiation shielding material and storage systems for radioactive materials incorporating the same. The PYRolytic Uranium Compound (''PYRUC'') shielding material is preferably formed by heat and/or pressure treatment of a precursor material comprising microspheres of a uranium compound, such as uranium dioxide or uranium carbide, and a suitable binder. The PYRUC shielding material provides improved radiation shielding, thermal characteristic, cost and ease of use in comparison with other shielding materials. The shielding material can be used to form containment systems, container vessels, shielding structures, and containment storage areas, all of which can be used to house radioactive waste. The preferred shielding system is in the form of a container for storage, transportation, and disposal of radioactive waste. In addition, improved methods for preparing uranium dioxide and uranium carbide microspheres for use in the radiation shielding materials are also provided.

Mosaic crystal algorithm for Monte Carlo simulations

CERN Document Server

Seeger, P A

2002-01-01

An algorithm is presented for calculating reflectivity, absorption, and scattering of mosaic crystals in Monte Carlo simulations of neutron instruments. The algorithm uses multi-step transport through the crystal with an exact solution of the Darwin equations at each step. It relies on the kinematical model for Bragg reflection (with parameters adjusted to reproduce experimental data). For computation of thermal effects (the Debye-Waller factor and coherent inelastic scattering), an expansion of the Debye integral as a rapidly converging series of exponential terms is also presented. Any crystal geometry and plane orientation may be treated. The algorithm has been incorporated into the neutron instrument simulation package NISP. (orig.)

Improved collaborative filtering recommendation algorithm of similarity measure

Science.gov (United States)

Zhang, Baofu; Yuan, Baoping

2017-05-01

The Collaborative filtering recommendation algorithm is one of the most widely used recommendation algorithm in personalized recommender systems. The key is to find the nearest neighbor set of the active user by using similarity measure. However, the methods of traditional similarity measure mainly focus on the similarity of user common rating items, but ignore the relationship between the user common rating items and all items the user rates. And because rating matrix is very sparse, traditional collaborative filtering recommendation algorithm is not high efficiency. In order to obtain better accuracy, based on the consideration of common preference between users, the difference of rating scale and score of common items, this paper presents an improved similarity measure method, and based on this method, a collaborative filtering recommendation algorithm based on similarity improvement is proposed. Experimental results show that the algorithm can effectively improve the quality of recommendation, thus alleviate the impact of data sparseness.

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

Directory of Open Access Journals (Sweden)

Kian Sheng Lim

2013-01-01

Full Text Available The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Interactive genetic algorithm for user-centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

Science.gov (United States)

Piemonti, Adriana Debora; Babbar-Sebens, Meghna; Mukhopadhyay, Snehasis; Kleinberg, Austin

2017-05-01

Interactive Genetic Algorithms (IGA) are advanced human-in-the-loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end-users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA-based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives.

A Coulomb collision algorithm for weighted particle simulations

Science.gov (United States)

Miller, Ronald H.; Combi, Michael R.

1994-01-01

A binary Coulomb collision algorithm is developed for weighted particle simulations employing Monte Carlo techniques. Charged particles within a given spatial grid cell are pair-wise scattered, explicitly conserving momentum and implicitly conserving energy. A similar algorithm developed by Takizuka and Abe (1977) conserves momentum and energy provided the particles are unweighted (each particle representing equal fractions of the total particle density). If applied as is to simulations incorporating weighted particles, the plasma temperatures equilibrate to an incorrect temperature, as compared to theory. Using the appropriate pairing statistics, a Coulomb collision algorithm is developed for weighted particles. The algorithm conserves energy and momentum and produces the appropriate relaxation time scales as compared to theoretical predictions. Such an algorithm is necessary for future work studying self-consistent multi-species kinetic transport.

Students' Perceived Preference for Visual and Auditory Assessment with E-Handwritten Feedback

Science.gov (United States)

Crews, Tena B.; Wilkinson, Kelly

2010-01-01

Undergraduate business communication students were surveyed to determine their perceived most effective method of assessment on writing assignments. The results indicated students' preference for a process that incorporates visual, auditory, and e-handwritten presentation via a tablet PC. Students also identified this assessment process would…

Behavioral and biochemical characteristics of rats preferring ethanol or water

International Nuclear Information System (INIS)

Kulikova, O.G.; Borodkin, Y.S.; Razumovskaya, N.I.; Shabanov, P.D.; Sokolovskaya, N.E.

1985-01-01

Considering that learning and memory processes are largely determined by the intensity of RNA synthesis in specific brain structure, the authors study the relationship between learning ability of rats preferring ethanol or water and the level of RNA-synthesizing activity of brain cell nuclei. RNA-synthesizing activity of cell nuclei from cortical gray matter of the animals was determined one month after selection by measuring incorporation of deuterium-uridine triphosphate. The numerical results were subjected to statistical analysis by Student's test at P 0.05. It is shown that the altered behavior of animals preferring ethanol is evidently based on disturbed interaction between mediator and genetic structures of brain cells

Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering

KAUST Repository

Fonarev, Alexander; Mikhalev, Alexander; Serdyukov, Pavel; Gusev, Gleb; Oseledets, Ivan

2017-01-01

preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation - a seed set of a

Evaluation of Activity Recognition Algorithms for Employee Performance Monitoring

OpenAIRE

Mehreen Mumtaz; Hafiz Adnan Habib

2012-01-01

Successful Human Resource Management plays a key role in success of any organization. Traditionally, human resource managers rely on various information technology solutions such as Payroll and Work Time Systems incorporating RFID and biometric technologies. This research evaluates activity recognition algorithms for employee performance monitoring. An activity recognition algorithm has been implemented that categorized the activity of employee into following in to classes: job activities and...

Specification of the Fast Fourier Transform algorithm as a term rewriting system

NARCIS (Netherlands)

Rodenburg, P.H.; Hoekzema, D.J.

1987-01-01

We specify an algorithm for multiplying polynomials with complex coefficients incorporating, the Fast Fourier Transform algorithm of Cooley and Tukey [CT]. The specification formalism we use is a variant of the formalism ASF described in. [BHK]. The difference with ASF is essentially a matter of

Stochastic split determinant algorithms

International Nuclear Information System (INIS)

Horvatha, Ivan

2000-01-01

I propose a large class of stochastic Markov processes associated with probability distributions analogous to that of lattice gauge theory with dynamical fermions. The construction incorporates the idea of approximate spectral split of the determinant through local loop action, and the idea of treating the infrared part of the split through explicit diagonalizations. I suggest that exact algorithms of practical relevance might be based on Markov processes so constructed

Men's Preferences for Physical Activity Interventions: An Exploratory Study Using a Factorial Survey Design Created With R Software.

Science.gov (United States)

Chatfield, Sheryl L; Gamble, Abigail; Hallam, Jeffrey S

2018-03-01

Effective exercise interventions are needed to improve quality of life and decrease the impact of chronic disease. Researchers suggest males have been underrepresented in exercise intervention studies, resulting in less understanding of their exercise practices. Findings from preference survey methods suggest reasonable association between preference and behavior. The purpose of the research described in this article was to use factorial survey, a preference method, to identify the characteristics of exercise interventions most likely to appeal to male participants, so preferences might be incorporated into future intervention research. The research was guided by the framework of Bandura's social cognitive theory, such that variations in individual, environmental, and behavioral factors were incorporated into vignettes. Participants included 53 adult male nonadministrative staff and contract employees at a public university in the Southeastern United States, who each scored 8 vignettes resulting in 423 observations. Multilevel models were used to assess the influence of the factors. Participants scored vignettes that included exercising with a single partner, playing basketball, and exercising in the evening higher than vignettes with other options. Qualitative analysis of an open response item identified additional alternatives in group size, participant desire for coaching support, and interest in programs that incorporate a range of activity alternatives. Findings from this research were consistent with elements of social cognitive theory as applied to health promotion. Factorial surveys potentially provide a resource effective means of identifying participants' preferences for use when planning interventions. The addition of a single qualitative item helped clarify and expand findings from statistical analysis.

A Case Study of a Multiobjective Elitist Recombinative Genetic Algorithm with Coevolutionary Sharing

NARCIS (Netherlands)

Neef, R.M.; Thierens, D.; Arciszewski, H.F.R.

1999-01-01

We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination,

Research and Implementation of the Practical Texture Synthesis Algorithms

Institute of Scientific and Technical Information of China (English)

孙家广; 周毅

1991-01-01

How to generate pictures real and esthetic objects is an important subject of computer graphics.The techniques of mapping textures onto the surfaces of an object in the 3D space are efficient approaches for the purpose.We developed and implemented algorithms for generating objects with appearances stone,wood grain,ice lattice,brick,doors and windows on Apollo workstations.All the algorithms have been incorporated into the 3D grometry modelling system (GEMS) developed by the CAD Center of Tsinghua University.This paper emphasizes the wood grain and the ice lattice algorithms.

A novel approach based on preference-based index for interval bilevel linear programming problem.

Science.gov (United States)

Ren, Aihong; Wang, Yuping; Xue, Xingsi

2017-01-01

This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers. In order to keep as much uncertainty of the original constraint region as possible, the original problem is first converted into an interval bilevel programming problem with interval coefficients in both objective functions only through normal variation of interval number and chance-constrained programming. With the consideration of different preferences of different decision makers, the concept of the preference level that the interval objective function is preferred to a target interval is defined based on the preference-based index. Then a preference-based deterministic bilevel programming problem is constructed in terms of the preference level and the order relation [Formula: see text]. Furthermore, the concept of a preference δ -optimal solution is given. Subsequently, the constructed deterministic nonlinear bilevel problem is solved with the help of estimation of distribution algorithm. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

A novel approach based on preference-based index for interval bilevel linear programming problem

Directory of Open Access Journals (Sweden)

Aihong Ren

2017-05-01

Full Text Available Abstract This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers. In order to keep as much uncertainty of the original constraint region as possible, the original problem is first converted into an interval bilevel programming problem with interval coefficients in both objective functions only through normal variation of interval number and chance-constrained programming. With the consideration of different preferences of different decision makers, the concept of the preference level that the interval objective function is preferred to a target interval is defined based on the preference-based index. Then a preference-based deterministic bilevel programming problem is constructed in terms of the preference level and the order relation ⪯ m w $\\preceq_{mw}$ . Furthermore, the concept of a preference δ-optimal solution is given. Subsequently, the constructed deterministic nonlinear bilevel problem is solved with the help of estimation of distribution algorithm. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms.

Science.gov (United States)

Sze, Sing-Hoi; Parrott, Jonathan J; Tarone, Aaron M

2017-12-06

While the continued development of high-throughput sequencing has facilitated studies of entire transcriptomes in non-model organisms, the incorporation of an increasing amount of RNA-Seq libraries has made de novo transcriptome assembly difficult. Although algorithms that can assemble a large amount of RNA-Seq data are available, they are generally very memory-intensive and can only be used to construct small assemblies. We develop a divide-and-conquer strategy that allows these algorithms to be utilized, by subdividing a large RNA-Seq data set into small libraries. Each individual library is assembled independently by an existing algorithm, and a merging algorithm is developed to combine these assemblies by picking a subset of high quality transcripts to form a large transcriptome. When compared to existing algorithms that return a single assembly directly, this strategy achieves comparable or increased accuracy as memory-efficient algorithms that can be used to process a large amount of RNA-Seq data, and comparable or decreased accuracy as memory-intensive algorithms that can only be used to construct small assemblies. Our divide-and-conquer strategy allows memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies.

Innovation of genetic algorithm code GenA for WWER fuel loading optimization

International Nuclear Information System (INIS)

Sustek, J.

2005-01-01

One of the stochastic search techniques - genetic algorithms - was recently used for optimization of arrangement of fuel assemblies (FA) in core of reactors WWER-440 and WWER-1000. Basic algorithm was modified by incorporation of SPEA scheme. Both were enhanced and some results are presented (Authors)

Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery.

Science.gov (United States)

Chen, Li; Tan, Chih-Hung; Kao, Shuh-Ji; Wang, Tai-Sheng

2008-01-01

Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors.

INCORPORATING ENVIRONMENTAL AND ECONOMIC CONSIDERATIONS INTO PROCESS DESIGN: THE WASTE REDUCTION (WAR) ALGORITHM

Science.gov (United States)

A general theory known as the WAste Reduction (WASR) algorithm has been developed to describe the flow and the generation of potential environmental impact through a chemical process. This theory integrates environmental impact assessment into chemical process design Potential en...

Incorporating Spatial Information for Microaneurysm Detection in Retinal Images

Directory of Open Access Journals (Sweden)

Mohamed M. Habib

2017-06-01

Full Text Available The presence of microaneurysms(MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR. This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method.

A DIFFERENTIAL EVOLUTION ALGORITHM DEVELOPED FOR A NURSE SCHEDULING PROBLEM

Directory of Open Access Journals (Sweden)

Shahnazari-Shahrezaei, P.

2012-11-01

Full Text Available Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers objectives and nurses preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics a differential evolution algorithm (DE and a greedy randomised adaptive search procedure (GRASP to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP.

Cell Formation in Industrial Engineering : Theory, Algorithms and Experiments

NARCIS (Netherlands)

Goldengorin, B.; Krushynskyi, D.; Pardalos, P.M.

2013-01-01

This book focuses on a development of optimal, flexible, and efficient models and algorithms for cell formation in group technology. Its main aim is to provide a reliable tool that can be used by managers and engineers to design manufacturing cells based on their own preferences and constraints

Medical Decision Algorithm for Pre-Hospital Trauma Care. Phase I.

Science.gov (United States)

1996-09-01

Algorithm for Pre-Hospital Trauma Care PRINCIPAL INVESTIGATOR: Donald K. Wedding, P.E., Ph.D CONTRACTING ORGANIZATION : Photonics Systems, Incorporated... ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Photonics Systems, Incorporated Northwood, Ohio 43619 9. SPONSORING...three areas: 1) data acquisition, 2) neural network design, and 3) system architechture design. In the first area of this research, a triage database

A Diagonal-Steering-Based Binaural Beamforming Algorithm Incorporating a Diagonal Speech Localizer for Persons With Bilateral Hearing Impairment.

Science.gov (United States)

Lee, Jun Chang; Nam, Kyoung Won; Jang, Dong Pyo; Kim, In Young

2015-12-01

Previously suggested diagonal-steering algorithms for binaural hearing support devices have commonly assumed that the direction of the speech signal is known in advance, which is not always the case in many real circumstances. In this study, a new diagonal-steering-based binaural speech localization (BSL) algorithm is proposed, and the performances of the BSL algorithm and the binaural beamforming algorithm, which integrates the BSL and diagonal-steering algorithms, were evaluated using actual speech-in-noise signals in several simulated listening scenarios. Testing sounds were recorded in a KEMAR mannequin setup and two objective indices, improvements in signal-to-noise ratio (SNRi ) and segmental SNR (segSNRi ), were utilized for performance evaluation. Experimental results demonstrated that the accuracy of the BSL was in the 90-100% range when input SNR was -10 to +5 dB range. The average differences between the γ-adjusted and γ-fixed diagonal-steering algorithms (for -15 to +5 dB input SNR) in the talking in the restaurant scenario were 0.203-0.937 dB for SNRi and 0.052-0.437 dB for segSNRi , and in the listening while car driving scenario, the differences were 0.387-0.835 dB for SNRi and 0.259-1.175 dB for segSNRi . In addition, the average difference between the BSL-turned-on and the BSL-turned-off cases for the binaural beamforming algorithm in the listening while car driving scenario was 1.631-4.246 dB for SNRi and 0.574-2.784 dB for segSNRi . In all testing conditions, the γ-adjusted diagonal-steering and BSL algorithm improved the values of the indices more than the conventional algorithms. The binaural beamforming algorithm, which integrates the proposed BSL and diagonal-steering algorithm, is expected to improve the performance of the binaural hearing support devices in noisy situations. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Realistic Free-Spins Features Increase Preference for Slot Machines.

Science.gov (United States)

Taylor, Lorance F; Macaskill, Anne C; Hunt, Maree J

2017-06-01

Despite increasing research into how the structural characteristics of slot machines influence gambling behaviour there have been no experimental investigations into the effect of free-spins bonus features-a structural characteristic that is commonly central to the design of slot machines. This series of three experiments investigated the free-spins feature using slot machine simulations to determine whether participants allocate more wagers to a machine with free spins, and, which components of free-spins features drive this preference. In each experiment, participants were exposed to two computer-simulated slot machines-one with a free-spins feature or similar bonus feature and one without. Participants then completed a testing phase where they could freely switch between the two machines. In Experiment 1, participants did not prefer the machine with a simple free-spins feature. In Experiment 2 the free-spins feature incorporated additional elements such as sounds, animations, and an increased win frequency; participants preferred to gamble on this machine. The Experiment 3 "bonus feature" machine resembled the free spins machine in Experiment 2 except spins were not free; participants showed a clear preference for this machine also. These findings indicate that (1) free-spins features have a major influence over machine choice and (2) the "freeness" of the free-spins bonus features is not an important driver of preference, contrary to self-report and interview research with gamblers.

Frequency-Domain Adaptive Algorithm for Network Echo Cancellation in VoIP

Directory of Open Access Journals (Sweden)

Patrick A. Naylor

2008-05-01

Full Text Available We propose a new low complexity, low delay, and fast converging frequency-domain adaptive algorithm for network echo cancellation in VoIP exploiting MMax and sparse partial (SP tap-selection criteria in the frequency domain. We incorporate these tap-selection techniques into the multidelay filtering (MDF algorithm in order to mitigate the delay inherent in frequency-domain algorithms. We illustrate two such approaches and discuss their tradeoff between convergence performance and computational complexity. Simulation results show an improvement in convergence rate for the proposed algorithm over MDF and significantly reduced complexity. The proposed algorithm achieves a convergence performance close to that of the recently proposed, but substantially more complex improved proportionate MDF (IPMDF algorithm.

Dose Calculation Accuracy of the Monte Carlo Algorithm for CyberKnife Compared with Other Commercially Available Dose Calculation Algorithms

International Nuclear Information System (INIS)

Sharma, Subhash; Ott, Joseph; Williams, Jamone; Dickow, Danny

2011-01-01

Monte Carlo dose calculation algorithms have the potential for greater accuracy than traditional model-based algorithms. This enhanced accuracy is particularly evident in regions of lateral scatter disequilibrium, which can develop during treatments incorporating small field sizes and low-density tissue. A heterogeneous slab phantom was used to evaluate the accuracy of several commercially available dose calculation algorithms, including Monte Carlo dose calculation for CyberKnife, Analytical Anisotropic Algorithm and Pencil Beam convolution for the Eclipse planning system, and convolution-superposition for the Xio planning system. The phantom accommodated slabs of varying density; comparisons between planned and measured dose distributions were accomplished with radiochromic film. The Monte Carlo algorithm provided the most accurate comparison between planned and measured dose distributions. In each phantom irradiation, the Monte Carlo predictions resulted in gamma analysis comparisons >97%, using acceptance criteria of 3% dose and 3-mm distance to agreement. In general, the gamma analysis comparisons for the other algorithms were <95%. The Monte Carlo dose calculation algorithm for CyberKnife provides more accurate dose distribution calculations in regions of lateral electron disequilibrium than commercially available model-based algorithms. This is primarily because of the ability of Monte Carlo algorithms to implicitly account for tissue heterogeneities, density scaling functions; and/or effective depth correction factors are not required.

Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children.

Directory of Open Access Journals (Sweden)

Natalia I Vargas-Cuentas

Full Text Available Autism spectrum disorder (ASD currently affects nearly 1 in 160 children worldwide. In over two-thirds of evaluations, no validated diagnostics are used and gold standard diagnostic tools are used in less than 5% of evaluations. Currently, the diagnosis of ASD requires lengthy and expensive tests, in addition to clinical confirmation. Therefore, fast, cheap, portable, and easy-to-administer screening instruments for ASD are required. Several studies have shown that children with ASD have a lower preference for social scenes compared with children without ASD. Based on this, eye-tracking and measurement of gaze preference for social scenes has been used as a screening tool for ASD. Currently available eye-tracking software requires intensive calibration, training, or holding of the head to prevent interference with gaze recognition limiting its use in children with ASD.In this study, we designed a simple eye-tracking algorithm that does not require calibration or head holding, as a platform for future validation of a cost-effective ASD potential screening instrument. This system operates on a portable and inexpensive tablet to measure gaze preference of children for social compared to abstract scenes. A child watches a one-minute stimulus video composed of a social scene projected on the left side and an abstract scene projected on the right side of the tablet's screen. We designed five stimulus videos by changing the social/abstract scenes. Every child observed all the five videos in random order. We developed an eye-tracking algorithm that calculates the child's gaze preference for the social and abstract scenes, estimated as the percentage of the accumulated time that the child observes the left or right side of the screen, respectively. Twenty-three children without a prior history of ASD and 8 children with a clinical diagnosis of ASD were evaluated. The recorded video of the child´s eye movement was analyzed both manually by an observer

TOPSIS-based consensus model for group decision-making with incomplete interval fuzzy preference relations.

Science.gov (United States)

Liu, Fang; Zhang, Wei-Guo

2014-08-01

Due to the vagueness of real-world environments and the subjective nature of human judgments, it is natural for experts to estimate their judgements by using incomplete interval fuzzy preference relations. In this paper, based on the technique for order preference by similarity to ideal solution method, we present a consensus model for group decision-making (GDM) with incomplete interval fuzzy preference relations. To do this, we first define a new consistency measure for incomplete interval fuzzy preference relations. Second, a goal programming model is proposed to estimate the missing interval preference values and it is guided by the consistency property. Third, an ideal interval fuzzy preference relation is constructed by using the induced ordered weighted averaging operator, where the associated weights of characterizing the operator are based on the defined consistency measure. Fourth, a similarity degree between complete interval fuzzy preference relations and the ideal one is defined. The similarity degree is related to the associated weights, and used to aggregate the experts' preference relations in such a way that more importance is given to ones with the higher similarity degree. Finally, a new algorithm is given to solve the GDM problem with incomplete interval fuzzy preference relations, which is further applied to partnership selection in formation of virtual enterprises.

A chaos wolf optimization algorithm with self-adaptive variable step-size

Science.gov (United States)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Mode of Delivery Preferences in a Diverse Population of Pregnant Women

Science.gov (United States)

YEE, Lynn M.; KAIMAL, Anjali J.; HOUSTON, Kathryn A.; WU, Erica; THIET, Mari-Paule; NAKAGAWA, Sanae; CAUGHEY, Aaron B.; FIROUZIAN, Atoosa; KUPPERMANN, Miriam

2014-01-01

OBJECTIVE To assess women’s preferences for vaginal versus cesarean delivery in four contexts: prior cesarean, twins, breech presentation, and absent indication for cesarean. STUDY DESIGN Cross-sectional study of pregnant women at 24-40 weeks gestation. After assessing stated preferences for vaginal or cesarean delivery, we used the standard gamble metric to measure the strength of these preferences and the time tradeoff metric to determine how women value the potential processes and outcomes associated with these two delivery approaches. RESULTS Among the 240 participants, 90.8% had a stated preference for vaginal delivery. Across the four contexts, these women indicated that, on average, they would accept a 59-75% chance of an attempted vaginal birth ending in a cesarean before choosing a planned cesarean, indicating strong preferences for spontaneous, uncomplicated vaginal delivery. Variations in preferences for labor processes emerged. While uncomplicated labor ending in vaginal birth was assigned mean utilities of 0.993 or higher (on a 0-to-1 scale with higher scores indicating more preferred outcomes), the need for oxytocin, antibiotics, or operative vaginal delivery resulted in lower mean scores, comparable to those assigned to uncomplicated cesarean delivery. Substantially lower scores (ranging from 0.432 to 0.598) were obtained for scenarios ending in severe maternal or neonatal morbidity. CONCLUSIONS While most women expressed strong preferences for vaginal delivery, their preferences regarding interventions frequently employed to achieve that goal varied. These data underscore the importance of educating patients about the process of labor and delivery to facilitate incorporation of informed patient preferences in shared decision making regarding delivery approach. PMID:25446662

An Automatic Web Service Composition Framework Using QoS-Based Web Service Ranking Algorithm.

Science.gov (United States)

Mallayya, Deivamani; Ramachandran, Baskaran; Viswanathan, Suganya

2015-01-01

Web service has become the technology of choice for service oriented computing to meet the interoperability demands in web applications. In the Internet era, the exponential addition of web services nominates the "quality of service" as essential parameter in discriminating the web services. In this paper, a user preference based web service ranking (UPWSR) algorithm is proposed to rank web services based on user preferences and QoS aspect of the web service. When the user's request cannot be fulfilled by a single atomic service, several existing services should be composed and delivered as a composition. The proposed framework allows the user to specify the local and global constraints for composite web services which improves flexibility. UPWSR algorithm identifies best fit services for each task in the user request and, by choosing the number of candidate services for each task, reduces the time to generate the composition plans. To tackle the problem of web service composition, QoS aware automatic web service composition (QAWSC) algorithm proposed in this paper is based on the QoS aspects of the web services and user preferences. The proposed framework allows user to provide feedback about the composite service which improves the reputation of the services.

Development of a preference-based index from the National Eye Institute Visual Function Questionnaire-25.

Science.gov (United States)

Rentz, Anne M; Kowalski, Jonathan W; Walt, John G; Hays, Ron D; Brazier, John E; Yu, Ren; Lee, Paul; Bressler, Neil; Revicki, Dennis A

2014-03-01

Understanding how individuals value health states is central to patient-centered care and to health policy decision making. Generic preference-based measures of health may not effectively capture the impact of ocular diseases. Recently, 6 items from the National Eye Institute Visual Function Questionnaire-25 were used to develop the Visual Function Questionnaire-Utility Index health state classification, which defines visual function health states. To describe elicitation of preferences for health states generated from the Visual Function Questionnaire-Utility Index health state classification and development of an algorithm to estimate health preference scores for any health state. Nonintervention, cross-sectional study of the general community in 4 countries (Australia, Canada, United Kingdom, and United States). A total of 607 adult participants were recruited from local newspaper advertisements. In the United Kingdom, an existing database of participants from previous studies was used for recruitment. Eight of 15,625 possible health states from the Visual Function Questionnaire-Utility Index were valued using time trade-off technique. A θ severity score was calculated for Visual Function Questionnaire-Utility Index-defined health states using item response theory analysis. Regression models were then used to develop an algorithm to assign health state preference values for all potential health states defined by the Visual Function Questionnaire-Utility Index. Health state preference values for the 8 states ranged from a mean (SD) of 0.343 (0.395) to 0.956 (0.124). As expected, preference values declined with worsening visual function. Results indicate that the Visual Function Questionnaire-Utility Index describes states that participants view as spanning most of the continuum from full health to dead. Visual Function Questionnaire-Utility Index health state classification produces health preference scores that can be estimated in vision-related studies that

Decision process in MCDM with large number of criteria and heterogeneous risk preferences

Directory of Open Access Journals (Sweden)

Jian Liu

Full Text Available A new decision process is proposed to address the challenge that a large number criteria in the multi-criteria decision making (MCDM problem and the decision makers with heterogeneous risk preferences. First, from the perspective of objective data, the effective criteria are extracted based on the similarity relations between criterion values and the criteria are weighted, respectively. Second, the corresponding types of theoretic model of risk preferences expectations will be built, based on the possibility and similarity between criterion values to solve the problem for different interval numbers with the same expectation. Then, the risk preferences (Risk-seeking, risk-neutral and risk-aversion will be embedded in the decision process. Later, the optimal decision object is selected according to the risk preferences of decision makers based on the corresponding theoretic model. Finally, a new algorithm of information aggregation model is proposed based on fairness maximization of decision results for the group decision, considering the coexistence of decision makers with heterogeneous risk preferences. The scientific rationality verification of this new method is given through the analysis of real case. Keywords: Heterogeneous, Risk preferences, Fairness, Decision process, Group decision

Base-pairing preferences, physicochemical properties and mutational behaviour of the DNA lesion 8-nitroguanine.

Science.gov (United States)

Bhamra, Inder; Compagnone-Post, Patricia; O'Neil, Ian A; Iwanejko, Lesley A; Bates, Andrew D; Cosstick, Richard

2012-11-01

8-Nitro-2'-deoxyguanosine (8-nitrodG) is a relatively unstable, mutagenic lesion of DNA that is increasingly believed to be associated with tissue inflammation. Due to the lability of the glycosidic bond, 8-nitrodG cannot be incorporated into oligodeoxynucleotides (ODNs) by chemical DNA synthesis and thus very little is known about its physicochemical properties and base-pairing preferences. Here we describe the synthesis of 8-nitro-2'-O-methylguanosine, a ribonucleoside analogue of this lesion, which is sufficiently stable to be incorporated into ODNs. Physicochemical studies demonstrated that 8-nitro-2'-O-methylguanosine adopts a syn conformation about the glycosidic bond; thermal melting studies and molecular modelling suggest a relatively stable syn-8-nitroG·anti-G base pair. Interestingly, when this lesion analogue was placed in a primer-template system, extension of the primer by either avian myeloblastosis virus reverse transcriptase (AMV-RT) or human DNA polymerase β (pol β), was significantly impaired, but where incorporation opposite 8-nitroguanine did occur, pol β showed a 2:1 preference to insert dA over dC, while AMV-RT incorporated predominantly dC. The fact that no 8-nitroG·G base pairing is seen in the primer extension products suggests that the polymerases may discriminate against this pairing system on the basis of its poor geometric match to a Watson-Crick pair.

Base-pairing preferences, physicochemical properties and mutational behaviour of the DNA lesion 8-nitroguanine†

Science.gov (United States)

Bhamra, Inder; Compagnone-Post, Patricia; O’Neil, Ian A.; Iwanejko, Lesley A.; Bates, Andrew D.; Cosstick, Richard

2012-01-01

8-Nitro-2′-deoxyguanosine (8-nitrodG) is a relatively unstable, mutagenic lesion of DNA that is increasingly believed to be associated with tissue inflammation. Due to the lability of the glycosidic bond, 8-nitrodG cannot be incorporated into oligodeoxynucleotides (ODNs) by chemical DNA synthesis and thus very little is known about its physicochemical properties and base-pairing preferences. Here we describe the synthesis of 8-nitro-2′-O-methylguanosine, a ribonucleoside analogue of this lesion, which is sufficiently stable to be incorporated into ODNs. Physicochemical studies demonstrated that 8-nitro-2′-O-methylguanosine adopts a syn conformation about the glycosidic bond; thermal melting studies and molecular modelling suggest a relatively stable syn-8-nitroG·anti-G base pair. Interestingly, when this lesion analogue was placed in a primer-template system, extension of the primer by either avian myeloblastosis virus reverse transcriptase (AMV-RT) or human DNA polymerase β (pol β), was significantly impaired, but where incorporation opposite 8-nitroguanine did occur, pol β showed a 2:1 preference to insert dA over dC, while AMV-RT incorporated predominantly dC. The fact that no 8-nitroG·G base pairing is seen in the primer extension products suggests that the polymerases may discriminate against this pairing system on the basis of its poor geometric match to a Watson–Crick pair. PMID:22965127

A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices

Directory of Open Access Journals (Sweden)

Dharmbir Prasad

2016-03-01

Full Text Available In this paper, symbiotic organisms search (SOS algorithm is proposed for the solution of optimal power flow (OPF problem of power system equipped with flexible ac transmission systems (FACTS devices. Inspired by interaction between organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. The performance of the proposed SOS algorithm is tested on the modified IEEE-30 bus and IEEE-57 bus test systems incorporating two types of FACTS devices, namely, thyristor controlled series capacitor and thyristor controlled phase shifter at fixed locations. The OPF problem of the present work is formulated with four different objective functions viz. (a fuel cost minimization, (b transmission active power loss minimization, (c emission reduction and (d minimization of combined economic and environmental cost. The simulation results exhibit the potential of the proposed SOS algorithm and demonstrate its effectiveness for solving the OPF problem of power system incorporating FACTS devices over the other evolutionary optimization techniques that surfaced in the recent state-of-the-art literature.

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

Science.gov (United States)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input

Algorithms for in-season nutrient management in cereals

Science.gov (United States)

The demand for improved decision making products for cereal production systems has placed added emphasis on using plant sensors in-season, and that incorporate real-time, site specific, growing environments. The objective of this work was to describe validated in-season sensor based algorithms prese...

Development of morphing algorithms for Histfactory using information geometry

Energy Technology Data Exchange (ETDEWEB)

Bandyopadhyay, Anjishnu; Brock, Ian [University of Bonn (Germany); Cranmer, Kyle [New York University (United States)

2016-07-01

Many statistical analyses are based on likelihood fits. In any likelihood fit we try to incorporate all uncertainties, both systematic and statistical. We generally have distributions for the nominal and ±1 σ variations of a given uncertainty. Using that information, Histfactory morphs the distributions for any arbitrary value of the given uncertainties. In this talk, a new morphing algorithm will be presented, which is based on information geometry. The algorithm uses the information about the difference between various probability distributions. Subsequently, we map this information onto geometrical structures and develop the algorithm on the basis of different geometrical properties. Apart from varying all nuisance parameters together, this algorithm can also probe both small (< 1 σ) and large (> 2 σ) variations. It will also be shown how this algorithm can be used for interpolating other forms of probability distributions.

Competitive Supply Chain Network Design Considering Marketing Strategies: A Hybrid Metaheuristic Algorithm

Directory of Open Access Journals (Sweden)

Ali Akbar Hasani

2016-11-01

Full Text Available In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

Design Optimization of Space Launch Vehicles Using a Genetic Algorithm

National Research Council Canada - National Science Library

Bayley, Douglas J

2007-01-01

.... A genetic algorithm (GA) was employed to optimize the design of the space launch vehicle. A cost model was incorporated into the optimization process with the goal of minimizing the overall vehicle cost...

Incorporating stand level risk management options into forest decision support systems

Directory of Open Access Journals (Sweden)

Kyle Eyvindson

2018-01-01

Full Text Available Aim of study: To examine methods of incorporating risk and uncertainty to stand level forest decisions. Area of study: A case study examines a small forest holding from Jönköping, Sweden. Material and methods: We incorporate empirically estimated uncertainty into the simulation through a Monte Carlo approach when simulating the forest stands for the next 100 years. For the iterations of the Monte Carlo approach, errors were incorporated into the input data which was simulated according to the Heureka decision support system. Both the Value at Risk and the Conditional Value at Risk of the net present value are evaluated for each simulated stand. Main results: Visual representation of the errors can be used to highlight which decision would be most beneficial dependent on the decision maker’s opinion of the forest inventory results. At a stand level, risk preferences can be rather easily incorporated into the current forest decision support software. Research highlights: Forest management operates under uncertainty and risk. Methods are available to describe this risk in an understandable fashion for the decision maker.

Combined spatial/angular domain decomposition SN algorithms for shared memory parallel machines

International Nuclear Information System (INIS)

Hunter, M.A.; Haghighat, A.

1993-01-01

Several parallel processing algorithms on the basis of spatial and angular domain decomposition methods are developed and incorporated into a two-dimensional discrete ordinates transport theory code. These algorithms divide the spatial and angular domains into independent subdomains so that the flux calculations within each subdomain can be processed simultaneously. Two spatial parallel algorithms (Block-Jacobi, red-black), one angular parallel algorithm (η-level), and their combinations are implemented on an eight processor CRAY Y-MP. Parallel performances of the algorithms are measured using a series of fixed source RZ geometry problems. Some of the results are also compared with those executed on an IBM 3090/600J machine. (orig.)

Zero-block mode decision algorithm for H.264/AVC.

Science.gov (United States)

Lee, Yu-Ming; Lin, Yinyi

2009-03-01

In the previous paper , we proposed a zero-block intermode decision algorithm for H.264 video coding based upon the number of zero-blocks of 4 x 4 DCT coefficients between the current macroblock and the co-located macroblock. The proposed algorithm can achieve significant improvement in computation, but the computation performance is limited for high bit-rate coding. To improve computation efficiency, in this paper, we suggest an enhanced zero-block decision algorithm, which uses an early zero-block detection method to compute the number of zero-blocks instead of direct DCT and quantization (DCT/Q) calculation and incorporates two adequate decision methods into semi-stationary and nonstationary regions of a video sequence. In addition, the zero-block decision algorithm is also applied to the intramode prediction in the P frame. The enhanced zero-block decision algorithm brings out a reduction of average 27% of total encoding time compared to the zero-block decision algorithm.

DNA polymerase preference determines PCR priming efficiency.

Science.gov (United States)

Pan, Wenjing; Byrne-Steele, Miranda; Wang, Chunlin; Lu, Stanley; Clemmons, Scott; Zahorchak, Robert J; Han, Jian

2014-01-30

Polymerase chain reaction (PCR) is one of the most important developments in modern biotechnology. However, PCR is known to introduce biases, especially during multiplex reactions. Recent studies have implicated the DNA polymerase as the primary source of bias, particularly initiation of polymerization on the template strand. In our study, amplification from a synthetic library containing a 12 nucleotide random portion was used to provide an in-depth characterization of DNA polymerase priming bias. The synthetic library was amplified with three commercially available DNA polymerases using an anchored primer with a random 3' hexamer end. After normalization, the next generation sequencing (NGS) results of the amplified libraries were directly compared to the unamplified synthetic library. Here, high throughput sequencing was used to systematically demonstrate and characterize DNA polymerase priming bias. We demonstrate that certain sequence motifs are preferred over others as primers where the six nucleotide sequences at the 3' end of the primer, as well as the sequences four base pairs downstream of the priming site, may influence priming efficiencies. DNA polymerases in the same family from two different commercial vendors prefer similar motifs, while another commercially available enzyme from a different DNA polymerase family prefers different motifs. Furthermore, the preferred priming motifs are GC-rich. The DNA polymerase preference for certain sequence motifs was verified by amplification from single-primer templates. We incorporated the observed DNA polymerase preference into a primer-design program that guides the placement of the primer to an optimal location on the template. DNA polymerase priming bias was characterized using a synthetic library amplification system and NGS. The characterization of DNA polymerase priming bias was then utilized to guide the primer-design process and demonstrate varying amplification efficiencies among three commercially

Selecting a general-purpose data compression algorithm

Science.gov (United States)

Mathews, Gary Jason

1995-01-01

The National Space Science Data Center's Common Data Formate (CDF) is capable of storing many types of data such as scalar data items, vectors, and multidimensional arrays of bytes, integers, or floating point values. However, regardless of the dimensionality and data type, the data break down into a sequence of bytes that can be fed into a data compression function to reduce the amount of data without losing data integrity and thus remaining fully reconstructible. Because of the diversity of data types and high performance speed requirements, a general-purpose, fast, simple data compression algorithm is required to incorporate data compression into CDF. The questions to ask are how to evaluate and compare compression algorithms, and what compression algorithm meets all requirements. The object of this paper is to address these questions and determine the most appropriate compression algorithm to use within the CDF data management package that would be applicable to other software packages with similar data compression needs.

Route planning algorithms: Planific@ Project

Directory of Open Access Journals (Sweden)

Gonzalo Martín Ortega

2009-12-01

Full Text Available Planific@ is a route planning project for the city of Madrid (Spain. Its main aim is to develop an intelligence system capable of routing people from one place in the city to any other using the public transport. In order to do this, it is necessary to take into account such things as: time, traffic, user preferences, etc. Before beginning to design the project is necessary to make a comprehensive study of the variety of main known route planning algorithms suitable to be used in this project.

Improved Degree Search Algorithms in Unstructured P2P Networks

Directory of Open Access Journals (Sweden)

Guole Liu

2012-01-01

Full Text Available Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P networks. Breadth-first search (BFS and depth-first search (DFS are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD and memory function preference degree algorithm (PD. We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.

Static economic dispatch incorporating wind farm using Flower pollination algorithm

Directory of Open Access Journals (Sweden)

Suresh Velamuri

2016-09-01

Full Text Available Renewable energy is one of the clean and cheapest forms of energy which helps in minimizing the carbon foot print. Due to the less environmental impact and economic issues integration of renewable energy sources with the existing network gained attention. In this paper, the impact of wind energy is analysed in a power system network using static economic dispatch (SED. The wind energy is integrated with the existing thermal systems. Here, the generation scheduling is optimized using Flower pollination algorithm (FPA due to its robustness in solving nonlinear problems. Integration of wind power in the existing system increases the complexity due to its stochastic nature. Weibull distribution function is used for solving the stochastic nature of wind. Scenarios without and with wind power penetration are discussed in detail. The analysis is carried out by considering the losses and installing the wind farm at different locations in the system. The proposed methodology is tested and validated on a standard IEEE 30 bus system.

Definition and Analysis of a System for the Automated Comparison of Curriculum Sequencing Algorithms in Adaptive Distance Learning

Science.gov (United States)

Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia

2011-01-01

LS-Lab provides automatic support to comparison/evaluation of the Learning Object Sequences produced by different Curriculum Sequencing Algorithms. Through this framework a teacher can verify the correspondence between the behaviour of different sequencing algorithms and her pedagogical preferences. In fact the teacher can compare algorithms…

A chaos wolf optimization algorithm with self-adaptive variable step-size

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Yong Zhu

2017-10-01

Full Text Available To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Community pharmacy-based asthma services--what do patients prefer?

Science.gov (United States)

Naik Panvelkar, Pradnya; Armour, Carol; Saini, Bandana

2010-12-01

identified. It would be important to identify the strength and magnitude of patient's preferences for different elements of such services. Future pharmacy-based services should incorporate patient preferences and tailor services to patient's needs to ensure their long-term viability.

A Mobile Application Recommendation Framework by Exploiting Personal Preference with Constraints

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Konglin Zhu

2017-01-01

Full Text Available Explosive mobile applications (Apps are proliferating with the popularity of mobile devices (e.g., smartphones, tablets. These Apps are developed to satisfy different function needs of users. Majority of existing App Stores have difficulty in recommending proper Apps for users. Therefore, it is of significance to recommend mobile Apps for users according to personal preference and various constraints of mobile devices (e.g., battery power. In this paper, we propose a mobile App recommendation framework by incorporating different requirements from users. We exploit modern portfolio theory (MPT to combine the popularity of mobile Apps, personal preference, and mobile device constraints for mobile App recommendation. Based on this framework, we discuss the recommendation approaches by constraints of phone power and limited mobile data plan. Extensive evaluations show that the proposed mobile App recommendation framework can well adapt to power and network data plan constraints. It satisfies the user App preference and mobile device constraints.

An optimal algorithm for configuring delivery options of a one-dimensional intensity-modulated beam

International Nuclear Information System (INIS)

Luan Shuang; Chen, Danny Z; Zhang, Li; Wu Xiaodong; Yu, Cedric X

2003-01-01

The problem of generating delivery options for one-dimensional intensity-modulated beams (1D IMBs) arises in intensity-modulated radiation therapy. In this paper, we present an algorithm with the optimal running time, based on the 'rightmost-preference' method, for generating all distinct delivery options for an arbitrary 1D IMB. The previously best known method for generating delivery options for a 1D IMB with N left leaf positions and N right leaf positions is a 'brute-force' solution, which first generates all N! possible combinations of the left and right leaf positions and then removes combinations that are not physically allowed delivery options. Compared with the brute-force method, our algorithm has several advantages: (1) our algorithm runs in an optimal time that is linearly proportional to the total number of distinct delivery options that it actually produces. Note that for a 1D IMB with multiple peaks, the total number of distinct delivery options in general tends to be considerably smaller than the worst case N!. (2) Our algorithm can be adapted to generating delivery options subject to additional constraints such as the 'minimum leaf separation' constraint. (3) Our algorithm can also be used to generate random subsets of delivery options; this feature is especially useful when the 1D IMBs in question have too many delivery options for a computer to store and process. The key idea of our method is that we impose an order on how left leaf positions should be paired with right leaf positions. Experiments indicated that our rightmost-preference algorithm runs dramatically faster than the brute-force algorithm. This implies that our algorithm can handle 1D IMBs whose sizes are substantially larger than those handled by the brute-force method. Applications of our algorithm in therapeutic techniques such as intensity-modulated arc therapy and 2D modulations are also discussed

On the relationship between Gaussian stochastic blockmodels and label propagation algorithms

International Nuclear Information System (INIS)

Zhang, Junhao; Hu, Junfeng; Chen, Tongfei

2015-01-01

The problem of community detection has received great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of the label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark networks. (paper)

Evaluation of an Efficient Method for Training Staff to Implement Stimulus Preference Assessments

Science.gov (United States)

Roscoe, Eileen M.; Fisher, Wayne W.

2008-01-01

We used a brief training procedure that incorporated feedback and role-play practice to train staff members to conduct stimulus preference assessments, and we used group-comparison methods to evaluate the effects of training. Staff members were trained to implement the multiple-stimulus-without-replacement assessment in a single session and the…

Research on Modified Root-MUSIC Algorithm of DOA Estimation Based on Covariance Matrix Reconstruction

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Changgan SHU

2014-09-01

Full Text Available In the standard root multiple signal classification algorithm, the performance of direction of arrival estimation will reduce and even lose effect in circumstances that a low signal noise ratio and a small signals interval. By reconstructing and weighting the covariance matrix of received signal, the modified algorithm can provide more accurate estimation results. The computer simulation and performance analysis are given next, which show that under the condition of lower signal noise ratio and stronger correlation between signals, the proposed modified algorithm could provide preferable azimuth estimating performance than the standard method.

An improved algorithm for personalized recommendation on MOOCs

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Yuqin Wang

2017-09-01

Full Text Available Purpose – In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs. MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users. Design/methodology/approach – This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF to select a recommendation set of courses for target MOOC users. Findings – The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is. Originality/value – This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

The Vocational Preference Inventory Scores and Environmental Preferences

Science.gov (United States)

Kunce, Joseph T.; Kappes, Bruno Maurice

1976-01-01

This study investigated the relationship between vocational interest measured by the Vocational Preference Inventory (VPI) and preferences of 175 undergraduates for structured or unstructured environments. Males having clear-cut preferences for structured situations had significantly higher Realistic-Conventional scores than those without…

Network-based recommendation algorithms: A review

Science.gov (United States)

Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš

2016-06-01

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.

Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network

DEFF Research Database (Denmark)

Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar

2013-01-01

Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines...... and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four...... well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable...

Managed ventricular pacing vs. conventional dual-chamber pacing for elective replacements: the PreFER MVP study: clinical background, rationale, and design.

Science.gov (United States)

Quesada, Aurelio; Botto, Gianluca; Erdogan, Ali; Kozak, Milan; Lercher, Peter; Nielsen, Jens Cosedis; Piot, Olivier; Ricci, Renato; Weiss, Christian; Becker, Daniel; Wetzels, Gwenn; De Roy, Luc

2008-03-01

Several clinical studies have shown that, in patients with intact atrioventricular (AV) conduction, unnecessary chronic right ventricular (RV) pacing can be detrimental. The managed ventricular pacing (MVP) algorithm is designed to give preference to spontaneous AV conduction, thus minimizing RV pacing. The clinical outcomes of MVP are being studied in several ongoing trials in patients undergoing a first device implantation, but it is unknown to what extent MVP is beneficial in patients with a history of ventricular pacing. The purpose of the Prefer for Elective Replacement MVP (PreFER MVP) study is to assess the superiority of the MVP algorithm to conventional pacemaker and implantable cardioverter-defibrillator programming in terms of freedom from hospitalization for cardiovascular causes in a population of patients exposed to long periods of ventricular pacing. PreFER MVP is a prospective, 1:1 parallel, randomized (MVP ON/MVP OFF), single-blinded multi-centre trial. The study population consists of patients with more than 40% ventricular pacing documented with their previous device. Approximately, 600 patients will be randomized and followed for at least 24 months. The primary endpoint comprises cardiovascular hospitalization. The PreFER MVP trial is the first large prospective randomized clinical trial evaluating the effect of MVP in patients with a history of RV pacing.

Application of third order stochastic dominance algorithm in investments ranking

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Lončar Sanja

2012-01-01

Full Text Available The paper presents the use of third order stochastic dominance in ranking Investment alternatives, using TSD algorithms (Levy, 2006for testing third order stochastic dominance. The main goal of using TSD rule is minimization of efficient investment set for investor with risk aversion, who prefers more money and likes positive skew ness.

Semiorders, Intervals Orders and Pseudo Orders Preference Structures in Multiple Criteria Decision Aid Methods

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Fernández Barberis, Gabriela

2013-06-01

Full Text Available During the last decades, an important number of Multicriteria Decision Aid Methods (MCDA has been proposed to help the decision maker to select the best compromise alternative. Meanwhile, the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations family of outranking method and their applications has attracted much attention from academics and practitioners. In this paper, an extension of these methods is presented, consisting of analyze its functioning under New Preference Structures (NPS. The preference structures taken into account are, namely: semiorders, intervals orders and pseudo orders. These structures outstandingly improve the modelization as they give more flexibility, amplitude and certainty at the preferences formulation, since they tend to abandon the Complete Transitive Comparability Axiom of Preferences in order to substitute it by the Partial Comparability Axiom of Preferences. It must be remarked the introduction of Incomparability relations to the analysis and the consideration of preference structures that accept the Indifference intransitivity. The NPS incorporation is carried out in three phases that the PROMETHEE Methodology takes in: preference structure enrichment, dominance relation enrichment and outranking relation exploitation for decision aid, in order to finally arrive at solving the alternatives ranking problem through the PROMETHEE I or the PROMETHEE II utilization, according to whether a partial ranking or a complete one, is respectively required under the NPS

Signal filtering algorithm for depth-selective diffuse optical topography

International Nuclear Information System (INIS)

Fujii, M; Nakayama, K

2009-01-01

A compact filtered backprojection algorithm that suppresses the undesirable effects of skin circulation for near-infrared diffuse optical topography is proposed. Our approach centers around a depth-selective filtering algorithm that uses an inverse problem technique and extracts target signals from observation data contaminated by noise from a shallow region. The filtering algorithm is reduced to a compact matrix and is therefore easily incorporated into a real-time system. To demonstrate the validity of this method, we developed a demonstration prototype for depth-selective diffuse optical topography and performed both computer simulations and phantom experiments. The results show that the proposed method significantly suppresses the noise from the shallow region with a minimal degradation of the target signal.

A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.

Science.gov (United States)

Zhang, Aizhu; Sun, Genyun; Ren, Jinchang; Li, Xiaodong; Wang, Zhenjie; Jia, Xiuping

2018-01-01

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.

Parameter optimization of differential evolution algorithm for automatic playlist generation problem

Science.gov (United States)

Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

2017-11-01

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

Kriging-based algorithm for nuclear reactor neutronic design optimization

International Nuclear Information System (INIS)

Kempf, Stephanie; Forget, Benoit; Hu, Lin-Wen

2012-01-01

Highlights: ► A Kriging-based algorithm was selected to guide research reactor optimization. ► We examined impacts of parameter values upon the algorithm. ► The best parameter values were incorporated into a set of best practices. ► Algorithm with best practices used to optimize thermal flux of concept. ► Final design produces thermal flux 30% higher than other 5 MW reactors. - Abstract: Kriging, a geospatial interpolation technique, has been used in the present work to drive a search-and-optimization algorithm which produces the optimum geometric parameters for a 5 MW research reactor design. The technique has been demonstrated to produce an optimal neutronic solution after a relatively small number of core calculations. It has additionally been successful in producing a design which significantly improves thermal neutron fluxes by 30% over existing reactors of the same power rating. Best practices for use of this algorithm in reactor design were identified and indicated the importance of selecting proper correlation functions.

Investigation of the three-dimensional lattice HP protein folding model using a genetic algorithm

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Fábio L. Custódio

2004-01-01

Full Text Available An approach to the hydrophobic-polar (HP protein folding model was developed using a genetic algorithm (GA to find the optimal structures on a 3D cubic lattice. A modification was introduced to the scoring system of the original model to improve the model's capacity to generate more natural-like structures. The modification was based on the assumption that it may be preferable for a hydrophobic monomer to have a polar neighbor than to be in direct contact with the polar solvent. The compactness and the segregation criteria were used to compare structures created by the original HP model and by the modified one. An islands' algorithm, a new selection scheme and multiple-points crossover were used to improve the performance of the algorithm. Ten sequences, seven with length 27 and three with length 64 were analyzed. Our results suggest that the modified model has a greater tendency to form globular structures. This might be preferable, since the original HP model does not take into account the positioning of long polar segments. The algorithm was implemented in the form of a program with a graphical user interface that might have a didactical potential in the study of GA and on the understanding of hydrophobic core formation.

HARDWARE REALIZATION OF CANNY EDGE DETECTION ALGORITHM FOR UNDERWATER IMAGE SEGMENTATION USING FIELD PROGRAMMABLE GATE ARRAYS

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ALEX RAJ S. M.

2017-09-01

Full Text Available Underwater images raise new challenges in the field of digital image processing technology in recent years because of its widespread applications. There are many tangled matters to be considered in processing of images collected from water medium due to the adverse effects imposed by the environment itself. Image segmentation is preferred as basal stage of many digital image processing techniques which distinguish multiple segments in an image and reveal the hidden crucial information required for a peculiar application. There are so many general purpose algorithms and techniques that have been developed for image segmentation. Discontinuity based segmentation are most promising approach for image segmentation, in which Canny Edge detection based segmentation is more preferred for its high level of noise immunity and ability to tackle underwater environment. Since dealing with real time underwater image segmentation algorithm, which is computationally complex enough, an efficient hardware implementation is to be considered. The FPGA based realization of the referred segmentation algorithm is presented in this paper.

Orientation estimation algorithm applied to high-spin projectiles

International Nuclear Information System (INIS)

Long, D F; Lin, J; Zhang, X M; Li, J

2014-01-01

High-spin projectiles are low cost military weapons. Accurate orientation information is critical to the performance of the high-spin projectiles control system. However, orientation estimators have not been well translated from flight vehicles since they are too expensive, lack launch robustness, do not fit within the allotted space, or are too application specific. This paper presents an orientation estimation algorithm specific for these projectiles. The orientation estimator uses an integrated filter to combine feedback from a three-axis magnetometer, two single-axis gyros and a GPS receiver. As a new feature of this algorithm, the magnetometer feedback estimates roll angular rate of projectile. The algorithm also incorporates online sensor error parameter estimation performed simultaneously with the projectile attitude estimation. The second part of the paper deals with the verification of the proposed orientation algorithm through numerical simulation and experimental tests. Simulations and experiments demonstrate that the orientation estimator can effectively estimate the attitude of high-spin projectiles. Moreover, online sensor calibration significantly enhances the estimation performance of the algorithm. (paper)

Orientation estimation algorithm applied to high-spin projectiles

Science.gov (United States)

Long, D. F.; Lin, J.; Zhang, X. M.; Li, J.

2014-06-01

High-spin projectiles are low cost military weapons. Accurate orientation information is critical to the performance of the high-spin projectiles control system. However, orientation estimators have not been well translated from flight vehicles since they are too expensive, lack launch robustness, do not fit within the allotted space, or are too application specific. This paper presents an orientation estimation algorithm specific for these projectiles. The orientation estimator uses an integrated filter to combine feedback from a three-axis magnetometer, two single-axis gyros and a GPS receiver. As a new feature of this algorithm, the magnetometer feedback estimates roll angular rate of projectile. The algorithm also incorporates online sensor error parameter estimation performed simultaneously with the projectile attitude estimation. The second part of the paper deals with the verification of the proposed orientation algorithm through numerical simulation and experimental tests. Simulations and experiments demonstrate that the orientation estimator can effectively estimate the attitude of high-spin projectiles. Moreover, online sensor calibration significantly enhances the estimation performance of the algorithm.

A Method Based on Dial's Algorithm for Multi-time Dynamic Traffic Assignment

Directory of Open Access Journals (Sweden)

Rongjie Kuang

2014-03-01

Full Text Available Due to static traffic assignment has poor performance in reflecting actual case and dynamic traffic assignment may incurs excessive compute cost, method of multi-time dynamic traffic assignment combining static and dynamic traffic assignment balances factors of precision and cost effectively. A method based on Dial's logit algorithm is proposed in the article to solve the dynamic stochastic user equilibrium problem in dynamic traffic assignment. Before that, a fitting function that can proximately reflect overloaded traffic condition of link is proposed and used to give corresponding model. Numerical example is given to illustrate heuristic procedure of method and to compare results with one of same example solved by other literature's algorithm. Results show that method based on Dial's algorithm is preferable to algorithm from others.

The development and initial validation of a clinical tool for patients' preferences on patient participation--The 4Ps.

OpenAIRE

Eldh, Ann Catrine; Luhr, Kristina; Ehnfors, Margareta

2015-01-01

AIMS: To report on the development and initial testing of a clinical tool, The Patient Preferences for Patient Participation tool (The 4Ps), which will allow patients to depict, prioritize, and evaluate their participation in health care. BACKGROUND: While patient participation is vital for high quality health care, a common definition incorporating all stakeholders' experience is pending. In order to support participation in health care, a tool for determining patients' preferences on partic...

Digital terrain model generalization incorporating scale, semantic and cognitive constraints

Science.gov (United States)

Partsinevelos, Panagiotis; Papadogiorgaki, Maria

2014-05-01

Cartographic generalization is a well-known process accommodating spatial data compression, visualization and comprehension under various scales. In the last few years, there are several international attempts to construct tangible GIS systems, forming real 3D surfaces using a vast number of mechanical parts along a matrix formation (i.e., bars, pistons, vacuums). Usually, moving bars upon a structured grid push a stretching membrane resulting in a smooth visualization for a given surface. Most of these attempts suffer either in their cost, accuracy, resolution and/or speed. Under this perspective, the present study proposes a surface generalization process that incorporates intrinsic constrains of tangible GIS systems including robotic-motor movement and surface stretching limitations. The main objective is to provide optimized visualizations of 3D digital terrain models with minimum loss of information. That is, to minimize the number of pixels in a raster dataset used to define a DTM, while reserving the surface information. This neighborhood type of pixel relations adheres to the basics of Self Organizing Map (SOM) artificial neural networks, which are often used for information abstraction since they are indicative of intrinsic statistical features contained in the input patterns and provide concise and characteristic representations. Nevertheless, SOM remains more like a black box procedure not capable to cope with possible particularities and semantics of the application at hand. E.g. for coastal monitoring applications, the near - coast areas, surrounding mountains and lakes are more important than other features and generalization should be "biased"-stratified to fulfill this requirement. Moreover, according to the application objectives, we extend the SOM algorithm to incorporate special types of information generalization by differentiating the underlying strategy based on topologic information of the objects included in the application. The final

Independent preferences

DEFF Research Database (Denmark)

Vind, Karl

1991-01-01

A simple mathematical result characterizing a subset of a product set is proved and used to obtain additive representations of preferences. The additivity consequences of independence assumptions are obtained for preferences which are not total or transitive. This means that most of the economic ...... theory based on additive preferences - expected utility, discounted utility - has been generalized to preferences which are not total or transitive. Other economic applications of the theorem are given...

A structure preserving Lanczos algorithm for computing the optical absorption spectrum

Energy Technology Data Exchange (ETDEWEB)

Shao, Meiyue [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Jornada, Felipe H. da [Univ. of California, Berkeley, CA (United States). Dept. of Physics; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Science Div.; Lin, Lin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Univ. of California, Berkeley, CA (United States). Dept. of Mathematics; Yang, Chao [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Deslippe, Jack [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Louie, Steven G. [Univ. of California, Berkeley, CA (United States). Dept. of Physics; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Science Div.

2016-11-16

We present a new structure preserving Lanczos algorithm for approximating the optical absorption spectrum in the context of solving full Bethe-Salpeter equation without Tamm-Dancoff approximation. The new algorithm is based on a structure preserving Lanczos procedure, which exploits the special block structure of Bethe-Salpeter Hamiltonian matrices. A recently developed technique of generalized averaged Gauss quadrature is incorporated to accelerate the convergence. We also establish the connection between our structure preserving Lanczos procedure with several existing Lanczos procedures developed in different contexts. Numerical examples are presented to demonstrate the effectiveness of our Lanczos algorithm.

Assistance algorithm of nursing for amiodarone intravenous infusion

Directory of Open Access Journals (Sweden)

Francimar Tinoco de Oliveira

2014-12-01

Full Text Available This study aimed at identifying scientific publication on phlebitis caused by amiodarone and proposes a nursing care algorithm for interventions in intravenous amiodarone administration grounded in the Infusion Nursing Society and the Center for Disease Control and Prevention. It is a descriptive study mediated by integrative review in MedLine, LILACS, IBECS, BDENF, Cochrane Library and Scielo bases, published from 2006 to 2013. The sample consisted of nine articles. The evidence pointed the incidence of phlebitis due to the infusion of amiodarone and the need to control this event. The algorithm proposed shows the materials to be used and the procedure of drug administration in order to minimize injury. Besides subsidizing the development of future studies, this algorithm also promotes the incorporation of the best recommendation for the interventionist clinical practice.

Evolutionary algorithm for optimization of nonimaging Fresnel lens geometry.

Science.gov (United States)

Yamada, N; Nishikawa, T

2010-06-21

In this study, an evolutionary algorithm (EA), which consists of genetic and immune algorithms, is introduced to design the optical geometry of a nonimaging Fresnel lens; this lens generates the uniform flux concentration required for a photovoltaic cell. Herein, a design procedure that incorporates a ray-tracing technique in the EA is described, and the validity of the design is demonstrated. The results show that the EA automatically generated a unique geometry of the Fresnel lens; the use of this geometry resulted in better uniform flux concentration with high optical efficiency.

Automated training for algorithms that learn from genomic data.

Science.gov (United States)

Cilingir, Gokcen; Broschat, Shira L

2015-01-01

Supervised machine learning algorithms are used by life scientists for a variety of objectives. Expert-curated public gene and protein databases are major resources for gathering data to train these algorithms. While these data resources are continuously updated, generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction. In this paper, we propose a new model of operation for supervised machine learning algorithms that learn from genomic data. By defining these algorithms in a pipeline in which the training data gathering procedure and the learning process are automated, one can create a system that generates a classifier or predictor using information available from public resources. The proposed model is explained using three case studies on SignalP, MemLoci, and ApicoAP in which existing machine learning models are utilized in pipelines. Given that the vast majority of the procedures described for gathering training data can easily be automated, it is possible to transform valuable machine learning algorithms into self-evolving learners that benefit from the ever-changing data available for gene products and to develop new machine learning algorithms that are similarly capable.

Predicting personal preferences in subjective video quality assessment

DEFF Research Database (Denmark)

Korhonen, Jari

2017-01-01

In this paper, we study the problem of predicting the visual quality of a specific test sample (e.g. a video clip) experienced by a specific user, based on the ratings by other users for the same sample and the same user for other samples. A simple linear model and algorithm is presented, where...... the characteristics of each test sample are represented by a set of parameters, and the individual preferences are represented by weights for the parameters. According to the validation experiment performed on public visual quality databases annotated with raw individual scores, the proposed model can predict...

Incorporation of quality updates for JPSS CGS Products

Science.gov (United States)

Cochran, S.; Grant, K. D.; Ibrahim, W.; Brueske, K. F.; Smit, P.

2016-12-01

NOAA's next-generation environmental satellite, the Joint Polar Satellite System (JPSS) replaces the current Polar-orbiting Operational Environmental Satellites (POES). JPSS satellites carry sensors which collect meteorological, oceanographic, climatological, and solar-geophysical observations of the earth, atmosphere, and space. The first JPSS satellite was launched in 2011 and is currently NOAA's primary operational polar satellite. The JPSS ground system is the Common Ground System (CGS), and provides command, control, and communications (C3) and data processing (DP). A multi-mission system, CGS provides combinations of C3/DP for numerous NASA, NOAA, DoD, and international missions. In preparation for the next JPSS satellite, CGS improved its multi-mission capabilities to enhance mission operations for larger constellations of earth observing satellites with the added benefit of streamlining mission operations for other NOAA missions. This paper will discuss both the theoretical basis and the actual practices used to date to identify, test and incorporate algorithm updates into the CGS processing baseline. To provide a basis for this support, Raytheon developed a theoretical analysis framework, and the application of derived engineering processes, for the maintenance of consistency and integrity of remote sensing operational algorithm outputs. The framework is an abstraction of the operationalization of the science-grade algorithm (Sci2Ops) process used throughout the JPSS program. By combining software and systems engineering controls, manufacturing disciplines to detect and reduce defects, and a standard process to control analysis, an environment to maintain operational algorithm maturity is achieved. Results of the use of this approach to implement algorithm changes into operations will also be detailed.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

Science.gov (United States)

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

An Adaptive Tradeoff Algorithm for Multi-issue SLA Negotiation

Science.gov (United States)

Son, Seokho; Sim, Kwang Mong

Since participants in a Cloud may be independent bodies, mechanisms are necessary for resolving different preferences in leasing Cloud services. Whereas there are currently mechanisms that support service-level agreement negotiation, there is little or no negotiation support for concurrent price and timeslot for Cloud service reservations. For the concurrent price and timeslot negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price and timeslot proposal is necessary. The contribution of this work is thus to design an adaptive tradeoff algorithm for multi-issue negotiation mechanism. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed and total utility and to reduce computational load by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using a testbed suggest that due to the concurrent price and timeslot negotiation mechanism with adaptive tradeoff algorithm: 1) both agents achieve the best performance in terms of negotiation speed and utility; 2) the number of evaluations of each proposal is comparatively lower than previous scheme (burst-N).

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

International Nuclear Information System (INIS)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

Science.gov (United States)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE

Algorithmic, LOCS and HOCS (chemistry) exam questions: performance and attitudes of college students

Science.gov (United States)

Zoller, Uri

2002-02-01

The performance of freshmen biology and physics-mathematics majors and chemistry majors as well as pre- and in-service chemistry teachers in two Israeli universities on algorithmic (ALG), lower-order cognitive skills (LOCS), and higher-order cognitive skills (HOCS) chemistry exam questions were studied. The driving force for the study was an interest in moving science and chemistry instruction from an algorithmic and factual recall orientation dominated by LOCS, to a decision-making, problem-solving and critical system thinking approach, dominated by HOCS. College students' responses to the specially designed ALG, LOCS and HOCS chemistry exam questions were scored and analysed for differences and correlation between the performance means within and across universities by the questions' category. This was followed by a combined student interview - 'speaking aloud' problem solving session for assessing the thinking processes involved in solving these types of questions and the students' attitudes towards them. The main findings were: (1) students in both universities performed consistently in each of the three categories in the order of ALG > LOCS > HOCS; their 'ideological' preference, was HOCS > algorithmic/LOCS, - referred to as 'computational questions', but their pragmatic preference was the reverse; (2) success on algorithmic/LOCS does not imply success on HOCS questions; algorithmic questions constitute a category on its own as far as students success in solving them is concerned. Our study and its results support the effort being made, worldwide, to integrate HOCS-fostering teaching and assessment strategies and, to develop HOCS-oriented science-technology-environment-society (STES)-type curricula within science and chemistry education.

Incorporating reliability evaluation into the uncertainty analysis of electricity market price

International Nuclear Information System (INIS)

Kang, Chongqing; Bai, Lichao; Xia, Qing; Jiang, Jianjian; Zhao, Jing

2005-01-01

A novel model and algorithm for analyzing the uncertainties in electricity market is proposed in this paper. In this model, bidding decision is formulated as a probabilistic model that takes into account the decision-maker's willingness to bid, risk preferences, the fluctuation of fuel-price, etc. At the same time, generating unit's uncertain output model is considered by its forced outage rate (FOR). Based on the model, the uncertainty of market price is then analyzed. Taking the analytical results into consideration, not only the reliability of the power system can be conventionally analyzed, but also the possible distribution of market prices can be easily obtained. The probability distribution of market prices can be further used to calculate the expected output and the sales income of generating unit in the market. Based on these results, it is also possible to evaluate the risk involved by generating units. A simple system with four generating units is used to illustrate the proposed algorithm. The proposed algorithm and the modeling technique are expected to helpful to the market participants in making their economic decisions

Application of particle swarm optimization algorithm in the heating system planning problem.

Science.gov (United States)

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

Pressure algorithm for elliptic flow calculations with the PDF method

Science.gov (United States)

Anand, M. S.; Pope, S. B.; Mongia, H. C.

1991-01-01

An algorithm to determine the mean pressure field for elliptic flow calculations with the probability density function (PDF) method is developed and applied. The PDF method is a most promising approach for the computation of turbulent reacting flows. Previous computations of elliptic flows with the method were in conjunction with conventional finite volume based calculations that provided the mean pressure field. The algorithm developed and described here permits the mean pressure field to be determined within the PDF calculations. The PDF method incorporating the pressure algorithm is applied to the flow past a backward-facing step. The results are in good agreement with data for the reattachment length, mean velocities, and turbulence quantities including triple correlations.

Sustainability assessment in forest management based on individual preferences.

Science.gov (United States)

Martín-Fernández, Susana; Martinez-Falero, Eugenio

2018-01-15

This paper presents a methodology to elicit the preferences of any individual in the assessment of sustainable forest management at the stand level. The elicitation procedure was based on the comparison of the sustainability of pairs of forest locations. A sustainability map of the whole territory was obtained according to the individual's preferences. Three forest sustainability indicators were pre-calculated for each point in a study area in a Scots pine forest in the National Park of Sierra de Guadarrama in the Madrid Region in Spain to obtain the best management plan with the sustainability map. We followed a participatory process involving fifty people to assess the sustainability of the forest management and the methodology. The results highlighted the demand for conservative forest management, the usefulness of the methodology for managers, and the importance and necessity of incorporating stakeholders into forestry decision-making processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transitivity of Preferences

Science.gov (United States)

Regenwetter, Michel; Dana, Jason; Davis-Stober, Clintin P.

2011-01-01

Transitivity of preferences is a fundamental principle shared by most major contemporary rational, prescriptive, and descriptive models of decision making. To have transitive preferences, a person, group, or society that prefers choice option "x" to "y" and "y" to "z" must prefer "x" to…

Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency.

Science.gov (United States)

Hisler, Garrett C; Phillips, Alison L; Krizan, Zlatan

2017-06-01

Diurnal preference (and chronotype more generally) has been implicated in exercise behavior, but this relation has not been examined using objective exercise measurements nor have potential psychosocial mediators been examined. Furthermore, time-of-day often moderates diurnal preference's influence on outcomes, and it is unknown whether time-of-exercise may influence the relation between chronotype and exercise frequency. The current study examined whether individual differences in diurnal preference ("morningness-eveningness") predict unique variance in exercise frequency and if commonly studied psychosocial variables mediate this relation (i.e., behavioral intentions, internal exercise control, external exercise control, and conscientiousness). Moreover, the study sought to test whether individuals' typical time-of-exercise moderated the impact of diurnal preference on exercise frequency. One hundred twelve healthy adults (mean age = 25.4; SD = 11.6 years) completed baseline demographics and then wore Fitbit Zips® for 4 weeks to objectively measure exercise frequency and typical time-of-exercise. At the end of the study, participants also self-reported recent exercise. Diurnal preference predicted both self-reported exercise and Fitbit-recorded exercise frequency. When evaluating mediators, only conscientiousness emerged as a partial mediator of the relation between diurnal preference and self-reported exercise. In addition, time-of-exercise moderated diurnal preference's relation to both self-reported exercise and Fitbit-recorded exercise frequency such that diurnal preference predicted higher exercise frequency when exercise occurred at a time that was congruent with one's diurnal preference. Based on these findings, diurnal preference is valuable, above and beyond other psychological constructs, in predicting exercise frequency and represents an important variable to incorporate into interventions seeking to increase exercise.

AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

Directory of Open Access Journals (Sweden)

V.G. Biju

2015-11-01

Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

A recursive economic dispatch algorithm for assessing the cost of thermal generator schedules

International Nuclear Information System (INIS)

Wong, K.P.; Doan, K.

1992-01-01

This paper develops an efficient, recursive algorithm for determining the economic power dispatch of thermal generators within the unit commitment environment. A method for incorporating the operation limits of all on-line generators and limits due to ramping generators is developed in the paper. The developed algorithm is amenable for computer implementation using the artificial intelligence programming language, Prolog. The performance of the developed algorithm is demonstrated through its application to evaluate the costs of dispatching 13 thermal generators within a generator schedule in a 24-hour schedule horizon

An Adaptive Large Neighborhood Search Algorithm for the Multi-mode RCPSP

DEFF Research Database (Denmark)

Muller, Laurent Flindt

We present an Adaptive Large Neighborhood Search algorithm for the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP). We incorporate techniques for deriving additional precedence relations and propose a new method, so-called mode-diminution, for removing modes during execution...

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.

Science.gov (United States)

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.

Iterative schemes for parallel Sn algorithms in a shared-memory computing environment

International Nuclear Information System (INIS)

Haghighat, A.; Hunter, M.A.; Mattis, R.E.

1995-01-01

Several two-dimensional spatial domain partitioning S n transport theory algorithms are developed on the basis of different iterative schemes. These algorithms are incorporated into TWOTRAN-II and tested on the shared-memory CRAY Y-MP C90 computer. For a series of fixed-source r-z geometry homogeneous problems, it is demonstrated that the concurrent red-black algorithms may result in large parallel efficiencies (>60%) on C90. It is also demonstrated that for a realistic shielding problem, the use of the negative flux fixup causes high load imbalance, which results in a significant loss of parallel efficiency

Algorithm integration using ADL (Algorithm Development Library) for improving CrIMSS EDR science product quality

Science.gov (United States)

Das, B.; Wilson, M.; Divakarla, M. G.; Chen, W.; Barnet, C.; Wolf, W.

2013-05-01

Algorithm Development Library (ADL) is a framework that mimics the operational system IDPS (Interface Data Processing Segment) that is currently being used to process data from instruments aboard Suomi National Polar-orbiting Partnership (S-NPP) satellite. The satellite was launched successfully in October 2011. The Cross-track Infrared and Microwave Sounder Suite (CrIMSS) consists of the Advanced Technology Microwave Sounder (ATMS) and Cross-track Infrared Sounder (CrIS) instruments that are on-board of S-NPP. These instruments will also be on-board of JPSS (Joint Polar Satellite System) that will be launched in early 2017. The primary products of the CrIMSS Environmental Data Record (EDR) include global atmospheric vertical temperature, moisture, and pressure profiles (AVTP, AVMP and AVPP) and Ozone IP (Intermediate Product from CrIS radiances). Several algorithm updates have recently been proposed by CrIMSS scientists that include fixes to the handling of forward modeling errors, a more conservative identification of clear scenes, indexing corrections for daytime products, and relaxed constraints between surface temperature and air temperature for daytime land scenes. We have integrated these improvements into the ADL framework. This work compares the results from ADL emulation of future IDPS system incorporating all the suggested algorithm updates with the current official processing results by qualitative and quantitative evaluations. The results prove these algorithm updates improve science product quality.

Case Study: A Bio-Inspired Control Algorithm for a Robotic Foot-Ankle Prosthesis Provides Adaptive Control of Level Walking and Stair Ascent

Directory of Open Access Journals (Sweden)

Uzma Tahir

2018-04-01

Full Text Available Powered ankle-foot prostheses assist users through plantarflexion during stance and dorsiflexion during swing. Provision of motor power permits faster preferred walking speeds than passive devices, but use of active motor power raises the issue of control. While several commercially available algorithms provide torque control for many intended activities and variations of terrain, control approaches typically exhibit no inherent adaptation. In contrast, muscles adapt instantaneously to changes in load without sensory feedback due to the intrinsic property that their stiffness changes with length and velocity. We previously developed a “winding filament” hypothesis (WFH for muscle contraction that accounts for intrinsic muscle properties by incorporating the giant titin protein. The goals of this study were to develop a WFH-based control algorithm for a powered prosthesis and to test its robustness during level walking and stair ascent in a case study of two subjects with 4–5 years of experience using a powered prosthesis. In the WFH algorithm, ankle moments produced by virtual muscles are calculated based on muscle length and activation. Net ankle moment determines the current applied to the motor. Using this algorithm implemented in a BiOM T2 prosthesis, we tested subjects during level walking and stair ascent. During level walking at variable speeds, the WFH algorithm produced plantarflexion angles (range = −8 to −19° and ankle moments (range = 1 to 1.5 Nm/kg similar to those produced by the BiOM T2 stock controller and to people with no amputation. During stair ascent, the WFH algorithm produced plantarflexion angles (range −15 to −19° that were similar to persons with no amputation and were ~5 times larger on average at 80 steps/min than those produced by the stock controller. This case study provides proof-of-concept that, by emulating muscle properties, the WFH algorithm provides robust, adaptive control of level walking at

Assessment of transport performance index for urban transport development strategies — Incorporating residents' preferences

Energy Technology Data Exchange (ETDEWEB)

Ambarwati, Lasmini, E-mail: L.Ambarwati@tudelft.nl [Department of Transport and Planning, TU Delft (Netherlands); Department of Civil Engineering, Brawijaya University (Indonesia); Verhaeghe, Robert, E-mail: R.Verhaeghe@tudelft.nl [Department of Transport and Planning, TU Delft (Netherlands); Arem, Bart van, E-mail: B.vanArem@tudelft.nl [Department of Transport and Planning, TU Delft (Netherlands); Pel, Adam J., E-mail: A.J.Pel@tudelft.nl [Department of Transport and Planning, TU Delft (Netherlands)

2017-03-15

The performance of urban transport depends on a variety of factors related to metropolitan structure; in particular, the patterns of commuting, roads and public transport (PT) systems. To evaluate urban transport planning efforts, there is a need for a metric expressing the aggregate performance of the city's transport systems which should relate to residents' preferences. The existing metrics have typically focused on a measure to express the proximity of job locations to residences. A Transport Performance Index (TPI) is proposed in which the total cost of transportation system (operational and environmental costs) is divided by willingness to pay (WTP) for transport plus the willingness to accept (WTA) the environmental effects on residents. Transport operational as well as the environmental costs are derived from a simulation of all transport systems, to particular designs of spatial development. Willingness to pay for transport and willingness to accept the environmental effects are derived from surveys among residents. Simulations were modelled of Surabaya's spatial structure and public transport expansion. The results indicate that the current TPI is high, which will double by 2030. With a hypothetical polycentric city structure and adjusted job housing balance, a lower index occurs because of the improvements in urban transport performance. A low index means that the residents obtain much benefit from the alternative proposed. This illustrates the importance of residents' preferences in urban spatial planning in order to achieve efficient urban transport. Applying the index suggests that city authorities should provide fair and equitable public transport systems for suburban residents in the effort to control the phenomenon of urban sprawl. This index is certainly a good tool and prospective benchmark for measuring sustainability in relation to urban development.

Assessment of transport performance index for urban transport development strategies — Incorporating residents' preferences

International Nuclear Information System (INIS)

Ambarwati, Lasmini; Verhaeghe, Robert; Arem, Bart van; Pel, Adam J.

2017-01-01

The performance of urban transport depends on a variety of factors related to metropolitan structure; in particular, the patterns of commuting, roads and public transport (PT) systems. To evaluate urban transport planning efforts, there is a need for a metric expressing the aggregate performance of the city's transport systems which should relate to residents' preferences. The existing metrics have typically focused on a measure to express the proximity of job locations to residences. A Transport Performance Index (TPI) is proposed in which the total cost of transportation system (operational and environmental costs) is divided by willingness to pay (WTP) for transport plus the willingness to accept (WTA) the environmental effects on residents. Transport operational as well as the environmental costs are derived from a simulation of all transport systems, to particular designs of spatial development. Willingness to pay for transport and willingness to accept the environmental effects are derived from surveys among residents. Simulations were modelled of Surabaya's spatial structure and public transport expansion. The results indicate that the current TPI is high, which will double by 2030. With a hypothetical polycentric city structure and adjusted job housing balance, a lower index occurs because of the improvements in urban transport performance. A low index means that the residents obtain much benefit from the alternative proposed. This illustrates the importance of residents' preferences in urban spatial planning in order to achieve efficient urban transport. Applying the index suggests that city authorities should provide fair and equitable public transport systems for suburban residents in the effort to control the phenomenon of urban sprawl. This index is certainly a good tool and prospective benchmark for measuring sustainability in relation to urban development.

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

International Nuclear Information System (INIS)

Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Vermandel, Maximilien; Baillet, Clio

2015-01-01

Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging.Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used.Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results.The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging. (paper)

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Science.gov (United States)

Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien

2015-12-01

Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

Fuzzy Logic-Based Model That Incorporates Personality Traits for Heterogeneous Pedestrians

Directory of Open Access Journals (Sweden)

Zhuxin Xue

2017-10-01

Full Text Available Most models designed to simulate pedestrian dynamical behavior are based on the assumption that human decision-making can be described using precise values. This study proposes a new pedestrian model that incorporates fuzzy logic theory into a multi-agent system to address cognitive behavior that introduces uncertainty and imprecision during decision-making. We present a concept of decision preferences to represent the intrinsic control factors of decision-making. To realize the different decision preferences of heterogeneous pedestrians, the Five-Factor (OCEAN personality model is introduced to model the psychological characteristics of individuals. Then, a fuzzy logic-based approach is adopted for mapping the relationships between the personality traits and the decision preferences. Finally, we have developed an application using our model to simulate pedestrian dynamical behavior in several normal or non-panic scenarios, including a single-exit room, a hallway with obstacles, and a narrowing passage. The effectiveness of the proposed model is validated with a user study. The results show that the proposed model can generate more reasonable and heterogeneous behavior in the simulation and indicate that individual personality has a noticeable effect on pedestrian dynamical behavior.

BootGraph: probabilistic fiber tractography using bootstrap algorithms and graph theory.

Science.gov (United States)

Vorburger, Robert S; Reischauer, Carolin; Boesiger, Peter

2013-02-01

Bootstrap methods have recently been introduced to diffusion-weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model. These methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain. Thereby, the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms. Graph based approaches have been proposed to overcome this drawback of streamline techniques. For this reason, the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method, called BootGraph. The acquired data set is thereby converted into a weighted, undirected graph by defining a vertex in each voxel and edges between adjacent vertices. By means of the cone of uncertainty, which is derived using the wild bootstrap, a weight is thereafter assigned to each edge. Two path finding algorithms are subsequently applied to derive connection probabilities. While the first algorithm is based on the shortest path approach, the second algorithm takes all existing paths between two vertices into consideration. Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm. The BootGraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints, such as a curvature threshold. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of

Caliko: An Inverse Kinematics Software Library Implementation of the FABRIK Algorithm

OpenAIRE

Lansley, Alastair; Vamplew, Peter; Smith, Philip; Foale, Cameron

2016-01-01

The Caliko library is an implementation of the FABRIK (Forward And Backward Reaching Inverse Kinematics) algorithm written in Java. The inverse kinematics (IK) algorithm is implemented in both 2D and 3D, and incorporates a variety of joint constraints as well as the ability to connect multiple IK chains together in a hierarchy. The library allows for the simple creation and solving of multiple IK chains as well as visualisation of these solutions. It is licensed under the MIT software license...

Automatic brightness control algorithms and their effect on fluoroscopic imaging

International Nuclear Information System (INIS)

Quinn, P.W.; Gagne, R.M.

1989-01-01

This paper reports a computer model used to investigate the effect on dose and image quality of three automatic brightness control (ABC) algorithms used in the imaging of barium during general-purpose fluoroscopy. A model incorporating all aspects of image formation - i.e., x- ray production, phantom attenuation, and energy absorption in the CSI phosphor - was driven according to each ABC algorithm as a function of patient thickness. The energy absorbed in the phosphor was kept constant, while the changes in exposure, integral dose, organ dose, and contrast were monitored

An approach to decision-making with triangular fuzzy reciprocal preference relations and its application

Science.gov (United States)

Meng, Fanyong

2018-02-01

Triangular fuzzy reciprocal preference relations (TFRPRs) are powerful tools to denoting decision-makers' fuzzy judgments, which permit the decision-makers to apply triangular fuzzy ratio rather than real numbers to express their judgements. Consistency analysis is one of the most crucial issues in preference relations that can guarantee the reasonable ranking order. However, all previous consistency concepts cannot well address this type of preference relations. Based on the operational laws on triangular fuzzy numbers, this paper introduces an additive consistency concept for TFRPRs by using quasi TFRPRs, which can be seen as a natural extension of the crisp case. Using this consistency concept, models to judging the additive consistency of TFRPRs and to estimating missing values in complete TFRPRs are constructed. Then, an algorithm to decision-making with TFRPRs is developed. Finally, two numerical examples are offered to illustrate the application of the proposed procedure, and comparison analysis is performed.

Enhanced Seamless Handover Algorithm for WiMAX and LTE Roaming

Directory of Open Access Journals (Sweden)

HINDIA, M. N.

2014-11-01

Full Text Available With the ever evolving mobile communication technology, achieving a high quality seamless mobility access across mobile networks is the present challenge to research and development engineers. Existing algorithms are used to make handover while a mobile station is roaming between cells. Such algorithms have some handover instability due to method of making handover decision. This paper proposes an enhanced handover algorithm that substantially reduces the handover redundancy in vertical and horizontal handovers. Also, it enables users to select the most appropriate target network technology based on their preferences even in the worst case where the mobile station roams between cell boundaries, and has high ability to have efficient performance in the critical area full of interferences. The proposed algorithm uses additional quality of service criteria, such as cost, delay, available bandwidth and network condition with two handover thresholds to achieve a better seamless handover process. After developing and testing this algorithm, the simulation results show a major reduction in the redundant handover, so high accuracy of horizontal and vertical handovers obtained. Moreover, the signal strength is kept at a level higher than the threshold during the whole simulation period, while maintaining low delay and connection cost compared to other two algorithms in both scenarios.

Automated measurement of spatial preference in the open field test with transmitted lighting.

Science.gov (United States)

Kulikov, Alexander V; Tikhonova, Maria A; Kulikov, Victor A

2008-05-30

New modification of the open field was designed to improve automation of the test. The main innovations were: (1) transmitted lighting and (2) estimation of probability to find pixels associated with an animal in the selected region of arena as an objective index of spatial preference. Transmitted (inverted) lighting significantly ameliorated the contrast between an animal and arena and allowed to track white animals with similar efficacy as colored ones. Probability as a measure of preference of selected region was mathematically proved and experimentally verified. A good correlation between probability and classic indices of spatial preference (number of region entries and time spent therein) was shown. The algorithm of calculation of probability to find pixels associated with an animal in the selected region was implemented in the EthoStudio software. Significant interstrain differences in locomotion and the central zone preference (index of anxiety) were shown using the inverted lighting and the EthoStudio software in mice of six inbred strains. The effects of arena shape (circle or square) and a novel object presence in the center of arena on the open field behavior in mice were studied.

An item-oriented recommendation algorithm on cold-start problem

Science.gov (United States)

Qiu, Tian; Chen, Guang; Zhang, Zi-Ke; Zhou, Tao

2011-09-01

Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A., 107 (2010) 4511), in this letter, we propose an improved method by introducing an item-oriented function, focusing on solving the dilemma of the recommendation accuracy between the cold and popular items. Differently from previous works, the present algorithm does not require any additional information (e.g., tags). Further experimental results obtained in three real datasets, RYM, Netflix and MovieLens, show that, compared with the original hybrid method, the proposed algorithm significantly enhances the recommendation accuracy of the cold items, while it keeps the recommendation accuracy of the overall and the popular items. This work might shed some light on both understanding and designing effective methods for long-tailed online applications of recommender systems.

Bidirectional Fano Algorithm for Lattice Coded MIMO Channels

KAUST Repository

Al-Quwaiee, Hessa

2013-05-08

Recently, lattices - a mathematical representation of infinite discrete points in the Euclidean space, have become an effective way to describe and analyze communication systems especially system those that can be modeled as linear Gaussian vector channel model. Channel codes based on lattices are preferred due to three facts: lattice codes have simple structure, the code can achieve the limits of the channel, and they can be decoded efficiently using lattice decoders which can be considered as the Closest Lattice Point Search (CLPS). Since the time lattice codes were introduced to Multiple Input Multiple Output (MIMO) channel, Sphere Decoder (SD) has been an efficient way to implement lattice decoders. Sphere decoder offers the optimal performance at the expense of high decoding complexity especially for low signal-to-noise ratios (SNR) and for high- dimensional systems. On the other hand, linear and non-linear receivers, Minimum Mean Square Error (MMSE), and MMSE Decision-Feedback Equalization (DFE), provide the lowest decoding complexity but unfortunately with poor performance. Several studies works have been conducted in the last years to address the problem of designing low complexity decoders for the MIMO channel that can achieve near optimal performance. It was found that sequential decoders using backward tree 
search can bridge the gap between SD and MMSE. The sequential decoder provides an interesting performance-complexity trade-off using a bias term. Yet, the sequential decoder still suffers from high complexity for mid-to-high SNR values. In this work, we propose a new algorithm for Bidirectional Fano sequential Decoder (BFD) in order to reduce the mid-to-high SNR complexity. Our algorithm consists of first constructing a unidirectional Sequential Decoder based on forward search using the QL decomposition. After that, BFD incorporates two searches, forward and backward, to work simultaneously till they merge and find the closest lattice point to the

Effects of organic additives on preferred plane and residual stress of copper electroplated on polyimide

International Nuclear Information System (INIS)

Kim, Jongsoo; Kim, Heesan

2010-01-01

Effects of the preferred plane and the residual stress of an electroplated copper on polyethylene glycol (PEG) and 3-N,N-dimethylaminodithiocarbamoyl-1-propanesulfonic acid (DPS) were studied. Polyimide film coated with sputtered copper was used as a substrate. Preferred plane, residual stress, and impurity level in the electroplated copper were measured by an X-ray diffractometry (XRD), calculated by Stoney's equation, and analyzed with secondary ion mass spectroscopy (SMS), respectively. With increasing the concentration of PEG, the preferred plane changed in the order (1 0 0) and (1 1 0) while with increasing the concentration of DPS, the preferred plane changed in the order (1 1 0), (1 0 0), and (1 1 1). Based on the modified preferred growth model, where the amount of additive adsorbed on a plane is newly assumed to be proportional to its surface energy in vacuum, the predicted preferred planes correspond to the experimental results. The residual stress of the electroplated copper depended on the type of additive as well as its concentration but was independent of the preferred plane. For example, PEG and DPS induced tensile and compressive residual stresses in the electroplated copper, respectively, and their magnitudes increased with their concentrations. The dependency of residual stress on the additives was explained by the incorporated additives into the electroplated copper.

Young women's contraceptive microbicide preferences: associations with contraceptive behavior and sexual relationship characteristics.

Science.gov (United States)

Best, Candace; Tanner, Amanda E; Hensel, Devon J; Fortenberry, J Dennis; Zimet, Gregory D

2014-03-01

In time, microbicides may provide women with dual prevention against pregnancy and STDs. Although several microbicide dimensions have been evaluated, little is known about women's preferences for contraceptive microbicides and correlates of these preferences. Acceptability of a hypothetical contraceptive microbicide cream or jelly was examined among a -clinic-based sample of 266 women in Indianapolis from 2004 (when participants were aged 14-22) to 2008. Group conjoint analyses and individual conjoint analyses were used to compare preferences with respect to four microbicide -dimensions: contraceptive ability, efficacy in relation to condoms, timing of use and texture. Pearson's product moment correlations were used to examine the relationship between preferences for a contraceptive microbicide and selected characteristics of the women. Overall, the top-rated microbicide dimensions were efficacy in relation to that of condoms and contraceptive ability (importance scores, 40.0 and 35.4 out of 100.0, respectively). When all dimension levels were compared, contraceptive ability was the most strongly preferred (part-worth utility score, 8.9), and lower efficacy than that of -condoms was the least strongly preferred (-11.9). Preference for contraceptive microbicides was positively -associated with current contraceptive use, sexual agency, partner communication, commitment to avoiding pregnancy and -perceived partner agreement about avoiding pregnancy (coefficients, 0.07-0.18). It was negatively associated with current or past nonuse of contraceptives, seeking pregnancy and perceived partner agreement about seeking -pregnancy (-0.08 to -0.14). Microbicides with dual prevention properties may be attractive to young women. Microbicide development and subsequent clinical trials should incorporate contraceptive microbicides. Copyright © 2013 by the Guttmacher Institute.

On Optimizing H. 264/AVC Rate Control by Improving R-D Model and Incorporating HVS Characteristics

Directory of Open Access Journals (Sweden)

Jiang Gangyi

2010-01-01

Full Text Available The state-of-the-art JVT-G012 rate control algorithm of H.264 is improved from two aspects. First, the quadratic rate-distortion (R-D model is modified based on both empirical observations and theoretical analysis. Second, based on the existing physiological and psychological research findings of human vision, the rate control algorithm is optimized by incorporating the main characteristics of the human visual system (HVS such as contrast sensitivity, multichannel theory, and masking effect. Experiments are conducted, and experimental results show that the improved algorithm can simultaneously enhance the overall subjective visual quality and improve the rate control precision effectively.

Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

International Nuclear Information System (INIS)

Martorell, S.; Carlos, S.; Villanueva, J.F.; Sanchez, A.I; Galvan, B.; Salazar, D.; Cepin, M.

2006-01-01

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability

Patient preferences versus physicians' judgement: does it make a difference in healthcare decision making?

Science.gov (United States)

Mühlbacher, Axel C; Juhnke, Christin

2013-06-01

and the patient. This in turn may keep physicians from fully appreciating the impact of certain medical conditions on patient preferences. Because differences exist between physicians' judgement and patient preferences, it is important to incorporate the needs and wants of the patient into treatment decisions.

Energetic integration of discontinuous processes by means of genetic algorithms, GABSOBHIN; Integration energetique de procedes discontinus a l'aide d'algorithmes genetiques, GABSOBHIN

Energy Technology Data Exchange (ETDEWEB)

Krummenacher, P.; Renaud, B.; Marechal, F.; Favrat, D.

2001-07-01

This report presents a new methodological approach for the optimal design of energy-integrated batch processes. The main emphasis is put on indirect and, to some extend, on direct heat exchange networks with the possibility of introducing closed or open storage systems. The study demonstrates the feasibility of optimising with genetic algorithms while highlighting the pros and cons of this type of approach. The study shows that the resolution of such problems should preferably be done in several steps to better target the expected solutions. Demonstration is made that in spite of relatively large computer times (on PCs) the use of genetic algorithm allows the consideration of both continuous decision variables (size, operational rating of equipment, etc.) and integer variables (related to the structure at design and during operation). Comparison of two optimisation strategies is shown with a preference for a two-steps optimisation scheme. One of the strengths of genetic algorithms is the capacity to accommodate heuristic rules, which can be introduced in the model. However, a rigorous modelling strategy is advocated to improve robustness and adequate coding of the decision variables. The practical aspects of the research work are converted into a software developed with MATLAB to solve the energy integration of batch processes with a reasonable number of closed or open stores. This software includes the model of superstructures, including the heat exchangers and the storage alternatives, as well as the link to the Struggle algorithm developed at MIT via a dedicated new interface. The package also includes a user-friendly pre-processing using EXCEL, which is to facilitate to application to other similar industrial problems. These software developments have been validated both on an academic and on an industrial type of problems. (author)

Factors Influencing Parents' Preferences and Parents' Perceptions of Child Preferences of Picturebooks

Science.gov (United States)

Wagner, Laura

2017-01-01

This study examined factors influencing parents' preferences and their perceptions of their children's preferences for picturebooks. First, a content analysis was conducted on a set of picturebooks (N = 87) drawn from the sample described in Wagner (2013); Then, parents (N = 149) rated the books and several content properties were examined for their ability to predict parents' preferences and their perception of their children's preferences. The initial content analysis found correlated clusters of disparate measures of complexity (linguistic, cognitive, narrative) and identified a distinctive sub-genre of modern books featuring female protagonists. The experimental preference analysis found that parents' own preferences were most influenced by the books' age and status; parents' perceptions of their children's preferences were influenced by gender, with parents perceiving their sons (but not daughters) as dis-preferring books with female protagnoists. In addition, influences of the child's reading ability and the linguistic complexity of the book on preferences suggested a sensitivity to the cultural practice of joint book-reading. PMID:28919869

Association of health literacy with health information-seeking preference in older people: A correlational, descriptive study.

Science.gov (United States)

Kim, Su Hyun; Utz, Sonja

2018-02-28

Low health literacy has been recognized as a potential barrier to obtaining knowledge and maintaining self-care in older people. However, little is known about information-seeking preference in relation to health literacy among older people. The aim of the present study was to understand the influence of health literacy on the information-seeking preference of older people. A total of 129 community-residing Korean older people completed a survey in 2016. The findings revealed that health literacy was a significant predictor of information-seeking preference in older people after controlling for demographic and illness variables. Our study highlights the important need to incorporate strategies to increase the desire for information seeking in older people, in addition to adopting communication strategies that address low health literacy. © 2018 John Wiley & Sons Australia, Ltd.

Genetic Algorithm Design of a 3D Printed Heat Sink

Energy Technology Data Exchange (ETDEWEB)

Wu, Tong [ORNL; Ozpineci, Burak [ORNL; Ayers, Curtis William [ORNL

2016-01-01

In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate the performance of the newly designed heat sinkcompared to commercially available heat sinks.

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA for Load Profiling Applications

Directory of Open Access Journals (Sweden)

Ioannis P. Panapakidis

2018-02-01

Full Text Available Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS. A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

A spectral algorithm for the seriation problem

Energy Technology Data Exchange (ETDEWEB)

Atkins, J.E. [Michigan Univ., Ann Arbor, MI (United States). Dept. of Mathematics; Boman, E.G. [Stanford Univ., CA (United States). Dept. of Computer Science; Hendrickson, B. [Sandia National Labs., Albuquerque, NM (United States)

1994-11-01

Given a set of objects and a correlation function f reflecting the desire for two items to be near each other, find all sequences {pi} of the items so that correlation preferences are preserved; that is if {pi}(i) < {pi}(j) < {pi}(k) then f(i,j) {ge} f(i,k) and f(j,k) {ge} f(i,k). This seriation problem has numerous applications, for instance, solving it yields a solution to the consecutive ones problem. We present a spectral algorithm for this problem that has a number of interesting features. Whereas most previous applications of spectral techniques provided bounds or heuristics, our result is an algorithm for a nontrivial combinatorial problem. Our analysis introduces powerful tools from matrix theory to the theoretical computer science community. Also, spectral methods are being applied as heuristics for a variety of sequencing problems and our result helps explain and justify these applications. Although the worst case running time for our approach is not competitive with that of existing methods for well posed problem instances, unlike combinatorial approaches our algorithm remains a credible heuristic for the important cases where there are errors in the data.

Incorporating a modified uniform crossover and 2-exchange neighborhood mechanism in a discrete bat algorithm to solve the quadratic assignment problem

Directory of Open Access Journals (Sweden)

Mohammed Essaid Riffi

2017-11-01

Full Text Available The bat algorithm is one of the recent nature-inspired algorithms, which has been emerged as a powerful search method for solving continuous as well as discrete problems. The quadratic assignment problem is a well-known NP-hard problem in combinatorial optimization. The goal of this problem is to assign n facilities to n locations in such a way as to minimize the assignment cost. For that purpose, this paper introduces a novel discrete variant of bat algorithm to deal with this combinatorial optimization problem. The proposed algorithm was evaluated on a set of benchmark instances from the QAPLIB library and the performance was compared to other algorithms. The empirical results of exhaustive experiments were promising and illustrated the efficacy of the suggested approach.

Proposed Fuzzy-NN Algorithm with LoRaCommunication Protocol for Clustered Irrigation Systems

Directory of Open Access Journals (Sweden)

Sotirios Kontogiannis

2017-11-01

Full Text Available Modern irrigation systems utilize sensors and actuators, interconnected together as a single entity. In such entities, A.I. algorithms are implemented, which are responsible for the irrigation process. In this paper, the authors present an irrigation Open Watering System (OWS architecture that spatially clusters the irrigation process into autonomous irrigation sections. Authors’ OWS implementation includes a Neuro-Fuzzy decision algorithm called FITRA, which originates from the Greek word for seed. In this paper, the FITRA algorithm is described in detail, as are experimentation results that indicate significant water conservations from the use of the FITRA algorithm. Furthermore, the authors propose a new communication protocol over LoRa radio as an alternative low-energy and long-range OWS clusters communication mechanism. The experimental scenarios confirm that the FITRA algorithm provides more efficient irrigation on clustered areas than existing non-clustered, time scheduled or threshold adaptive algorithms. This is due to the FITRA algorithm’s frequent monitoring of environmental conditions, fuzzy and neural network adaptation as well as adherence to past irrigation preferences.

Sustainable logistics and transportation optimization models and algorithms

CERN Document Server

Gakis, Konstantinos; Pardalos, Panos

2017-01-01

Focused on the logistics and transportation operations within a supply chain, this book brings together the latest models, algorithms, and optimization possibilities. Logistics and transportation problems are examined within a sustainability perspective to offer a comprehensive assessment of environmental, social, ethical, and economic performance measures. Featured models, techniques, and algorithms may be used to construct policies on alternative transportation modes and technologies, green logistics, and incentives by the incorporation of environmental, economic, and social measures. Researchers, professionals, and graduate students in urban regional planning, logistics, transport systems, optimization, supply chain management, business administration, information science, mathematics, and industrial and systems engineering will find the real life and interdisciplinary issues presented in this book informative and useful.

Preferences and beliefs in ingroup favoritism.

Science.gov (United States)

Everett, Jim A C; Faber, Nadira S; Crockett, Molly

2015-01-01

Ingroup favoritism-the tendency to favor members of one's own group over those in other groups-is well documented, but the mechanisms driving this behavior are not well understood. In particular, it is unclear to what extent ingroup favoritism is driven by preferences concerning the welfare of ingroup over outgroup members, vs. beliefs about the behavior of ingroup and outgroup members. In this review we analyze research on ingroup favoritism in economic games, identifying key gaps in the literature and providing suggestions on how future work can incorporate these insights to shed further light on when, why, and how ingroup favoritism occurs. In doing so, we demonstrate how social psychological theory and research can be integrated with findings from behavioral economics, providing new theoretical and methodological directions for future research.

Growth Inhibition of Sporomusa ovata by Incorporation of Benzimidazole Bases into Cobamides

Science.gov (United States)

Mok, Kenny C.

2013-01-01

Phenolyl cobamides are unique members of a class of cobalt-containing cofactors that includes vitamin B12 (cobalamin). Cobamide cofactors facilitate diverse reactions in prokaryotes and eukaryotes. Phenolyl cobamides are structurally and chemically distinct from the more commonly used benzimidazolyl cobamides such as cobalamin, as the lower axial ligand is a phenolic group rather than a benzimidazole. The functional significance of this difference is not well understood. Here we show that in the bacterium Sporomusa ovata, the only organism known to synthesize phenolyl cobamides, several cobamide-dependent acetogenic metabolisms have a requirement or preference for phenolyl cobamides. The addition of benzimidazoles to S. ovata cultures results in a decrease in growth rate when grown on methanol, 3,4-dimethoxybenzoate, H2 plus CO2, or betaine. Suppression of native p-cresolyl cobamide synthesis and production of benzimidazolyl cobamides occur upon the addition of benzimidazoles, indicating that benzimidazolyl cobamides are not functionally equivalent to the phenolyl cobamide cofactors produced by S. ovata. We further show that S. ovata is capable of incorporating other phenolic compounds into cobamides that function in methanol metabolism. These results demonstrate that S. ovata can incorporate a wide range of compounds as cobamide lower ligands, despite its preference for phenolyl cobamides in the metabolism of certain energy substrates. To our knowledge, S. ovata is unique among cobamide-dependent organisms in its preferential utilization of phenolyl cobamides. PMID:23417488

Incorporating creditors' seniority into contingent claim models: Applicarion to peripheral euro area countries

OpenAIRE

Gómez-Puig, Marta; Singh, Manish Kumar; Sosvilla Rivero, Simón, 1961-

2018-01-01

This paper highlights the role of multilateral creditors (i.e., the ECB, IMF, ESM etc.) and their preferred creditor status in explaining the sovereign default risk of peripheral euro area (EA) countries. Incorporating lessons from sovereign debt crises in general, and from the Greek debt restructuring in particular, we define the priority structure of sovereigns' creditors that is most relevant for peripheral EA countries in severe crisis episodes. This new priority structure of creditors, t...

Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset.

Science.gov (United States)

Schedl, Markus

2017-01-01

Recently, the LFM-1b dataset has been proposed to foster research and evaluation in music retrieval and music recommender systems, Schedl (Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR). New York, 2016). It contains more than one billion music listening events created by more than 120,000 users of Last.fm. Each listening event is characterized by artist, album, and track name, and further includes a timestamp. Basic demographic information and a selection of more elaborate listener-specific descriptors are included as well, for anonymized users. In this article, we reveal information about LFM-1b's acquisition and content and we compare it to existing datasets. We furthermore provide an extensive statistical analysis of the dataset, including basic properties of the item sets, demographic coverage, distribution of listening events (e.g., over artists and users), and aspects related to music preference and consumption behavior (e.g., temporal features and mainstreaminess of listeners). Exploiting country information of users and genre tags of artists, we also create taste profiles for populations and determine similar and dissimilar countries in terms of their populations' music preferences. Finally, we illustrate the dataset's usage in a simple artist recommendation task, whose results are intended to serve as baseline against which more elaborate techniques can be assessed.

Patient preference compared with random allocation in short-term psychodynamic supportive psychotherapy with indicated addition of pharmacotherapy for depression.

NARCIS (Netherlands)

Van, H.L.; Dekker, J.J.M.; Koelen, J.; Kool, S.; van Aalst, G.; Hendriksen, I.J.M.; Peen, J.; Schoevers, R.A.

2009-01-01

Depressed patients randomized to psychotherapy were compared with those who had been chosen for psychotherapy in a treatment algorithm, including addition of an antidepressant in case of early nonresponse. There were no differences between randomized and by-preference patients at baseline in

A Multidisciplinary Algorithm for the 3-D Design Optimization of Transonic Axial Compressor Blades

National Research Council Canada - National Science Library

Jones, James

2002-01-01

...) that can be easily manipulated to achieve true 3-D changes in blade shape. The algorithm Incorporates zero and first-order optimization techniques including sensitivity analyses and one-dimensional search methodology...

Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint

Energy Technology Data Exchange (ETDEWEB)

Wang, Dexin; Yang, Liuqing; Florita, Anthony; Alam, S.M. Shafiul; Elgindy, Tarek; Hodge, Bri-Mathias

2016-08-01

The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.

Genetic algorithm based on virus theory of evolution for traveling salesman problem; Virus shinkaron ni motozuku identeki algorithm no junkai salesman mondai eno oyo

Energy Technology Data Exchange (ETDEWEB)

Kubota, N. [Osaka Inst. of Technology, Osaka (Japan); Fukuda, T. [Nagoya University, Nagoya (Japan)

1998-05-31

This paper deals with virus evolutionary genetic algorithm. The genetic algorithms (GAs) have been demonstrated its effectiveness in optimization problems in these days. In general, the GAs simulate the survival of fittest by natural selection and the heredity of the Darwin`s theory of evolution. However, some types of evolutionary hypotheses such as neutral theory of molecular evolution, Imanishi`s evolutionary theory, serial symbiosis theory, and virus theory of evolution, have been proposed in addition to the Darwinism. Virus theory of evolution is based on the view that the virus transduction is a key mechanism for transporting segments of DNA across species. This paper proposes genetic algorithm based on the virus theory of evolution (VE-GA), which has two types of populations: host population and virus population. The VE-GA is composed of genetic operators and virus operators such as reverse transcription and incorporation. The reverse transcription operator transcribes virus genes on the chromosome of host individual and the incorporation operator creates new genotype of virus from host individual. These operators by virus population make it possible to transmit segment of DNA between individuals in the host population. Therefore, the VE-GA realizes not only vertical but also horizontal propagation of genetic information. Further, the VE-GA is applied to the traveling salesman problem in order to show the effectiveness. 20 refs., 10 figs., 3 tabs.

Continuing Education Preferences, Facilitators, and Barriers for Nursing Home Nurses.

Science.gov (United States)

Dyck, Mary J; Kim, Myoung Jin

2018-01-01

The purpose of the study was to determine the continuing education needs for nursing home nurses in rural central Illinois and to determine any potential facilitators or barriers to obtaining continuing education. Data were collected using the Educational Needs Assessment questionnaire. Descriptive statistics were computed to examine continuing education preferences, facilitators, and barriers among nursing home nurses. Independent samples t tests were used to compare preferences between administrative and staff nurses. The sample included 317 nurses from 34 facilities. The five top needs were related to clinical problems. Administrative nurses had greater needs for professional issues, managerial skills, and quality improvement than staff nurses. Barriers included rural settings, need for vacation time for programs, and inadequate staffing. Continuing education needs of nursing home nurses in Illinois are similar to previous studies conducted in Arizona and North Carolina. Continuing education barriers were mostly organizational, rather than personal. J Contin Nurs Educ. 2018;49(1):26-33. Copyright 2018, SLACK Incorporated.

Simulated annealing algorithm for solving chambering student-case assignment problem

Science.gov (United States)

Ghazali, Saadiah; Abdul-Rahman, Syariza

2015-12-01

The problem related to project assignment problem is one of popular practical problem that appear nowadays. The challenge of solving the problem raise whenever the complexity related to preferences, the existence of real-world constraints and problem size increased. This study focuses on solving a chambering student-case assignment problem by using a simulated annealing algorithm where this problem is classified under project assignment problem. The project assignment problem is considered as hard combinatorial optimization problem and solving it using a metaheuristic approach is an advantage because it could return a good solution in a reasonable time. The problem of assigning chambering students to cases has never been addressed in the literature before. For the proposed problem, it is essential for law graduates to peruse in chambers before they are qualified to become legal counselor. Thus, assigning the chambering students to cases is a critically needed especially when involving many preferences. Hence, this study presents a preliminary study of the proposed project assignment problem. The objective of the study is to minimize the total completion time for all students in solving the given cases. This study employed a minimum cost greedy heuristic in order to construct a feasible initial solution. The search then is preceded with a simulated annealing algorithm for further improvement of solution quality. The analysis of the obtained result has shown that the proposed simulated annealing algorithm has greatly improved the solution constructed by the minimum cost greedy heuristic. Hence, this research has demonstrated the advantages of solving project assignment problem by using metaheuristic techniques.

Agent assisted interactive algorithm for computationally demanding multiobjective optimization problems

OpenAIRE

Ojalehto, Vesa; Podkopaev, Dmitry; Miettinen, Kaisa

2015-01-01

We generalize the applicability of interactive methods for solving computationally demanding, that is, time-consuming, multiobjective optimization problems. For this purpose we propose a new agent assisted interactive algorithm. It employs a computationally inexpensive surrogate problem and four different agents that intelligently update the surrogate based on the preferences specified by a decision maker. In this way, we decrease the waiting times imposed on the decision maker du...

Neural modelling of ranking data with an application to stated preference data

Directory of Open Access Journals (Sweden)

Catherine Krier

2013-05-01

Full Text Available Although neural networks are commonly encountered to solve classification problems, ranking data present specificities which require adapting the model. Based on a latent utility function defined on the characteristics of the objects to be ranked, the approach suggested in this paper leads to a perceptron-based algorithm for a highly non linear model. Data on stated preferences obtained through a survey by face-to-face interviews, in the field of freight transport, are used to illustrate the method. Numerical difficulties are pinpointed and a Pocket type algorithm is shown to provide an efficient heuristic to minimize the discrete error criterion. A substantial merit of this approach is to provide a workable estimation of contextually interpretable parameters along with a statistical evaluation of the goodness of fit.

Firefly Algorithm for Polynomial Bézier Surface Parameterization

Directory of Open Access Journals (Sweden)

Akemi Gálvez

2013-01-01

reality, medical imaging, computer graphics, computer animation, and many others. Very often, the preferred approximating surface is polynomial, usually described in parametric form. This leads to the problem of determining suitable parametric values for the data points, the so-called surface parameterization. In real-world settings, data points are generally irregularly sampled and subjected to measurement noise, leading to a very difficult nonlinear continuous optimization problem, unsolvable with standard optimization techniques. This paper solves the parameterization problem for polynomial Bézier surfaces by applying the firefly algorithm, a powerful nature-inspired metaheuristic algorithm introduced recently to address difficult optimization problems. The method has been successfully applied to some illustrative examples of open and closed surfaces, including shapes with singularities. Our results show that the method performs very well, being able to yield the best approximating surface with a high degree of accuracy.

A Location-Aware Vertical Handoff Algorithm for Hybrid Networks

KAUST Repository

Mehbodniya, Abolfazl

2010-07-01

One of the main objectives of wireless networking is to provide mobile users with a robust connection to different networks so that they can move freely between heterogeneous networks while running their computing applications with no interruption. Horizontal handoff, or generally speaking handoff, is a process which maintains a mobile user\\'s active connection as it moves within a wireless network, whereas vertical handoff (VHO) refers to handover between different types of networks or different network layers. Optimizing VHO process is an important issue, required to reduce network signalling and mobile device power consumption as well as to improve network quality of service (QoS) and grade of service (GoS). In this paper, a VHO algorithm in multitier (overlay) networks is proposed. This algorithm uses pattern recognition to estimate user\\'s position, and decides on the handoff based on this information. For the pattern recognition algorithm structure, the probabilistic neural network (PNN) which has considerable simplicity and efficiency over existing pattern classifiers is used. Further optimization is proposed to improve the performance of the PNN algorithm. Performance analysis and comparisons with the existing VHO algorithm are provided and demonstrate a significant improvement with the proposed algorithm. Furthermore, incorporating the proposed algorithm, a structure is proposed for VHO from the medium access control (MAC) layer point of view. © 2010 ACADEMY PUBLISHER.

Automated characterization of perceptual quality of clinical chest radiographs: Validation and calibration to observer preference

International Nuclear Information System (INIS)

Samei, Ehsan; Lin, Yuan; Choudhury, Kingshuk R.; Page McAdams, H.

2014-01-01

Purpose: The authors previously proposed an image-based technique [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] to assess the perceptual quality of clinical chest radiographs. In this study, an observer study was designed and conducted to validate the output of the program against rankings by expert radiologists and to establish the ranges of the output values that reflect the acceptable image appearance so the program output can be used for image quality optimization and tracking. Methods: Using an IRB-approved protocol, 2500 clinical chest radiographs (PA/AP) were collected from our clinical operation. The images were processed through our perceptual quality assessment program to measure their appearance in terms of ten metrics of perceptual image quality: lung gray level, lung detail, lung noise, rib–lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm–lung contrast, and subdiaphragm area. From the results, for each targeted appearance attribute/metric, 18 images were selected such that the images presented a relatively constant appearance with respect to all metrics except the targeted one. The images were then incorporated into a graphical user interface, which displayed them into three panels of six in a random order. Using a DICOM calibrated diagnostic display workstation and under low ambient lighting conditions, each of five participating attending chest radiologists was tasked to spatially order the images based only on the targeted appearance attribute regardless of the other qualities. Once ordered, the observer also indicated the range of image appearances that he/she considered clinically acceptable. The observer data were analyzed in terms of the correlations between the observer and algorithmic rankings and interobserver variability. An observer-averaged acceptable image appearance was also statistically derived for each quality attribute based on the collected individual acceptable ranges

Automated characterization of perceptual quality of clinical chest radiographs: Validation and calibration to observer preference

Energy Technology Data Exchange (ETDEWEB)

Samei, Ehsan, E-mail: samei@duke.edu [Carl E. Ravin Advanced Imaging Laboratories, Departments of Radiology, Physics, Biomedical Engineering, Electrical and Computer Engineering, Medical Physics Graduate Program, Duke Clinical Imaging Physics Group, Duke University, Durham, North Carolina 27710 (United States); Lin, Yuan [Carl E. Ravin Advanced Imaging Laboratories, Departments of Radiology and Physics, Duke University, Durham, North Carolina 27710 (United States); Choudhury, Kingshuk R. [Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology and Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27710 (United States); Page McAdams, H. [Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, North Carolina 27710 (United States)

2014-11-01

Purpose: The authors previously proposed an image-based technique [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] to assess the perceptual quality of clinical chest radiographs. In this study, an observer study was designed and conducted to validate the output of the program against rankings by expert radiologists and to establish the ranges of the output values that reflect the acceptable image appearance so the program output can be used for image quality optimization and tracking. Methods: Using an IRB-approved protocol, 2500 clinical chest radiographs (PA/AP) were collected from our clinical operation. The images were processed through our perceptual quality assessment program to measure their appearance in terms of ten metrics of perceptual image quality: lung gray level, lung detail, lung noise, rib–lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm–lung contrast, and subdiaphragm area. From the results, for each targeted appearance attribute/metric, 18 images were selected such that the images presented a relatively constant appearance with respect to all metrics except the targeted one. The images were then incorporated into a graphical user interface, which displayed them into three panels of six in a random order. Using a DICOM calibrated diagnostic display workstation and under low ambient lighting conditions, each of five participating attending chest radiologists was tasked to spatially order the images based only on the targeted appearance attribute regardless of the other qualities. Once ordered, the observer also indicated the range of image appearances that he/she considered clinically acceptable. The observer data were analyzed in terms of the correlations between the observer and algorithmic rankings and interobserver variability. An observer-averaged acceptable image appearance was also statistically derived for each quality attribute based on the collected individual acceptable ranges

How Spatial Relationships Influence Economic Preferences for Wind Power—A Review

Directory of Open Access Journals (Sweden)

Lauren Knapp

2015-06-01

Full Text Available An increasing number of studies in the environmental and resource economic literature suggest that preferences for changes or improvements in environmental amenities, from water quality to recreation, are spatially heterogeneous. One of these effects in particular, distance decay, suggests that respondents exhibit a higher willingness to pay (WTP the closer they live to a proposed environmental improvement and vice versa. The importance of spatial effects cannot be underestimated. Several of these studies find significant biases in aggregate WTP values, and therefore social welfare, from models that disregard spatial factors. This relationship between spatial aspects and preferences, however, remains largely ignored in the non-market valuation literature applied to valuing preferences for renewable energy, generally, and wind power, specifically. To our knowledge, fourteen peer-reviewed studies have been conducted to estimate stated preferences (SP for onshore and/or offshore wind development, yet less than half of those utilize any measure to account for the relationship between spatial effects and preferences. Fewer still undertake more robust measures that account for these spatially dependent relationships, such as via GIS, outside incorporating a single ‘distance’ attribute within the choice experiment (CE referenda. This paper first reviews the methodologies of the SP wind valuation studies that have integrated measure(s to account for spatial effects. We then categorize these effects into three dimensions—distance to a proposed wind project, distance to existing wind project(s, and cumulative effects—supporting each with a discussion of significant findings, including those found in the wind hedonic and acceptance literature. Policy implications that can be leveraged to maximize social welfare when siting future wind projects as well as recommendations for additional research to control for preference spatial heterogeneity in wind

TITRATION: A Randomized Study to Assess 2 Treatment Algorithms with New Insulin Glargine 300 units/mL.

Science.gov (United States)

Yale, Jean-François; Berard, Lori; Groleau, Mélanie; Javadi, Pasha; Stewart, John; Harris, Stewart B

2017-10-01

It was uncertain whether an algorithm that involves increasing insulin dosages by 1 unit/day may cause more hypoglycemia with the longer-acting insulin glargine 300 units/mL (GLA-300). The objective of this study was to compare safety and efficacy of 2 titration algorithms, INSIGHT and EDITION, for GLA-300 in people with uncontrolled type 2 diabetes mellitus, mainly in a primary care setting. This was a 12-week, open-label, randomized, multicentre pilot study. Participants were randomly assigned to 1 of 2 algorithms: they either increased their dosage by 1 unit/day (INSIGHT, n=108) or the dose was adjusted by the investigator at least once weekly, but no more often than every 3 days (EDITION, n=104). The target fasting self-monitored blood glucose was in the range of 4.4 to 5.6 mmol/L. The percentages of participants reaching the primary endpoint of fasting self-monitored blood glucose ≤5.6 mmol/L without nocturnal hypoglycemia were 19.4% (INSIGHT) and 18.3% (EDITION). At week 12, 26.9% (INSIGHT) and 28.8% (EDITION) of participants achieved a glycated hemoglobin value of ≤7%. No differences in the incidence of hypoglycemia of any category were noted between algorithms. Participants in both arms of the study were much more satisfied with their new treatment as assessed by the Diabetes Treatment Satisfaction Questionnaire. Most health-care professionals (86%) preferred the INSIGHT over the EDITION algorithm. The frequency of adverse events was similar between algorithms. A patient-driven titration algorithm of 1 unit/day with GLA-300 is effective and comparable to the previously tested EDITION algorithm and is preferred by health-care professionals. Copyright © 2017 Diabetes Canada. Published by Elsevier Inc. All rights reserved.

Transient analysis and leakage detection algorithm using GA and HS algorithm for a pipeline system

Energy Technology Data Exchange (ETDEWEB)

Kim, Sang Hyun; Yoo, Wan Suk; Oh, Kwang Jung; Hwang, In Sung; Oh, Jeong Eun [Pusan National University, Pusan (Korea, Republic of)

2006-03-15

The impact of leakage was incorporated into the transfer functions of the complex head and discharge. The impedance transfer functions for the various leaking pipeline systems were also derived. Hydraulic transients could be efficiently analyzed by the developed method. The simulation of normalized pressure variation using the method of characteristics and the impulse response method shows good agreement to the condition of turbulent flow. The leak calibration could be performed by incorporation of the impulse response method with Genetic Algorithm (GA) and Harmony Search (HS). The objective functions for the leakage detection can be made using the pressure-head response at the valve, or the pressure-head or the flow response at a certain point of the pipeline located upstream from the valve. The proposed method is not constrained by the Courant number to control the numerical dissipation of the method of characteristics. The limitations associated with the discreteness of the pipeline system in the inverse transient analysis can be neglected in the proposed method.

Transient analysis and leakage detection algorithm using GA and HS algorithm for a pipeline system

International Nuclear Information System (INIS)

Kim, Sang Hyun; Yoo, Wan Suk; Oh, Kwang Jung; Hwang, In Sung; Oh, Jeong Eun

2006-01-01

The impact of leakage was incorporated into the transfer functions of the complex head and discharge. The impedance transfer functions for the various leaking pipeline systems were also derived. Hydraulic transients could be efficiently analyzed by the developed method. The simulation of normalized pressure variation using the method of characteristics and the impulse response method shows good agreement to the condition of turbulent flow. The leak calibration could be performed by incorporation of the impulse response method with Genetic Algorithm (GA) and Harmony Search (HS). The objective functions for the leakage detection can be made using the pressure-head response at the valve, or the pressure-head or the flow response at a certain point of the pipeline located upstream from the valve. The proposed method is not constrained by the Courant number to control the numerical dissipation of the method of characteristics. The limitations associated with the discreteness of the pipeline system in the inverse transient analysis can be neglected in the proposed method

Application of genetic algorithms to in-core nuclear fuel management optimization

International Nuclear Information System (INIS)

Poon, P.W.; Parks, G.T.

1993-01-01

The search for an optimal arrangement of fresh and burnt fuel and control material within the core of a PWR represents a formidable optimization problem. The approach of combining the robust optimization capabilities of the Simulated Annealing (SA) algorithm with the computational speed of a Generalized Perturbation Theory (GPT) based evaluation methodology in the code FORMOSA has proved to be very effective. In this paper, we show that the incorporation of another stochastic search technique, a Genetic Algorithm, results in comparable optimization performance on serial computers and offers substantially superior performance on parallel machines. (orig.)

The development of PubMed search strategies for patient preferences for treatment outcomes.

Science.gov (United States)

van Hoorn, Ralph; Kievit, Wietske; Booth, Andrew; Mozygemba, Kati; Lysdahl, Kristin Bakke; Refolo, Pietro; Sacchini, Dario; Gerhardus, Ansgar; van der Wilt, Gert Jan; Tummers, Marcia

2016-07-29

The importance of respecting patients' preferences when making treatment decisions is increasingly recognized. Efficiently retrieving papers from the scientific literature reporting on the presence and nature of such preferences can help to achieve this goal. The objective of this study was to create a search filter for PubMed to help retrieve evidence on patient preferences for treatment outcomes. A total of 27 journals were hand-searched for articles on patient preferences for treatment outcomes published in 2011. Selected articles served as a reference set. To develop optimal search strategies to retrieve this set, all articles in the reference set were randomly split into a development and a validation set. MeSH-terms and keywords retrieved using PubReMiner were tested individually and as combinations in PubMed and evaluated for retrieval performance (e.g. sensitivity (Se) and specificity (Sp)). Of 8238 articles, 22 were considered to report empirical evidence on patient preferences for specific treatment outcomes. The best search filters reached Se of 100 % [95 % CI 100-100] with Sp of 95 % [94-95 %] and Sp of 97 % [97-98 %] with 75 % Se [74-76 %]. In the validation set these queries reached values of Se of 90 % [89-91 %] with Sp 94 % [93-95 %] and Se of 80 % [79-81 %] with Sp of 97 % [96-96 %], respectively. Narrow and broad search queries were developed which can help in retrieving literature on patient preferences for treatment outcomes. Identifying such evidence may in turn enhance the incorporation of patient preferences in clinical decision making and health technology assessment.

Nature Disaster Risk Evaluation with a Group Decision Making Method Based on Incomplete Hesitant Fuzzy Linguistic Preference Relations

Directory of Open Access Journals (Sweden)

Ming Tang

2018-04-01

Full Text Available Because the natural disaster system is a very comprehensive and large system, the disaster reduction scheme must rely on risk analysis. Experts’ knowledge and experiences play a critical role in disaster risk assessment. The hesitant fuzzy linguistic preference relation is an effective tool to express experts’ preference information when comparing pairwise alternatives. Owing to the lack of knowledge or a heavy workload, information may be missed in the hesitant fuzzy linguistic preference relation. Thus, an incomplete hesitant fuzzy linguistic preference relation is constructed. In this paper, we firstly discuss some properties of the additive consistent hesitant fuzzy linguistic preference relation. Next, the incomplete hesitant fuzzy linguistic preference relation, the normalized hesitant fuzzy linguistic preference relation, and the acceptable hesitant fuzzy linguistic preference relation are defined. Afterwards, three procedures to estimate the missing information are proposed. The first one deals with the situation in which there are only n − 1 known judgments involving all the alternatives; the second one is used to estimate the missing information of the hesitant fuzzy linguistic preference relation with more known judgments; while the third procedure is used to deal with ignorance situations in which there is at least one alternative with totally missing information. Furthermore, an algorithm for group decision making with incomplete hesitant fuzzy linguistic preference relations is given. Finally, we illustrate our model with a case study about flood disaster risk evaluation. A comparative analysis is presented to testify the advantage of our method.

Nature Disaster Risk Evaluation with a Group Decision Making Method Based on Incomplete Hesitant Fuzzy Linguistic Preference Relations.

Science.gov (United States)

Tang, Ming; Liao, Huchang; Li, Zongmin; Xu, Zeshui

2018-04-13

Because the natural disaster system is a very comprehensive and large system, the disaster reduction scheme must rely on risk analysis. Experts' knowledge and experiences play a critical role in disaster risk assessment. The hesitant fuzzy linguistic preference relation is an effective tool to express experts' preference information when comparing pairwise alternatives. Owing to the lack of knowledge or a heavy workload, information may be missed in the hesitant fuzzy linguistic preference relation. Thus, an incomplete hesitant fuzzy linguistic preference relation is constructed. In this paper, we firstly discuss some properties of the additive consistent hesitant fuzzy linguistic preference relation. Next, the incomplete hesitant fuzzy linguistic preference relation, the normalized hesitant fuzzy linguistic preference relation, and the acceptable hesitant fuzzy linguistic preference relation are defined. Afterwards, three procedures to estimate the missing information are proposed. The first one deals with the situation in which there are only n-1 known judgments involving all the alternatives; the second one is used to estimate the missing information of the hesitant fuzzy linguistic preference relation with more known judgments; while the third procedure is used to deal with ignorance situations in which there is at least one alternative with totally missing information. Furthermore, an algorithm for group decision making with incomplete hesitant fuzzy linguistic preference relations is given. Finally, we illustrate our model with a case study about flood disaster risk evaluation. A comparative analysis is presented to testify the advantage of our method.

Optimal platform design using non-dominated sorting genetic algorithm II and technique for order of preference by similarity to ideal solution; application to automotive suspension system

Science.gov (United States)

Shojaeefard, Mohammad Hassan; Khalkhali, Abolfazl; Faghihian, Hamed; Dahmardeh, Masoud

2018-03-01

Unlike conventional approaches where optimization is performed on a unique component of a specific product, optimum design of a set of components for employing in a product family can cause significant reduction in costs. Increasing commonality and performance of the product platform simultaneously is a multi-objective optimization problem (MOP). Several optimization methods are reported to solve these MOPs. However, what is less discussed is how to find the trade-off points among the obtained non-dominated optimum points. This article investigates the optimal design of a product family using non-dominated sorting genetic algorithm II (NSGA-II) and proposes the employment of technique for order of preference by similarity to ideal solution (TOPSIS) method to find the trade-off points among the obtained non-dominated results while compromising all objective functions together. A case study for a family of suspension systems is presented, considering performance and commonality. The results indicate the effectiveness of the proposed method to obtain the trade-off points with the best possible performance while maximizing the common parts.

Learning-style preferences of Latino/Hispanic community college students enrolled in an introductory biology course

Science.gov (United States)

Sarantopoulos, Helen D.

Purpose. The purpose of this study was to identify, according to the Productivity Environment Preference Survey (PEPS) instrument, which learning-style domains (environmental, emotional, sociological, and physiological) were favored among Latino/Hispanic community college students enrolled in introductory biology classes in a large, urban community college. An additional purpose of this study was to determine whether statistically significant differences existed between the learning-style preferences and the demographic variables of age, gender, number of prior science courses, second language learner status, and earlier exposure to scientific information. Methodology. The study design was descriptive and ex post facto. The sample consisted of a total of 332 Latino/Hispanic students enrolled in General Biology 3. Major findings. The study revealed that Latino/Hispanic students enrolled in introductory biology at a large urban community college scored higher for the learning preference element of structure. Students twenty-five years and older scored higher for the learning preference elements of light, design, persistence, responsibility, and morning time (p learning-style preferences were found between second English language learners and those who learned English as their primary language (p tactile (p learning-style model and instruments and on recent learning-style research articles on ethnically diverse groups of adult learners; and (2) Instructors should plan their instruction to incorporate the learning-style preferences of their students.

Going direct to the consumer: Examining treatment preferences for veterans with insomnia, PTSD, and depression.

Science.gov (United States)

Gutner, Cassidy A; Pedersen, Eric R; Drummond, Sean P A

2018-05-01

Inclusion of consumer preferences to disseminate evidence-based psychosocial treatment (EBPT) is crucial to effectively bridge the science-to-practice quality chasm. We examined this treatment gap for insomnia, posttraumatic stress disorder (PTSD), depression, and comorbid symptoms in a sample of 622 young adult veterans through preference in symptom focus, treatment modality, and related gender differences among those screening positive for each problem. Data were collected from veteran drinkers recruited through targeted Facebook advertisements as part of a brief online alcohol intervention. Analyses demonstrated that veterans reported greater willingness to seek insomnia-focused treatment over PTSD- or depression-focused care. Notably, even when participants screened negative for insomnia, they preferred sleep-focused care to PTSD- or depression-focused care. Although one in five veterans with a positive screen would not consider care, veterans screening for both insomnia and PTSD who would consider care had a preference for in-person counseling, and those screening for both insomnia and depression had similar preferences for in-person and mobile app-based/computer self-help treatment. Marginal gender differences were found. Incorporating direct-to-consumer methods into research can help educate stakeholders about methods to expand EBPT access. Though traditional in-person counseling was often preferred, openness to app-based/computer interventions offers alternative methods to provide veterans with EBPTs. Published by Elsevier B.V.

Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis

DEFF Research Database (Denmark)

Kaltoft, Mette Kjer; Turner, Robin; Cunich, Michelle

2015-01-01

The use of subgroups based on biological-clinical and socio-demographic variables to deal with population heterogeneity is well-established in public policy. The use of subgroups based on preferences is rare, except when religion based, and controversial. If it were decided to treat subgroup...... preferences as valid determinants of public policy, a transparent analytical procedure is needed. In this proof of method study we show how public preferences could be incorporated into policy decisions in a way that respects both the multi-criterial nature of those decisions, and the heterogeneity...... techniques of CA to demonstrate that not only do different techniques produce different clusters, but that choosing among techniques (as well as developing the MCDA structure) is an important task to be undertaken in implementing the approach outlined in any specific policy context. Data for the illustrative...

Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems

International Nuclear Information System (INIS)

Ahmadi, Mohamadreza; Mojallali, Hamed

2012-01-01

Highlights: ► A new meta-heuristic optimization algorithm. ► Integration of invasive weed optimization and chaotic search methods. ► A novel parameter identification scheme for chaotic systems. - Abstract: This paper introduces a novel hybrid optimization algorithm by taking advantage of the stochastic properties of chaotic search and the invasive weed optimization (IWO) method. In order to deal with the weaknesses associated with the conventional method, the proposed chaotic invasive weed optimization (CIWO) algorithm is presented which incorporates the capabilities of chaotic search methods. The functionality of the proposed optimization algorithm is investigated through several benchmark multi-dimensional functions. Furthermore, an identification technique for chaotic systems based on the CIWO algorithm is outlined and validated by several examples. The results established upon the proposed scheme are also supplemented which demonstrate superior performance with respect to other conventional methods.

Happy faces are preferred regardless of familiarity--sad faces are preferred only when familiar.

Science.gov (United States)

Liao, Hsin-I; Shimojo, Shinsuke; Yeh, Su-Ling

2013-06-01

Familiarity leads to preference (e.g., the mere exposure effect), yet it remains unknown whether it is objective familiarity, that is, repetitive exposure, or subjective familiarity that contributes to preference. In addition, it is unexplored whether and how different emotions influence familiarity-related preference. The authors investigated whether happy or sad faces are preferred or perceived as more familiar and whether this subjective familiarity judgment correlates with preference for different emotional faces. An emotional face--happy or sad--was paired with a neutral face, and participants rated the relative preference and familiarity of each of the paired faces. For preference judgment, happy faces were preferred and sad faces were less preferred, compared with neutral faces. For familiarity judgment, happy faces did not show any bias, but sad faces were perceived as less familiar than neutral faces. Item-by-item correlational analyses show preference for sad faces--but not happy faces--positively correlate with familiarity. These results suggest a direct link between positive emotion and preference, and argue at least partly against a common cause for familiarity and preference. Instead, facial expression of different emotional valence modulates the link between familiarity and preference.

A reconstruction algorithm for electrical impedance tomography based on sparsity regularization

KAUST Repository

Jin, Bangti

2011-08-24

This paper develops a novel sparse reconstruction algorithm for the electrical impedance tomography problem of determining a conductivity parameter from boundary measurements. The sparsity of the \\'inhomogeneity\\' with respect to a certain basis is a priori assumed. The proposed approach is motivated by a Tikhonov functional incorporating a sparsity-promoting ℓ 1-penalty term, and it allows us to obtain quantitative results when the assumption is valid. A novel iterative algorithm of soft shrinkage type was proposed. Numerical results for several two-dimensional problems with both single and multiple convex and nonconvex inclusions were presented to illustrate the features of the proposed algorithm and were compared with one conventional approach based on smoothness regularization. © 2011 John Wiley & Sons, Ltd.

Bayesian PET image reconstruction incorporating anato-functional joint entropy

International Nuclear Information System (INIS)

Tang Jing; Rahmim, Arman

2009-01-01

We developed a maximum a posterior (MAP) reconstruction method for positron emission tomography (PET) image reconstruction incorporating magnetic resonance (MR) image information, with the joint entropy between the PET and MR image features serving as the regularization constraint. A non-parametric method was used to estimate the joint probability density of the PET and MR images. Using realistically simulated PET and MR human brain phantoms, the quantitative performance of the proposed algorithm was investigated. Incorporation of the anatomic information via this technique, after parameter optimization, was seen to dramatically improve the noise versus bias tradeoff in every region of interest, compared to the result from using conventional MAP reconstruction. In particular, hot lesions in the FDG PET image, which had no anatomical correspondence in the MR image, also had improved contrast versus noise tradeoff. Corrections were made to figures 3, 4 and 6, and to the second paragraph of section 3.1 on 13 November 2009. The corrected electronic version is identical to the print version.

Ultrasonic particle image velocimetry for improved flow gradient imaging: algorithms, methodology and validation

International Nuclear Information System (INIS)

Niu Lili; Qian Ming; Yu Wentao; Jin Qiaofeng; Ling Tao; Zheng Hairong; Wan Kun; Gao Shen

2010-01-01

This paper presents a new algorithm for ultrasonic particle image velocimetry (Echo PIV) for improving the flow velocity measurement accuracy and efficiency in regions with high velocity gradients. The conventional Echo PIV algorithm has been modified by incorporating a multiple iterative algorithm, sub-pixel method, filter and interpolation method, and spurious vector elimination algorithm. The new algorithms' performance is assessed by analyzing simulated images with known displacements, and ultrasonic B-mode images of in vitro laminar pipe flow, rotational flow and in vivo rat carotid arterial flow. Results of the simulated images show that the new algorithm produces much smaller bias from the known displacements. For laminar flow, the new algorithm results in 1.1% deviation from the analytically derived value, and 8.8% for the conventional algorithm. The vector quality evaluation for the rotational flow imaging shows that the new algorithm produces better velocity vectors. For in vivo rat carotid arterial flow imaging, the results from the new algorithm deviate 6.6% from the Doppler-measured peak velocities averagely compared to 15% of that from the conventional algorithm. The new Echo PIV algorithm is able to effectively improve the measurement accuracy in imaging flow fields with high velocity gradients.

Artificial immune system algorithm in VLSI circuit configuration

Science.gov (United States)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

A similarity based agglomerative clustering algorithm in networks

Science.gov (United States)

Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

2018-04-01

The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

Application of genetic algorithm in the fuel management optimization for the high flux engineering test reactor

International Nuclear Information System (INIS)

Shi Xueming; Wu Hongchun; Sun Shouhua; Liu Shuiqing

2003-01-01

The in-core fuel management optimization model based on the genetic algorithm has been established. An encode/decode technique based on the assemblies position is presented according to the characteristics of HFETR. Different reproduction strategies have been studied. The expert knowledge and the adaptive genetic algorithms are incorporated into the code to get the optimized loading patterns that can be used in HFETR

Intelligent decision support algorithm for distribution system restoration.

Science.gov (United States)

Singh, Reetu; Mehfuz, Shabana; Kumar, Parmod

2016-01-01

Distribution system is the means of revenue for electric utility. It needs to be restored at the earliest if any feeder or complete system is tripped out due to fault or any other cause. Further, uncertainty of the loads, result in variations in the distribution network's parameters. Thus, an intelligent algorithm incorporating hybrid fuzzy-grey relation, which can take into account the uncertainties and compare the sequences is discussed to analyse and restore the distribution system. The simulation studies are carried out to show the utility of the method by ranking the restoration plans for a typical distribution system. This algorithm also meets the smart grid requirements in terms of an automated restoration plan for the partial/full blackout of network.

Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

DEFF Research Database (Denmark)

Wang, Yong; Cai, Zixing; Zhou, Yuren

2009-01-01

A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

Preferences and beliefs in ingroup favouritism

Directory of Open Access Journals (Sweden)

Jim Albert Charlton Everett

2015-02-01

Full Text Available Ingroup favouritism – the tendency to favour members of one’s own group over those in other groups – is well documented, but the mechanisms driving this behavior are not well understood. In particular, it is unclear to what extent ingroup favouritism is driven by preferences concerning the welfare of ingroup over outgroup members, versus beliefs about the behaviour of ingroup and outgroup members. In this review we analyse research on ingroup favouritism in economic games, identifying key gaps in the literature and providing suggestions on how future work can incorporate these insights to shed further light on when, why, and how ingroup favouritism occurs. In doing so, we demonstrate how social psychological theory and research can be integrated with findings from behavioral economics, providing new theoretical and methodological directions for future research.

The algorithmic level is the bridge between computation and brain.

Science.gov (United States)

Love, Bradley C

2015-04-01

Every scientist chooses a preferred level of analysis and this choice shapes the research program, even determining what counts as evidence. This contribution revisits Marr's (1982) three levels of analysis (implementation, algorithmic, and computational) and evaluates the prospect of making progress at each individual level. After reviewing limitations of theorizing within a level, two strategies for integration across levels are considered. One is top-down in that it attempts to build a bridge from the computational to algorithmic level. Limitations of this approach include insufficient theoretical constraint at the computation level to provide a foundation for integration, and that people are suboptimal for reasons other than capacity limitations. Instead, an inside-out approach is forwarded in which all three levels of analysis are integrated via the algorithmic level. This approach maximally leverages mutual data constraints at all levels. For example, algorithmic models can be used to interpret brain imaging data, and brain imaging data can be used to select among competing models. Examples of this approach to integration are provided. This merging of levels raises questions about the relevance of Marr's tripartite view. Copyright © 2015 Cognitive Science Society, Inc.

The Six-Legged Subject: A Survey of Secondary Science Teachers' Incorporation of Insects into U.S. Life Science Instruction.

Science.gov (United States)

Ingram, Erin; Golick, Douglas

2018-03-14

To improve students' understanding and appreciation of insects, entomology education efforts have supported insect incorporation in formal education settings. While several studies have explored student ideas about insects and the incorporation of insects in elementary and middle school classrooms, the topic of how and why insects are incorporated in secondary science classrooms remains relatively unexplored. Using survey research methods, this study addresses the gap in the literature by (1) describing in-service secondary science teachers' incorporation of insects in science classrooms; (2) identifying factors that support or deter insect incorporation and (3) identifying teachers' preferred resources to support future entomology education efforts. Findings indicate that our sample of U.S. secondary science teachers commonly incorporate various insects in their classrooms, but that incorporation is infrequent throughout the academic year. Insect-related lesson plans are commonly used and often self-created to meet teachers' need for standards-aligned curriculum materials. Obstacles to insect incorporation include a perceived lack of alignment of insect education materials to state or national science standards and a lack of time and professional training to teach about insects. Recommendations are provided for entomology and science education organizations to support teachers in overcoming these obstacles.

The Six-Legged Subject: A Survey of Secondary Science Teachers’ Incorporation of Insects into U.S. Life Science Instruction

Science.gov (United States)

Ingram, Erin

2018-01-01

To improve students’ understanding and appreciation of insects, entomology education efforts have supported insect incorporation in formal education settings. While several studies have explored student ideas about insects and the incorporation of insects in elementary and middle school classrooms, the topic of how and why insects are incorporated in secondary science classrooms remains relatively unexplored. Using survey research methods, this study addresses the gap in the literature by (1) describing in-service secondary science teachers’ incorporation of insects in science classrooms; (2) identifying factors that support or deter insect incorporation and (3) identifying teachers’ preferred resources to support future entomology education efforts. Findings indicate that our sample of U.S. secondary science teachers commonly incorporate various insects in their classrooms, but that incorporation is infrequent throughout the academic year. Insect-related lesson plans are commonly used and often self-created to meet teachers’ need for standards-aligned curriculum materials. Obstacles to insect incorporation include a perceived lack of alignment of insect education materials to state or national science standards and a lack of time and professional training to teach about insects. Recommendations are provided for entomology and science education organizations to support teachers in overcoming these obstacles. PMID:29538297

Preferences over Social Risk

DEFF Research Database (Denmark)

Harrison, Glenn W.; Lau, Morten; Rutström, E. Elisabet

2013-01-01

that subjects systematically reveal different risk attitudes in a social setting with no prior knowledge about the risk preferences of others compared to when they solely bear the consequences of the decision. However, we also find that subjects are significantly more risk averse when they know the risk......We elicit individual preferences over social risk. We identify the extent to which these preferences are correlated with preferences over individual risk and the well-being of others. We examine these preferences in the context of laboratory experiments over small, anonymous groups, although...... the methodological issues extend to larger groups that form endogenously (e.g., families, committees, communities). Preferences over social risk can be closely approximated by individual risk attitudes when subjects have no information about the risk preferences of other group members. We find no evidence...

FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.

Directory of Open Access Journals (Sweden)

Shouheng Tuo

Full Text Available Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS. Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models.In this study, two scoring functions (Bayesian network based K2-score and Gini-score are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models.We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR, specificity (SPC, positive predictive value (PPV and accuracy (ACC. Our method has identified two SNPs (rs3775652 and rs10511467 that may be also associated with disease in AMD dataset.

A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

Science.gov (United States)

Ju, Chunhua; Xu, Chonghuan

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Chunhua Ju

2013-01-01

Full Text Available Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users’ preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

CodonTest: modeling amino acid substitution preferences in coding sequences.

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Wayne Delport

2010-08-01

Full Text Available Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes.

Comparative study between ultrahigh spatial frequency algorithm and high spatial frequency algorithm in high-resolution CT of the lungs

International Nuclear Information System (INIS)

Oh, Yu Whan; Kim, Jung Kyuk; Suh, Won Hyuck

1994-01-01

To date, the high spatial frequency algorithm (HSFA) which reduces image smoothing and increases spatial resolution has been used for the evaluation of parenchymal lung diseases in thin-section high-resolution CT. In this study, we compared the ultrahigh spatial frequency algorithm (UHSFA) with the high spatial frequency algorithm in the assessment of thin section images of the lung parenchyma. Three radiologists compared the UHSFA and HSFA on identical CT images in a line-pair resolution phantom, one lung specimen, 2 patients with normal lung and 18 patients with abnormal lung parenchyma. Scanning of a line-pair resolution phantom demonstrated no difference in resolution between two techniques but it showed that outer lines of the line pairs with maximal resolution looked thicker on UHSFA than those on HSFA. Lung parenchymal detail with UHSFA was judged equal or superior to HSFA in 95% of images. Lung parenchymal sharpness was improved with UHSFA in all images. Although UHSFA resulted in an increase in visible noise, observers did not found that image noise interfered with image interpretation. The visual CT attenuation of normal lung parenchyma is minimally increased in images with HSFA. The overall visual preference of the images reconstructed on UHSFA was considered equal to or greater than that of those reconstructed on HSFA in 78% of images. The ultrahigh spatial frequency algorithm improved the overall visual quality of the images in pulmonary parenchymal high-resolution CT

Preferred sets of states, predictability, classicality, and the environment-induced decoherence

International Nuclear Information System (INIS)

Zurek, W.H.

1992-01-01

Selection of the preferred classical set of states in the process of decoherence -- so important for cosmological considerations -- is discussed with an emphasis on the role of information loss and entropy. Persistence of correlations between the observables of two systems (for instance, a record and a state of a system evolved from the initial conditions described by that record) in the presence of the environment is used to define classical behavior. From the view point of an observer (or any system capable of maintaining records) predictability is a measure of such persistence. Predictability sieve -- a procedure which employs both the statistical and algorithmic entropies to systematicaly explore all of the Hilbert space of open system in order to eliminate the majority of the unpredictable and non-classical states and to locate the islands of predictability including the preferred pointer basis is proposed. Predictably evolving states of decohering systems along with the time-ordered sequences of records of their evolution define the effectively classical branches of the universal wavefunction in the context of the ''Many Worlds Interpretation''. The relation between the consistent histories approach and the preferred basis is considered. It is demonstrated that histories of sequences of events corresponding to projections onto the states of the pointer basis are consistent

The development of PubMed search strategies for patient preferences for treatment outcomes

Directory of Open Access Journals (Sweden)

Ralph van Hoorn

2016-07-01

Full Text Available Abstract Background The importance of respecting patients’ preferences when making treatment decisions is increasingly recognized. Efficiently retrieving papers from the scientific literature reporting on the presence and nature of such preferences can help to achieve this goal. The objective of this study was to create a search filter for PubMed to help retrieve evidence on patient preferences for treatment outcomes. Methods A total of 27 journals were hand-searched for articles on patient preferences for treatment outcomes published in 2011. Selected articles served as a reference set. To develop optimal search strategies to retrieve this set, all articles in the reference set were randomly split into a development and a validation set. MeSH-terms and keywords retrieved using PubReMiner were tested individually and as combinations in PubMed and evaluated for retrieval performance (e.g. sensitivity (Se and specificity (Sp. Results Of 8238 articles, 22 were considered to report empirical evidence on patient preferences for specific treatment outcomes. The best search filters reached Se of 100 % [95 % CI 100-100] with Sp of 95 % [94–95 %] and Sp of 97 % [97–98 %] with 75 % Se [74–76 %]. In the validation set these queries reached values of Se of 90 % [89–91 %] with Sp 94 % [93–95 %] and Se of 80 % [79–81 %] with Sp of 97 % [96–96 %], respectively. Conclusions Narrow and broad search queries were developed which can help in retrieving literature on patient preferences for treatment outcomes. Identifying such evidence may in turn enhance the incorporation of patient preferences in clinical decision making and health technology assessment.

Research on Large-Scale Road Network Partition and Route Search Method Combined with Traveler Preferences

Directory of Open Access Journals (Sweden)

De-Xin Yu

2013-01-01

Full Text Available Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.

Improved Passive Microwave Algorithms for North America and Eurasia

Science.gov (United States)

Foster, James; Chang, Alfred; Hall, Dorothy

1997-01-01

Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.

Incorporating prior knowledge into beam orientation optimization in IMRT

International Nuclear Information System (INIS)

Pugachev, Andrei M.S.; Lei Xing

2002-01-01

Purpose: Selection of beam configuration in currently available intensity-modulated radiotherapy (IMRT) treatment planning systems is still based on trial-and-error search. Computer beam orientation optimization has the potential to improve the situation, but its practical implementation is hindered by the excessive computing time associated with the calculation. The purpose of this work is to provide an effective means to speed up the beam orientation optimization by incorporating a priori geometric and dosimetric knowledge of the system and to demonstrate the utility of the new algorithm for beam placement in IMRT. Methods and Materials: Beam orientation optimization was performed in two steps. First, the quality of each possible beam orientation was evaluated using beam's-eye-view dosimetrics (BEVD) developed in our previous study. A simulated annealing algorithm was then employed to search for the optimal set of beam orientations, taking into account the BEVD scores of different incident beam directions. During the calculation, sampling of gantry angles was weighted according to the BEVD score computed before the optimization. A beam direction with a higher BEVD score had a higher probability of being included in the trial configuration, and vice versa. The inclusion of the BEVD weighting in the stochastic beam angle sampling process made it possible to avoid spending valuable computing time unnecessarily at 'bad' beam angles. An iterative inverse treatment planning algorithm was used for beam intensity profile optimization during the optimization process. The BEVD-guided beam orientation optimization was applied to an IMRT treatment of paraspinal tumor. The advantage of the new optimization algorithm was demonstrated by comparing the calculation with the conventional scheme without the BEVD weighting in the beam sampling. Results: The BEVD tool provided useful guidance for the selection of the potentially good directions for the beams to incident and was used

Considerations and Algorithms for Compression of Sets

DEFF Research Database (Denmark)

Larsson, Jesper

We consider compression of unordered sets of distinct elements. After a discus- sion of the general problem, we focus on compressing sets of fixed-length bitstrings in the presence of statistical information. We survey techniques from previous work, suggesting some adjustments, and propose a novel...... compression algorithm that allows transparent incorporation of various estimates for probability distribution. Our experimental results allow the conclusion that set compression can benefit from incorporat- ing statistics, using our method or variants of previously known techniques....

Programming Algorithms of load balancing with HA-Proxy in HTTP services

Directory of Open Access Journals (Sweden)

José Teodoro Mejía Viteri

2018-02-01

Full Text Available The access to the public and private services through the web gains daily protagonism, and sometimes they must support amounts of requests that a team can not process, so there are solutions that use algorithms that allow to distribute the load of requests of a web application in several equipment; the objective of this work is to perform an analysis of load balancing scheduling algorithms through the HA-Proxy tool, and deliver an instrument that identifies the load distribution algorithm to be used and the technological infrastructure, to largely cover implementation. The information used for this work is based on a bibliographic analysis, eld study and implementation of the different load balancing algorithms in equipment, where the distribution and its performance will be analyzed. The incorporation of this technology to the management of services on the web, improves availability, helps business continuity and through the different forms of distribution of the requests of the algorithms that can be implemented in HA-Proxy to provide those responsible for information technology systems with a view of their advantages and disadvantages.

Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets.

Science.gov (United States)

Lamu, Admassu N; Chen, Gang; Gamst-Klaussen, Thor; Olsen, Jan Abel

2018-03-22

To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets. Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalised linear model, beta binomial regression, fractional regression, MM estimation and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r 2 . Cross-validation with fivefold approach was conducted to test the generalizability of each model. The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r 2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models. The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.

A Machine Learning Recommender System to Tailor Preference Assessments to Enhance Person-Centered Care Among Nursing Home Residents.

Science.gov (United States)

Gannod, Gerald C; Abbott, Katherine M; Van Haitsma, Kimberly; Martindale, Nathan; Heppner, Alexandra

2018-05-21

Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items (72) to be a barrier to using the assessment. Using a sample of n = 255 NH resident responses to the PELI-NH, we used the 16 preference items from the MDS 3.0 Section F to develop a machine learning recommender system to identify additional PELI-NH items that may be important to specific residents. Much like the Netflix recommender system, our system is based on the concept of collaborative filtering whereby insights and predictions (e.g., filters) are created using the interests and preferences of many users. The algorithm identifies multiple sets of "you might also like" patterns called association rules, based upon responses to the 16 MDS preferences that recommends an additional set of preferences with a high likelihood of being important to a specific resident. In the evaluation of the combined apriori and logistic regression approach, we obtained a high recall performance (i.e., the ratio of correctly predicted preferences compared with all predicted preferences and nonpreferences) and high precision (i.e., the ratio of correctly predicted rules with respect to the rules predicted to be true) of 80.2% and 79.2%, respectively. The recommender system successfully provides guidance on how to best tailor the preference items asked of residents and can support preference capture in busy clinical environments, contributing to the feasibility of delivering person-centered care.

Partially Adaptive STAP Algorithm Approaches to functional MRI

OpenAIRE

Huang, Lejian; Thompson, Elizabeth A.; Schmithorst, Vincent; Holland, Scott K.; Talavage, Thomas M.

2008-01-01

In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing tim...

Caliko: An Inverse Kinematics Software Library Implementation of the FABRIK Algorithm

Directory of Open Access Journals (Sweden)

Alastair Lansley

2016-09-01

Full Text Available The Caliko library is an implementation of the FABRIK (Forward And Backward Reaching Inverse Kinematics algorithm written in Java. The inverse kinematics (IK algorithm is implemented in both 2D and 3D, and incorporates a variety of joint constraints as well as the ability to connect multiple IK chains together in a hierarchy. The library allows for the simple creation and solving of multiple IK chains as well as visualisation of these solutions. It is licensed under the MIT software license and the source code is freely available for use and modification at: https://github.com/feduni/caliko

New Parallel Algorithms for Structural Analysis and Design of Aerospace Structures

Science.gov (United States)

Nguyen, Duc T.

1998-01-01

Subspace and Lanczos iterations have been developed, well documented, and widely accepted as efficient methods for obtaining p-lowest eigen-pair solutions of large-scale, practical engineering problems. The focus of this paper is to incorporate recent developments in vectorized sparse technologies in conjunction with Subspace and Lanczos iterative algorithms for computational enhancements. Numerical performance, in terms of accuracy and efficiency of the proposed sparse strategies for Subspace and Lanczos algorithm, is demonstrated by solving for the lowest frequencies and mode shapes of structural problems on the IBM-R6000/590 and SunSparc 20 workstations.

High resolution reconstruction of PET images using the iterative OSEM algorithm

International Nuclear Information System (INIS)

Doll, J.; Bublitz, O.; Werling, A.; Haberkorn, U.; Semmler, W.; Adam, L.E.; Pennsylvania Univ., Philadelphia, PA; Brix, G.

2004-01-01

Aim: Improvement of the spatial resolution in positron emission tomography (PET) by incorporation of the image-forming characteristics of the scanner into the process of iterative image reconstruction. Methods: All measurements were performed at the whole-body PET system ECAT EXACT HR + in 3D mode. The acquired 3D sinograms were sorted into 2D sinograms by means of the Fourier rebinning (FORE) algorithm, which allows the usage of 2D algorithms for image reconstruction. The scanner characteristics were described by a spatially variant line-spread function (LSF), which was determined from activated copper-64 line sources. This information was used to model the physical degradation processes in PET measurements during the course of 2D image reconstruction with the iterative OSEM algorithm. To assess the performance of the high-resolution OSEM algorithm, phantom measurements performed at a cylinder phantom, the hotspot Jaszczack phantom, and the 3D Hoffmann brain phantom as well as different patient examinations were analyzed. Results: Scanner characteristics could be described by a Gaussian-shaped LSF with a full-width at half-maximum increasing from 4.8 mm at the center to 5.5 mm at a radial distance of 10.5 cm. Incorporation of the LSF into the iteration formula resulted in a markedly improved resolution of 3.0 and 3.5 mm, respectively. The evaluation of phantom and patient studies showed that the high-resolution OSEM algorithm not only lead to a better contrast resolution in the reconstructed activity distributions but also to an improved accuracy in the quantification of activity concentrations in small structures without leading to an amplification of image noise or even the occurrence of image artifacts. Conclusion: The spatial and contrast resolution of PET scans can markedly be improved by the presented image restauration algorithm, which is of special interest for the examination of both patients with brain disorders and small animals. (orig.)

The Goals and Effects of Music Listening and Their Relationship to the Strength of Music Preference.

Science.gov (United States)

Schäfer, Thomas

2016-01-01

Individual differences in the strength of music preference are among the most intricate psychological phenomena. While one person gets by very well without music, another person needs to listen to music every day and spends a lot of temporal and financial resources on listening to music, attending concerts, or buying concert tickets. Where do these differences come from? The hypothesis presented in this article is that the strength of music preference is mainly informed by the functions that music fulfills in people's lives (e.g., to regulate emotions, moods, or physiological arousal; to promote self-awareness; to foster social relatedness). Data were collected with a diary study, in which 121 respondents documented the goals they tried to attain and the effects that actually occurred for up to 5 music-listening episodes per day for 10 successive days. As expected, listeners reporting more intense experience of the functional use of music in the past (1) had a stronger intention to listen to music to attain specific goals in specific situations and (2) showed a larger overall strength of music preference. It is concluded that the functional effectiveness of music listening should be incorporated in existing models and frameworks of music preference to produce better predictions of interindividual differences in the strength of music preference. The predictability of musical style/genre preferences is also discussed with regard to the present results.

Incorporating a multi-criteria decision procedure into the combined dynamic programming/production simulation algorithm for generation expansion planning

International Nuclear Information System (INIS)

Yang, H.T.; Chen, S.L.

1989-01-01

A multi-objective optimization approach to generation expansion planning is presented. The approach is designed by adding a new multi-criteria decision (MCD) procedure to the conventional algorithm which combines dynamic programming with production simulation method. The MCD procedure can help decision makers weight the relative importance of multiple attributes associated with the decision alternatives, and find the near-best compromise solution efficiently at each optimization step of the conventional algorithm. Practical application of proposed approach to feasibility evaluation of the fourth nuclear power plant of Tawian is also presented, demonstrating the effectiveness and limitations of the approach

Preferred and actual relative height among homosexual male partners vary with preferred dominance and sex role.

Science.gov (United States)

Valentova, Jaroslava Varella; Stulp, Gert; Třebický, Vít; Havlíček, Jan

2014-01-01

Previous research has shown repeatedly that human stature influences mate preferences and mate choice in heterosexuals. In general, it has been shown that tall men and average height women are most preferred by the opposite sex, and that both sexes prefer to be in a relationship where the man is taller than the woman. However, little is known about such partner preferences in homosexual individuals. Based on an online survey of a large sample of non-heterosexual men (N = 541), we found that the majority of men prefer a partner slightly taller than themselves. However, these preferences were dependent on the participant's own height, such that taller men preferred shorter partners, whereas shorter men preferred taller partners. We also examined whether height preferences predicted the preference for dominance and the adoption of particular sexual roles within a couple. Although a large proportion of men preferred to be in an egalitarian relationship with respect to preferred dominance (although not with respect to preferred sexual role), men that preferred a more dominant and more "active" sexual role preferred shorter partners, whereas those that preferred a more submissive and more "passive" sexual role preferred taller partners. Our results indicate that preferences for relative height in homosexual men are modulated by own height, preferred dominance and sex role, and do not simply resemble those of heterosexual women or men.

Preferred and actual relative height among homosexual male partners vary with preferred dominance and sex role.

Directory of Open Access Journals (Sweden)

Jaroslava Varella Valentova

Full Text Available Previous research has shown repeatedly that human stature influences mate preferences and mate choice in heterosexuals. In general, it has been shown that tall men and average height women are most preferred by the opposite sex, and that both sexes prefer to be in a relationship where the man is taller than the woman. However, little is known about such partner preferences in homosexual individuals. Based on an online survey of a large sample of non-heterosexual men (N = 541, we found that the majority of men prefer a partner slightly taller than themselves. However, these preferences were dependent on the participant's own height, such that taller men preferred shorter partners, whereas shorter men preferred taller partners. We also examined whether height preferences predicted the preference for dominance and the adoption of particular sexual roles within a couple. Although a large proportion of men preferred to be in an egalitarian relationship with respect to preferred dominance (although not with respect to preferred sexual role, men that preferred a more dominant and more "active" sexual role preferred shorter partners, whereas those that preferred a more submissive and more "passive" sexual role preferred taller partners. Our results indicate that preferences for relative height in homosexual men are modulated by own height, preferred dominance and sex role, and do not simply resemble those of heterosexual women or men.

Preferred and Actual Relative Height among Homosexual Male Partners Vary with Preferred Dominance and Sex Role

Science.gov (United States)

Valentova, Jaroslava Varella; Stulp, Gert; Třebický, Vít; Havlíček, Jan

2014-01-01

Previous research has shown repeatedly that human stature influences mate preferences and mate choice in heterosexuals. In general, it has been shown that tall men and average height women are most preferred by the opposite sex, and that both sexes prefer to be in a relationship where the man is taller than the woman. However, little is known about such partner preferences in homosexual individuals. Based on an online survey of a large sample of non-heterosexual men (N = 541), we found that the majority of men prefer a partner slightly taller than themselves. However, these preferences were dependent on the participant’s own height, such that taller men preferred shorter partners, whereas shorter men preferred taller partners. We also examined whether height preferences predicted the preference for dominance and the adoption of particular sexual roles within a couple. Although a large proportion of men preferred to be in an egalitarian relationship with respect to preferred dominance (although not with respect to preferred sexual role), men that preferred a more dominant and more “active” sexual role preferred shorter partners, whereas those that preferred a more submissive and more “passive” sexual role preferred taller partners. Our results indicate that preferences for relative height in homosexual men are modulated by own height, preferred dominance and sex role, and do not simply resemble those of heterosexual women or men. PMID:24466136

A Cooperative Framework for Fireworks Algorithm.

Science.gov (United States)

Zheng, Shaoqiu; Li, Junzhi; Janecek, Andreas; Tan, Ying

2017-01-01

This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that ( i) the current selection strategy has the drawback that the contribution of the firework with the best fitness (denoted as core firework) overwhelms the contributions of all other fireworks (non-core fireworks) in the explosion operator, ( ii) the Gaussian mutation operator is not as effective as it is designed to be. To overcome these limitations, the CoFFWA is proposed, which significantly improves the exploitation capability by using an independent selection method and also increases the exploration capability by incorporating a crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions indicate that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, and the standard particle swarm optimization SPSO2007/SPSO2011 in terms of convergence performance.

Clustering Using Boosted Constrained k-Means Algorithm

Directory of Open Access Journals (Sweden)

Masayuki Okabe

2018-03-01

Full Text Available This article proposes a constrained clustering algorithm with competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learning-based methods. Since it simply adds a function into the data assignment process of the k-means algorithm to check for constraint violations, it often exploits only a small number of constraints. Metric learning-based methods, which exploit constraints to create a new metric for data similarity, have shown promising results although the methods proposed so far are often slow depending on the amount of data or number of feature dimensions. We present a method that exploits the advantages of the constrained k-means and metric learning approaches. It incorporates a mechanism for accepting constraint priorities and a metric learning framework based on the boosting principle into a constrained k-means algorithm. In the framework, a metric is learned in the form of a kernel matrix that integrates weak cluster hypotheses produced by the constrained k-means algorithm, which works as a weak learner under the boosting principle. Experimental results for 12 data sets from 3 data sources demonstrated that our method has performance competitive to those of state-of-the-art constrained clustering methods for most data sets and that it takes much less computation time. Experimental evaluation demonstrated the effectiveness of controlling the constraint priorities by using the boosting principle and that our constrained k-means algorithm functions correctly as a weak learner of boosting.

Implementation of an algorithm for cylindrical object identification using range data

Science.gov (United States)

Bozeman, Sylvia T.; Martin, Benjamin J.

1989-01-01

One of the problems in 3-D object identification and localization is addressed. In robotic and navigation applications the vision system must be able to distinguish cylindrical or spherical objects as well as those of other geometric shapes. An algorithm was developed to identify cylindrical objects in an image when range data is used. The algorithm incorporates the Hough transform for line detection using edge points which emerge from a Sobel mask. Slices of the data are examined to locate arcs of circles using the normal equations of an over-determined linear system. Current efforts are devoted to testing the computer implementation of the algorithm. Refinements are expected to continue in order to accommodate cylinders in various positions. A technique is sought which is robust in the presence of noise and partial occlusions.

Value redefined for inflammatory bowel disease patients: a choice-based conjoint analysis of patients' preferences.

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van Deen, Welmoed K; Nguyen, Dominic; Duran, Natalie E; Kane, Ellen; van Oijen, Martijn G H; Hommes, Daniel W

2017-02-01

Value-based healthcare is an upcoming field. The core idea is to evaluate care based on achieved outcomes divided by the costs. Unfortunately, the optimal way to evaluate outcomes is ill-defined. In this study, we aim to develop a single, preference based, outcome metric, which can be used to quantify overall health value in inflammatory bowel disease (IBD). IBD patients filled out a choice-based conjoint (CBC) questionnaire in which patients chose preferable outcome scenarios with different levels of disease control (DC), quality of life (QoL), and productivity (Pr). A CBC analysis was performed to estimate the relative value of DC, QoL, and Pr. A patient-centered composite score was developed which was weighted based on the stated preferences. We included 210 IBD patients. Large differences in stated preferences were observed. Increases from low to intermediate outcome levels were valued more than increases from intermediate to high outcome levels. Overall, QoL was more important to patients than DC or Pr. Individual outcome scores were calculated based on the stated preferences. This score was significantly different from a score not weighted based on patient preferences in patients with active disease. We showed the feasibility of creating a single outcome metric in IBD which incorporates patients' values using a CBC. Because this metric changes significantly when weighted according to patients' values, we propose that success in healthcare should be measured accordingly.

VIERS- User Preference Service

Data.gov (United States)

Department of Veterans Affairs — The Preferences service provides a means to store, retrieve, and manage user preferences. The service supports definition of enterprise wide preferences, as well as...

On the meaningfulness of testing preference axioms in stated preference discrete choice experiments

DEFF Research Database (Denmark)

Hougaard, Jens Leth; Tjur, Carl Tue; Østerdal, Lars Peter Raahave

2012-01-01

A stream of studies on evaluation of health care services and public goods have developed tests of the preference axioms of completeness and transitivity and methods for detecting other preference phenomena such as unstability, learning- and tiredness effects, and random error, in stated preference...... discrete choice experiments. This methodological paper tries to identify the role of the preference axioms and other preference phenomena in the context of such experiments and discusses whether or howsuch axioms and phenomena can be subject to meaningful (statistical) tests....

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

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Bin Ju

Full Text Available Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

Science.gov (United States)

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

Preferred Methods of Learning for Nursing Students in an On-Line Degree Program.

Science.gov (United States)

Hampton, Debra; Pearce, Patricia F; Moser, Debra K

study demonstrate that there are distinct student preferences and generational differences in preferred teaching/learning methods for on-line students. Faculty need to incorporate various teaching methodologies within on-line courses to include both synchronous and asynchronous activities and interactive and passive methodologies. Copyright © 2016 Elsevier Inc. All rights reserved.

GASPACHO: a generic automatic solver using proximal algorithms for convex huge optimization problems

Science.gov (United States)

Goossens, Bart; Luong, Hiêp; Philips, Wilfried

2017-08-01

Many inverse problems (e.g., demosaicking, deblurring, denoising, image fusion, HDR synthesis) share various similarities: degradation operators are often modeled by a specific data fitting function while image prior knowledge (e.g., sparsity) is incorporated by additional regularization terms. In this paper, we investigate automatic algorithmic techniques for evaluating proximal operators. These algorithmic techniques also enable efficient calculation of adjoints from linear operators in a general matrix-free setting. In particular, we study the simultaneous-direction method of multipliers (SDMM) and the parallel proximal algorithm (PPXA) solvers and show that the automatically derived implementations are well suited for both single-GPU and multi-GPU processing. We demonstrate this approach for an Electron Microscopy (EM) deconvolution problem.

Training nuclei detection algorithms with simple annotations

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Henning Kost

2017-01-01

Full Text Available Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.

Genetic algorithm-based improved DOA estimation using fourth-order cumulants

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Ahmed, Ammar; Tufail, Muhammad

2017-05-01

Genetic algorithm (GA)-based direction of arrival (DOA) estimation is proposed using fourth-order cumulants (FOC) and ESPRIT principle which results in Multiple Invariance Cumulant ESPRIT algorithm. In the existing FOC ESPRIT formulations, only one invariance is utilised to estimate DOAs. The unused multiple invariances (MIs) must be exploited simultaneously in order to improve the estimation accuracy. In this paper, a fitness function based on a carefully designed cumulant matrix is developed which incorporates MIs present in the sensor array. Better DOA estimation can be achieved by minimising this fitness function. Moreover, the effectiveness of Newton's method as well as GA for this optimisation problem has been illustrated. Simulation results show that the proposed algorithm provides improved estimation accuracy compared to existing algorithms, especially in the case of low SNR, less number of snapshots, closely spaced sources and high signal and noise correlation. Moreover, it is observed that the optimisation using Newton's method is more likely to converge to false local optima resulting in erroneous results. However, GA-based optimisation has been found attractive due to its global optimisation capability.

Many-Objective Distinct Candidates Optimization using Differential Evolution

DEFF Research Database (Denmark)

Justesen, Peter; Ursem, Rasmus Kjær

2010-01-01

for each objective. The Many-Objective Distinct Candidates Optimization using Differential Evolution (MODCODE) algorithm takes a novel approach by focusing search using a user-defined number of subpopulations each returning a distinct optimal solution within the preferred region of interest. In this paper......, we present the novel MODCODE algorithm incorporating the ROD measure to measure and control candidate distinctiveness. MODCODE is tested against GDE3 on three real world centrifugal pump design problems supplied by Grundfos. Our algorithm outperforms GDE3 on all problems with respect to all...

A Framework To Support Management Of HIVAIDS Using K-Means And Random Forest Algorithm

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Gladys Iseu

2017-06-01

Full Text Available Healthcare industry generates large amounts of complex data about patients hospital resources disease management electronic patient records and medical devices among others. The availability of these huge amounts of medical data creates a need for powerful mining tools to support health care professionals in diagnosis treatment and management of HIVAIDS. Several data mining techniques have been used in management of different data sets. Data mining techniques have been categorized into regression algorithms segmentation algorithms association algorithms sequence analysis algorithms and classification algorithms. In the medical field there has not been a specific study that has incorporated two or more data mining algorithms hence limiting decision making levels by medical practitioners. This study identified the extent to which K-means algorithm cluster patient characteristics it has also evaluated the extent to which random forest algorithm can classify the data for informed decision making as well as design a framework to support medical decision making in the treatment of HIVAIDS related diseases in Kenya. The paper further used random forest classification algorithm to compute proximities between pairs of cases that can be used in clustering locating outliers or by scaling to give interesting views of the data.

Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.

Science.gov (United States)

Tang, Jing; Kuwabara, Hiroto; Wong, Dean F; Rahmim, Arman

2010-08-07

We developed an anatomy-guided 4D closed-form algorithm to directly reconstruct parametric images from projection data for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 2D/3D PET data, followed by graphical analysis on the sequence of reconstructed image frames. The proposed direct reconstruction approach maintains the simplicity and accuracy of the expectation-maximization (EM) algorithm by extending the system matrix to include the relation between the parametric images and the measured data. A closed-form solution was achieved using a different hidden complete-data formulation within the EM framework. Furthermore, the proposed method was extended to maximum a posterior reconstruction via incorporation of MR image information, taking the joint entropy between MR and parametric PET features as the prior. Using realistic simulated noisy [(11)C]-naltrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise versus bias performance were demonstrated when performing direct parametric reconstruction, and additionally upon extending the algorithm to its Bayesian counterpart using the MR-PET joint entropy measure.

Volkov transform generalized projection algorithm for attosecond pulse characterization

International Nuclear Information System (INIS)

Keathley, P D; Bhardwaj, S; Moses, J; Laurent, G; Kärtner, F X

2016-01-01

An algorithm for characterizing attosecond extreme ultraviolet pulses that is not bandwidth-limited, requires no interpolation of the experimental data, and makes no approximations beyond the strong-field approximation is introduced. This approach fully incorporates the dipole transition matrix element into the retrieval process. Unlike attosecond retrieval methods such as phase retrieval by omega oscillation filtering (PROOF), or improved PROOF, it simultaneously retrieves both the attosecond and infrared (IR) pulses, without placing fundamental restrictions on the IR pulse duration, intensity or bandwidth. The new algorithm is validated both numerically and experimentally, and is also found to have practical advantages. These include an increased robustness to noise, and relaxed requirements for the size of the experimental dataset and the intensity of the streaking pulse. (paper)

Parallel SN algorithms in shared- and distributed-memory environments

International Nuclear Information System (INIS)

Haghighat, Alireza; Hunter, Melissa A.; Mattis, Ronald E.

1995-01-01

Different 2-D spatial domain partitioning Sn transport theory algorithms have been developed on the basis of the Block-Jacobi iterative scheme. These algorithms have been incorporated into TWOTRAN-II, and tested on a shared-memory CRAY Y-MP C90 and a distributed-memory IBM SP1. For a series of fixed source r-z geometry homogeneous problems, parallel efficiencies in a range of 50-90% are achieved on the C90 with 6 processors, and lower values (20-60%) are obtained on the SP1. It is demonstrated that better performance is attainable if one addresses issues such as convergence rate, load-balancing, and granularity for both architectures, as well as message passing (network bandwidth and latency) for SP1. (author). 17 refs, 4 figs

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Directory of Open Access Journals (Sweden)

Yeou-Jiunn Chen

2016-09-01

Full Text Available Subjects with amyotrophic lateral sclerosis (ALS consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

Zombie algorithms: a timesaving remote sensing systems engineering tool

Science.gov (United States)

Ardanuy, Philip E.; Powell, Dylan C.; Marley, Stephen

2008-08-01

In modern horror fiction, zombies are generally undead corpses brought back from the dead by supernatural or scientific means, and are rarely under anyone's direct control. They typically have very limited intelligence, and hunger for the flesh of the living [1]. Typical spectroradiometric or hyperspectral instruments providess calibrated radiances for a number of remote sensing algorithms. The algorithms typically must meet specified latency and availability requirements while yielding products at the required quality. These systems, whether research, operational, or a hybrid, are typically cost constrained. Complexity of the algorithms can be high, and may evolve and mature over time as sensor characterization changes, product validation occurs, and areas of scientific basis improvement are identified and completed. This suggests the need for a systems engineering process for algorithm maintenance that is agile, cost efficient, repeatable, and predictable. Experience on remote sensing science data systems suggests the benefits of "plug-n-play" concepts of operation. The concept, while intuitively simple, can be challenging to implement in practice. The use of zombie algorithms-empty shells that outwardly resemble the form, fit, and function of a "complete" algorithm without the implemented theoretical basis-provides the ground systems advantages equivalent to those obtained by integrating sensor engineering models onto the spacecraft bus. Combined with a mature, repeatable process for incorporating the theoretical basis, or scientific core, into the "head" of the zombie algorithm, along with associated scripting and registration, provides an easy "on ramp" for the rapid and low-risk integration of scientific applications into operational systems.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

Science.gov (United States)

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-10-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

An Effective Tri-Clustering Algorithm Combining Expression Data with Gene Regulation Information

Directory of Open Access Journals (Sweden)

Ao Li

2009-04-01

Full Text Available Motivation: Bi-clustering algorithms aim to identify sets of genes sharing similar expression patterns across a subset of conditions. However direct interpretation or prediction of gene regulatory mechanisms may be difficult as only gene expression data is used. Information about gene regulators may also be available, most commonly about which transcription factors may bind to the promoter region and thus control the expression level of a gene. Thus a method to integrate gene expression and gene regulation information is desirable for clustering and analyzing. Methods: By incorporating gene regulatory information with gene expression data, we define regulated expression values (REV as indicators of how a gene is regulated by a specific factor. Existing bi-clustering methods are extended to a three dimensional data space by developing a heuristic TRI-Clustering algorithm. An additional approach named Automatic Boundary Searching algorithm (ABS is introduced to automatically determine the boundary threshold. Results: Results based on incorporating ChIP-chip data representing transcription factor-gene interactions show that the algorithms are efficient and robust for detecting tri-clusters. Detailed analysis of the tri-cluster extracted from yeast sporulation REV data shows genes in this cluster exhibited significant differences during the middle and late stages. The implicated regulatory network was then reconstructed for further study of defined regulatory mechanisms. Topological and statistical analysis of this network demonstrated evidence of significant changes of TF activities during the different stages of yeast sporulation, and suggests this approach might be a general way to study regulatory networks undergoing transformations.

Mass Conservation and Positivity Preservation with Ensemble-type Kalman Filter Algorithms

Science.gov (United States)

Janjic, Tijana; McLaughlin, Dennis B.; Cohn, Stephen E.; Verlaan, Martin

2013-01-01

Maintaining conservative physical laws numerically has long been recognized as being important in the development of numerical weather prediction (NWP) models. In the broader context of data assimilation, concerted efforts to maintain conservation laws numerically and to understand the significance of doing so have begun only recently. In order to enforce physically based conservation laws of total mass and positivity in the ensemble Kalman filter, we incorporate constraints to ensure that the filter ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. We show that the analysis steps of ensemble transform Kalman filter (ETKF) algorithm and ensemble Kalman filter algorithm (EnKF) can conserve the mass integral, but do not preserve positivity. Further, if localization is applied or if negative values are simply set to zero, then the total mass is not conserved either. In order to ensure mass conservation, a projection matrix that corrects for localization effects is constructed. In order to maintain both mass conservation and positivity preservation through the analysis step, we construct a data assimilation algorithms based on quadratic programming and ensemble Kalman filtering. Mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate constraints. Some simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. The results show clear improvements in both analyses and forecasts, particularly in the presence of localized features. Behavior of the algorithm is also tested in presence of model error.

Impact of whey protein coating incorporated with Bifidobacterium and Lactobacillus on sliced ham properties.

Science.gov (United States)

Odila Pereira, Joana; Soares, José; J P Monteiro, Maria; Gomes, Ana; Pintado, Manuela

2018-05-01

Edible coatings/films with functional ingredients may be a solution to consumers' demands for high-quality food products and an extended shelf-life. The aim of this work was to evaluate the antimicrobial efficiency of edible coatings incorporated with probiotics on sliced ham preservation. Coatings was developed based on whey protein isolates with incorporation of Bifidobacterium animalis Bb-12® or Lactobacillus casei-01. The physicochemical analyses showed that coating decreased water and weight loss on the ham. Furthermore, color analysis showed that coated sliced ham, exhibited no color change, comparatively to uncoated slices. The edible coatings incorporating the probiotic strains inhibited detectable growth of Staphylococcus spp., Pseudomonas spp., Enterobacteriaceae and yeasts/molds, at least, for 45days of storage at 4°C. The sensory evaluation demonstrated that there was a preference for the sliced coated ham. Probiotic bacteria viable cell numbers were maintained at ca. 10 8 CFU/g throughout storage time, enabling the slice of ham to act as a suitable carrier for the beneficial bacteria. Copyright © 2018 Elsevier Ltd. All rights reserved.

Color preferences are not universal.

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Taylor, Chloe; Clifford, Alexandra; Franklin, Anna

2013-11-01

Claims of universality pervade color preference research. It has been argued that there are universal preferences for some colors over others (e.g., Eysenck, 1941), universal sex differences (e.g., Hurlbert & Ling, 2007), and universal mechanisms or dimensions that govern these preferences (e.g., Palmer & Schloss, 2010). However, there have been surprisingly few cross-cultural investigations of color preference and none from nonindustrialized societies that are relatively free from the common influence of global consumer culture. Here, we compare the color preferences of British adults to those of Himba adults who belong to a nonindustrialized culture in rural Namibia. British and Himba color preferences are found to share few characteristics, and Himba color preferences display none of the so-called "universal" patterns or sex differences. Several significant predictors of color preference are identified, such as cone-contrast between stimulus and background (Hurlbert & Ling, 2007), the valence of color-associated objects (Palmer & Schloss, 2010), and the colorfulness of the color. However, the relationship of these predictors to color preference was strikingly different for the two cultures. No one model of color preference is able to account for both British and Himba color preferences. We suggest that not only do patterns of color preference vary across individuals and groups but the underlying mechanisms and dimensions of color preference vary as well. The findings have implications for broader debate on the extent to which our perception and experience of color is culturally relative or universally constrained. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Clinical effectiveness of a Bayesian algorithm for the diagnosis and management of heparin-induced thrombocytopenia.

Science.gov (United States)

Raschke, R A; Gallo, T; Curry, S C; Whiting, T; Padilla-Jones, A; Warkentin, T E; Puri, A

2017-08-01

Essentials We previously published a diagnostic algorithm for heparin-induced thrombocytopenia (HIT). In this study, we validated the algorithm in an independent large healthcare system. The accuracy was 98%, sensitivity 82% and specificity 99%. The algorithm has potential to improve accuracy and efficiency in the diagnosis of HIT. Background Heparin-induced thrombocytopenia (HIT) is a life-threatening drug reaction caused by antiplatelet factor 4/heparin (anti-PF4/H) antibodies. Commercial tests to detect these antibodies have suboptimal operating characteristics. We previously developed a diagnostic algorithm for HIT that incorporated 'four Ts' (4Ts) scoring and a stratified interpretation of an anti-PF4/H enzyme-linked immunosorbent assay (ELISA) and yielded a discriminant accuracy of 0.97 (95% confidence interval [CI], 0.93-1.00). Objectives The purpose of this study was to validate the algorithm in an independent patient population and quantitate effects that algorithm adherence could have on clinical care. Methods A retrospective cohort comprised patients who had undergone anti-PF4/H ELISA and serotonin release assay (SRA) testing in our healthcare system from 2010 to 2014. We determined the algorithm recommendation for each patient, compared recommendations with the clinical care received, and enumerated consequences of discrepancies. Operating characteristics were calculated for algorithm recommendations using SRA as the reference standard. Results Analysis was performed on 181 patients, 10 of whom were ruled in for HIT. The algorithm accurately stratified 98% of patients (95% CI, 95-99%), ruling out HIT in 158, ruling in HIT in 10 and recommending an SRA in 13 patients. Algorithm adherence would have obviated 165 SRAs and prevented 30 courses of unnecessary antithrombotic therapy for HIT. Diagnostic sensitivity was 0.82 (95% CI, 0.48-0.98), specificity 0.99 (95% CI, 0.97-1.00), PPV 0.90 (95% CI, 0.56-0.99) and NPV 0.99 (95% CI, 0.96-1.00). Conclusions An

Numeric algorithms for parallel processors computer architectures with applications to the few-groups neutron diffusion equations

International Nuclear Information System (INIS)

Zee, S.K.

1987-01-01

A numeric algorithm and an associated computer code were developed for the rapid solution of the finite-difference method representation of the few-group neutron-diffusion equations on parallel computers. Applications of the numeric algorithm on both SIMD (vector pipeline) and MIMD/SIMD (multi-CUP/vector pipeline) architectures were explored. The algorithm was successfully implemented in the two-group, 3-D neutron diffusion computer code named DIFPAR3D (DIFfusion PARallel 3-Dimension). Numerical-solution techniques used in the code include the Chebyshev polynomial acceleration technique in conjunction with the power method of outer iteration. For inner iterations, a parallel form of red-black (cyclic) line SOR with automated determination of group dependent relaxation factors and iteration numbers required to achieve specified inner iteration error tolerance is incorporated. The code employs a macroscopic depletion model with trace capability for selected fission products' transients and critical boron. In addition to this, moderator and fuel temperature feedback models are also incorporated into the DIFPAR3D code, for realistic simulation of power reactor cores. The physics models used were proven acceptable in separate benchmarking studies

Fire behavior simulation in Mediterranean forests using the minimum travel time algorithm

Science.gov (United States)

Kostas Kalabokidis; Palaiologos Palaiologou; Mark A. Finney

2014-01-01

Recent large wildfires in Greece exemplify the need for pre-fire burn probability assessment and possible landscape fire flow estimation to enhance fire planning and resource allocation. The Minimum Travel Time (MTT) algorithm, incorporated as FlamMap's version five module, provide valuable fire behavior functions, while enabling multi-core utilization for the...

Single-Spot Yellow Laser Versus Conventional Green Laser on Panretinal Photocoagulation: Patient Pain Scores and Preferences.

Science.gov (United States)

González-Saldivar, Gerardo; Rojas-Juárez, Sergio; Espinosa-Soto, Itzel; Sánchez-Ramos, Jorge; Jaurieta-Hinojosa, Noel; Ramírez-Estudillo, Abel

2017-11-01

Panretinal photocoagulation (PRP) is the mainstay therapy for proliferative diabetic retinopathy. Pain during and after its application is a complication that affects patients' therapeutic adherence. This study aimed to compare pain perception and patient preference for the 577-nm yellow laser (YL-577) (LIGHTL as 577; LIGHTMED, San Clemente, CA) and the conventional 532-nm green laser (GL-532) (Purepoint Laser; Alcon, Fort Worth, TX) with PRP. A total of 92 patient eyes with proliferative diabetic retinopathy treated with PRP were randomly assigned to receive both GL-532 and YL-577 (184 eyes) - one on each eye, with the order of application randomized, as well. Afterward, verbal rapid answer and visual analogue scale (VAS) scores for pain perception and patient preference were evaluated. VAS score was 7 ± 2 for the GL-532 group compared to 5 ± 3 in the YL-577 group (P = .001). Overall, 75% of the patients preferred YL-577 therapy if they were to receive a second PRP session. The use of YL-577 as an alternative approach for PRP reduces pain perception and is preferred by patients. [Ophthalmic Surg Lasers Imaging Retina. 2017;48:902-905.]. Copyright 2017, SLACK Incorporated.

A preference for migration

OpenAIRE

Stark, Oded

2007-01-01

At least to some extent migration behavior is the outcome of a preference for migration. The pattern of migration as an outcome of a preference for migration depends on two key factors: imitation technology and migration feasibility. We show that these factors jointly determine the outcome of a preference for migration and we provide examples that illustrate how the prevalence and transmission of a migration-forming preference yield distinct migration patterns. In particular, the imitation of...

Magnet sorting algorithms for insertion devices for the Advanced Light Source

International Nuclear Information System (INIS)

Humphries, D.; Hoyer, E.; Kincaid, B.; Marks, S.; Schlueter, R.

1994-01-01

Insertion devices for the Advanced Light Source (ALS) incorporate up to 3,000 magnet blocks each for pole energization. In order to minimize field errors, these magnets must be measured, sorted and assigned appropriate locations and orientation in the magnetic structures. Sorting must address multiple objectives, including pole excitation and minimization of integrated multipole fields from minor field components in the magnets. This is equivalent to a combinatorial minimization problem with a large configuration space. Multi-stage sorting algorithms use ordering and pairing schemes in conjunction with other combinatorial methods to solve the minimization problem. This paper discusses objective functions, solution algorithms and results of application to magnet block measurement data

A parallel algorithm for switch-level timing simulation on a hypercube multiprocessor

Science.gov (United States)

Rao, Hariprasad Nannapaneni

1989-01-01

The parallel approach to speeding up simulation is studied, specifically the simulation of digital LSI MOS circuitry on the Intel iPSC/2 hypercube. The simulation algorithm is based on RSIM, an event driven switch-level simulator that incorporates a linear transistor model for simulating digital MOS circuits. Parallel processing techniques based on the concepts of Virtual Time and rollback are utilized so that portions of the circuit may be simulated on separate processors, in parallel for as large an increase in speed as possible. A partitioning algorithm is also developed in order to subdivide the circuit for parallel processing.

Multi-scale graph-cut algorithm for efficient water-fat separation.

Science.gov (United States)

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies

DEFF Research Database (Denmark)

Rasmussen, Matias Sevel; Justesen, Tor Fog; Dohn, Anders Høeg

2012-01-01

In the Home Care Crew Scheduling Problem a staff of home carers has to be assigned a number of visits to patients’ homes, such that the overall service level is maximised. The problem is a generalisation of the vehicle routing problem with time windows. Required travel time between visits and time...... preference constraints. The algorithm is tested both on real-life problem instances and on generated test instances inspired by realistic settings. The use of the specialised branching scheme on real-life problems is novel. The visit clustering decreases run times significantly, and only gives a loss...... windows of the visits must be respected. The challenge when assigning visits to home carers lies in the existence of soft preference constraints and in temporal dependencies between the start times of visits.We model the problem as a set partitioning problem with side constraints and develop an exact...

Assessing Women's Preferences and Preference Modeling for Breast Reconstruction Decision-Making.

Science.gov (United States)

Sun, Clement S; Cantor, Scott B; Reece, Gregory P; Crosby, Melissa A; Fingeret, Michelle C; Markey, Mia K

2014-03-01

Women considering breast reconstruction must make challenging trade-offs amongst issues that often conflict. It may be useful to quantify possible outcomes using a single summary measure to aid a breast cancer patient in choosing a form of breast reconstruction. In this study, we used multiattribute utility theory to combine multiple objectives to yield a summary value using nine different preference models. We elicited the preferences of 36 women, aged 32 or older with no history of breast cancer, for the patient-reported outcome measures of breast satisfaction, psychosocial well-being, chest well-being, abdominal well-being, and sexual wellbeing as measured by the BREAST-Q in addition to time lost to reconstruction and out-of-pocket cost. Participants ranked hypothetical breast reconstruction outcomes. We examined each multiattribute utility preference model and assessed how often each model agreed with participants' rankings. The median amount of time required to assess preferences was 34 minutes. Agreement among the nine preference models with the participants ranged from 75.9% to 78.9%. None of the preference models performed significantly worse than the best performing risk averse multiplicative model. We hypothesize an average theoretical agreement of 94.6% for this model if participant error is included. There was a statistically significant positive correlation with more unequal distribution of weight given to the seven attributes. We recommend the risk averse multiplicative model for modeling the preferences of patients considering different forms of breast reconstruction because it agreed most often with the participants in this study.

Providing health information for culturally and linguistically diverse women: priorities and preferences of new migrants and refugees.

Science.gov (United States)

Lee, Susan K; Sulaiman-Hill, Cheryl M R; Thompson, Sandra C

2013-08-01

Preferences for topics and means of access to health information among newly arrived, culturally and linguistically diverse women in Perth, Western Australia, were explored. A mixed-methods approach was adopted. Qualitative material obtained from focus groups and interviews with 22 service providers and 26 migrant women was used to develop a questionnaire, which was then administered to 268 newly arrived migrant and refugee women from 50 countries. Participants' information and support priorities were ascertained from a ranking exercise conducted in a non-threatening context. Responses of migrant and refugee women were compared quantitatively. Women's top priorities for information and support included employment advice, as well as information regarding mental health issues, women's health, exercise and nutrition, family violence and alcohol and other drug issues. Their preferred methods for receiving information were interactive talks or presentations, with written material support. Audiovisual and Web-based material were also considered useful. There were differences between refugee women's and other migrants' preferences for means of receiving information and topics of most concern. The use of a non-threatening ranking process encouraged women to prioritise sensitive topics, such as family violence, and revealed a need for such topics to be incorporated within general health information presentations. Internet-based technologies are becoming increasingly important methods for disseminating information to migrant women. SO WHAT? Differences between migrant and refugee women's priority health issues and their preferred methods for receiving information highlight the desirability of tailoring information to particular groups. Although advice on employment pathways and mental health concerns were top priorities, the study revealed a need for more discussion on other sensitive topics, such as family violence and alcohol-related issues, and that ideally these should

Improved dynamic-programming-based algorithms for segmentation of masses in mammograms

International Nuclear Information System (INIS)

Dominguez, Alfonso Rojas; Nandi, Asoke K.

2007-01-01

In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID 2 PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID 2 PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions

The Goals and Effects of Music Listening and Their Relationship to the Strength of Music Preference.

Directory of Open Access Journals (Sweden)

Thomas Schäfer

Full Text Available Individual differences in the strength of music preference are among the most intricate psychological phenomena. While one person gets by very well without music, another person needs to listen to music every day and spends a lot of temporal and financial resources on listening to music, attending concerts, or buying concert tickets. Where do these differences come from? The hypothesis presented in this article is that the strength of music preference is mainly informed by the functions that music fulfills in people's lives (e.g., to regulate emotions, moods, or physiological arousal; to promote self-awareness; to foster social relatedness. Data were collected with a diary study, in which 121 respondents documented the goals they tried to attain and the effects that actually occurred for up to 5 music-listening episodes per day for 10 successive days. As expected, listeners reporting more intense experience of the functional use of music in the past (1 had a stronger intention to listen to music to attain specific goals in specific situations and (2 showed a larger overall strength of music preference. It is concluded that the functional effectiveness of music listening should be incorporated in existing models and frameworks of music preference to produce better predictions of interindividual differences in the strength of music preference. The predictability of musical style/genre preferences is also discussed with regard to the present results.

The Goals and Effects of Music Listening and Their Relationship to the Strength of Music Preference

Science.gov (United States)

Schäfer, Thomas

2016-01-01

Individual differences in the strength of music preference are among the most intricate psychological phenomena. While one person gets by very well without music, another person needs to listen to music every day and spends a lot of temporal and financial resources on listening to music, attending concerts, or buying concert tickets. Where do these differences come from? The hypothesis presented in this article is that the strength of music preference is mainly informed by the functions that music fulfills in people’s lives (e.g., to regulate emotions, moods, or physiological arousal; to promote self-awareness; to foster social relatedness). Data were collected with a diary study, in which 121 respondents documented the goals they tried to attain and the effects that actually occurred for up to 5 music-listening episodes per day for 10 successive days. As expected, listeners reporting more intense experience of the functional use of music in the past (1) had a stronger intention to listen to music to attain specific goals in specific situations and (2) showed a larger overall strength of music preference. It is concluded that the functional effectiveness of music listening should be incorporated in existing models and frameworks of music preference to produce better predictions of interindividual differences in the strength of music preference. The predictability of musical style/genre preferences is also discussed with regard to the present results. PMID:26985998

Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN

Directory of Open Access Journals (Sweden)

Ahmad Ali

2018-03-01

Full Text Available Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs. Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF. In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle.

MODA: an efficient algorithm for network motif discovery in biological networks.

Science.gov (United States)

Omidi, Saeed; Schreiber, Falk; Masoudi-Nejad, Ali

2009-10-01

In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/

Algebraic dynamics algorithm: Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm

Institute of Scientific and Technical Information of China (English)

WANG ShunJin; ZHANG Hua

2007-01-01

Based on the exact analytical solution of ordinary differential equations,a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm.A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models.The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision,and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

Algebraic dynamics algorithm:Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm

Institute of Scientific and Technical Information of China (English)

2007-01-01

Based on the exact analytical solution of ordinary differential equations, a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm. A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models. The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision, and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

SWIM: A Semi-Analytical Ocean Color Inversion Algorithm for Optically Shallow Waters

Science.gov (United States)

McKinna, Lachlan I. W.; Werdell, P. Jeremy; Fearns, Peter R. C. S.; Weeks, Scarla J.; Reichstetter, Martina; Franz, Bryan A.; Shea, Donald M.; Feldman, Gene C.

2014-01-01

Ocean color remote sensing provides synoptic-scale, near-daily observations of marine inherent optical properties (IOPs). Whilst contemporary ocean color algorithms are known to perform well in deep oceanic waters, they have difficulty operating in optically clear, shallow marine environments where light reflected from the seafloor contributes to the water-leaving radiance. The effect of benthic reflectance in optically shallow waters is known to adversely affect algorithms developed for optically deep waters [1, 2]. Whilst adapted versions of optically deep ocean color algorithms have been applied to optically shallow regions with reasonable success [3], there is presently no approach that directly corrects for bottom reflectance using existing knowledge of bathymetry and benthic albedo.To address the issue of optically shallow waters, we have developed a semi-analytical ocean color inversion algorithm: the Shallow Water Inversion Model (SWIM). SWIM uses existing bathymetry and a derived benthic albedo map to correct for bottom reflectance using the semi-analytical model of Lee et al [4]. The algorithm was incorporated into the NASA Ocean Biology Processing Groups L2GEN program and tested in optically shallow waters of the Great Barrier Reef, Australia. In-lieu of readily available in situ matchup data, we present a comparison between SWIM and two contemporary ocean color algorithms, the Generalized Inherent Optical Property Algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA).

Data-driven Development of ROTEM and TEG Algorithms for the Management of Trauma Hemorrhage

DEFF Research Database (Denmark)

Baksaas-Aasen, Kjersti; Van Dieren, Susan; Balvers, Kirsten

2018-01-01

for ROTEM, TEG, and CCTs to be used in addition to ratio driven transfusion and tranexamic acid. CONCLUSIONS: We describe a systematic approach to define threshold parameters for ROTEM and TEG. These parameters were incorporated into algorithms to support data-driven adjustments of resuscitation...

CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

Science.gov (United States)

Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

2014-12-01

Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

Directory of Open Access Journals (Sweden)

Dazhi Jiang

2015-01-01

Full Text Available At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.

Fatigue evaluation algorithms: Review

Energy Technology Data Exchange (ETDEWEB)

Passipoularidis, V.A.; Broendsted, P.

2009-11-15

A progressive damage fatigue simulator for variable amplitude loads named FADAS is discussed in this work. FADAS (Fatigue Damage Simulator) performs ply by ply stress analysis using classical lamination theory and implements adequate stiffness discount tactics based on the failure criterion of Puck, to model the degradation caused by failure events in ply level. Residual strength is incorporated as fatigue damage accumulation metric. Once the typical fatigue and static properties of the constitutive ply are determined,the performance of an arbitrary lay-up under uniaxial and/or multiaxial load time series can be simulated. The predictions are validated against fatigue life data both from repeated block tests at a single stress ratio as well as against spectral fatigue using the WISPER, WISPERX and NEW WISPER load sequences on a Glass/Epoxy multidirectional laminate typical of a wind turbine rotor blade construction. Two versions of the algorithm, the one using single-step and the other using incremental application of each load cycle (in case of ply failure) are implemented and compared. Simulation results confirm the ability of the algorithm to take into account load sequence effects. In general, FADAS performs well in predicting life under both spectral and block loading fatigue. (author)

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

Energy Technology Data Exchange (ETDEWEB)

Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)

2014-03-15

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

International Nuclear Information System (INIS)

Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao

2014-01-01

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm

Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm

DEFF Research Database (Denmark)

Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar

2017-01-01

India's ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging...... stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context......, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...

Comparison of SAR calculation algorithms for the finite-difference time-domain method

International Nuclear Information System (INIS)

Laakso, Ilkka; Uusitupa, Tero; Ilvonen, Sami

2010-01-01

Finite-difference time-domain (FDTD) simulations of specific-absorption rate (SAR) have several uncertainty factors. For example, significantly varying SAR values may result from the use of different algorithms for determining the SAR from the FDTD electric field. The objective of this paper is to rigorously study the divergence of SAR values due to different SAR calculation algorithms and to examine if some SAR calculation algorithm should be preferred over others. For this purpose, numerical FDTD results are compared to analytical solutions in a one-dimensional layered model and a three-dimensional spherical object. Additionally, the implications of SAR calculation algorithms for dosimetry of anatomically realistic whole-body models are studied. The results show that the trapezium algorithm-based on the trapezium integration rule-is always conservative compared to the analytic solution, making it a good choice for worst-case exposure assessment. In contrast, the mid-ordinate algorithm-named after the mid-ordinate integration rule-usually underestimates the analytic SAR. The linear algorithm-which is approximately a weighted average of the two-seems to be the most accurate choice overall, typically giving the best fit with the shape of the analytic SAR distribution. For anatomically realistic models, the whole-body SAR difference between different algorithms is relatively independent of the used body model, incident direction and polarization of the plane wave. The main factors affecting the difference are cell size and frequency. The choice of the SAR calculation algorithm is an important simulation parameter in high-frequency FDTD SAR calculations, and it should be explained to allow intercomparison of the results between different studies. (note)

Classification and characterization of Japanese consumers' beef preferences by external preference mapping.

Science.gov (United States)

Sasaki, Keisuke; Ooi, Motoki; Nagura, Naoto; Motoyama, Michiyo; Narita, Takumi; Oe, Mika; Nakajima, Ikuyo; Hagi, Tatsuro; Ojima, Koichi; Kobayashi, Miho; Nomura, Masaru; Muroya, Susumu; Hayashi, Takeshi; Akama, Kyoko; Fujikawa, Akira; Hokiyama, Hironao; Kobayashi, Kuniyuki; Nishimura, Takanori

2017-08-01

Over the past few decades, beef producers in Japan have improved marbling in their beef products. It was recently reported that marbling is not well correlated with palatability as rated by Japanese consumers. This study sought to identify the consumer segments in Japan that prefer sensory characteristics of beef other than high marbling. Three Wagyu beef, one Holstein beef and two lean imported beef longissimus samples were subjected to a descriptive sensory test, physicochemical analysis and a consumer (n = 307) preference test. According to consumer classification and external preference mapping, four consumer segments were identified as 'gradual high-fat likers', 'moderate-fat and distinctive taste likers', 'Wagyu likers' and 'distinctive texture likers'. Although the major trend of Japanese consumers' beef preference was 'marbling liking', 16.9% of the consumers preferred beef samples that had moderate marbling and distinctive taste. The consumers' attitudes expressed in a questionnaire survey were in good agreement with the preference for marbling among the 'moderate-fat and distinctive taste likers'. These results indicate that moderately marbled beef is a potent category in the Japanese beef market. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Sour Taste preferences of children relate to preference of novel and intense stimuli

NARCIS (Netherlands)

Liem, D.G.; Westerbeek, A.; Wolterink, S.; Kok, F.J.; Graaf, de C.

2004-01-01

Previous research has suggested that some children have a preference for sour tastes. The origin of this preference remains unclear. We investigated whether preference for sour tastes is related to a difference in rated sour intensity due to physiological properties of saliva, or to an overall

A novel biomedical image indexing and retrieval system via deep preference learning.

Science.gov (United States)

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state

What are colorectal cancer survivors' preferences for dietary advice? A best-worst discrete choice experiment.

Science.gov (United States)

Wright, Stuart J; Gibson, Debbie; Eden, Martin; Lal, Simon; Todd, Chris; Ness, Andy; Burden, Sorrel

2017-12-01

Studies on healthy lifestyle interventions in survivors of colorectal cancer have been disappointing, demonstrating only modest changes. This study aims to quantify people's preferences for different aspects of dietary intervention. A best-worst discrete choice experiment was designed and incorporated into a questionnaire including participants' characteristics and a self-assessment of lifestyle. The response rate was 68% and 179 questionnaires were analysed. When analysing aggregate preferences, the modes of information provision selected as the most preferred were "face-to-face" (willingness to pay (WTP) £63.97, p ≤ 0.001) and "telephone" (WTP £62.36, p WTP -£118.96, p ≤ 0.001). Scenarios that included hospitals were most preferred (WTP £17.94, p = 0.031), and the favoured provider was bowel cancer nurses (WTP £75.11, p ≤ 0.001). When investigating preference heterogeneity, three sub-groups were identified: Firstly, "technophiles" preferring email (WTP £239.60, p ≤ 0.001) were male, were younger and had fewer risk factors. Secondly, a "one-to-one" group had strong preference for interventions over the telephone or at their local doctors and were older (WTP £642.13, p ≤ 0.001). Finally, a "person-centred" group preferred face-to-face individual or group sessions (WTP £358.79, p < 0.001) and had a high risk lifestyle. For survivors of colorectal cancer, there is not one approach that suits all when it comes to providing dietary advice. This is important information to consider when planning healthy lifestyle interventions which include dietary advice for survivors of colorectal cancer. Aligning services to individuals' preferences has the potential to improve patient experience and outcomes by increasing uptake of healthy lifestyle advice services and promoting a more tailored approach to dietary modifications, acknowledging sub-groups of people within the total population of colorectal cancer survivors.

Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH

International Nuclear Information System (INIS)

Volk, Jochen; Herrmann, Torsten; Wuethrich, Kurt

2008-01-01

MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics guarantees high efficiency and robustness. MATCH thus makes combined use of the two predominant concepts in use for automated NMR assignment of proteins. Dynamic transition and inherent mutation are new techniques that enable automatic adaptation to variable quality of the experimental input data. The concept of dynamic transition is incorporated in all major building blocks of the algorithm, where it enables switching between local and global optimization heuristics at any time during the assignment process. Inherent mutation restricts the intrinsically required randomness of the evolutionary algorithm to those regions of the conformation space that are compatible with the experimental input data. Using intact and artificially deteriorated APSY-NMR input data of proteins, MATCH performed sequence-specific resonance assignment with high efficiency and robustness

Multi-objective optimization of an underwater compressed air energy storage system using genetic algorithm

International Nuclear Information System (INIS)

Cheung, Brian C.; Carriveau, Rupp; Ting, David S.K.

2014-01-01

This paper presents the findings from a multi-objective genetic algorithm optimization study on the design parameters of an underwater compressed air energy storage system (UWCAES). A 4 MWh UWCAES system was numerically simulated and its energy, exergy, and exergoeconomics were analysed. Optimal system configurations were determined that maximized the UWCAES system round-trip efficiency and operating profit, and minimized the cost rate of exergy destruction and capital expenditures. The optimal solutions obtained from the multi-objective optimization model formed a Pareto-optimal front, and a single preferred solution was selected using the pseudo-weight vector multi-criteria decision making approach. A sensitivity analysis was performed on interest rates to gauge its impact on preferred system designs. Results showed similar preferred system designs for all interest rates in the studied range. The round-trip efficiency and operating profit of the preferred system designs were approximately 68.5% and $53.5/cycle, respectively. The cost rate of the system increased with interest rates. - Highlights: • UWCAES system configurations were developed using multi-objective optimization. • System was optimized for energy efficiency, exergy, and exergoeconomics • Pareto-optimal solution surfaces were developed at different interest rates. • Similar preferred system configurations were found at all interest rates studied

Using data mining to segment healthcare markets from patients' preference perspectives.

Science.gov (United States)

Liu, Sandra S; Chen, Jie

2009-01-01

This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics. Data were derived from a number of individuals who received in-patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables. Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles. The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters. Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.

Explicit symplectic algorithms based on generating functions for relativistic charged particle dynamics in time-dependent electromagnetic field

Science.gov (United States)

Zhang, Ruili; Wang, Yulei; He, Yang; Xiao, Jianyuan; Liu, Jian; Qin, Hong; Tang, Yifa

2018-02-01

Relativistic dynamics of a charged particle in time-dependent electromagnetic fields has theoretical significance and a wide range of applications. The numerical simulation of relativistic dynamics is often multi-scale and requires accurate long-term numerical simulations. Therefore, explicit symplectic algorithms are much more preferable than non-symplectic methods and implicit symplectic algorithms. In this paper, we employ the proper time and express the Hamiltonian as the sum of exactly solvable terms and product-separable terms in space-time coordinates. Then, we give the explicit symplectic algorithms based on the generating functions of orders 2 and 3 for relativistic dynamics of a charged particle. The methodology is not new, which has been applied to non-relativistic dynamics of charged particles, but the algorithm for relativistic dynamics has much significance in practical simulations, such as the secular simulation of runaway electrons in tokamaks.

Incorporating single detector failure into the ROP detector layout optimization for CANDU reactors

Energy Technology Data Exchange (ETDEWEB)

Kastanya, Doddy, E-mail: Doddy.Kastanya@snclavalin.com

2015-12-15

Highlights: • ROP TSP value needs to be adjusted when any detector in the system fails. • Single detector failure criterion has been incorporated into the detector layout optimization as a constraint. • Results show that the optimized detector layout is more robust with respect to its vulnerability to a single detector failure. • An early rejection scheme has been introduced to speed-up the optimization process. - Abstract: In CANDU{sup ®} reactors, the regional overpower protection (ROP) systems are designed to protect the reactor against overpower in the fuel which could reduce the safety margin-to-dryout. In the CANDU{sup ®} 600 MW (CANDU 6) design, there are two ROP systems in the core, each of which is connected to a fast-acting shutdown system. Each ROP system consists of a number of fast-responding, self-powered flux detectors suitably distributed throughout the core within vertical and horizontal flux detector assemblies. The placement of these ROP detectors is a challenging discrete optimization problem. In the past few years, two algorithms, DETPLASA and ADORE, have been developed to optimize the detector layout for the ROP systems in CANDU reactors. These algorithms utilize the simulated annealing (SA) technique to optimize the placement of the detectors in the core. The objective of the optimization process is typically either to maximize the TSP value for a given number of detectors in the system or to minimize the number of detectors in the system to obtain a target TSP value. One measure to determine the robustness of the optimized detector layout is to evaluate the maximum decrease (penalty) in TSP value when any single detector in the system fails. The smaller the penalty, the more robust the design is. Therefore, in order to ensure that the optimized detector layout is robust, the single detector failure (SDF) criterion has been incorporated as an additional constraint into the ADORE algorithm. Results from this study indicate that there

An improved algorithm for finding all minimal paths in a network

International Nuclear Information System (INIS)

Bai, Guanghan; Tian, Zhigang; Zuo, Ming J.

2016-01-01

Minimal paths (MPs) play an important role in network reliability evaluation. In this paper, we report an efficient recursive algorithm for finding all MPs in two-terminal networks, which consist of a source node and a sink node. A linked path structure indexed by nodes is introduced, which accepts both directed and undirected form of networks. The distance between each node and the sink node is defined, and a simple recursive algorithm is presented for labeling the distance for each node. Based on the distance between each node and the sink node, additional conditions for backtracking are incorporated to reduce the number of search branches. With the newly introduced linked node structure, the distances between each node and the sink node, and the additional backtracking conditions, an improved backtracking algorithm for searching for all MPs is developed. In addition, the proposed algorithm can be adapted to search for all minimal paths for each source–sink pair in networks consisting of multiple source nodes and/or multiple sink nodes. Through computational experiments, it is demonstrated that the proposed algorithm is more efficient than existing algorithms when the network size is not too small. The proposed algorithm becomes more advantageous as the size of the network grows. - Highlights: • A linked path structure indexed by nodes is introduced to represent networks. • Additional conditions for backtracking are proposed based on the distance of each node. • An efficient algorithm is developed to find all MPs for two-terminal networks. • The computational efficiency of the algorithm for two-terminal networks is investigated. • The computational efficiency of the algorithm for multi-terminal networks is investigated.

Semi-empirical Algorithm for the Retrieval of Ecology-Relevant Water Constituents in Various Aquatic Environments

Directory of Open Access Journals (Sweden)

Robert Shuchman

2009-03-01

Full Text Available An advanced operational semi-empirical algorithm for processing satellite remote sensing data in the visible region is described. Based on the Levenberg-Marquardt multivariate optimization procedure, the algorithm is developed for retrieving major water colour producing agents: chlorophyll-a, suspended minerals and dissolved organics. Two assurance units incorporated by the algorithm are intended to flag pixels with inaccurate atmospheric correction and specific hydro-optical properties not covered by the applied hydro-optical model. The hydro-optical model is a set of spectral cross-sections of absorption and backscattering of the colour producing agents. The combination of the optimization procedure and a replaceable hydro-optical model makes the developed algorithm not specific to a particular satellite sensor or a water body. The algorithm performance efficiency is amply illustrated for SeaWiFS, MODIS and MERIS images over a variety of water bodies.

Compression of fingerprint data using the wavelet vector quantization image compression algorithm. 1992 progress report

Energy Technology Data Exchange (ETDEWEB)

Bradley, J.N.; Brislawn, C.M.

1992-04-11

This report describes the development of a Wavelet Vector Quantization (WVQ) image compression algorithm for fingerprint raster files. The pertinent work was performed at Los Alamos National Laboratory for the Federal Bureau of Investigation. This document describes a previously-sent package of C-language source code, referred to as LAFPC, that performs the WVQ fingerprint compression and decompression tasks. The particulars of the WVQ algorithm and the associated design procedure are detailed elsewhere; the purpose of this document is to report the results of the design algorithm for the fingerprint application and to delineate the implementation issues that are incorporated in LAFPC. Special attention is paid to the computation of the wavelet transform, the fast search algorithm used for the VQ encoding, and the entropy coding procedure used in the transmission of the source symbols.

Preferences for prenatal testing among pregnant women, partners and health professionals.

Science.gov (United States)

Lund, Ida Charlotte Bay; Becher, Naja; Petersen, Olav Bjørn; Hill, Melissa; Chitty, Lyn; Vogel, Ida

2018-05-01

Cell-free DNA testing (cfDNA testing) in maternal plasma has recently been implemented in Danish healthcare. Prior to that we wanted to evaluate the preferences among pregnant women, partners and health professionals regarding cfDNA testing compared with invasive prenatal diagnostics. Responders were recruited at public hospitals in the Central and North Denmark Regions. Stated preferences for prenatal testing were obtained through an online questionnaire incorporating a discrete choice experiment. Test choices differed according to attributes such as risk of miscarriage (none or small) and genetic information provided by the test; simple (Down syndrome only) or comprehensive (chromosomal abnormalities beyond Down syndrome). No risk of miscarriage was the key attribute affecting the preferences of women (n = 315) and partners (n = 102). However, women with experiences of invasive testing placed more emphasis on comprehensive genetic information and less on risk of miscarriage compared with other women. Likewise, foetal medicine experts, obstetricians and sonographers (n = 57) had a greater preference for comprehensive genetic information than midwives who were not directly involved in counselling for prenatal testing (n = 48). As safety seems to affect the majority of pregnant couples' choice behaviour, thorough pre-test counselling by trained health professionals is of paramount importance. Aarhus University and The Foundation of 17-12-1981. This study was registered with the Danish Data Protection Agency (1-16-02-586-13/ 2007-58-0010). Articles published in the DMJ are “open access”. This means that the articles are distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits any non-commercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

An efficient algorithm for removal of inactive blocks in reservoir simulation

Energy Technology Data Exchange (ETDEWEB)

Abou-Kassem, J.H.; Ertekin, T. (Pennsylvania State Univ., PA (United States))

1992-02-01

In the efficient simulation of reservoirs having irregular boundaries one is confronted with two problems: the removal of inactive blocks at the matrix level and the development and application of a variable band-width solver. A simple algorithm is presented that provides effective solutions to these two problems. The algorithm is demonstrated for both the natural ordering and D4 ordering schemes. It can be easily incorporated in existing simulators and results in significant savings in CPU and matrix storage requirements. The removal of the inactive blocks at the matrix level plays a major role in effecting these savings whereas the application of a variable band-width solver plays an enhancing role only. The value of this algorithm lies in the fact that it takes advantage of irregular reservoir boundaries that are invariably encountered in almost all practical applications of reservoir simulation. 11 refs., 3 figs., 3 tabs.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

Science.gov (United States)

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-06-12

Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

Characterization of active paper packaging incorporated with ginger pulp oleoresin

Science.gov (United States)

Wiastuti, T.; Khasanah, L. U.; Atmaka Kawiji, W.; Manuhara, G. J.; Utami, R.

2016-02-01

Utilization of ginger pulp waste from herbal medicine and instant drinks industry in Indonesia currently used for fertilizer and fuel, whereas the ginger pulp still contains high oleoresin. Active paper packaging were developed incorporated with ginger pulp oleoresin (0%, 2%, 4%, and 6% w/w). Physical (thickness, tensile strength, and folding endurance, moisture content), sensory characteristics and antimicrobial activity of the active paper were evaluated. Selected active paper then were chemically characterized (functional groups). The additional of ginger pulp oleoresin levels are reduced tensile strength, folding endurance and sensory characteristic (color, texture and overall) and increased antimicrobial activity. Due to physical, sensory characteristic and antimicrobial activity, active paper with 2% ginger pulp oleoresin incorporation was selected. Characteristics of selected paper were 9.93% of water content; 0.81 mm of thickness; 0.54 N / mm of tensile strength; 0.30 of folding endurance; 8.43 mm inhibits the growth of Pseudomonas fluorescence and 27.86 mm inhibits the growth of Aspergillus niger (antimicrobial activity) and neutral preference response for sensory properties. For chemical characteristic, selected paper had OH functional group of ginger in 3422.83 cm-1 of wave number and indicated contain red ginger active compounds.

Great apes prefer cooked food.

Science.gov (United States)

Wobber, Victoria; Hare, Brian; Wrangham, Richard

2008-08-01

The cooking hypothesis proposes that a diet of cooked food was responsible for diverse morphological and behavioral changes in human evolution. However, it does not predict whether a preference for cooked food evolved before or after the control of fire. This question is important because the greater the preference shown by a raw-food-eating hominid for the properties present in cooked food, the more easily cooking should have been adopted following the control of fire. Here we use great apes to model food preferences by Paleolithic hominids. We conducted preference tests with various plant and animal foods to determine whether great apes prefer food items raw or cooked. We found that several populations of captive apes tended to prefer their food cooked, though with important exceptions. These results suggest that Paleolithic hominids would likewise have spontaneously preferred cooked food to raw, exapting a pre-existing preference for high-quality, easily chewed foods onto these cooked items. The results, therefore, challenge the hypothesis that the control of fire preceded cooking by a significant period.

An innovative artificial bee colony algorithm and its application to a practical intercell scheduling problem

Science.gov (United States)

Li, Dongni; Guo, Rongtao; Zhan, Rongxin; Yin, Yong

2018-06-01

In this article, an innovative artificial bee colony (IABC) algorithm is proposed, which incorporates two mechanisms. On the one hand, to provide the evolutionary process with a higher starting level, genetic programming (GP) is used to generate heuristic rules by exploiting the elements that constitute the problem. On the other hand, to achieve a better balance between exploration and exploitation, a leading mechanism is proposed to attract individuals towards a promising region. To evaluate the performance of IABC in solving practical and complex problems, it is applied to the intercell scheduling problem with limited transportation capacity. It is observed that the GP-generated rules incorporate the elements of the most competing human-designed rules, and they are more effective than the human-designed ones. Regarding the leading mechanism, the strategies of the ageing leader and multiple challengers make the algorithm less likely to be trapped in local optima.

An effective algorithm for approximating adaptive behavior in seasonal environments

DEFF Research Database (Denmark)

Sainmont, Julie; Andersen, Ken Haste; Thygesen, Uffe Høgsbro

2015-01-01

Behavior affects most aspects of ecological processes and rates, and yet modeling frameworks which efficiently predict and incorporate behavioral responses into ecosystem models remain elusive. Behavioral algorithms based on life-time optimization, adaptive dynamics or game theory are unsuited...... for large global models because of their high computational demand. We compare an easily integrated, computationally efficient behavioral algorithm known as Gilliam's rule against the solution from a life-history optimization. The approximation takes into account only the current conditions to optimize...... behavior; the so-called "myopic approximation", "short sighted", or "static optimization". We explore the performance of the myopic approximation with diel vertical migration (DVM) as an example of a daily routine, a behavior with seasonal dependence that trades off predation risk with foraging...

Fuzzy model predictive control algorithm applied in nuclear power plant

International Nuclear Information System (INIS)

Zuheir, Ahmad

2006-01-01

The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

Partially Adaptive STAP Algorithm Approaches to functional MRI

Science.gov (United States)

Huang, Lejian; Thompson, Elizabeth A.; Schmithorst, Vincent; Holland, Scott K.; Talavage, Thomas M.

2010-01-01

In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis. PMID:19272913

Runway Operations Planning: A Two-Stage Heuristic Algorithm

Science.gov (United States)

Anagnostakis, Ioannis; Clarke, John-Paul

2003-01-01

The airport runway is a scarce resource that must be shared by different runway operations (arrivals, departures and runway crossings). Given the possible sequences of runway events, careful Runway Operations Planning (ROP) is required if runway utilization is to be maximized. From the perspective of departures, ROP solutions are aircraft departure schedules developed by optimally allocating runway time for departures given the time required for arrivals and crossings. In addition to the obvious objective of maximizing throughput, other objectives, such as guaranteeing fairness and minimizing environmental impact, can also be incorporated into the ROP solution subject to constraints introduced by Air Traffic Control (ATC) procedures. This paper introduces a two stage heuristic algorithm for solving the Runway Operations Planning (ROP) problem. In the first stage, sequences of departure class slots and runway crossings slots are generated and ranked based on departure runway throughput under stochastic conditions. In the second stage, the departure class slots are populated with specific flights from the pool of available aircraft, by solving an integer program with a Branch & Bound algorithm implementation. Preliminary results from this implementation of the two-stage algorithm on real-world traffic data are presented.

Measuring Preference for Supernormal Over Natural Rewards

Directory of Open Access Journals (Sweden)

B. C. Goodwin

2015-10-01

Full Text Available Supernormal (SN stimuli are artificial products that activate reward pathways and approach behavior more so than naturally occurring stimuli for which these systems were intended. Many modern consumer products (e.g., snack foods, alcohol, and pornography appear to incorporate SN features, leading to excessive consumption, in preference to naturally occurring alternatives. No measure currently exists for the self-report assessment of individual differences or changes in susceptibility to such stimuli. Therefore, an anticipatory pleasure scale was modified to include items that represented both SN and natural (N classes of rewarding stimuli. Exploratory factor analysis yielded a two-factor solution, and as predicted, N and SN items reliably loaded on separate dimensions. Internal reliability for the two scales was high, ρ =.93 and ρ =.90, respectively. The two-dimensional measure was evaluated via regression using the N and SN scale means as predictors and self-reports of daily consumption of 21 products with SN features as outcomes. As expected, SN pleasure ratings were related to higher SN product consumption, while N pleasure ratings had either negative or neutral associations to consumption of these products. We conclude that the resulting two-dimensional measure is a potentially reliable and valid self-report measure of differential preference for SN stimuli. While further evaluation is needed (e.g., using experimental measures, the proposed scale may play a useful role in the study of both trait- and state-based variation in human susceptibility to SN stimuli.

Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift

Science.gov (United States)

Ortíz Díaz, Agustín; Ramos-Jiménez, Gonzalo; Frías Blanco, Isvani; Caballero Mota, Yailé; Morales-Bueno, Rafael

2015-01-01

The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts. PMID:25879051

To develop a universal gamut mapping algorithm

International Nuclear Information System (INIS)

Morovic, J.

1998-10-01

When a colour image from one colour reproduction medium (e.g. nature, a monitor) needs to be reproduced on another (e.g. on a monitor or in print) and these media have different colour ranges (gamuts), it is necessary to have a method for mapping between them. If such a gamut mapping algorithm can be used under a wide range of conditions, it can also be incorporated in an automated colour reproduction system and considered to be in some sense universal. In terms of preliminary work, a colour reproduction system was implemented, for which a new printer characterisation model (including grey-scale correction) was developed. Methods were also developed for calculating gamut boundary descriptors and for calculating gamut boundaries along given lines from them. The gamut mapping solution proposed in this thesis is a gamut compression algorithm developed with the aim of being accurate and universally applicable. It was arrived at by way of an evolutionary gamut mapping development strategy for the purposes of which five test images were reproduced between a CRT and printed media obtained using an inkjet printer. Initially, a number of previously published algorithms were chosen and psychophysically evaluated whereby an important characteristic of this evaluation was that it also considered the performance of algorithms for individual colour regions within the test images used. New algorithms were then developed on their basis, subsequently evaluated and this process was repeated once more. In this series of experiments the new GCUSP algorithm, which consists of a chroma-dependent lightness compression followed by a compression towards the lightness of the reproduction cusp on the lightness axis, gave the most accurate and stable performance overall. The results of these experiments were also useful for improving the understanding of some gamut mapping factors - in particular gamut difference. In addition to looking at accuracy, the pleasantness of reproductions obtained

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

Science.gov (United States)

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

An on-line modified least-mean-square algorithm for training neurofuzzy controllers.

Science.gov (United States)

Tan, Woei Wan

2007-04-01

The problem hindering the use of data-driven modelling methods for training controllers on-line is the lack of control over the amount by which the plant is excited. As the operating schedule determines the information available on-line, the knowledge of the process may degrade if the setpoint remains constant for an extended period. This paper proposes an identification algorithm that alleviates "learning interference" by incorporating fuzzy theory into the normalized least-mean-square update rule. The ability of the proposed methodology to achieve faster learning is examined by employing the algorithm to train a neurofuzzy feedforward controller for controlling a liquid level process. Since the proposed identification strategy has similarities with the normalized least-mean-square update rule and the recursive least-square estimator, the on-line learning rates of these algorithms are also compared.

Implementation and preliminary evaluation of 'C-tone': A novel algorithm to improve lexical tone recognition in Mandarin-speaking cochlear implant users.

Science.gov (United States)

Ping, Lichuan; Wang, Ningyuan; Tang, Guofang; Lu, Thomas; Yin, Li; Tu, Wenhe; Fu, Qian-Jie

2017-09-01

Because of limited spectral resolution, Mandarin-speaking cochlear implant (CI) users have difficulty perceiving fundamental frequency (F0) cues that are important to lexical tone recognition. To improve Mandarin tone recognition in CI users, we implemented and evaluated a novel real-time algorithm (C-tone) to enhance the amplitude contour, which is strongly correlated with the F0 contour. The C-tone algorithm was implemented in clinical processors and evaluated in eight users of the Nurotron NSP-60 CI system. Subjects were given 2 weeks of experience with C-tone. Recognition of Chinese tones, monosyllables, and disyllables in quiet was measured with and without the C-tone algorithm. Subjective quality ratings were also obtained for C-tone. After 2 weeks of experience with C-tone, there were small but significant improvements in recognition of lexical tones, monosyllables, and disyllables (P C-tone were greater for disyllables than for monosyllables. Subjective quality ratings showed no strong preference for or against C-tone, except for perception of own voice, where C-tone was preferred. The real-time C-tone algorithm provided small but significant improvements for speech performance in quiet with no change in sound quality. Pre-processing algorithms to reduce noise and better real-time F0 extraction would improve the benefits of C-tone in complex listening environments. Chinese CI users' speech recognition in quiet can be significantly improved by modifying the amplitude contour to better resemble the F0 contour.

Base-pairing preferences, physicochemical properties and mutational behaviour of the DNA lesion 8-nitroguanine †

OpenAIRE

Bhamra, Inder; Compagnone-Post, Patricia; O’Neil, Ian A.; Iwanejko, Lesley A.; Bates, Andrew D.; Cosstick, Richard

2012-01-01

8-Nitro-2′-deoxyguanosine (8-nitrodG) is a relatively unstable, mutagenic lesion of DNA that is increasingly believed to be associated with tissue inflammation. Due to the lability of the glycosidic bond, 8-nitrodG cannot be incorporated into oligodeoxynucleotides (ODNs) by chemical DNA synthesis and thus very little is known about its physicochemical properties and base-pairing preferences. Here we describe the synthesis of 8-nitro-2′-O-methylguanosine, a ribonucleoside analogue of this lesi...

An Algorithm to Automate Yeast Segmentation and Tracking

Science.gov (United States)

Doncic, Andreas; Eser, Umut; Atay, Oguzhan; Skotheim, Jan M.

2013-01-01

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation. PMID:23520484

Fully 3D PET image reconstruction using a fourier preconditioned conjugate-gradient algorithm

International Nuclear Information System (INIS)

Fessler, J.A.; Ficaro, E.P.

1996-01-01

Since the data sizes in fully 3D PET imaging are very large, iterative image reconstruction algorithms must converge in very few iterations to be useful. One can improve the convergence rate of the conjugate-gradient (CG) algorithm by incorporating preconditioning operators that approximate the inverse of the Hessian of the objective function. If the 3D cylindrical PET geometry were not truncated at the ends, then the Hessian of the penalized least-squares objective function would be approximately shift-invariant, i.e. G'G would be nearly block-circulant, where G is the system matrix. We propose a Fourier preconditioner based on this shift-invariant approximation to the Hessian. Results show that this preconditioner significantly accelerates the convergence of the CG algorithm with only a small increase in computation

Implementation of pencil kernel and depth penetration algorithms for treatment planning of proton beams

International Nuclear Information System (INIS)

Russell, K.R.; Saxner, M.; Ahnesjoe, A.; Montelius, A.; Grusell, E.; Dahlgren, C.V.

2000-01-01

The implementation of two algorithms for calculating dose distributions for radiation therapy treatment planning of intermediate energy proton beams is described. A pencil kernel algorithm and a depth penetration algorithm have been incorporated into a commercial three-dimensional treatment planning system (Helax-TMS, Helax AB, Sweden) to allow conformal planning techniques using irregularly shaped fields, proton range modulation, range modification and dose calculation for non-coplanar beams. The pencil kernel algorithm is developed from the Fermi-Eyges formalism and Moliere multiple-scattering theory with range straggling corrections applied. The depth penetration algorithm is based on the energy loss in the continuous slowing down approximation with simple correction factors applied to the beam penumbra region and has been implemented for fast, interactive treatment planning. Modelling of the effects of air gaps and range modifying device thickness and position are implicit to both algorithms. Measured and calculated dose values are compared for a therapeutic proton beam in both homogeneous and heterogeneous phantoms of varying complexity. Both algorithms model the beam penumbra as a function of depth in a homogeneous phantom with acceptable accuracy. Results show that the pencil kernel algorithm is required for modelling the dose perturbation effects from scattering in heterogeneous media. (author)

Color preference in red–green dichromats

Science.gov (United States)

Álvaro, Leticia; Moreira, Humberto; Lillo, Julio; Franklin, Anna

2015-01-01

Around 2% of males have red–green dichromacy, which is a genetic disorder of color vision where one type of cone photoreceptor is missing. Here we investigate the color preferences of dichromats. We aim (i) to establish whether the systematic and reliable color preferences of normal trichromatic observers (e.g., preference maximum at blue, minimum at yellow-green) are affected by dichromacy and (ii) to test theories of color preference with a dichromatic sample. Dichromat and normal trichromat observers named and rated how much they liked saturated, light, dark, and focal colors twice. Trichromats had the expected pattern of preference. Dichromats had a reliable pattern of preference that was different to trichromats, with a preference maximum rather than minimum at yellow and a much weaker preference for blue than trichromats. Color preference was more affected in observers who lacked the cone type sensitive to long wavelengths (protanopes) than in those who lacked the cone type sensitive to medium wavelengths (deuteranopes). Trichromats’ preferences were summarized effectively in terms of cone-contrast between color and background, and yellow-blue cone-contrast could account for dichromats’ pattern of preference, with some evidence for residual red–green activity in deuteranopes’ preference. Dichromats’ color naming also could account for their color preferences, with colors named more accurately and quickly being more preferred. This relationship between color naming and preference also was present for trichromat males but not females. Overall, the findings provide novel evidence on how dichromats experience color, advance the understanding of why humans like some colors more than others, and have implications for general theories of aesthetics. PMID:26170287

抑制扩频系统中窄带干扰的新卡尔曼滤波算法%New Kalman Filtering Algorithm for Narrowband Interference Suppression in Spread Spectrum Systems

Institute of Scientific and Technical Information of China (English)

许光辉; 胡光锐

2005-01-01

A new Kalman filtering algorithm based on estimation of spread spectrum signal before suppression of narrowband interference (NBI) in spread spectrum systems, using the dependence of autoregressive (AR) interference, is presented compared with performance of the ACM nonlinear filtering algorithm, simulation results show that the proposed algorithm has preferable performance, there is about 5 dB SNR improvement in average.

Tears or Fears? Comparing Gender Stereotypes about Movie Preferences to Actual Preferences.

Science.gov (United States)

Wühr, Peter; Lange, Benjamin P; Schwarz, Sascha

2017-01-01

This study investigated the accuracy of gender-specific stereotypes about movie-genre preferences for 17 genres. In Study 1, female and male participants rated the extent to which 17 movie genres are preferred by women or men. In Study 2, another sample of female and male participants rated their own preference for each genre. There were three notable results. First, Study 1 revealed the existence of gender stereotypes for the majority of genres (i.e., for 15 of 17 genres). Second, Study 2 revealed the existence of actual gender differences in preferences for the majority of genres (i.e., for 11 of 17 genres). Third, in order to assess the accuracy of gender stereotypes on movie preferences, we compared the results of both studies and found that the majority of gender stereotypes were accurate in direction, but inaccurate in size. In particular, the stereotypes overestimated actual gender differences for the majority of movie genres (i.e., 10 of 17). Practical and theoretical implications of these findings are discussed.

Tears or Fears? Comparing Gender Stereotypes about Movie Preferences to Actual Preferences

Science.gov (United States)

Wühr, Peter; Lange, Benjamin P.; Schwarz, Sascha

2017-01-01

This study investigated the accuracy of gender-specific stereotypes about movie-genre preferences for 17 genres. In Study 1, female and male participants rated the extent to which 17 movie genres are preferred by women or men. In Study 2, another sample of female and male participants rated their own preference for each genre. There were three notable results. First, Study 1 revealed the existence of gender stereotypes for the majority of genres (i.e., for 15 of 17 genres). Second, Study 2 revealed the existence of actual gender differences in preferences for the majority of genres (i.e., for 11 of 17 genres). Third, in order to assess the accuracy of gender stereotypes on movie preferences, we compared the results of both studies and found that the majority of gender stereotypes were accurate in direction, but inaccurate in size. In particular, the stereotypes overestimated actual gender differences for the majority of movie genres (i.e., 10 of 17). Practical and theoretical implications of these findings are discussed. PMID:28392774

The Causes of Preference Reversal.

OpenAIRE

Tversky, Amos; Slovic, Paul; Kahneman, Daniel

1990-01-01

Observed preference reversal cannot be adequately explained by violations of independence, the reduction axiom, or transitivity. The primary cause of preference reversal is the failure of procedure invariance, especially the overpricing of low-probability, high-payoff bets. This result violates regret theory and generalized (nonindependent) utility models. Preference reversal and a new reversal involving time preferences are explained by scale compatibility, which implies that payoffs are wei...

An integer optimization algorithm for robust identification of non-linear gene regulatory networks

Directory of Open Access Journals (Sweden)

Chemmangattuvalappil Nishanth

2012-09-01

Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

Science.gov (United States)

Floares, Alexandru George

2008-01-01

Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

Application of Grey Wolf Optimizer Algorithm for Optimal Power Flow of Two-Terminal HVDC Transmission System

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Heba Ahmed Hassan

2017-01-01

Full Text Available This paper applies a relatively new optimization method, the Grey Wolf Optimizer (GWO algorithm for Optimal Power Flow (OPF of two-terminal High Voltage Direct Current (HVDC electrical power system. The OPF problem of pure AC power systems considers the minimization of total costs under equality and inequality constraints. Hence, the OPF problem of integrated AC-DC power systems is extended to incorporate HVDC links, while taking into consideration the power transfer control characteristics using a GWO algorithm. This algorithm is inspired by the hunting behavior and social leadership of grey wolves in nature. The proposed algorithm is applied to two different case-studies: the modified 5-bus and WSCC 9-bus test systems. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization techniques. Analysis of the obtained results show that the proposed GWO algorithm is able to achieve shorter CPU time, as well as minimized total cost when compared with already existing optimization techniques. This conclusion proves the efficiency of the GWO algorithm.

Recent advances in fuzzy preference modelling

International Nuclear Information System (INIS)

Van de Walle, B.; De Baets, B.; Kerre, E.

1996-01-01

Preference structures are well-known mathematical concepts having numerous applications in a variety of disciplines, such as economics, sociology and psychology. The generalization of preference structures to the fuzzy case has received considerable attention over the past years. Fuzzy preference structures allow a decision maker to express degrees of preference instead of the rigid classical yes-or-no preference assignment. This paper reports on the recent insights gained into the existence, construction and characterization of these fuzzy preference structures

Developing a Valuation Function for the Preference-Based Multiple Sclerosis Index: Comparison of Standard Gamble and Rating Scale.

Directory of Open Access Journals (Sweden)

Ayse Kuspinar

Full Text Available The standard gamble (SG and rating scale (RS are two approaches that can be employed to elicit health state preferences from patients in order to inform decision making. The objectives of this study were: (i to contribute evidence towards the similarities and differences in the SG and the RS to reflect patient preferences, and (ii to develop a multi-attribute utility function (MAUF (i.e., scoring algorithm for the PBMSI.Two samples were recruited for the study. The first sample provided cross-sectional data to generate the preference weights which were then used to develop (D the MAUFD. The distribution of SG and RS were compared across levels of perceived difficulty. The second sample provided additional data to validate (V the MAUF, termed MAUFV.The mean RS values ranged from 0.39 to 0.65, whereas the mean SG values were much higher ranging from 0.80 to 0.91. Correlations between the two methods were very low ranging from -0.29 to 0.15. Bland-Altman plots revealed the extent of differences in values produced by the two methods.In contemplating trade-offs in the selection of a preference-based elicitation approach for a MAUF that could guide clinical decision making, results suggest the RS is preferable in terms of feasibility and validity for MS patients. The PBMSI with patient preferences shows promise as a measure of health-related quality of life for MS.

Spiral-CT-angiography of acute pulmonary embolism: factors that influence the implementation into standard diagnostic algorithms

International Nuclear Information System (INIS)

Bankier, A.; Herold, C.J.; Fleischmann, D.; Janata-Schwatczek, K.

1998-01-01

Purpose: Debate about the potential implementation of Spiral-CT in diagnostic algorithms of pulmonary embolism are often focussed on sensitivity and specificity in the context of comparative methodologic studies. We intend to investigate whether additional factors might influence this debate. Results: The factors availability, acceptance, patient-outcome, and cost-effectiveness-studies do have substantial influence on the implementation of Spiral-CT in the diagnostic algorithms of pulmonary embolism. Incorporation of these factors into the discussion might lead to more flexible and more patient-oriented algorithms for the diagnosis of pulmonary embolism. Conclusion: Availability of equipment, acceptance among clinicians, patient-out-come, and cost-effectiveness evaluations should be implemented into the debate about potential implementation of Spiral-CT in routine diagnostic imaging algorithms of pulmonary embolism. (orig./AJ) [de

Cloud detection algorithm comparison and validation for operational Landsat data products

Science.gov (United States)

Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady

2017-01-01

nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.

Preference Handling for Artificial Intelligence

OpenAIRE

Goldsmith, Judy; University of Kentucky; Junker, Ulrich; ILOG

2009-01-01

This article explains the benefits of preferences for AI systems and draws a picture of current AI research on preference handling. It thus provides an introduction to the topics covered by this special issue on preference handling.

Development of Data Processing Algorithms for the Upgraded LHCb Vertex Locator

CERN Document Server

AUTHOR|(CDS)2101352

The LHCb detector will see a major upgrade during LHC Long Shutdown II, which is planned for 2019/20. The silicon Vertex Locator subdetector will be upgraded for operation under the new run conditions. The detector will be read out using a data acquisition board based on an FPGA. The work presented in this thesis is concerned with the development of the data processing algorithms to be used in this data acquisition board. In particular, work in three different areas of the FPGA is covered: the data processing block, the low level interface, and the post router block. The algorithms produced have been simulated and tested, and shown to provide the required performance. Errors in the initial implementation of the Gigabit Wireline Transmitter serialized data in the low level interface were discovered and corrected. The data scrambling algorithm and the post router block have been incorporated in the front end readout chip.

Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference.

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David A Bridwell

Full Text Available Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33% and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%, with accuracies ranging from 52.9% (Silver Linings Playbook to 11.75% (Seinfeld within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference.

Science.gov (United States)

Bridwell, David A; Roth, Cullen; Gupta, Cota Navin; Calhoun, Vince D

2015-01-01

Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's) emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33%) and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%), with accuracies ranging from 52.9% (Silver Linings Playbook) to 11.75% (Seinfeld) within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz) primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

Identifying the structure of a narrative via an agent-based logic of preferences and beliefs: Formalizations of episodes from CSI: Crime Scene Investigation™

NARCIS (Netherlands)

Löwe, B.; Pacuit, E.; Saraf, S.

2009-01-01

Finding out what makes two stories equivalent is a daunting task for a formalization of narratives. Using a high-level language of beliefs and preferences for describing stories and a simple algorithm for analyzing them, we determine the doxastic game fragment of actual narratives from the TV crime

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

Science.gov (United States)

Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B

2011-02-01

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Age Preferences for Professional Helpers.

Science.gov (United States)

Furchtgott, Ernest; Busemeyer, Jerome R.

1981-01-01

For all occupations except clergyman, a relationship between the age of the respondent and preferred age of the professional existed. Older individuals preferred older service providers with one exception, their physician. Highly educated respondents preferred younger physicians. (Author)

Tears or Fears? Comparing Gender Stereotypes about Movie Preferences to Actual Preferences

OpenAIRE

Wühr, Peter; Lange, Benjamin P.; Schwarz, Sascha

2017-01-01

This study investigated the accuracy of gender-specific stereotypes about movie-genre preferences for 17 genres. In Study 1, female and male participants rated the extent to which 17 movie genres are preferred by women or men. In Study 2, another sample of female and male participants rated their own preference for each genre. There were three notable results. First, Study 1 revealed the existence of gender stereotypes for the majority of genres (i.e., for 15 of 17 genres). Second, Study 2 re...

Adaptive inversion algorithm for 1 . 5 μm visibility lidar incorporating in situ Angstrom wavelength exponent

Science.gov (United States)

Shang, Xiang; Xia, Haiyun; Dou, Xiankang; Shangguan, Mingjia; Li, Manyi; Wang, Chong

2018-07-01

An eye-safe 1 . 5 μm visibility lidar is presented in this work considering in situ particle size distribution, which can be deployed in crowded places like airports. In such a case, the measured extinction coefficient at 1 . 5 μm should be converted to that at 0 . 55 μm for visibility retrieval. Although several models have been established since 1962, the accurate wavelength conversion remains a challenge. An adaptive inversion algorithm for 1 . 5 μm visibility lidar is proposed and demonstrated by using the in situ Angstrom wavelength exponent, which is derived from an aerosol spectrometer. The impact of the particle size distribution of atmospheric aerosols and the Rayleigh backscattering of atmospheric molecules are taken into account. Using the 1 . 5 μm visibility lidar, the visibility with a temporal resolution of 5 min is detected over 48 h in Hefei (31 . 83∘ N, 117 . 25∘ E). The average visibility error between the new method and a visibility sensor (Vaisala, PWD52) is 5.2% with the R-square value of 0.96, while the relative error between another reference visibility lidar at 532 nm and the visibility sensor is 6.7% with the R-square value of 0.91. All results agree with each other well, demonstrating the accuracy and stability of the algorithm.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

Science.gov (United States)

Doble, Brett; Lorgelly, Paula

2016-04-01

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

Directory of Open Access Journals (Sweden)

Jiayin Liu

2017-06-01

Full Text Available Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC, which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF, which is estimated by Kernel Density Estimation (KDE with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

Preferences for Pink and Blue: The Development of Color Preferences as a Distinct Gender-Typed Behavior in Toddlers.

Science.gov (United States)

Wong, Wang I; Hines, Melissa

2015-07-01

Many gender differences are thought to result from interactions between inborn factors and sociocognitive processes that occur after birth. There is controversy, however, over the causes of gender-typed preferences for the colors pink and blue, with some viewing these preferences as arising solely from sociocognitive processes of gender development. We evaluated preferences for gender-typed colors, and compared them to gender-typed toy and activity preferences in 126 toddlers on two occasions separated by 6-8 months (at Time 1, M = 29 months; range 20-40). Color preferences were assessed using color cards and neutral toys in gender-typed colors. Gender-typed toy and activity preferences were assessed using a parent-report questionnaire, the Preschool Activities Inventory. Color preferences were also assessed for the toddlers' parents using color cards. A gender difference in color preferences was present between 2 and 3 years of age and strengthened near the third birthday, at which time it was large (d > 1). In contrast to their parents, toddlers' gender-typed color preferences were stronger and unstable. Gender-typed color preferences also appeared to establish later and were less stable than gender-typed toy and activity preferences. Gender-typed color preferences were largely uncorrelated with gender-typed toy and activity preferences. These results suggest that the factors influencing gender-typed color preferences and gender-typed toy and activity preferences differ in some respects. Our findings suggest that sociocognitive influences and play with gender-typed toys that happen to be made in gender-typed colors contribute to toddlers' gender-typed color preferences.

Linear energy transfer incorporated intensity modulated proton therapy optimization

Science.gov (United States)

Cao, Wenhua; Khabazian, Azin; Yepes, Pablo P.; Lim, Gino; Poenisch, Falk; Grosshans, David R.; Mohan, Radhe

2018-01-01

The purpose of this study was to investigate the feasibility of incorporating linear energy transfer (LET) into the optimization of intensity modulated proton therapy (IMPT) plans. Because increased LET correlates with increased biological effectiveness of protons, high LETs in target volumes and low LETs in critical structures and normal tissues are preferred in an IMPT plan. However, if not explicitly incorporated into the optimization criteria, different IMPT plans may yield similar physical dose distributions but greatly different LET, specifically dose-averaged LET, distributions. Conventionally, the IMPT optimization criteria (or cost function) only includes dose-based objectives in which the relative biological effectiveness (RBE) is assumed to have a constant value of 1.1. In this study, we added LET-based objectives for maximizing LET in target volumes and minimizing LET in critical structures and normal tissues. Due to the fractional programming nature of the resulting model, we used a variable reformulation approach so that the optimization process is computationally equivalent to conventional IMPT optimization. In this study, five brain tumor patients who had been treated with proton therapy at our institution were selected. Two plans were created for each patient based on the proposed LET-incorporated optimization (LETOpt) and the conventional dose-based optimization (DoseOpt). The optimized plans were compared in terms of both dose (assuming a constant RBE of 1.1 as adopted in clinical practice) and LET. Both optimization approaches were able to generate comparable dose distributions. The LET-incorporated optimization achieved not only pronounced reduction of LET values in critical organs, such as brainstem and optic chiasm, but also increased LET in target volumes, compared to the conventional dose-based optimization. However, on occasion, there was a need to tradeoff the acceptability of dose and LET distributions. Our conclusion is that the

A density distribution algorithm for bone incorporating local orthotropy, modal analysis and theories of cellular solids.

Science.gov (United States)

Impelluso, Thomas J

2003-06-01

An algorithm for bone remodeling is presented which allows for both a redistribution of density and a continuous change of principal material directions for the orthotropic material properties of bone. It employs a modal analysis to add density for growth and a local effective strain based analysis to redistribute density. General re-distribution functions are presented. The model utilizes theories of cellular solids to relate density and strength. The code predicts the same general density distributions and local orthotropy as observed in reality.

Preferences of older patient regarding hip fracture rehabilitation service configuration: A feasibility discrete choice experiment.

Science.gov (United States)

Charles, Joanna M; Roberts, Jessica L; Din, Nafees Ud; Williams, Nefyn H; Yeo, Seow Tien; Edwards, Rhiannon T

2018-05-14

As part of a wider feasibility study, the feasibility of gaining older patients' views for hip fracture rehabilitation services was tested using a discrete choice experiment in a UK context. Discrete choice experiment is a method used for eliciting individuals' preferences about goods and services. The discrete choice experiment was administered to 41 participants who had experienced hip fracture (mean age 79.3 years; standard deviation (SD) 7.5 years), recruited from a larger feasibility study exploring a new multidisciplinary rehabilitation for hip fracture. Attributes and levels for this discrete choice experiment were identified from a systematic review and focus groups. The questionnaire was administered at the 3-month follow-up. Participants indicated a significant preference for a fully-qualified physiotherapist or occupational therapist to deliver the rehabilitation sessions (β = 0·605, 95% confidence interval (95% CI) 0.462-0.879), and for their rehabilitation session to last less than 90 min (β = -0.192, 95% CI -0.381 to -0.051). The design of the discrete choice experiment using attributes associated with service configuration could have the potential to inform service implementation, and assist rehabilitation service design that incorporates the preferences of patients.

Human preference for air movement

DEFF Research Database (Denmark)

Toftum, Jørn; Melikov, Arsen Krikor; Tynel, A.

2002-01-01

Human preference for air movement was studied at slightly cool, neutral, and slightly warm overall thermal sensations and at temperatures ranging from 18 deg.C to 28 deg.C. Air movement preference depended on both thermal sensation and temperature, but large inter-individual differences existed...... between subjects. Preference for less air movement was linearly correlated with draught discomfort, but the percentage of subjects who felt draught was lower than the percentage who preferred less air movement....

Improved hybridization of Fuzzy Analytic Hierarchy Process (FAHP) algorithm with Fuzzy Multiple Attribute Decision Making - Simple Additive Weighting (FMADM-SAW)

Science.gov (United States)

Zaiwani, B. E.; Zarlis, M.; Efendi, S.

2018-03-01

In this research, the improvement of hybridization algorithm of Fuzzy Analytic Hierarchy Process (FAHP) with Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) in selecting the best bank chief inspector based on several qualitative and quantitative criteria with various priorities. To improve the performance of the above research, FAHP algorithm hybridization with Fuzzy Multiple Attribute Decision Making - Simple Additive Weighting (FMADM-SAW) algorithm was adopted, which applied FAHP algorithm to the weighting process and SAW for the ranking process to determine the promotion of employee at a government institution. The result of improvement of the average value of Efficiency Rate (ER) is 85.24%, which means that this research has succeeded in improving the previous research that is equal to 77.82%. Keywords: Ranking and Selection, Fuzzy AHP, Fuzzy TOPSIS, FMADM-SAW.

Incorporation of composite defects from ultrasonic NDE into CAD and FE models

Science.gov (United States)

Bingol, Onur Rauf; Schiefelbein, Bryan; Grandin, Robert J.; Holland, Stephen D.; Krishnamurthy, Adarsh

2017-02-01

Fiber-reinforced composites are widely used in aerospace industry due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. While, ultrasonic testing (UT) is the preferred NDE method to identify the presence of defects in composites, there are no reasonable ways to model the damage and evaluate the structural integrity of composites. We have developed an automated framework to incorporate flaws and known composite damage automatically into a finite element analysis (FEA) model of composites, ultimately aiding in accessing the residual life of composites and make informed decisions regarding repairs. The framework can be used to generate a layer-by-layer 3D structural CAD model of the composite laminates replicating their manufacturing process. Outlines of structural defects, such as delaminations, are automatically detected from UT of the laminate and are incorporated into the CAD model between the appropriate layers. In addition, the framework allows for direct structural analysis of the resulting 3D CAD models with defects by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept for the composite model builder with capabilities of incorporating delaminations between laminate layers and automatically preparing the CAD model for structural analysis using a FEA software.

Tobacco brand preference among Mexican adolescents.

Science.gov (United States)

West, Joshua H; Hall, P Cougar; Page, Randy M; Trinidad, Dennis R; Lindsay, Gordon B

2012-01-01

Advertising plays a major role in smoking behavior and forming brand preferences. Additionally, the most advertised tobacco brands have also been the most preferred. Maintaining brand loyalty in Latin America remains a priority for the tobacco industry. The purpose of this study was to explore tobacco brand preference trends from 2003 to 2006, and explore marketing and advertising factors that might be associated with these trends. Data for this study came from Mexican adolescents residing in cities that participated in the Global Youth Tobacco Survey in both 2003 and 2006 and reported smoking either Marlboro or Camel cigarettes in the past 30 days. Respondents reported the brand name of their preferred cigarette during the past 30 days. Multivariate regression analysis was used to determine differences by brand preference and exposure to tobacco marketing and advertising, which was assessed using six items. In 2003, most adolescents preferred Marlboro. By 2006, older boys preferred Camel cigarettes to Marlboro, while girls' preference for Camel was similar to their preference for Marlboro. Adolescents that preferred Camel cigarettes in 2003 also reported greater exposure to tobacco marketing and advertising. Findings indicate that there are ongoing shifts in youth brand preference in Mexico, and that these shifts might be related to marketing and advertising practices. There is an ongoing need for monitoring marketing and advertising practices in an effort to protect adolescents from tobacco company exploits.

Incorporation of Socio-Economic Features' Ranking in Multicriteria Analysis Based on Ecosystem Services for Marine Protected Area Planning.

Directory of Open Access Journals (Sweden)

Michelle E Portman

Full Text Available Developed decades ago for spatial choice problems related to zoning in the urban planning field, multicriteria analysis (MCA has more recently been applied to environmental conflicts and presented in several documented cases for the creation of protected area management plans. Its application is considered here for the development of zoning as part of a proposed marine protected area management plan. The case study incorporates specially-explicit conservation features while considering stakeholder preferences, expert opinion and characteristics of data quality. It involves the weighting of criteria using a modified analytical hierarchy process. Experts ranked physical attributes which include socio-economically valued physical features. The parameters used for the ranking of (physical attributes important for socio-economic reasons are derived from the field of ecosystem services assessment. Inclusion of these feature values results in protection that emphasizes those areas closest to shore, most likely because of accessibility and familiarity parameters and because of data biases. Therefore, other spatial conservation prioritization methods should be considered to supplement the MCA and efforts should be made to improve data about ecosystem service values farther from shore. Otherwise, the MCA method allows incorporation of expert and stakeholder preferences and ecosystem services values while maintaining the advantages of simplicity and clarity.

Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

DEFF Research Database (Denmark)

Martínez, Héctor Pérez

strategies (error functions and training algorithms) for artificial neural networks are examined across synthetic and psycho-physiological datasets, and compared against support vector machines and Cohen’s method. Results reveal the best training strategies for neural networks and suggest their superiority...... difficulties, ordinal reports such as rankings and ratings can yield more reliable affect annotations than alternative tools. This thesis explores preference learning methods to automatically learn computational models from ordinal annotations of affect. In particular, an extensive collection of training...... over the other examined methods. The second challenge addressed in this thesis refers to the extraction of relevant information from physiological modalities. Deep learning is proposed as an automatic approach to extract input features for models of affect from physiological signals. Experiments...

An algorithm to automate yeast segmentation and tracking.

Directory of Open Access Journals (Sweden)

Andreas Doncic

Full Text Available Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.

A Flexible Reservation Algorithm for Advance Network Provisioning

Energy Technology Data Exchange (ETDEWEB)

Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex

2010-04-12

Many scientific applications need support from a communication infrastructure that provides predictable performance, which requires effective algorithms for bandwidth reservations. Network reservation systems such as ESnet's OSCARS, establish guaranteed bandwidth of secure virtual circuits for a certain bandwidth and length of time. However, users currently cannot inquire about bandwidth availability, nor have alternative suggestions when reservation requests fail. In general, the number of reservation options is exponential with the number of nodes n, and current reservation commitments. We present a novel approach for path finding in time-dependent networks taking advantage of user-provided parameters of total volume and time constraints, which produces options for earliest completion and shortest duration. The theoretical complexity is only O(n2r2) in the worst-case, where r is the number of reservations in the desired time interval. We have implemented our algorithm and developed efficient methodologies for incorporation into network reservation frameworks. Performance measurements confirm the theoretical predictions.

Student learning style preferences in college-level biology courses: Implications for teaching and academic performance

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