Consumer food choices: the role of price and pricing strategies.

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Steenhuis, Ingrid H M; Waterlander, Wilma E; de Mul, Anika

2011-12-01

To study differences in the role of price and value in food choice between low-income and higher-income consumers and to study the perception of consumers about pricing strategies that are of relevance during grocery shopping. A cross-sectional study was conducted using structured, written questionnaires. Food choice motives as well as price perceptions and opinion on pricing strategies were measured. The study was carried out in point-of-purchase settings, i.e. supermarkets, fast-food restaurants and sports canteens. Adults (n 159) visiting a point-of-purchase setting were included. Price is an important factor in food choice, especially for low-income consumers. Low-income consumers were significantly more conscious of value and price than higher-income consumers. The most attractive strategies, according to the consumers, were discounting healthy food more often and applying a lower VAT (Value Added Tax) rate on healthy food. Low-income consumers differ in their preferences for pricing strategies. Since price is more important for low-income consumers we recommend mainly focusing on their preferences and needs.

Generalized outcome-based strategy classification: comparing deterministic and probabilistic choice models.

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Hilbig, Benjamin E; Moshagen, Morten

2014-12-01

Model comparisons are a vital tool for disentangling which of several strategies a decision maker may have used--that is, which cognitive processes may have governed observable choice behavior. However, previous methodological approaches have been limited to models (i.e., decision strategies) with deterministic choice rules. As such, psychologically plausible choice models--such as evidence-accumulation and connectionist models--that entail probabilistic choice predictions could not be considered appropriately. To overcome this limitation, we propose a generalization of Bröder and Schiffer's (Journal of Behavioral Decision Making, 19, 361-380, 2003) choice-based classification method, relying on (1) parametric order constraints in the multinomial processing tree framework to implement probabilistic models and (2) minimum description length for model comparison. The advantages of the generalized approach are demonstrated through recovery simulations and an experiment. In explaining previous methods and our generalization, we maintain a nontechnical focus--so as to provide a practical guide for comparing both deterministic and probabilistic choice models.

Work conditions and the food choice coping strategies of employed parents.

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Devine, Carol M; Farrell, Tracy J; Blake, Christine E; Jastran, Margaret; Wethington, Elaine; Bisogni, Carole A

2009-01-01

How work conditions relate to parents' food choice coping strategies. Pilot telephone survey. City in the northeastern United States (US). Black, white, and Hispanic employed mothers (25) and fathers (25) randomly recruited from low-/moderate-income zip codes; 78% of those reached and eligible participated. Sociodemographic characteristics; work conditions (hours, shift, job schedule, security, satisfaction, food access); food choice coping strategies (22 behavioral items for managing food in response to work and family demands (ie, food prepared at/away from home, missing meals, individualizing meals, speeding up, planning). Two-tailed chi-square and Fisher exact tests (P restaurant meals, missed breakfast, and prepared entrees. Job security, satisfaction, and food access were also associated with gender-specific strategies. Structural work conditions among parents such as job hours, schedule, satisfaction, and food access are associated with food choice coping strategies with importance for dietary quality. Findings have implications for worksite interventions but need examination in a larger sample.

Discrete strategies to reduce intake of discretionary food choices: a scoping review.

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Grieger, Jessica A; Wycherley, Thomas P; Johnson, Brittany J; Golley, Rebecca K

2016-05-06

On a population level, dietary improvement strategies have had limited success in preventing the surge in overweight and obesity or reducing risk factors for chronic disease. While numerous multi-component studies have examined whole-of-diet strategies, and single component (i.e. discrete) dietary intervention strategies have targeted an increase in core foods (e.g. fruits, vegetables, dairy), there is a paucity of evidence on the effectiveness of dietary intervention strategies targeting a decrease in discretionary choices. The aim of this review was to identify dietary intervention strategies that are potentially relevant to reducing intake of discretionary choices in 2-65 year olds. A scoping review was carried out to map the literature on key discrete dietary intervention strategies that are potentially applicable to reducing discretionary choices, and to identify the targeted health/nutrition effects (e.g. improve nutrient intake, decrease sugar intake, and reduce body weight) of these strategies. Studies conducted in participants aged 2-65 years and published in English by July 20, 2015, were located through electronic searches including the Cochrane Library, Medline, Embase, CINAHL, and Scopus. Three thousand two hundred and eighty three studies were identified from the search, of which 44 met the selection criteria. The dietary intervention strategies included reformulation (n = 13), substitution (n = 5), restriction/elimination (n = 9), supplementation (n = 13), and nutrition education/messages (n = 4). The key findings of the review were: restricting portion size was consistently beneficial for reducing energy intake in the acute setting; reformulating foods from higher fat to lower fat could be useful to reduce saturated fat intake; substituting discretionary choices for high fibre snacks, fruit, or low/no-calorie beverages may be an effective strategy for reducing energy intake; supplementing nutrient dense foods such as nuts and

The Evaluation of Surface Integrity During Machining of Inconel 718 with Various Laser Assistance Strategies

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Wojciechowski Szymon

2017-01-01

Full Text Available The paper is focused on the evaluation of surface integrity formed during turning of Inconel 718 with the application of various laser assistance strategies. The primary objective of the work was to determine the relations between the applied machining strategy and the obtained surface integrity, in order to select the effective cutting conditions allowing the obtainment of high surface quality. The carried out experiment included the machining of Inconel 718 in the conventional turning conditions, as well as during the continuous laser assisted machining and sequential laser assistance. The surface integrity was evaluated by the measurements of machined surface topographies, microstructures and the microhardness. Results revealed that surface integrity of Inconel 718 is strongly affected by the selected machining strategy. The significant improvement of the surface roughness formed during machining of Inconel 718, can be reached by the application of simultaneous laser heating and cutting (LAM.

Choice and Application of Marketing Strategies of Selected Book ...

African Journals Online (AJOL)

The study was designed to identity the type of marketing strategies employed by book publishers in Nigeria, the criteria for the choice and application of marketing strategies, being used to reach each segment of the market. The survey research method was adopted for the study. Forty market managers and 60 sales ...

Children's Choice Strategies: The Effects of Age and Task Demands

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Bereby-Meyer, Yoella; Assor, Avi; Katz, Idit

2004-01-01

Two experiments examined the effect of age and cognitive demands on children's choice strategies. Children aged 8-9 and 12-13 years were asked to choose among either two or four products that differed in several attributes of varying importance to them. Choice tasks were designed to differentiate between the lexicographic and the equal-weighting…

Sleep Deprivation Alters Choice Strategy Without Altering Uncertainty or Loss Aversion Preferences

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O'Dhaniel A Mullette-Gillman

2015-10-01

Full Text Available Sleep deprivation alters decision making; however, it is unclear what specific cognitive processes are modified to drive altered choices. In this manuscript, we examined how one night of total sleep deprivation (TSD alters economic decision making. We specifically examined changes in uncertainty preferences dissociably from changes in the strategy with which participants engage with presented choice information. With high test-retest reliability, we show that TSD does not alter uncertainty preferences or loss aversion. Rather, TSD alters the information the participants rely upon to make their choices. Utilizing a choice strategy metric which contrasts the influence of maximizing and satisficing information on choice behavior, we find that TSD alters the relative reliance on maximizing information and satisficing information, in the gains domain. This alteration is the result of participants both decreasing their reliance on cognitively-complex maximizing information and a concomitant increase in the use of readily-available satisficing information. TSD did not result in a decrease in overall information use in either domain. These results show that sleep deprivation alters decision making by altering the informational strategies that participants employ, without altering their preferences.

Sleep deprivation alters choice strategy without altering uncertainty or loss aversion preferences.

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Mullette-Gillman, O'Dhaniel A; Kurnianingsih, Yoanna A; Liu, Jean C J

2015-01-01

Sleep deprivation alters decision making; however, it is unclear what specific cognitive processes are modified to drive altered choices. In this manuscript, we examined how one night of total sleep deprivation (TSD) alters economic decision making. We specifically examined changes in uncertainty preferences dissociably from changes in the strategy with which participants engage with presented choice information. With high test-retest reliability, we show that TSD does not alter uncertainty preferences or loss aversion. Rather, TSD alters the information the participants rely upon to make their choices. Utilizing a choice strategy metric which contrasts the influence of maximizing and satisficing information on choice behavior, we find that TSD alters the relative reliance on maximizing information and satisficing information, in the gains domain. This alteration is the result of participants both decreasing their reliance on cognitively-complex maximizing information and a concomitant increase in the use of readily-available satisficing information. TSD did not result in a decrease in overall information use in either domain. These results show that sleep deprivation alters decision making by altering the informational strategies that participants employ, without altering their preferences.

Spontaneous gestures influence strategy choices in problem solving.

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Alibali, Martha W; Spencer, Robert C; Knox, Lucy; Kita, Sotaro

2011-09-01

Do gestures merely reflect problem-solving processes, or do they play a functional role in problem solving? We hypothesized that gestures highlight and structure perceptual-motor information, and thereby make such information more likely to be used in problem solving. Participants in two experiments solved problems requiring the prediction of gear movement, either with gesture allowed or with gesture prohibited. Such problems can be correctly solved using either a perceptual-motor strategy (simulation of gear movements) or an abstract strategy (the parity strategy). Participants in the gesture-allowed condition were more likely to use perceptual-motor strategies than were participants in the gesture-prohibited condition. Gesture promoted use of perceptual-motor strategies both for participants who talked aloud while solving the problems (Experiment 1) and for participants who solved the problems silently (Experiment 2). Thus, spontaneous gestures influence strategy choices in problem solving.

Expanding the Education Universe: A Fifty-State Strategy for Course Choice

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Brickman, Michael

2014-01-01

After twenty years of expanding school-choice options, state leaders, educators, and families have a new tool: course choice, a strategy for students to learn from unconventional providers that might range from top-tier universities or innovative community colleges to local employers, labs, or hospitals. In "Expanding the Education Universe:…

A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

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Yanbing Liu

2014-01-01

Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

Self-Efficacy, Attitudes, and Choice of Strategies for English Pronunciation Learning

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Sardegna, Veronica G.; Lee, Juhee; Kusey, Crystal

2018-01-01

This article proposes a structural model of English language learners' self-efficacy beliefs, attitudes toward learning pronunciation skills, and choice of pronunciation learning strategies. Participants' responses (N = 704) to two self-reported questionnaires--Strategies for Pronunciation Improvement (SPI) inventory and Learner Attitudes for…

Freedom of choice in technology strategy? An analysis of technology strategy in the large commercial aircraft industry

NARCIS (Netherlands)

de Bruijn, E.J.; Steenhuis, H.J.

2004-01-01

Many companies experience difficult situations as a result of their selected strategy. Strategic management theories implicitly assume that companies have a free choice in setting their strategy. Hence, when companies experience difficult situations this is because of management inadequacy. It is

Influence of the large-small split effect on strategy choice in complex subtraction.

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Xiang, Yan Hui; Wu, Hao; Shang, Rui Hong; Chao, Xiaomei; Ren, Ting Ting; Zheng, Li Ling; Mo, Lei

2018-04-01

Two main theories have been used to explain the arithmetic split effect: decision-making process theory and strategy choice theory. Using the inequality paradigm, previous studies have confirmed that individuals tend to adopt a plausibility-checking strategy and a whole-calculation strategy to solve large and small split problems in complex addition arithmetic, respectively. This supports strategy choice theory, but it is unknown whether this theory also explains performance in solving different split problems in complex subtraction arithmetic. This study used small, intermediate and large split sizes, with each split condition being further divided into problems requiring and not requiring borrowing. The reaction times (RTs) for large and intermediate splits were significantly shorter than those for small splits, while accuracy was significantly higher for large and middle splits than for small splits, reflecting no speed-accuracy trade-off. Further, RTs and accuracy differed significantly between the borrow and no-borrow conditions only for small splits. This study indicates that strategy choice theory is suitable to explain the split effect in complex subtraction arithmetic. That is, individuals tend to choose the plausibility-checking strategy or the whole-calculation strategy according to the split size. © 2016 International Union of Psychological Science.

Integrating Regret Psychology to Travel Mode Choice for a Transit-Oriented Evacuation Strategy

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Shi An

2015-06-01

Full Text Available Facing the potential dangers from sudden disasters in urban cities, emergency administrators have to make an appropriate evacuation plan to mitigate negative consequences. However, little attention has been paid to evacuee real decision psychology when developing a strategy. The aim of this paper is to analyze evacuee mode choice behavior considering regret aversion psychology during evacuation. First, the utility-based and regret-based models are formulated to obtain evacuees’ preferences on travel mode choice, respectively. According to the data collected from the stated preference (SP survey on evacuee mode choice, the estimation results show that the regret-based model performs better than the utility model. Moreover, based on the estimates from behavioral analysis, the elasticities of evacuee mode choices are calculated, and transit strategy simulation is undertaken to investigate the influence on evacuee mode switching from private automobile to public transit. The results are expected to help emergency administrators to make a transit-oriented strategy for a sustainable evacuation plan, especially for the benefit of carless people.

Understanding consumer acceptance of intervention strategies for healthy food choices: a qualitative study.

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Bos, Colin; Van der Lans, Ivo A; Van Rijnsoever, Frank J; Van Trijp, Hans C M

2013-11-13

The increasing prevalence of overweight and obesity poses a major threat to public health. Intervention strategies for healthy food choices potentially reduce obesity rates. Reviews of the effectiveness of interventions, however, show mixed results. To maximise effectiveness, interventions need to be accepted by consumers. The aim of the present study is to explore consumer acceptance of intervention strategies for low-calorie food choices. Beliefs that are associated with consumer acceptance are identified. Data was collected in the Netherlands in 8 semi-structured interviews and 4 focus group discussions (N = 39). Nine archetypical strategies representing educational, marketing and legal interventions served as reference points. Verbatim transcriptions were coded both inductively and deductively with the framework approach. We found that three beliefs are related to consumer acceptance: 1) general beliefs regarding obesity, such as who is responsible for food choice; 2) the perceived effectiveness of interventions; and 3) the perceived fairness of interventions. Furthermore, the different aspects underlying these general and intervention-specific beliefs were identified. General and intervention-specific beliefs are associated with consumer acceptance of interventions for low-calorie food choices. Policymakers in the food domain can use the findings to negotiate the development of interventions and to assess the feasibility of interventions. With respect to future research, we recommend that segments of consumers based on perceptions of intervention strategies are identified.

Understanding consumer acceptance of intervention strategies for healthy food choices: a qualitative study

Science.gov (United States)

2013-01-01

Background The increasing prevalence of overweight and obesity poses a major threat to public health. Intervention strategies for healthy food choices potentially reduce obesity rates. Reviews of the effectiveness of interventions, however, show mixed results. To maximise effectiveness, interventions need to be accepted by consumers. The aim of the present study is to explore consumer acceptance of intervention strategies for low-calorie food choices. Beliefs that are associated with consumer acceptance are identified. Methods Data was collected in the Netherlands in 8 semi-structured interviews and 4 focus group discussions (N = 39). Nine archetypical strategies representing educational, marketing and legal interventions served as reference points. Verbatim transcriptions were coded both inductively and deductively with the framework approach. Results We found that three beliefs are related to consumer acceptance: 1) general beliefs regarding obesity, such as who is responsible for food choice; 2) the perceived effectiveness of interventions; and 3) the perceived fairness of interventions. Furthermore, the different aspects underlying these general and intervention-specific beliefs were identified. Conclusions General and intervention-specific beliefs are associated with consumer acceptance of interventions for low-calorie food choices. Policymakers in the food domain can use the findings to negotiate the development of interventions and to assess the feasibility of interventions. With respect to future research, we recommend that segments of consumers based on perceptions of intervention strategies are identified. PMID:24225034

From Multilatina to Global Latina: Unveiling the corporate-level international strategy choices of Grupo Nutresa

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MARIA A DE VILLA

Full Text Available Research on Multilatinas has underexplored multinationals from Colombia and their corporate-level international strategy choices to develop into Global Latinas. Building on interviews, documents, and archival data about Grupo Nutresa -Colombia's most international firm in manufactured goods-, this study unveils and discusses this firm's corporate-level international strategy choices between 1960 and 2014. A prevailing notion is that most multinationals from Latin America continue to target international operations to focus mainly on their home region through an export, multidomestic or transnational corporate-level international strategy. In contrast, data show that Grupo Nutresa chose to evolve through a sequential approach from an export to a transnational corporate-level international strategy while its international operations were able to transcend its home region to reach North America, Asia, Europe, Africa, and Oceania. These results add to international business research on emergent market multinational companies (EMNCs from Latin America by unveiling the corporate-level international strategy choices of a Colombian origin Multilatina that transformed into a Global Latina.

Strategies and Principles of Distributed Machine Learning on Big Data

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Eric P. Xing

2016-06-01

Full Text Available The rise of big data has led to new demands for machine learning (ML systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area

Healthier choices in an Australian health service: a pre-post audit of an intervention to improve the nutritional value of foods and drinks in vending machines and food outlets.

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Bell, Colin; Pond, Nicole; Davies, Lynda; Francis, Jeryl Lynn; Campbell, Elizabeth; Wiggers, John

2013-11-25

Vending machines and shops located within health care facilities are a source of food and drinks for staff, visitors and outpatients and they have the potential to promote healthy food and drink choices. This paper describes perceptions of parents and managers of health-service located food outlets towards the availability and labelling of healthier food options and the food and drinks offered for sale in health care facilities in Australia. It also describes the impact of an intervention to improve availability and labelling of healthier foods and drinks for sale. Parents (n = 168) and food outlet managers (n = 17) were surveyed. Food and drinks for sale in health-service operated food outlets (n = 5) and vending machines (n = 90) in health care facilities in the Hunter New England region of NSW were audited pre (2007) and post (2010/11) the introduction of policy and associated support to increase the availability of healthier choices. A traffic light system was used to classify foods from least (red) to most healthy choices (green). Almost all (95%) parents and most (65%) food outlet managers thought food outlets on health service sites should have signs clearly showing healthy choices. Parents (90%) also thought all food outlets on health service sites should provide mostly healthy items compared to 47% of managers. The proportion of healthier beverage slots in vending machines increased from 29% to 51% at follow-up and the proportion of machines that labelled healthier drinks increased from 0 to 26%. No outlets labelled healthier items at baseline compared to 4 out of 5 after the intervention. No changes were observed in the availability or labelling of healthier food in vending machines or the availability of healthier food or drinks in food outlets. Baseline availability and labelling of healthier food and beverage choices for sale in health care facilities was poor in spite of the support of parents and outlet managers for such initiatives. The intervention

Mental Computation or Standard Algorithm? Children's Strategy Choices on Multi-Digit Subtractions

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Torbeyns, Joke; Verschaffel, Lieven

2016-01-01

This study analyzed children's use of mental computation strategies and the standard algorithm on multi-digit subtractions. Fifty-eight Flemish 4th graders of varying mathematical achievement level were individually offered subtractions that either stimulated the use of mental computation strategies or the standard algorithm in one choice and two…

The Effect of Compliance-Gaining Strategy Choice and Communicator Style on Sales Success.

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Parrish-Sprowl, John; And Others

1994-01-01

Explores the relationship among compliance-gaining strategy choice, communicator image, and sales person effectiveness. Finds no statistically significant relationship between the use of compliance-gaining strategies and sales success, but indicates a link between communicator image and sales success. (SR)

Machine learning approach for single molecule localisation microscopy.

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Colabrese, Silvia; Castello, Marco; Vicidomini, Giuseppe; Del Bue, Alessio

2018-04-01

Single molecule localisation (SML) microscopy is a fundamental tool for biological discoveries; it provides sub-diffraction spatial resolution images by detecting and localizing "all" the fluorescent molecules labeling the structure of interest. For this reason, the effective resolution of SML microscopy strictly depends on the algorithm used to detect and localize the single molecules from the series of microscopy frames. To adapt to the different imaging conditions that can occur in a SML experiment, all current localisation algorithms request, from the microscopy users, the choice of different parameters. This choice is not always easy and their wrong selection can lead to poor performance. Here we overcome this weakness with the use of machine learning. We propose a parameter-free pipeline for SML learning based on support vector machine (SVM). This strategy requires a short supervised training that consists in selecting by the user few fluorescent molecules (∼ 10-20) from the frames under analysis. The algorithm has been extensively tested on both synthetic and real acquisitions. Results are qualitatively and quantitatively consistent with the state of the art in SML microscopy and demonstrate that the introduction of machine learning can lead to a new class of algorithms competitive and conceived from the user point of view.

Beyond labelling: what strategies do nut allergic individuals employ to make food choices? A qualitative study.

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Julie Barnett

Full Text Available OBJECTIVE: Food labelling is an important tool that assists people with peanut and tree nut allergies to avoid allergens. Nonetheless, other strategies are also developed and used in food choice decision making. In this paper, we examined the strategies that nut allergic individuals deploy to make safe food choices in addition to a reliance on food labelling. METHODS: THREE QUALITATIVE METHODS: an accompanied shop, in-depth semi-structured interviews, and the product choice reasoning task - were used with 32 patients that had a clinical history of reactions to peanuts and/or tree nuts consistent with IgE-mediated food allergy. Thematic analysis was applied to the transcribed data. RESULTS: Three main strategies were identified that informed the risk assessments and food choice practices of nut allergic individuals. These pertained to: (1 qualities of product such as the product category or the country of origin, (2 past experience of consuming a food product, and (3 sensory appreciation of risk. Risk reasoning and risk management behaviours were often contingent on the context and other physiological and socio-psychological needs which often competed with risk considerations. CONCLUSIONS: Understanding and taking into account the complexity of strategies and the influences of contextual factors will allow healthcare practitioners, allergy nutritionists, and caregivers to advise and educate patients more effectively in choosing foods safely. Governmental bodies and policy makers could also benefit from an understanding of these food choice strategies when risk management policies are designed and developed.

Strategy choice mediates the link between auditory processing and spelling.

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Kwong, Tru E; Brachman, Kyle J

2014-01-01

Relations among linguistic auditory processing, nonlinguistic auditory processing, spelling ability, and spelling strategy choice were examined. Sixty-three undergraduate students completed measures of auditory processing (one involving distinguishing similar tones, one involving distinguishing similar phonemes, and one involving selecting appropriate spellings for individual phonemes). Participants also completed a modified version of a standardized spelling test, and a secondary spelling test with retrospective strategy reports. Once testing was completed, participants were divided into phonological versus nonphonological spellers on the basis of the number of words they spelled using phonological strategies only. Results indicated a) moderate to strong positive correlations among the different auditory processing tasks in terms of reaction time, but not accuracy levels, and b) weak to moderate positive correlations between measures of linguistic auditory processing (phoneme distinction and phoneme spelling choice in the presence of foils) and spelling ability for phonological spellers, but not for nonphonological spellers. These results suggest a possible explanation for past contradictory research on auditory processing and spelling, which has been divided in terms of whether or not disabled spellers seemed to have poorer auditory processing than did typically developing spellers, and suggest implications for teaching spelling to children with good versus poor auditory processing abilities.

Neural Signatures of Rational and Heuristic Choice Strategies: A Single Trial ERP Analysis

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Szymon Wichary

2017-08-01

Full Text Available In multi-attribute choice, people use heuristics to simplify decision problems. We studied the use of heuristic and rational strategies and their electrophysiological correlates. Since previous work linked the P3 ERP component to attention and decision making, we were interested whether the amplitude of this component is associated with decision strategy use. To this end, we recorded EEG when participants performed a two-alternative choice task, where they could acquire decision cues in a sequential manner and use them to make choices. We classified participants’ choices as consistent with a rational Weighted Additive rule (WADD or a simple heuristic Take The Best (TTB. Participants differed in their preference for WADD and TTB. Using a permutation-based single trial approach, we analyzed EEG responses to consecutive decision cues and their relation to the individual strategy preference. The preference for WADD over TTB was associated with overall higher signal amplitudes to decision cues in the P3 time window. Moreover, the preference for WADD was associated with similar P3 amplitudes to consecutive cues, whereas the preference for TTB was associated with substantial decreases in P3 amplitudes to consecutive cues. We also found that the preference for TTB was associated with enhanced N1 component to cues that discriminated decision alternatives, suggesting very early attention allocation to such cues by TTB users. Our results suggest that preference for either WADD or TTB has an early neural signature reflecting differences in attentional weighting of decision cues. In light of recent findings and hypotheses regarding P3, we interpret these results as indicating the involvement of catecholamine arousal systems in shaping predecisional information processing and strategy selection.

Neural Signatures of Rational and Heuristic Choice Strategies: A Single Trial ERP Analysis.

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Wichary, Szymon; Magnuski, Mikołaj; Oleksy, Tomasz; Brzezicka, Aneta

2017-01-01

In multi-attribute choice, people use heuristics to simplify decision problems. We studied the use of heuristic and rational strategies and their electrophysiological correlates. Since previous work linked the P3 ERP component to attention and decision making, we were interested whether the amplitude of this component is associated with decision strategy use. To this end, we recorded EEG when participants performed a two-alternative choice task, where they could acquire decision cues in a sequential manner and use them to make choices. We classified participants' choices as consistent with a rational Weighted Additive rule (WADD) or a simple heuristic Take The Best (TTB). Participants differed in their preference for WADD and TTB. Using a permutation-based single trial approach, we analyzed EEG responses to consecutive decision cues and their relation to the individual strategy preference. The preference for WADD over TTB was associated with overall higher signal amplitudes to decision cues in the P3 time window. Moreover, the preference for WADD was associated with similar P3 amplitudes to consecutive cues, whereas the preference for TTB was associated with substantial decreases in P3 amplitudes to consecutive cues. We also found that the preference for TTB was associated with enhanced N1 component to cues that discriminated decision alternatives, suggesting very early attention allocation to such cues by TTB users. Our results suggest that preference for either WADD or TTB has an early neural signature reflecting differences in attentional weighting of decision cues. In light of recent findings and hypotheses regarding P3, we interpret these results as indicating the involvement of catecholamine arousal systems in shaping predecisional information processing and strategy selection.

Individual Differences in Strategy Choices: Good Students, Not-So-Good Students, and Perfectionists.

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Siegler, Robert S.

1988-01-01

Issues include consistent individual differences in children's strategy choices, interpretation of differences within a framework, and the relation of differences to standardized test performance. (RJC)

Interaction between behavioral and pharmacological treatment strategies to decrease cocaine choice in rhesus monkeys.

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Banks, Matthew L; Blough, Bruce E; Negus, S Stevens

2013-02-01

Behavioral and pharmacotherapeutic approaches constitute two prominent strategies for treating cocaine dependence. This study investigated interactions between behavioral and pharmacological strategies in a preclinical model of cocaine vs food choice. Six rhesus monkeys, implanted with a chronic indwelling double-lumen venous catheter, initially responded under a concurrent schedule of food delivery (1-g pellets, fixed-ratio (FR) 100 schedule) and cocaine injections (0-0.1 mg/kg/injection, FR 10 schedule) during continuous 7-day treatment periods with saline or the agonist medication phenmetrazine (0.032-0.1 mg/kg/h). Subsequently, the FR response requirement for cocaine or food was varied (food, FR 100; cocaine, FR 1-100; cocaine, FR 10; food, FR 10-300), and effects of phenmetrazine on cocaine vs food choice were redetermined. Decreases in the cocaine FR or increases in the food FR resulted in leftward shifts in the cocaine choice dose-effect curve, whereas increases in the cocaine FR or decreases in the food FR resulted in rightward shifts in the cocaine choice dose-effect curve. The efficacy of phenmetrazine to decrease cocaine choice varied systematically as a function of the prevailing response requirements, such that phenmetrazine efficacy was greatest when cocaine choice was maintained by relatively low unit cocaine doses. These results suggest that efficacy of pharmacotherapies to modulate cocaine use can be influenced by behavioral contingencies of cocaine availability. Agonist medications may be most effective under contingencies that engender choice of relatively low cocaine doses.

Pricing effects on food choices.

Science.gov (United States)

French, Simone A

2003-03-01

Individual dietary choices are primarily influenced by such considerations as taste, cost, convenience and nutritional value of foods. The current obesity epidemic has been linked to excessive consumption of added sugars and fat, as well as to sedentary lifestyles. Fat and sugar provide dietary energy at very low cost. Food pricing and marketing practices are therefore an essential component of the eating environment. Recent studies have applied economic theories to changing dietary behavior. Price reduction strategies promote the choice of targeted foods by lowering their cost relative to alternative food choices. Two community-based intervention studies used price reductions to promote the increased purchase of targeted foods. The first study examined lower prices and point-of-purchase promotion on sales of lower fat vending machine snacks in 12 work sites and 12 secondary schools. Price reductions of 10%, 25% and 50% on lower fat snacks resulted in an increase in sales of 9%, 39% and 93%, respectively, compared with usual price conditions. The second study examined the impact of a 50% price reduction on fresh fruit and baby carrots in two secondary school cafeterias. Compared with usual price conditions, price reductions resulted in a four-fold increase in fresh fruit sales and a two-fold increase in baby carrot sales. Both studies demonstrate that price reductions are an effective strategy to increase the purchase of more healthful foods in community-based settings such as work sites and schools. Results were generalizable across various food types and populations. Reducing prices on healthful foods is a public health strategy that should be implemented through policy initiatives and industry collaborations.

Work Conditions and the Food Choice Coping Strategies of Employed Parents

Science.gov (United States)

Devine, Carol M.; Farrell, Tracy J.; Blake, Christine E.; Jastran, Margaret; Wethington, Elaine; Bisogni, Carole A.

2009-01-01

Objective: How work conditions relate to parents' food choice coping strategies. Design: Pilot telephone survey. Setting: City in the northeastern United States (US). Participants: Black, white, and Hispanic employed mothers (25) and fathers (25) randomly recruited from low-/moderate-income zip codes; 78% of those reached and eligible…

Quantifying rural livelihood strategies in developing countries using an activity choice approach

DEFF Research Database (Denmark)

Nielsen, Øystein Juul; Rayamajhi, Santosh; Uberhuaga de Arratia, Patricia D C

2013-01-01

outcomes are compared across strategies and household differences in asset holdings are analyzed using multinomial logit regression. Findings reveal that income diversification is the norm, that a higher degree of specialization does not characterize more remunerative livelihood strategies, that nonfarm......This article uses a quantitative activity choice approach, based on identification of activity variables and application of latent class cluster analysis, to identify five major rural livelihood strategies pursued by households (n= 576) in Bolivia, Nepal, and Mozambique. Income sources and welfare...... income significantly contributes to higher income earnings, that environmental reliance does not vary across strategies, and that small-scale farmers are the largest and poorest livelihood group. Some livelihood strategies are superior to all other strategies in terms of income earned; access to more...

Food choices coping strategies of eating disorder patients' parents: what happens when both mother and father work?

Science.gov (United States)

Jáuregui-Lobera, I; Ruiz-Prieto, I; Bolaños-Ríos, P; Garrido-Casals, O

2013-11-01

Recently, it has been reported that food choices of relatives of eating disorder (ED) patients are not adequate having in mind a healthy model of eating habits. The aim of this study was to analyse how work conditions relate to parents' food choice coping strategies in both families with a member suffering from an ED and families with no sick members. In addition, the differences in those strategies between the two types of working parents were studied. A total of 80 employed fathers (n = 27) and mothers (n = 53) of patients with an ED (n =50) and healthy offsprings (n = 30) were interviewed. The mean age was 43.57 ± 5.69 and they had moderate incomes. Food choice coping strategies, used by working parents to integrate work and family demands, were measured by means of 22 items included in five categories. Considering the food choice coping strategies, ED patients' relatives show better skills than relatives of healthy offsprings do. The fact of preparing more meals at home and less fast food as main meal are good examples of those better strategies as well as to miss less number of breakfasts and lunches because of work-family conflict, grabbing less frequently and overeat less after missing a meal. The therapeutic effort to improve the food choices of ED patients' relatives, especially when both father and mother work, are a key point to improve the eating habits of ED patients, thus contributing to a better outcome. Copyright AULA MEDICA EDICIONES 2013. Published by AULA MEDICA. All rights reserved.

Consumer Acceptance of Population-Level Intervention Strategies for Healthy Food Choices: The Role of Perceived Effectiveness and Perceived Fairness

NARCIS (Netherlands)

Bos, C.; Lans, van der I.A.; Rijnsoever, F.J.; Trijp, van J.C.M.

2015-01-01

The present study investigates acceptance of intervention strategies for low-calorie snack choices that vary regarding the effect they have on consumers’ freedom of choice (providing information, guiding choice through (dis)incentives, and restricting choice). We examine the mediating effects of

Pricing and availability intervention in vending machines at four bus garages.

Science.gov (United States)

French, Simone A; Hannan, Peter J; Harnack, Lisa J; Mitchell, Nathan R; Toomey, Traci L; Gerlach, Anne

2010-01-01

To evaluate the effects of lowering prices and increasing availability on sales of healthy foods and beverages from 33 vending machines in 4 bus garages as part of a multicomponent worksite obesity prevention intervention. Availability of healthy items was increased to 50% and prices were lowered at least 10% in the vending machines in two metropolitan bus garages for an 18-month period. Two control garages offered vending choices at usual availability and prices. Sales data were collected monthly from each of the vending machines at the four garages. Increases in availability to 50% and price reductions of an average of 31% resulted in 10% to 42% higher sales of the healthy items. Employees were mostly price responsive for snack purchases. Greater availability and lower prices on targeted food and beverage items from vending machines was associated with greater purchases of these items over an 18-month period. Efforts to promote healthful food purchases in worksite settings should incorporate these two strategies.

Behavioral Contexts, Food-Choice Coping Strategies, and Dietary Quality of a Multiethnic Sample of Employed Parents

Science.gov (United States)

Blake, Christine E.; Wethington, Elaine; Farrell, Tracy J.; Bisogni, Carole A.; Devine, Carol M.

2012-01-01

Employed parents’ work and family conditions provide behavioral contexts for their food choices. Relationships between employed parents’ food-choice coping strategies, behavioral contexts, and dietary quality were evaluated. Data on work and family conditions, sociodemographic characteristics, eating behavior, and dietary intake from two 24-hour dietary recalls were collected in a random sample cross-sectional pilot telephone survey in the fall of 2006. Black, white, and Latino employed mothers (n=25) and fathers (n=25) were recruited from a low/moderate income urban area in upstate New York. Hierarchical cluster analysis (Ward’s method) identified three clusters of parents differing in use of food-choice coping strategies (ie, Individualized Eating, Missing Meals, and Home Cooking). Cluster sociodemographic, work, and family characteristics were compared using χ2 and Fisher’s exact tests. Cluster differences in dietary quality (Healthy Eating Index 2005) were analyzed using analysis of variance. Clusters differed significantly (P≤0.05) on food-choice coping strategies, dietary quality, and behavioral contexts (ie, work schedule, marital status, partner’s employment, and number of children). Individualized Eating and Missing Meals clusters were characterized by nonstandard work hours, having a working partner, single parenthood and with family meals away from home, grabbing quick food instead of a meal, using convenience entrées at home, and missing meals or individualized eating. The Home Cooking cluster included considerably more married fathers with nonemployed spouses and more home-cooked family meals. Food-choice coping strategies affecting dietary quality reflect parents’ work and family conditions. Nutritional guidance and family policy needs to consider these important behavioral contexts for family nutrition and health. PMID:21338739

Role of the man-machine interface in accident management strategies

International Nuclear Information System (INIS)

Oewre, Fridtjov

2001-01-01

First, this paper gives a short general review on important safety issues in the field of man-machine interaction as expressed by important nuclear safety organisations. Then follows a summary discussion on what constitutes a modern Man-Machine Interface (MMI) and what is normally meant with accident management and accident management strategies. Furthermore, the paper focuses on three major issues in the context of accident management. First, the need for reliable information in accidents and how this can be obtained by additional computer technology. Second, the use of procedures is discussed, and basic MMI aspects of computer support for procedure presentation are identified followed by a presentation of a new approach on how to computerise procedures. Third, typical information needs for characteristic end-users in accidents, such as the control room operators, technical support staff and plant emergency teams, is discussed. Some ideas on how to apply virtual reality technology in accident management is also presented

Understanding patients’ decision-making strategies in hospital choice: Literature review and a call for experimental research

Directory of Open Access Journals (Sweden)

Sophia Fischer

2015-12-01

Full Text Available Insights from psychology and cognitive science have, as yet, barely entered hospital choice research. This conceptual article closes this gap by reviewing and conceptually framing the current literature on hospital choice and patient information behavior and by discussing which tools are needed to advance scientific methodology in the study of patient decision-making strategies in hospital choice. Specifically, we make a call for more experimental research in hospital choice in order to complement existing theories, methods, and tools. This article introduces computerized process-tracing tools in hospital choice research, and also outlines a hands-on example, to provide a basis for future research.

Choice of jumping strategy in two standard jumps, squat and countermovement jump--effect of training background or inherited preference?

DEFF Research Database (Denmark)

Ravn, Susanne; Voigt, M; Simonsen, Erik Bruun

1999-01-01

. The jumps were recorded on highspeed film (500 Hz) combined with registration of ground reaction forces, and net joint moments were calculated by inverse dynamics. The purpose was to investigate the choice of strategy in two standard jumps, squat jump and countermovement jump. The volleyball jump...... was performed with a sequential strategy and the ballet jump was performed with a simultaneous strategy. In the two standard jumps, the choice of strategy was individual and not related to training background. This was additionally confirmed in a test of seven ballet dancers and seven volleyball players....

Leadership style and choice of strategy in conflict management among Israeli nurse managers in general hospitals.

Science.gov (United States)

Hendel, Tova; Fish, Miri; Galon, Vered

2005-03-01

To identify conflict mode choices of head nurses in general hospitals and examine the relationship between leadership style, choice of strategy in handling conflicts and demographic characteristics. Nurse managers deal with conflicts daily. The choice of conflict management mode is associated with managerial effectiveness. The ability to creatively manage conflict situations, towards constructive outcomes is becoming a standard requirement. Head nurses (N = 60) in five general hospitals in central Israel were surveyed, using a 3-part questionnaire: The Thomas-Kilmann Conflict Mode Instrument, the Multi-factor Leadership Questionnaire, Form 5X-Short (MLQ 5X) and demographic data. Head nurses perceive themselves significantly more as transformational leaders than as transactional leaders. Compromise was found to be the most commonly used conflict management strategy. Approximately half of the nurses surveyed used only one mode in conflict management. Transformational leadership significantly affected the conflict strategy chosen. Head nurses tend to choose a conflict-handling mode which is concerned a form of a Lose-Lose approach. Preparation in conflict management should start from undergraduate education.

Virtual Class Support at the Virtual Machine Level

DEFF Research Database (Denmark)

Nielsen, Anders Bach; Ernst, Erik

2009-01-01

This paper describes how virtual classes can be supported in a virtual machine.  Main-stream virtual machines such as the Java Virtual Machine and the .NET platform dominate the world today, and many languages are being executed on these virtual machines even though their embodied design choices...... conflict with the design choices of the virtual machine.  For instance, there is a non-trivial mismatch between the main-stream virtual machines mentioned above and dynamically typed languages.  One language concept that creates an even greater mismatch is virtual classes, in particular because fully...... general support for virtual classes requires generation of new classes at run-time by mixin composition.  Languages like CaesarJ and ObjectTeams can express virtual classes restricted to the subset that does not require run-time generation of classes, because of the restrictions imposed by the Java...

Promoting the purchase of low-calorie foods from school vending machines: a cluster-randomized controlled study.

Science.gov (United States)

Kocken, Paul L; Eeuwijk, Jennifer; Van Kesteren, Nicole M C; Dusseldorp, Elise; Buijs, Goof; Bassa-Dafesh, Zeina; Snel, Jeltje

2012-03-01

Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. A school-based randomized controlled trial was conducted in 13 experimental schools and 15 control schools. Three strategies were tested within each experimental school: increasing the availability of lower-calorie products in vending machines, labeling products, and reducing the price of lower-calorie products. The experimental schools introduced the strategies in 3 consecutive phases, with phase 3 incorporating all 3 strategies. The control schools remained the same. The sales volumes from the vending machines were registered. Products were grouped into (1) extra foods containing empty calories, for example, candies and potato chips, (2) nutrient-rich basic foods, and (3) beverages. They were also divided into favorable, moderately unfavorable, and unfavorable products. Total sales volumes for experimental and control schools did not differ significantly for the extra and beverage products. Proportionally, the higher availability of lower-calorie extra products in the experimental schools led to higher sales of moderately unfavorable extra products than in the control schools, and to higher sales of favorable extra products in experimental schools where students have to stay during breaks. Together, availability, labeling, and price reduction raised the proportional sales of favorable beverages. Results indicate that when the availability of lower-calorie foods is increased and is also combined with labeling and reduced prices, students make healthier choices without buying more or fewer products from school vending machines. Changes to school vending machines help to create a healthy school environment. © 2012, American School Health Association.

Pricing and Availability Intervention in Vending Machines at Four Bus Garages

Science.gov (United States)

Hannan, Peter J; Harnack, Lisa J; Mitchell, Nathan R; Toomey, Traci L; Gerlach, Anne

2009-01-01

Objective To evaluate the effects of lowering prices and increasing availability on sales of healthy foods and beverages from 33 vending machines in four bus garages as part of a multi-component worksite obesity prevention intervention. Methods Availability of healthy items was increased to 50% and prices were lowered at least 10% in the vending machines in two metropolitan bus garages for an 18-month period. Two control garages offered vending choices at usual availability and prices. Sales data were collected monthly from each of the vending machines at the four garages. Results Increases in availability to 50% and price reductions of an average of 31% resulted in 10-42% higher sales of the healthy items. Employees were most price-responsive for snack purchases. Conclusions Greater availability and lower prices on targeted food and beverage items from vending machines was associated with greater purchases of these items over an eighteen-month period. Efforts to promote healthful food purchases in worksite settings should incorporate these two strategies. PMID:20061884

The Complexity of Abstract Machines

Directory of Open Access Journals (Sweden)

Beniamino Accattoli

2017-01-01

Full Text Available The lambda-calculus is a peculiar computational model whose definition does not come with a notion of machine. Unsurprisingly, implementations of the lambda-calculus have been studied for decades. Abstract machines are implementations schema for fixed evaluation strategies that are a compromise between theory and practice: they are concrete enough to provide a notion of machine and abstract enough to avoid the many intricacies of actual implementations. There is an extensive literature about abstract machines for the lambda-calculus, and yet—quite mysteriously—the efficiency of these machines with respect to the strategy that they implement has almost never been studied. This paper provides an unusual introduction to abstract machines, based on the complexity of their overhead with respect to the length of the implemented strategies. It is conceived to be a tutorial, focusing on the case study of implementing the weak head (call-by-name strategy, and yet it is an original re-elaboration of known results. Moreover, some of the observation contained here never appeared in print before.

Rational choice and the political bases of changing Israeli counterinsurgency strategy.

Science.gov (United States)

Brym, Robert J; Andersen, Robert

2011-09-01

Israeli counterinsurgency doctrine holds that the persistent use of credible threat and disproportionate military force results in repeated victories that eventually teach the enemy the futility of aggression. The doctrine thus endorses classical rational choice theory's claim that narrow cost-benefit calculations shape fixed action rationales. This paper assesses whether Israel's strategic practice reflects its counterinsurgency doctrine by exploring the historical record and the association between Israeli and Palestinian deaths due to low-intensity warfare. In contrast to the expectations of classical rational choice theory, the evidence suggests that institutional, cultural and historical forces routinely override simple cost-benefit calculations. Changing domestic and international circumstances periodically cause revisions in counterinsurgency strategy. Credible threat and disproportionate military force lack the predicted long-term effect. © London School of Economics and Political Science 2011.

Barriers and Strategies for Healthy Food Choices among American Indian Tribal College Students: A Qualitative Analysis.

Science.gov (United States)

Keith, Jill F; Stastny, Sherri; Brunt, Ardith; Agnew, Wanda

2018-06-01

American Indian and Alaskan Native individuals experience disproportionate levels of chronic health conditions such as type 2 diabetes and overweight and obesity that are influenced by dietary patterns and food choices. Understanding factors that influence healthy food choices among tribal college students can enrich education and programs that target dietary intake. To build an understanding of factors that influence healthy food choices among tribal college students at increased risk for college attrition. A nonexperimental cohort design was used for qualitative descriptive analysis. Participants (N=20) were purposively sampled, newly enrolled, academically underprepared tribal college students enrolled in a culturally relevant life skills course at an upper Midwest tribal college between September 2013 and May 2015. Participant demographic characteristics included various tribal affiliations, ages, and number of dependents. Participant responses to qualitative research questions about dietary intake, food choices, self-efficacy for healthy food choices, psychosocial determinants, and barriers to healthy food choices during telephone interviews were used as measures. Qualitative analysis included prestudy identification of researcher bias/assumptions, audiorecording and transcription, initial analysis (coding), secondary analysis (sorting and identifying meaning), and verification (comparative pattern analysis). Qualitative analysis revealed a variety of themes and subthemes about healthy food choices. Main themes related to barriers included taste, food gathering and preparation, and difficulty clarifying healthy food choices. Main themes related to strategies included taste, cultural traditions and practices, and personal motivation factors. Qualitative analysis identified barrier and strategy themes that may assist nutrition and dietetics practitioners working with tribal/indigenous communities, tribal college educators and health specialists, and tribal

A Concrete Framework for Environment Machines

DEFF Research Database (Denmark)

Biernacka, Malgorzata; Danvy, Olivier

2007-01-01

calculus with explicit substitutions), we extend it minimally so that it can also express one-step reduction strategies, and we methodically derive a series of environment machines from the specification of two one-step reduction strategies for the lambda-calculus: normal order and applicative order....... The derivation extends Danvy and Nielsen’s refocusing-based construction of abstract machines with two new steps: one for coalescing two successive transitions into one, and the other for unfolding a closure into a term and an environment in the resulting abstract machine. The resulting environment machines...... include both the Krivine machine and the original version of Krivine’s machine, Felleisen et al.’s CEK machine, and Leroy’s Zinc abstract machine....

Predictors of return rate discrimination in slot machine play.

Science.gov (United States)

Coates, Ewan; Blaszczynski, Alex

2014-09-01

The purpose of this study was to investigate the extent to which accurate estimates of payback percentages and volatility combined with prior learning, enabled players to successfully discriminate between multi-line/multi-credit slot machines that provided differing rates of reinforcement. The aim was to determine if the capacity to discriminate structural characteristics of gaming machines influenced player choices in selecting 'favourite' slot machines. Slot machine gambling history, gambling beliefs and knowledge, impulsivity, illusions of control, and problem solving style were assessed in a sample of 48 first year undergraduate psychology students. Participants were subsequently exposed to a choice paradigm where they could freely select to play either of two concurrently presented PC-simulated slot machines programmed to randomly differ in expected player return rates (payback percentage) and win frequency (volatility). Results suggest that prior learning and cognitions (particularly gambler's fallacy) but not payback, were major contributors to the ability of a player to discriminate volatility between slot machines. Participants displayed a general tendency to discriminate payback, but counter-intuitively placed more bets on the slot machine with lower payback percentage rates.

A singular choice for multiple choice

DEFF Research Database (Denmark)

Frandsen, Gudmund Skovbjerg; Schwartzbach, Michael Ignatieff

2006-01-01

How should multiple choice tests be scored and graded, in particular when students are allowed to check several boxes to convey partial knowledge? Many strategies may seem reasonable, but we demonstrate that five self-evident axioms are sufficient to determine completely the correct strategy. We ...

The Scenario Approach to the Development of Strategy of Prevention of Raider Seizure for Machine-Building Enterprise

Directory of Open Access Journals (Sweden)

Momot Tetiana V.

2017-12-01

Full Text Available The article proposes the methodical approach to the choice and substantiation of efficiency of managerial decisions on ensuring economic safety at counteraction of raiding, based on an intellectual instrumental analysis. The ranking of alternatives of managerial decisions on the basis of the received weighted estimates and their fuzzy composition is used. A graphical interpretation of the membership functions of the calculated fuzzy expected utilities of management alternatives for the machine-building enterprises has been constructed and is presented.

An (un)healthy poster: When environmental cues affect consumers' food choices at vending machines.

Science.gov (United States)

Stöckli, Sabrina; Stämpfli, Aline E; Messner, Claude; Brunner, Thomas A

2016-01-01

Environmental cues can affect food decisions. There is growing evidence that environmental cues influence how much one consumes. This article demonstrates that environmental cues can similarly impact the healthiness of consumers' food choices. Two field studies examined this effect with consumers of vending machine foods who were exposed to different posters. In field study 1, consumers with a health-evoking nature poster compared to a pleasure-evoking fun fair poster or no poster in their visual sight were more likely to opt for healthy snacks. Consumers were also more likely to buy healthy snacks when primed by an activity poster than when exposed to the fun fair poster. In field study 2, this consumer pattern recurred with a poster of skinny Giacometti sculptures. Overall, the results extend the mainly laboratory-based evidence by demonstrating the health-relevant impact of environmental cues on food decisions in the field. Results are discussed in light of priming literature emphasizing the relevance of preexisting associations, mental concepts and goals. Copyright © 2015 Elsevier Ltd. All rights reserved.

Consumer Acceptance of Population-Level Intervention Strategies for Healthy Food Choices: The Role of Perceived Effectiveness and Perceived Fairness

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Colin Bos

2015-09-01

Full Text Available The present study investigates acceptance of intervention strategies for low-calorie snack choices that vary regarding the effect they have on consumers’ freedom of choice (providing information, guiding choice through (disincentives, and restricting choice. We examine the mediating effects of perceived effectiveness and perceived fairness, and the moderating effects of barriers to choose low-calorie snacks and perceived responsibility for food choice. Data was collected through an online survey, involving three waves that were completed over a seven week timespan. Information was collected on barriers and perceived responsibility, and evaluations of a total of 128 intervention strategies with varying levels of intrusiveness that were further systematically varied in terms of source, location, approach/avoidance, type, and severity. A total of 1173 respondents completed all three waves. We found that the effect of intervention intrusiveness on acceptance was mediated by the perceived personal- and societal effectiveness, and the perceived fairness of interventions. For barriers and perceived responsibility, only main effects on intervention-specific beliefs were found. Government interventions were accepted less than interventions by food manufacturers. In conclusion, the present study shows that acceptance of interventions depends on perceptions of personal- and societal effectiveness and fairness, thereby providing novel starting points for increasing acceptance of both existing and new food choice interventions.

Consumer Acceptance of Population-Level Intervention Strategies for Healthy Food Choices: The Role of Perceived Effectiveness and Perceived Fairness

Science.gov (United States)

Bos, Colin; Van Der Lans, Ivo; Van Rijnsoever, Frank; Van Trijp, Hans

2015-01-01

The present study investigates acceptance of intervention strategies for low-calorie snack choices that vary regarding the effect they have on consumers’ freedom of choice (providing information, guiding choice through (dis)incentives, and restricting choice). We examine the mediating effects of perceived effectiveness and perceived fairness, and the moderating effects of barriers to choose low-calorie snacks and perceived responsibility for food choice. Data was collected through an online survey, involving three waves that were completed over a seven week timespan. Information was collected on barriers and perceived responsibility, and evaluations of a total of 128 intervention strategies with varying levels of intrusiveness that were further systematically varied in terms of source, location, approach/avoidance, type, and severity. A total of 1173 respondents completed all three waves. We found that the effect of intervention intrusiveness on acceptance was mediated by the perceived personal- and societal effectiveness, and the perceived fairness of interventions. For barriers and perceived responsibility, only main effects on intervention-specific beliefs were found. Government interventions were accepted less than interventions by food manufacturers. In conclusion, the present study shows that acceptance of interventions depends on perceptions of personal- and societal effectiveness and fairness, thereby providing novel starting points for increasing acceptance of both existing and new food choice interventions. PMID:26389949

Consumer Acceptance of Population-Level Intervention Strategies for Healthy Food Choices: The Role of Perceived Effectiveness and Perceived Fairness.

Science.gov (United States)

Bos, Colin; Lans, Ivo Van Der; Van Rijnsoever, Frank; Van Trijp, Hans

2015-09-15

The present study investigates acceptance of intervention strategies for low-calorie snack choices that vary regarding the effect they have on consumers' freedom of choice (providing information, guiding choice through (dis)incentives, and restricting choice). We examine the mediating effects of perceived effectiveness and perceived fairness, and the moderating effects of barriers to choose low-calorie snacks and perceived responsibility for food choice. Data was collected through an online survey, involving three waves that were completed over a seven week timespan. Information was collected on barriers and perceived responsibility, and evaluations of a total of 128 intervention strategies with varying levels of intrusiveness that were further systematically varied in terms of source, location, approach/avoidance, type, and severity. A total of 1173 respondents completed all three waves. We found that the effect of intervention intrusiveness on acceptance was mediated by the perceived personal- and societal effectiveness, and the perceived fairness of interventions. For barriers and perceived responsibility, only main effects on intervention-specific beliefs were found. Government interventions were accepted less than interventions by food manufacturers. In conclusion, the present study shows that acceptance of interventions depends on perceptions of personal- and societal effectiveness and fairness, thereby providing novel starting points for increasing acceptance of both existing and new food choice interventions.

The influence of affect on suboptimal strategy choice in the Monty Hall dilemma

Directory of Open Access Journals (Sweden)

Efendic Emir

2015-01-01

Full Text Available The Monty Hall dilemma (MHD presents an intriguing choice anomaly that offers insight into human reasoning. It presents a specific subclass of decision tasks that require the adequate use of Bayes theorem in order to make optimal decisions. In the MHD, participants are presented with three doors with only one door hiding the prize. After their initial choice of a door, they are offered additional information. A different door (one that does not hide the prize and one not chosen by the participant is opened to reveal nothing behind it. Afterwards, the participants are offered to stay with their initial choice or to switch to the other remaining door. The better strategy is to always switch; a counterintuitive one for most people. We examine the notorious difficulty of the MHD from an affective perspective while relying on the dual processing approach to thinking. We varied participants’ reliance on their affective reactions as opposed to a neutral condition and hypothesized that the affective reactions associated with the staying option contribute to worse performance on the task. Indeed, the participants in the affective condition chose the staying option more often than our control participants. Using the MHD as an appropriate paradigm of conditional probability reasoning we show that, for this type of task, an affective strategy is highly inefficient. We attribute these results to the affective reactions associated with the staying option, with regret avoidance associated with the switch option, and the conditional probability construction of the dilemma.

Energy conversion loops for flux-switching PM machine analysis

NARCIS (Netherlands)

Ilhan, E.; Motoasca, T.E.; Paulides, J.J.H.; Lomonova, E.

2012-01-01

Induction and synchronous machines have traditionally been the first choice of automotive manufacturers for electric/hybrid vehicles. However, these conventional machines are not able anymore to meet the increasing demands for a higher energy density due to space limitation in cars. Flux-switching

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

Science.gov (United States)

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Geological Hazards analysis in Urban Tunneling by EPB Machine (Case study: Tehran subway line 7 tunnel

Directory of Open Access Journals (Sweden)

Hassan Bakhshandeh Amnieh

2016-06-01

Full Text Available Technological progress in tunneling has led to modern and efficient tunneling methods in vast underground spaces even under inappropriate geological conditions. Identification and access to appropriate and sufficient geological hazard data are key elements to successful construction of underground structures. Choice of the method, excavation machine, and prediction of suitable solutions to overcome undesirable conditions depend on geological studies and hazard analysis. Identifying and investigating the ground hazards in excavating urban tunnels by an EPB machine could augment the strategy for improving soil conditions during excavation operations. In this paper, challenges such as geological hazards, abrasion of the machine cutting tools, clogging around these tools and inside the chamber, diverse work front, severe water level fluctuations, existence of water, and fine-grained particles in the route were recognized in a study of Tehran subway line 7, for which solutions such as low speed boring, regular cutter head checks, application of soil improving agents, and appropriate grouting were presented and discussed. Due to the presence of fine particles in the route, foam employment was suggested as the optimum strategy where no filler is needed.

Decomposition of the compound Atwood machine

Science.gov (United States)

Lopes Coelho, R.

2017-11-01

Non-standard solving strategies for the compound Atwood machine problem have been proposed. The present strategy is based on a very simple idea. Taking an Atwood machine and replacing one of its bodies by another Atwood machine, we have a compound machine. As this operation can be repeated, we can construct any compound Atwood machine. This rule of construction is transferred to a mathematical model, whereby the equations of motion are obtained. The only difference between the machine and its model is that instead of pulleys and bodies, we have reference frames that move solidarily with these objects. This model provides us with the accelerations in the non-inertial frames of the bodies, which we will use to obtain the equations of motion. This approach to the problem will be justified by the Lagrange method and exemplified by machines with six and eight bodies.

A measurement strategy and an error-compensation model for the on-machine laser measurement of large-scale free-form surfaces

International Nuclear Information System (INIS)

Li, Bin; Li, Feng; Liu, Hongqi; Cai, Hui; Mao, Xinyong; Peng, Fangyu

2014-01-01

This study presents a novel measurement strategy and an error-compensation model for the measurement of large-scale free-form surfaces in on-machine laser measurement systems. To improve the measurement accuracy, the effects of the scan depth, surface roughness, incident angle and azimuth angle on the measurement results were investigated experimentally, and a practical measurement strategy considering the position and orientation of the sensor is presented. Also, a semi-quantitative model based on geometrical optics is proposed to compensate for the measurement error associated with the incident angle. The normal vector of the measurement point is determined using a cross-curve method from the acquired surface data. Then, the azimuth angle and incident angle are calculated to inform the measurement strategy and error-compensation model, respectively. The measurement strategy and error-compensation model are verified through the measurement of a large propeller blade on a heavy machine tool in a factory environment. The results demonstrate that the strategy and the model are effective in increasing the measurement accuracy. (paper)

Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress

Science.gov (United States)

Ambur, Manjula Y.; Yagle, Jeremy J.; Reith, William; McLarney, Edward

2016-01-01

In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.

Construction machine control guidance implementation strategy.

Science.gov (United States)

2010-07-01

Machine Controlled Guidance (MCG) technology may be used in roadway and bridge construction to improve construction efficiencies, potentially resulting in reduced project costs and accelerated schedules. The technology utilizes a Global Positioning S...

Effect of the Machined Surfaces of AISI 4337 Steel to Cutting Conditions on Dry Machining Lathe

Science.gov (United States)

Rahim, Robbi; Napid, Suhardi; Hasibuan, Abdurrozzaq; Rahmah Sibuea, Siti; Yusmartato, Y.

2018-04-01

The objective of the research is to obtain a cutting condition which has a good chance of realizing dry machining concept on AISI 4337 steel material by studying surface roughness, microstructure and hardness of machining surface. The data generated from the experiment were then processed and analyzed using the standard Taguchi method L9 (34) orthogonal array. Testing of dry and wet machining used surface test and micro hardness test for each of 27 test specimens. The machining results of the experiments showed that average surface roughness (Raavg) was obtained at optimum cutting conditions when VB 0.1 μm, 0.3 μm and 0.6 μm respectively 1.467 μm, 2.133 μm and 2,800 μm fo r dry machining while which was carried out by wet machining the results obtained were 1,833 μm, 2,667 μm and 3,000 μm. It can be concluded that dry machining provides better surface quality of machinery results than wet machining. Therefore, dry machining is a good choice that may be realized in the manufacturing and automotive industries.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

Science.gov (United States)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Employed parents’ satisfaction with food choice coping strategies: influence of gender and structure

OpenAIRE

Blake, Christine E.; Devine, Carol M.; Wethington, Elaine; Jastran, Margaret; Farrell, Tracy J.; Bisogni, Carole A.

2009-01-01

This study aimed to understand parents’ evaluations of the way they integrated work-family demands to manage food and eating. Employed, low/moderate-income, urban, U.S., Black, White, and Latino mothers (35) and fathers (34) participated in qualitative interviews exploring work and family conditions and spillover, food roles, and food-choice coping and family-adaptive strategies. Parents expressed a range of evaluations from overall satisfaction to overall dissatisfaction as well as dissatisf...

Motivation, justification, normalization: talk strategies used by Canadian medical tourists regarding their choices to go abroad for hip and knee surgeries.

Science.gov (United States)

Cameron, Keri; Crooks, Valorie A; Chouinard, Vera; Snyder, Jeremy; Johnston, Rory; Casey, Victoria

2014-04-01

Contributing to health geography scholarship on the topic, the objective of this paper is to reveal Canadian medical tourists' perspectives regarding their choices to seek knee replacement or hip replacement or resurfacing (KRHRR) at medical tourism facilities abroad rather than domestically. We address this objective by examining the 'talk strategies' used by these patients in discussing their choices and the ways in which such talk is co-constructed by others. Fourteen interviews were conducted with Canadians aged 42-77 who had gone abroad for KRHRR. Three types of talk strategies emerged through thematic analysis of their narratives: motivation, justification, and normalization talk. Motivation talk referenced participants' desires to maintain or resume physical activity, employment, and participation in daily life. Justification talk emerged when participants described how limitations in the domestic system drove them abroad. Finally, being a medical tourist was talked about as being normal on several bases. Among other findings, the use of these three talk strategies in patients' narratives surrounding medical tourism for KRHRR offers new insight into the language-health-place interconnection. Specifically, they reveal the complex ways in which medical tourists use talk strategies to assert the soundness of their choice to shift the site of their own medical care on a global scale while also anticipating, if not even guarding against, criticism of what ultimately is their own patient mobility. These talk strategies provide valuable insight into why international patients are opting to engage in the spatially explicit practice of medical tourism and who and what are informing their choices. Copyright © 2014 Elsevier Ltd. All rights reserved.

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

International Nuclear Information System (INIS)

Kong, Xiangyong; Gao, Liqun; Ouyang, Haibin; Li, Steven

2015-01-01

In most research on redundancy allocation problem (RAP), the redundancy strategy for each subsystem is assumed to be predetermined and fixed. This paper focuses on a specific RAP with multiple strategy choices (RAP-MSC), in which both active redundancy and cold standby redundancy can be selected as an additional decision variable for individual subsystems. To do so, the component type, redundancy strategy and redundancy level for each subsystem should be chosen subject to the system constraints appropriately such that the system reliability is maximized. Meanwhile, imperfect switching for cold standby redundancy is considered and a k-Erlang distribution is introduced to model the time-to-failure component as well. Given the importance and complexity of RAP-MSC, we propose a new efficient simplified version of particle swarm optimization (SPSO) to solve such NP-hard problems. In this method, a new position updating scheme without velocity is presented with stochastic disturbance and a low probability. Moreover, it is compared with several well-known PSO variants and other state-of-the-art approaches in the literature to evaluate its performance. The experiment results demonstrate the superiority of SPSO as an alternative for solving the RAP-MSC. - Highlights: • A more realistic RAP form with multiple strategy choices is focused. • Redundancy strategies are to be selected rather than fixed in general RAP. • A new simplified particle swarm optimization is proposed. • Higher reliabilities are achieved than the state-of-the-art approaches.

The impact of two multiple-choice question formats on the problem-solving strategies used by novices and experts.

Science.gov (United States)

Coderre, Sylvain P; Harasym, Peter; Mandin, Henry; Fick, Gordon

2004-11-05

Pencil-and-paper examination formats, and specifically the standard, five-option multiple-choice question, have often been questioned as a means for assessing higher-order clinical reasoning or problem solving. This study firstly investigated whether two paper formats with differing number of alternatives (standard five-option and extended-matching questions) can test problem-solving abilities. Secondly, the impact of the alternatives number on psychometrics and problem-solving strategies was examined. Think-aloud protocols were collected to determine the problem-solving strategy used by experts and non-experts in answering Gastroenterology questions, across the two pencil-and-paper formats. The two formats demonstrated equal ability in testing problem-solving abilities, while the number of alternatives did not significantly impact psychometrics or problem-solving strategies utilized. These results support the notion that well-constructed multiple-choice questions can in fact test higher order clinical reasoning. Furthermore, it can be concluded that in testing clinical reasoning, the question stem, or content, remains more important than the number of alternatives.

A strategy for man-machine system development in process industries

International Nuclear Information System (INIS)

Wirstad, J.

1986-12-01

A framework for Man-Machine System design in process industry projects is reported. It is based in the Guidelines for the Design of Man-Machine interfaces which have been generated in cooperation within the European Workshop for Industrial Computer Systems (EWICS). The application of EWICS Guidelines in industrial projects is demonstrated by six User Scenarios, which represent typical projects from different industries, e.g. electrical power generation and distribution, water control, pulp and paper production, oil and gas production. In all these projects Man-Machine System design has been conducted. It is recommended in the report that each Company develops its set of Man-Machine Systems Standard techniques/procedures. At present there are several techniques/procedures available which, for moderate costs, can be adapted to specific Company conditions. A menu of such Man-Machine System techniques/procedures is presented. Means of estimating the costs and benefits of Man-Machine System design are also described. (author)

Tool path strategy and cutting process monitoring in intelligent machining

Science.gov (United States)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

A user-specific human-machine interaction strategy for a prosthetic shank adapter

Directory of Open Access Journals (Sweden)

Stuhlenmiller Florian

2017-09-01

Full Text Available For people with lower limb amputation, a user-specific human-machine interaction with their prostheses is required to ensure safe and comfortable assistance. Especially during dynamic turning manoeuvres, users experience high loads at the stump, which decreases comfort and may lead to long-term tissue damage. Preliminary experiments with users wearing a configurable, passive torsional adaptor indicate increased comfort and safety achieved by adaptation of torsional stiffness and foot alignment. Moreover, the results show that the individual preference regarding both parameters depend on gait situation and individual preference. Hence, measured loads in the structure of the prosthesis and subjective feedback regarding comfort and safety during different turning motions are considered in a user-specific human-machine interaction strategy for a prosthetic shank adaptor. Therefore, the interrelations of gait parameters with optimal configuration are stored in an individual preference-setting matrix. Stiffness and foot alignment are actively adjusted to the optimal parameters by a parallel elastic actuator. Two subjects reported that they experienced appropriate variation of stiffness and foot alignment, a noticeable reduction of load at the stump and that they could turn with less effort.

Research on machine learning framework based on random forest algorithm

Science.gov (United States)

Ren, Qiong; Cheng, Hui; Han, Hai

2017-03-01

With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

A Virtual Inertia Control Strategy for DC Microgrids Analogized with Virtual Synchronous Machines

DEFF Research Database (Denmark)

Wu, Wenhua; Chen, Yandong; Luo, An

2017-01-01

In a DC microgrid (DC-MG), the dc bus voltage is vulnerable to power fluctuation derived from the intermittent distributed energy or local loads variation. In this paper, a virtual inertia control strategy for DC-MG through bidirectional grid-connected converters (BGCs) analogized with virtual...... synchronous machine (VSM) is proposed to enhance the inertia of the DC-MG, and to restrain the dc bus voltage fluctuation. The small-signal model of the BGC system is established, and the small-signal transfer function between the dc bus voltage and the dc output current of the BGC is deduced. The dynamic...... for the BGC is introduced to smooth the dynamic response of the dc bus voltage. By analyzing the control system stability, the appropriate virtual inertia control parameters are selected. Finally, simulations and experiments verified the validity of the proposed control strategy....

Vulnerability to Weather Disasters: the Choice of Coping Strategies in Rural Uganda

Directory of Open Access Journals (Sweden)

Jennifer F. Helgeson

2013-06-01

Full Text Available When a natural disaster hits, the affected households try to cope with its impacts. A variety of coping strategies, from reducing current consumption to disposing of productive assets, may be employed. The latter strategies are especially worrisome because they may reduce the capacity of the household to generate income in the future, possibly leading to chronic poverty. We used the results of a household survey in rural Uganda to ask, first, what coping strategies would tend to be employed in the event of a weather disaster, second, given that multiple strategies can be chosen, in what combinations would they tend to be employed, and, third, given that asset-liquidation strategies can be particularly harmful for the future income prospects of households, what determines their uptake? Our survey is one of the largest of its kind, containing over 3000 observations garnered by local workers using smartphone technology. We found that in this rural sample, by far, the most frequently reported choice would be to sell livestock. This is rather striking because asset-based theories would predict more reliance on strategies like eating and spending less today, which avoid disposal of productive assets. It may well be that livestock is held as a form of liquid savings to, among other things, help bounce back from a weather disaster. Although, we did find that other strategies that might undermine future prospects were avoided, notably selling land or the home and disrupting the children's education. Our econometric analysis revealed a fairly rich set of determinants of different subsets of coping strategies. Perhaps most notably, households with a more educated head are much less likely to choose coping strategies involving taking their own children out of education.

On synchronous parallel computations with independent probabilistic choice

International Nuclear Information System (INIS)

Reif, J.H.

1984-01-01

This paper introduces probabilistic choice to synchronous parallel machine models; in particular parallel RAMs. The power of probabilistic choice in parallel computations is illustrate by parallelizing some known probabilistic sequential algorithms. The authors characterize the computational complexity of time, space, and processor bounded probabilistic parallel RAMs in terms of the computational complexity of probabilistic sequential RAMs. They show that parallelism uniformly speeds up time bounded probabilistic sequential RAM computations by nearly a quadratic factor. They also show that probabilistic choice can be eliminated from parallel computations by introducing nonuniformity

Vector control of induction machines

CERN Document Server

Robyns, Benoit

2012-01-01

After a brief introduction to the main law of physics and fundamental concepts inherent in electromechanical conversion, ""Vector Control of Induction Machines"" introduces the standard mathematical models for induction machines - whichever rotor technology is used - as well as several squirrel-cage induction machine vector-control strategies. The use of causal ordering graphs allows systematization of the design stage, as well as standardization of the structure of control devices. ""Vector Control of Induction Machines"" suggests a unique approach aimed at reducing parameter sensitivity for

Fiscal 1999 research report on long-term energy technology strategy. Basic research on industrial technology strategy (Individual technology strategy). Machine industry technology field (Machine tool); 1999 nendo choki energy gijutsu senryaku nado ni kansuru chosa hokokusho. Sangyo gijutsu senryaku sakutei kiban chosa (bun'yabetsu gijjtsu senryaku) kikai sangyo gijutsu bun'ya (kosaku kikai bun'ya)

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-03-01

This report summarizes the fiscal 1999 basic research result on industrial technology strategy of a machine tool field. Corresponding to construction of an environment-friendly recycling economic society and economic growth as estimated change in social and industrial structure during 2010-2025, major themes are reuse of machine tools, energy saving, environment design, chip treatment and resource recovery. With preparation of information infrastructures in an advanced information society, major issues are 3D-CAD, remote diagnosis, remote control and self-restoration. Because of decline of the birth rate, positive employment of aged persons and women is desirable. New machining technology for new materials, and fusion of various machining technologies are important. For micro-technology, study should be made on the concept and effect of micro-factory (energy saving, space saving, cost reduction, higher accuracy and higher speed). For higher-accuracy machine tool, static, dynamic, thermal and movement characteristics are the key for improving the proper technology. For faster machining. fine basic structural element, faster main spindle and faster feed are important. (NEDO)

Organizational Lerning and Strategy: Information Processing Approach of Organizaitonal Learning to Perform Strategic Choice Analysis

Directory of Open Access Journals (Sweden)

Agustian Budi Prasetya

2017-03-01

Full Text Available Study of organizational learning required to discuss the issue of strategy to understand company’s organizational knowledge and how company applied the organizational knowledge toward the changing of the environment. Method of the analysis for this research was based on desk research thoroughly on the existing literature. This research analyzed the viewpoints of different researchers in organizational learning and elaborates the information processing abilities approach of Organizational Learning (OL. Based on desk research on literature, the research discussed information processing approach to explain organizational learning and strategy choice by describing the importance of information and assumptions, the activities of knowledge acquisition, interpreting and distribution of the knowledge, typology of exploitation and exploration learning. It proposed the importance of the company to perform alignment between internal managerial process arrangement and external environment while doing the learning, based on the strategic choice space, as theatrical clustering map of the learning, the fit, the alignment, and the alliances of the organization. This research finds that the strategic space might help the analysis of balancing between exploitation and exploration learning while applying the analysis of varied firm characteristics, strategic orientation, and industrial environments.

Healthier snacks in school vending machines: a pilot project in four Ontario high schools.

Science.gov (United States)

Callaghan, Christine; Mandich, Gillian; He, Meizi

2010-01-01

The Healthy Vending Machine Pilot Project (HVMPP) was a public health initiative intended to create a healthier school nutrition environment by making healthier snacks available in vending machines, while maintaining a profit margin. The HVMPP was evaluated using quantitative and qualitative measures. Vending machines were stocked with healthier choices and conventional vending products at a 50:50 ratio. The HVMPP was implemented from February to May 2007 in four Ontario secondary schools in Middlesex-London, Elgin, and Oxford counties. Product sales were tracked, and focus groups were conducted to obtain students' opinions about healthy eating and vending choices. "Healthier choice" sales ranged from 14% to 17%. In all schools, vending revenues declined from 0.7% to 66%. A majority of participants had substantial knowledge of healthy eating and were in favour of healthier choices in vending machines; however, price, value, and taste were barriers that led them to purchase these products rarely. Students preferred to have "real" healthy snacks, such as yogurt, fruit, and vegetables, available in schools. Replacing 50% of vending stock with healthier snacks resulted in a decline in vending revenues. Future health programs in schools need to provide "real" healthy snacks, such as low-fat dairy products, fruits, and vegetables.

Control strategy minimizing the converter-alternating current motor losses: application to electric traction; Strategies de commande minimisant les pertes d'un ensemble convertisseur - machine alternative: application a la traction electrique

Energy Technology Data Exchange (ETDEWEB)

Bastiani, Ph.

2001-02-01

Improving the efficiency of the converter-alternating current motor system is a major task in electric traction. Global energy optimisation implies a specific approach at system scale. To reach this goal, we have chosen an algebraic method using sub-system models. To start with, a synchronous machine Park model is developed to take account magnetic saturation and iron losses. Then, an averaged model of the voltage inverter is used in order to obtain a simplified model of the losses to be implemented in our optimisation method. This is how the global model is built including losses in the synchronous machine along with the losses of the power converter. Experimental results are there to validate our approach. This study proposes a method based on algebraic formulation of the general laws to control torque. Algorithms take into account magnetic circuits saturation and power losses in both the machine and its converter. Here again, experimental results validate the algorithm on several test benches. Achieved efficiency improvement is important compared to existing usual control strategies. The proposed method can be generalised to other machine-converter systems. As a matter of fact we have extended our study to the induction machine. As a complement ti this study we have looked at the effects natural limitations of voltages and currents in the torque-speed plane. Therefore algebraic formulation of the torque-speed plane and optimisation strategies are proposed including those constraints. (author)

Machine Learning

Energy Technology Data Exchange (ETDEWEB)

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

2017-04-21

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

Substantiating Recommendations on the Choice of an Efficient Strategy of the State Regulation for System of Monitoring Economy Branches

Directory of Open Access Journals (Sweden)

Ashchaulov Vitalii V.

2016-01-01

Full Text Available The article is concerned with substantiating recommendations on the choice of an effective strategy of the State regulation for system of monitoring economy branches. To display the financial aspect of the enterprise's strategic positioning in the sectoral environment, a modified ADL matrix was used. A matrix of choosing an optimum variant (scenario of the enterprise's financial strategy has been considered taking into account possibilities of its use in the system of monitoring the development of economic sectors. Disadvantages in the use of modified ADL matrices and in choosing an optimum variant (scenario financial strategy of the enterprise's financial strategy have been identified. Factors of internal and external environment, which affect the strategic choice of enterprises, have been determined and analyzed. The SPACE matrix for building the model of choosing a strategy of financing the development of enterprises in the economy branches has been modified in the context of the system of monitoring the development of economy branches. A description of the strategies proposed by author for financing the development of enterprise has been provided, their advantages and disadvantages have been determined. Individual tools of the proposed models of actions can be accepted as a way of improving the strategic positions with a view to achieving a higher quantitative-qualitative situation as well as adjustment of the existing model of behavior.

Methods of equipment choice in shotcreting

Science.gov (United States)

Sharapov, R. R.; Yadykina, V. V.; Stepanov, M. A.; Kitukov, B. A.

2018-03-01

Shotcrete is widely used in architecture, hydraulic engineering structures, finishing works in tunnels, arc covers and ceilings. The problem of the equipment choice in shotcreting is very important. The main issues influencing the equipment choice are quality improvement and intensification of shotcreting. Main parameters and rational limits of technological characteristic of machines used in solving different problems in shotcreting are described. It is suggested to take into account peculiarities of shotcrete mixing processes and peculiarities of applying these mixtures with compressed air kinetic energy. The described method suggests choosing a mixer with the account of energy capacity, Reynolds number and rotational frequency of the mixing drum. The suggested choice procedure of the equipment nomenclature allows decreasing exploitation costs, increasing the quality of shotcrete and shotcreting in general.

Energy Conversion Loops for Flux-Switching PM Machine Analysis

Directory of Open Access Journals (Sweden)

E. Ilhan

2012-10-01

Full Text Available Induction and synchronous machines have traditionally been the first choice of automotive manufacturers for electric/hybrid vehicles. However, these conventional machines are not able anymore to meet the increasing demands for a higher energy density due to space limitation in cars. Flux-switching PM (FSPM machines with their high energy density are very suitable to answer this demand. In this paper, the energy conversion loop technique is implemented on FSPM for the first time. The energy conversion technique is a powerful tool for the visualization of machine characteristics, both linear and nonlinear. Further, the technique provides insight into the torque production mechanism. A stepwise explanation is given on how to create these loops for FSPM along with the machine operation.

Fundamentals of machine design

CERN Document Server

Karaszewski, Waldemar

2011-01-01

A forum of researchers, educators and engineers involved in various aspects of Machine Design provided the inspiration for this collection of peer-reviewed papers. The resultant dissemination of the latest research results, and the exchange of views concerning the future research directions to be taken in this field will make the work of immense value to all those having an interest in the topics covered. The book reflects the cooperative efforts made in seeking out the best strategies for effecting improvements in the quality and the reliability of machines and machine parts and for extending

Machine learning strategies for systems with invariance properties

Science.gov (United States)

Ling, Julia; Jones, Reese; Templeton, Jeremy

2016-08-01

In many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds Averaged Navier Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high performance computing has led to a growing availability of high fidelity simulation data. These data open up the possibility of using machine learning algorithms, such as random forests or neural networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these empirical models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first method, a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance at significantly reduced computational training costs.

An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies

International Nuclear Information System (INIS)

Chambari, Amirhossain; Najafi, Amir Abbas; Rahmati, Seyed Habib A.; Karimi, Aida

2013-01-01

The redundancy allocation problem (RAP) is an important reliability optimization problem. This paper studies a specific RAP in which redundancy strategies are chosen. To do so, the choice of the redundancy strategies among active and cold standby is considered as decision variables. The goal is to select the redundancy strategy, component, and redundancy level for each subsystem such that the system reliability is maximized. Since RAP is a NP-hard problem, we propose an efficient simulated annealing algorithm (SA) to solve it. In addition, to evaluating the performance of the proposed algorithm, it is compared with well-known algorithms in the literature for different test problems. The results of the performance analysis show a relatively satisfactory efficiency of the proposed SA algorithm

Search predicts and changes patience in intertemporal choice

Science.gov (United States)

Johnson, Eric J.

2017-01-01

Intertemporal choice impacts many important outcomes, such as decisions about health, education, wealth, and the environment. However, the psychological processes underlying decisions involving outcomes at different points in time remain unclear, limiting opportunities to intervene and improve people’s patience. This research examines information-search strategies used during intertemporal choice and their impact on decisions. In experiment 1, we demonstrate that search strategies vary substantially across individuals. We subsequently identify two distinct search strategies across individuals. Comparative searchers, who compare features across options, discount future options less and are more susceptible to acceleration versus delay framing than integrative searchers, who integrate the features of an option. Experiment 2 manipulates search using an unobtrusive method to establish a causal relationship between strategy and choice, randomly assigning participants to conditions promoting either comparative or integrative search. Again, comparative search promotes greater patience than integrative search. Additionally, when participants adopt a comparative search strategy, they also exhibit greater effects of acceleration versus delay framing. Although most participants reported that the manipulation did not change their behavior, promoting comparative search decreased discounting of future rewards substantially and speeded patient choices. These findings highlight the central role that heterogeneity in psychological processes plays in shaping intertemporal choice. Importantly, these results indicate that theories that ignore variability in search strategies may be inadvertently aggregating over different subpopulations that use very different processes. The findings also inform interventions in choice architecture to increase patience and improve consumer welfare. PMID:29078303

Exploring how individuals complete the choice tasks in a discrete choice experiment: an interview study

Directory of Open Access Journals (Sweden)

Jorien Veldwijk

2016-04-01

Full Text Available Abstract Background To be able to make valid inferences on stated preference data from a Discrete Choice Experiment (DCE it is essential that researchers know if participants were actively involved, understood and interpreted the provided information correctly and whether they used complex decision strategies to make their choices and thereby acted in accordance with the continuity axiom. Methods During structured interviews, we explored how 70 participants evaluated and completed four discrete choice tasks aloud. Hereafter, additional questions were asked to further explore if participants understood the information that was provided to them and whether they used complex decision strategies (continuity axiom when making their choices. Two existing DCE questionnaires on rotavirus vaccination and prostate cancer-screening served as case studies. Results A large proportion of the participants was not able to repeat the exact definition of the risk attributes as explained to them in the introduction of the questionnaire. The majority of the participants preferred more optimal over less optimal risk attribute levels. Most participants (66 % mentioned three or more attributes when motivating their decisions, thereby acting in accordance with the continuity axiom. However, 16 out of 70 participants continuously mentioned less than three attributes when motivating their decision. Lower educated and less literate participants tended to mention less than three attributes when motivating their decision and used trading off between attributes less often as a decision-making strategy. Conclusion The majority of the participants seemed to have understood the provided information about the choice tasks, the attributes, and the levels. They used complex decision strategies (continuity axiom and are therefore capable to adequately complete a DCE. However, based on the participants’ age, educational level and health literacy additional, actions should be

Search predicts and changes patience in intertemporal choice.

Science.gov (United States)

Reeck, Crystal; Wall, Daniel; Johnson, Eric J

2017-11-07

Intertemporal choice impacts many important outcomes, such as decisions about health, education, wealth, and the environment. However, the psychological processes underlying decisions involving outcomes at different points in time remain unclear, limiting opportunities to intervene and improve people's patience. This research examines information-search strategies used during intertemporal choice and their impact on decisions. In experiment 1, we demonstrate that search strategies vary substantially across individuals. We subsequently identify two distinct search strategies across individuals. Comparative searchers, who compare features across options, discount future options less and are more susceptible to acceleration versus delay framing than integrative searchers, who integrate the features of an option. Experiment 2 manipulates search using an unobtrusive method to establish a causal relationship between strategy and choice, randomly assigning participants to conditions promoting either comparative or integrative search. Again, comparative search promotes greater patience than integrative search. Additionally, when participants adopt a comparative search strategy, they also exhibit greater effects of acceleration versus delay framing. Although most participants reported that the manipulation did not change their behavior, promoting comparative search decreased discounting of future rewards substantially and speeded patient choices. These findings highlight the central role that heterogeneity in psychological processes plays in shaping intertemporal choice. Importantly, these results indicate that theories that ignore variability in search strategies may be inadvertently aggregating over different subpopulations that use very different processes. The findings also inform interventions in choice architecture to increase patience and improve consumer welfare. Copyright © 2017 the Author(s). Published by PNAS.

Automation of a universal machine

International Nuclear Information System (INIS)

Rodriguez S, J.

1997-01-01

The development of the hardware and software of a control system for a servo-hydraulic machine is presented. The universal machine is an Instron, model 1331, used to make mechanical tests. The software includes the acquisition of data from the measurements, processing and graphic presentation of the results in the assay of the 'tension' type. The control is based on a PPI (Programmable Peripheral Interface) 8255, in which the different states of the machine are set. The control functions of the machine are: a) Start of an assay, b) Pause in the assay, c) End of the assay, d) Choice of the control mode of the machine, that they could be in load, stroke or strain modes. For the data acquisition, a commercial card, National Products, model DAS-16, plugged in a slot of a Pc was used. Three transducers provide the analog signals, a cell of load, a LVDT and a extensometer. All the data are digitalized and handled in order to get the results in the appropriate working units. A stress-strain graph is obtained in the screen of the Pc for a tension test for a specific material. The points of maximum stress, rupture stress and the yield stress of the material under test are shown. (Author)

Making Healthy Choices Easier

DEFF Research Database (Denmark)

Guldborg Hansen, Pelle; Skov, Laurits Rohden; Lund Skov, Katrine

2016-01-01

. However, integration and testing of the nudge approach as part of more comprehensive public health strategies aimed at making healthy choices easier is being threatened by inadequate understandings of its scientific character, relationship with regulation and its ethical implications. This article reviews...... working with or incorporating the nudge approach into programs or policies aimed at making healthy choices easier...

Educational Choice. A Background Paper.

Science.gov (United States)

Quality Education for Minorities Network, Washington, DC.

This paper addresses school choice, one proposal to address parental involvement concerns, focusing on historical background, definitions, rationale for advocating choice, implementation strategies, and implications for minorities and low-income families. In the past, transfer payment programs such as tuition tax credits and vouchers were…

Machinability of Stellite 6 hardfacing

Directory of Open Access Journals (Sweden)

Dudzinski D.

2010-06-01

Full Text Available This paper reports some experimental findings concerning the machinability at high cutting speed of nickel-base weld-deposited hardfacings for the manufacture of hot tooling. The forging work involves extreme impacts, forces, stresses and temperatures. Thus, mould dies must be extremely resistant. The aim of the project is to create a rapid prototyping process answering to forging conditions integrating a Stellite 6 hardfacing deposed PTA process. This study talks about the dry machining of the hardfacing, using a two tips machining tool and a high speed milling machine equipped by a power consumption recorder Wattpilote. The aim is to show the machinability of the hardfacing, measuring the power and the tip wear by optical microscope and white light interferometer, using different strategies and cutting conditions.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

Science.gov (United States)

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Maintenance strategies to reduce downtime due to machine positional errors

OpenAIRE

Shagluf, Abubaker; Longstaff, A.P.; Fletcher, S.

2014-01-01

Proceedings of Maintenance Performance Measurement and Management (MPMM) Conference 2014 Manufacturing strives to reduce waste and increase Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime competentely and similiarly identify the machines performance. Total productive maintenance (TPM) is a maintenance progr...

Ex-vivo machine perfusion for kidney preservation.

Science.gov (United States)

Hamar, Matyas; Selzner, Markus

2018-06-01

Machine perfusion is a novel strategy to decrease preservation injury, improve graft assessment, and increase organ acceptance for transplantation. This review summarizes the current advances in ex-vivo machine-based kidney preservation technologies over the last year. Ex-vivo perfusion technologies, such as hypothermic and normothermic machine perfusion and controlled oxygenated rewarming, have gained high interest in the field of organ preservation. Keeping kidney grafts functionally and metabolically active during the preservation period offers a unique chance for viability assessment, reconditioning, and organ repair. Normothermic ex-vivo kidney perfusion has been recently translated into clinical practice. Preclinical results suggest that prolonged warm perfusion appears superior than a brief end-ischemic reconditioning in terms of renal function and injury. An established standardized protocol for continuous warm perfusion is still not available for human grafts. Ex-vivo machine perfusion represents a superior organ preservation method over static cold storage. There is still an urgent need for the optimization of the perfusion fluid and machine technology and to identify the optimal indication in kidney transplantation. Recent research is focusing on graft assessment and therapeutic strategies.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

Science.gov (United States)

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

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.

Parental attitudes towards soft drink vending machines in high schools.

Science.gov (United States)

Hendel-Paterson, Maia; French, Simone A; Story, Mary

2004-10-01

Soft drink vending machines are available in 98% of US high schools. However, few data are available about parents' opinions regarding the availability of soft drink vending machines in schools. Six focus groups with 33 parents at three suburban high schools were conducted to describe the perspectives of parents regarding soft drink vending machines in their children's high school. Parents viewed the issue of soft drink vending machines as a matter of their children's personal choice more than as an issue of a healthful school environment. However, parents were unaware of many important details about the soft drink vending machines in their children's school, such as the number and location of machines, hours of operation, types of beverages available, or whether the school had contracts with soft drink companies. Parents need more information about the number of soft drink vending machines at their children's school, the beverages available, the revenue generated by soft drink vending machine sales, and the terms of any contracts between the school and soft drink companies.

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Tavakkoli-Moghaddam, R. [Department of Industrial Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365/4563, Tehran (Iran, Islamic Republic of); Department of Mechanical Engineering, The University of British Columbia, Vancouver (Canada)], E-mail: tavakoli@ut.ac.ir; Safari, J. [Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of)], E-mail: jalalsafari@pideco.com; Sassani, F. [Department of Mechanical Engineering, The University of British Columbia, Vancouver (Canada)], E-mail: sassani@mech.ubc.ca

2008-04-15

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed.

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

International Nuclear Information System (INIS)

Tavakkoli-Moghaddam, R.; Safari, J.; Sassani, F.

2008-01-01

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed

Constructing food choice decisions.

Science.gov (United States)

Sobal, Jeffery; Bisogni, Carole A

2009-12-01

Food choice decisions are frequent, multifaceted, situational, dynamic, and complex and lead to food behaviors where people acquire, prepare, serve, give away, store, eat, and clean up. Many disciplines and fields examine decision making. Several classes of theories are applicable to food decision making, including social behavior, social facts, and social definition perspectives. Each offers some insights but also makes limiting assumptions that prevent fully explaining food choice decisions. We used constructionist social definition perspectives to inductively develop a food choice process model that organizes a broad scope of factors and dynamics involved in food behaviors. This food choice process model includes (1) life course events and experiences that establish a food choice trajectory through transitions, turning points, timing, and contexts; (2) influences on food choices that include cultural ideals, personal factors, resources, social factors, and present contexts; and (3) a personal system that develops food choice values, negotiates and balances values, classifies foods and situations, and forms/revises food choice strategies, scripts, and routines. The parts of the model dynamically interact to make food choice decisions leading to food behaviors. No single theory can fully explain decision making in food behavior. Multiple perspectives are needed, including constructionist thinking.

Reliability analysis in intelligent machines

Science.gov (United States)

Mcinroy, John E.; Saridis, George N.

1990-01-01

Given an explicit task to be executed, an intelligent machine must be able to find the probability of success, or reliability, of alternative control and sensing strategies. By using concepts for information theory and reliability theory, new techniques for finding the reliability corresponding to alternative subsets of control and sensing strategies are proposed such that a desired set of specifications can be satisfied. The analysis is straightforward, provided that a set of Gaussian random state variables is available. An example problem illustrates the technique, and general reliability results are presented for visual servoing with a computed torque-control algorithm. Moreover, the example illustrates the principle of increasing precision with decreasing intelligence at the execution level of an intelligent machine.

Strategies in electro-chemical machining of tungsten for divertor application

International Nuclear Information System (INIS)

Krauss, W.; Holstein, N.; Konys, J.

2007-01-01

For future application in a fusion power system a modular structured He cooled divertor concept is investigated under the framework of EFDA which is based on the use of pure W or W alloys for the thermally highly loaded parts. Due to the underlying physico-chemical principles electro-chemical machining (ECM) is the only shaping process which will not introduce microstructural defects, e.g. microcracks into work pieces as known by example from electro-discharge machining (EDM). However, ECM processes have no industrial application in W machining up to yet due to passivation effects using standard electrolytes known from steel working. Therefore, a systematical electrochemical development program was launched, and the electrochemical behavior of W was examined and passivation effects could be eliminated, successfully. The electrochemical shaping processes can be divided into two main categories. The first one is M-ECM, which represents the lithographic route based on structured anode masks, and the other is C-ECM, working with a negatively structured cathode as tool which is copied by electro-chemical dissolution. Both ECM branches are discussed on base of first machined structured parts, showing their process depending advantages and potential enhancements are revealed by applying pulsed currents instead of DC dissolution technique

Which Infidelity Type Makes You More Jealous? Decision Strategies in a Forced-Choice between Sexual and Emotional Infidelity

Directory of Open Access Journals (Sweden)

Achim Schützwohl

2004-01-01

Full Text Available This study tested the prediction derived from the evolutionary psychological analysis of jealousy that men and women selecting the adaptively primary infidelity type (i.e., female sexual and male emotional infidelity, respectively in a forced-choice response format need to engage in less elaborate decision strategies than men and women selecting the adaptively secondary infidelity type (i.e., male sexual and female emotional infidelity, respectively. Unknown to the participants, decision times were registered as an index of the elaborateness of their decision strategies. The results clearly support the prediction. Implications and limitations of the present findings are discussed.

Employed parents' satisfaction with food-choice coping strategies. Influence of gender and structure.

Science.gov (United States)

Blake, Christine E; Devine, Carol M; Wethington, Elaine; Jastran, Margaret; Farrell, Tracy J; Bisogni, Carole A

2009-06-01

This study aimed to understand parents' evaluations of the way they integrated work-family demands to manage food and eating. Employed, low/moderate-income, urban, U.S., Black, White, and Latino mothers (35) and fathers (34) participated in qualitative interviews exploring work and family conditions and spillover, food roles, and food-choice coping and family-adaptive strategies. Parents expressed a range of evaluations from overall satisfaction to overall dissatisfaction as well as dissatisfaction limited to work, family life, or daily schedule. Evaluation criteria differed by gender. Mothers evaluated satisfaction on their ability to balance work and family demands through flexible home and work conditions, while striving to provide healthy meals for their families. Fathers evaluated satisfaction on their ability to achieve schedule stability and participate in family meals, while meeting expectations to contribute to food preparation. Household, and especially work structural conditions, often served as sizeable barriers to parents fulfilling valued family food roles. These relationships highlight the critical need to consider the intersecting influences of gender and social structure as influences on adults' food choices and dietary intake and to address the challenges of work and family integration among low income employed parents as a way to promote family nutrition in a vulnerable population.

Strategy Use and Strategy Choice in Fraction Magnitude Comparison

Science.gov (United States)

Fazio, Lisa K.; DeWolf, Melissa; Siegler, Robert S.

2016-01-01

We examined, on a trial-by-trial basis, fraction magnitude comparison strategies of adults with more and less mathematical knowledge. College students with high mathematical proficiency used a large variety of strategies that were well tailored to the characteristics of the problems and that were guaranteed to yield correct performance if executed…

Using Machine Learning to Search for MSSM Higgs Bosons

CERN Document Server

Diesing, Rebecca

2016-01-01

This paper examines the performance of machine learning in the identification of Minimally Su- persymmetric Standard Model (MSSM) Higgs Bosons, and compares this performance to that of traditional cut strategies. Two boosted decision tree algorithms were tested, scikit-learn and XGBoost. These tests indicated that machine learning can perform significantly better than traditional cuts. However, since machine learning in this form cannot be directly implemented in a real MSSM Higgs analysis, this performance information was instead used to better understand the relationships between training variables. Further studies might use this information to construct an improved cut strategy.

Russian consumers' motives for food choice

NARCIS (Netherlands)

Honkanen, P.; Frewer, L.J.

2009-01-01

Knowledge about food choice motives which have potential to influence consumer consumption decisions is important when designing food and health policies, as well as marketing strategies. Russian consumers¿ food choice motives were studied in a survey (1081 respondents across four cities), with the

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

Science.gov (United States)

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

Directory of Open Access Journals (Sweden)

Yu-Shuang Dong

2014-01-01

Full Text Available The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Boltzmann machines as a model for parallel annealing

NARCIS (Netherlands)

Aarts, E.H.L.; Korst, J.H.M.

1991-01-01

The potential of Boltzmann machines to cope with difficult combinatorial optimization problems is investigated. A discussion of various (parallel) models of Boltzmann machines is given based on the theory of Markov chains. A general strategy is presented for solving (approximately) combinatorial

Influence of learning strategy on response time during complex value-based learning and choice.

Directory of Open Access Journals (Sweden)

Shiva Farashahi

Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

On-machine measurement of a slow slide servo diamond-machined 3D microstructure with a curved substrate

International Nuclear Information System (INIS)

Zhu, Wu-Le; Yang, Shunyao; Ju, Bing-Feng; Jiang, Jiacheng; Sun, Anyu

2015-01-01

A scanning tunneling microscope-based multi-axis measuring system is specially developed for the on-machine measurement of three-dimensional (3D) microstructures, to address the quality control difficulty with the traditional off-line measurement process. A typical 3D microstructure of the curved compound eye was diamond-machined by the slow slide servo technique, and then the whole surface was on-machine scanned three-dimensionally based on the tip-tracking strategy by utilizing a spindle, two linear motion stages, and an additional rotary stage. The machined surface profile and its shape deviation were accurately measured on-machine. The distortion of imaged ommatidia on the curved substrate was distinctively evaluated based on the characterized points extracted from the measured surface. Furthermore, the machining errors were investigated in connection with the on-machine measured surface and its characteristic parameters. Through experiments, the proposed measurement system is demonstrated to feature versatile on-machine measurement of 3D microstructures with a curved substrate, which is highly meaningful for quality control in the fabrication field. (paper)

Formal Analysis and Design of Supervisor and User Interface Allowing for Non-Deterministic Choices Using Weak Bi-Simulation

Directory of Open Access Journals (Sweden)

Shazada Muhammad Umair Khan

2018-01-01

Full Text Available In human machine systems, a user display should contain sufficient information to encapsulate expressive and normative human operator behavior. Failure in such system that is commanded by supervisor can be difficult to anticipate because of unexpected interactions between the different users and machines. Currently, most interfaces have non-deterministic choices at state of machine. Inspired by the theories of single user of an interface established on discrete event system, we present a formal model of multiple users, multiple machines, a supervisor and a supervisor machine. The syntax and semantics of these models are based on the system specification using timed automata that adheres to desirable specification properties conducive to solving the non-deterministic choices for usability properties of the supervisor and user interface. Further, the succinct interface developed by applying the weak bi-simulation relation, where large classes of potentially equivalent states are refined into a smaller one, enables the supervisor and user to perform specified task correctly. Finally, the proposed approach is applied to a model of a manufacturing system with several users interacting with their machines, a supervisor with several users and a supervisor with a supervisor machine to illustrate the design procedure of human–machine systems. The formal specification is validated by z-eves toolset.

Separate neural mechanisms underlie choices and strategic preferences in risky decision making.

Science.gov (United States)

Venkatraman, Vinod; Payne, John W; Bettman, James R; Luce, Mary Frances; Huettel, Scott A

2009-05-28

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using an economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual's preferred strategy. Choices that maximized gains or minimized losses were predicted by functional magnetic resonance imaging activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending on strategies, traits, and context.

Peak thrust operation of linear induction machines from parameter identification

Energy Technology Data Exchange (ETDEWEB)

Zhang, Z.; Eastham, T.R.; Dawson, G.E. [Queen`s Univ., Kingston, Ontario (Canada). Dept. of Electrical and Computer Engineering

1995-12-31

Various control strategies are being used to achieve high performance operation of linear drives. To maintain minimum volume and weight of the power supply unit on board the transportation vehicle, peak thrust per unit current operation is a desirable objective. True peak thrust per unit current through slip control is difficult to achieve because the parameters of linear induction machines vary during normal operation. This paper first develops a peak thrust per unit current control law based on the per-phase equivalent circuit for linear induction machines. The algorithm for identification of the variable parameters in induction machines is then presented. Application to an operational linear induction machine (LIM) demonstrates the utility of this algorithm. The control strategy is then simulated, based on an operational transit LIM, to show the capability of achieving true peak thrust operation for linear induction machines.

Relevance as a metric for evaluating machine learning algorithms

NARCIS (Netherlands)

Kota Gopalakrishna, A.; Ozcelebi, T.; Liotta, A.; Lukkien, J.J.

2013-01-01

In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel

The Milling Assistant, Case-Based Reasoning, and machining strategy: A report on the development of automated numerical control programming systems at New Mexico State University

Energy Technology Data Exchange (ETDEWEB)

Burd, W. [Sandia National Labs., Albuquerque, NM (United States); Culler, D.; Eskridge, T.; Cox, L.; Slater, T. [New Mexico State Univ., Las Cruces, NM (United States)

1993-08-01

The Milling Assistant (MA) programming system demonstrates the automated development of tool paths for Numerical Control (NC) machine tools. By integrating a Case-Based Reasoning decision processor with a commercial CAD/CAM software, intelligent tool path files for milled and point-to-point features can be created. The operational system is capable of reducing the time required to program a variety of parts and improving product quality by collecting and utilizing ``best of practice`` machining strategies.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

Science.gov (United States)

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

How the choice of Operating System can affect databases on a Virtual Machine

OpenAIRE

Karlsson, Jan; Eriksson, Patrik

2014-01-01

As databases grow in size, the need for optimizing databases is becoming a necessity. Choosing the right operating system to support your database becomes paramount to ensure that the database is fully utilized. Furthermore with the virtualization of operating systems becoming more commonplace, we find ourselves with more choices than we ever faced before. This paper demonstrates why the choice of operating system plays an integral part in deciding the right database for your system in a virt...

Which Infidelity Type Makes You More Jealous? Decision Strategies in a Forced-Choice between Sexual and Emotional Infidelity

OpenAIRE

Achim Schützwohl

2004-01-01

This study tested the prediction derived from the evolutionary psychological analysis of jealousy that men and women selecting the adaptively primary infidelity type (i.e., female sexual and male emotional infidelity, respectively) in a forced-choice response format need to engage in less elaborate decision strategies than men and women selecting the adaptively secondary infidelity type (i.e., male sexual and female emotional infidelity, respectively). Unknown to the participants, decision ti...

Controls and Machine Protection Systems

CERN Document Server

Carrone, E.

2016-01-01

Machine protection, as part of accelerator control systems, can be managed with a 'functional safety' approach, which takes into account product life cycle, processes, quality, industrial standards and cybersafety. This paper will discuss strategies to manage such complexity and the related risks, with particular attention to fail-safe design and safety integrity levels, software and hardware standards, testing, and verification philosophy. It will also discuss an implementation of a machine protection system at the SLAC National Accelerator Laboratory's Linac Coherent Light Source (LCLS).

Hold it! The influence of lingering rewards on choice diversification and persistence.

Science.gov (United States)

Schulze, Christin; van Ravenzwaaij, Don; Newell, Ben R

2017-11-01

Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of sequential choice-outcome dependencies was implemented by manipulating a single task aspect between conditions: the retention/withdrawal of reward across individual choice trials. The study addresses how people adapt to these learning environments and to what extent they engage in 2 choice strategies often contrasted as paradigmatic examples of striking violation of versus nominal adherence to rational choice: diversification and persistent probability maximizing, respectively. Results show that decisions approached adaptive choice diversification and persistence when sufficient feedback was provided on the dynamic rules of the probabilistic environments. The findings of divergent behavior in the 2 environments indicate that diversified choices represented a response to the reward retention manipulation rather than to the mere variability of outcome probabilities. Choice in both environments was well accounted for by the generalized matching law, and computational modeling-based strategy analyses indicated that adaptive choice arose mainly from reliance on reinforcement learning strategies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Optimization of line configuration and balancing for flexible machining lines

Science.gov (United States)

Liu, Xuemei; Li, Aiping; Chen, Zurui

2016-05-01

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

Evaluation of implementation of a healthy food and drink supply strategy throughout the whole school environment in Queensland state schools, Australia.

Science.gov (United States)

Dick, M; Lee, A; Bright, M; Turner, K; Edwards, R; Dawson, J; Miller, J

2012-10-01

This paper reports on the evaluation of the Smart Choices healthy food and drink supply strategy for Queensland schools (Smart Choices) implementation across the whole school environment in state government primary and secondary schools in Queensland, Australia. Three concurrent surveys using different methods for each group of stakeholders that targeted all 1275 school Principals, all 1258 Parent and Citizens' Associations (P&Cs) and a random sample of 526 tuckshop convenors throughout Queensland. Nine hundred and seventy-three Principals, 598 P&Cs and 513 tuckshop convenors participated with response rates of 78%, 48% and 98%, respectively. Nearly all Principals (97%), P&Cs (99%) and tuckshop convenors (97%) reported that their school tuckshop had implemented Smart Choices. The majority of Principals and P&Cs reported implementation, respectively, in: school breakfast programs (98 and 92%); vending machine stock (94 and 83%); vending machine advertising (85 and 84%); school events (87 and 88%); school sporting events (81 and 80%); sponsorship and advertising (93 and 84%); fundraising events (80 and 84%); and sporting clubs (73 and 75%). Implementation in curriculum activities, classroom rewards and class parties was reported, respectively, by 97%, 86% and 75% of Principals. Respondents also reported very high levels of understanding of Smart Choices and engagement of the school community. The results demonstrated that food supply interventions to promote nutrition across all domains of the school environment can be implemented successfully.

An introduction to quantum machine learning

OpenAIRE

Schuld, M.; Sinayskiy, I.; Petruccione, F.

2014-01-01

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum compute...

PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology.

Science.gov (United States)

Araki, Tadashi; Ikeda, Nobutaka; Shukla, Devarshi; Jain, Pankaj K; Londhe, Narendra D; Shrivastava, Vimal K; Banchhor, Sumit K; Saba, Luca; Nicolaides, Andrew; Shafique, Shoaib; Laird, John R; Suri, Jasjit S

2016-05-01

Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary

Strategy of valid 14C dates choice in syngenetic permafrost

Science.gov (United States)

Vasil'chuk, Y. K.; Vasil'chuk, A. C.

2014-11-01

The main problem of radiocarbon dating within permafrost is the uncertain reliability of the 14C dates. Syngenetic sediments contain allochthonous organic deposit that originated at a distance from its present position. Due to the very good preservation of organic materials in permafrost conditions and numerous re-burials of the fossils from ancient deposits into younger ones the dates could be both younger and older than the true age of dated material. The strategy for the most authentic radiocarbon date selection for dating of syncryogenic sediments is considered taking into account the fluvial origin of the syngenetic sediments. The re-deposition of organic material is discussed in terms of cyclic syncryogenic sedimentation and also the possible re-deposition of organic material in subaerial-subaqueous conditions. The advantages and the complications of dating organic micro-inclusions from ice wedges by the accelerator mass spectrometry (AMS) method are discussed applying to true age of dated material search. Radiocarbon dates of different organic materials from the same samples are compared. The younger age of the yedoma from cross-sections of Duvanny Yar in Kolyma River and Mamontova Khayata in the mouth of Lena River is substantiated due to the principle of the choice of the youngest 14C date from the set.

User-driven sampling strategies in image exploitation

Science.gov (United States)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

Introduction to machine learning: k-nearest neighbors.

Science.gov (United States)

Zhang, Zhongheng

2016-06-01

Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The article introduces some basic ideas underlying the kNN algorithm, and then focuses on how to perform kNN modeling with R. The dataset should be prepared before running the knn() function in R. After prediction of outcome with kNN algorithm, the diagnostic performance of the model should be checked. Average accuracy is the mostly widely used statistic to reflect the kNN algorithm. Factors such as k value, distance calculation and choice of appropriate predictors all have significant impact on the model performance.

Automated reasoning in man-machine control systems

International Nuclear Information System (INIS)

Stratton, R.C.; Lusk, E.L.

1983-01-01

This paper describes a project being undertaken at Argonne National Laboratory to demonstrate the usefulness of automated reasoning techniques in the implementation of a man-machine control system being designed at the EBR-II nuclear power plant. It is shown how automated reasoning influences the choice of optimal roles for both man and machine in the system control process, both for normal and off-normal operation. In addition, the requirements imposed by such a system for a rigorously formal specification of operating states, subsystem states, and transition procedures have a useful impact on the analysis phase. The definitions and rules are discussed for a prototype system which is physically simple yet illustrates some of the complexities inherent in real systems

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

Directory of Open Access Journals (Sweden)

Mareike Ließ

Full Text Available Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Porter's Five Competitive Forces Framework and Other Factors That Influence the Choice of Response Strategies Adopted by Public Universities in Kenya

Science.gov (United States)

Mathooko, Francis M.; Ogutu, Martin

2015-01-01

Purpose: The purpose of this paper is to establish the extent to which Porter's five competitive forces (PFCF) framework, among other factors drive the choice of response strategies adopted by public universities in Kenya. Design/Methodology/Approach: The study design was descriptive and utilized a cross-sectional survey of all the public…

Transition Towards Energy Efficient Machine Tools

CERN Document Server

Zein, André

2012-01-01

Energy efficiency represents a cost-effective and immediate strategy of a sustainable development. Due to substantial environmental and economic implications, a strong emphasis is put on the electrical energy requirements of machine tools for metalworking processes. The improvement of energy efficiency is however confronted with diverse barriers, which sustain an energy efficiency gap of unexploited potential. The deficiencies lie in the lack of information about the actual energy requirements of machine tools, a minimum energy reference to quantify improvement potential and the possible actions to improve the energy demand. Therefore, a comprehensive concept for energy performance management of machine tools is developed which guides the transition towards energy efficient machine tools. It is structured in four innovative concept modules, which are embedded into step-by-step workflow models. The capability of the performance management concept is demonstrated in an automotive manufacturing environment. The ...

Measuring Diagnostic Stand for Experimental Researches in Technology Machining

Directory of Open Access Journals (Sweden)

A. E. Dreval'

2014-01-01

Full Text Available The paper reviews applied techniques, methods, and structure of the control and measuring means to conduct experimental and scientific researches of cutting processes. Existing research methods in cutting the metals are divided by features, such as essence of methods, the number of records of physical indicators, the number of studied factors, duration of tests. The groups of methods are briefly characterized.The chair "Tool Engineering and Technologies" of BMSTU developed and made a diagnostic stand of control and measurements for conducting research activities in the field of materials processing technology by cutting to define rational technological decisions, when machining, and carry out an analysis of efficiency and economic feasibility of made decisions. The diagnostic stand contains modern the electronic equipment. Record of measuring parameters is made in real time with a possibility for visual representation of read results and mathematical and statistical processing of measurement results. The stand can be used in research laboratories of machine-building enterprises, laboratories of higher education institutions, and other scientific divisions.The paper presents a justification that the stand is reasonable to use for the following: completion and choice of rational cutting modes, workability assessment of new constructional materials, technical and operational characteristics of the processed surfaces, and operational properties of the cutting tools of various producers, choice of optimum geometrical parameters of the cutting tools and brands of the lubricant cooling technological means, as well as the energy consumption for the chosen machining process. The stand allows us to make an assessment of wear resistance and tribology-technical characteristics of tool materials, as well as an accuracy, rigidity, vibration stability of machines, both new and being in operation.

Vending Machines: A Narrative Review of Factors Influencing Items Purchased.

Science.gov (United States)

Hua, Sophia V; Ickovics, Jeannette R

2016-10-01

Vending machines are a ubiquitous part of our food environments. Unfortunately, items found in vending machines tend to be processed foods and beverages high in salt, sugar, and/or fat. The purpose of this review is to describe intervention and case studies designed to promote healthier vending purchases by consumers and identify which manipulations are most effective. All studies analyzed were intervention or case studies that manipulated vending machines and analyzed sales or revenue data. This literature review is limited to studies conducted in the United States within the past 2 decades (ie, 1994 to 2015), regardless of study population or setting. Ten articles met these criteria based on a search conducted using PubMed. Study manipulations included price changes, increase in healthier items, changes to the advertisements wrapped around vending machines, and promotional signs such as a stoplight system to indicate healthfulness of items and to remind consumers to make healthy choices. Overall, seven studies had manipulations that resulted in statistically significant positive changes in purchasing behavior. Two studies used manipulations that did not influence consumer behavior, and one study was equivocal. Although there was no intervention pattern that ensured changes in purchasing, price reductions were most effective overall. Revenue from vending sales did not change substantially regardless of intervention, which will be important to foster initiation and sustainability of healthier vending. Future research should identify price changes that would balance healthier choices and revenue as well as better marketing to promote purchase of healthier items. Copyright © 2016 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

The impact of the availability of school vending machines on eating behavior during lunch: the Youth Physical Activity and Nutrition Survey.

Science.gov (United States)

Park, Sohyun; Sappenfield, William M; Huang, Youjie; Sherry, Bettylou; Bensyl, Diana M

2010-10-01

Childhood obesity is a major public health concern and is associated with substantial morbidities. Access to less-healthy foods might facilitate dietary behaviors that contribute to obesity. However, less-healthy foods are usually available in school vending machines. This cross-sectional study examined the prevalence of students buying snacks or beverages from school vending machines instead of buying school lunch and predictors of this behavior. Analyses were based on the 2003 Florida Youth Physical Activity and Nutrition Survey using a representative sample of 4,322 students in grades six through eight in 73 Florida public middle schools. Analyses included χ2 tests and logistic regression. The outcome measure was buying a snack or beverage from vending machines 2 or more days during the previous 5 days instead of buying lunch. The survey response rate was 72%. Eighteen percent of respondents reported purchasing a snack or beverage from a vending machine 2 or more days during the previous 5 school days instead of buying school lunch. Although healthier options were available, the most commonly purchased vending machine items were chips, pretzels/crackers, candy bars, soda, and sport drinks. More students chose snacks or beverages instead of lunch in schools where beverage vending machines were also available than did students in schools where beverage vending machines were unavailable: 19% and 7%, respectively (P≤0.05). The strongest risk factor for buying snacks or beverages from vending machines instead of buying school lunch was availability of beverage vending machines in schools (adjusted odds ratio=3.5; 95% confidence interval, 2.2 to 5.7). Other statistically significant risk factors were smoking, non-Hispanic black race/ethnicity, Hispanic ethnicity, and older age. Although healthier choices were available, the most common choices were the less-healthy foods. Schools should consider developing policies to reduce the availability of less-healthy choices

Strategy Choices of Potential Entrepreneurs

Science.gov (United States)

Alstete, Jeffrey W.

2014-01-01

The author examined the written business plans of 380 students who completed courses in entrepreneurship and small business management over an 11-year period. An analysis categorized the plans into five generic competitive strategy types, and the results found that 58% chose a traditional, focused differentiation approach. A large portion (28%)…

Aging impairs deliberation and behavioral flexibility in inter-temporal choice

Directory of Open Access Journals (Sweden)

Yannick-Andre eBreton

2015-03-01

Full Text Available Inter-temporal choice depends on multiple, interacting systems, some of which may be compromised with age. Some of these systems may be responsible for ongoing trial-by-trial choice strategies. Some may represent the consequences of action. Some may be necessary for the coupling between anticipated consequences and strategies currently in use, flexibly guiding behavior. When faced with a difficult decision, rats will orient back and forth, a behavior termed ``vicarious trial and error'' (VTE. Recent experiments have linked the occurrence of VTE to hippocampal search processes and behavioral flexibility. We tested 5 month (n=6, 9 month (n=8 and over-27 month-old (n=10 rats on a Spatial Adjusting Delay Discounting task to examine how aging impacted lap-by-lap strategies and VTE during inter-temporal choice. Rats chose between spatially separated food goals that provided a smaller-sooner or larger-later reward. On each lap, the delay to the larger-later reward was adjusted as a function of the rat's decisions, increasing by 1 second after delayed-side choices and decreasing by 1 second after non-delayed side choices. The strategies that aged rats used differed from those used in young and adult rats. Moreover, aged rats produced reliably more VTE behaviors, for protracted periods of time, uncoupled from behavioral flexibility.

Human Reliability and the Current Dilemma in Human-Machine Interface Design Strategies

International Nuclear Information System (INIS)

Passalacqua, Roberto; Yamada, Fumiaki

2002-01-01

Since human error dominates the probability of failures of still-existing human-requiring systems (as the Monju reactor), the human-machine interface needs to be improved. Several rationales may lead to the conclusion that 'humans' should limit themselves to monitor the 'machine'. For example, this is the trend in the aviation industry: newest aircrafts are designed to be able to return to a safe state by the use of control systems, which do not need human intervention. Thus, the dilemma whether we really need operators (for example in the nuclear industry) might arise. However, social-technical approaches in recent human error analyses are pointing out the so-called 'organizational errors' and the importance of a human-machine interface harmonization. Typically plant's operators are a 'redundant' safety system with a much lower reliability (than the machine): organizational factors and harmonization requirements suggest designing the human-machine interface in a way that allows improvement of operator's reliability. In addition, taxonomy studies of accident databases have also proved that operators' training should promote processes of decision-making. This is accomplished in the latest trends of PSA technology by introducing the concept of a 'Safety Monitor' that is a computer-based tool that uses a level 1 PSA model of the plant. Operators and maintenance schedulers of the Monju FBR will be able to perform real-time estimations of the plant risk level. The main benefits are risk awareness and improvements in decision-making by operators. Also scheduled maintenance can be approached in a more rational (safe and economic) way. (authors)

A Navier-Strokes Chimera Code on the Connection Machine CM-5: Design and Performance

Science.gov (United States)

Jespersen, Dennis C.; Levit, Creon; Kwak, Dochan (Technical Monitor)

1994-01-01

We have implemented a three-dimensional compressible Navier-Stokes code on the Connection Machine CM-5. The code is set up for implicit time-stepping on single or multiple structured grids. For multiple grids and geometrically complex problems, we follow the 'chimera' approach, where flow data on one zone is interpolated onto another in the region of overlap. We will describe our design philosophy and give some timing results for the current code. A parallel machine like the CM-5 is well-suited for finite-difference methods on structured grids. The regular pattern of connections of a structured mesh maps well onto the architecture of the machine. So the first design choice, finite differences on a structured mesh, is natural. We use centered differences in space, with added artificial dissipation terms. When numerically solving the Navier-Stokes equations, there are liable to be some mesh cells near a solid body that are small in at least one direction. This mesh cell geometry can impose a very severe CFL (Courant-Friedrichs-Lewy) condition on the time step for explicit time-stepping methods. Thus, though explicit time-stepping is well-suited to the architecture of the machine, we have adopted implicit time-stepping. We have further taken the approximate factorization approach. This creates the need to solve large banded linear systems and creates the first possible barrier to an efficient algorithm. To overcome this first possible barrier we have considered two options. The first is just to solve the banded linear systems with data spread over the whole machine, using whatever fast method is available. This option is adequate for solving scalar tridiagonal systems, but for scalar pentadiagonal or block tridiagonal systems it is somewhat slower than desired. The second option is to 'transpose' the flow and geometry variables as part of the time-stepping process: Start with x-lines of data in-processor. Form explicit terms in x, then transpose so y-lines of data are

Mate choice for major histocompatibility complex genetic divergence as a bet-hedging strategy in the Atlantic salmon (Salmo salar)

Science.gov (United States)

Evans, Melissa L.; Dionne, Mélanie; Miller, Kristina M.; Bernatchez, Louis

2012-01-01

Major histocompatibility complex (MHC)-dependent mating preferences have been observed across vertebrate taxa and these preferences are expected to promote offspring disease resistance and ultimately, viability. However, little empirical evidence linking MHC-dependent mate choice and fitness is available, particularly in wild populations. Here, we explore the adaptive potential of previously observed patterns of MHC-dependent mate choice in a wild population of Atlantic salmon (Salmo salar) in Québec, Canada, by examining the relationship between MHC genetic variation and adult reproductive success and offspring survival over 3 years of study. While Atlantic salmon choose their mates in order to increase MHC diversity in offspring, adult reproductive success was in fact maximized between pairs exhibiting an intermediate level of MHC dissimilarity. Moreover, patterns of offspring survival between years 0+ and 1+, and 1+ and 2+ and population genetic structure at the MHC locus relative to microsatellite loci indicate that strong temporal variation in selection is likely to be operating on the MHC. We interpret MHC-dependent mate choice for diversity as a likely bet-hedging strategy that maximizes parental fitness in the face of temporally variable and unpredictable natural selection pressures. PMID:21697172

MoleculeNet: a benchmark for molecular machine learning.

Science.gov (United States)

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S; Leswing, Karl; Pande, Vijay

2018-01-14

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

Food labeling; calorie labeling of articles of food in vending machines. Final rule.

Science.gov (United States)

2014-12-01

To implement the vending machine food labeling provisions of the Patient Protection and Affordable Care Act of 2010 (ACA), the Food and Drug Administration (FDA or we) is establishing requirements for providing calorie declarations for food sold from certain vending machines. This final rule will ensure that calorie information is available for certain food sold from a vending machine that does not permit a prospective purchaser to examine the Nutrition Facts Panel before purchasing the article, or does not otherwise provide visible nutrition information at the point of purchase. The declaration of accurate and clear calorie information for food sold from vending machines will make calorie information available to consumers in a direct and accessible manner to enable consumers to make informed and healthful dietary choices. This final rule applies to certain food from vending machines operated by a person engaged in the business of owning or operating 20 or more vending machines. Vending machine operators not subject to the rules may elect to be subject to the Federal requirements by registering with FDA.

Nudging consumers towards healthier choices: a systematic review of positional influences on food choice.

Science.gov (United States)

Bucher, Tamara; Collins, Clare; Rollo, Megan E; McCaffrey, Tracy A; De Vlieger, Nienke; Van der Bend, Daphne; Truby, Helen; Perez-Cueto, Federico J A

2016-06-01

Nudging or 'choice architecture' refers to strategic changes in the environment that are anticipated to alter people's behaviour in a predictable way, without forbidding any options or significantly changing their economic incentives. Nudging strategies may be used to promote healthy eating behaviour. However, to date, the scientific evidence has not been systematically reviewed to enable practitioners and policymakers to implement, or argue for the implementation of, specific measures to support nudging strategies. This systematic review investigated the effect of positional changes of food placement on food choice. In total, seven scientific databases were searched using relevant keywords to identify interventions that manipulated food position (proximity or order) to generate a change in food selection, sales or consumption, among normal-weight or overweight individuals across any age group. From 2576 identified articles, fifteen articles comprising eighteen studies met our inclusion criteria. This review has identified that manipulation of food product order or proximity can influence food choice. Such approaches offer promise in terms of impacting on consumer behaviour. However, there is a need for high-quality studies that quantify the magnitude of positional effects on food choice in conjunction with measuring the impact on food intake, particularly in the longer term. Future studies should use outcome measures such as change in grams of food consumed or energy intake to quantify the impact on dietary intake and potential impacts on nutrition-related health. Research is also needed to evaluate potential compensatory behaviours secondary to such interventions.

Punishment Strategies: First Choice or Last Resort

Science.gov (United States)

Lukowiak, Twila; Bridges, Jennifer

2010-01-01

Is it appropriate to implement punishment strategies in the home and school settings when children display disrespectful and inappropriate behaviors? This article depicts the advantages and disadvantages of teachers and parents utilizing an array of punishment strategies including: (a) reprimands, (b) response cost, (c) timeout, and (d) corporal…

An introduction to quantum machine learning

Science.gov (United States)

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

2015-04-01

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Consumer support for healthy food and drink vending machines in public places.

Science.gov (United States)

Carrad, Amy M; Louie, Jimmy Chun-Yu; Milosavljevic, Marianna; Kelly, Bridget; Flood, Victoria M

2015-08-01

To investigate the feasibility of introducing vending machines for healthier food into public places, and to examine the effectiveness of two front-of-pack labelling systems in the vending machine context. A survey was conducted with 120 students from a university and 120 employees, patients and visitors of a hospital in regional NSW, Australia. Questions explored vending machine use, attitudes towards healthier snack products and price, and the performance of front-of-pack labelling formats for vending machine products. Most participants viewed the current range of snacks and drinks as "too unhealthy" (snacks 87.5%; drinks 56.7%). Nuts and muesli bars were the most liked healthier vending machine snack. Higher proportions of participants were able to identify the healthier snack in three of the five product comparisons when products were accompanied with any type of front-of-pack label (all pvending machines. Front-of-pack label formats on vending machines may assist consumers to identify healthier products. Public settings, such as universities and hospitals, should support consumers to make healthy dietary choices by improving food environments. © 2015 Public Health Association of Australia.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

Science.gov (United States)

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Machine Protection: Availability for Particle Accelerators

CERN Document Server

Apollonio, Andrea; Schmidt, Ruediger

2015-03-16

Machine availability is a key indicator for the performance of the next generation of particle accelerators. Availability requirements need to be carefully considered during the design phase to achieve challenging objectives in different fields, as e.g. particle physics and material science. For existing and future High-Power facilities, such as ESS (European Spallation Source) and HL-LHC (High-Luminosity LHC), operation with unprecedented beam power requires highly dependable Machine Protection Systems (MPS) to avoid any damage-induced downtime. Due to the high complexity of accelerator systems, finding the optimal balance between equipment safety and accelerator availability is challenging. The MPS architecture, as well as the choice of electronic components, have a large influence on the achievable level of availability. In this thesis novel methods to address the availability of accelerators and their protection systems are presented. Examples of studies related to dependable MPS architectures are given i...

Characterization of wood dust emission from hand-held woodworking machines.

Science.gov (United States)

Keller, F-X; Chata, F

2018-01-01

This article focuses on the prevention of exposure to wood dust when operating electrical hand-held sawing and sanding machines. A laboratory methodology was developed to measure the dust concentration around machines during operating processes. The main objective was to characterize circular saws and sanders, with the aim of classifying the different power tools tested in terms of dust emission (high dust emitter vs. low dust emitter). A test set-up was developed and is described and a measurement methodology was determined for each of the two operations studied. The robustness of the experimental results is discussed and shows good tendencies. The impact of air-flow extraction rate was assessed and the pressure loss of the system for each machine established. For the circular saws, three machines over the nine tested could be classified in the low dust emitter group. Their mean concentration values measured are between 0.64 and 0.98 mg/m 3 for the low dust emitter group and from 2.55 and 4.37 mg/m 3 for the high dust emitter group. From concentration measurements, a machine classification is possible-one for sanding machines and one for sawing machines-and a ratio from 1-7 is obtained when comparing the results. This classification will be helpful when a choice of high performance power tools, in terms of dust emission, must be made by professionals.

Derailing healthy choices: an audit of vending machines at train stations in NSW.

Science.gov (United States)

Kelly, Bridget; Flood, Victoria M; Bicego, Cecilia; Yeatman, Heather

2012-04-01

Train stations provide opportunities for food purchases and many consumers are exposed to these venues daily, on their commute to and from work. This study aimed to describe the food environment that commuters are exposed to at train stations in NSW. One hundred train stations were randomly sampled from the Greater Sydney Metropolitan region, representing a range of demographic areas. A purpose-designed instrument was developed to collect information on the availability, promotion and cost of food and beverages in vending machines. Items were classified as high/low in energy according to NSW school canteen criteria. Of the 206 vending machines identified, 84% of slots were stocked with high-energy food and beverages. The most frequently available items were chips and extruded snacks (33%), sugar-sweetened soft drinks (18%), chocolate (12%) and confectionery (10%). High energy foods were consistently cheaper than lower-energy alternatives. Transport sites may cumulatively contribute to excess energy consumption as the items offered are energy dense. Interventions are required to improve train commuters' access to healthy food and beverages.

A strategy for quantum algorithm design assisted by machine learning

International Nuclear Information System (INIS)

Bang, Jeongho; Lee, Jinhyoung; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin

2014-01-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum–classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch–Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method. (paper)

A strategy for quantum algorithm design assisted by machine learning

Science.gov (United States)

Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung

2014-07-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

Nano Mechanical Machining Using AFM Probe

Science.gov (United States)

Mostofa, Md. Golam

Complex miniaturized components with high form accuracy will play key roles in the future development of many products, as they provide portability, disposability, lower material consumption in production, low power consumption during operation, lower sample requirements for testing, and higher heat transfer due to their very high surface-to-volume ratio. Given the high market demand for such micro and nano featured components, different manufacturing methods have been developed for their fabrication. Some of the common technologies in micro/nano fabrication are photolithography, electron beam lithography, X-ray lithography and other semiconductor processing techniques. Although these methods are capable of fabricating micro/nano structures with a resolution of less than a few nanometers, some of the shortcomings associated with these methods, such as high production costs for customized products, limited material choices, necessitate the development of other fabricating techniques. Micro/nano mechanical machining, such an atomic force microscope (AFM) probe based nano fabrication, has, therefore, been used to overcome some the major restrictions of the traditional processes. This technique removes material from the workpiece by engaging micro/nano size cutting tool (i.e. AFM probe) and is applicable on a wider range of materials compared to the photolithographic process. In spite of the unique benefits of nano mechanical machining, there are also some challenges with this technique, since the scale is reduced, such as size effects, burr formations, chip adhesions, fragility of tools and tool wear. Moreover, AFM based machining does not have any rotational movement, which makes fabrication of 3D features more difficult. Thus, vibration-assisted machining is introduced into AFM probe based nano mechanical machining to overcome the limitations associated with the conventional AFM probe based scratching method. Vibration-assisted machining reduced the cutting forces

Consumer acceptance of intervention strategies for healthy food choices

NARCIS (Netherlands)

Bos, Colin

2016-01-01

The need for more effective interventions to combat the obesity problem has been expressed by many public health experts. While consumer support is important for intervention effectiveness, little is known about why consumers accept or do not accept food choice interventions. The present thesis

Food choice as a key management strategy for functional gastrointestinal symptoms.

Science.gov (United States)

Gibson, Peter R; Shepherd, Susan J

2012-05-01

Recognition of food components that induce functional gut symptoms in patient's functional bowel disorders (FBD) has been challenging. Food directly or indirectly provides considerable afferent input into the enteric nervous system. There is an altered relationship between the afferent input and perception/efferent response in FBD. Defining the nature of food-related stimuli may provide a means of minimizing such an input and gut symptoms. Using this premise, reducing the intake of FODMAPs (fermentable oligo-, di-, and mono-saccharides and polyols)--poorly absorbed short-chain carbohydrates that, by virtue of their small molecular size and rapid fermentability, will distend the intestinal lumen with liquid and gas--improves symptoms in the majority of patients. Well-developed methodologies to deliver the diet via dietician-led education are available. Another abundant source of afferent input is natural and added food chemicals (such as salicylates, amines, and glutamates). Studies are needed to assess the efficacy of the low food chemical dietary approach. A recent placebo-controlled trial of FODMAP-poor gluten provided the first valid evidence that non-celiac gluten intolerance might actually exist, but its prevalence and underlying mechanisms require elucidation. Food choice via the low FODMAP and potentially other dietary strategies is now a realistic and efficacious therapeutic approach for functional gut symptoms.

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

Electricity contract choices of Finnish residential customers. A choice based conjoint analysis

Energy Technology Data Exchange (ETDEWEB)

Rouvinen, S.; Matero, J. (Univ. of Eastern Finland, Joensuu (Finland), School of Forest Sciences), e-mail: seppo.rouvinen@uef.fi, e-mail: jukka.matero@uef.fi

2010-07-01

Our aim is to examine how different environmental attributes of electricity contracts affect the residential customer choices when heterogeneity in customer preferences and motivations is taken into account. The data was acquired by a mail questionnaire to random sample of Finnish people in October-November 2009 with a response rate of 38 %. In addition to conventional questions, like questions on socio-demographic and agreements of energy related statements, the discrete choice experiment (DCE) of electricity contracts was included. The choice sets in the DCE had three electricity contract alternatives with varying levels of predetermined attributes (including unit price, supplier type, frequency of power outages, energy source and CO{sub 2} emissions). In this paper, we present the findings of our DCE design. Modeling respondent choices resulted in implicit prices for various electricity contract attributes that provide guidance for green marketing strategies of electricity suppliers and energy related informational activities of public institutions. We conclude that currently the potential for increasing demand-based environmental competitiveness from the wood electricity differentiation remains limited as we did not find any significant market segment of residential customers with strong preferences for wood over other sources of electricity (including 'mixture'). (orig.)

How to make moral choices.

Science.gov (United States)

Chambers, David W

2011-01-01

Moral choice is committing to act for what one believes is right and good. It is less about what we know than about defining who we are. Three cases typical of those used in the principles or dilemmas approach to teaching ethics are presented. But they are analyzed using an alternative approach based on seven moral choice heuristics--approaches proven to increase the likelihood of locating the best course of action. The approaches suggested for analyzing moral choice situations include: (a) identify the outcomes of available alternative courses of action; (b) rule out strategies that involve deception, coercion, reneging on promises, collusion, and contempt for others; (c) be authentic (do not deceive yourself); (d) relate to others on a human basis; (e) downplay rational justifications; (f) match the solution to the problem, not the other way around; (g) execute on the best solution, do not hold out for the perfect one; and (h) take action to improve the choice after it has been made.

Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

Directory of Open Access Journals (Sweden)

F. Khammar

2016-01-01

Full Text Available The induction machine is experiencing a growing success for two decades by gradually replacing the DC machines and synchronous in many industrial applications. This paper is devoted to the study of advanced methods applied to the command of the asynchronous machine in order to obtain a system of control of high performance. While the criteria for response time, overtaking, and static error can be assured by the techniques of conventional control, the criterion of robustness remains a challenge for researchers. This criterion can be satisfied only by applying advanced techniques of command. After mathematical modeling of the asynchronous machine, it defines the control strategies based on the orientation of the rotor flux. The results of the different simulation tests highlight the properties of robustness of algorithms proposed and suggested to compare the different control strategies.

Traffic-light labels and choice architecture: promoting healthy food choices.

Science.gov (United States)

Thorndike, Anne N; Riis, Jason; Sonnenberg, Lillian M; Levy, Douglas E

2014-02-01

Preventing obesity requires maintenance of healthy eating behaviors over time. Food labels and strategies that increase visibility and convenience of healthy foods (choice architecture) promote healthier choices, but long-term effectiveness is unknown. Assess effectiveness of traffic-light labeling and choice architecture cafeteria intervention over 24 months. Longitudinal pre-post cohort follow-up study between December 2009 and February 2012. Data were analyzed in 2012. Large hospital cafeteria with a mean of 6511 transactions daily. Cafeteria sales were analyzed for (1) all cafeteria customers and (2) a longitudinal cohort of 2285 hospital employees who used the cafeteria regularly. After a 3-month baseline period, cafeteria items were labeled green (healthy); yellow (less healthy); or red (unhealthy) and rearranged to make healthy items more accessible. Proportion of cafeteria sales that were green or red during each 3-month period from baseline to 24 months. Changes in 12- and 24-month sales were compared to baseline for all transactions and transactions by the employee cohort. The proportion of sales of red items decreased from 24% at baseline to 20% at 24 months (pchoice architecture cafeteria intervention resulted in sustained healthier choices over 2 years, suggesting that food environment interventions can promote long-term changes in population eating behaviors. © 2013 American Journal of Preventive Medicine Published by American Journal of Preventive Medicine All rights reserved.

The Total Energy Efficiency Index for machine tools

International Nuclear Information System (INIS)

Schudeleit, Timo; Züst, Simon; Weiss, Lukas; Wegener, Konrad

2016-01-01

Energy efficiency in industries is one of the dominating challenges of the 21st century. Since the release of the eco-design directive 2005/32/EC in 2005, great research effort has been spent on the energy efficiency assessment for energy using products. The ISO (International Organization for Standardization) standardization body (ISO/TC 39 WG 12) currently works on the ISO 14955 series in order to enable the assessment of energy efficient design of machine tools. A missing piece for completion of the ISO 14955 series is a metric to quantify the design of machine tools regarding energy efficiency based on the respective assembly of components. The metric needs to take into account each machine tool components' efficiency and the need-oriented utilization in combination with the other components while referring to efficiency limits. However, a state of the art review reveals that none of the existing metrics is feasible to adequately match this goal. This paper presents a metric that matches all these criteria to promote the development of the ISO 14955 series. The applicability of the metric is proven in a practical case study on a turning machine. - Highlights: • Study for pushing forward the standardization work on the ISO 14955 series. • Review of existing energy efficiency indicators regarding three basic strategies to foster sustainability. • Development of a metric comprising the three basic strategies to foster sustainability. • Metric application for quantifying the energy efficiency of a turning machine.

Effects of cutting parameters on machinability characteristics of Ni-based superalloys: a review

Directory of Open Access Journals (Sweden)

Kaya Eren

2017-12-01

Full Text Available Nickel based superalloys offer high strength, corrosion resistance, thermal stability and superb thermal fatigue properties. However, they have been one of the most difficult materials to machine due to these properties. Although we are witnessing improved machining strategies with the developing machining, tooling and inspection technologies, machining of nickel based superalloys is still a challenging task due to in-process strains and post process part quality demands.

Strategy Choice in Solving Arithmetic Word Problems: Are There Differences between Students with Learning Disabilities, G-V Poor Performance, and Typical Achievement Students?

Science.gov (United States)

Gonzalez, Juan E. Jimenez; Espinel, Ana Isabel Garcia

2002-01-01

A study was designed to test whether there are differences between Spanish children (ages 7-9) with arithmetic learning disabilities (n=60), garden-variety (G-V) poor performance (n=44), and typical children (n=44) in strategy choice when solving arithmetic word problems. No significant differences were found between children with dyscalculia and…

Machine learning in cardiovascular medicine: are we there yet?

Science.gov (United States)

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Which, When, and How: Hierarchical Clustering with Human–Machine Cooperation

Directory of Open Access Journals (Sweden)

Huanyang Zheng

2016-12-01

Full Text Available Human–Machine Cooperations (HMCs can balance the advantages and disadvantages of human computation (accurate but costly and machine computation (cheap but inaccurate. This paper studies HMCs in agglomerative hierarchical clusterings, where the machine can ask the human some questions. The human will return the answers to the machine, and the machine will use these answers to correct errors in its current clustering results. We are interested in the machine’s strategy on handling the question operations, in terms of three problems: (1 Which question should the machine ask? (2 When should the machine ask the question (early or late? (3 How does the machine adjust the clustering result, if the machine’s mistake is found by the human? Based on the insights of these problems, an efficient algorithm is proposed with five implementation variations. Experiments on image clusterings show that the proposed algorithm can improve the clustering accuracy with few question operations.

Active vibration isolation of high precision machines

CERN Document Server

Collette, C; Artoos, K; Hauviller, C

2010-01-01

This paper provides a review of active control strategies used to isolate high precisionmachines (e.g. telescopes, particle colliders, interferometers, lithography machines or atomic force microscopes) from external disturbances. The objective of this review is to provide tools to develop the best strategy for a given application. Firstly, the main strategies are presented and compared, using single degree of freedom models. Secondly, the case of huge structures constituted of a large number of elements, like particle colliders or segmented telescopes, is considered.

Making Communication Strategy Choices in a Fast Evolving Crisis Situation—Results from a Table-Top Discussion on an Anthrax Scenario

Directory of Open Access Journals (Sweden)

Aino Ruggiero

2016-05-01

Full Text Available This paper aims at clarifying a timely topic of how communication strategy choices are made in evolving, complex crises, such as those caused by terrorism involving chemical, biological, radiological, or nuclear (CBRN agents. This is done by examining data gathered from a table-top discussion among crisis communication experts, focusing on a scenario of an anthrax attack and analysed qualitatively. The communication experts followed the evolving crisis situation by gathering inputs from various actors in the crisis management network, thereby creating situational understanding, and interpreted these inputs for decision-making on communication strategies. The underlying process of coping with complexity in evolving CBRN terrorism crises can be described as a continuous, dynamic process that can best be explained with a combination of traditional and more modern crisis communication approaches. Strategy-making in crisis situations by communication experts is still largely a black box. In this study, a novel approach of decomposing strategy-making by observing a table-top discussion is chosen to clarify the process. By identifying the core elements involved, a more detailed picture of communication strategy-making is created, thus promoting preparedness and professional resilience in the field.

A Review of Design Optimization Methods for Electrical Machines

Directory of Open Access Journals (Sweden)

Gang Lei

2017-11-01

Full Text Available Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines

Directory of Open Access Journals (Sweden)

Tarek ASKRI

2017-04-01

Full Text Available This paper deals with a preventive maintenance strategy optimization correlated to production for a manufacturing system made by several parallel machines under lease contract. In order to minimize the total cost of production and maintenance by reducing the production system interruptions due to maintenance activities, a correlated group preventive maintenance policy is developed using the gravity center approach (GCA. The aim of this study is to determine an economical production plan and an optimal group preventive maintenance interval Tn at which all machines are maintained simultaneously. An analytical correlation between failure rate of machines and production level is considered and the impact of the preventive maintenance policy on the production plan is studied. Finally, the proposed maintenance policy GPM is compared with an individual simple strategy approach IPM in order to illustrate its efficiency.

Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies

International Nuclear Information System (INIS)

Safari, Jalal

2012-01-01

This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed.

Mate choice when males are in patches: optimal strategies and good rules of thumb.

Science.gov (United States)

Hutchinson, John M C; Halupka, Konrad

2004-11-07

In standard mate-choice models, females encounter males sequentially and decide whether to inspect the quality of another male or to accept a male already inspected. What changes when males are clumped in patches and there is a significant cost to travel between patches? We use stochastic dynamic programming to derive optimum strategies under various assumptions. With zero costs to returning to a male in the current patch, the optimal strategy accepts males above a quality threshold which is constant whenever one or more males in the patch remain uninspected; this threshold drops when inspecting the last male in the patch, so returns may occur only then and are never to a male in a previously inspected patch. With non-zero within-patch return costs, such a two-threshold rule still performs extremely well, but a more gradual decline in acceptance threshold is optimal. Inability to return at all need not decrease performance by much. The acceptance threshold should also decline if it gets harder to discover the last males in a patch. Optimal strategies become more complex when mean male quality varies systematically between patches or years, and females estimate this in a Bayesian manner through inspecting male qualities. It can then be optimal to switch patch before inspecting all males on a patch, or, exceptionally, to return to an earlier patch. We compare performance of various rules of thumb in these environments and in ones without a patch structure. A two-threshold rule performs excellently, as do various simplifications of it. The best-of-N rule outperforms threshold rules only in non-patchy environments with between-year quality variation. The cutoff rule performs poorly.

In silico machine learning methods in drug development.

Science.gov (United States)

Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

2014-01-01

Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

ENTERPRISE RESOURCE STRATEGIC PLANNING: TARGET CHOICE TECHNIQUES

Directory of Open Access Journals (Sweden)

A. S. Lankin

2011-01-01

Full Text Available Choice of the targets is one of most important elements of the resource planning system. Particular feature of the strategic planning is development of future alternatives for the enterprise. Main resource strategic planning cycle elements: examination of principal external and internal environment components; forming the company mission; development of long-term targets; concretization of the long-term targets through short-term aims; examination of strategies and final choice.

Programming and machining of complex parts based on CATIA solid modeling

Science.gov (United States)

Zhu, Xiurong

2017-09-01

The complex parts of the use of CATIA solid modeling programming and simulation processing design, elaborated in the field of CNC machining, programming and the importance of processing technology. In parts of the design process, first make a deep analysis on the principle, and then the size of the design, the size of each chain, connected to each other. After the use of backstepping and a variety of methods to calculate the final size of the parts. In the selection of parts materials, careful study, repeated testing, the final choice of 6061 aluminum alloy. According to the actual situation of the processing site, it is necessary to make a comprehensive consideration of various factors in the machining process. The simulation process should be based on the actual processing, not only pay attention to shape. It can be used as reference for machining.

Transition towards energy efficient machine tools

Energy Technology Data Exchange (ETDEWEB)

Zein, Andre [Technische Univ. Braunschweig (Germany). Inst. fuer Werkzeugmaschinen und Fertigungstechnik

2012-07-01

Provides unique data about industrial trends affecting the energy demand of machine tools. Presents a comprehensive methodology to assess the energy efficiency of machining processes. Contains an integrated management concept to implement energy performance measures into existing industrial systems. Includes an industrial case study with two exemplary applications. Energy efficiency represents a cost-effective and immediate strategy of a sustainable development. Due to substantial environmental and economic implications, a strong emphasis is put on the electrical energy requirements of machine tools for metalworking processes. The improvement of energy efficiency is however confronted with diverse barriers, which sustain an energy efficiency gap of unexploited potential. The deficiencies lie in the lack of information about the actual energy requirements of machine tools, a minimum energy reference to quantify improvement potential and the possible actions to improve the energy demand. Therefore, a comprehensive concept for energy performance management of machine tools is developed which guides the transition towards energy efficient machine tools. It is structured in four innovative concept modules, which are embedded into step-by-step workflow models. The capability of the performance management concept is demonstrated in an automotive manufacturing environment. The target audience primarily comprises researchers and practitioners challenged to enhance energy efficiency in manufacturing. The book may also be beneficial for graduate students who want to specialize in this field.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

Science.gov (United States)

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

A linear maglev guide for machine tools

Energy Technology Data Exchange (ETDEWEB)

Tieste, K D [Inst. of Mechanics, Univ. of Hannover (Germany); Popp, K [Inst. of Mechanics, Univ. of Hannover (Germany)

1996-12-31

Machine tools require linear guides with high slide velocity and very high position accuracy. The three tasks of a linear guide - supporting, guiding and driving - shall be realised by means of active magnetic bearings (AMB). The resulting linear magnetically levitated (maglev) guide has to accomplish the following characteristics: High stiffness, good damping and low noise as well as low heat production. First research on a one degree-of-freedom (DOF) support magnet unit aimed at the development of components and efficient control strategies for the linear maglev guide. The actual research is directed to realise a five DOF linear maglev guide for machine tools without drive to answer the question whether the maglev principle can be used for a linear axis in a machine tool. (orig.)

An analysis of switching and non-switching slot machine player behaviour.

Science.gov (United States)

Coates, Ewan; Blaszczynski, Alex

2013-12-01

Learning theory predicts that, given the repeated choice to bet between two concurrently available slot machines, gamblers will learn to bet more money on the machine with higher expected return (payback percentage) or higher win probability per spin (volatility). The purpose of this study was to investigate whether this occurs when the two machines vary orthogonally on payback percentage and volatility. The sample comprised 52 first year psychology students (mean age = 20.3 years, 20 females, 32 males) who had played a gaming machine at least once in the previous 12 months. Participants were administered a battery of questionnaires designed to assess level of knowledge on the characteristics and operation of poker machines, frequency of poker machine play in the past 12 months, personality traits of impulsivity and capacity for cognitive reflection, and gambling beliefs. For the experimental task, participants were instructed to play on two PC-simulated electronic gaming machines (EGMs or slot machines) that differed on payback percentage and volatility, with the option of freely switching between EGMs after a practice phase. Results indicated that participants were able to easily discriminate between machines and manifested a preference to play machines offering higher payback or volatility. These findings diverged from previous findings of no preference for play on higher payback/volatility machines, potentially due to of the current study's absence of the option to make multi-line and multi-credit bets. It was concluded that return rate parameters like payback percentage and volatility strongly influenced slot machine preference in the absence of betting options like multi-line bets, though more research is needed to determine the effects of such betting options on player distribution of money between multiple EGMs.

Transitivity of an entangled choice

International Nuclear Information System (INIS)

Makowski, Marcin; Piotrowski, Edward W

2011-01-01

We describe a quantum model of a simple choice game (constructed upon the entangled state of two qubits), which involves the fundamental problem of transitive-intransitive preferences. We compare attainability of optimal intransitive strategies in both classical and quantum models with the use of geometrical interpretation.

Effects of pole flux distribution in a homopolar linear synchronous machine

Science.gov (United States)

Balchin, M. J.; Eastham, J. F.; Coles, P. C.

1994-05-01

Linear forms of synchronous electrical machine are at present being considered as the propulsion means in high-speed, magnetically levitated (Maglev) ground transportation systems. A homopolar form of machine is considered in which the primary member, which carries both ac and dc windings, is supported on the vehicle. Test results and theoretical predictions are presented for a design of machine intended for driving a 100 passenger vehicle at a top speed of 400 km/h. The layout of the dc magnetic circuit is examined to locate the best position for the dc winding from the point of view of minimum core weight. Measurements of flux build-up under the machine at different operating speeds are given for two types of secondary pole: solid and laminated. The solid pole results, which are confirmed theoretically, show that this form of construction is impractical for high-speed drives. Measured motoring characteristics are presented for a short length of machine which simulates conditions at the leading and trailing ends of the full-sized machine. Combination of the results with those from a cylindrical version of the machine make it possible to infer the performance of the full-sized traction machine. This gives 0.8 pf and 0.9 efficiency at 300 km/h, which is much better than the reported performance of a comparable linear induction motor (0.52 pf and 0.82 efficiency). It is therefore concluded that in any projected high-speed Maglev systems, a linear synchronous machine should be the first choice as the propulsion means.

Automation of a universal machine; Automatizacion de una maquina universal

Energy Technology Data Exchange (ETDEWEB)

Rodriguez S, J

1997-09-01

The development of the hardware and software of a control system for a servo-hydraulic machine is presented. The universal machine is an Instron, model 1331, used to make mechanical tests. The software includes the acquisition of data from the measurements, processing and graphic presentation of the results in the assay of the `tension` type. The control is based on a PPI (Programmable Peripheral Interface) 8255, in which the different states of the machine are set. The control functions of the machine are: (a) Start of an assay, (b) Pause in the assay, (c) End of the assay, (d) Choice of the control mode of the machine, that they could be in load, stroke or strain modes. For the data acquisition, a commercial card, National Products, model DAS-16, plugged in a slot of a Pc was used. Three transducers provide the analog signals, a cell of load, a LVDT and a extensometer. All the data are digitalized and handled in order to get the results in the appropriate working units. A stress-strain graph is obtained in the screen of the Pc for a tension test for a specific material. The points of maximum stress, rupture stress and the yield stress of the material under test are shown. (Author).

The choices before us.

Science.gov (United States)

Streeten, P P

1980-01-01

This introduction is from the 16th World Conference of SID in Colombo, Sri Lanka, August 1979, which addressed the theme of development choices for the 1980's and beyond. Choices may refer to different political, ideological or social systems. Choices may refer to strategies and technical issues, e.g. agriculture vs. industry. A third meaning of choice is implicit in the idea of a Third World, or alternative, method of development. The third meaning implies a rejection of Western institutions, values, and standards. In the past, the transfer of Western or in this case Northern, institutions and standards has disappointed and created obstacles to development. The rapid rate of population growth forces choices of population control and resource management. Common themes of development have emerged from conference discussions: the need to build development efforts on indigenous values; the need for new institutions both at the sub-national and at the super-national level; and, the need to adjust to inevitable changes rationally and with foresight. The nation state is too large for many functions that are better decentralized and left to village or district administrations, yet it is too small to respond to global challenges and environmental risks like harvest failure, credit risks, marketing risks, failure of supplies. The interests of the state are not identical with those of society or particular groups in society.

Hydraulic Power Plant Machine Dynamic Diagnosis

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Hans Günther Poll

2006-01-01

Full Text Available A method how to perform an entire structural and hydraulic diagnosis of prototype Francis power machines is presented and discussed in this report. Machine diagnosis of Francis units consists on a proper evaluation of acquired mechanical, thermal and hydraulic data obtained in different operating conditions of several rotary and non rotary machine components. Many different physical quantities of a Francis machine such as pressure, strains, vibration related data, water flow, air flow, position of regulating devices and displacements are measured in a synchronized way so that a relation of cause an effect can be developed for each operating condition and help one to understand all phenomena that are involved with such kind of machine. This amount of data needs to be adequately post processed in order to allow correct interpretation of the machine dynamics and finally these data must be compared with the expected calculated data not only to fine tuning the calculation methods but also to accomplish fully understanding of the influence of the water passages on such machines. The way how the power plant owner has to operate its Francis machines, many times also determined by a central dispatcher, has a high influence on the fatigue life time of the machine components. The diagnostic method presented in this report helps one to understand the importance of adequate operation to allow a low maintenance cost for the entire power plant. The method how to acquire these quantities is discussed in details together with the importance of correct sensor balancing, calibration and adequate correlation with the physical quantities. Typical results of the dynamic machine behavior, with adequate interpretation, obtained in recent measurement campaigns of some important hydraulic turbines were presented. The paper highlights the investigation focus of the hydraulic machine behavior and how to tailor the measurement strategy to accomplish all goals. Finally some

Star marketer’s impact on the market strategy choice

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Vlašić Goran

2017-01-01

Full Text Available We focus on understanding the role of star marketers in pursuing a market-driven vs. a market-driving strategy. Results indicate that market-driving and market-driven strategies are two approaches that can be pursued by market-oriented firms. A star marketer has a robust positive influence on market-driving strategy. In contrast, a star marketer has no meaningful influence on market-driven strategy. In short, while star marketers are very important for market-driving strategy and long term success, they represent an unnecessary cost and provide no added value to companies focusing on market-driven strategies and short term results.

Shopping for food with children: A strategy for directing their choices toward novel foods containing vegetables.

Science.gov (United States)

Allirot, Xavier; Maiz, Edurne; Urdaneta, Elena

2018-01-01

Involving children in the different steps of meal preparation has been suggested as a strategy for enhancing dietary habits in childhood. It has previously been shown that involving children in cooking can increase their willingness to taste novel foods and direct their food choices towards foods containing vegetables. The objective of the present study was to assess the effect of involving children in food purchasing on food choices, intake, liking and appetite. A between-subject experiment was conducted with 86 children (from 8 to 10 years old). Forty-three children (PURCHASE group) participated in a workshop dedicated to purchasing the necessary ingredients online for the preparation of three unfamiliar foods containing vegetables: apple and beetroot juice, zucchini tortilla sandwich and spinach cookies. Forty-three children (CONTROL group) participated instead in a creativity workshop. Afterwards, all the children were invited to choose, for an afternoon snack, between three familiar vs. unfamiliar foods: orange vs. apple and beetroot juice, potatoes vs. zucchini tortilla sandwich and chocolate vs. spinach cookie. The mean number of unfamiliar foods chosen per child was higher in the PURCHASE (0.70 ± 0.14) vs. CONTROL (0.19 ± 0.07) group (P = 0.003). The liking for 1 of the 3 unfamiliar foods was higher in the PURCHASE group (P < 0.05). We did not find any difference between the two groups in food intake estimation and in the levels of subjective appetite. This study demonstrates that involving children in purchasing food can help in directing their food choices towards unfamiliar foods containing vegetables. It highlights the importance of involving children in the different steps of meal preparation for decreasing food neophobia. Copyright © 2017 Elsevier Ltd. All rights reserved.

Star marketer’s impact on the market strategy choice

OpenAIRE

Goran, Vlašić; Hair, Joe F.; Krupka, Zoran

2017-01-01

We focus on understanding the role of star marketers in pursuing a market-driven vs. a market-driving strategy. Results indicate that market-driving and market-driven strategies are two approaches that can be pursued by market-oriented firms. A star marketer has a robust positive influence on market-driving strategy. In contrast, a star marketer has no meaningful influence on market-driven strategy. In short, while star marketers are very important for market-driving strategy and long term su...

Behavioral Economics and the Supplemental Nutrition Assistance Program:: Making the Healthy Choice the Easy Choice.

Science.gov (United States)

Ammerman, Alice S; Hartman, Terry; DeMarco, Molly M

2017-02-01

The Supplemental Nutrition Assistance Program (SNAP) serves as an important nutritional safety net program for many Americans. Given its aim to use traditional economic levers to provide access to food, the SNAP program includes minimal nutritional requirements and restrictions. As food choices are influenced by more than just economic constraints, behavioral economics may offer insights and tools for altering food purchases for SNAP users. This manuscript outlines behavioral economics strategies that have potential to encourage healthier food choices within the SNAP program. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Proactive condition monitoring of low-speed machines

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Stamboliska, Zhaklina; Moczko, Przemyslaw

2015-01-01

This book broadens readers’ understanding of proactive condition monitoring of low-speed machines in heavy industries. It focuses on why low-speed machines are different than others and how maintenance of these machines should be implemented with particular attention. The authors explain the best available monitoring techniques for various equipment and the principle of how to get proactive information from each technique. They further put forward possible strategies for application of FEM for detection of faults and technical assessment of machinery. Implementation phases are described and industrial case-studies of proactive condition monitoring are included. Proactive Condition Monitoring of Low-Speed Machines is an essential resource for engineers and technical managers across a range of industries as well as design engineers working in industrial product development. This book also: ·         Explains the practice of proactive condition monitoring and illustrates implementation phases ·   ...

Progress Evaluation for the Restaurant Industry Assessed by a Voluntary Marketing-Mix and Choice-Architecture Framework That Offers Strategies to Nudge American Customers toward Healthy Food Environments, 2006-2017.

Science.gov (United States)

Kraak, Vivica; Englund, Tessa; Misyak, Sarah; Serrano, Elena

2017-07-12

Consumption of restaurant food and beverage products high in fat, sugar and sodium contribute to obesity and non-communicable diseases. We evaluated restaurant-sector progress to promote healthy food environments for Americans. We conducted a desk review of seven electronic databases (January 2006-January 2017) to examine restaurant strategies used to promote healthful options in the United States (U.S.). Evidence selection ( n = 84) was guided by the LEAD principles (i.e., locate, evaluate, and assemble evidence to inform decisions) and verified by data and investigator triangulation. A marketing-mix and choice-architecture framework was used to examine eight voluntary strategies (i.e., place, profile, portion, pricing, promotion, healthy default picks, priming or prompting and proximity) to evaluate progress (i.e., no, limited, some or extensive) toward 12 performance metrics based on available published evidence. The U.S. restaurant sector has made limited progress to use pricing, profile (reformulation), healthy default picks (choices), promotion (responsible marketing) and priming and prompting (information and labeling); and some progress to reduce portions. No evidence was available to assess progress for place (ambience) and proximity (positioning) to promote healthy choices during the 10-year review period. Chain and non-chain restaurants can apply comprehensive marketing-mix and nudge strategies to promote healthy food environments for customers.

Machine learning in materials informatics: recent applications and prospects

Science.gov (United States)

Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam; Mannodi-Kanakkithodi, Arun; Kim, Chiho

2017-12-01

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as "descriptors", may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven "materials informatics" strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

Good vs complementary genes for parasite resistance and the evolution of mate choice

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Lively Curtis M

2004-11-01

Full Text Available Abstract Background Female mate choice may be adaptive when males exhibit heritable genetic variation at loci encoding resistance to infectious disease. The Hamilton-Zuk hypothesis predicts that females should assess the genetic quality of males by monitoring traits that indicate health and vigor (condition-dependent choice, or CD. Alternatively, some females may employ a more direct method of screening and select mates based on the dissimilarity of alleles at the major histocompatibility loci (we refer to this as opposites-attract, or OA. Empirical studies suggest that both forms of mate choice exist, but little is known about the potential for natural selection to shape the two strategies in nature. Results We used computer simulation models to examine the evolutionary fates of the two forms of mate choice in populations at risk for infection by debilitating parasites. We found that populations exhibiting random mating (no mate choice can be invaded and replaced completely by individuals practicing CD type mate choice. We also found that an allele encoding OA choice can increase when rare in randomly mating populations, but that it does not go to fixation under selection. A similar result was obtained when the OA strategy was introduced into populations practicing CD mate choice. As before, we found that the OA choice allele will increase when rare, and that it will not go to fixation under selection. The converse however was not true, as CD individuals gain no rare advantage when introduced into an OA population. Conclusions Taken together, the results suggest that, when rare, OA is the best strategy for parasite evasion (of those considered here. The consequence of OA increasing in the population, however, is to reduce the parasite driven genotype oscillations and facilitate the breakdown of linkage disequilibrium at the disease-resistance loci. This leads to a neutrally stable situation in which different strategies have equal fitness, and

Educational strategies to reduce risk: a choice of social responsibility

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Federica La Longa

2012-07-01

Full Text Available This study develops the critical reflections of the activities for information, training and education that have been conducted by a group of researchers of the Istituto Nazionale di Geofisica e Vulcanologia in recent years. In particular, from an epistemological point of view, our analysis involves: (i science outreach, the link between science and the world; (ii science teaching and its role in the contact between science and schools; and (iii risk education, seen as a process that can develop a culture of risk in relation to the territory in which we live. These issues are critically analyzed on the basis of experience gained since 1995. The educational methodologies tested in ‘peacetime’ (in the absence of seismic events with the EDURISK Project are compared with those experienced during an emergency in Abruzzo, Italy. Today, we increasingly refer to prevention as the primary strategy of defense against risk. However, very often the responsibility of prevention falls on others, such as the government, institutions and/or local authorities. The citizens then perceive themselves as powerless against the inevitability of natural events, and they refer to these ‘rulers’ for the implementation of effective prevention policies. So, as researchers, what are the most effective actions we can take to influence risk reduction and to motivate the choices of the people? Must the effectiveness of our interventions be based on scientific information or on specific training, or must it be reached through the development of values, actions and awareness? Must our interventions be oriented and developed to inform, to train or to educate?

Machine rates for selected forest harvesting machines

Science.gov (United States)

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Progressive Tool Wear in Cryogenic Machining: The Effect of Liquid Nitrogen and Carbon Dioxide

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Yusuf Kaynak

2018-05-01

Full Text Available This experimental study focuses on various cooling strategies and lubrication-assisted cooling strategies to improve machining performance in the turning process of AISI 4140 steel. Liquid nitrogen (LN2 and carbon dioxide (CO2 were used as cryogenic coolants, and their performances were compared with respect to progression of tool wear. Minimum quantity lubrication (MQL was also used with carbon dioxide. Progression of wear, including flank and nose, are the main outputs examined during experimental study. This study illustrates that carbon dioxide-assisted cryogenic machining alone and with minimum quantity lubrication does not contribute to decreasing the progression of wear within selected cutting conditions. This study also showed that carbon dioxide-assisted cryogenic machining helps to increase chip breakability. Liquid nitrogen-assisted cryogenic machining results in a reduction of tool wear, including flank and nose wear, in the machining process of AISI 4140 steel material. It was also observed that in the machining process of this material at a cutting speed of 80 m/min, built-up edges occurred in both cryogenic cooling conditions. Additionally, chip flow damage occurs in particularly dry machining.

Portfolio Optimization and Mortgage Choice

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Maj-Britt Nordfang

2017-01-01

Full Text Available This paper studies the optimal mortgage choice of an investor in a simple bond market with a stochastic interest rate and access to term life insurance. The study is based on advances in stochastic control theory, which provides analytical solutions to portfolio problems with a stochastic interest rate. We derive the optimal portfolio of a mortgagor in a simple framework and formulate stylized versions of mortgage products offered in the market today. This allows us to analyze the optimal investment strategy in terms of optimal mortgage choice. We conclude that certain extreme investors optimally choose either a traditional fixed rate mortgage or an adjustable rate mortgage, while investors with moderate risk aversion and income prefer a mix of the two. By matching specific investor characteristics to existing mortgage products, our study provides a better understanding of the complex and yet restricted mortgage choice faced by many household investors. In addition, the simple analytical framework enables a detailed analysis of how changes to market, income and preference parameters affect the optimal mortgage choice.

Prediction of tunnel boring machine performance using machine and rock mass data

International Nuclear Information System (INIS)

Dastgir, G.

2012-01-01

Performance of the tunnel boring machine and its prediction by different methods has been a hot issue since the first TBM came into being. For the sake of safe and sound transport, improvement of hydro-power, mining, civil and many other tunneling projects that cannot be driven efficiently and economically by conventional drill and blast, TBMs are quite frequently used. TBM parameters and rock mass properties, which heavily influence machine performance, should be estimated or known before choice of TBM-type and start of excavation. By applying linear regression analysis (SPSS19), fuzzy logic tools and a special Math-Lab code on actual field data collected from seven TBM driven tunnels (Hieflau expansion, Queen water tunnel, Vereina, Hemerwald, Maen, Pieve and Varzo tunnel), an attempt was made to provide prediction of rock mass class (RMC), rock fracture class (RFC), penetration rate (PR) and advance rate (AR). For detailed analysis of TBM performance, machine parameters (thrust, machine rpm, torque, power etc.), machine types and specification and rock mass properties (UCS, discontinuity in rock mass, RMC, RFC, RMR, etc.) were analyzed by 3-D surface plotting using statistical software R. Correlations between machine parameters and rock mass properties which effectively influence prediction models, are presented as well. In Hieflau expansion tunnel AR linearly decreases with increase of thrust due to high dependence of machine advance rate upon rock strength. For Hieflau expansion tunnel three types of data (TBM, rock mass and seismic data e.g. amplitude, pseudo velocity etc.) were coupled and simultaneously analyzed by plotting 3-D surfaces. No appreciable correlation between seismic data (Amplitude and Pseudo velocity) and rock mass properties and machine parameters could be found. Tool wear as a function of TBM operational parameters was analyzed which revealed that tool wear is minimum if applied thrust is moderate and that tool wear is high when thrust is

Food Choice and Nutrition: A Social Psychological Perspective.

Science.gov (United States)

Hardcastle, Sarah J; Thøgersen-Ntoumani, Cecilie; Chatzisarantis, Nikos L D

2015-10-01

In this Special Issue, entitled "Food choice and Nutrition: A Social Psychological Perspective", three broad themes have been identified: (1) social and environmental influences on food choice; (2) psychological influences on eating behaviour; and (3) eating behaviour profiling.The studies that addressed the social and environmental influences indicated that further research would do well to promote positive food choices rather than reduce negative food choices; promote the reading and interpretation of food labels and find ways to effectively market healthy food choices through accessibility, availability and presentation. The studies on psychological influences found that intentions, perceived behavioural control, and confidence were predictors of healthy eating. Given the importance of psychological factors, such as perceived behavioural control and self-efficacy, healthy eating interventions should reduce barriers to healthy eating and foster perceptions of confidence to consume a healthy diet. The final theme focused on the clustering of individuals according to eating behaviour. Some "types" of individuals reported more frequent consumption of fast foods, ready meals or convenience meals or greater levels of disinhibitiona nd less control over food cravings. Intervention designs which make use of multi-level strategies as advocated by the Ecological Model of Behaviour change that proposes multi-level (combining psychological, social and environmental) strategies are likely to be more effective in reaching and engaging individuals susceptible to unhealthy eating habits than interventions operating on a single level.

A human-machine cooperation route planning method based on improved A* algorithm

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Zhang, Zhengsheng; Cai, Chao

2011-12-01

To avoid the limitation of common route planning method to blindly pursue higher Machine Intelligence and autoimmunization, this paper presents a human-machine cooperation route planning method. The proposed method includes a new A* path searing strategy based on dynamic heuristic searching and a human cooperated decision strategy to prune searching area. It can overcome the shortage of A* algorithm to fall into a local long term searching. Experiments showed that this method can quickly plan a feasible route to meet the macro-policy thinking.

Emotion regulation and risk taking: predicting risky choice in deliberative decision making.

Science.gov (United States)

Panno, Angelo; Lauriola, Marco; Figner, Bernd

2013-01-01

Only very recently has research demonstrated that experimentally induced emotion regulation strategies (cognitive reappraisal and expressive suppression) affect risky choice (e.g., Heilman et al., 2010). However, it is unknown whether this effect also operates via habitual use of emotion regulation strategies in risky choice involving deliberative decision making. We investigated the role of habitual use of emotion regulation strategies in risky choice using the "cold" deliberative version of the Columbia Card Task (CCT; Figner et al., 2009). Fifty-three participants completed the Emotion Regulation Questionnaire (ERQ; Gross & John, 2003) and--one month later--the CCT and the PANAS. Greater habitual cognitive reappraisal use was related to increased risk taking, accompanied by decreased sensitivity to changes in probability and loss amount. Greater habitual expressive suppression use was related to decreased risk taking. The results show that habitual use of reappraisal and suppression strategies predict risk taking when decisions involve predominantly cognitive-deliberative processes.

Support vector machine for diagnosis cancer disease: A comparative study

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Nasser H. Sweilam

2010-12-01

Full Text Available Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, Quantum-behave Particle Swarm for training SVM is introduced. Another approach named least square support vector machine (LSSVM and active set strategy are introduced. The obtained results by these methods are tested on a breast cancer dataset and compared with the exact solution model problem.

The Composition of Consideration and Choice Sets in Undergraduate University Choice: An Exploratory Study

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Dawes, Philip L.; Brown, Jennifer

2004-01-01

We examine university choice as a case of consumer decision making and adopt a brand elimination framework. This approach is predicated on the grounds that a large amount of research in consumer behavior has shown that in markets where there are many alternative brands, consumers use phased-decision strategies. In these research studies, the…

Mechanism of crud migration into the fuelling machine and its effects

International Nuclear Information System (INIS)

Sie, T.

2003-01-01

'Full text:' The objective of this paper is to summarize the opinion of experts on the mechanism of crud deposit formation and its migration into the fueling machine. Also to point out the negative effects of crud on the performance of the fueling machine head and the head overhaul / maintenance program in general. There are numerous moving/rotating components (ball screws, linear and rotating bearings, mechanical gears, mechanical seals, etc.) inside the fueling machine. By design, all these are lubricated by D2O. Because of the delicate nature of the moving components, crud contaminated D2O is obviously not a good choice of lubricant. Crud causes poor performance of the FM drive systems, premature wear of the mechanical seals, and other internal components. Due to the fuelling machine's role in maintaining reactor power and safety related functions, it is of extreme importance that the performance of the fueling machine is controlled. Major field functional failures must be prevented. In the extreme case the effect of the crud contaminated D2O could lead to a major functional failure while the fueling machine is locked on channel or has irradiated fuel on board. The next worse scenario is intolerably frequent process stops, thus requiring costly and premature fuelling machine overhaul / repairs with its associated negative effects: maintenance cost, radiation exposure, reduced fueling rates, and major upsets to the general head overhaul schedule. (author)

Motives for food choice among Serbian consumers

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Gagić Snježana

2014-01-01

Full Text Available People's motives for food choice depend on a number of very complex economic, social and individual factors. A Food Choice Questionnaire (FCQ, an instrument that measures the importance of factors underlying food choice, was used to reveal the Serbian consumers' food choice motives by survey of 450 respondents of different age groups. A confirmatory factor analysis was conducted on the motive items, using 11 factors. Previous research shows that the nutrition in Serbia is not balanced enough, and therefore the analysis of motives for food choice is considered a useful tool for the planning of more efficient public policies and interventions aimed at influencing healthier eating habits. Hence the results can be useful for researchers as well as for public institutions which deal with creating the strategy of public health or businessmen who produce and sell food products, because knowing consumer behaviour is necessary for product success on the market.

Support vector machine for the diagnosis of malignant mesothelioma

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Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

The choice of strategy of development of the enterprise entering into the integrated structure on the basis of obtaining additional competitive benefits by the enterprise

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A. I. Khorev

2017-01-01

Full Text Available In article process of the choice of strategy of development of the enterprise, entering into the integrated structure (IS taking into account a possibility of obtaining additional competitive benefits by the enterprise from this occurrence is considered. The enterprises as a part of IS has to use a possibility of realization of effect of a synergy. In relation to the enterprise which is in IS, the synergy has not one concept, but three interrelations, different according to types, between the enterprises, namely: material, non-material and competitive. Such opinion of M. Porter shares most of the experts working in the sphere of strategic management. Realization of a synergy allows to improve overall performance not only IS (2 + 2 = 5 in general, but also and separately the enterprises entering IS. Overall performance of the enterprises improves thanks to obtaining additional competitive benefits by the enterprises in the form of decrease in expenses and differentiation of production that is realized by means of competitive strategy. Realization of effect of a synergy thanks to establishment of interrelations, as a rule, is followed also by increase in production of the enterprises entering IS that can be realized by means of growth strategy. Thus, it is desirable to develop strategy of development of the enterprise entering IS as the combined strategy consisting of two strategy: competitive and growth. The matrix of alternative strategy of development of the enterprise entering IS in the form of combinations competitive and strategy of growth, and also model of the choice of the similar combined strategy taking into account a possibility of obtaining additional competitive benefits by the enterprise is given in article. For an assessment of competitive advantages of the enterprise as a part of IS by means of ball estimates of experts the technique of a similar assessment, and a matrix of the arising interrelations of the enterprises in IS is

Toolpath Strategy and Optimum Combination of Machining Parameter during Pocket Mill Process of Plastic Mold Steels Material

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Wibowo, Y. T.; Baskoro, S. Y.; Manurung, V. A. T.

2018-02-01

Plastic based products spread all over the world in many aspects of life. The ability to substitute other materials is getting stronger and wider. The use of plastic materials increases and become unavoidable. Plastic based mass production requires injection process as well Mold. The milling process of plastic mold steel material was done using HSS End Mill cutting tool that is widely used in a small and medium enterprise for the reason of its ability to be re sharpened and relatively inexpensive. Study on the effect of the geometry tool states that it has an important effect on the quality improvement. Cutting speed, feed rate, depth of cut and radii are input parameters beside to the tool path strategy. This paper aims to investigate input parameter and cutting tools behaviors within some different tool path strategy. For the reason of experiments efficiency Taguchi method and ANOVA were used. Response studied is surface roughness and cutting behaviors. By achieving the expected quality, no more additional process is required. Finally, the optimal combination of machining parameters will deliver the expected roughness and of course totally reduced cutting time. However actually, SMEs do not optimally use this data for cost reduction.

Traffic-Light Labels and Choice Architecture Promoting Healthy Food Choices

Science.gov (United States)

Thorndike, Anne N.; Riis, Jason; Sonnenberg, Lillian M.; Levy, Douglas E.

2014-01-01

Background Preventing obesity requires maintenance of healthy eating behaviors over time. Food labels and strategies that increase visibility and convenience of healthy foods (choice architecture) promote healthier choices, but long-term effectiveness is unknown. Purpose Assess effectiveness of traffic-light labeling and choice architecture cafeteria intervention over 24 months. Design Longitudinal pre–post cohort follow-up study between December 2009 and February 2012. Data were analyzed in 2012. Setting/participants Large hospital cafeteria with mean of 6511 transactions daily. Cafeteria sales were analyzed for: (1) all cafeteria customers and (2) longitudinal cohort of 2285 hospital employees who used the cafeteria regularly. Intervention After 3-month baseline period, cafeteria items were labeled green (healthy), yellow (less healthy) or red (unhealthy) and rearranged to make healthy items more accessible. Main outcome measures Proportion of cafeteria sales that were green or red during each 3-month period from baseline to 24 months. Changes in 12- and 24-month sales were compared to baseline for all transactions and transactions by the employee cohort. Results The proportion of sales of red items decreased from 24% at baseline to 20% at 24 months (p<0.001), and green sales increased from 41% to 46% (p<0.001). Red beverages decreased from 26% of beverage sales at baseline to 17% at 24 months (p<0.001); green beverages increased from 52% to 60% (p<0.001). Similar patterns were observed for the cohort of employees, with largest change for red beverages (23% to 14%, p<0.001). Conclusions A traffic-light and choice architecture cafeteria intervention resulted in sustained healthier choices over 2 years, suggesting food environment interventions can promote long-term changes in population eating behaviors. PMID:24439347

Working memory, strategy knowledge, and strategy instruction in children with reading disabilities.

Science.gov (United States)

Swanson, H Lee; Kehler, Pam; Jerman, Olga

2010-01-01

Two experiments investigated the effects of strategy knowledge and strategy training on the working memory (WM) performance in children (ages 10-11) with and without reading disabilities (RD). Experiment 1 examined the relationship between strategy knowledge (stability of strategy choices) and WM performance as a function of initial, gain (cued), and maintenance conditions. WM performance was significantly improved for both groups under cued conditions; however, the performances of children with RD were inferior to those of children without RD across all memory conditions. Measures of WM capacity rather than strategy stability or processing efficiency best predicted reading comprehension performance. Experiment 2 assessed the effects of strategy training on WM performance by randomly assigning children to strategy instruction or control conditions. Significant improvements in WM performance occurred as a function of training conditions, but the residual WM differences between the reading groups remained. Although the results showed that stable strategy choices, cued performance, and strategy instruction significantly bolstered WM performance in children with RD, their overall WM performance, however, was constrained by capacity limitations.

Progress Evaluation for the Restaurant Industry Assessed by a Voluntary Marketing-Mix and Choice-Architecture Framework That Offers Strategies to Nudge American Customers toward Healthy Food Environments, 2006–2017

Science.gov (United States)

Misyak, Sarah; Serrano, Elena

2017-01-01

Consumption of restaurant food and beverage products high in fat, sugar and sodium contribute to obesity and non-communicable diseases. We evaluated restaurant-sector progress to promote healthy food environments for Americans. We conducted a desk review of seven electronic databases (January 2006–January 2017) to examine restaurant strategies used to promote healthful options in the United States (U.S.). Evidence selection (n = 84) was guided by the LEAD principles (i.e., locate, evaluate, and assemble evidence to inform decisions) and verified by data and investigator triangulation. A marketing-mix and choice-architecture framework was used to examine eight voluntary strategies (i.e., place, profile, portion, pricing, promotion, healthy default picks, priming or prompting and proximity) to evaluate progress (i.e., no, limited, some or extensive) toward 12 performance metrics based on available published evidence. The U.S. restaurant sector has made limited progress to use pricing, profile (reformulation), healthy default picks (choices), promotion (responsible marketing) and priming and prompting (information and labeling); and some progress to reduce portions. No evidence was available to assess progress for place (ambience) and proximity (positioning) to promote healthy choices during the 10-year review period. Chain and non-chain restaurants can apply comprehensive marketing-mix and nudge strategies to promote healthy food environments for customers. PMID:28704965

Physician trainees' decision making and information processing: choice size and Medicare Part D.

Science.gov (United States)

Barnes, Andrew J; Hanoch, Yaniv; Martynenko, Melissa; Wood, Stacey; Rice, Thomas; Federman, Alex D

2013-01-01

Many patients expect their doctor to help them choose a Medicare prescription drug plan. Whether the size of the choice set affects clinicians' decision processes and strategy selection, and the quality of their choice, as it does their older patients, is an important question with serious financial consequences. Seventy medical students and internal medicine residents completed a within-subject design using Mouselab, a computer program that allows the information-acquisition process to be examined. We examined highly numerate physician trainees' decision processes, strategy, and their ability to pick the cheapest drug plan-as price was deemed the most important factor in Medicare beneficiaries' plan choice-from either 3 or 9 drug plans. Before adjustment, participants were significantly more likely to identify the lowest cost plan when facing three versus nine choices (67.3% vs. 32.8%, pinformation on each attribute (pdecision strategies focusing on comparing alternate plans across a single attribute (search pattern, pdecision process and strategy, numeracy, and amount of medical training, the odds were 10.75 times higher that trainees would choose the lowest cost Medicare Part D drug plan when facing 3 versus 9 drug plans (pdecision environment are needed and suggest physicians' role in their patients' Part D choices may be most productive when assisting seniors with forecasting their expected medication needs and then referring them to the Medicare website or helpline.

A Novel Machine Learning Strategy Based on Two-Dimensional Numerical Models in Financial Engineering

Directory of Open Access Journals (Sweden)

Qingzhen Xu

2013-01-01

Full Text Available Machine learning is the most commonly used technique to address larger and more complex tasks by analyzing the most relevant information already present in databases. In order to better predict the future trend of the index, this paper proposes a two-dimensional numerical model for machine learning to simulate major U.S. stock market index and uses a nonlinear implicit finite-difference method to find numerical solutions of the two-dimensional simulation model. The proposed machine learning method uses partial differential equations to predict the stock market and can be extensively used to accelerate large-scale data processing on the history database. The experimental results show that the proposed algorithm reduces the prediction error and improves forecasting precision.

A new optimization tool path planning for 3-axis end milling of free-form surfaces based on efficient machining intervals

Science.gov (United States)

Vu, Duy-Duc; Monies, Frédéric; Rubio, Walter

2018-05-01

A large number of studies, based on 3-axis end milling of free-form surfaces, seek to optimize tool path planning. Approaches try to optimize the machining time by reducing the total tool path length while respecting the criterion of the maximum scallop height. Theoretically, the tool path trajectories that remove the most material follow the directions in which the machined width is the largest. The free-form surface is often considered as a single machining area. Therefore, the optimization on the entire surface is limited. Indeed, it is difficult to define tool trajectories with optimal feed directions which generate largest machined widths. Another limiting point of previous approaches for effectively reduce machining time is the inadequate choice of the tool. Researchers use generally a spherical tool on the entire surface. However, the gains proposed by these different methods developed with these tools lead to relatively small time savings. Therefore, this study proposes a new method, using toroidal milling tools, for generating toolpaths in different regions on the machining surface. The surface is divided into several regions based on machining intervals. These intervals ensure that the effective radius of the tool, at each cutter-contact points on the surface, is always greater than the radius of the tool in an optimized feed direction. A parallel plane strategy is then used on the sub-surfaces with an optimal specific feed direction for each sub-surface. This method allows one to mill the entire surface with efficiency greater than with the use of a spherical tool. The proposed method is calculated and modeled using Maple software to find optimal regions and feed directions in each region. This new method is tested on a free-form surface. A comparison is made with a spherical cutter to show the significant gains obtained with a toroidal milling cutter. Comparisons with CAM software and experimental validations are also done. The results show the

A Machine-to-Machine protocol benchmark for eHealth applications - Use case: Respiratory rehabilitation.

Science.gov (United States)

Talaminos-Barroso, Alejandro; Estudillo-Valderrama, Miguel A; Roa, Laura M; Reina-Tosina, Javier; Ortega-Ruiz, Francisco

2016-06-01

M2M (Machine-to-Machine) communications represent one of the main pillars of the new paradigm of the Internet of Things (IoT), and is making possible new opportunities for the eHealth business. Nevertheless, the large number of M2M protocols currently available hinders the election of a suitable solution that satisfies the requirements that can demand eHealth applications. In the first place, to develop a tool that provides a benchmarking analysis in order to objectively select among the most relevant M2M protocols for eHealth solutions. In the second place, to validate the tool with a particular use case: the respiratory rehabilitation. A software tool, called Distributed Computing Framework (DFC), has been designed and developed to execute the benchmarking tests and facilitate the deployment in environments with a large number of machines, with independence of the protocol and performance metrics selected. DDS, MQTT, CoAP, JMS, AMQP and XMPP protocols were evaluated considering different specific performance metrics, including CPU usage, memory usage, bandwidth consumption, latency and jitter. The results obtained allowed to validate a case of use: respiratory rehabilitation of chronic obstructive pulmonary disease (COPD) patients in two scenarios with different types of requirement: Home-Based and Ambulatory. The results of the benchmark comparison can guide eHealth developers in the choice of M2M technologies. In this regard, the framework presented is a simple and powerful tool for the deployment of benchmark tests under specific environments and conditions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[A new machinability test machine and the machinability of composite resins for core built-up].

Science.gov (United States)

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Choice Criteria of Cosmetics among Chinese Consumers

OpenAIRE

LI, ZHU

2014-01-01

Becoming familiar with consumers’ choice criteria towards a certain kind of product can help marketers tailor more efficient market strategies. Cosmetics play a very important part in the lives of women. Plautus asserted, “A woman without paint is like food without salt”. In recent years, the Chinese cosmetic market has flourished. The aim of this dissertation is to understand the choice criteria of cosmetics in the context of the Chinese market. Country-of-origin, brand image and quality are...

Food Choice and Nutrition: A Social Psychological Perspective

Directory of Open Access Journals (Sweden)

Sarah J. Hardcastle

2015-10-01

Full Text Available In this Special Issue, entitled “Food choice and Nutrition: A Social Psychological Perspective”, three broad themes have been identified: (1 social and environmental influences on food choice; (2 psychological influences on eating behaviour; and (3 eating behaviour profiling. The studies that addressed the social and environmental influences indicated that further research would do well to promote positive food choices rather than reduce negative food choices; promote the reading and interpretation of food labels and find ways to effectively market healthy food choices through accessibility, availability and presentation. The studies on psychological influences found that intentions, perceived behavioural control, and confidence were predictors of healthy eating. Given the importance of psychological factors, such as perceived behavioural control and self-efficacy, healthy eating interventions should reduce barriers to healthy eating and foster perceptions of confidence to consume a healthy diet. The final theme focused on the clustering of individuals according to eating behaviour. Some “types” of individuals reported more frequent consumption of fast foods, ready meals or convenience meals or greater levels of disinhibition and less control over food cravings. Intervention designs which make use of multi-level strategies as advocated by the Ecological Model of Behaviour change that proposes multi-level (combining psychological, social and environmental strategies are likely to be more effective in reaching and engaging individuals susceptible to unhealthy eating habits than interventions operating on a single level.

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

Science.gov (United States)

Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

Directory of Open Access Journals (Sweden)

George L Sutphin

2016-11-01

Full Text Available The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

Conceptual models in man-machine design verification

International Nuclear Information System (INIS)

Rasmussen, J.

1985-01-01

The need for systematic methods for evaluation of design concepts for new man-machine systems has been rapidly increasing in consequence of the introduction of modern information technology. Direct empirical methods are difficult to apply when functions during rare conditions and support of operator decisions during emergencies are to be evaluated. In this paper, the problems of analytical evaluations based on conceptual models of the man-machine interaction are discussed, and the relations to system design and analytical risk assessment are considered. Finally, a conceptual framework for analytical evaluation is proposed, including several domains of description: 1. The problem space, in the form of a means-end hierarchy; 2. The structure of the decision process; 3. The mental strategies and heuristics used by operators; 4. The levels of cognitive control and the mechanisms related to human errors. Finally, the need for models representing operators' subjective criteria for choosing among available mental strategies and for accepting advice from intelligent interfaces is discussed

Machine performance and its effects on experiments in JT-60U

International Nuclear Information System (INIS)

Kondo, I.

1995-01-01

The operational results of JT-60U were reviewed in light of the strategy made at the design stage. The operational plan for better confinement shifted from that of low q to high poloidal beta plasma configuration with higher q value according to the revealed machine properties. Some technical and operational skills helped bring about the recent results out of the machine. (orig.)

Food labels promote healthy choices by a decision bias in the amygdala.

Science.gov (United States)

Grabenhorst, Fabian; Schulte, Frank P; Maderwald, Stefan; Brand, Matthias

2013-07-01

Food labeling is the major health policy strategy to counter rising obesity rates. Based on traditional economic theory, such strategies assume that detailed nutritional information will necessarily help individuals make better, healthier choices. However, in contrast to the well-known utility of labels in food marketing, evidence for the efficacy of nutritional labeling is mixed. Psychological and behavioral economic theories suggest that successful marketing strategies activate automatic decision biases and emotions, which involve implicit emotional brain systems. Accordingly, simple, intuitive food labels that engage these neural systems could represent a promising approach for promoting healthier choices. Here we used functional MRI to investigate this possibility. Healthy, mildly hungry subjects performed a food evaluation task and a food choice task. The main experimental manipulation was to pair identical foods with simple labels that emphasized either taste benefits or health-related food properties. We found that such labels biased food evaluations in the amygdala, a core emotional brain system. When labels biased the amygdala's evaluations towards health-related food properties, the strength of this bias predicted behavioral shifts towards healthier choices. At the time of decision-making, amygdala activity encoded key decision variables, potentially reflecting active amygdala participation in food choice. Our findings underscore the potential utility of food labeling in health policy and indicate a principal role for emotional brain systems when labels guide food choices. Copyright © 2013 Elsevier Inc. All rights reserved.

Platform Expansion Design as Strategic Choice

DEFF Research Database (Denmark)

Staykova, Kalina S.; Damsgaard, Jan

2016-01-01

In this paper, we address how the strategic choice of platform expansion design impacts the subse-quent platform strategy. We identify two distinct approaches to platform expansion – platform bun-dling and platform constellations, which currently co-exist. The purpose of this paper is to outline...

ADVANCED DESIGN SOLUTIONS FOR HIGH-PRECISION WOODWORKING MACHINES

Directory of Open Access Journals (Sweden)

Giuseppe Lucisano

2016-03-01

Full Text Available With the aim at performing the highest precision during woodworking, a mix of alternative approaches, fruitfully integrated in a common design strategy, is essential. This paper represents an overview of technical solutions, recently developed by authors, in design of machine tools and their final effects on manufacturing. The most advanced solutions in machine design are reported side by side with common practices or little everyday expedients. These design actions are directly or indirectly related to the rational use of materials, sometimes very uncommon, as in the case of magnetorheological fluids chosen to implement an active control in speed and force on the electro-spindle, and permitting to improve the quality of wood machining. Other actions are less unusual, as in the case of the adoption of innovative anti-vibration supports for basement. Tradition or innovation, all these technical solutions contribute to the final result: the highest precision in wood machining.

Believing Is Doing: Emotion Regulation Beliefs Are Associated With Emotion Regulation Behavioral Choices and Subjective Well-Being

Directory of Open Access Journals (Sweden)

Catherine Nicole Marie Ortner

2017-03-01

Full Text Available Research in emotion regulation has begun to examine various predictors of emotion regulation choices, including individual differences and contextual variables. However, scant attention has been paid to the extent to which people’s beliefs about the specific consequences of emotion regulation strategies for the components of an emotional response and long-term well-being predict their behavioral regulatory choices and, in turn, their subjective well-being. Participants completed measures to assess their beliefs about the consequences of functional and dysfunctional strategies, behavioral choices of emotion regulation strategies in negative scenarios, and subjective well-being. The model that fit the data indicated partial mediation whereby beliefs were associated with approximately 9% of the variance in choices. Emotion regulation choices were related to subjective well-being, with an additional direct effect between beliefs and well-being. This suggests beliefs play a role in people’s regulatory choices. Future research should explore how beliefs interact with individual differences and contextual variables to better understand why people regulate their emotions in different ways and, ultimately, to help individuals make healthy emotion regulation choices.

Believing Is Doing: Emotion Regulation Beliefs Are Associated With Emotion Regulation Behavioral Choices and Subjective Well-Being.

Science.gov (United States)

Ortner, Catherine Nicole Marie; Briner, Esther Lydia; Marjanovic, Zdravko

2017-03-01

Research in emotion regulation has begun to examine various predictors of emotion regulation choices, including individual differences and contextual variables. However, scant attention has been paid to the extent to which people's beliefs about the specific consequences of emotion regulation strategies for the components of an emotional response and long-term well-being predict their behavioral regulatory choices and, in turn, their subjective well-being. Participants completed measures to assess their beliefs about the consequences of functional and dysfunctional strategies, behavioral choices of emotion regulation strategies in negative scenarios, and subjective well-being. The model that fit the data indicated partial mediation whereby beliefs were associated with approximately 9% of the variance in choices. Emotion regulation choices were related to subjective well-being, with an additional direct effect between beliefs and well-being. This suggests beliefs play a role in people's regulatory choices. Future research should explore how beliefs interact with individual differences and contextual variables to better understand why people regulate their emotions in different ways and, ultimately, to help individuals make healthy emotion regulation choices.

The machine intelligence Hex project

Science.gov (United States)

Chalup, Stephan K.; Mellor, Drew; Rosamond, Fran

2005-12-01

Hex is a challenging strategy board game for two players. To enhance students’ progress in acquiring understanding and practical experience with complex machine intelligence and programming concepts we developed the Machine Intelligence Hex (MIHex) project. The associated undergraduate student assignment is about designing and implementing Hex players and evaluating them in an automated tournament of all programs developed by the class. This article surveys educational aspects of the MIHex project. Additionally, fundamental techniques for game programming as well as specific concepts for Hex board evaluation are reviewed. The MIHex game server and possibilities of tournament organisation are described. We summarise and discuss our experiences from running the MIHex project assignment over four consecutive years. The impact on student motivation and learning benefits are evaluated using questionnaires and interviews.

Constructive Consumer Choice Processes.

OpenAIRE

Bettman, James R; Luce, Mary Frances; Payne, John W

1998-01-01

Consumer decision making has been a focal interest in consumer research, and consideration of current marketplace trends ( e.g., technological change, an information explosion) indicates that this topic will continue to be critically important. We argue that consumer choice is inherently constructive. Due to limited processing capacity, consumers often do not have well-defined existing preferences, but construct them using a variety of strategies contingent on task demands. After describing c...

Parameter choice in Banach space regularization under variational inequalities

International Nuclear Information System (INIS)

Hofmann, Bernd; Mathé, Peter

2012-01-01

The authors study parameter choice strategies for the Tikhonov regularization of nonlinear ill-posed problems in Banach spaces. The effectiveness of any parameter choice for obtaining convergence rates depends on the interplay of the solution smoothness and the nonlinearity structure, and it can be expressed concisely in terms of variational inequalities. Such inequalities are link conditions between the penalty term, the norm misfit and the corresponding error measure. The parameter choices under consideration include an a priori choice, the discrepancy principle as well as the Lepskii principle. For the convenience of the reader, the authors review in an appendix a few instances where the validity of a variational inequality can be established. (paper)

Linkage studies on Gilles de la Tourette syndrome: What is the strategy of choice?

Energy Technology Data Exchange (ETDEWEB)

Heutink, P.; Wetering, J.M. van de; Oostra, B.A. [Erasmus Univ. Rotterdam (Netherlands)] [and others

1995-08-01

For a linkage study it is important to ascertain family material that is sufficiently informative. The statistical power of linkage sample can be determined via computer simulation. For complex traits uncertain parameters such as incomplete penetrance, frequency of phenocopies, gene frequency and variable expression have to be taken into account. One can either include only the most severe phenotype in the analysis or apply multiple linkage tests for a gradually broadened disease phenotype. Gilles de la Tourette syndrome (GTS) is a chronic neurological disorder characterized by multiple, intermittent motor and vocal tics. Segregation analyses suggests that GTS and milder phenotypes are caused by a single dominant gene. We report here the results of an extensive simulation study on a large set of families. We compared the effectiveness of linkage tests with only the GTS phenotype versus multiple tests that included various milder phenotypes and different gene frequencies. The scenario of multiple tests yielded superior power. Our results show that computer simulation can indicate the strategy of choice in linkage studies of multiple, complex phenotypes. 33 refs., 2 figs., 3 tabs.

The Machine within the Machine

CERN Multimedia

Katarina Anthony

2014-01-01

Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines

DEFF Research Database (Denmark)

van Tulder, Gijs; de Bruijne, Marleen

2016-01-01

The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may...... outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from...... unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both...

Effects of student choice on engagement and understanding in a junior high science class

Science.gov (United States)

Foreback, Laura Elizabeth

The purpose of this study was to determine the effects of increasing individual student choice in assignments on student engagement and understanding of content. It was predicted that if students are empowered to choose learning activities based on individual readiness, learning style, and interests, they would be more engaged in the curriculum and consequently would develop deeper understanding of the material. During the 2009--2010 school year, I implemented differentiated instructional strategies that allowed for an increased degree of student choice in five sections of eighth grade science at DeWitt Junior High School. These strategies, including tiered lessons and student-led, project-based learning, were incorporated into the "Earth History and Geologic Time Scale" unit of instruction. The results of this study show that while offering students choices can be used as an effective motivational strategy, their academic performance was not increased compared to their performance during an instructional unit that did not offer choice.

Availability of Vending Machines and School Stores in California Schools.

Science.gov (United States)

Cisse-Egbuonye, Nafissatou; Liles, Sandy; Schmitz, Katharine E; Kassem, Nada; Irvin, Veronica L; Hovell, Melbourne F

2016-01-01

This study examined the availability of foods sold in vending machines and school stores in United States public and private schools, and associations of availability with students' food purchases and consumption. Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected from 521 students aged 8 to 15 years recruited from orthodontic offices in California. Vending machines were more common in private schools than in public schools, whereas school stores were common in both private and public schools. The food items most commonly available in both vending machines and school stores in all schools were predominately foods of minimal nutritional value (FMNV). Participant report of availability of food items in vending machines and/or school stores was significantly correlated with (1) participant purchase of each item from those sources, except for energy drinks, milk, fruits, and vegetables; and (2) participants' friends' consumption of items at lunch, for 2 categories of FMNV (candy, cookies, or cake; soda or sports drinks). Despite the Child Nutrition and Women, Infants, and Children (WIC) Reauthorization Act of 2004, FMNV were still available in schools, and may be contributing to unhealthy dietary choices and ultimately to health risks. © 2015, American School Health Association.

Quantum Cournot equilibrium for the Hotelling–Smithies model of product choice

International Nuclear Information System (INIS)

Rahaman, Ramij; Majumdar, Priyadarshi; Basu, B

2012-01-01

This paper demonstrates the quantization of a spatial Cournot duopoly model with product choice, a two stage game focusing on non-cooperation in locations and quantities. With quantization, the players can access a continuous set of strategies, using a continuous variable quantum mechanical approach. The presence of quantum entanglement in the initial state identifies a quantity equilibrium for each location pair choice with any transport cost. Also higher profit is obtained by the firms at Nash equilibrium. Adoption of quantum strategies rewards us by the existence of a larger quantum strategic space at equilibrium. (paper)

Progress Evaluation for the Restaurant Industry Assessed by a Voluntary Marketing-Mix and Choice-Architecture Framework That Offers Strategies to Nudge American Customers toward Healthy Food Environments, 2006–2017

Directory of Open Access Journals (Sweden)

Vivica Kraak

2017-07-01

Full Text Available Consumption of restaurant food and beverage products high in fat, sugar and sodium contribute to obesity and non-communicable diseases. We evaluated restaurant-sector progress to promote healthy food environments for Americans. We conducted a desk review of seven electronic databases (January 2006–January 2017 to examine restaurant strategies used to promote healthful options in the United States (U.S.. Evidence selection (n = 84 was guided by the LEAD principles (i.e., locate, evaluate, and assemble evidence to inform decisions and verified by data and investigator triangulation. A marketing-mix and choice-architecture framework was used to examine eight voluntary strategies (i.e., place, profile, portion, pricing, promotion, healthy default picks, priming or prompting and proximity to evaluate progress (i.e., no, limited, some or extensive toward 12 performance metrics based on available published evidence. The U.S. restaurant sector has made limited progress to use pricing, profile (reformulation, healthy default picks (choices, promotion (responsible marketing and priming and prompting (information and labeling; and some progress to reduce portions. No evidence was available to assess progress for place (ambience and proximity (positioning to promote healthy choices during the 10-year review period. Chain and non-chain restaurants can apply comprehensive marketing-mix and nudge strategies to promote healthy food environments for customers.

Individual Differences in Strategy Use on Division Problems: Mental versus Written Computation

Science.gov (United States)

Hickendorff, Marian; van Putten, Cornelis M.; Verhelst, Norman D.; Heiser, Willem J.

2010-01-01

Individual differences in strategy use (choice and accuracy) were analyzed. A sample of 362 Grade 6 students solved complex division problems under 2 different conditions. In the choice condition students were allowed to use either a mental or a written strategy. In the subsequent no-choice condition, they were required to use a written strategy.…

Probability matching and strategy availability.

Science.gov (United States)

Koehler, Derek J; James, Greta

2010-09-01

Findings from two experiments indicate that probability matching in sequential choice arises from an asymmetry in strategy availability: The matching strategy comes readily to mind, whereas a superior alternative strategy, maximizing, does not. First, compared with the minority who spontaneously engage in maximizing, the majority of participants endorse maximizing as superior to matching in a direct comparison when both strategies are described. Second, when the maximizing strategy is brought to their attention, more participants subsequently engage in maximizing. Third, matchers are more likely than maximizers to base decisions in other tasks on their initial intuitions, suggesting that they are more inclined to use a choice strategy that comes to mind quickly. These results indicate that a substantial subset of probability matchers are victims of "underthinking" rather than "overthinking": They fail to engage in sufficient deliberation to generate a superior alternative to the matching strategy that comes so readily to mind.

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

Machine learning and data science in soft materials engineering

Science.gov (United States)

Ferguson, Andrew L.

2018-01-01

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by ‘de-jargonizing’ data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Machine learning and data science in soft materials engineering.

Science.gov (United States)

Ferguson, Andrew L

2018-01-31

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

Directory of Open Access Journals (Sweden)

Marc Wieland

2014-03-01

Full Text Available In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS and very high resolution (WorldView-2, Quickbird, Ikonos multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based, have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.

The methodic of calculation for the need of basic construction machines on construction site when developing organizational and technological documentation

Science.gov (United States)

Zhadanovsky, Boris; Sinenko, Sergey

2018-03-01

Economic indicators of construction work, particularly in high-rise construction, are directly related to the choice of optimal number of machines. The shortage of machinery makes it impossible to complete the construction & installation work on scheduled time. Rates of performance of construction & installation works and labor productivity during high-rise construction largely depend on the degree of provision of construction project with machines (level of work mechanization). During calculation of the need for machines in construction projects, it is necessary to ensure that work is completed on scheduled time, increased level of complex mechanization, increased productivity and reduction of manual work, and improved usage and maintenance of machine fleet. The selection of machines and determination of their numbers should be carried out by using formulas presented in this work.

Making Healthy Choices Easier: Regulation versus Nudging.

Science.gov (United States)

Hansen, Pelle Guldborg; Skov, Laurits Rohden; Skov, Katrine Lund

2016-01-01

In recent years, the nudge approach to behavior change has emerged from the behavioral sciences to challenge the traditional use of regulation in public health strategies to address modifiable individual-level behaviors related to the rise of noncommunicable diseases and their treatment. However, integration and testing of the nudge approach as part of more comprehensive public health strategies aimed at making healthy choices easier are being threatened by inadequate understandings of its scientific character, its relationship with regulation, and its ethical implications. This article reviews this character and its ethical implication with a special emphasis on the compatibility of nudging with traditional regulation, special domains of experience, and the need for a more nuanced approach to the ethical debate. The aim is to advance readers' understanding and give guidance to those who have considered working with or incorporating the nudge approach into programs or policies aimed at making healthful choices easier.

Spectral methods in machine learning and new strategies for very large datasets

Science.gov (United States)

Belabbas, Mohamed-Ali; Wolfe, Patrick J.

2009-01-01

Spectral methods are of fundamental importance in statistics and machine learning, because they underlie algorithms from classical principal components analysis to more recent approaches that exploit manifold structure. In most cases, the core technical problem can be reduced to computing a low-rank approximation to a positive-definite kernel. For the growing number of applications dealing with very large or high-dimensional datasets, however, the optimal approximation afforded by an exact spectral decomposition is too costly, because its complexity scales as the cube of either the number of training examples or their dimensionality. Motivated by such applications, we present here 2 new algorithms for the approximation of positive-semidefinite kernels, together with error bounds that improve on results in the literature. We approach this problem by seeking to determine, in an efficient manner, the most informative subset of our data relative to the kernel approximation task at hand. This leads to two new strategies based on the Nyström method that are directly applicable to massive datasets. The first of these—based on sampling—leads to a randomized algorithm whereupon the kernel induces a probability distribution on its set of partitions, whereas the latter approach—based on sorting—provides for the selection of a partition in a deterministic way. We detail their numerical implementation and provide simulation results for a variety of representative problems in statistical data analysis, each of which demonstrates the improved performance of our approach relative to existing methods. PMID:19129490

ASCERTAINMENT OF THE EQUIVALENT CIRCUIT PARAMETERS OF THE ASYNCHRONOUS MACHINE

Directory of Open Access Journals (Sweden)

V. S. Safaryan

2015-01-01

Full Text Available The article considers experimental and analytical determination of the asynchronous machine equivalent-circuit parameters with application of the reference data. Transient processes investigation of the asynchronous machines necessitates the equivalent circuit parameters (resistance impedance, inductances and coefficient of the stator-rotor contours mutual inductance that help form the transitory-process mathematical simulation model. The reference books do not provide those parameters; they instead give the rated ones (active power, voltage, slide, coefficient of performance and capacity coefficient as well as the ratio of starting and nominal currents and torques. The noted studies on the asynchronous machine equivalent-circuits parametrization fail to solve the problems ad finem or solve them with admissions. The paper presents experimental and analytical determinations of the asynchronous machine equivalent-circuit parameters: the experimental one based on the results of two measurements and the analytical one where the problem boils down to solving a system of nonlineal algebraic equations. The authors investigate the equivalent asynchronous machine input-resistance properties and adduce the dependence curvatures of the input-resistances on the slide. They present a symbolic model for analytical parameterization of the asynchronous machine equivalent-circuit that represents a system of nonlineal equations and requires one of the rotor-parameters arbitrary assignment. The article demonstrates that for the asynchronous machine equivalent-circuit experimental parameterization the measures are to be conducted of the stator-circuit voltage, current and active power with two different slides and arbitrary assignment of one of the rotor parameters. The paper substantiates the fact that additional measurement does not discard the rotor-parameter choice arbitrariness. The authors establish that in motoring mode there is a critical slide by which the

Models for formation and choice of variants for organizing digital electronics manufacturing

Science.gov (United States)

Korshunov, G. I.; Lapkova, M. Y.; Polyakov, S. L.; Frolova, E. A.

2018-03-01

The directions of organizing digital electronics manufacturing are considered by the example of surface mount technology. The basic equipment choice has to include not only individual characteristics, but also mutual influence of individual machines and the results of design for manufacturing. Application of special cases of the Utility function which are complicated in the general representation of polynomial functions are proposed for estimation of product quality in a staged automation.

Working Capital Management, Corporate Performance, and Strategic Choices of the Wholesale and Retail Industry in China

Directory of Open Access Journals (Sweden)

Chuan-guo Li

2014-01-01

Full Text Available We examine the influence of strategic choice on working capital configurations and observe how the relationship between working capital ratio and operational performance differs depending on strategy. By clustering the strategic factors of the wholesale and retail industry, we find three categories of strategies: terminal market strategy, middle market strategy, and hybrid strategy. Using the panel data of the listed companies of the wholesale and retail industry as our sample, we analyze the differences in the ways companies configure working capital, the speed with which working capital adjusts to its target, and the effects of working capital on performance for companies that make different strategic choices. The empirical results suggest that working capital is configured and adjusted to its target in different ways under different competitive strategic choices. This effect is finally transferred to influence the relationship between working capital configuration and operational performance.

Working capital management, corporate performance, and strategic choices of the wholesale and retail industry in China.

Science.gov (United States)

Li, Chuan-guo; Dong, Hui-min; Chen, Shou; Yang, Yan

2014-01-01

We examine the influence of strategic choice on working capital configurations and observe how the relationship between working capital ratio and operational performance differs depending on strategy. By clustering the strategic factors of the wholesale and retail industry, we find three categories of strategies: terminal market strategy, middle market strategy, and hybrid strategy. Using the panel data of the listed companies of the wholesale and retail industry as our sample, we analyze the differences in the ways companies configure working capital, the speed with which working capital adjusts to its target, and the effects of working capital on performance for companies that make different strategic choices. The empirical results suggest that working capital is configured and adjusted to its target in different ways under different competitive strategic choices. This effect is finally transferred to influence the relationship between working capital configuration and operational performance.

Working Capital Management, Corporate Performance, and Strategic Choices of the Wholesale and Retail Industry in China

Science.gov (United States)

Li, Chuan-guo; Dong, Hui-min; Chen, Shou; Yang, Yan

2014-01-01

We examine the influence of strategic choice on working capital configurations and observe how the relationship between working capital ratio and operational performance differs depending on strategy. By clustering the strategic factors of the wholesale and retail industry, we find three categories of strategies: terminal market strategy, middle market strategy, and hybrid strategy. Using the panel data of the listed companies of the wholesale and retail industry as our sample, we analyze the differences in the ways companies configure working capital, the speed with which working capital adjusts to its target, and the effects of working capital on performance for companies that make different strategic choices. The empirical results suggest that working capital is configured and adjusted to its target in different ways under different competitive strategic choices. This effect is finally transferred to influence the relationship between working capital configuration and operational performance. PMID:25121141

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Syringe vending machines for injection drug users: an experiment in Marseille, France.

Science.gov (United States)

Obadia, Y; Feroni, I; Perrin, V; Vlahov, D; Moatti, J P

1999-01-01

OBJECTIVES: This study evaluated the usefulness of vending machines in providing injection drug users with access to sterile syringes in Marseille, France. METHODS: Self-administered questionnaires were offered to 485 injection drug users obtaining syringes from 32 pharmacies, 4 needle exchange programs, and 3 vending machines. RESULTS: Of the 343 respondents (response rate = 70.7%), 21.3% used the vending machines as their primary source of syringes. Primary users of vending machines were more likely than primary users of other sources to be younger than 30 years, to report no history of drug maintenance treatment, and to report no sharing of needles or injection paraphernalia. CONCLUSIONS: Vending machines may be an appropriate strategy for providing access to syringes for younger injection drug users, who have typically avoided needle exchange programs and pharmacies. PMID:10589315

Prototype Vector Machine for Large Scale Semi-Supervised Learning

Energy Technology Data Exchange (ETDEWEB)

Zhang, Kai; Kwok, James T.; Parvin, Bahram

2009-04-29

Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.

Optimizing the way kinematical feed chains with great distance between slides are chosen for CNC machine tools

Science.gov (United States)

Lucian, P.; Gheorghe, S.

2017-08-01

This paper presents a new method, based on FRISCO formula, for optimizing the choice of the best control system for kinematical feed chains with great distance between slides used in computer numerical controlled machine tools. Such machines are usually, but not limited to, used for machining large and complex parts (mostly in the aviation industry) or complex casting molds. For such machine tools the kinematic feed chains are arranged in a dual-parallel drive structure that allows the mobile element to be moved by the two kinematical branches and their related control systems. Such an arrangement allows for high speed and high rigidity (a critical requirement for precision machining) during the machining process. A significant issue for such an arrangement it’s the ability of the two parallel control systems to follow the same trajectory accurately in order to address this issue it is necessary to achieve synchronous motion control for the two kinematical branches ensuring that the correct perpendicular position it’s kept by the mobile element during its motion on the two slides.

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

International Nuclear Information System (INIS)

Yahya, Noorazrul; Ebert, Martin A.; Bulsara, Max; House, Michael J.; Kennedy, Angel; Joseph, David J.; Denham, James W.

2016-01-01

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥ 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

Energy Technology Data Exchange (ETDEWEB)

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au [School of Physics, University of Western Australia, Western Australia 6009, Australia and School of Health Sciences, National University of Malaysia, Bangi 43600 (Malaysia); Ebert, Martin A. [School of Physics, University of Western Australia, Western Australia 6009, Australia and Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008 (Australia); Bulsara, Max [Institute for Health Research, University of Notre Dame, Fremantle, Western Australia 6959 (Australia); House, Michael J. [School of Physics, University of Western Australia, Western Australia 6009 (Australia); Kennedy, Angel [Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008 (Australia); Joseph, David J. [Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia and School of Surgery, University of Western Australia, Western Australia 6009 (Australia); Denham, James W. [School of Medicine and Public Health, University of Newcastle, New South Wales 2308 (Australia)

2016-05-15

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥ 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions

Modelling machine ensembles with discrete event dynamical system theory

Science.gov (United States)

Hunter, Dan

1990-01-01

Discrete Event Dynamical System (DEDS) theory can be utilized as a control strategy for future complex machine ensembles that will be required for in-space construction. The control strategy involves orchestrating a set of interactive submachines to perform a set of tasks for a given set of constraints such as minimum time, minimum energy, or maximum machine utilization. Machine ensembles can be hierarchically modeled as a global model that combines the operations of the individual submachines. These submachines are represented in the global model as local models. Local models, from the perspective of DEDS theory , are described by the following: a set of system and transition states, an event alphabet that portrays actions that takes a submachine from one state to another, an initial system state, a partial function that maps the current state and event alphabet to the next state, and the time required for the event to occur. Each submachine in the machine ensemble is presented by a unique local model. The global model combines the local models such that the local models can operate in parallel under the additional logistic and physical constraints due to submachine interactions. The global model is constructed from the states, events, event functions, and timing requirements of the local models. Supervisory control can be implemented in the global model by various methods such as task scheduling (open-loop control) or implementing a feedback DEDS controller (closed-loop control).

Sandwiching it in: spillover of work onto food choices and family roles in low- and moderate-income urban households.

Science.gov (United States)

Devine, Carol M; Connors, Margaret M; Sobal, Jeffery; Bisogni, Carole A

2003-02-01

Lower status jobs, high workloads and lack of control at work have been associated with less healthful diets, but the ways through which work is connected to food choices are not well understood. This analysis was an examination of workers' experience of the relationship of their jobs to their food choices. Fifty-one multi-ethnic, urban, low- and moderate-income adults living in Upstate New York in 1995 participated in a qualitative interview study of fruit and vegetable choices and discussed employment and food choices. The workers who participated in this study described a dynamic relationship between work and food choices that they experienced in the context of their other roles and values. These workers presented a relationship that was characterized by positive and negative spillover between their jobs and their ability to fulfill family roles and promote personal health, linked by a spectrum of food choice strategies. Participants' narratives fit into three different domains: characterizations of work and their resources for food choice, strategies used to manage food choices within the constraints of work, and affect related to the negative and positive spillover of these strategies on family roles and on personal food choices. Characterizations of work as demanding and limiting or demanding and manageable differentiated participants who experienced their food choice strategies as a source of guilt and dissatisfaction (negative spillover) from those who experienced food choices as a source of pride and satisfaction (positive spillover). Ideals and values related to food choice and health were balanced against other values for family closeness and nurturing and personal achievement. Some participants found work unproblematic. These findings direct attention to a broad conceptualization of the relationship of work to food choices in which the demands and resources of the work role are viewed as they spill over into the social and temporal context of other

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

Machinability of Al 6061 Deposited with Cold Spray Additive Manufacturing

Science.gov (United States)

Aldwell, Barry; Kelly, Elaine; Wall, Ronan; Amaldi, Andrea; O'Donnell, Garret E.; Lupoi, Rocco

2017-10-01

Additive manufacturing techniques such as cold spray are translating from research laboratories into more mainstream high-end production systems. Similar to many additive processes, finishing still depends on removal processes. This research presents the results from investigations into aspects of the machinability of aluminum 6061 tubes manufactured with cold spray. Through the analysis of cutting forces and observations on chip formation and surface morphology, the effect of cutting speed, feed rate, and heat treatment was quantified, for both cold-sprayed and bulk aluminum 6061. High-speed video of chip formation shows changes in chip form for varying material and heat treatment, which is supported by the force data and quantitative imaging of the machined surface. The results shown in this paper demonstrate that parameters involved in cold spray directly impact on machinability and therefore have implications for machining parameters and strategy.

Molecular machines open cell membranes.

Science.gov (United States)

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B; Robinson, Jacob T; Wang, Gufeng; Pal, Robert; Tour, James M

2017-08-30

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

Molecular machines open cell membranes

Science.gov (United States)

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G.; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B.; Robinson, Jacob T.; Wang, Gufeng; Pal, Robert; Tour, James M.

2017-08-01

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

Impact of Perceived Healthiness of Food on Food Choices and Intake.

Science.gov (United States)

Provencher, Véronique; Jacob, Raphaëlle

2016-03-01

Healthy eating is an important determinant of health, but adherence to dietary guidelines remains a public health concern. Identifying factors that impact dietary habits is therefore important to facilitate healthy eating. One widely used strategy to help consumers make healthier food choices is nutrition information, such as labeling and claims. Despite the intention of these strategies to improve decision making, they can also be misunderstood or misinterpreted by consumers. The aim of this review is to explore food perceptions by examining how cognitive factors influence perceived healthiness of food, and the impact of perceived healthiness of food on food choices and intake. Overall findings of this review suggest that cognitive factors, such as type of food and branding, significantly contribute to judgmental bias and have an impact on perceived healthiness while not consistently or systematically influencing choice and intake.

Nudging consumers towards healthier choices: a systematic review of positional influences on food choice

OpenAIRE

Bucher Tamara; Collins Clare; Rollo Megan E.; McCaffrey Tracy A.; De Vlieger Nienke; Van der Bend Daphne; Truby Helen; Perez-Cueto Federico J. A.

2016-01-01

Nudging or 'choice architecture' refers to strategic changes in the environment that are anticipated to alter people's behaviour in a predictable way without forbidding any options or significantly changing their economic incentives. Nudging strategies may be used to promote healthy eating behaviour. However to date the scientific evidence has not been systematically reviewed to enable practitioners and policymakers to implement or argue for the implementation of specific measures to support ...

Machine learning and computer vision approaches for phenotypic profiling.

Science.gov (United States)

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

An Interactive Web-based Learning System for Assisting Machining Technology Education

Directory of Open Access Journals (Sweden)

Min Jou

2008-05-01

Full Text Available The key technique of manufacturing methods is machining. The degree of technique of machining directly affects the quality of the product. Therefore, the machining technique is of primary importance in promoting student practice ability during the training process. Currently, practical training is applied in shop floor to discipline student’s practice ability. Much time and cost are used to teach these techniques. Particularly, computerized machines are continuously increasing in use. The development of educating engineers on computerized machines becomes much more difficult than with traditional machines. This is because of the limitation of the extremely expensive cost of teaching. The quality and quantity of teaching cannot always be promoted in this respect. The traditional teaching methods can not respond well to the needs of the future. Therefore, this research aims to the following topics; (1.Propose the teaching strategies for the students to learning machining processing planning through web-based learning system. (2.Establish on-line teaching material for the computer-aided manufacturing courses including CNC coding method, CNC simulation. (3.Develop the virtual machining laboratory to bring the machining practical training to web-based learning system. (4.Integrate multi-media and virtual laboratory in the developed e-learning web-based system to enhance the effectiveness of machining education through web-based system.

Motivating Students by Increasing Student Choice

Science.gov (United States)

Birdsell, Becky S.; Ream, Sarah M.; Seyller, Ann M.; Zobott, Pam L.

2009-01-01

The purpose of this study was to increase motivation in 7th grade students. Four teacher researchers examined the change in motivational levels as a result of choice strategies. They gathered data from four different classes, 101 students in all, to track levels of motivation. They monitored their levels of observable behavioral patterns with a…

The Influence of School on the Choice of Language Learning ...

African Journals Online (AJOL)

This research seeks to examine the role that context or learning situation plays in strategy choice by comparing the strategy patterns of a private English medium secondary and a government secondary school in Botswana. More specifically, the main objectives of this study are to, firstly, investigate whether the 'type of ...

Effect of nutrition label format and product assortment on healthfulness of food choice

DEFF Research Database (Denmark)

Aschemann-Witzel, Jessica; Grunert, Klaus G; van Trijp, Hans

2013-01-01

This study aims to find out whether front-of-pack nutrition label formats influence the healthfulness of consumers’ food choices and important predictors of healthful choices, depending on the size of the choice set that is made available to consumers. The predictors explored were health motivati...... the results revealed no consistent differences in the effects between the formats, they indicate that manipulating choice sets by including healthier options is an effective strategy to increase the healthfulness of food choices........ The results showed that food choices were more healthful in the extended 20-product (vs. 10-product) choice set and that this effect is stronger than a random choice would produce. The formats colour coding and texts, particularly colour coding in Germany, increased the healthfulness of product choices when...

Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

Directory of Open Access Journals (Sweden)

Chi-Man Vong

2012-01-01

Full Text Available Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs, a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

Choice and maintenance of equipment for electron crystallography.

Science.gov (United States)

Mills, Deryck J; Vonck, Janet

2013-01-01

The choice of equipment for an electron crystallography laboratory will ultimately be determined by the available budget; nevertheless, the ideal lab will have two electron microscopes: a dedicated 300 kV cryo-EM with a field emission gun and a smaller LaB(6) machine for screening. The high-end machine should be equipped with photographic film or a very large CCD or CMOS camera for 2D crystal data collection; the screening microscope needs a mid-size CCD for rapid evaluation of crystal samples. The microscope room installations should provide adequate space and a special environment that puts no restrictions on the collection of high-resolution data. Equipment for specimen preparation includes a carbon coater, glow discharge unit, light microscope, plunge freezer, and liquid nitrogen containers and storage dewars. When photographic film is to be used, additional requirements are a film desiccator, dark room, optical diffractometer, and a film scanner. Having the electron microscopes and ancillary equipment well maintained and always in optimum condition facilitates the production of high-quality data.

Fighting Terrorism With Strategy: Revisiting Competing Visions

National Research Council Canada - National Science Library

Schluckebier, Thomas

2002-01-01

... the threat of international terrorism requires America to make a grand strategic choice, This paper examines those choices by presenting four post-Cold War strategy options neo-isolationism, selective...

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

This book describes various hybrid machining and finishing processes. It gives a critical review of the past work based on them as well as the current trends and research directions. For each hybrid machining process presented, the authors list the method of material removal, machining system, process variables and applications. This book provides a deep understanding of the need, application and mechanism of hybrid machining processes.

Russian consumers' motives for food choice.

Science.gov (United States)

Honkanen, Pirjo; Frewer, Lynn

2009-04-01

Knowledge about food choice motives which have potential to influence consumer consumption decisions is important when designing food and health policies, as well as marketing strategies. Russian consumers' food choice motives were studied in a survey (1081 respondents across four cities), with the purpose of identifying consumer segments based on these motives. These segments were then profiled using consumption, attitudinal and demographic variables. Face-to-face interviews were used to sample the data, which were analysed with two-step cluster analysis (SPSS). Three clusters emerged, representing 21.5%, 45.8% and 32.7% of the sample. The clusters were similar in terms of the order of motivations, but differed in motivational level. Sensory factors and availability were the most important motives for food choice in all three clusters, followed by price. This may reflect the turbulence which Russia has recently experienced politically and economically. Cluster profiles differed in relation to socio-demographic factors, consumption patterns and attitudes towards health and healthy food.

Man-machine interface builders at the Advanced Photon Source

International Nuclear Information System (INIS)

Anderson, M.D.

1991-01-01

Argonne National Laboratory is constructing a 7-GeV Advanced Photon Source for use as a synchrotron radiation source in basic and applied research. The controls and computing environment for this accelerator complex includes graphical operator interfaces to the machine based on Motif, X11, and PHIGS/PEX. Construction and operation of the control system for this accelerator relies upon interactive interface builder and diagram/editor type tools, as well as a run-time environment for the constructed displays which communicate with the physical machine via network connections. This paper discusses our experience with several commercial CUI builders, the inadequacies found in these, motivation for the development of an application- specific builder, and design and implementation strategies employed in the development of our own Man-Machine Interface builder. 5 refs

A control approach for plasma density in tokamak machines

Energy Technology Data Exchange (ETDEWEB)

Boncagni, Luca, E-mail: luca.boncagni@enea.it [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Pucci, Daniele; Piesco, F.; Zarfati, Emanuele [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy); Mazzitelli, G. [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Monaco, S. [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy)

2013-10-15

Highlights: •We show a control approach for line plasma density in tokamak. •We show a control approach for pressure in a tokamak chamber. •We show experimental results using one valve. -- Abstract: In tokamak machines, chamber pre-fill is crucial to attain plasma breakdown, while plasma density control is instrumental for several tasks such as machine protection and achievement of desired plasma performances. This paper sets the principles of a new control strategy for attaining both chamber pre-fill and plasma density regulation. Assuming that the actuation mean is a piezoelectric valve driven by a varying voltage, the proposed control laws ensure convergence to reference values of chamber pressure during pre-fill, and of plasma density during plasma discharge. Experimental results at FTU are presented to discuss weaknesses and strengths of the proposed control strategy. The whole system has been implemented by using the MARTe framework [1].

Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke.

Science.gov (United States)

McConnell, Alistair C; Moioli, Renan C; Brasil, Fabricio L; Vallejo, Marta; Corne, David W; Vargas, Patricia A; Stokes, Adam A

2017-06-28

To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation. The development progression of robotic-aided hand physiotherapy devices and brain-machine interface systems is outlined, focussing on those with mechanisms and control strategies designed to improve recovery outcomes of the hand post-stroke. A total of 110 commercial and non-commercial hand and wrist devices, spanning the 2 major core designs: end-effector and exoskeleton are reviewed. The growing body of evidence on the efficacy and relevance of incorporating brain-machine interfaces in stroke rehabilitation is summarized. The challenges involved in integrating robotic rehabilitation into the healthcare system are discussed. This review provides novel insights into the use of robotics in physiotherapy practice, and may help system designers to develop new devices.

ENERGY EFFICIENCY DETERMINATION OF LOADING-BACK SYSTEM OF ELECTRIC TRACTION MACHINES

Directory of Open Access Journals (Sweden)

A. M. Afanasov

2014-03-01

Full Text Available Purpose.Acceptance post-repair testsof electric traction machinesare conducted onloading-backstandsthat reducethe overall power costsfor the tests.Currentlya numberof possiblecircuit designs of loading-backsystems of electric machines are known, but there is nomethod of determiningtheir energy efficiency. This in turn makes difficult the choiceof rationaloptions. The purpose of the article is the development of the corresponding methodo-logy to make easier this process. Methodology. Expressions for determining theenergy efficiency ofa stand for testingof electric traction machineswere obtained using the generalizedscheme analysisof energy transformationsin the loading-backsystems of universal structure. Findings.Thetechnique wasoffered and the analytical expressions for determining the energy efficiency of loading-backsystemsof electric traction machines wereobtained. Energy efficiency coefficientofloading-backsystemisproposed to consider as the ratio of the total actionenergy of the mechanical and electromotive forces, providing anchors rotation and flowof currents in electric machines, which are being tested,to the total energy, consumed during the test from the external network. Originality. The concept was introduced and the analytical determination method of the energy efficiency of loading-backsystem in electric traction machines was offered. It differs by efficiency availability of power sources and converters, as well as energy efficiency factors of indirect methods of loss compensation. Practical value. The proposed technique of energy efficiency estimation of a loading-backsystemcan be used in solving the problem of rational options choice of schematics stands decisions for electric traction machines acceptance tests of main line and industrial transport.

Mexico's critical choices

International Nuclear Information System (INIS)

Marcos, E.

1990-01-01

In Mexico, the 1982 fall in international oil prices shook the national conscience and pushed the Mexican people in search of a new national image and toward the choices they must make to attain that image. But, according to the author of this paper, the country as a whole has already made critical choices for overall strategy and there are reasons for optimism. In the current economic environment of growing domestic demand and enhanced international competitiveness, the author sees PEMEX (the Mexican national oil company) facing not only the challenge of responding to the rapid changes taking place in the Mexican economy, but also making a significant contribution toward the solid and stable growth of the country. The relevant question is how PEMEX will live up to these expectations. This paper describes several steps PEMEX has taken already or is preparing to take in order to meet this challenge, including: investment in the domestic petrochemical industry; entry into the Eurobond market; development of new methods of project financing

A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation.

Science.gov (United States)

2015-12-01

This study adopts a dwelling unit level of analysis and considers a probabilistic choice set generation approach for residential choice modeling. In doing so, we accommodate the fact that housing choices involve both characteristics of the dwelling u...

Machinability of titanium metal matrix composites (Ti-MMCs)

Science.gov (United States)

Aramesh, Maryam

Titanium metal matrix composites (Ti-MMCs), as a new generation of materials, have various potential applications in aerospace and automotive industries. The presence of ceramic particles enhances the physical and mechanical properties of the alloy matrix. However, the hard and abrasive nature of these particles causes various issues in the field of their machinability. Severe tool wear and short tool life are the most important drawbacks of machining this class of materials. There is very limited work in the literature regarding the machinability of this class of materials especially in the area of tool life estimation and tool wear. By far, polycrystalline diamond (PCD) tools appear to be the best choice for machining MMCs from researchers' point of view. However, due to their high cost, economical alternatives are sought. Cubic boron nitride (CBN) inserts, as the second hardest available tools, show superior characteristics such as great wear resistance, high hardness at elevated temperatures, a low coefficient of friction and a high melting point. Yet, so far CBN tools have not been studied during machining of Ti-MMCs. In this study, a comprehensive study has been performed to explore the tool wear mechanisms of CBN inserts during turning of Ti-MMCs. The unique morphology of the worn faces of the tools was investigated for the first time, which led to new insights in the identification of chemical wear mechanisms during machining of Ti-MMCs. Utilizing the full tool life capacity of cutting tools is also very crucial, due to the considerable costs associated with suboptimal replacement of tools. This strongly motivates development of a reliable model for tool life estimation under any cutting conditions. In this study, a novel model based on the survival analysis methodology is developed to estimate the progressive states of tool wear under any cutting conditions during machining of Ti-MMCs. This statistical model takes into account the machining time in

Foreign IPO capital market choice: Understanding the institutional fit of corporate governance

OpenAIRE

Moore, C. B.; Bell, R. G.; Filatotchev, I.; Rasheed, A. A.

2012-01-01

While product market choices were central to strategy formulation for firms in the past, the integration of financial markets makes the choice of capital markets an equally important strategic decision. We advance a comparative institutional perspective to explain capital market choice by firms making an IPO in a foreign market. Based on a sample of 103 and 99 foreign IPOs in the US and UK respectively during the period 2002-2006, we find that internal governance characteristics (founder CEO,...

Choice certainty in Discrete Choice Experiments

DEFF Research Database (Denmark)

Uggeldahl, Kennet Christian; Jacobsen, Catrine; Lundhede, Thomas

2016-01-01

In this study, we conduct a Discrete Choice Experiment (DCE) using eye tracking technology to investigate if eye movements during the completion of choice sets reveal information about respondents’ choice certainty. We hypothesise that the number of times that respondents shift their visual...

Mechanics of Wood Machining

CERN Document Server

Csanády, Etele

2013-01-01

Wood is one of the most valuable materials for mankind, and since our earliest days wood materials have been widely used. Today we have modern woodworking machine and tools; however, the raw wood materials available are continuously declining. Therefore we are forced to use this precious material more economically, reducing waste wherever possible. This new textbook on the “Mechanics of Wood Machining” combines the quantitative, mathematical analysis of the mechanisms of wood processing with practical recommendations and solutions. Bringing together materials from many sources, the book contains new theoretical and experimental approaches and offers a clear and systematic overview of the theory of wood cutting, thermal loading in wood-cutting tools, dynamic behaviour of tool and work piece, optimum choice of operational parameters and energy consumption, the wear process of the tools, and the general regularities of wood surface roughness. Diagrams are provided for the quick estimation of various process ...

Leveraging delay discounting for health: Can time delays influence food choice?

Science.gov (United States)

Appelhans, Bradley M; French, Simone A; Olinger, Tamara; Bogucki, Michael; Janssen, Imke; Avery-Mamer, Elizabeth F; Powell, Lisa M

2018-03-15

Delay discounting, the tendency to choose smaller immediate rewards over larger delayed rewards, is theorized to promote consumption of immediately rewarding but unhealthy foods at the expense of long-term weight maintenance and nutritional health. An untested implication of delay discounting models of decision-making is that selectively delaying access to less healthy foods may promote selection of healthier (immediately available) alternatives, even if they may be less desirable. The current study tested this hypothesis by measuring healthy versus regular vending machine snack purchasing before and during the implementation of a 25-s time delay on the delivery of regular snacks. Purchasing was also examined under a $0.25 discount on healthy snacks, a $0.25 tax on regular snacks, and the combination of both pricing interventions with the 25-s time delay. Across 32,019 vending sales from three separate vending locations, the 25-s time delay increased healthy snack purchasing from 40.1% to 42.5%, which was comparable to the impact of a $0.25 discount (43.0%). Combining the delay and the discount had a roughly additive effect (46.0%). However, the strongest effects were seen under the $0.25 tax on regular snacks (53.7%) and the combination of the delay and the tax (50.2%). Intervention effects varied substantially between vending locations. Importantly, time delays did not harm overall vending sales or revenue, which is relevant to the real-world feasibility of this intervention. More investigation is needed to better understand how the impact of time delays on food choice varies across populations, evaluate the effects of time delays on beverage vending choices, and extend this approach to food choices in contexts other than vending machines. ClinicalTrials.gov, NCT02359916. Copyright © 2018 Elsevier Ltd. All rights reserved.

Promoting the purchase of low-calorie foods from school vending machines: A cluster-randomized controlled study

NARCIS (Netherlands)

Kocken, P.L.; Eeuwijk, J.; Kesten, N.M.C. van; Dusseldorp, E.; Buijs, G.; Bassa-Dafesh, Z.; Snel, J.

2012-01-01

BACKGROUND: Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. METHODS: A school-based randomized controlled trial was conducted in 13 experimental

Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics

Directory of Open Access Journals (Sweden)

Haejoon Jung

2018-01-01

Full Text Available As an intrinsic part of the Internet of Things (IoT ecosystem, machine-to-machine (M2M communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.

Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics.

Science.gov (United States)

Jung, Haejoon; Lee, In-Ho

2018-01-12

As an intrinsic part of the Internet of Things (IoT) ecosystem, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave) communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC) device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs) with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy) of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.

Transient characteristics of current lead losses for the large scale high-temperature superconducting rotating machine

International Nuclear Information System (INIS)

Le, T. D.; Kim, J. H.; Park, S. I.; Kim, D. J.; Kim, H. M.; Lee, H. G.; Yoon, Y. S.; Jo, Y. S.; Yoon, K. Y.

2014-01-01

To minimize most heat loss of current lead for high-temperature superconducting (HTS) rotating machine, the choice of conductor properties and lead geometry - such as length, cross section, and cooling surface area - are one of the various significant factors must be selected. Therefore, an optimal lead for large scale of HTS rotating machine has presented before. Not let up with these trends, this paper continues to improve of diminishing heat loss for HTS part according to different model. It also determines the simplification conditions for an evaluation of the main flux flow loss and eddy current loss transient characteristics during charging and discharging period.

76 FR 46853 - International Business Machines Corporation, ITD Business Unit, Division 7, E-mail and...

Science.gov (United States)

2011-08-03

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-73,218; TA-W-73,218A] International Business Machines Corporation, ITD Business Unit, Division 7, E-mail and Collaboration Group, Including Workers Off-Site From Various States in the United States Reporting to Armonk, NY; International Business Machines Corporation, Web Strategy...

A comparison of numerical and machine-learning modeling of soil water content with limited input data

Science.gov (United States)

Karandish, Fatemeh; Šimůnek, Jiří

2016-12-01

Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a

A Review of Factors Influencing Athletes' Food Choices.

Science.gov (United States)

Birkenhead, Karen L; Slater, Gary

2015-11-01

Athletes make food choices on a daily basis that can affect both health and performance. A well planned nutrition strategy that includes the careful timing and selection of appropriate foods and fluids helps to maximize training adaptations and, thus, should be an integral part of the athlete's training programme. Factors that motivate food selection include taste, convenience, nutrition knowledge and beliefs. Food choice is also influenced by physiological, social, psychological and economic factors and varies both within and between individuals and populations. This review highlights the multidimensional nature of food choice and the depth of previous research investigating eating behaviours. Despite numerous studies with general populations, little exploration has been carried out with athletes, yet the energy demands of sport typically require individuals to make more frequent and/or appropriate food choices. While factors that are important to general populations also apply to athletes, it seems likely, given the competitive demands of sport, that performance would be an important factor influencing food choice. It is unclear if athletes place the same degree of importance on these factors or how food choice is influenced by involvement in sport. There is a clear need for further research exploring the food choice motives of athletes, preferably in conjunction with research investigating dietary intake to establish if intent translates into practice.

Maintenance Strategies to Reduce Downtime Due to\\ud Machine Positional Errors

OpenAIRE

Shagluf, Abubaker; Longstaff, Andrew P.; Fletcher, Simon

2014-01-01

Manufacturing strives to reduce waste and increase\\ud Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime\\ud competently and similarly identify the machines’ performance.\\ud Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a pow...

NEW ASPECTS OF MANUFACTURING ON MACHINE TOOLS

Directory of Open Access Journals (Sweden)

Dorian ŞTEF

2012-11-01

Full Text Available In the paper are presented the modality to minimize the production time and increase the machining accuracy in the milling operations and to analyze different milling strategies. In this analyze the only on modification for face milling operation was to change the tool geometry by mounted a special shape insert WIPER, that have a different geometry, and for pocketing operations the changes was by using different milling strategies for manufacturing pockets. The application for this analyze is a simulation between the process technologies in virtual fabrication made using Esprit CAM (Computer Aided Manufacturing software.

Effects of Computer-Assisted Instruction in Using Formal Decision-Making Strategies to Choose a College Major.

Science.gov (United States)

Mau, Wei-Cheng; Jepsen, David A.

1992-01-01

Compared decision-making strategies and college major choice among 113 first-year students assigned to Elimination by Aspects Strategy (EBA), Subjective Expected Utility Strategy (SEU), and control groups. "Rational" EBA students scored significantly higher on choice certainty; lower on choice anxiety and career indecision than "rational"…

Machine terms dictionary

Energy Technology Data Exchange (ETDEWEB)

NONE

1979-04-15

This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

Melioration as rational choice: sequential decision making in uncertain environments.

Science.gov (United States)

Sims, Chris R; Neth, Hansjörg; Jacobs, Robert A; Gray, Wayne D

2013-01-01

Melioration-defined as choosing a lesser, local gain over a greater longer term gain-is a behavioral tendency that people and pigeons share. As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality. In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies. In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience. When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior. In the present article, we examine human performance in a sequential decision-making experiment that is known to induce meliorating behavior. In keeping with previous results using this paradigm, we find that the majority of participants in the experiment fail to adopt the optimal decision strategy and instead demonstrate a significant bias toward melioration. To explore the origins of this behavior, we develop a rational analysis (Anderson, 1990) of the learning problem facing individuals in uncertain decision environments. Our analysis demonstrates that an unbiased learner would adopt melioration as the optimal response strategy for maximizing long-term gain. We suggest that many documented cases of melioration can be reinterpreted not as irrational choice but rather as globally optimal choice under uncertainty.

Addressing health workforce distribution concerns: a discrete choice experiment to develop rural retention strategies in Cameroon.

Science.gov (United States)

Robyn, Paul Jacob; Shroff, Zubin; Zang, Omer Ramses; Kingue, Samuel; Djienouassi, Sebastien; Kouontchou, Christian; Sorgho, Gaston

2015-03-01

Nearly every nation in the world faces shortages of health workers in remote areas. Cameroon is no exception to this. The Ministry of Public Health (MoPH) is currently considering several rural retention strategies to motivate qualified health personnel to practice in remote rural areas. To better calibrate these mechanisms and to develop evidence-based retention strategies that are attractive and motivating to health workers, a Discrete Choice Experiment (DCE) was conducted to examine what job attributes are most attractive and important to health workers when considering postings in remote areas. The study was carried out between July and August 2012 among 351 medical students, nursing students and health workers in Cameroon. Mixed logit models were used to analyze the data. Among medical and nursing students a rural retention bonus of 75% of base salary (aOR= 8.27, 95% CI: 5.28-12.96, Pimpact measurements were also estimated to identify combination of incentives that health workers would find most attractive. Based on these findings, the study recommends the introduction of a system of substantial monetary bonuses for rural service along with ensuring adequate and functional equipment and uninterrupted supplies. By focusing on the analysis of locally relevant, actionable incentives, generated through the involvement of policy-makers at the design stage, this study provides an example of research directly linked to policy action to address a vitally important issue in global health.

The Relation of Students’ Choice of Private Higher Education and Marketing Strategies in Bosnia and Herzegovina

Directory of Open Access Journals (Sweden)

Ozbal Okai

2015-09-01

Full Text Available Severe market conditions and advancing technology demand a well-planned and a strategic marketing approach in all sectors as well as in education sector. This study examined the relationship between the students’ choice and the marketing strategies of private higher education (HE in Bosnia and Herzegovina (BIH. To perceive this relation, we chose one of the private universities in Sarajevo. The study is based on a student survey which employed a questionnaire of 55 questions under 4 sub-groups and was done by 146 students. The first step of the questionnaire was determining the motives of the students to attend a private HE institution. The next part was to investigate the information sources of the students. These two steps followed the understanding of the evaluation criteria and the decision process of the students. Finally, the survey was concluded with the post enrollment observations of the students. The data obtained analyzed by SPSS software. The results revealed a strong consistency. The students were willing to have quality education and improve themselves via HE. They were using internet as a prior information source but would not make a final decision without parents’ confirmation. The overall satisfaction of the students showed that our subject university communicates with the target groups efficiently and enjoys the benefits of positive ‘word of mouth’ marketing. Finally, we addressed the marketing strategies that we reaped throughout the study to suggest a course of action to private HEIs in the conclusion part.

The effects of single instance, multiple instance, and general case training on generalized vending machine use by moderately and severely handicapped students.

OpenAIRE

Sprague, J R; Horner, R H

1984-01-01

This report provides an experimental analysis of generalized vending machine use by six moderately or severely retarded high school students. Dependent variables were training trials to criterion and performance on 10 nontrained "generalization" vending machines. A multiple-baseline design across subjects was used to compare three strategies for teaching generalized vending machine use. Training occurred with (a) a single vending machine, (b) three similar machines, or (c) three machines that...

MRR and TWR evaluation on electrical discharge machining of Ti-6Al-4V using tungsten : copper composite electrode

Science.gov (United States)

Prasanna, J.; Rajamanickam, S.; Amith Kumar, O.; Karthick Raj, G.; Sathya Narayanan, P. V. V.

2017-05-01

In this paper Ti-6Al-4V used as workpiece material and it is keenly seen in variety of field including medical, chemical, marine, automotive, aerospace, aviation, electronic industries, nuclear reactor, consumer products etc., The conventional machining of Ti-6Al-4V is very difficult due to its distinctive properties. The Electrical Discharge Machining (EDM) is right choice of machining this material. The tungsten copper composite material is employed as tool material. The gap voltage, peak current, pulse on time and duty factor is considered as the machining parameter to analyze the machining characteristics Material Removal Rate (MRR) and Tool Wear Rate (TWR). The Taguchi method is provided to work for finding the significant parameter of EDM. It is found that for MRR significant parameters rated in the following order Gap Voltage, Pulse On-Time, Peak Current and Duty Factor. On the other hand for TWR significant parameters are listed in line of Gap Voltage, Duty Factor, Peak Current and Pulse On-Time.

Probability matching and strategy availability

OpenAIRE

J. Koehler, Derek; Koehler, Derek J.; James, Greta

2010-01-01

Findings from two experiments indicate that probability matching in sequential choice arises from an asymmetry in strategy availability: The matching strategy comes readily to mind, whereas a superior alternative strategy, maximizing, does not. First, compared with the minority who spontaneously engage in maximizing, the majority of participants endorse maximizing as superior to matching in a direct comparison when both strategies are described. Second, when the maximizing strategy is brought...

Some relations between quantum Turing machines and Turing machines

OpenAIRE

Sicard, Andrés; Vélez, Mario

1999-01-01

For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

Strategies and logics of internationalization

Directory of Open Access Journals (Sweden)

Mahjouba Ben Salem

2013-07-01

Full Text Available The race between firms to acquire capacities worldwide has evolved in a chronological order which centered at first around products, then around position to move later on to skills and to focus currently on networks. Similarly, when observing the evolution of the different international development strategies, it was found out that they have started by the exportation and the setting up of production subsidiaries to move more recently to such strategies as mergers & acquisitions and international alliances. The present paper investigates the relationship between the internationalization strategies and logics and comes to the conclusion that, a particular logic is behind every choice made. Indeed, the present work was conducted within the Tunisian food enterprises and helped confirm this hypothesis as it was found out that the position logic is behind the choice of exportation and the creation of production subsidiaries while the choice of partnership is based on the logic of skills. The option for merger, on the other hand, is motivated by the networks logic.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Application of discrete choice experiments to enhance stakeholder engagement as a strategy for advancing implementation: a systematic review.

Science.gov (United States)

Salloum, Ramzi G; Shenkman, Elizabeth A; Louviere, Jordan J; Chambers, David A

2017-11-23

One of the key strategies to successful implementation of effective health-related interventions is targeting improvements in stakeholder engagement. The discrete choice experiment (DCE) is a stated preference technique for eliciting individual preferences over hypothetical alternative scenarios that is increasingly being used in health-related applications. DCEs are a dynamic approach to systematically measure health preferences which can be applied in enhancing stakeholder engagement. However, a knowledge gap exists in characterizing the extent to which DCEs are used in implementation science. We conducted a systematic literature search (up to December 2016) of the English literature to identify and describe the use of DCEs in engaging stakeholders as an implementation strategy. We searched the following electronic databases: MEDLINE, Econlit, PsychINFO, and the CINAHL using mesh terms. Studies were categorized according to application type, stakeholder(s), healthcare setting, and implementation outcome. Seventy-five publications were selected for analysis in this systematic review. Studies were categorized by application type: (1) characterizing demand for therapies and treatment technologies (n = 32), (2) comparing implementation strategies (n = 22), (3) incentivizing workforce participation (n = 11), and (4) prioritizing interventions (n = 10). Stakeholders included providers (n = 27), patients (n = 25), caregivers (n = 5), and administrators (n = 2). The remaining studies (n = 16) engaged multiple stakeholders (i.e., combination of patients, caregivers, providers, and/or administrators). The following implementation outcomes were discussed: acceptability (n = 75), appropriateness (n = 34), adoption (n = 19), feasibility (n = 16), and fidelity (n = 3). The number of DCE studies engaging stakeholders as an implementation strategy has been increasing over the past decade. As DCEs are more widely used as a

Machine-learning-assisted materials discovery using failed experiments

Science.gov (United States)

Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.; Falk, Casey; Wenny, Malia B.; Mollo, Aurelio; Zeller, Matthias; Friedler, Sorelle A.; Schrier, Joshua; Norquist, Alexander J.

2016-05-01

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on ‘dark’ reactions—failed or unsuccessful hydrothermal syntheses—collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted

Chance, choice, and the future of reproduction.

Science.gov (United States)

Miller, W B

1983-11-01

The evolution of reproduction has been characterized by the development of complex biological and behavioral mechanisms that serve to regulate chance events. Human reproduction has been characterized by the increasing importance of individual choice. Some contemporary manifestations of this broad trend are the high incidence of contraceptive and "proceptive" behavior among couples in Western, industrialized nations. The former behavior willingly attempts to prevent conception while the latter actively attempts to induce conception (such as concentrating intercourse around the time of ovulation). Both patterns of behavior indicate that a choice is being made. A 3-year study of 1000 women revealed proceptive behavior as the most important factor predicting occurance of conception among married couples in the United States. The general strategeis people follow while making childbearing decisions: termination, sequencing, and pre-planning form a continuum following the historical trend toward greater reproductive control. In the terminating strategy, a couple makes no decision about child bearing until the number of children they have become enough or too much. In the sequencing strategy, decisions to have children are made 1 child at a time until a satisfactory limit is reached. In the pre-planning strategy, a plan is worked out ahead of time and is subsequently carried out. As new reproductive technology is introduced and as progressive change is made in society's reproductive related values and beliefs, choice will continue to dominate chance as the highly likely trend for the future of reproduction. Surrogate maternity is just 1 example of this trend. However, these new options, which culminate in the theory and practice of "progensis," (still in its infancy), as well as offering a rich opportunity, can also incur psychological burdens on a couple. Thus, as with any kind of freedom, these developments will require care, caution and responsibility.

A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

Directory of Open Access Journals (Sweden)

Fang Su

2013-01-01

Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

What does location choice reveal about knowledge-seeking strategies of emerging market multinationals in the EU?

DEFF Research Database (Denmark)

Jindra, Bjørn; Hassan, Sohaib Shahzad; Cantner, Uwe

2016-01-01

The European Union is one of the largest recipients of outward foreign direct investment from emerging economies. We apply different discrete choice models to analyze the location choice of 4555 emerging market firms in 93 sub-national regions of the European Union. In particular, we test to what...... extent these firms’ location choices are related to agglomeration economies and knowledge externalities, because these have been suggested as potential sources to propel learning and technological catching-up. Our results indicate that emerging market firms’ location choices are positively affected...... by agglomeration economies and knowledge externalities. In addition, we can identify differences in the valuation of various sub-national location factors as well as differences in the substitution pattern between alternative regions for firms originating from emerging markets. The evidence supports the argument...

Combining extreme learning machines using support vector machines for breast tissue classification.

Science.gov (United States)

Daliri, Mohammad Reza

2015-01-01

In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

Simulations of Quantum Turing Machines by Quantum Multi-Stack Machines

OpenAIRE

Qiu, Daowen

2005-01-01

As was well known, in classical computation, Turing machines, circuits, multi-stack machines, and multi-counter machines are equivalent, that is, they can simulate each other in polynomial time. In quantum computation, Yao [11] first proved that for any quantum Turing machines $M$, there exists quantum Boolean circuit $(n,t)$-simulating $M$, where $n$ denotes the length of input strings, and $t$ is the number of move steps before machine stopping. However, the simulations of quantum Turing ma...

An effect of loudness of advisory speech on a choice response task

Science.gov (United States)

Utsuki, Narisuke; Takeuchi, Yoshinori; Nomiyama, Takenori

1995-03-01

Recent technologies have realized talking advisory/guidance systems in which machines give advice and guidance to operators in speech. However, nonverbal aspects of spoken messages may have significant effects on an operator's behavior. Twelve subjects participated in a TV game-like choice response task where they were asked to choose a 'true' target from three invader-like figures displayed on a CRT screen. The subjects had received a prerecorded advice designating either left, center, or right target that would be true before each choice. The position of the 'true' targets and advice were preprogrammed in pseudorandom sequences. In other words, there was no way for the subjects to predict the 'true' target and there was no relationship between spoken advice and the true target position. The subjects tended to make more choices corresponding to the presented messages when the messages were presented in a louder voice than in a softer voice. Choice response time was significantly shorter when the response was the same as the advice indicated. The shortening of response time was slightly greater when advice was presented in a louder voice. This study demonstrates that spoken advice may result in faster and less deliberate reponses in accordance with the presented messages which are given by talking guidance systems.

Development of Mathematical Model for Lifecycle Management Process of New Type of Multirip Saw Machine

Directory of Open Access Journals (Sweden)

B. V. Phung

2017-01-01

variables. Based on the obtained unified information model, a multi-criterion problem has been formulated for the process of automated synthesis and rational choice to design and manufacture the multirip saw machine of new generation.

The eyes have it: Using eye tracking to inform information processing strategies in multi-attributes choices.

Science.gov (United States)

Ryan, Mandy; Krucien, Nicolas; Hermens, Frouke

2018-04-01

Although choice experiments (CEs) are widely applied in economics to study choice behaviour, understanding of how individuals process attribute information remains limited. We show how eye-tracking methods can provide insight into how decisions are made. Participants completed a CE, while their eye movements were recorded. Results show that although the information presented guided participants' decisions, there were also several processing biases at work. Evidence was found of (a) top-to-bottom, (b) left-to-right, and (c) first-to-last order biases. Experimental factors-whether attributes are defined as "best" or "worst," choice task complexity, and attribute ordering-also influence information processing. How individuals visually process attribute information was shown to be related to their choices. Implications for the design and analysis of CEs and future research are discussed. Copyright © 2017 John Wiley & Sons, Ltd.

Machine tool structures

CERN Document Server

Koenigsberger, F

1970-01-01

Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

US fusion community discussion on fusion strategies

International Nuclear Information System (INIS)

Marton, W.A.

1998-01-01

On April 26 - May 1, 1998, a US Fusion Community Forum for Major Next-Step Experiments was held at Madison, Wisconsin, USA. Both the Single Integrated Step strategy and the Multiple Machine strategy have substantial support from the about 180 scientists and engineers who participated

The technopolitics of big infrastructure and the Chinese water machine

Directory of Open Access Journals (Sweden)

Britt Crow-Miller

2017-06-01

Full Text Available Despite widespread recognition of the problems caused by relying on engineering approaches to water management issues, since 2000 China has raised its commitment to a concrete-heavy approach to water management. While, historically, China’s embrace of modernist water management could be understood as part of a broader set of ideas about controlling nature, in the post-reform era this philosophical view has merged with a technocratic vision of national development. In the past two decades, a Chinese Water Machine has coalesced: the institutional embodiment of China’s commitment to large infrastructure. The technocratic vision of the political and economic elite at the helm of this Machine has been manifest in the form of some of the world’s largest water infrastructure projects, including the Three Gorges Dam and the South-North Water Transfer Project, and in the exporting of China’s vision of concrete-heavy development beyond its own borders. This paper argues that China’s approach to water management is best described as a techno-political regime that extends well beyond infrastructure, and is fundamentally shaped by both past choices and current political-economic conditions. Emerging from this regime, the Chinese Water Machine is one of the forces driving the (return to big water infrastructure globally.

Learning features for tissue classification with the classification restricted Boltzmann machine

DEFF Research Database (Denmark)

van Tulder, Gijs; de Bruijne, Marleen

2014-01-01

Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convo...... outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy....

Electricity of machine tool

International Nuclear Information System (INIS)

Gijeon media editorial department

1977-10-01

This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

Promoting the Purchase of Low-Calorie Foods from School Vending Machines: A Cluster-Randomized Controlled Study

Science.gov (United States)

Kocken, Paul L.; Eeuwijk, Jennifer; van Kesteren, Nicole M.C.; Dusseldorp, Elise; Buijs, Goof; Bassa-Dafesh, Zeina; Snel, Jeltje

2012-01-01

Background: Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. Methods: A school-based randomized controlled trial was conducted in 13 experimental schools and 15 control schools. Three strategies…

Machine medical ethics

CERN Document Server

Pontier, Matthijs

2015-01-01

The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

Humanizing machines: Anthropomorphization of slot machines increases gambling.

Science.gov (United States)

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

Informed choice and the nanny state: learning from the tobacco industry.

Science.gov (United States)

Hoek, Janet

2015-08-01

To examine the 'nanny state' arguments used by tobacco companies, explore the cognitive biases that impede smokers' ability to make fully informed choices, and analyse the implications for those working to limit the harmful effects of other risk products. A critical analysis of the practices engaged in by the tobacco industry, the logic on which they relied, and the extent to which their work has informed approaches used by other industries. The tobacco industry's deliberate strategy of challenging scientific evidence undermines smokers' ability to understand the harms smoking poses and questions arguments that smoking is an informed choice. Cognitive biases predispose smokers to discount risk information, particularly when this evidence is disputed and framed as uncertain. Only state intervention has held the tobacco industry to account and begun ameliorating the effects of their sustained duplicity. Evidence other industries are now adopting similar tactics, particularly use of 'nanny state' claims to oppose proportionate interventions, is concerning. Some marketing strategies have deliberately mis-informed consumers thus directly contributing to many public health problems. Far from removing free choice, government policies that restrain commercial communications and stimuli are prerequisites necessary to promote free choice. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Known Unknowns in Judgment and Choice

OpenAIRE

Walters, Daniel

2017-01-01

This dissertation investigates how people make inferences about missing information. Whereas most prior literature focuses on how people process known information, I show that the extent to which people make inferences about missing information impacts judgments and choices. Specifically, I investigate how (1) awareness of known unknowns affects overconfidence in judgment in Chapter 1, (2) beliefs about the knowability of unknowns impacts investment strategies in Chapter 2, and (3) inferences...

Assessing Scientific Practices Using Machine-Learning Methods: How Closely Do They Match Clinical Interview Performance?

Science.gov (United States)

Beggrow, Elizabeth P.; Ha, Minsu; Nehm, Ross H.; Pearl, Dennis; Boone, William J.

2014-02-01

The landscape of science education is being transformed by the new Framework for Science Education (National Research Council, A framework for K-12 science education: practices, crosscutting concepts, and core ideas. The National Academies Press, Washington, DC, 2012), which emphasizes the centrality of scientific practices—such as explanation, argumentation, and communication—in science teaching, learning, and assessment. A major challenge facing the field of science education is developing assessment tools that are capable of validly and efficiently evaluating these practices. Our study examined the efficacy of a free, open-source machine-learning tool for evaluating the quality of students' written explanations of the causes of evolutionary change relative to three other approaches: (1) human-scored written explanations, (2) a multiple-choice test, and (3) clinical oral interviews. A large sample of undergraduates (n = 104) exposed to varying amounts of evolution content completed all three assessments: a clinical oral interview, a written open-response assessment, and a multiple-choice test. Rasch analysis was used to compute linear person measures and linear item measures on a single logit scale. We found that the multiple-choice test displayed poor person and item fit (mean square outfit >1.3), while both oral interview measures and computer-generated written response measures exhibited acceptable fit (average mean square outfit for interview: person 0.97, item 0.97; computer: person 1.03, item 1.06). Multiple-choice test measures were more weakly associated with interview measures (r = 0.35) than the computer-scored explanation measures (r = 0.63). Overall, Rasch analysis indicated that computer-scored written explanation measures (1) have the strongest correspondence to oral interview measures; (2) are capable of capturing students' normative scientific and naive ideas as accurately as human-scored explanations, and (3) more validly detect understanding

Age-Related Differences of Individuals' Arithmetic Strategy Utilization with Different Level of Math Anxiety.

Science.gov (United States)

Si, Jiwei; Li, Hongxia; Sun, Yan; Xu, Yanli; Sun, Yu

2016-01-01

The present study used the choice/no-choice method to investigate the effect of math anxiety on the strategy used in computational estimation and mental arithmetic tasks and to examine age-related differences in this regard. Fifty-seven fourth graders, 56 sixth graders, and 60 adults were randomly selected to participate in the experiment. Results showed the following: (1) High-anxious individuals were more likely to use a rounding-down strategy in the computational estimation task under the best-choice condition. Additionally, sixth-grade students and adults performed faster than fourth-grade students on the strategy execution parameter. Math anxiety affected response times (RTs) and the accuracy with which strategies were executed. (2) The execution of the partial-decomposition strategy was superior to that of the full-decomposition strategy on the mental arithmetic task. Low-math-anxious persons provided more accurate answers than did high-math-anxious participants under the no-choice condition. This difference was significant for sixth graders. With regard to the strategy selection parameter, the RTs for strategy selection varied with age.

Age-Related Differences of Individuals’ Arithmetic Strategy Utilization with Different Level of Math Anxiety

Science.gov (United States)

Si, Jiwei; Li, Hongxia; Sun, Yan; Xu, Yanli; Sun, Yu

2016-01-01

The present study used the choice/no-choice method to investigate the effect of math anxiety on the strategy used in computational estimation and mental arithmetic tasks and to examine age-related differences in this regard. Fifty-seven fourth graders, 56 sixth graders, and 60 adults were randomly selected to participate in the experiment. Results showed the following: (1) High-anxious individuals were more likely to use a rounding-down strategy in the computational estimation task under the best-choice condition. Additionally, sixth-grade students and adults performed faster than fourth-grade students on the strategy execution parameter. Math anxiety affected response times (RTs) and the accuracy with which strategies were executed. (2) The execution of the partial-decomposition strategy was superior to that of the full-decomposition strategy on the mental arithmetic task. Low-math-anxious persons provided more accurate answers than did high-math-anxious participants under the no-choice condition. This difference was significant for sixth graders. With regard to the strategy selection parameter, the RTs for strategy selection varied with age. PMID:27803685

Age-related Differences of Individuals’ Arithmetic Strategy Utilization with Different Level of Math Anxiety

Directory of Open Access Journals (Sweden)

Jiwei Si

2016-10-01

Full Text Available The present study used the choice/no-choice method to investigate the effect of math anxiety on the strategy used in computational estimation and mental arithmetic tasks and to examine age-related differences in this regard. 57 fourth graders, 56 sixth graders, and 60 adults were randomly selected to participate in the experiment. Results showed the following: (1 High-anxious individuals were more likely to use a rounding-down strategy in the computational estimation task under the best-choice condition. Additionally, sixth-grade students and adults performed faster than fourth-grade students on the strategy execution parameter. Math anxiety affected response times (RTs and the accuracy with which strategies were executed. (2 The execution of the partial-decomposition strategy was superior to that of the full-decomposition strategy on the mental arithmetic task. Low-math-anxious persons provided more accurate answers than did high-math-anxious participants under the no-choice condition. This difference was significant for sixth graders. With regard to the strategy selection parameter, the RTs for strategy selection varied with age.

Choice in HIV testing: the acceptability and anticipated use of a self ...

African Journals Online (AJOL)

Combination HIV prevention is being widely promoted by funders. This strategy aims to offer HIV prevention choices that can be selected and combined to decrease HIV risk in ways that fit with each individual's situation. Treatment as prevention and pre-exposure prophylaxis are two new evidence-based strategies to ...

Code-expanded radio access protocol for machine-to-machine communications

DEFF Research Database (Denmark)

Thomsen, Henning; Kiilerich Pratas, Nuno; Stefanovic, Cedomir

2013-01-01

The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated b...... subframes and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the massive support of Machine-Type Communication users that is beyond the reach of current systems.......The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated...... by the random access method employed in LTE, which significantly increases the amount of contention resources without increasing the system resources, such as contention subframes and preambles. This is accomplished by a logical, rather than physical, extension of the access method in which the available system...

Chimpanzee choice rates in competitive games match equilibrium game theory predictions.

Science.gov (United States)

Martin, Christopher Flynn; Bhui, Rahul; Bossaerts, Peter; Matsuzawa, Tetsuro; Camerer, Colin

2014-06-05

The capacity for strategic thinking about the payoff-relevant actions of conspecifics is not well understood across species. We use game theory to make predictions about choices and temporal dynamics in three abstract competitive situations with chimpanzee participants. Frequencies of chimpanzee choices are extremely close to equilibrium (accurate-guessing) predictions, and shift as payoffs change, just as equilibrium theory predicts. The chimpanzee choices are also closer to the equilibrium prediction, and more responsive to past history and payoff changes, than two samples of human choices from experiments in which humans were also initially uninformed about opponent payoffs and could not communicate verbally. The results are consistent with a tentative interpretation of game theory as explaining evolved behavior, with the additional hypothesis that chimpanzees may retain or practice a specialized capacity to adjust strategy choice during competition to perform at least as well as, or better than, humans have.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Science.gov (United States)

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Direct torque control design and experimental evaluation for the brushless doubly fed machine

International Nuclear Information System (INIS)

Sarasola, Izaskun; Poza, Javier; Rodriguez, Miguel A.; Abad, Gonzalo

2011-01-01

In this paper, a direct torque control (DTC) strategy for the brushless doubly fed machine (BDFM) is presented. After analyzing the mathematical model of this machine, the voltage vectors look-up table of classical DTC techniques is derived. Then, the behavior of the machine is studied when it is controlled by the developed DTC technique, concluding that under some specific operation conditions, a BDFM could present a time interval where the torque and the flux can not be controlled simultaneously. In these cases, two different control solutions have been defined: 'flux priority' and 'torque priority'. Finally, simulation and experimental results validate the effectiveness of the proposed control algorithms.

Human X-chromosome inactivation pattern distributions fit a model of genetically influenced choice better than models of completely random choice

Science.gov (United States)

Renault, Nisa K E; Pritchett, Sonja M; Howell, Robin E; Greer, Wenda L; Sapienza, Carmen; Ørstavik, Karen Helene; Hamilton, David C

2013-01-01

In eutherian mammals, one X-chromosome in every XX somatic cell is transcriptionally silenced through the process of X-chromosome inactivation (XCI). Females are thus functional mosaics, where some cells express genes from the paternal X, and the others from the maternal X. The relative abundance of the two cell populations (X-inactivation pattern, XIP) can have significant medical implications for some females. In mice, the ‘choice' of which X to inactivate, maternal or paternal, in each cell of the early embryo is genetically influenced. In humans, the timing of XCI choice and whether choice occurs completely randomly or under a genetic influence is debated. Here, we explore these questions by analysing the distribution of XIPs in large populations of normal females. Models were generated to predict XIP distributions resulting from completely random or genetically influenced choice. Each model describes the discrete primary distribution at the onset of XCI, and the continuous secondary distribution accounting for changes to the XIP as a result of development and ageing. Statistical methods are used to compare models with empirical data from Danish and Utah populations. A rigorous data treatment strategy maximises information content and allows for unbiased use of unphased XIP data. The Anderson–Darling goodness-of-fit statistics and likelihood ratio tests indicate that a model of genetically influenced XCI choice better fits the empirical data than models of completely random choice. PMID:23652377

Drug choice as a self-handicapping strategy in response to noncontingent success.

Science.gov (United States)

Berglas, S; Jones, E E

1978-04-01

In two closely related experiments, college student subjects were instructed to choose between a drug that allegedly interfered with performance and a drug that allegedly enhanced performance. This choice was the main dependent measure of the experiment. The drug choice intervened between work on soluble or insoluble problems and a promised retest on similar problems. In Experiment 1, all subjects received success feedback after their initial problem-solving attempts, thus creating one condition in which the success appeared to be accidental (noncontingent on performance) and one in which the success appeared to be contingent on appropriate knowledge. Males in the noncontingent-success condition were alone in preferring the performance-inhibiting drug, presumably because they wished to externalize probable failure on the retest. The predicted effect, however, did not hold for female subjects. Experiment 2 replicated the unique preference shown by males after noncontingent success and showed the critical importance of success feedback.

Strategy and New Media

DEFF Research Database (Denmark)

Plesner, Ursula; Gulbrandsen, Ib Tunby

2015-01-01

Despite current attention to the materiality of organizations and the performative role of tools, devices, artefacts and objects in processes of strategy-making, the impact of new media has not been thoroughly conceptualized in the strategy literature. We argue that new media challenge core...... assumptions in strategy about control, boundaries and choice. To understand their constitutive effects and the implications for strategy-making, it is necessary to develop a research agenda oriented towards understanding technological affordances – but not only in local practices. Due to vital characteristics...

The validation and assessment of machine learning: a game of prediction from high-dimensional data

DEFF Research Database (Denmark)

Pers, Tune Hannes; Albrechtsen, A; Holst, C

2009-01-01

In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often...... the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively....

The impact of choice context on consumers' choice heuristics

DEFF Research Database (Denmark)

Mueller Loose, Simone; Scholderer, Joachim; Corsi, Armando M.

2012-01-01

Context effects in choice settings have received recent attention but little is known about the impact of context on choice consistency and the extent to which consumers apply choice heuristics. The sequence of alternatives in a choice set is examined here as one specific context effect. We compare...... how a change from a typical price order to a sensory order in wine menus affects consumer choice. We use pre-specified latent heuristic classes to analyse the existence of different choice processes, which begins to untangle the ‘black box’ of how consumers choose. Our findings indicate...... that in the absence of price order, consumers are less price-sensitive, pay more attention to visually salient cues, are less consistent in their choices and employ other simple choice heuristics more frequently than price. Implications for consumer research, marketing and consumer policy are discussed....

Evaluation of consumers' choice of wooden dining furniture in ...

African Journals Online (AJOL)

Evaluation of consumers' choice of wooden dining furniture in Southwestern Nigeria: A market strategy for furniture manufacturers and marketers. ... The study recommended increase use of durable Lesser Used Species (LUS) of trees for producing dining furniture and location of showrooms to target high income earners.

The validation and assessment of machine learning: a game of prediction from high-dimensional data.

Directory of Open Access Journals (Sweden)

Tune H Pers

Full Text Available In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often implies that multiple methods are tested and compared on the same set of data. This is particularly difficult in situations that are prone to over-fitting where the number of subjects is low compared to the number of potential predictors. The article presents a game which provides some grounds for conducting a fair model comparison. Each player selects a modeling strategy for predicting individual response from potential predictors. A strictly proper scoring rule, bootstrap cross-validation, and a set of rules are used to make the results obtained with different strategies comparable. To illustrate the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively.

Knowledge-oriented strategies in the metal industry (empirical studies

Directory of Open Access Journals (Sweden)

A. Krawczyk-Sołtys

2016-07-01

Full Text Available The aim of this article is an attempt to determine which knowledge-oriented strategies can give metal industry enterprises the best results in achieving and maintaining a competitive advantage. To determine which of these discussed in the literature and implemented in various organizations knowledge-oriented strategies may prove to be the most effective in the metal industry, empirical research has begun. A chosen strategy of knowledge management and supporting strategies are the basis of a choice of methods and means of intended implementation. The choice of a specific knowledge management strategy may also result in the need for changes in an organization, particularly in an information system, internal communication, work organization and human resource management.

DEFINITION AND CHOICE OF FINANCIAL STRATEGY OF CORPORATION

Directory of Open Access Journals (Sweden)

V. S. Michel

2015-01-01

Full Text Available The current stage of economic development of theRussian Federationmakes the question of the analysis of the financial condition of corporations, as well as the development of their financial strategy. This is due to a large share of unprofitable enterprises in theRussian Federation, as well as low levels of liquidity and autonomy. Given that the financial condition of the company depends largely on the success of its activities, the analysis of the financial condition of the company paid much attention. Pledge of survival and the foundation stability of enterprises and organizations in a market economy is financial stability. Financial stability of the company is able to manipulate the funds to ensure the sustainability of production and sales through effective use, and to minimize the cost of its expansion and renovation. View of this article is devoted to the most important task of strategic management, namely the identification and selection of effective financial strategy of the corporation. 

Two-motor single-inverter field-oriented induction machine drive ...

Indian Academy of Sciences (India)

is a challenge since no two motors will have exactly the same operating ... system consisting of multiple machines fed by a single inverter offers an .... Time (s). T em_2. (Nm). (b)Mean strategy. Figure 4. Multimachine dynamics: ..... oscillations in the torque response, and can be identified as rotor eigenvalues, and the real ...

陕西电信天翼3G双模智能机市场发展策略研究%Shanxi Telecom Tianyi 3G Dual-mode Intelligent Machine Market Development Strategy Research

Institute of Scientific and Technical Information of China (English)

贾琳

2013-01-01

中国电信陕西公司天翼3G智能终端销售在2011年至2012年间快速上升，但2012年下半年，智能机发展出现阶段性波动，增长率降低；同时竞争对手发力双模智能机，快速挖转中国移动用户。分析和论证发展天翼3G双模智能机的必要性，并从产品、价格、渠道、宣传和促销4方面提出了天翼3G双模智能机发展的具体策略，对于陕西电信进一步加快智能机的发展，拓展社会开放渠道，保持3G市场领先优势具有重要意义。%3G intelligent terminal sales of Shaanxi China Telecom Tianyi form 2011 to 2012 years the rises rapidly,but the second half of 2012, intelligent machine development stage of volatility, growth rate decreased,rival force of dual-mode intelligent machine, fast digging to China Mobile users at the same time. This paper focuses on a telecommunications enterprise employee's point of view, fully aware of the changing market environment and competition strategy, the necessity analysis and demonstration of development day wing 3G dual-mode intelligent machines, and specific strategies day wing development 3G dual-mode intelligent machine is put forward from the product, price, channel, promotion and promotion four aspects, for Shaanxi to further accelerate the development of the telecom intelligent machine, expand social open channel, it is important to keep the 3G market advantage.

Simple machines

CERN Document Server

Graybill, George

2007-01-01

Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center

1998-03-19

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess the status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.

Recent Educational Experiences in Electric Machine Maintenance Teaching

Directory of Open Access Journals (Sweden)

Jose Alfonso Antonino-Daviu

2013-05-01

Full Text Available Maintenance of electric machines and installations is a particularly important area; eventual faults in these devices may lead to significant losses in terms of time and money. The investment and concern in developing proper maintenance protocols have been gradually increasing over recent decades. As a consequence, there is a need to instruct future engineers in the electric machines and installations maintenance area. The subject "Maintenance of Electric Machines and Installations" has been designed under this idea. It is taught within an official master degree in Maintenance Engineering. This work describes the educational experiences reached during the initial years of the teaching of the subject. Aspects such as student profiles, subject approaches, design of the syllabus, methodology and structure of the laboratory sessions are remarked in the work. In addition, the paper discusses other educational strategies which are being introduced to increase the interest in the subject, such as integration of Information and Communication Technologies (ICT, promotion of the collaborative work, inclusion of the possibility of remote learning or development of new assessment systems.

Superconducting rotating machines

International Nuclear Information System (INIS)

Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.

1975-01-01

The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed

Report of an EU-US symposium on understanding nutrition-related consumer behavior: strategies to promote a lifetime of healthy food choices.

Science.gov (United States)

Friedl, Karl E; Rowe, Sylvia; Bellows, Laura L; Johnson, Susan L; Hetherington, Marion M; de Froidmont-Görtz, Isabelle; Lammens, Veerle; Hubbard, Van S

2014-01-01

This report summarizes an EU-US Task Force on Biotechnology Research symposium on healthy food choices and nutrition-related purchasing behaviors. This meeting was unique in its transdisciplinary approach to obesity and in bringing together scientists from academia, government, and industry. Discussion relevant to funders and researchers centered on (1) increased use of public-private partnerships, (2) the complexity of food behaviors and obesity risk and multilevel aspects that must be considered, and (3) the importance of transatlantic cooperation and collaboration that could accelerate advances in this field. A call to action stressed these points along with a commitment to enhanced communication strategies. Copyright © 2014 Society for Nutrition Education and Behavior. All rights reserved.

Deep neural mapping support vector machines.

Science.gov (United States)

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modelling Choice of Information Sources

Directory of Open Access Journals (Sweden)

Agha Faisal Habib Pathan

2013-04-01

Full Text Available This paper addresses the significance of traveller information sources including mono-modal and multimodal websites for travel decisions. The research follows a decision paradigm developed earlier, involving an information acquisition process for travel choices, and identifies the abstract characteristics of new information sources that deserve further investigation (e.g. by incorporating these in models and studying their significance in model estimation. A Stated Preference experiment is developed and the utility functions are formulated by expanding the travellers' choice set to include different combinations of sources of information. In order to study the underlying choice mechanisms, the resulting variables are examined in models based on different behavioural strategies, including utility maximisation and minimising the regret associated with the foregone alternatives. This research confirmed that RRM (Random Regret Minimisation Theory can fruitfully be used and can provide important insights for behavioural studies. The study also analyses the properties of travel planning websites and establishes a link between travel choices and the content, provenance, design, presence of advertisements, and presentation of information. The results indicate that travellers give particular credence to governmentowned sources and put more importance on their own previous experiences than on any other single source of information. Information from multimodal websites is more influential than that on train-only websites. This in turn is more influential than information from friends, while information from coachonly websites is the least influential. A website with less search time, specific information on users' own criteria, and real time information is regarded as most attractive

Automatic fitting of conical envelopes to free-form surfaces for flank CNC machining

OpenAIRE

Bo P.; Bartoň M.; Pottmann H.

2017-01-01

We propose a new algorithm to detect patches of free-form surfaces that can be well approximated by envelopes of a rotational cone under a rigid body motion. These conical envelopes are a preferable choice from the manufacturing point of view as they are, by-definition, manufacturable by computer numerically controlled (CNC) machining using the efficient flank (peripheral) method with standard conical tools. Our geometric approach exploits multi-valued vector fields that consist of vectors in...

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

Science.gov (United States)

Salehi, Hadi; Das, Saptarshi; Chakrabartty, Shantanu; Biswas, Subir; Burgueño, Rigoberto

2017-04-01

This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

The Effect of Strategic Choices and Management Control Systems on Organizational Performance

Directory of Open Access Journals (Sweden)

Emanuel Junqueira

Full Text Available ABSTRACT The study investigates the effect of generic strategic choices and management control systems (MCS on the organizational performance of large and medium-sized companies located in Espírito Santo, using Contingency Theory as the theoretical framework. It is a quantitative study, using a survey as the data collection technique. 73 questionnaires were validated, after being completed by those responsible for the controlling or related area of these enterprises over the period between February and April of 2014. The data analysis was performed using the structural equations modeling technique. The main results indicate that: (i competitive forces shape the strategy adopted by the organizations surveyed, however, contrary to what the literature predicts, those companies that operate in more competitive environments choose a strategy of cost leadership instead of differentiation; (ii the design and use of the MCS is influenced by the strategy chosen, and the use of contemporary management practices is associated with a differentiation strategy; (iii strategic choices and the MCS have a positive impact on organizational performance. In addition, those companies that combine differentiation strategy with contemporary management practices perform better than the other companies analyzed.

Sustainable machining

CERN Document Server

2017-01-01

This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

Different strategy of hand choice after learning of constant and incremental dynamical perturbation in arm reaching

Directory of Open Access Journals (Sweden)

Chie eHabagishi

2014-02-01

Full Text Available In daily life, we encounter situations where we must quickly decide which hand to use for a motor action. Here, we investigated whether the hand chosen for a motor action varied over a short timescale (i.e., hours with changes in arm dynamics. Participants performed a reaching task in which they moved a specified hand to reach a target on a virtual reality display. During the task, a resistive viscous force field was abruptly applied to only the dominant hand. To evaluate changes in hand choice caused by this perturbation, participants performed an interleaved choice test in which they could freely choose either hand for reaching. Furthermore, to investigate the effect of temporal changes on arm dynamics and hand choice, we exposed the same participants to another condition in which the force field was introduced gradually. When the abrupt force was applied, use of the perturbed hand significantly decreased and not changed during the training. In contrast, when the incremental force was applied, use of the perturbed hand gradually decreased as force increased. Surprisingly, even though the final amount of force was identical between the two conditions, hand choice was significantly biased toward the unperturbed hand in the gradual condition. These results suggest that time-varying changes in arm dynamics may have a greater influence on hand choice than the amplitude of the resistant force itself.

Laser beam machining of polycrystalline diamond for cutting tool manufacturing

Science.gov (United States)

Wyszyński, Dominik; Ostrowski, Robert; Zwolak, Marek; Bryk, Witold

2017-10-01

The paper concerns application of DPSS Nd: YAG 532nm pulse laser source for machining of polycrystalline WC based diamond inserts (PCD). The goal of the research was to determine optimal laser cutting parameters for cutting tool shaping. Basic criteria to reach the goal was cutting edge quality (minimalization of finishing operations), material removal rate (time and cost efficiency), choice of laser beam characteristics (polarization, power, focused beam diameter). The research was planned and realised and analysed according to design of experiment rules (DOE). The analysis of the cutting edge was prepared with use of Alicona Infinite Focus measurement system.

MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae.

Science.gov (United States)

Zhou, Yikang; Li, Gang; Dong, Junkai; Xing, Xin-Hui; Dai, Junbiao; Zhang, Chong

2018-05-01

Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using β-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2-5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of an artificial neural network (ANN) ensemble to avoid over-fitting problem when dealing with a small number of training samples. We then applied MiYA to improve the biosynthesis of violacein. Feed with initial data from a colorimetric plate-based, pre-screened pool of 24 strains producing violacein, MiYA successfully predicted, and verified experimentally, the existence of a strain that showed a 2.42-fold titer improvement in violacein production among 3125 possible designs. Furthermore, MiYA was able to largely avoid the branch pathway of violacein biosynthesis that makes deoxyviolacein, and produces very pure violacein. Together, MiYA combines the advantages of standardized building blocks and machine learning to accelerate the Design-Build-Test-Learn (DBTL) cycle for combinatorial optimization of metabolic pathways, which could significantly accelerate the development of microbial cell factories. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

Directory of Open Access Journals (Sweden)

Ming Cheng

2015-09-01

Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

Asynchronized synchronous machines

CERN Document Server

Botvinnik, M M

1964-01-01

Asynchronized Synchronous Machines focuses on the theoretical research on asynchronized synchronous (AS) machines, which are "hybrids” of synchronous and induction machines that can operate with slip. Topics covered in this book include the initial equations; vector diagram of an AS machine; regulation in cases of deviation from the law of full compensation; parameters of the excitation system; and schematic diagram of an excitation regulator. The possible applications of AS machines and its calculations in certain cases are also discussed. This publication is beneficial for students and indiv

Discrete choice experiment to evaluate factors that influence preferences for antibiotic prophylaxis in pediatric oncology.

Science.gov (United States)

Regier, Dean A; Diorio, Caroline; Ethier, Marie-Chantal; Alli, Amanda; Alexander, Sarah; Boydell, Katherine M; Gassas, Adam; Taylor, Jonathan; Kellow, Charis; Mills, Denise; Sung, Lillian

2012-01-01

Bacterial and fungal infections in pediatric oncology patients cause morbidity and mortality. The clinical utility of antimicrobial prophylaxis in children is uncertain and the personal utility of these agents is disputed. Objectives were to use a discrete choice experiment to: (1) describe the importance of attributes to parents and healthcare providers when deciding between use and non-use of antibacterial and antifungal prophylaxis; and (2) estimate willingness-to-pay for prophylactic strategies. Attributes were chances of infection, death and side effects, route of administration and cost of pharmacotherapy. Respondents were randomized to a discrete choice experiment outlining hypothetical treatment options to prevent antibacterial or antifungal infections. Each respondent was presented 16 choice tasks and was asked to choose between two unlabeled treatment options and an opt-out alternative (no prophylaxis). 102 parents and 60 healthcare providers participated. For the antibacterial discrete choice experiment, frequency of administration was significantly associated with utility for parents but not for healthcare providers. Increasing chances of infection, death, side effects and cost were all significantly associated with decreased utility for parents and healthcare providers in both the antibacterial and antifungal discrete choice experiment. Parental willingness-to-pay was higher than healthcare providers for both strategies. Chances of infection, death, side effects and costs were all significantly associated with utility. Parents have higher willingness-to-pay for these strategies compared with healthcare providers. This knowledge can help to develop prophylaxis programs.

Discrete choice experiment to evaluate factors that influence preferences for antibiotic prophylaxis in pediatric oncology.

Directory of Open Access Journals (Sweden)

Dean A Regier

Full Text Available Bacterial and fungal infections in pediatric oncology patients cause morbidity and mortality. The clinical utility of antimicrobial prophylaxis in children is uncertain and the personal utility of these agents is disputed. Objectives were to use a discrete choice experiment to: (1 describe the importance of attributes to parents and healthcare providers when deciding between use and non-use of antibacterial and antifungal prophylaxis; and (2 estimate willingness-to-pay for prophylactic strategies.Attributes were chances of infection, death and side effects, route of administration and cost of pharmacotherapy. Respondents were randomized to a discrete choice experiment outlining hypothetical treatment options to prevent antibacterial or antifungal infections. Each respondent was presented 16 choice tasks and was asked to choose between two unlabeled treatment options and an opt-out alternative (no prophylaxis.102 parents and 60 healthcare providers participated. For the antibacterial discrete choice experiment, frequency of administration was significantly associated with utility for parents but not for healthcare providers. Increasing chances of infection, death, side effects and cost were all significantly associated with decreased utility for parents and healthcare providers in both the antibacterial and antifungal discrete choice experiment. Parental willingness-to-pay was higher than healthcare providers for both strategies.Chances of infection, death, side effects and costs were all significantly associated with utility. Parents have higher willingness-to-pay for these strategies compared with healthcare providers. This knowledge can help to develop prophylaxis programs.

Strategy selection in the minority game

Science.gov (United States)

D'hulst, R.; Rodgers, G. J.

2000-04-01

We investigate the dynamics of the choice of an active strategy in the minority game. A history distribution is introduced as an analytical tool to study the asymmetry between the two choices offered to the agents. Its properties are studied numerically. It allows us to show that the departure from uniformity in the initial attribution of strategies to the agents is important even in the efficient market. Also, an approximate expression for the variance of the number of agents at one side in the efficient phase is proposed. All the analytical propositions are supported by numerical simulations of the system.

PECULIARITIES OF MOTIVATION AND SELF-ATTITUDE DURING THE CHOICE OF SPECIALIZATION IN A MEDICAL HIGH SCHOOL

Directory of Open Access Journals (Sweden)

T. V. Malyutina

2015-05-01

Full Text Available Personal backgrounds of the choice of specialization by graduates of a medical high school are studied in this article. The hypothesis is checked that the strategies of testees in the situation of the choice of specialization are determined by the peculiarities of self-attitude and motivation. Depending on the specifity of perception of the situation of specialization 4 groups of testees are distinguished and described. They have different types of an individual strategy: functionally efficient, optimal, investigative and troublous.

Machine Shop Lathes.

Science.gov (United States)

Dunn, James

This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

Application of discrete choice experiments to enhance stakeholder engagement as a strategy for advancing implementation: a systematic review

Directory of Open Access Journals (Sweden)

Ramzi G. Salloum

2017-11-01

Full Text Available Abstract Background One of the key strategies to successful implementation of effective health-related interventions is targeting improvements in stakeholder engagement. The discrete choice experiment (DCE is a stated preference technique for eliciting individual preferences over hypothetical alternative scenarios that is increasingly being used in health-related applications. DCEs are a dynamic approach to systematically measure health preferences which can be applied in enhancing stakeholder engagement. However, a knowledge gap exists in characterizing the extent to which DCEs are used in implementation science. Methods We conducted a systematic literature search (up to December 2016 of the English literature to identify and describe the use of DCEs in engaging stakeholders as an implementation strategy. We searched the following electronic databases: MEDLINE, Econlit, PsychINFO, and the CINAHL using mesh terms. Studies were categorized according to application type, stakeholder(s, healthcare setting, and implementation outcome. Results Seventy-five publications were selected for analysis in this systematic review. Studies were categorized by application type: (1 characterizing demand for therapies and treatment technologies (n = 32, (2 comparing implementation strategies (n = 22, (3 incentivizing workforce participation (n = 11, and (4 prioritizing interventions (n = 10. Stakeholders included providers (n = 27, patients (n = 25, caregivers (n = 5, and administrators (n = 2. The remaining studies (n = 16 engaged multiple stakeholders (i.e., combination of patients, caregivers, providers, and/or administrators. The following implementation outcomes were discussed: acceptability (n = 75, appropriateness (n = 34, adoption (n = 19, feasibility (n = 16, and fidelity (n = 3. Conclusions The number of DCE studies engaging stakeholders as an implementation strategy has been increasing over the

Sweet and salty. An assessment of the snacks and beverages sold in vending machines on US post-secondary institution campuses.

Science.gov (United States)

Byrd-Bredbenner, Carol; Johnson, Michelle; Quick, Virginia M; Walsh, Jennifer; Greene, Geoffrey W; Hoerr, Sharon; Colby, Sarah M; Kattelmann, Kendra K; Phillips, Beatrice W; Kidd, Tandalayo; Horacek, Tanya M

2012-06-01

This study assessed the nutritional quality of snacks and beverages sold in vending machines. The contents of snack and beverage vending machines in 78 buildings on 11 US post-secondary education campuses were surveyed. Of the 2607 snack machine slots surveyed, the most common snacks vended were salty snacks (e.g., chips, pretzels) and sweets (i.e., candy and candy bars). The 1650 beverage machine slots assessed contained twice as many sugar-sweetened beverages as non-calorie-containing beverages. Only two institutions sold both milk and 100% juice in vending machines. The portion of snacks and beverages sold averaged more than 200 cal. Neither snacks nor beverages were nutrient dense. The majority of snacks were low in fiber and high in calories and fat and almost half were high in sugar. Most beverages were high in calories and sugar. This study's findings suggest that vending machines provide limited healthful choices. Findings from benchmark assessments of components of the food environment, like the vending options reported here, can provide valuable input to campus administrators, health services, food service, and students who want to establish campus policies to promote healthful eating. Copyright © 2012 Elsevier Ltd. All rights reserved.

What does the new breed of decision-making methodologies mean for choices and norms in hydrological science?

Science.gov (United States)

Wikman-Svahn, Per

2013-04-01

Hydrological sciences are increasingly utilized in decision-making contexts that need to manage deep uncertainty, changing conditions and very long-lead times and lifetimes. Traditional optimizing approaches become problematic in such situations. For example, optimizing approaches may underestimate the importance of low probability outcomes, or very uncertain outcomes. Alternative decision-making strategies are therefore increasingly used in hydrological applications, including "bottom-up/top-down", "context-first", "decision-scaling", "assess risk of policy", "robust", "resilient" or "flexible" approaches. These kinds of strategies are typically designed to handle very uncertain and diverse outcomes, and often start from the particular decision-making context, in contrast to more traditional "predict-then-act" or "science first" approaches. Contemporary research in philosophy of science stress the influence of value judgments and norms in scientific assessments. In particular, this literature points out that implicit anticipated applications often influence choices made in scientific assessments. Furthermore, this literature also emphasize that choices made at within scientific assessments have consequences for decision-making later on. One reason is that it is often difficult for decision-makers to see what choices are made and the implications of these choices. Another reason is that information that could be of use for decision-makers are lost at an early stage. For example, the choice to focus on central estimates and not providing assessments on more unlikely outcomes is a choice that has consequences for what outcomes are taken into account in the decision-making process. This paper develops this argument and then analyzes the implications of these new developments for hydrological science. One implication of the increasing use of the new breed of planning strategies is that a broader range of uncertainty in scientific assessments becomes desirable in order

Self-Handicapping by Task Choice: An Attribute Ambiguity Analysis.

Science.gov (United States)

Handelsman, Mitchell M.; And Others

Self-handicapping strategies are behaviors or choices of performance settings which allow people to maintain self-esteem by avoiding negative self-relevant attributions. People will behave in such a way that accurate, nonambiguous attributions about their performance cannot be made. Research on self-handicapping has focused on clinically relevant…

Risky choice with heuristics: reply to Birnbaum (2008), Johnson, Schulte-Mecklenbeck, and Willemsen (2008), and Rieger and Wang (2008).

Science.gov (United States)

Brandstätter, Eduard; Gigerenzer, Gerd; Hertwig, Ralph

2008-01-01

E. Brandstätter, G. Gigerenzer, and R. Hertwig (2006) showed that the priority heuristic matches or outperforms modifications of expected utility theory in predicting choice in 4 diverse problem sets. M. H. Birnbaum (2008) argued that sets exist in which the opposite is true. The authors agree--but stress that all choice strategies have regions of good and bad performance. The accuracy of various strategies systematically depends on choice difficulty, which the authors consider a triggering variable underlying strategy selection. Agreeing with E. J. Johnson, M. Schulte-Mecklenbeck, and M. C. Willemsen (2008) that process (not "as-if") models need to be formulated, the authors show how quantitative predictions can be derived and test them. Finally, they demonstrate that many of Birnbaum's and M. O. Rieger and M. Wang's (2008) case studies championing their preferred models involved biased tests in which the priority heuristic predicted data, whereas the parameterized models were fitted to the same data. The authors propose an adaptive toolbox approach of risky choice, according to which people first seek a no-conflict solution before resorting to conflict-resolving strategies such as the priority heuristic. (c) 2008 APA, all rights reserved

Design of Human – Machine Interface and Altering of Pelvic Obliquity with RGR Trainer

OpenAIRE

Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

2011-01-01

The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system’s ability to alter the pattern of movement of the pelvis...

Involving children in cooking activities: A potential strategy for directing food choices toward novel foods containing vegetables.

Science.gov (United States)

Allirot, Xavier; da Quinta, Noelia; Chokupermal, Krithika; Urdaneta, Elena

2016-08-01

Involving children in cooking has been suggested as a strategy to improve dietary habits in childhood. Interventions in schools including cooking, gardening and tasting activities have showed promising results. Several cross-sectional surveys demonstrated associations between frequency of involvement in food preparation and better diet quality. However, experimental studies confirming the beneficial effect of cooking on food choices in children are missing from the literature. The objective of the present study was to assess the effect of involving children in cooking on their willingness to taste novel foods, food intake, liking and hunger. A between-subject experiment was conducted with 137 children between 7 and 11 years old. 69 children (COOK group) participated in the preparation of three unfamiliar foods containing vegetables: apple/beetroot juice, zucchini tortilla sandwich and spinach cookies. 68 children (CONTROL group) participated, instead, in a creative workshop. Afterwards, the children were invited to choose, for an afternoon snack, between three familiar vs. unfamiliar foods: orange vs. apple/beetroot juice, potato vs. zucchini tortilla sandwich and chocolate vs. spinach cookie. The mean number of unfamiliar foods chosen per child was higher in the COOK vs. CONTROL group (P = 0.037). The overall willingness to taste the unfamiliar foods was also higher in the COOK group (P = 0.011). The liking for the whole afternoon snack (P = 0.034), for 2 of 3 unfamiliar foods and for 1 of 3 familiar foods was higher in the COOK group (P food intake and hunger/satiety scores. This study demonstrated that involving children in cooking can increase their willingness to taste novel foods and direct food choices towards foods containing vegetables. Copyright © 2016 Elsevier Ltd. All rights reserved.

Machining of Machine Elements Made of Polymer Composite Materials

Science.gov (United States)

Baurova, N. I.; Makarov, K. A.

2017-12-01

The machining of the machine elements that are made of polymer composite materials (PCMs) or are repaired using them is considered. Turning, milling, and drilling are shown to be most widely used among all methods of cutting PCMs. Cutting conditions for the machining of PCMs are presented. The factors that most strongly affect the roughness parameters and the accuracy of cutting PCMs are considered.

Design Comparison of Inner and Outer Rotor of Permanent Magnet Flux Switching Machine for Electric Bicycle Application

Science.gov (United States)

Jusoh, L. I.; Sulaiman, E.; Bahrim, F. S.; Kumar, R.

2017-08-01

Recent advancements have led to the development of flux switching machines (FSMs) with flux sources within the stators. The advantage of being a single-piece machine with a robust rotor structure makes FSM an excellent choice for speed applications. There are three categories of FSM, namely, the permanent magnet (PM) FSM, the field excitation (FE) FSM, and the hybrid excitation (HE) FSM. The PMFSM and the FEFSM have their respective PM and field excitation coil (FEC) as their key flux sources. Meanwhile, as the name suggests, the HEFSM has a combination of PM and FECs as the flux sources. The PMFSM is a simple and cheap machine, and it has the ability to control variable flux, which would be suitable for an electric bicycle. Thus, this paper will present a design comparison between an inner rotor and an outer rotor for a single-phase permanent magnet flux switching machine with 8S-10P, designed specifically for an electric bicycle. The performance of this machine was validated using the 2D- FEA. As conclusion, the outer-rotor has much higher torque approximately at 54.2% of an innerrotor PMFSM. From the comprehensive analysis of both designs it can be conclude that output performance is lower than the SRM and IPMSM design machine. But, it shows that the possibility to increase the design performance by using “deterministic optimization method”.

Speakers' choice of frame in binary choice

Directory of Open Access Journals (Sweden)

Marc van Buiten

2009-02-01

Full Text Available A distinction is proposed between extit{recommending for} preferred choice options and extit{recommending against} non-preferred choice options. In binary choice, both recommendation modes are logically, though not psychologically, equivalent. We report empirical evidence showing that speakers recommending for preferred options predominantly select positive frames, which are less common when speakers recommend against non-preferred options. In addition, option attractiveness is shown to affect speakers' choice of frame, and adoption of recommendation mode. The results are interpreted in terms of three compatibility effects, (i extit{recommendation mode---valence framing compatibility}: speakers' preference for positive framing is enhanced under extit{recommending for} and diminished under extit{recommending against} instructions, (ii extit{option attractiveness---valence framing compatibility}: speakers' preference for positive framing is more pronounced for attractive than for unattractive options, and (iii extit{recommendation mode---option attractiveness compatibility}: speakers are more likely to adopt a extit{recommending for} approach for attractive than for unattractive binary choice pairs.

Operation of micro and molecular machines: a new concept with its origins in interface science.

Science.gov (United States)

Ariga, Katsuhiko; Ishihara, Shinsuke; Izawa, Hironori; Xia, Hong; Hill, Jonathan P

2011-03-21

A landmark accomplishment of nanotechnology would be successful fabrication of ultrasmall machines that can work like tweezers, motors, or even computing devices. Now we must consider how operation of micro- and molecular machines might be implemented for a wide range of applications. If these machines function only under limited conditions and/or require specialized apparatus then they are useless for practical applications. Therefore, it is important to carefully consider the access of functionality of the molecular or nanoscale systems by conventional stimuli at the macroscopic level. In this perspective, we will outline the position of micro- and molecular machines in current science and technology. Most of these machines are operated by light irradiation, application of electrical or magnetic fields, chemical reactions, and thermal fluctuations, which cannot always be applied in remote machine operation. We also propose strategies for molecular machine operation using the most conventional of stimuli, that of macroscopic mechanical force, achieved through mechanical operation of molecular machines located at an air-water interface. The crucial roles of the characteristics of an interfacial environment, i.e. connection between macroscopic dimension and nanoscopic function, and contact of media with different dielectric natures, are also described.

Combining human and machine processes (CHAMP)

Science.gov (United States)

Sudit, Moises; Sudit, David; Hirsch, Michael

2015-05-01

Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don't push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas: • Providing consistency within a mission: Injection of human reasoning and intelligence within the reliability and temporal needs of a mission to attain situational awareness, impact assessment, and COA development. • Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Development of mathematical models to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns. • Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.

Social choice for one: On the rationality of intertemporal decisions.

Science.gov (United States)

Paglieri, Fabio

2016-06-01

When faced with an intertemporal choice between a smaller short-term reward and a larger long-term prize, is opting for the latter always indicative of delay tolerance? And is delay tolerance always to be regarded as a manifestation of self-control, and thus as a rational solution to intertemporal dilemmas? I argue in favor of a negative answer to both questions, based on evidence collected in the delay discounting literature. This highlights the need for a nuanced understanding of rationality in intertemporal choice, to capture also situations in which waiting is not the optimal strategy. This paper suggests that such an understanding is fostered by adopting social choice theory as a promising framework to model intertemporal decision making. Some preliminary results of this approach are discussed, and its potential is compared with a much more studied formal model for intertemporal choice, i.e. game theory. Copyright © 2016 Elsevier B.V. All rights reserved.

Entry Mode Choice in Emerging Markets

OpenAIRE

Gundersen, Anne Kathrine Navestad

2012-01-01

As the mature markets of developed economies have become increasingly saturated, firms are turning their attention towards emerging markets for further enterprise growth. However, these countries often present significant challenges for foreign entrants, forcing firms to adapt their strategies to the new context. While MNEs? entry mode choice is an extensively studied field, there is a deficit in the entry mode research on SMEs, and even more so when it comes to entry into emerging markets in...

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

Science.gov (United States)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Machinability of nickel based alloys using electrical discharge machining process

Science.gov (United States)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

The achievements of the Z-machine; Les exploits de la Z-machine

Energy Technology Data Exchange (ETDEWEB)

Larousserie, D

2008-03-15

The ZR-machine that represents the latest generation of Z-pinch machines has recently begun preliminary testing before its full commissioning in Albuquerque (Usa). During its test the machine has well operated with electrical currents whose intensities of 26 million Ampere are already 2 times as high as the intensity of the operating current of the previous Z-machine. In 2006 the Z-machine reached temperatures of 2 billions Kelvin while 100 million Kelvin would be sufficient to ignite thermonuclear fusion. In fact the concept of Z-pinch machines was imagined in the fifties but the technological breakthrough that has allowed this recent success and the reborn of Z-machine, was the replacement of gas by an array of metal wires through which the electrical current flows and vaporizes it creating an imploding plasma. It is not well understood why Z-pinch machines generate far more radiation than theoretically expected. (A.C.)

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Science.gov (United States)

Howard, Rebecca; Rattray, Magnus; Prosperi, Mattia; Custovic, Adnan

2015-07-01

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.

Quantum machine learning.

Science.gov (United States)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Career choice and perceptions of nursing among healthcare students in higher educational institutions.

Science.gov (United States)

Liaw, Sok Ying; Wu, Ling Ting; Chow, Yeow Leng; Lim, Siriwan; Tan, Khoon Kiat

2017-05-01

Due to the ageing population and competition from other healthcare courses, a greater demand in the healthcare workforce has made it challenging for educational institutions to attract school leavers to enter nursing courses. Understanding the considerations of students who have chosen non-nursing healthcare courses and their perceptions of nursing can help identify specific strategies to enhance the attractiveness of nursing course. This study aims to examine the differences between healthcare career choices and perceptions of nursing as a career choice among first-year non-nursing healthcare students. A descriptive survey design was conducted at the beginning of the healthcare courses of seven healthcare groups and from four higher educational institutions in Singapore. A total of 451 students responded, yielding an overall response rate of 52.7%. The online survey was administered using a valid and reliable 35-item parallel scale, known as the Healthcare Career Choice and Nursing Career Choice. The participants perceived prior healthcare exposure as the most influential factor and self-efficacy as the least influential factor when choosing nursing as a career. In comparison to their own healthcare career choices, nursing was perceived to have greater gender stigma and, as nurses, they would be less likely to achieve higher qualifications and career advancements, and they would be less likely to enjoy fulfilling careers. They also perceived that they would be less likely to gain their parents' support to pursue nursing and to make their parents proud. This study provides educators and policy-makers with vital information to develop key strategies to improve nursing enrolment in educational institutions. These strategies include early exposure to nursing as a rewarding career during school years, addressing the issue of gender stigma, and promoting information on the career and educational advancement of a registered nurse to parents of school leavers. Copyright �

Machine protection systems

CERN Document Server

Macpherson, A L

2010-01-01

A summary of the Machine Protection System of the LHC is given, with particular attention given to the outstanding issues to be addressed, rather than the successes of the machine protection system from the 2009 run. In particular, the issues of Safe Machine Parameter system, collimation and beam cleaning, the beam dump system and abort gap cleaning, injection and dump protection, and the overall machine protection program for the upcoming run are summarised.

I can't wait: Methods for measuring and moderating individual differences in impulsive choice.

Science.gov (United States)

Peterson, Jennifer R; Hill, Catherine C; Marshall, Andrew T; Stuebing, Sarah L; Kirkpatrick, Kimberly

2015-01-01

Impulsive choice behavior occurs when individuals make choices without regard for future consequences. This behavior is often maladaptive and is a common symptom in many disorders, including drug abuse, compulsive gambling, and obesity. Several proposed mechanisms may influence impulsive choice behavior. These mechanisms provide a variety of pathways that may provide the basis for individual differences that are often evident when measuring choice behavior. This review provides an overview of these different pathways to impulsive choice, and the behavioral intervention strategies being developed to moderate impulsive choice. Because of the compelling link between impulsive choice behavior and the near-epidemic pervasiveness of obesity in the United States, we focus on the relationship between impulsive choice behavior and obesity as a test case for application of the multiple pathways approach. Choosing immediate gratification over healthier long term food choices is a contributing factor to the obesity crisis. Behavioral interventions can lead to more self controlled choices in a rat pre-clinical model, suggesting a possible gateway for translation to human populations. Designing and implementing effective impulsive choice interventions is crucial to improving the overall health and well-being of impulsive individuals.

Big Data and Machine Learning-Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference.

Science.gov (United States)

Kruskal, Jonathan B; Berkowitz, Seth; Geis, J Raymond; Kim, Woojin; Nagy, Paul; Dreyer, Keith

2017-06-01

The 38th radiology Intersociety Committee reviewed the current state and future direction of clinical data science and its application to radiology practice. The assembled participants discussed the need to use current technology to better generate and demonstrate radiologists' value for our patients and referring providers. The attendants grappled with the potentially disruptive applications of machine learning to image analysis. Although the prospect of algorithms' interpreting images automatically initially shakes the core of the radiology profession, the group emerged with tremendous optimism about the future of radiology. Emerging technologies will provide enormous opportunities for radiologists to augment and improve the quality of care they provide to their patients. Radiologists must maintain an active role in guiding the development of these technologies. The conference ended with a call to action to develop educational strategies for future leaders, communicate optimism for our profession's future, and engage with industry to ensure the ethics and clinical relevance of developing technologies. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

First turn around strategy for RHIC

International Nuclear Information System (INIS)

Milutinovic, J.; Ruggiero, A.G.

1991-01-01

The authors present a strategy for achieving the so-called first turn around in RHIC. The strategy is based on the same method proposed to correct a distorted closed orbit in RHIC, i.e. on a generalization of the local three-bump method. They found out that the method is very effective in passing the beam through a non-ideal, insufficiently known, machine. The perturbed lattice was generated by the code PATRIS, which was also adapted to control the newly developed software. In ten distributions of errors the software was capable of passing the beam through in 2-3 injection attempts, at full sextupole strength. It was also determined that once the beam makes the first turn around and all the correctors are energized, it stays in the machine for at least several hundred turns

Processes in arithmetic strategy selection: A fMRI study.

Directory of Open Access Journals (Sweden)

Julien eTaillan

2015-02-01

Full Text Available This neuroimaging (fMRI study investigated neural correlates of strategy selection. Young adults performed an arithmetic task in two different conditions. In both conditions, participants had to provide estimates of two-digit multiplication problems like 54 x 78. In the choice condition, participants had to select the better of two available rounding strategies, rounding-up strategy (RU (i.e., doing 60x80 = 4,800 or rounding-down strategy (RD (i.e., doing 50x70=3,500 to estimate product of 54x78. In the no-choice condition, participants did not have to select strategy on each problem but were told which strategy to use; they executed RU and RD strategies each on a series of problems. Participants also had a control task (i.e., providing correct products of multiplication problems like 40x50. Brain activations and performance were analyzed as a function of these conditions. Participants were able to frequently choose the better strategy in the choice condition; they were also slower when they executed the difficult RU than the easier RD. Neuroimaging data showed greater brain activations in right anterior cingulate cortex (ACC, dorso-lateral prefrontal cortex (DLPFC, and angular gyrus (ANG, when selecting (relative to executing the better strategy on each problem. Moreover, RU was associated with more parietal cortex activation than RD. These results suggest an important role of fronto-parietal network in strategy selection and have important implications for our further understanding and modelling cognitive processes underlying strategy selection.

Processes in arithmetic strategy selection: a fMRI study.

Science.gov (United States)

Taillan, Julien; Ardiale, Eléonore; Anton, Jean-Luc; Nazarian, Bruno; Félician, Olivier; Lemaire, Patrick

2015-01-01

This neuroimaging (functional magnetic resonance imaging) study investigated neural correlates of strategy selection. Young adults performed an arithmetic task in two different conditions. In both conditions, participants had to provide estimates of two-digit multiplication problems like 54 × 78. In the choice condition, participants had to select the better of two available rounding strategies, rounding-up (RU) strategy (i.e., doing 60 × 80 = 4,800) or rounding-down (RD) strategy (i.e., doing 50 × 70 = 3,500 to estimate product of 54 × 78). In the no-choice condition, participants did not have to select strategy on each problem but were told which strategy to use; they executed RU and RD strategies each on a series of problems. Participants also had a control task (i.e., providing correct products of multiplication problems like 40 × 50). Brain activations and performance were analyzed as a function of these conditions. Participants were able to frequently choose the better strategy in the choice condition; they were also slower when they executed the difficult RU than the easier RD. Neuroimaging data showed greater brain activations in right anterior cingulate cortex (ACC), dorso-lateral prefrontal cortex (DLPFC), and angular gyrus (ANG), when selecting (relative to executing) the better strategy on each problem. Moreover, RU was associated with more parietal cortex activation than RD. These results suggest an important role of fronto-parietal network in strategy selection and have important implications for our further understanding and modeling cognitive processes underlying strategy selection.

Machine learning molecular dynamics for the simulation of infrared spectra.

Science.gov (United States)

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

An adaptive regularization parameter choice strategy for multispectral bioluminescence tomography

Energy Technology Data Exchange (ETDEWEB)

Feng Jinchao; Qin Chenghu; Jia Kebin; Han Dong; Liu Kai; Zhu Shouping; Yang Xin; Tian Jie [Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China) and School of Life Sciences and Technology, Xidian University, Xi' an 710071 (China)

2011-11-15

Purpose: Bioluminescence tomography (BLT) provides an effective tool for monitoring physiological and pathological activities in vivo. However, the measured data in bioluminescence imaging are corrupted by noise. Therefore, regularization methods are commonly used to find a regularized solution. Nevertheless, for the quality of the reconstructed bioluminescent source obtained by regularization methods, the choice of the regularization parameters is crucial. To date, the selection of regularization parameters remains challenging. With regards to the above problems, the authors proposed a BLT reconstruction algorithm with an adaptive parameter choice rule. Methods: The proposed reconstruction algorithm uses a diffusion equation for modeling the bioluminescent photon transport. The diffusion equation is solved with a finite element method. Computed tomography (CT) images provide anatomical information regarding the geometry of the small animal and its internal organs. To reduce the ill-posedness of BLT, spectral information and the optimal permissible source region are employed. Then, the relationship between the unknown source distribution and multiview and multispectral boundary measurements is established based on the finite element method and the optimal permissible source region. Since the measured data are noisy, the BLT reconstruction is formulated as l{sub 2} data fidelity and a general regularization term. When choosing the regularization parameters for BLT, an efficient model function approach is proposed, which does not require knowledge of the noise level. This approach only requests the computation of the residual and regularized solution norm. With this knowledge, we construct the model function to approximate the objective function, and the regularization parameter is updated iteratively. Results: First, the micro-CT based mouse phantom was used for simulation verification. Simulation experiments were used to illustrate why multispectral data were used

Preliminary Test of Upgraded Conventional Milling Machine into PC Based CNC Milling Machine

International Nuclear Information System (INIS)

Abdul Hafid

2008-01-01

CNC (Computerized Numerical Control) milling machine yields a challenge to make an innovation in the field of machining. With an action job is machining quality equivalent to CNC milling machine, the conventional milling machine ability was improved to be based on PC CNC milling machine. Mechanically and instrumentally change. As a control replacing was conducted by servo drive and proximity were used. Computer programme was constructed to give instruction into milling machine. The program structure of consists GUI model and ladder diagram. Program was put on programming systems called RTX software. The result of up-grade is computer programming and CNC instruction job. The result was beginning step and it will be continued in next time. With upgrading ability milling machine becomes user can be done safe and optimal from accident risk. By improving performance of milling machine, the user will be more working optimal and safely against accident risk. (author)

Changes in a middle school food environment affect food behavior and food choices.

Science.gov (United States)

Wordell, Doug; Daratha, Kenn; Mandal, Bidisha; Bindler, Ruth; Butkus, Sue Nicholson

2012-01-01

Increasing rates of obesity among children ages 12 to 19 years have led to recommendations to alter the school food environment. The purpose of this study was to determine whether there are associations between an altered school food environment and food choices of middle school students both in and outside of school. In a midsized western city, two of six middle schools allowed only bottled water in vending machines, only milk and fruit on à la carte menus, and offered a seasonal fruit and vegetable bar. Three years after the intervention was initiated, seventh- and eighth-grade students attending the two intervention schools and four control middle schools were surveyed about their food choices. A total of 2,292 surveys were completed. Self-reported frequency of consumption for nine food groups in the survey was low; consumption was higher outside than in school. Boys consumed more milk than girls although girls consumed more fruits and vegetables. Significant socioeconomic differences existed. Compared with students who paid the full lunch fee, students qualifying for free and reduced-price meals consumed more milk and juice in schools but less outside school; more candy and energy drinks in school; and more sweet drinks, candy, pastries, and energy drinks outside school. Students in intervention schools were 24% more likely to consume milk outside school, 27% less likely to consume juice in school, and 56% less likely to consume sweet pastries in school. There were no differences in fruit and vegetable consumption reported by children in control and intervention schools. Overall, there was a positive association between a modified school food environment and student food behavior in and outside school. Policies related to the school food environment are an important strategy to address the obesity epidemic in our country. Copyright © 2012 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Strategies identification in an experimental reading comprehension task

Directory of Open Access Journals (Sweden)

Stanković Sanda

2010-01-01

Full Text Available Standardized reading comprehension tests (RCTs usually consist of a small number of texts each accompanied by several multiple-choice questions, with texts and questions simultaneously presented. The score the common measure of reading comprehension ability in RCTs is the score. Literature review suggests that strategies subjects employ may influence their performance on RCT, however the score itself provides no information on the specific strategy employed. Knowledge of test-taking strategies could have impact on understanding of the actual purpose and benefits of using RCTs in pedagogical and psychological practice. With the ultimate objective of constructing a first standard RCT in Serbian language, the preliminary step we took was to conduct an experimental reading comprehension task (ERCT consisting of 27 short texts displayed in succession, each followed by a single multiplechoice question. Using qualitative analysis of subjects’ responses in semi-structured postexperimental interview, we identified four overall strategies used on ERCT. Our results show that groups of students who used specific strategies differed significantly from one another in text reading time, with no differences found regarding the question reading and answering time. More importantly, there were no significant between-group differences found in terms of ERCT score. These findings suggest that choice of strategy is a way to optimize the relation between one’s own potential and ERCT task requirements. RCT based on ERCT principles would allow for a flexible choice of strategy which would not influence the final score.

Strategy Scriptions: On sociomaterial devices staging strategy formulation European Management Journal

DEFF Research Database (Denmark)

Friis, Ole Uhrskov

2014-01-01

is making strategic choices, then chose a strategy and then making and action plan. Most project groups worked as expected, one group constituting a rare exception, as staging and the mobilisation of devices lead to unanticipated events, triggering extraordinary management activity. Our study shows......Strategy formulation can be viewed as an entanglement of social and material elements, developing sociomaterial strategy devices. Devices contribute to arranging and staging occasions of strategy formulation. The article investigates which arenas are staged for strategy formulation, which types...... of devices is purposely mobilised by management. and consultants. First, one central device, based on a balanced scorecard, is staging the managers as active strategists in a more traditional strategy workshop, with the employees as distanced spectators. Second, the employees are staged in an open space...

Choices and control: parental experiences in pediatric terminal home care.

Science.gov (United States)

Vickers, J L; Carlisle, C

2000-01-01

During the past decade, palliative care at home has become an alternative option to hospital care for terminally ill children. This study describes the experience of caring for a dying child at home from a parent's perspective. A qualitative research design was used to conduct and analyze data. Nonstandardized, focused interviews were conducted with 10 families. Thematic content analysis assisted in deriving themes from the transcripts of the interviews. "Choice and control" was the major theme that linked all the other concepts, and it appeared to be fundamental to parental coping strategies. Most parents were willing to take responsibility for the nursing care of their child, including administration of intravenous medication. The patient's home was the overwhelming choice of parents for delivery of terminal care, with most parents perceiving it as their child's choice also.

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

Science.gov (United States)

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

Study of on-machine error identification and compensation methods for micro machine tools

International Nuclear Information System (INIS)

Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng

2016-01-01

Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results

Low-beta investment strategies

OpenAIRE

Korn, Olaf; Kuntz, Laura-Chloé

2015-01-01

This paper investigates investment strategies that exploit the low-beta anomaly. Although the notion of buying low-beta stocks and selling high-beta stocks is natural, a choice is necessary with respect to the relative weighting of high-beta stocks and low-beta stocks in the investment portfolio. Our empirical results for US large-cap stocks show that this choice is very important for the risk-return characteristics of the resulting portfolios and their sensitivities to common risk factors. W...

National Machine Guarding Program: Part 1. Machine safeguarding practices in small metal fabrication businesses.

Science.gov (United States)

Parker, David L; Yamin, Samuel C; Brosseau, Lisa M; Xi, Min; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2015-11-01

Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business. Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc.

High-pressure coolant effect on the surface integrity of machining titanium alloy Ti-6Al-4V: a review

Science.gov (United States)

Liu, Wentao; Liu, Zhanqiang

2018-03-01

Machinability improvement of titanium alloy Ti-6Al-4V is a challenging work in academic and industrial applications owing to its low thermal conductivity, low elasticity modulus and high chemical affinity at high temperatures. Surface integrity of titanium alloys Ti-6Al-4V is prominent in estimating the quality of machined components. The surface topography (surface defects and surface roughness) and the residual stress induced by machining Ti-6Al-4V occupy pivotal roles for the sustainability of Ti-6Al-4V components. High-pressure coolant (HPC) is a potential choice in meeting the requirements for the manufacture and application of Ti-6Al-4V. This paper reviews the progress towards the improvements of Ti-6Al4V surface integrity under HPC. Various researches of surface integrity characteristics have been reported. In particularly, surface roughness, surface defects, residual stress as well as work hardening are investigated in order to evaluate the machined surface qualities. Several coolant parameters (including coolant type, coolant pressure and the injection position) deserve investigating to provide the guidance for a satisfied machined surface. The review also provides a clear roadmap for applications of HPC in machining Ti-6Al4V. Experimental studies and analysis are reviewed to better understand the surface integrity under HPC machining process. A distinct discussion has been presented regarding the limitations and highlights of the prospective for machining Ti-6Al4V under HPC.

Controlling Motion at the Nanoscale: Rise of the Molecular Machines.

Science.gov (United States)

Abendroth, John M; Bushuyev, Oleksandr S; Weiss, Paul S; Barrett, Christopher J

2015-08-25

As our understanding and control of intra- and intermolecular interactions evolve, ever more complex molecular systems are synthesized and assembled that are capable of performing work or completing sophisticated tasks at the molecular scale. Commonly referred to as molecular machines, these dynamic systems comprise an astonishingly diverse class of motifs and are designed to respond to a plethora of actuation stimuli. In this Review, we outline the conditions that distinguish simple switches and rotors from machines and draw from a variety of fields to highlight some of the most exciting recent examples of opportunities for driven molecular mechanics. Emphasis is placed on the need for controllable and hierarchical assembly of these molecular components to display measurable effects at the micro-, meso-, and macroscales. As in Nature, this strategy will lead to dramatic amplification of the work performed via the collective action of many machines organized in linear chains, on functionalized surfaces, or in three-dimensional assemblies.

The Media Strategy Game : Fostering Discussion on Media Strategy

NARCIS (Netherlands)

Dr. H.M.M. van Vliet; Rogier Brussee; Jeroen Nobel; Charlotte van Nus

2013-01-01

All social media should have a sticker saying 'Don't Jump for the Tool!' While it is tempting 'to use Twitter', the choice of a medium like Twitter cannot be seen in isolation of strategic goals, instruments and expected results, i.e. a communication strategy. We designed a board game, called the

Using Likert-type and ipsative/forced choice items in sequence to generate a preference.

Science.gov (United States)

Ried, L Douglas

2014-01-01

Collaboration and implementation of a minimum, standardized set of core global educational and professional competencies seems appropriate given the expanding international evolution of pharmacy practice. However, winnowing down hundreds of competencies from a plethora of local, national and international competency frameworks to select the most highly preferred to be included in the core set is a daunting task. The objective of this paper is to describe a combination of strategies used to ascertain the most highly preferred items among a large number of disparate items. In this case, the items were >100 educational and professional competencies that might be incorporated as the core components of new and existing competency frameworks. Panelists (n = 30) from the European Union (EU) and United States (USA) were chosen to reflect a variety of practice settings. Each panelist completed two electronic surveys. The first survey presented competencies in a Likert-type format and the second survey presented many of the same competencies in an ipsative/forced choice format. Item mean scores were calculated for each competency, the competencies were ranked, and non-parametric statistical tests were used to ascertain the consistency in the rankings achieved by the two strategies. This exploratory study presented over 100 competencies to the panelists in the beginning. The two methods provided similar results, as indicated by the significant correlation between the rankings (Spearman's rho = 0.30, P < 0.09). A two-step strategy using Likert-type and ipsative/forced choice formats in sequence, appears to be useful in a situation where a clear preference is required from among a large number of choices. The ipsative/forced choice format resulted in some differences in the competency preferences because the panelists could not rate them equally by design. While this strategy was used for the selection of professional educational competencies in this exploratory study, it is

Non-conventional electrical machines

CERN Document Server

Rezzoug, Abderrezak

2013-01-01

The developments of electrical machines are due to the convergence of material progress, improved calculation tools, and new feeding sources. Among the many recent machines, the authors have chosen, in this first book, to relate the progress in slow speed machines, high speed machines, and superconducting machines. The first part of the book is dedicated to materials and an overview of magnetism, mechanic, and heat transfer.

Machine Learning Methods to Predict Diabetes Complications.

Science.gov (United States)

Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

2018-03-01

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

Contributions a l'etude et a l'application industrielle de la machine asynchrone

Science.gov (United States)

Ouhrouche, Mohand-Ameziane

The work presented in this thesis, done in the Electrical Drives Laboratory of Electrical and Computer Engineering Department, deals with the industrial applications of a three-phase induction machine (electrical drives and electricity generation). This thesis, characterized by its multidisciplinary content, has two major parts. The first one deals with the on-line and off-line parametric identification of the induction machine model necessary to achieve accurate vector control strategy. The second part, which is a resume of a research work sponsored by Hydro-Quebec, deals with the application of an induction machine in Asynchronous Non Utility Generators units (ANUG). As it is shown in the following, major scientific contributions are made in both two parts. In the first part of our research work, we propose a new speed sensorless vector control strategy for an induction machine, which is adaptive to the rotor resistance variations. The proposed control strategy is based on the Extended Kalman Filter approach and a decoupling controller which takes into account the rotor resistance variations. The consideration of coupled electrical and mechanical modes leads to a fifth order nonlinear model of the induction machine. The load torque is taken as a function of the rotor angular speed. The Extended Kalman Filter, based on the process's nonlinear (bilinear) model, estimate simultaneously the rotor resistance, angular speed and the flux vector from the startup to the steady state equilibrium point. The machine-converter-control system is implemented in MATLAB/SIMULINK environment and the obtained results confirm the robustness of the proposed scheme. As in the electrical drives erea, the induction machine is now widely used by small to medium power Non Utility Generator units (NUG) to produce electricity. In Quebec, these NUGs units are integrated into the Hydro-Quebec 25 kV distribution system via transformer which exhibit nonlinear characteristics. We have shown by

Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum.

Directory of Open Access Journals (Sweden)

Makoto Ito

2015-11-01

Full Text Available Previous theoretical studies of animal and human behavioral learning have focused on the dichotomy of the value-based strategy using action value functions to predict rewards and the model-based strategy using internal models to predict environmental states. However, animals and humans often take simple procedural behaviors, such as the "win-stay, lose-switch" strategy without explicit prediction of rewards or states. Here we consider another strategy, the finite state-based strategy, in which a subject selects an action depending on its discrete internal state and updates the state depending on the action chosen and the reward outcome. By analyzing choice behavior of rats in a free-choice task, we found that the finite state-based strategy fitted their behavioral choices more accurately than value-based and model-based strategies did. When fitted models were run autonomously with the same task, only the finite state-based strategy could reproduce the key feature of choice sequences. Analyses of neural activity recorded from the dorsolateral striatum (DLS, the dorsomedial striatum (DMS, and the ventral striatum (VS identified significant fractions of neurons in all three subareas for which activities were correlated with individual states of the finite state-based strategy. The signal of internal states at the time of choice was found in DMS, and for clusters of states was found in VS. In addition, action values and state values of the value-based strategy were encoded in DMS and VS, respectively. These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum.

Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum.

Science.gov (United States)

Ito, Makoto; Doya, Kenji

2015-11-01

Previous theoretical studies of animal and human behavioral learning have focused on the dichotomy of the value-based strategy using action value functions to predict rewards and the model-based strategy using internal models to predict environmental states. However, animals and humans often take simple procedural behaviors, such as the "win-stay, lose-switch" strategy without explicit prediction of rewards or states. Here we consider another strategy, the finite state-based strategy, in which a subject selects an action depending on its discrete internal state and updates the state depending on the action chosen and the reward outcome. By analyzing choice behavior of rats in a free-choice task, we found that the finite state-based strategy fitted their behavioral choices more accurately than value-based and model-based strategies did. When fitted models were run autonomously with the same task, only the finite state-based strategy could reproduce the key feature of choice sequences. Analyses of neural activity recorded from the dorsolateral striatum (DLS), the dorsomedial striatum (DMS), and the ventral striatum (VS) identified significant fractions of neurons in all three subareas for which activities were correlated with individual states of the finite state-based strategy. The signal of internal states at the time of choice was found in DMS, and for clusters of states was found in VS. In addition, action values and state values of the value-based strategy were encoded in DMS and VS, respectively. These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

Energy Technology Data Exchange (ETDEWEB)

Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

2014-08-15

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

International Nuclear Information System (INIS)

Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

2014-01-01

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology