Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi
The past years have brought some significant changes in the world energy market, where the nuclear power plants and utilities are operating. Part of NPPs is privatised now; the electricity markets are liberalized and become more and more international. Due to the increase of competition, the power production costs are now monitored more closely than before. The opening of electricity markets has led the nuclear power plants to be under the serious economic pressure with a demand for continuous cost reduction. All these require from NPPs to make their personnel training more cost-effective. In addition, based on modern technology, a great amount of new training tools, aids and technologies have been introduced during the last 2-3 years, these new opportunities can be quite useful for training cost optimization. On the basis of experience gained worldwide in the application of the systematic approach to training (SAT), SAT based training is now a broad integrated approach emphasizing not only technical knowledge and skills but also human factor related knowledge, skills and attitudes. In this way, all competency requirements for attaining and maintaining personnel competence and qualification can be met, thus promoting and strengthening quality culture and safety culture, which should be fostered throughout the initial and continuing training programmes. The subject of the present technical meeting was suggested by the members of the Technical Working Group on Training and Qualification of NPP Personnel (TWG-T and Q) and supported by a number of the IAEA meetings on NPP personnel training. The technical Meeting on 'Lessons Learned with Respect to SAT Implementation, Including Development of Trainers and Use of Cost Effective Training Methods' was organized by the IAEA in co-operation with the Tecnatom A.S. and was held from 21 to 24 October 2002 in San Sebastian de los Reyes/ Madrid, Spain. The main objective of the meeting was to provide an international forum for
Erbil, Deniz Gökçe; Kocabas, Ayfer
In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…
Yu, S.; Zemdegs, R.; Boyle, S.; Soulard, M., E-mail: firstname.lastname@example.org [Candu Energy Inc., Mississauga, Ontario (Canada)
The Enhanced CANDU 6 (EC6) is the new Generation III CANDU reactor design that meets the most up to date regulatory requirements and customer expectations. EC6 builds on the proven high performance design inch as the Qinshan CANDU 6 units and has made improvements to safety and operational performance, and has incorporated extensive operational feedback including Fukushima. The Fukushima Dai-ichi March 11, 2011 event has demonstrated the importance of defence-in-depth considerations for beyond-design basis events, including severe accidents. The EC6 design is based on the defence-in-depth principles and provides further design features that address the lessons learned from Fukushima. (author)
Zayapragassarazan, Z.; Kumar, Santosh
Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…
Methods of learning in the workplace will be introduced. The methods are connect to competence development and to the process of conducting development discussions in a dialogical way. The tools developed and applied are a fourfold table, a cycle of work identity, a plan of personal development targets, a learning meeting and a learning map. The methods introduced will aim to better learning at work.
Burdet, G.; Combe, Ph.; Nencka, H.
The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine
Van Norman, Staci A.; Aston, Victoria J.; Weimer, Alan W.
Structures, catalysts, and reactors suitable for use for a variety of applications, including gas-to-liquid and coal-to-liquid processes and methods of forming the structures, catalysts, and reactors are disclosed. The catalyst material can be deposited onto an inner wall of a microtubular reactor and/or onto porous tungsten support structures using atomic layer deposition techniques.
Bauch, Garland T.
Most failures occur at interfaces between organizations and hardware. Processing interface requirements at the start of a project life cycle will reduce the likelihood of costly interface changes/failures later. This can be done by adding Interface Control Documents (ICDs) to the Project top level drawing tree, providing technical direction to the Projects for interface requirements, and by funding the interface requirements function directly from the Project Manager's office. The interface requirements function within the Project Systems Engineering and Integration (SE&I) Office would work in-line with the project element design engineers early in the life cycle to enhance communications and negotiate technical issues between the elements. This function would work as the technical arm of the Project Manager to help ensure that the Project cost, schedule, and risk objectives can be met during the Life Cycle. Some ICD Lessons Learned during the Space Shuttle Program (SSP) Life Cycle will include the use of hardware interface photos in the ICD, progressive life cycle design certification by analysis, test, & operations experience, assigning interface design engineers to Element Interface (EI) and Project technical panels, and linking interface design drawings with project build drawings
Hatch, Anson V; Sommer, Gregory J; Singh, Anup K; Wang, Ying-Chih; Abhyankar, Vinay V
Microfluidic devices and methods including porous polymer monoliths are described. Polymerization techniques may be used to generate porous polymer monoliths having pores defined by a liquid component of a fluid mixture. The fluid mixture may contain iniferters and the resulting porous polymer monolith may include surfaces terminated with iniferter species. Capture molecules may then be grafted to the monolith pores.
Mann, Nicholas R; Tranter, Troy J
Methods of producing a metal oxide are disclosed. The method comprises dissolving a metal salt in a reaction solvent to form a metal salt/reaction solvent solution. The metal salt is converted to a metal oxide and a caustic solution is added to the metal oxide/reaction solvent solution to adjust the pH of the metal oxide/reaction solvent solution to less than approximately 7.0. The metal oxide is precipitated and recovered. A method of producing adsorption media including the metal oxide is also disclosed, as is a precursor of an active component including particles of a metal oxide.
Maria Valéria Pena; Barbara Brakarz
In the aftermath of Hurricane Mitch in 1998, the Honduras Interactive Environmental Learning and Science Promotion Project "Profuturo" was launched as a multi-sectoral effort designed to encourage and expand scientific, environmental, and cultural knowledge and management in the context of Honduras' sustainable development needs and ethnic diversity. Profuturo benefits Hondurans by providi...
Van Zyl, Lourens H
Full Text Available implementations of the DLM are however not very versatile in terms of geometries that can be modeled. The ZONA6 code offers a versatile surface panel body model including a separated wake model, but uses a pressure panel method for lifting surfaces. This paper...
Daniels, Michael A.; Condit, Reston A.; Rasmussen, Nikki; Wallace, Ronald S.
Initiation devices may include at least one substrate, an initiation element positioned on a first side of the at least one substrate, and a spark gap electrically coupled to the initiation element and positioned on a second side of the at least one substrate. Initiation devices may include a plurality of substrates where at least one substrate of the plurality of substrates is electrically connected to at least one adjacent substrate of the plurality of substrates with at least one via extending through the at least one substrate. Initiation systems may include such initiation devices. Methods of igniting energetic materials include passing a current through a spark gap formed on at least one substrate of the initiation device, passing the current through at least one via formed through the at least one substrate, and passing the current through an explosive bridge wire of the initiation device.
What. This chapter concerns how visual methods and visual materials can support visually oriented, collaborative, and creative learning processes in education. The focus is on facilitation (guiding, teaching) with visual methods in learning processes that are designerly or involve design. Visual...... methods are exemplified through two university classroom cases about collaborative idea generation processes. The visual methods and materials in the cases are photo elicitation using photo cards, and modeling with LEGO Serious Play sets. Why. The goal is to encourage the reader, whether student...... or professional, to facilitate with visual methods in a critical, reflective, and experimental way. The chapter offers recommendations for facilitating with visual methods to support playful, emergent designerly processes. The chapter also has a critical, situated perspective. Where. This chapter offers case...
Soroush, Masoud; Weinberger, Charles B.
This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…
This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...
MaCoy, Katherine W.
The methods used and the results obtained by means of the accelerated language learning techniques developed by Georgi Lozanov, Director of the Institute of Suggestology in Bulgaria, are discussed. The following topics are included: (1) discussion of hypermnesia, "super memory," and the reasons foreign languages were chosen for purposes…
Background: It is now expected that projects addressing the lives of people with learning disabilities include people with learning disabilities in the research process. In the past, such research often excluded people with learning disabilities, favouring the opinions of family members, carers and professionals. The inclusion of the voices of…
Topal, Kenan; Sarıkaya, Özlem; Basturk, Ramazan; Buke, Akile
Objectives: The process of development and evaluation of undergraduate medical education programs should include analysis of learners’ characteristics, needs, and perceptions about learning methods. This study aims to evaluate medical students’ perceptions about problem-based learning methods and to compare these results with their individual learning styles.Materials and Methods: The survey was conducted at Marmara University Medical School where problem-based learning was implemented in the...
Icaza, José I.; Heredia, Yolanda; Borch, Ole M.
A pedagogical approach called “project oriented immersion learning” is presented and tested on a graduate online course. The approach combines the Project Oriented Learning method with immersion learning in a virtual enterprise. Students assumed the role of authors hired by a fictitious publishing...... house that develops digital products including e-books, tutorials, web sites and so on. The students defined the problem that their product was to solve; choose the type of product and the content; and built the product following a strict project methodology. A wiki server was used as a platform to hold...
Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: email@example.com
The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.
The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms
The present invention offers a method for separating dry matter from a medium. A separation chamber is at least partly defined by a plurality of rollers (2,7) and is capable of being pressure regulated. At least one of the rollers is a pore roller (7) having a surface with pores allowing permeabi...
Bryant, D P; Bryant, B R
Cooperative learning (CL) is a common instructional arrangement that is used by classroom teachers to foster academic achievement and social acceptance of students with and without learning disabilities. Cooperative learning is appealing to classroom teachers because it can provide an opportunity for more instruction and feedback by peers than can be provided by teachers to individual students who require extra assistance. Recent studies suggest that students with LD may need adaptations during cooperative learning activities. The use of assistive technology adaptations may be necessary to help some students with LD compensate for their specific learning difficulties so that they can engage more readily in cooperative learning activities. A process for integrating technology adaptations into cooperative learning activities is discussed in terms of three components: selecting adaptations, monitoring the use of the adaptations during cooperative learning activities, and evaluating the adaptations' effectiveness. The article concludes with comments regarding barriers to and support systems for technology integration, technology and effective instructional practices, and the need to consider technology adaptations for students who have learning disabilities.
Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G
This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
Bawendi, Moungi G.; Sundar, Vikram C.
Temperature-sensing compositions can include an inorganic material, such as a semiconductor nanocrystal. The nanocrystal can be a dependable and accurate indicator of temperature. The intensity of emission of the nanocrystal varies with temperature and can be highly sensitive to surface temperature. The nanocrystals can be processed with a binder to form a matrix, which can be varied by altering the chemical nature of the surface of the nanocrystal. A nanocrystal with a compatibilizing outer layer can be incorporated into a coating formulation and retain its temperature sensitive emissive properties.
Hansen, Bodil Winther; Hatt, Camusa
The university have years of experience with interprofessional student groups, from seven different health professions, learning through participating in an interprofessional module. Evaluations have shown a continuos massive challenge concerning the student´s motivation for learning...... and their level of participation in this three-week course of “Conflict management”. To meet these challenges the university started a project within the frame of problembased learning and drama games. The idea was to develop strategies to motivate students and create a dynamic and stimulating learning...... as an important aspect of carrying out a successful PBL course. Among the students, there was a significantly higher level of satisfaction in the experimental classes than in the comparison classes, regarding 10 out of 12 questions asked about both academic achievement and satisfaction with the learning...
Rapp, R.A.; Urquhart, A.W.; Nagelberg, A.S.; Newkirk, M.S.
This patent describes a method for producing a superconducting complex oxidation reaction product of two or more metals in an oxidized state. It comprises positioning at least one parent metal source comprising one of the metals adjacent to a permeable mass comprising at least one metal-containing compound capable of reaction to form the complex oxidation reaction product in step below, the metal component of the at least one metal-containing compound comprising at least a second of the two or more metals, and orienting the parent metal source and the permeable mass relative to each other so that formation of the complex oxidation reaction product will occur in a direction towards and into the permeable mass; and heating the parent metal source in the presence of an oxidant to a temperature region above its melting point to form a body of molten parent metal to permit infiltration and reaction of the molten parent metal into the permeable mass and with the oxidant and the at least one metal-containing compound to form the complex oxidation reaction product, and progressively drawing the molten parent metal source through the complex oxidation reaction product towards the oxidant and towards and into the adjacent permeable mass so that fresh complex oxidation reaction product continues to form within the permeable mass; and recovering the resulting complex oxidation reaction product
Lai, Zhiping; Huang, Kuo-Wei; Chen, Wei
In accordance with the purpose(s) of the present disclosure, as embodied and broadly described herein, embodiments of the present disclosure provide membranes, methods of making the membrane, systems including the membrane, methods of separation, methods of desalination, and the like.
In accordance with the purpose(s) of the present disclosure, as embodied and broadly described herein, embodiments of the present disclosure provide membranes, methods of making the membrane, systems including the membrane, methods of separation, methods of desalination, and the like.
Full Text Available Distance learning has facilitated innovative means to include Cooperative Learning (CL in virtual settings. This study, conducted at a Hispanic-Serving Institution, compared the effectiveness of online CL strategies in discussion forums with traditional online forums. Quantitative and qualitative data were collected from 56 graduate student participants. Quantitative results revealed no significant difference on student success between CL and Traditional formats. The qualitative data revealed that students in the cooperative learning groups found more learning benefits than the Traditional group. The study will benefit instructors and students in distance learning to improve teaching and learning practices in a virtual classroom.
Full Text Available The objective of this paper is to present a new e-learning method that use databases. The solution could pe implemented for any typeof e-learning system in any domain. The article will purpose a solution to improve the learning process for virtual classes.
This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…
Mitsel, A. A.; Cherniaeva, N. V.
The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.
Thornton, James E.
This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in…
Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain
Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed....
J.G. Bagi; N.K. Hashilkar
Background: Blended learning includes an integration of face to face classroom learning with technology enhanced online material. It provides the convenience, speed and cost effectiveness of e-learning with the personal touch of traditional learning. Objective: The objective of the present study was to assess the effectiveness of a combination of e-learning module and traditional teaching (Blended learning) as compared to traditional teaching alone to teach acid base homeostasis to Phase I MB...
teaching-learning methods of entrepreneurship curriculum. Moreover, the value for Kaiser Meyer Olkin measure of sampling adequacy equaled 0.72 and the value for Bartlett’s test of variances homogeneity was significant at the 0.0001 level. Except for internship element, the rest had a factor load of higher than 0.3. Also, the results of confirmatory factor analysis showed the model appropriateness, and the criteria for qualitative accreditation were acceptable. Conclusion: Developed model can help instructors in selecting an appropriate method of entrepreneurship teaching, and it can also make sure that the teaching is on the right path. Moreover, the model is comprehensive and includes all the effective teaching methods in entrepreneurship education. It is also based on qualities, conditions, and requirements of Higher Education Institutions in Iranian cultural environment.
Karp Peter D
Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.
Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214
We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...
Li, Hongxin; Ding, Mengchun
Reasons for learning the management include (1) perfecting the knowledge structure, (2) the management is the base of all organizations, (3) one person may be the manager or the managed person, (4) the management is absolutely not simple knowledge, and (5) the learning of the theoretical knowledge of the management can not be replaced by the…
Esmi, Keramat; Marzoughi, Rahmatallah; Torkzadeh, Jafar
One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners' needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation. This is a mixed method research of a sequential exploratory kind conducted through two stages: a) developing teaching methods of entrepreneurship curriculum, and b) validating developed framework. Data were collected through "triangulation" (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts). Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability), and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach's alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects) with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of teaching-learning methods of entrepreneurship curriculum
Christensen, Hans Peter; Vigild, Martin E.; Thomsen, Erik; Szabo, Peter; Horsewell, Andy
Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed. Peer Reviewed
Tjalla, Awaluddin; Sofiah, Evi
This research aims to reveal the influence of learning methods and self-regulated learning on students learning scores for Social Studies object. The research was done in Islamic Junior High School (MTs Manba'ul Ulum), Batuceper City Tangerang using quasi-experimental method. The research employed simple random technique to 28 students. Data were…
The study investigated the effect of using cooperative learning method on tenth grade students' learning achievement in biology and their attitude towards the subject in a Higher Secondary School in Bhutan. The study used a mixed method approach. The quantitative component included an experimental design where cooperative learning was the…
Gosselin, Philippe Henri; Cord, Matthieu
Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.
Prosper Harrison B.
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Jackson, Dontae L.
In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.
Ryszard Józef Panfil
Full Text Available The dynamics of the environment in which educational institutions operate have a significant influence on the basic activity of these institutions, i.e. the process of educating, and particularly teaching and learning methods used during that process: traditional teaching, tutoring, mentoring and coaching. The identity of an educational institution and the appeal of its services depend on how flexible, diverse and adaptable is the educational process it offers as a core element of its services. Such a process is determined by how its pragmatism is displayed in the operational relativism of methods, their applicability, as well as practical dimension of achieved results and values. Based on the above premises, this publication offers a pragmatic-systemic identification of contemporary teaching and learning methods, while taking into account the differences between them and the scope of their compatibility. Secondly, using the case of sport coaches’ education, the author exemplifies the pragmatic theory of perception of contemporary teaching and learning methods.
This paper outlines the development of a generic Business Research Methods course from a simple name in a box to a full e-Learning web based module. It highlights particular issues surrounding the nature of the discipline and the integration of a large number of cross faculty subject specific research methods courses into a single generic module.…
Prosper Harrison B.
A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...
extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P
Current track reconstructing methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast Simulation might not be as realistic as real data but tacking has been done for that with 100 percent efficiency while by using real data we would probably be limited to current efficiency.
Pedersen, Kamilla; Moeller, Martin Holdgaard; Paltved, Charlotte
OBJECTIVES: The aim of this study was to explore medical students' learning experiences from the didactic teaching formats using either text-based patient cases or video-based patient cases with similar content. The authors explored how the two different patient case formats influenced students......' perceptions of psychiatric patients and students' reflections on meeting and communicating with psychiatric patients. METHODS: The authors conducted group interviews with 30 medical students who volunteered to participate in interviews and applied inductive thematic content analysis to the transcribed...
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...
Kocabas, Ayfer; Erbil, Deniz Gokce
Cooperative learning method is a learning method studied both in Turkey and in the world for long years as an active learning method. Although cooperative learning method takes place in training programs, it cannot be implemented completely in the direction of its principles. The results of the researches point out that teachers have problems with…
Lillo, Thomas M.; Chu, Henry S.; Harrison, William M.; Bailey, Derek
Methods of forming composite materials include coating particles of titanium dioxide with a substance including boron (e.g., boron carbide) and a substance including carbon, and reacting the titanium dioxide with the substance including boron and the substance including carbon to form titanium diboride. The methods may be used to form ceramic composite bodies and materials, such as, for example, a ceramic composite body or material including silicon carbide and titanium diboride. Such bodies and materials may be used as armor bodies and armor materials. Such methods may include forming a green body and sintering the green body to a desirable final density. Green bodies formed in accordance with such methods may include particles comprising titanium dioxide and a coating at least partially covering exterior surfaces thereof, the coating comprising a substance including boron (e.g., boron carbide) and a substance including carbon.
Pamungkas, Bian Dwi
This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...
Enever, Janet, Ed.; Lindgren, Eva, Ed.
This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…
Siadat, M. Vali; Musial, Paul M.; Sagher, Yoram
This study reports the effects of an integrated instructional program (the Keystone Method) on the students' performance in mathematics and reading, and tracks students' persistence and retention. The subject of the study was a large group of students in remedial mathematics classes at the college, willing to learn but lacking basic educational…
Aim of this study is to investigate students' ideas on cooperative learning method. For that purpose students who are studying at elementary science education program are distributed into two groups through an experimental design. Factors threaten the internal validity are either eliminated or reduced to minimum value. Data analysis is done…
Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R
Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.
Dwi Nur Rachmah
Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas La...
Liu, Di; Li, YingChun
In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.
Full Text Available There are very few studies concerning the importance of teaching methods in biology education and environmental education including outdoor education for promoting sustainability at the levels of primary and secondary schools and pre-service teacher education. The material was selected using special keywords from biology and sustainable education in several scientific databases. The article provides an overview of 24 selected articles published in peer-reviewed scientific journals from 2006–2016. The data was analyzed using qualitative content analysis. Altogether, 16 journals were selected and 24 articles were analyzed in detail. The foci of the analyses were teaching methods, learning environments, knowledge and thinking skills, psychomotor skills, emotions and attitudes, and evaluation methods. Additionally, features of good methods were investigated and their implications for teaching were emphasized. In total, 22 different teaching methods were found to improve sustainability education in different ways. The most emphasized teaching methods were those in which students worked in groups and participated actively in learning processes. Research points toward the value of teaching methods that provide a good introduction and supportive guidelines and include active participation and interactivity.
Full Text Available The authors describe the method of global learning of foreign languages, which is based on the principles of neurolinguistic programming (NLP. According to this theory, the educator should use the method of the so-called periphery learning, where students learn relaxation techniques and at the same time they »incidentally « or subconsciously learn a foreign language. The method of global learning imitates successful strategies of learning in early childhood and therefore creates a relaxed attitude towards learning. Global learning is also compared with standard methods.
Bakr, Osman; Peng, Wei; Wang, Lingfei
Embodiments of the present disclosure provide for solar cells including an organometallic halide perovskite monocrystalline film (see fig. 1.1B), other devices including the organometallic halide perovskite monocrystalline film, methods of making
Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen
Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data.  Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.
Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin
Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.
Pi, Zhongling; Hong, Jianzhong
Video podcasts have become one of the fastest developing trends in learning and teaching. The study explored the effect of the presenting mode of educational video podcasts on the learning process and learning outcomes. Prior to viewing a video podcast, the 94 Chinese undergraduates participating in the study completed a demographic questionnaire…
Ward, Logan; Liu, Ruoqian; Krishna, Amar; Hegde, Vinay I.; Agrawal, Ankit; Choudhary, Alok; Wolverton, Chris
While high-throughput density functional theory (DFT) has become a prevalent tool for materials discovery, it is limited by the relatively large computational cost. In this paper, we explore using DFT data from high-throughput calculations to create faster, surrogate models with machine learning (ML) that can be used to guide new searches. Our method works by using decision tree models to map DFT-calculated formation enthalpies to a set of attributes consisting of two distinct types: (i) composition-dependent attributes of elemental properties (as have been used in previous ML models of DFT formation energies), combined with (ii) attributes derived from the Voronoi tessellation of the compound's crystal structure. The ML models created using this method have half the cross-validation error and similar training and evaluation speeds to models created with the Coulomb matrix and partial radial distribution function methods. For a dataset of 435 000 formation energies taken from the Open Quantum Materials Database (OQMD), our model achieves a mean absolute error of 80 meV/atom in cross validation, which is lower than the approximate error between DFT-computed and experimentally measured formation enthalpies and below 15% of the mean absolute deviation of the training set. We also demonstrate that our method can accurately estimate the formation energy of materials outside of the training set and be used to identify materials with especially large formation enthalpies. We propose that our models can be used to accelerate the discovery of new materials by identifying the most promising materials to study with DFT at little additional computational cost.
Harst-Wintraecken, van der Eugenie; Potting, José; Kroeze, Carolien
Many methods have been reported and used to include recycling in life cycle assessments (LCAs). This paper evaluates six widely used methods: three substitution methods (i.e. substitution based on equal quality, a correction factor, and alternative material), allocation based on the number of
Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.
Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.
Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.
Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis
We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.
Collaborative learning is one, among other, active learning methods, widely acclaimed in higher education. Consequently, instructors in fields that lack pedagogical training often implement new learning methods such as collaborative learning on the basis of trial and error. Moreover, even though the benefits in academic circles are broadly touted,…
Hofmann, Douglas C. (Inventor); Kennett, Andrew (Inventor)
Systems and methods to fabricate objects including metallic glass-based materials using low-pressure casting techniques are described. In one embodiment, a method of fabricating an object that includes a metallic glass-based material includes: introducing molten alloy into a mold cavity defined by a mold using a low enough pressure such that the molten alloy does not conform to features of the mold cavity that are smaller than 100 microns; and cooling the molten alloy such that it solidifies, the solid including a metallic glass-based material.
Kivotides, Demosthenes, E-mail: firstname.lastname@example.org
An asymptotically exact method for the direct computation of turbulent polymeric liquids that includes (a) fully resolved, creeping microflow fields due to hydrodynamic interactions between chains, (b) exact account of (subfilter) residual stresses, (c) polymer Brownian motion, and (d) direct calculation of chain entanglements, is formulated. Although developed in the context of polymeric fluids, the method is equally applicable to turbulent colloidal dispersions and aerosols. - Highlights: • An asymptotically exact method for the computation of polymer and colloidal fluids is developed. • The method is valid for all flow inertia and all polymer volume fractions. • The method models entanglements and hydrodynamic interactions between polymer chains.
Verpoorten, Dominique; Poumay, M; Leclercq, D
Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence
Paixao, S.B.; Marzo, M.A.S.; Alvim, A.C.M.
The calculation method used in WIGLE code is studied. Because of the non availability of such a praiseworthy solution, expounding the method minutely has been tried. This developed method has been applied for the solution of the one-dimensional, two-group, diffusion equations in slab, axial analysis, including non-boiling heat transfer, accountig for feedback. A steady-state program (CITER-1D), written in FORTRAN 4, has been implemented, providing excellent results, ratifying the developed work quality. (Author) [pt
Bakr, Osman M.
Embodiments of the present disclosure provide for solar cells including an organometallic halide perovskite monocrystalline film (see fig. 1.1B), other devices including the organometallic halide perovskite monocrystalline film, methods of making organometallic halide perovskite monocrystalline film, and the like.
Andrusyszyn, M A; Cragg, C E; Humbert, J
The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.
Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: email@example.com; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)
We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.
Zimmermann, J.; Kiesling, C.
We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application
Full Text Available We report the learning curves of three eye surgeons converting from sutureless extracapsular cataract extraction to phacoemulsification using different teaching methods. Posterior capsule rupture (PCR as a per-operative complication and visual outcome of the first 100 operations were analysed. The PCR rate was 4% and 15% in supervised and unsupervised surgery respectively. Likewise, an uncorrected visual acuity of > or = 6/18 on the first postoperative day was seen in 62 (62% of patients and in 22 (22% in supervised and unsupervised surgery respectively.
Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen
Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.
Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu
Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.
From the assumption that matching a student's learning style with the learning method best suited for the student, it follows that developing courses that correlate learning method with learning style would be more successful for students. Albuquerque Technical Vocational Institute (TVI) in New Mexico has attempted to provide students with more…
Most Chinese students are not interested in English learning, especially English words. In this paper, I focus on English vocabulary learning, for example, the study of high school students English word learning method, and also introduce several ways to make vocabulary memory becomes more effective. The purpose is to make high school students grasp more English word learning skills.
DeWall, Kevin George [Pocatello, ID; Garcia, Humberto Enrique [Idaho Falls, ID; McKellar, Michael George [Idaho Falls, ID
Methods of evaluating a fluid condition may include stroking a valve member and measuring a force acting on the valve member during the stroke. Methods of evaluating a fluid condition may include measuring a force acting on a valve member in the presence of fluid flow over a period of time and evaluating at least one of the frequency of changes in the measured force over the period of time and the magnitude of the changes in the measured force over the period of time to identify the presence of an anomaly in a fluid flow and, optionally, its estimated location. Methods of evaluating a valve condition may include directing a fluid flow through a valve while stroking a valve member, measuring a force acting on the valve member during the stroke, and comparing the measured force to a reference force. Valve assemblies and related systems are also disclosed.
Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, and demographic variables. Given the various sources of information, we can develop computerized methods that can be a great help to clinicians to discover hidden patterns in the data. The computerized methods often employ data mining and machine learning algorithms, lending themselves as the computer-aided diagnosis (CAD) tool that assists clinicians in making diagnostic decisions. In this chapter, we review state-of-the-art methods used in dementia research, and briefly introduce some recently proposed algorithms subsequently.
Kong, Peter C; Grandy, Jon D; Detering, Brent A; Zuck, Larry D
Electrode assemblies for plasma reactors include a structure or device for constraining an arc endpoint to a selected area or region on an electrode. In some embodiments, the structure or device may comprise one or more insulating members covering a portion of an electrode. In additional embodiments, the structure or device may provide a magnetic field configured to control a location of an arc endpoint on the electrode. Plasma generating modules, apparatus, and systems include such electrode assemblies. Methods for generating a plasma include covering at least a portion of a surface of an electrode with an electrically insulating member to constrain a location of an arc endpoint on the electrode. Additional methods for generating a plasma include generating a magnetic field to constrain a location of an arc endpoint on an electrode.
McDowell, Jenny; Marriott, Jennifer L.; Calandra, Angela; Duncan, Gregory
Objective To design and evaluate a preregistration course utilizing asynchronous online learning as the primary distance education delivery method. Design Online course components including tutorials, quizzes, and moderated small-group asynchronous case-based discussions were implemented. Online delivery was supplemented with self-directed and face-to-face learning. Assessment Pharmacy graduates who had completed the course in 2004 and 2005 were surveyed. The majority felt they had benefited from all components of the course, and that online delivery provided benefits including increased peer support, shared learning, and immediate feedback on performance. A majority of the first cohort reported that the workload associated with asynchronous online discussions was too great. The course was altered in 2005 to reduce the online component. Participant satisfaction improved, and most felt that the balance of online to face-to-face delivery was appropriate. Conclusion A new pharmacy preregistration course was successfully implemented. Online teaching and learning was well accepted and appeared to deliver benefits over traditional distance education methods once workload issues were addressed. PMID:19777092
Background Health policy makers now have access to a greater number and variety of systematic reviews to inform different stages in the policy making process, including reviews of qualitative research. The inclusion of mixed methods studies in systematic reviews is increasing, but these studies pose particular challenges to methods of review. This article examines the quality of the reporting of mixed methods and qualitative-only studies. Methods We used two completed systematic reviews to generate a sample of qualitative studies and mixed method studies in order to make an assessment of how the quality of reporting and rigor of qualitative-only studies compares with that of mixed-methods studies. Results Overall, the reporting of qualitative studies in our sample was consistently better when compared with the reporting of mixed methods studies. We found that mixed methods studies are less likely to provide a description of the research conduct or qualitative data analysis procedures and less likely to be judged credible or provide rich data and thick description compared with standalone qualitative studies. Our time-related analysis shows that for both types of study, papers published since 2003 are more likely to report on the study context, describe analysis procedures, and be judged credible and provide rich data. However, the reporting of other aspects of research conduct (i.e. descriptions of the research question, the sampling strategy, and data collection methods) in mixed methods studies does not appear to have improved over time. Conclusions Mixed methods research makes an important contribution to health research in general, and could make a more substantial contribution to systematic reviews. Through our careful analysis of the quality of reporting of mixed methods and qualitative-only research, we have identified areas that deserve more attention in the conduct and reporting of mixed methods research. PMID:22545681
Meier, M.; Yadigaroglu, G. [Swiss Federal Institute of Technology, Nuclear Engineering Lab. ETH-Zentrum, CLT, Zurich (Switzerland); Smith, B. [Paul Scherrer Inst. (PSI), Villigen (Switzerland). Lab. for Thermal-Hydraulics
Various versions of Volume-of-Fluid (VOF) methods have been used successfully for the numerical simulation of gas-liquid flows with an explicit tracking of the phase interface. Of these, Piecewise-Linear Interface Construction (PLIC-VOF) appears as a fairly accurate, although somewhat more involved variant. Including effects due to surface tension remains a problem, however. The most prominent methods, Continuum Surface Force (CSF) of Brackbill et al. and the method of Zaleski and co-workers (both referenced later), both induce spurious or 'parasitic' currents, and only moderate accuracy in regards to determining the curvature. We present here a new method to determine curvature accurately using an estimator function, which is tuned with a least-squares-fit against reference data. Furthermore, we show how spurious currents may be drastically reduced using the reconstructed interfaces from the PLIC-VOF method. (authors)
Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.
van der Harst, Eugenie; Potting, José; Kroeze, Carolien
Many methods have been reported and used to include recycling in life cycle assessments (LCAs). This paper evaluates six widely used methods: three substitution methods (i.e. substitution based on equal quality, a correction factor, and alternative material), allocation based on the number of recycling loops, the recycled-content method, and the equal-share method. These six methods were first compared, with an assumed hypothetical 100% recycling rate, for an aluminium can and a disposable polystyrene (PS) cup. The substitution and recycled-content method were next applied with actual rates for recycling, incineration and landfilling for both product systems in selected countries. The six methods differ in their approaches to credit recycling. The three substitution methods stimulate the recyclability of the product and assign credits for the obtained recycled material. The choice to either apply a correction factor, or to account for alternative substituted material has a considerable influence on the LCA results, and is debatable. Nevertheless, we prefer incorporating quality reduction of the recycled material by either a correction factor or an alternative substituted material over simply ignoring quality loss. The allocation-on-number-of-recycling-loops method focusses on the life expectancy of material itself, rather than on a specific separate product. The recycled-content method stimulates the use of recycled material, i.e. credits the use of recycled material in products and ignores the recyclability of the products. The equal-share method is a compromise between the substitution methods and the recycled-content method. The results for the aluminium can follow the underlying philosophies of the methods. The results for the PS cup are additionally influenced by the correction factor or credits for the alternative material accounting for the drop in PS quality, the waste treatment management (recycling rate, incineration rate, landfilling rate), and the
Rollins, Harry W [Idaho Falls, ID; Petkovic, Lucia M [Idaho Falls, ID; Ginosar, Daniel M [Idaho Falls, ID
Catalytic structures include a catalytic material disposed within a zeolite material. The catalytic material may be capable of catalyzing a formation of methanol from carbon monoxide and/or carbon dioxide, and the zeolite material may be capable of catalyzing a formation of hydrocarbon molecules from methanol. The catalytic material may include copper and zinc oxide. The zeolite material may include a first plurality of pores substantially defined by a crystal structure of the zeolite material and a second plurality of pores dispersed throughout the zeolite material. Systems for synthesizing hydrocarbon molecules also include catalytic structures. Methods for synthesizing hydrocarbon molecules include contacting hydrogen and at least one of carbon monoxide and carbon dioxide with such catalytic structures. Catalytic structures are fabricated by forming a zeolite material at least partially around a template structure, removing the template structure, and introducing a catalytic material into the zeolite material.
Atkins, Salla; Launiala, Annika; Kagaha, Alexander; Smith, Helen
Health policy makers now have access to a greater number and variety of systematic reviews to inform different stages in the policy making process, including reviews of qualitative research. The inclusion of mixed methods studies in systematic reviews is increasing, but these studies pose particular challenges to methods of review. This article examines the quality of the reporting of mixed methods and qualitative-only studies. We used two completed systematic reviews to generate a sample of qualitative studies and mixed method studies in order to make an assessment of how the quality of reporting and rigor of qualitative-only studies compares with that of mixed-methods studies. Overall, the reporting of qualitative studies in our sample was consistently better when compared with the reporting of mixed methods studies. We found that mixed methods studies are less likely to provide a description of the research conduct or qualitative data analysis procedures and less likely to be judged credible or provide rich data and thick description compared with standalone qualitative studies. Our time-related analysis shows that for both types of study, papers published since 2003 are more likely to report on the study context, describe analysis procedures, and be judged credible and provide rich data. However, the reporting of other aspects of research conduct (i.e. descriptions of the research question, the sampling strategy, and data collection methods) in mixed methods studies does not appear to have improved over time. Mixed methods research makes an important contribution to health research in general, and could make a more substantial contribution to systematic reviews. Through our careful analysis of the quality of reporting of mixed methods and qualitative-only research, we have identified areas that deserve more attention in the conduct and reporting of mixed methods research.
Chung, Beom Sun; Chung, Min Suk
The authors have operated the homepage (http://anatomy.co.kr) to provide the learning contents of anatomy. From the homepage, sectioned images, volume models, and surface models-all Visible Korean products-can be downloaded. The realistic images can be interactively manipulated, which will give rise to the interest in anatomy. The various anatomy comics (learning comics, comic strips, plastination comics, etc.) are approachable. Visitors can obtain the regional anatomy book with concise contents, mnemonics, and schematics as well as the simplified dissection manual and the pleasant anatomy essay. Medical students, health allied professional students, and even laypeople are expected to utilize the easy and comforting anatomy contents. It is hoped that other anatomists successively produce and distribute their own informative contents.
Chung, Beom Sun
The authors have operated the homepage (http://anatomy.co.kr) to provide the learning contents of anatomy. From the homepage, sectioned images, volume models, and surface models—all Visible Korean products—can be downloaded. The realistic images can be interactively manipulated, which will give rise to the interest in anatomy. The various anatomy comics (learning comics, comic strips, plastination comics, etc.) are approachable. Visitors can obtain the regional anatomy book with concise contents, mnemonics, and schematics as well as the simplified dissection manual and the pleasant anatomy essay. Medical students, health allied professional students, and even laypeople are expected to utilize the easy and comforting anatomy contents. It is hoped that other anatomists successively produce and distribute their own informative contents. PMID:29644104
Lowis, A; Ellington, H
The results of a survey in the United Kingdom in the late 1980s indicated that many occupational health nurses were not being sent for formal training because of the length of time nurses needed to be away from their employment and the difficulty employers had in finding nurse replacements during training. To meet the needs of occupational health nurses and their employers, the Robert Gordon Institute of Technology (RGIT) instituted a modular training course that offers full time attendance or distance learning options. RGIT's course consists of six modules over a 1 to 3 year period, which students can take in any order after completing a short Return to Study course. Using the innovative distance learning option, occupational health nurses can earn a Diploma in Occupational Health Nursing while completing most of their courses at the workplace, thus avoiding conflicts between training and work schedules.
NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners' activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current
The cases deals about learner centered learning in a commercial program and a technical program.......The cases deals about learner centered learning in a commercial program and a technical program....
Mougaard, J.F.; Poulsen, P.N.; Nielsen, L.O.
the crack geometry parameters, such as the crack length and the crack direction directly in the virtual work formulation. For efficiency, it is essential to obtain a complete tangent stiffness. A new method in this work is presented to include an incremental form the crack growth parameters on equal terms......The eXtended Finite Element Method (XFEM) is a useful tool for modeling the growth of discrete cracks in structures made of concrete and other quasi‐brittle and brittle materials. However, in a standard application of XFEM, the tangent stiffness is not complete. This is a result of not including...... with the degrees of freedom in the FEM‐equations. The complete tangential stiffness matrix is based on the virtual work together with the constitutive conditions at the crack tip. Introducing the crack growth parameters as direct unknowns, both equilibrium equations and the crack tip criterion can be handled...
Chopra, A.K.; Guttierrez, J.A.
A general substructure method for analysis of response of nuclear power plant structures to earthquake ground motion, including the effects of structure-soil interaction, is summarized. The method is applicable to complex structures idealized as finite element systems and the soil region treated as either a continuum, for example as a viscoelastic halfspace, or idealized as a finite element system. The halfspace idealization permits reliable analysis for sites where essentially similar soils extend to large depths and there is no rigid boundary such as soil-rock interface. For sites where layers of soft soil are underlain by rock at shallow depth, finite element idealization of the soil region is appropriate; in this case, the direct and substructure methods would lead to equivalent results but the latter provides the better alternative. Treating the free field motion directly as the earthquake input in the substructure method eliminates the deconvolution calculations and the related assumption -regarding type and direction of earthquake waves- required in the direct method. The substructure method is computationally efficient because the two substructures-the structure and the soil region- are analyzed separately; and, more important, it permits taking advantage of the important feature that response to earthquake ground motion is essentially contained in the lower few natural modes of vibration of the structure on fixed base. For sites where essentially similar soils extend to large depths and there is no obvious rigid boundary such as a soil-rock interface, numerical results for earthquake response of a nuclear reactor structure are presented to demonstrate that the commonly used finite element method may lead to unacceptable errors; but the substructure method leads to reliable results
Jacques van der Meer
Full Text Available Peer learning has long been recognised as an effective way to induct first-year students into the academic skills required to succeed at university. One recognised successful model that has been extensively researched is the Supplemental Instruction (SI model; it has operated in the US since the mid-1970s. This model is commonly known in Australasia as the Peer Assisted Study Sessions (PASS program. Although there is a considerable amount of research into SI and PASS, very little has been published about the impact of peer learning on different student groups, for example indigenous and other ethnic groups. This article reports on the results from one New Zealand university of the effectiveness of PASS for Māori and Pasifika students. The questions this article seeks to address are whether attendance of the PASS program results in better final marks for these two groups of students, and whether the number of sessions attended has an impact on the final marks.
Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.
In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to
Balan, Peter; Clark, Michele; Restall, Gregory
Purpose: Teaching methods such as Flipped Learning and Team-Based Learning require students to pre-learn course materials before a teaching session, because classroom exercises rely on students using self-gained knowledge. This is the reverse to "traditional" teaching when course materials are presented during a lecture, and students are…
Park, Jeong Yoon; Kim, Kyung Hyun; Kuh, Sung Uk; Chin, Dong Kyu; Kim, Keun Su; Cho, Yong Eun
Surgeon spine angle during surgery was studied ergonomically and the kinematics of the surgeon's spine was related with musculoskeletal fatigue and pain. Spine angles varied depending on operation table height and visualization method, and in a previous paper we showed that the use of a loupe and a table height at the midpoint between the umbilicus and the sternum are optimal for reducing musculoskeletal loading. However, no studies have previously included a microscope as a possible visualization method. The objective of this study is to assess differences in surgeon spine angles depending on operating table height and visualization method, including microscope. We enrolled 18 experienced spine surgeons for this study, who each performed a discectomy using a spine surgery simulator. Three different methods were used to visualize the surgical field (naked eye, loupe, microscope) and three different operating table heights (anterior superior iliac spine, umbilicus, the midpoint between the umbilicus and the sternum) were studied. Whole spine angles were compared for three different views during the discectomy simulation: midline, ipsilateral, and contralateral. A 16-camera optoelectronic motion analysis system was used, and 16 markers were placed from the head to the pelvis. Lumbar lordosis, thoracic kyphosis, cervical lordosis, and occipital angle were compared between the different operating table heights and visualization methods as well as a natural standing position. Whole spine angles differed significantly depending on visualization method. All parameters were closer to natural standing values when discectomy was performed with a microscope, and there were no differences between the naked eye and the loupe. Whole spine angles were also found to differ from the natural standing position depending on operating table height, and became closer to natural standing position values as the operating table height increased, independent of the visualization method
Chopra, A.K.; Guttierrez, J.A.
A general substructure method for analysis of response of nuclear power plant structures to earthquake ground motion, including the effects of structure-soil interaction, is summarized. The method is applicable to complex structures idealized as finite element systems and the soil region treated as either a continuum, for example as a viscoelastic halfspace, or idealized as a finite element system. The halfspace idealization permits reliable analysis for sites where essentially similar soils extend to large depths and there is no rigid boundary such as soil-rock interface. For sites where layers of soft soil are underlain by rock at shallow depth, finite element idealization of the soil region is appropriate; in this case, the direct and substructure methods would lead to equivalent results but the latter provides the better alternative. Treating the free field motion directly as the earthquake input in the substructure eliminates the deconvolution calculations and the related assumption-regarding type and direction of earthquake waves-required in the direct method. (Auth.)
Bunch, Seleta LeAnn
Enrollment in online degree programs is rapidly expanding due to the convenience and affordability offered to students and improvements in technology. The purpose of this hermeneutical phenomenological study was to understand the shared experiences of students with documented specific learning disorders (including Attention-Deficit/Hyperactivity…
Hessell, Steven M.; Morris, Robert L.; McGrogan, Sean W.; Heap, Anthony H.; Mendoza, Gil J.
A powertrain including an engine and torque machines is configured to transfer torque through a multi-mode transmission to an output member. A method for controlling the powertrain includes employing a closed-loop speed control system to control torque commands for the torque machines in response to a desired input speed. Upon approaching a power limit of a power storage device transferring power to the torque machines, power limited torque commands are determined for the torque machines in response to the power limit and the closed-loop speed control system is employed to determine an engine torque command in response to the desired input speed and the power limited torque commands for the torque machines.
Dwi Nur Rachmah
Full Text Available Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas Lambung Mangkurat. The method used in the study is the experimental method using one group pre-test and post-test design. The results of the measurements before and after the use of jigsaw learning method were compared using paired samples t-test. The results showed that there is a difference in students’ self-efficacy and motivation to learn before and after subjected to the treatments; therefore, it can be said that jigsaw learning method had significant effects on self-efficacy and motivation to learn. The application of jigsaw learning model in a classroom with large number of students was the discussion of this study.
He, Xiaoxian; Zhu, Yunlong; Hu, Kunyuan; Niu, Ben
Inspired by cooperative transport behaviors of ants, on the basis of Q-learning, a new learning method, Neighbor-Information-Reference (NIR) learning method, is present in the paper. This is a swarm-based learning method, in which principles of swarm intelligence are strictly complied with. In NIR learning, the i-interval neighbor's information, namely its discounted reward, is referenced when an individual selects the next state, so that it can make the best decision in a computable local neighborhood. In application, different policies of NIR learning are recommended by controlling the parameters according to time-relativity of concrete tasks. NIR learning can remarkably improve individual efficiency, and make swarm more "intelligent".
This thematic volume explores the relationship between the arts and learning in various educational contexts and across cultures, but with a focus on higher education and organizational learning. Arts-based interventions are at the heart of this volume, which addresses how they are conceived, des...
Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin
Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…
Holmberg, Andreas; Kierkegaard, Axel; Weng, Chenyang
In this paper, a method for including damping of acoustic energy in regions of strong turbulence is derived for a linearized Navier-Stokes method in the frequency domain. The proposed method is validated and analyzed in 2D only, although the formulation is fully presented in 3D. The result is applied in a study of the linear interaction between the acoustic and the hydrodynamic field in a 2D T-junction, subject to grazing flow at Mach 0.1. Part of the acoustic energy at the upstream edge of the junction is shed as harmonically oscillating disturbances, which are conveyed across the shear layer over the junction, where they interact with the acoustic field. As the acoustic waves travel in regions of strong shear, there is a need to include the interaction between the background turbulence and the acoustic field. For this purpose, the oscillation of the background turbulence Reynold's stress, due to the acoustic field, is modeled using an eddy Newtonian model assumption. The time averaged flow is first solved for using RANS along with a k-ε turbulence model. The spatially varying turbulent eddy viscosity is then added to the spatially invariant kinematic viscosity in the acoustic set of equations. The response of the 2D T-junction to an incident acoustic field is analyzed via a plane wave scattering matrix model, and the result is compared to experimental data for a T-junction of rectangular ducts. A strong improvement in the agreement between calculation and experimental data is found when the modification proposed in this paper is implemented. Discrepancies remaining are likely due to inaccuracies in the selected turbulence model, which is known to produce large errors e.g. for flows with significant rotation, which the grazing flow across the T-junction certainly is. A natural next step is therefore to test the proposed methodology together with more sophisticated turbulence models.
Tokuyasu, Yoshiki; Kusakabe, Kiyoko; Yamazaki, Toshio
Electrocardiography (ECG), echocardiography, nuclear method, cardiac catheterization, left ventriculography and endomyocardial biopsy (biopsy) were performed in 40 cases of cardiomyopathy (CM), 9 of endocardial fibroelastosis and 19 of specific heart muscle disease, and the usefulness and limitation of each method was comparatively estimated. In CM, various methods including biopsy were performed. The 40 patients were classified into 3 groups, i.e., hypertrophic (17), dilated (20) and non-hypertrophic.non-dilated (3) on the basis of left ventricular ejection fraction and hypertrophy of the ventricular wall. The hypertrophic group was divided into 4 subgroups: 9 septal, 4 apical, 2 posterior and 2 anterior. The nuclear study is useful in assessing the site of the abnormal ventricular thickening, perfusion defect and ventricular function. Echocardiography is most useful in detecting asymmetric septal hypertrophy. The biopsy gives the sole diagnostic clue, especially in non-hypertrophic.non-dilated cardiomyopathy. ECG is useful in all cases but correlation with the site of disproportional hypertrophy was not obtained. (J.P.N.)
Isotalo, A.E.; Wieselquist, W.A.
Highlights: • A method for handling external feed in depletion calculations with CRAM. • Source term can have polynomial or exponentially decaying time-dependence. • CRAM with source term and adjoint capability implemented to ORIGEN in SCALE. • The new solver is faster and more accurate than the original solver of ORIGEN. - Abstract: A method for including external feed with polynomial time dependence in depletion calculations with the Chebyshev Rational Approximation Method (CRAM) is presented and the implementation of CRAM to the ORIGEN module of the SCALE suite is described. In addition to being able to handle time-dependent feed rates, the new solver also adds the capability to perform adjoint calculations. Results obtained with the new CRAM solver and the original depletion solver of ORIGEN are compared to high precision reference calculations, which shows the new solver to be orders of magnitude more accurate. Furthermore, in most cases, the new solver is up to several times faster due to not requiring similar substepping as the original one
Full Text Available Free vibration equations for non-cylindrical (conical, barrel, and hyperboloidal types helical springs with noncircular cross-sections, which consist of 14 first-order ordinary differential equations with variable coefficients, are theoretically derived using spatially curved beam theory. In the formulation, the warping effect upon natural frequencies and vibrating mode shapes is first studied in addition to including the rotary inertia, the shear and axial deformation influences. The natural frequencies of the springs are determined by the use of improved Riccati transfer matrix method. The element transfer matrix used in the solution is calculated using the Scaling and Squaring method and Pad'e approximations. Three examples are presented for three types of springs with different cross-sectional shapes under clamped-clamped boundary condition. The accuracy of the proposed method has been compared with the FEM results using three-dimensional solid elements (Solid 45 in ANSYS code. Numerical results reveal that the warping effect is more pronounced in the case of non-cylindrical helical springs than that of cylindrical helical springs, which should be taken into consideration in the free vibration analysis of such springs.
Zhu, J; Liang, J; Chen, S; Qin, A; Yan, D [Beaumont Health Systeml, Royal Oak, MI (United States)
Purpose: Organ changes shape and size during radiation treatment due to both mechanical stress and radiation dose response. However, the dose response induced deformation has not been considered in conventional deformable image registration (DIR). A novel DIR approach is proposed to include both tissue elasticity and radiation dose induced organ deformation. Methods: Assuming that organ sub-volume shrinkage was proportional to the radiation dose induced cell killing/absorption, the dose induced organ volume change was simulated applying virtual temperature on each sub-volume. Hence, both stress and heterogeneity temperature induced organ deformation. Thermal stress finite element method with organ surface boundary condition was used to solve deformation. Initial boundary correspondence on organ surface was created from conventional DIR. Boundary condition was updated by an iterative optimization scheme to minimize elastic deformation energy. The registration was validated on a numerical phantom. Treatment dose was constructed applying both the conventional DIR and the proposed method using daily CBCT image obtained from HN treatment. Results: Phantom study showed 2.7% maximal discrepancy with respect to the actual displacement. Compared with conventional DIR, subvolume displacement difference in a right parotid had the mean±SD (Min, Max) to be 1.1±0.9(−0.4∼4.8), −0.1±0.9(−2.9∼2.4) and −0.1±0.9(−3.4∼1.9)mm in RL/PA/SI directions respectively. Mean parotid dose and V30 constructed including the dose response induced shrinkage were 6.3% and 12.0% higher than those from the conventional DIR. Conclusion: Heterogeneous dose distribution in normal organ causes non-uniform sub-volume shrinkage. Sub-volume in high dose region has a larger shrinkage than the one in low dose region, therefore causing more sub-volumes to move into the high dose area during the treatment course. This leads to an unfavorable dose-volume relationship for the normal organ
Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean
Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further
Engel, F.L.; Geerings, M.P.W.
Four different methods of question presentation, in interactive computeraided learning of Dutch-English word pairs are evaluated experimentally. These methods are: 1) the 'open-question method', 2) the 'multiple-choice method', 3) the 'sequential method' and 4) the 'true/ false method'. When
Cowgill, Joel [White Lake, MI
An exhaust valve control method may include displacing an exhaust valve in communication with the combustion chamber of an engine to an open position using a hydraulic exhaust valve actuation system and returning the exhaust valve to a closed position using the hydraulic exhaust valve actuation assembly. During closing, the exhaust valve may be displaced for a first duration from the open position to an intermediate closing position at a first velocity by operating the hydraulic exhaust valve actuation assembly in a first mode. The exhaust valve may be displaced for a second duration greater than the first duration from the intermediate closing position to a fully closed position at a second velocity at least eighty percent less than the first velocity by operating the hydraulic exhaust valve actuation assembly in a second mode.
Blizzard, John; Tonge, James Steven; Weidner, William Kenneth
A flexible barrier film has a thickness of from greater than zero to less than 5,000 nanometers and a water vapor transmission rate of no more than 1.times.10.sup.-2 g/m.sup.2/day at 22.degree. C. and 47% relative humidity. The flexible barrier film is formed from a composition, which comprises a multi-functional acrylate. The composition further comprises the reaction product of an alkoxy-functional organometallic compound and an alkoxy-functional organosilicon compound. A method of forming the flexible barrier film includes the steps of disposing the composition on a substrate and curing the composition to form the flexible barrier film. The flexible barrier film may be utilized in organic electronic devices.
Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik
We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.
Hindriks, Koen V.; Tykhonov, Dmytro
In automated negotiation, information gained about an opponent's preference profile by means of learning techniques may significantly improve an agent's negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent's preference profile and discuss our findings.
Mariani, Robert Dominick
Zirconium-based metal alloy compositions comprise zirconium, a first additive in which the permeability of hydrogen decreases with increasing temperatures at least over a temperature range extending from 350.degree. C. to 750.degree. C., and a second additive having a solubility in zirconium over the temperature range extending from 350.degree. C. to 750.degree. C. At least one of a solubility of the first additive in the second additive over the temperature range extending from 350.degree. C. to 750.degree. C. and a solubility of the second additive in the first additive over the temperature range extending from 350.degree. C. to 750.degree. C. is higher than the solubility of the second additive in zirconium over the temperature range extending from 350.degree. C. to 750.degree. C. Nuclear fuel rods include a cladding material comprising such metal alloy compositions, and nuclear reactors include such fuel rods. Methods are used to fabricate such zirconium-based metal alloy compositions.
Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.
Burgos, Daniel; Specht, Marcus
Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp.
Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.
García-Fernández, Pablo; Wojdeł, Jacek C.; Íñiguez, Jorge; Junquera, Javier
We present a first-principles-based (second-principles) scheme that permits large-scale materials simulations including both atomic and electronic degrees of freedom on the same footing. The method is based on a predictive quantum-mechanical theory—e.g., density functional theory—and its accuracy can be systematically improved at a very modest computational cost. Our approach is based on dividing the electron density of the system into a reference part—typically corresponding to the system's neutral, geometry-dependent ground state—and a deformation part—defined as the difference between the actual and reference densities. We then take advantage of the fact that the bulk part of the system's energy depends on the reference density alone; this part can be efficiently and accurately described by a force field, thus avoiding explicit consideration of the electrons. Then, the effects associated to the difference density can be treated perturbatively with good precision by working in a suitably chosen Wannier function basis. Further, the electronic model can be restricted to the bands of interest. All these features combined yield a very flexible and computationally very efficient scheme. Here we present the basic formulation of this approach, as well as a practical strategy to compute model parameters for realistic materials. We illustrate the accuracy and scope of the proposed method with two case studies, namely, the relative stability of various spin arrangements in NiO (featuring complex magnetic interactions in a strongly-correlated oxide) and the formation of a two-dimensional electron gas at the interface between band insulators LaAlO3 and SrTiO3 (featuring subtle electron-lattice couplings and screening effects). We conclude by discussing ways to overcome the limitations of the present approach (most notably, the assumption of a fixed bonding topology), as well as its many envisioned possibilities and future extensions.
Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao
With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.
Full Text Available Virtual learning is a type of electronic learning system based on the web. It models traditional in- person learning by providing virtual access to classes, tests, homework, feedbacks and etc. Students and teachers can interact through chat rooms or other virtual environments. Web 2.0 services are usually used for this method. Internet audio-visual tools, multimedia systems, a disco CD-ROMs, videotapes, animation, video conferencing, and interactive phones can all be used to deliver data to the students. E-learning can occur in or out of the classroom. It is time saving with lower costs compared to traditional methods. It can be self-paced, it is suitable for distance learning and it is flexible. It is a great learning style for continuing education and students can independently solve their problems but it has its disadvantages too. Thereby, blended learning (combination of conventional and virtual education is being used worldwide and has improved knowledge, skills and confidence of pharmacy students.The aim of this study is to review, discuss and introduce different methods of virtual learning for pharmacy students.Google scholar, Pubmed and Scupus databases were searched for topics related to virtual, electronic and blended learning and different styles like computer simulators, virtual practice environment technology, virtual mentor, virtual patient, 3D simulators, etc. are discussed in this article.Our review on different studies on these areas shows that the students are highly satisfied withvirtual and blended types of learning.
Mohammadjani, Farzad; Tonkaboni, Forouzan
The aim of the present research is to investigate a comparison between the effect of cooperative learning teaching method and lecture teaching method on students' learning and satisfaction level. The research population consisted of all the fourth grade elementary school students of educational district 4 in Shiraz. The statistical population…
This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data
Ross, J. C.
The ability of a lower order panel method VSAERO, to accurately predict the lift and pitching moment of a complete forward-swept-wing/canard configuration was investigated. The program can simulate nonlinear effects including boundary-layer displacement thickness, wake roll up, and to a limited extent, separated wakes. The predictions were compared with experimental data obtained using a small-scale model in the 7- by 10- Foot Wind Tunnel at NASA Ames Research Center. For the particular configuration under investigation, wake roll up had only a small effect on the force and moment predictions. The effect of the displacement thickness modeling was to reduce the lift curve slope slightly, thus bringing the predicted lift into good agreement with the measured value. Pitching moment predictions were also improved by the boundary-layer simulation. The separation modeling was found to be sensitive to user inputs, but appears to give a reasonable representation of a separated wake. In general, the nonlinear capabilities of the code were found to improve the agreement with experimental data. The usefullness of the code would be enhanced by improving the reliability of the separated wake modeling and by the addition of a leading edge separation model.
Chu, Henry Shiu-Hung [Idaho Falls, ID; Lillo, Thomas Martin [Idaho Falls, ID
The invention includes methods of forming an aluminum oxynitride-comprising body. For example, a mixture is formed which comprises A:B:C in a respective molar ratio in the range of 9:3.6-6.2:0.1-1.1, where "A" is Al.sub.2O.sub.3, "B" is AlN, and "C" is a total of one or more of B.sub.2O.sub.3, SiO.sub.2, Si--Al--O--N, and TiO.sub.2. The mixture is sintered at a temperature of at least 1,600.degree. C. at a pressure of no greater than 500 psia effective to form an aluminum oxynitride-comprising body which is at least internally transparent and has at least 99% maximum theoretical density.
Everly, Marcee C
To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.
Scholz, G.; Dewulf, A.; Pahl-Wostl, C.
Social learning among different stakeholders is often a goal in problem solving contexts such as environmental management. Participatory methods (e.g., group model-building and role playing games) are frequently assumed to stimulate social learning. Yet understanding if and why this assumption is
Neruda, Roman; Kudová, Petra
Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005
Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo
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.
Full Text Available The paper presents the application of a hybrid method (blended learning - linking traditional education with on-line education to teach selected problems of mathematical statistics. This includes the teaching of the application of mathematical statistics to evaluate laboratory experimental results. An on-line statistics course was developed to form an integral part of the module ‘methods of statistical evaluation of experimental results’. The course complies with the principles outlined in the Polish National Framework of Qualifications with respect to the scope of knowledge, skills and competencies that students should have acquired at course completion. The paper presents the structure of the course and the educational content provided through multimedia lessons made accessible on the Moodle platform. Following courses which used the traditional method of teaching and courses which used the hybrid method of teaching, students test results were compared and discussed to evaluate the effectiveness of the hybrid method of teaching when compared to the effectiveness of the traditional method of teaching.
Cutanda Henriquez, Vicente; Juhl, Peter Møller
The implementation of viscous and thermal losses using the Boundary Element Method (BEM) is based on the Kirchhoff’s dispersion relation and has been tested in previous work using analytical test cases and comparison with measurements. Numerical methods that can simulate sound fields in fluids...
Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank
Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability
Zeng, Irene Sui Lan; Lumley, Thomas
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.
Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dyna...
Hasegawa, Yuki; Shimayoshi, Takao; Amano, Akira; Matsuda, Tetsuya
Multi-scale models of the cardiovascular system provide new insight that was unavailable with in vivo and in vitro experiments. For the cardiovascular system, multi-scale simulations provide a valuable perspective in analyzing the interaction of three phenomenons occurring at different spatial scales: circulatory hemodynamics, ventricular structural dynamics, and myocardial excitation-contraction. In order to simulate these interactions, multiscale cardiovascular simulation systems couple models that simulate different phenomena. However, coupling methods require a significant amount of calculation, since a system of non-linear equations must be solved for each timestep. Therefore, we proposed a coupling method which decreases the amount of calculation by using the Kalman filter. In our method, the Kalman filter calculates approximations for the solution to the system of non-linear equations at each timestep. The approximations are then used as initial values for solving the system of non-linear equations. The proposed method decreases the number of iterations required by 94.0% compared to the conventional strong coupling method. When compared with a smoothing spline predictor, the proposed method required 49.4% fewer iterations.
Stoia, Lucas John; Melton, Patrick Benedict; Johnson, Thomas Edward; Stevenson, Christian Xavier; Vanselow, John Drake; Westmoreland, James Harold
A turbomachine combustor nozzle includes a monolithic nozzle component having a plate element and a plurality of nozzle elements. Each of the plurality of nozzle elements includes a first end extending from the plate element to a second end. The plate element and plurality of nozzle elements are formed as a unitary component. A plate member is joined with the nozzle component. The plate member includes an outer edge that defines first and second surfaces and a plurality of openings extending between the first and second surfaces. The plurality of openings are configured and disposed to register with and receive the second end of corresponding ones of the plurality of nozzle elements.
Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen
.... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...
Specht, Marcus; Burgos, Daniel
Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org
Kim, Man Cheol
Conventional PSA (probabilistic safety analysis) is performed in the framework of event tree analysis and fault tree analysis. In conventional PSA, I and C systems and human operators are assumed to be independent for simplicity. But, the dependency of human operators on I and C systems and the dependency of I and C systems on human operators are gradually recognized to be significant. I believe that it is time to consider the interdependency between I and C systems and human operators in the framework of PSA. But, unfortunately it seems that we do not have appropriate methods for incorporating the interdependency between I and C systems and human operators in the framework of Pasa. Conventional human reliability analysis (HRA) methods are not developed to consider the interdependecy, and the modeling of the interdependency using conventional event tree analysis and fault tree analysis seem to be, event though is does not seem to be impossible, quite complex. To incorporate the interdependency between I and C systems and human operators, we need a new method for HRA and a new method for modeling the I and C systems, man-machine interface (MMI), and human operators for quantitative safety assessment. As a new method for modeling the I and C systems, MMI and human operators, I develop a new system reliability analysis method, reliability graph with general gates (RGGG), which can substitute conventional fault tree analysis. RGGG is an intuitive and easy-to-use method for system reliability analysis, while as powerful as conventional fault tree analysis. To demonstrate the usefulness of the RGGG method, it is applied to the reliability analysis of Digital Plant Protection System (DPPS), which is the actual plant protection system of Ulchin 5 and 6 nuclear power plants located in Republic of Korea. The latest version of the fault tree for DPPS, which is developed by the Integrated Safety Assessment team in Korea Atomic Energy Research Institute (KAERI), consists of 64
Lewandowski, E.F.; Peterson, L.L.
This invention teaches a method of cutting a narrow slot in an extrusion die with an electrical discharge machine by first drilling spaced holes at the ends of where the slot will be, whereby the oil can flow through the holes and slot to flush the material eroded away as the slot is being cut. The invention further teaches a method of extruding a very thin ribbon of solid highly reactive material such as lithium or sodium through the die in an inert atmosphere of nitrogen, argon or the like as in a glovebox. The invention further teaches a method of stamping out sample discs from the ribbon and of packaging each disc by sandwiching it between two aluminum sheets and cold welding the sheets together along an annular seam beyond the outer periphery of the disc. This provides a sample of high purity reactive material that can have a long shelf life
ABSTRACT: Active Learning Method which requires students to take an active role in the process of learning in the classroom has been applied in Department of Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia for Unit Operations II subject in the Even Semester of Academic Year 2015/2016. The purpose of implementation of the learning method is to assist students in achieving competencies associated with the Unit Operations II subject and to help in creating...
Meenal J. Patel
Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.
Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J
Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.
Good daylight conditions in office buildings have become an important issue due to new European regulatory demands which include energy consumption for electrical lighting in the building energy frame. Good daylight conditions in offices are thus in increased focus as an energy conserving measure....... In order to evaluate whether a certain design is good daylight design or not building designers must perform detailed evaluation of daylight levels, including the daylight performance of dynamic solar shadings, and include these in the energy performance evaluation. However, the mandatory national...... calculation tool in Denmark (Be06) for evaluating the energy performance of buildings is currently using a simple representation of available daylight in a room and simple assumptions regarding the control of shading devices. In a case example, this is leading to an overestimation of the energy consumption...
Wooten, Rachel; Quinn, John; Macek, Joseph
Landau level mixing should influence the quantum Hall effect for all except the strongest applied magnetic fields. We propose a simple method for examining the effects of Landau level mixing by incorporating multiple Landau levels into the Haldane pseudopotentials through exact numerical diagonalization. Some of the resulting pseudopotentials for the lowest and first excited Landau levels will be presented
Yanagihara, Kota; Kubo, Shin; Dodin, Ilya; Nakamura, Hiroaki; Tsujimura, Toru
Geometrical Optics Ray-tracing is a reasonable numerical analytic approach for describing the Electron Cyclotron resonance Wave (ECW) in slowly varying spatially inhomogeneous plasma. It is well known that the result with this conventional method is adequate in most cases. However, in the case of Helical fusion plasma which has complicated magnetic structure, strong magnetic shear with a large scale length of density can cause a mode coupling of waves outside the last closed flux surface, and complicated absorption structure requires a strong focused wave for ECH. Since conventional Ray Equations to describe ECW do not have any terms to describe the diffraction, polarization and wave decay effects, we can not describe accurately a mode coupling of waves, strong focus waves, behavior of waves in inhomogeneous absorption region and so on. For fundamental solution of these problems, we consider the extension of the Ray-tracing method. Specific process is planned as follows. First, calculate the reference ray by conventional method, and define the local ray-base coordinate system along the reference ray. Then, calculate the evolution of the distributions of amplitude and phase on ray-base coordinate step by step. The progress of our extended method will be presented.
Trögl, J.; Pavlorková, Jana; Packová, P.; Seják, J.; Kuráň, P.; Kuráň, J.; Popelka, J.; Pacina, J.
Roč. 8, č. 3 (2016), č. článku 253. ISSN 2071-1050 Institutional support: RVO:67985858 Keywords : biotope assessment * biotope valuation method * soil microbial communities Subject RIV: DJ - Water Pollution ; Quality Impact factor: 1.789, year: 2016
Tomczuk, Zygmunt; Olszanski, Theodore W.; Battles, James E.
A negative electrode that includes a lithium alloy as active material is prepared by briefly submerging a porous, electrically conductive substrate within a melt of the alloy. Prior to solidification, excess melt can be removed by vibrating or otherwise manipulating the filled substrate to expose interstitial surfaces. Electrodes of such as solid lithium-aluminum filled within a substrate of metal foam are provided.
Xiao, Jie; Lu, Dongping; Liu, Jun; Zhang, Jiguang; Graff, Gordon L.
Electrodes having nanostructure and/or utilizing nanoparticles of active materials and having high mass loadings of the active materials can be made to be physically robust and free of cracks and pinholes. The electrodes include nanoparticles having electroactive material, which nanoparticles are aggregated with carbon into larger secondary particles. The secondary particles can be bound with a binder to form the electrode.
Dane, C. Brent; Hackel, Lloyd; Harris, Fritz B.
A laser system, such as a master oscillator/power amplifier system, comprises a gain medium and a stimulated Brillouin scattering SBS mirror system. The SBS mirror system includes an in situ filtered SBS medium that comprises a compound having a small negative non-linear index of refraction, such as a perfluoro compound. An SBS relay telescope having a telescope focal point includes a baffle at the telescope focal point which blocks off angle beams. A beam splitter is placed between the SBS mirror system and the SBS relay telescope, directing a fraction of the beam to an alternate beam path for an alignment fiducial. The SBS mirror system has a collimated SBS cell and a focused SBS cell. An adjustable attenuator is placed between the collimated SBS cell and the focused SBS cell, by which pulse width of the reflected beam can be adjusted.
Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain
Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching....
Slater, T. F.; Elfring, L.; Novodvorsky, I.; Talanquer, V.; Quintenz, J.
Science education reform documents universally call for students to have authentic and meaningful experiences using real data in the context of their science education. The underlying philosophical position is that students analyzing data can have experiences that mimic actual research. In short, research experiences that reflect the scientific spirit of inquiry potentially can: prepare students to address real world complex problems; develop students' ability to use scientific methods; prepare students to critically evaluate the validity of data or evidence and of the consequent interpretations or conclusions; teach quantitative skills, technical methods, and scientific concepts; increase verbal, written, and graphical communication skills; and train students in the values and ethics of working with scientific data. However, it is unclear what the broader pre-service teacher preparation community is doing in preparing future teachers to promote, manage, and successful facilitate their own students in conducting authentic scientific inquiry. Surveys of undergraduates in secondary science education programs suggests that students have had almost no experiences themselves in conducting open scientific inquiry where they develop researchable questions, design strategies to pursue evidence, and communicate data-based conclusions. In response, the College of Science Teacher Preparation Program at the University of Arizona requires all students enrolled in its various science teaching methods courses to complete an open inquiry research project and defend their findings at a specially designed inquiry science mini-conference at the end of the term. End-of-term surveys show that students enjoy their research experience and believe that this experience enhances their ability to facilitate their own future students in conducting open inquiry.
Sidik, S. M.
Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.
Full Text Available Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG and electroencephalogram (EEG, considering that they are both unique to individuals, and more reliable than token (identity card and knowledge-based (username/password methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health.
Tereshin, G.S.; Kharitonova, L.K.; Kuznetsova, O.B.
Heterogeneous systems Y(NO 3 ) 3 (YCl 3 )-Hsub(n)L-KNO 3 (KCl)-H 2 O are investigated by potentiometric titration (with coulomb-meter generation of oH - ions). Hsub(n)L is one of the following: oxyethylidendiphosphonic; aminobenzilidendiphosphonic; glycine-bis-methyl-phosphonic; nitrilotrimethylphosphonic (H 6 L) and ethylenediaminetetramethylphosphonic acids. The range of the exsistence of YHsub(nL3)LxyH 2 O has been determined. The possibility of using potentiometric titration for investigating heterogeneous systems is demonstrated by the stUdy of the system Y(NO 3 ) 3 -H 6 L-KOH-H 2 o by the method of residual concentration. The two methods have shown that at pH 3 LxyH 2 O; at pH=6, KYH 2 Lxy'H 2 O, and at pH=7, K 2 YHLxy''H 2 O. The complete solubility products of nitrilotrimethylphosphonates are evaluated
Booth, J.T.; Zavgorodni, S.F.; Royal Adelaide Hospital, SA
Full text: The random treatment delivery errors (organ motion and set-up error) can be incorporated into the treatment planning software using a convolution method. Mean treatment dose is computed as the convolution of a static dose distribution with a variation kernel. Typically this variation kernel is Gaussian with variance equal to the sum of the organ motion and set-up error variances. We propose a novel variation kernel for the convolution technique that additionally considers the position of the mobile organ in the planning CT image. The systematic error of organ position in the planning CT image can be considered random for each patient over a population. Thus the variance of the variation kernel will equal the sum of treatment delivery variance and organ motion variance at planning for the population of treatments. The kernel is extended to deal with multiple pre-treatment CT scans to improve tumour localisation for planning. Mean treatment doses calculated with the convolution technique are compared to benchmark Monte Carlo (MC) computations. Calculations of mean treatment dose using the convolution technique agreed with MC results for all cases to better than ± 1 Gy in the planning treatment volume for a prescribed 60 Gy treatment. Convolution provides a quick method of incorporating random organ motion (captured in the planning CT image and during treatment delivery) and random set-up errors directly into the dose distribution. Copyright (2000) Australasian College of Physical Scientists and Engineers in Medicine
Koh, Chung-Yan; Piccini, Matthew E.; Singh, Anup K.
Examples are described including measurement systems for conducting competition assays. A first chamber of an assay device may be loaded with a sample containing a target antigen. The target antigen in the sample may be allowed to bind to antibody-coated beads in the first chamber. A control layer separating the first chamber from a second chamber may then be opened to allow a labeling agent loaded in a first portion of the second chamber to bind to any unoccupied sites on the antibodies. A centrifugal force may then be applied to transport the beads through a density media to a detection region for measurement by a detection unit.
Koh, Chung-Yan; Piccini, Matthew E.; Singh, Anup K.
Examples are described including measurement systems for conducting competition assays. A first chamber of an assay device may be loaded with a sample containing a target antigen. The target antigen in the sample may be allowed to bind to antibody-coated beads in the first chamber. A control layer separating the first chamber from a second chamber may then be opened to allow a labeling agent loaded in a first portion of the second chamber to bind to any unoccupied sites on the antibodies. A centrifugal force may then be applied to transport the beads through a density media to a detection region for measurement by a detection unit.
Milton, Kimball A
This is a graduate level textbook on the theory of electromagnetic radiation and its application to waveguides, transmission lines, accelerator physics and synchrotron radiation. It has grown out of lectures and manuscripts by Julian Schwinger prepared during the war at MIT's Radiation Laboratory, updated with material developed by Schwinger at UCLA in the 1970s and 1980s, and by Milton at the University of Oklahoma since 1994. The book includes a great number of straightforward and challenging exercises and problems. It is addressed to students in physics, electrical engineering, and applied mathematics seeking a thorough introduction to electromagnetism with emphasis on radiation theory and its applications.
Dwiyogo, Wasis D.
The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…
Full Text Available Building relationships in the classroom is an essential part of any teacher's career. Having healthy teacher-to-learner and learner-to-learner relationships is an effective way to help prevent pedagogical failure, social conflict and quarrelsome behavior. Many strategies are available that can be used to achieve good long-lasting relationships in the classroom setting. Successful teachers’ pedagogical work in the classroom requires detailed knowledge of learners’ relationships. Good understanding of the relationships is necessary, especially in the case of teenagers’ class. This sensitive period of adolescence demands attention of all teachers who should deal with the problems of their learners. Special care should be focused on children that are out of their classmates’ interest (so called isolated learners or isolates in such class and on possibilities to integrate them into the class. Natural idea how to do it is that of using some modern non-traditional teaching/learning methods, especially the methods based on work in small groups involving learners’ cooperation. Small group education (especially problem-based learning, project-based learning, cooperative learning, collaborative learning or inquire-based learning as one of these methods involves a high degree of interaction. The effectiveness of learning groups is determined by the extent to which the interaction enables members to clarify their own understanding, build upon each other's contributions, sift out meanings, ask and answer questions. An influence of this kind of methods (especially cooperative learning (CL on learners’ relationships was a subject of the further described research. Within the small group education, students work with their classmates to solve complex and authentic problems that help develop content knowledge as well as problem-solving, reasoning, communication, and self-assessment skills. The aim of the research was to answer the question: Can the
Garg, Anil K; Garg, Seema
The evidence suggests that our perception of physical beauty is based on how closely the features of one's face reflect phi (the golden ratio) in their proportions. By that extension, it must certainly be possible to use a mathematical parameter to design an anterior hairline in all faces. To establish a user-friendly method to design an anterior hairline in cases of male pattern alopecia. We need a flexible measuring tape and skin marker. A reference point A (glabella) is taken in between eyebrows. Mark point E, near the lateral canthus, 8 cm horizontal on either side from the central point A. A mid-frontal point (point B) is marked 8 cm from point A on the forehead in a mid-vertical plane. The frontotemporal points (C and C') are marked on the frontotemporal area, 8 cm in a horizontal plane from point B and 8 cm in a vertical plane from point E. The temporal peak points (D and D') are marked on the line joining the frontotemporal point C to the lateral canthus point E, slightly more than halfway toward lateral canthus, usually 5 cm from the frontotemporal point C. This line makes an anterior border of the temporal triangle. We have conducted a study with 431 cases of male pattern alopecia. The average distance of the mid-frontal point from glabella was 7.9 cm. The patient satisfaction reported was 94.7%. Our method gives a skeletal frame of the anterior hairline with minimal criteria, with no need of visual imagination and experience of the surgeon. It automatically takes care of the curvature of the forehead and is easy to use for a novice surgeon.
Full Text Available This research aims to find out the application of Think Pair Share (TPS learning method in improving learning motivation and learning achievement in the subject of Introduction to Accounting I of the Accounting Study Program students of Politeknik Harapan Bersama. The Method of data collection in this study used observation method, test method, and documentation method. The research instruments used observation sheet, questionnaire and test question. This research used Class Action Research Design which is an action implementation oriented research, with the aim of improving quality or problem solving in a group by carefully and observing the success rate due to the action. The method of analysis used descriptive qualitative and quantitative analysis method. The results showed that the application of Think Pair Share Learning (TPS Method can improve the Learning Motivation and Achievement. Before the implementation of the action, the obtained score is 67% then in the first cycle increases to 72%, and in the second cycle increasws to 80%. In addition, based on questionnaires distributed to students, it also increases the score of Accounting Learning Motivation where the score in the first cycle of 76% increases to 79%. In addition, in the first cycle, the score of pre test and post test of the students has increased from 68.86 to 76.71 while in the second cycle the score of pre test and post test of students has increased from 79.86 to 84.86.
Isupova, Olga; Kuzin, Danil; Mihaylova, Lyudmila
Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.
Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati
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.
Full Text Available Teaching methods in MBA and Lifelong Learning Programmes (LLP for managers should be topically relevant in terms of content as well as the teaching methods used. In terms of the content, the integral part of MBA and Lifelong Learning Programmes for managers should be the development of participants’ leadership competencies and their understanding of current leadership concepts. The teaching methods in educational programmes for managers as adult learners should correspond to the strategy of learner-centred teaching that focuses on the participants’ learning process and their active involvement in class. The focus on the participants’ learning process also raises questions about whether the programme’s participants perceive the teaching methods used as useful and relevant for their development as leaders. The paper presents the results of the analysis of the responses to these questions in a sample of 54 Czech participants in the MBA programme and of lifelong learning programmes at the University of Economics, Prague. The data was acquired based on written or electronically submitted questionnaires. The data was analysed in relation to the usefulness of the teaching methods for understanding the concepts of leadership, leadership skills development as well as respondents’ personal growth. The results show that the respondents most valued the methods that enabled them to get feedback, activated them throughout the programme and got them involved in discussions with others in class. Implications for managerial education practices are discussed.
Ni Putu Wulan Purnama Sari
Full Text Available Background and Purpose: Caring is the essence of nursing profession. Stimulation of caring attitude should start early. Effective teaching methods needed to foster caring attitude and improve learning achievement. This study aimed to explain the effect of applying flipped classroom learning method for improving caring attitude and learning achievement of new student nurses at nursing institutions in Surabaya. Method: This is a pre-experimental study using the one group pretest posttest and posttest only design. Population was all new student nurses on nursing institutions in Surabaya. Inclusion criteria: female, 18-21 years old, majoring in nursing on their own volition and being first choice during students selection process, status were active in the even semester of 2015/2016 academic year. Sample size was 67 selected by total sampling. Variables: 1 independent: application of flipped classroom learning method; 2 dependent: caring attitude, learning achievement. Instruments: teaching plan, assignment descriptions, presence list, assignment assessment rubrics, study materials, questionnaires of caring attitude. Data analysis: paired and one sample t test. Ethical clearance was available. Results: Most respondents were 20 years old (44.8%, graduated from high school in Surabaya (38.8%, living with parents (68.7% in their homes (64.2%. All data were normally distributed. Flipped classroom learning method could improve caring attitude by 4.13%. Flipped classroom learning method was proved to be effective for improving caring attitude (p=0.021 and learning achievement (p=0.000. Conclusion and Recommendation: Flipped classroom was effective for improving caring attitude and learning achievement of new student nurse. It is recommended to use mix-method and larger sample for further study.
Sulisworo, Dwi; Sutadi, Novitasari
There have been many studies related to the implementation of cooperative learning. However, there are still many problems in school related to the learning outcomes on science lesson, especially in physics. The aim of this study is to observe the application of science learning cycle (SLC) model on improving scientific literacy for secondary…
Kauvar, Arielle N B; Cronin, Terrence; Roenigk, Randall; Hruza, George; Bennett, Richard
Basal cell carcinoma (BCC) is the most common cancer in the US population affecting approximately 2.8 million people per year. Basal cell carcinomas are usually slow-growing and rarely metastasize, but they do cause localized tissue destruction, compromised function, and cosmetic disfigurement. To provide clinicians with guidelines for the management of BCC based on evidence from a comprehensive literature review, and consensus among the authors. An extensive review of the medical literature was conducted to evaluate the optimal treatment methods for cutaneous BCC, taking into consideration cure rates, recurrence rates, aesthetic and functional outcomes, and cost-effectiveness of the procedures. Surgical approaches provide the best outcomes for BCCs. Mohs micrographic surgery provides the highest cure rates while maximizing tissue preservation, maintenance of function, and cosmesis. Mohs micrographic surgery is an efficient and cost-effective procedure and remains the treatment of choice for high-risk BCCs and for those in cosmetically sensitive locations. Nonsurgical modalities may be used for low-risk BCCs when surgery is contraindicated or impractical, but the cure rates are lower.
Jaime Leonardo Bobadilla Molina
Full Text Available The increasing amount of protein three-dimensional (3D structures determined by x-ray and NMR technologies as well as structures predicted by computational methods results in the need for automated methods to provide inital annotations. We have developed a new method for recognizing sites in three-dimensional protein structures. Our method is based on a previosly reported algorithm for creating descriptions of protein microenviroments using physical and chemical properties at multiple levels of detail. The recognition method takes three inputs: 1. A set of control nonsites that share some structural or functional role. 2. A set of control nonsites that lack this role. 3. A single query site. A support vector machine classifier is built using feature vectors where each component represents a property in a given volume. Validation against an independent test set shows that this recognition approach has high sensitivity and specificity. We also describe the results of scanning four calcium binding proteins (with the calcium removed using a three dimensional grid of probe points at 1.25 angstrom spacing. The system finds the sites in the proteins giving points at or near the blinding sites. Our results show that property based descriptions along with support vector machines can be used for recognizing protein sites in unannotated structures.
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.
Johansen, Steffen Kjær
T becomes a learning method rather than a teaching method. Besides discussing the pedagogical characteristics of EiT, the study also gives a general introduction to EiT as it was taught at SDU fall 2016 as well as a brief review of the basic theory behind experiential learning. As such this study serves...... courses. Most of the practical courses are group work along the lines of project based learning. EiT is in a way both. It is a practical course in as much as our students get hands-on experience with interdisciplinary team work and innovation processes. EiT is a theoretical course in as much as our...... both as an introduction to e.g. new teachers of EiT but also as a starting point for a clarification of the features that makes EiT an experiential learning endeavor....
Ustinov, A; Khayrullina, A; Khmelik, M; Sveshnikova, A; Borzenko, V
Development of fuel cell (FC) and hydrogen metal-hydride storage (MH) technologies continuously demonstrate higher efficiency rates and higher safety, as hydrogen is stored at low pressures of about 2 bar in a bounded state. A combination of a FC/MH system with an electrolyser, powered with a renewable source, allows creation of an almost fully autonomous power system, which could potentially replace a diesel-generator as a back-up power supply. However, the system must be extended with an electro-chemical battery to start-up the FC and compensate the electric load when FC fails to deliver the necessary power. Present paper delivers the results of experimental and theoretical investigation of a hybrid energy system, including a proton exchange membrane (PEM) FC, MH- accumulator and an electro-chemical battery, development methodology for such systems and the modelling of different battery types, using hardware-in-the-loop approach. The economic efficiency of the proposed solution is discussed using an example of power supply of a real town of Batamai in Russia. (paper)
Ustinov, A.; Khayrullina, A.; Borzenko, V.; Khmelik, M.; Sveshnikova, A.
Development of fuel cell (FC) and hydrogen metal-hydride storage (MH) technologies continuously demonstrate higher efficiency rates and higher safety, as hydrogen is stored at low pressures of about 2 bar in a bounded state. A combination of a FC/MH system with an electrolyser, powered with a renewable source, allows creation of an almost fully autonomous power system, which could potentially replace a diesel-generator as a back-up power supply. However, the system must be extended with an electro-chemical battery to start-up the FC and compensate the electric load when FC fails to deliver the necessary power. Present paper delivers the results of experimental and theoretical investigation of a hybrid energy system, including a proton exchange membrane (PEM) FC, MH- accumulator and an electro-chemical battery, development methodology for such systems and the modelling of different battery types, using hardware-in-the-loop approach. The economic efficiency of the proposed solution is discussed using an example of power supply of a real town of Batamai in Russia.
Full Text Available Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher vocational students to learn Business English Visual-audio-oral Course, master Business English knowledge, and improve communicative competence of Business English.
Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher voca...
Üce, Musa; Ates, Ismail
In this research; aim was determining student achievement by comparing problem-based learning method with teacher-centered traditional method of teaching 10th grade chemistry lesson mixtures topic. Pretest-posttest control group research design is implemented. Research sample includes; two classes of (total of 48 students) an Anatolian High School…
What are the competencies for tommorow´s enginnering education and the implications of these regarding the choice of teaching content and learning methods? The paper analyses two trends: the traditional and the techo-science approach. These two trends are based on technological innovation...... and change processes and impact on educational content and methods....
Liu, Shuang; Breit, Rhonda
The capacity to conduct research is essential for university graduates to survive and thrive in their future career. However, research methods courses have often been considered by students as "abstract", "uninteresting", and "hard". Thus, motivating students to engage in the process of learning research methods has become a crucial challenge for…
Branney, Jonathan; Priego-Hernández, Jacqueline
It is important for nurses to have a thorough understanding of the biosciences such as pathophysiology that underpin nursing care. These courses include content that can be difficult to learn. Team-based learning is emerging as a strategy for enhancing learning in nurse education due to the promotion of individual learning as well as learning in teams. In this study we sought to evaluate the use of team-based learning in the teaching of applied pathophysiology to undergraduate student nurses. A mixed methods observational study. In a year two, undergraduate nursing applied pathophysiology module circulatory shock was taught using Team-based Learning while all remaining topics were taught using traditional lectures. After the Team-based Learning intervention the students were invited to complete the Team-based Learning Student Assessment Instrument, which measures accountability, preference and satisfaction with Team-based Learning. Students were also invited to focus group discussions to gain a more thorough understanding of their experience with Team-based Learning. Exam scores for answers to questions based on Team-based Learning-taught material were compared with those from lecture-taught material. Of the 197 students enrolled on the module, 167 (85% response rate) returned the instrument, the results from which indicated a favourable experience with Team-based Learning. Most students reported higher accountability (93%) and satisfaction (92%) with Team-based Learning. Lectures that promoted active learning were viewed as an important feature of the university experience which may explain the 76% exhibiting a preference for Team-based Learning. Most students wanted to make a meaningful contribution so as not to let down their team and they saw a clear relevance between the Team-based Learning activities and their own experiences of teamwork in clinical practice. Exam scores on the question related to Team-based Learning-taught material were comparable to those
Full Text Available Abstract Background Patient reported outcome measures (PROMs are self-report measures of health status increasingly promoted for use in healthcare quality improvement. However people with low literacy skills or learning disabilities may find PROMs hard to complete. Our study investigated stakeholder views on the accessibility and use of PROMs to develop suggestions for more inclusive practice. Methods Taking PROMs recommended for chronic obstructive pulmonary disease (COPD as an example, we conducted 8 interviews with people with low literacy skills and/or learning disabilities, and 4 focus groups with 20 health professionals and people with COPD. Discussions covered the format and delivery of PROMs using the EQ-5D and St George Respiratory Questionnaire as prompts. Thematic framework analysis focused on three main themes: Accessibility, Ease of Use, and Contextual factors. Results Accessibility included issues concerning the questionnaire format, and suggestions for improvement included larger font sizes and more white space. Ease of Use included discussion about PROMs’ administration. While health professionals suggested PROMs could be completed in waiting rooms, patients preferred settings with more privacy and where they could access help from people they know. Contextual Factors included other challenges and wider issues associated with completing PROMs. While health professionals highlighted difficulties created by the system in managing patients with low literacy/learning disabilities, patient participants stressed that understanding the purpose of PROMs was important to reduce intimidation. Conclusions Adjusting PROMs’ format, giving an explicit choice of where patients can complete them, and clearly conveying PROMs’ purpose and benefit to patients may help to prevent inequality when using PROMs in health services.
Geary, W.J.; James, A.M. (ed.)
This book presents the analytical uses of radioactive isotopes within the context of radiochemistry as a whole. It is designed for scientists with relatively little background knowledge of the subject. Thus the initial emphasis is on developing the basic concepts of radioactive decay, particularly as they affect the potential usage of radioisotopes. Discussion of the properties of various types of radiation, and of factors such as half-life, is related to practical considerations such as counting and preparation methods, and handling/disposal problems. Practical aspects are then considered in more detail, and the various radioanalytical methods are outlined with particular reference to their applicability. The approach is 'user friendly' and the use of self assessment questions allows the reader to test his/her understanding of individual sections easily. For those who wish to develop their knowledge further, a reading list is provided.
The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.
Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query the users about their preferences and give recommendations based on the system’s belief about the utility function. Critical to these applications is th...... is the acquisition of prior distribution about the utility parameters and the possibility of real time Bayesian inference. In this paper we consider Monte Carlo methods for these problems....
Ariana, Armin; Amin, Moein; Pakneshan, Sahar; Dolan-Evans, Elliot; Lam, Alfred K
Dental students require a basic ability to explain and apply general principles of pathology to systemic, dental, and oral pathology. Although there have been recent advances in electronic and online resources, the academic effectiveness of using self-directed e-learning tools in pathology courses for dental students is unclear. The aim of this study was to determine if blended learning combining e-learning with traditional learning methods of lectures and tutorials would improve students' scores and satisfaction over those who experienced traditional learning alone. Two consecutive cohorts of Bachelor of Dentistry and Oral Health students taking the general pathology course at Griffith University in Australia were compared. The control cohort experienced traditional methods only, while members of the study cohort were also offered self-directed learning materials including online resources and online microscopy classes. Final assessments for the course were used to compare the differences in effectiveness of the intervention, and students' satisfaction with the teaching format was evaluated using questionnaires. On the final course assessments, students in the study cohort had significantly higher scores than students in the control cohort (plearning tools such as virtual microscopy and interactive online resources for delivering pathology instruction can be an effective supplement for developing dental students' competence, confidence, and satisfaction.
Taylor, Estelle; Breed, Marnus; Hauman, Ilette; Homann, Armando
Our aim is to determine which teaching methods students in Computer Science and Information Systems prefer. There are in total 5 different paradigms (behaviorism, cognitivism, constructivism, design-based and humanism) with 32 models between them. Each model is unique and states different learning methods. Recommendations are made on methods that…
Czimber, Kornél; Gálos, Borbála; Mátyás, Csaba; Bidló, András; Gribovszki, Zoltán
Hungarian forests are highly sensitive to the changing climate, especially to the available precipitation amount. Over the past two decades several drought damages were observed for tree species which are in the lower xeric limit of their distribution. From year to year these affected forest stands become more difficult to reforest with the same native species because these are not able to adapt to the increasing probability of droughts. The climate related parameter set of the Hungarian forest stand database needs updates. Air humidity that was formerly used to define the forest climate zones is not measured anymore and its value based on climate model outputs is highly uncertain. The aim was to develop a novel computerized and objective method to describe the species-specific climate conditions that is essential for survival, growth and optimal production of the forest ecosystems. The method is expected to project the species spatial distribution until 2100 on the basis of regional climate model simulations. Until now, Hungarian forest managers have been using a carefully edited spreadsheet for reforestation purposes. Applying binding regulations this spreadsheet prescribes the stand-forming and admixed tree species and their expected growth rate for each forest site types. We are going to present a new machine learning based method to replace the former spreadsheet. We took into great consideration of various methods, such as maximum likelihood, Bayesian networks, Fuzzy logic. The method calculates distributions, setups classification, which can be validated and modified by experts if necessary. Projected climate change conditions makes necessary to include into this system an additional climate zone that does not exist in our region now, as well as new options for potential tree species. In addition to or instead of the existing ones, the influence of further limiting parameters (climatic extremes, soil water retention) are also investigated. Results will be
Full Text Available This paper discusses a research project carried out with 82 final and third year undergraduate students, learning Research Methods prior to undertaking an undergraduate thesis during the academic years 2010 and 2011. The research had two separate, linked objectives, (a to develop a Research Methods module that embraces an activity-based approach to learning in a group environment, (b to improve engagement by all students. The Research Methods module was previously taught through a traditional lecture-based format. Anecdotally, it was felt that student engagement was poor and learning was limited. It was believed that successful completion of the development of this Module would equip students with a deeply-learned battery of research skills to take into their further academic and professional careers. Student learning was achieved through completion of a series of activities based on different research methods. In order to encourage student engagement, a wide variety of activities were used. These activities included workshops, brainstorming, mind-mapping, presentations, written submissions, peer critiquing, lecture/seminar, and ‘speed dating’ with more senior students and self reflection. Student engagement was measured through a survey based on a U.S. National Survey of Student Engagement (2000. A questionnaire was devised to establish whether, and to what degree, students were engaged in the material that they were learning, while they were learning it. The results of the questionnaire were very encouraging with between 63% and 96% of students answering positively to a range of questions concerning engagement. In terms of the two objectives set, these were satisfactorily met. The module was successfully developed and continues to be delivered, based upon this new and significant level of student engagement.
Pedersen, Kamilla; Moeller, Martin Holdgaard; Paltved, Charlotte; Mors, Ole; Ringsted, Charlotte; Morcke, Anne Mette
The aim of this study was to explore medical students' learning experiences from the didactic teaching formats using either text-based patient cases or video-based patient cases with similar content. The authors explored how the two different patient case formats influenced students' perceptions of psychiatric patients and students' reflections on meeting and communicating with psychiatric patients. The authors conducted group interviews with 30 medical students who volunteered to participate in interviews and applied inductive thematic content analysis to the transcribed interviews. Students taught with text-based patient cases emphasized excitement and drama towards the personal clinical narratives presented by the teachers during the course, but never referred to the patient cases. Authority and boundary setting were regarded as important in managing patients. Students taught with video-based patient cases, in contrast, often referred to the patient cases when highlighting new insights, including the importance of patient perspectives when communicating with patients. The format of patient cases included in teaching may have a substantial impact on students' patient-centeredness. Video-based patient cases are probably more effective than text-based patient cases in fostering patient-centered perspectives in medical students. Teachers sharing stories from their own clinical experiences stimulates both engagement and excitement, but may also provoke unintended stigma and influence an authoritative approach in medical students towards managing patients in clinical psychiatry.
Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent
Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.
Santos, Rui C.; Leal, Joao P.; Martinho Simoes, Jose A.
A revised parameterization of the extended Laidler method for predicting standard molar enthalpies of atomization and standard molar enthalpies of formation at T = 298.15 K for several families of hydrocarbons (alkanes, alkenes, alkynes, polyenes, poly-ynes, cycloalkanes, substituted cycloalkanes, cycloalkenes, substituted cycloalkenes, benzene derivatives, and bi and polyphenyls) is presented. Data for a total of 265 gas-phase and 242 liquid-phase compounds were used for the calculation of the parameters. Comparison of the experimental values with those obtained using the additive scheme led to an average absolute difference of 0.73 kJ . mol -1 for the gas-phase standard molar enthalpy of formation and 0.79 kJ . mol -1 for the liquid-phase standard molar enthalpy of formation. The database used to establish the parameters was carefully reviewed by using, whenever possible, the original publications. A worksheet to simplify the calculation of standard molar enthalpies of formation and standard molar enthalpies of atomization at T = 298.15 K based on the extended Laidler parameters defined in this paper is provided as supplementary material.
Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.
Wilson, Penne L.
interviews; Part II is a series of embedded, explanatory case studies which present an in-depth examination of three of the participants of this study to better understand the factors that influenced their learning of the HbL method of teaching science. Findings of this study indicate that teachers did learn the HbL method of teaching science through the online HbL workshop, the only place instruction in the HbL method was available. The structure of the online workshop which first introduced an element of the HbL process to teachers, next asked them to conduct a personal activity, and then to use a similar activity in their classrooms with students, and to reflect on the outcome of the activity, was successful in teaching the HbL method. Teachers expressed satisfaction with the structure of the online workshop and with the HbL method which they believed made learning science fun and which encouraged students to become more creative and critical thinkers, and also increased their knowledge of science concepts. The main motivation for learning HbL and the primary factor that led to teachers' satisfaction was the students' positive reaction to the HbL method. The teachers were encouraged because the students loved to do science after being introduced to HbL. Also identified in this study was the need by a participant for the inclusion of video models of teachers using the HbL method within the HbL online workshop. This suggestion demonstrated the need to incorporate more learning styles in the activities included in the HbL workshop in order to appeal to a wider audience of online learners.
Rankin, Jean; Brown, Val
Traditional ways of teaching in Higher Education are enhanced with adult-based approaches to learning within the curriculum. Adult-based learning enables students to take ownership of their own learning, working in independence using a holistic approach. Introducing creative activities promotes students to think in alternative ways to the traditional learning models. The study aimed to explore student midwives perceptions of a creative teaching method as a learning strategy. A qualitative design was used adopting a phenomenological approach to gain the lived experience of students within this learning culture. Purposive sampling was used to recruit student midwives (n=30). Individual interviews were conducted using semi-structured interviews with open-ended questions to gain subjective information. Data were transcribed and analyzed into useful and meaningful themes and emerging themes using Colaizzi's framework for analyzing qualitative data in a logical and systematic way. Over 500 meaningful statements were identified from the transcripts. Three key themes strongly emerged from the transcriptions. These included'meaningful learning','inspired to learn and achieve', and 'being connected'. A deep meaningful learning experience was found to be authentic in the context of theory and practice. Students were inspired to learn and achieve and positively highlighted the safe learning environment. The abilities of the facilitators were viewed positively in supporting student learning. This approach strengthened the relationships and social engagement with others in the peer group and the facilitators. On a less positive note, tensions and conflict were noted in group work and indirect negative comments about the approach from the teaching team. Incorporating creative teaching activities is a positive addition to the healthcare curriculum. Creativity is clearly an asset to the range of contemporary learning strategies. In doing so, higher education will continue to keep
Pedersen, Kamilla; Holdgaard, Martin Møller; Paltved, Charlotte
' perceptions of psychiatric patients and students' reflections on meeting and communicating with psychiatric patients. METHODS: The authors conducted group interviews with 30 medical students who volunteered to participate in interviews and applied inductive thematic content analysis to the transcribed....... Students taught with video-based patient cases, in contrast, often referred to the patient cases when highlighting new insights, including the importance of patient perspectives when communicating with patients. CONCLUSION: The format of patient cases included in teaching may have a substantial impact...... unintended stigma and influence an authoritative approach in medical students towards managing patients in clinical psychiatry....
Coya, Liliam de Barbosa; Perez-Coffie, Jorge
"Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…
Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A
Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.
Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro
Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones
Lu, Jiamei; Li, Daqi; Stevens, Carla; Ye, Renmin
Using PISA 2009, an international education database, this study compares gifted and talented (GT) students in three groups with normal (non-GT) students by examining student characteristics, reading, schooling, learning methods, and use of strategies for understanding and memorizing. Results indicate that the GT and non-GT gender distributions…
This study examines alternative method of teaching and learning of the concept of diffusion. An improvised U-shape glass tube called ionic mobility tube was used to observed and measure the rate of movement of divalent metal ions in an aqueous medium in the absence of an electric current. The study revealed that the ...
Abrahamsen, Trine Julie
Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...
Oxford, Rebecca; Crookall, David
Surveys research on formal and informal second-language learning strategies, covering the effectiveness of research methods involving making lists, interviews and thinking aloud, note-taking, diaries, surveys, and training. Suggestions for future and improved research are presented. (131 references) (CB)
Ivanov, V.V.; Purehvdorzh, B.; Puzynin, I.V.
First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs
Иван Николаевич Куринин
Full Text Available The article describes a method of interactive learning based on educational integrating projects. Some examples of content of such projects for the disciplines related to the study of information and Internet technologies and their application in management are presented.
Jönsson, Lise Høgh
, and people with learning disabilities worked together to develop five new visual and digital methods for interviewing in special education. Thereby not only enhancing the students’ competences, knowledge and proficiency in innovation and research, but also proposing a new teaching paradigm for university...
Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun
Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…
Audio visual education that incorporates devices and materials which involve sight, sound, or both has become a sine qua non in recent times in the teaching and learning process. An automated physical model of mining methods aided with video instructions was designed and constructed by harnessing locally available ...
Full Text Available I documented my strategies for learning sound-symbol correspondences during a Khmer course. I used a mnemonic strategy that I call the keyimage method. In this method, a character evokes an image (the keyimage, which evokes the corresponding sound. For example, the keyimage for the character 2 could be a swan with its head tucked in. This evokes the sound "kaw" that a swan makes, which sounds similar to the Khmer sound corresponding to 2. The method has some similarities to the keyword method. Considering the results of keyword studies, I hypothesize that the keyimage method is more effective than rote learning and that peer-generated keyimages are more effective than researcher- or teacher-generated keyimages, which are more effective than learner-generated ones. In Dr. Andrew Cohen's plenary presentation at the Hawaii TESOL 2007 conference, he mentioned that more case studies are needed on learning strategies (LSs. One reason to study LSs is that what learners do with input to produce output is unclear, and knowing what strategies learners use may help us understand that process (Dornyei, 2005, p. 170. Hopefully, we can use that knowledge to improve language learning, perhaps by teaching learners to use the strategies that we find. With that in mind, I have examined the LSs that I used in studying Khmer as a foreign language, focusing on learning the syllabic alphabet.
Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng
Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.
Minowa, Hirotsugu; Gofuku, Akio
Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)
Patel, Meenal J.; Khalaf, Alexander; Aizenstein, Howard J.
Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presen...
Towill, Denis R
The purpose of this article is to look at method study, as devised by the Gilbreths at the beginning of the twentieth century, which found early application in hospital quality assurance and surgical "best practice". It has since become a core activity in all modern methods, as applied to healthcare delivery improvement programmes. The article traces the origin of what is now currently and variously called "business process re-engineering", "business process improvement" and "lean healthcare" etc., by different management gurus back to the century-old pioneering work of Frank Gilbreth. The outcome is a consistent framework involving "width", "length" and "depth" dimensions within which healthcare delivery systems can be analysed, designed and successfully implemented to achieve better and more consistent performance. Healthcare method (saving time plus saving motion) study is best practised as co-joint action learning activity "owned" by all "players" involved in the re-engineering process. However, although process mapping is a key step forward, in itself it is no guarantee of effective re-engineering. It is not even the beginning of the end of the change challenge, although it should be the end of the beginning. What is needed is innovative exploitation of method study within a healthcare organisational learning culture accelerated via the Gilbreth Knowledge Flywheel. It is shown that effective healthcare delivery pipeline improvement is anchored into a team approach involving all "players" in the system especially physicians. A comprehensive process study, constructive dialogue, proper and highly professional re-engineering plus managed implementation are essential components. Experience suggests "learning" is thereby achieved via "natural groups" actively involved in healthcare processes. The article provides a proven method for exploiting Gilbreths' outputs and their many successors in enabling more productive evidence-based healthcare delivery as summarised
Full Text Available The «PBL working environment» is a virtual environment developed in the framework of SCENE project (profeSsional development for an effeCtive PBL approach: a practical experiENce through ICT-enabled lEarning solution, co-funded by the European Lifelong Learning Program. The «PBL working environment» is devoted to prepare headmasters and teachers of secondary and vocational schools to use Problem-Based Learning (PBL pedagogy effectively. It is a student-centered pedagogy where learners are «actively» engaged in real world problems to solve or challenges to meet. Students develop problem-solving, self-directed learning and team skills. The «PBL working environment» is an virtual tool including three main elements: e-learning platform, virtual facilitator and PBL repository. Teachers, trainers and headmasters/school managers learn the PBL pedagogy by attending an on-line course (e-learning platform delivered through the «inductive method». It allows learners to experience PBL approach, by practicing it stage by stage, and then learn to turn practice into theory by abstracting their experience to build a theoretical understanding. Since generating the proper scenario is the most critical aspect of PBL, after benefiting from the on-line course, users can benefit from a further support: the Virtual Facilitator. It provides tips and hints on how correctly design a problem scenario and by asking questions to collect data on user's specific needs. The Virtual Facilitator is able to provide a/or more suitable example(s which match as closest as possible the teacher/trainer need. Finally, users can share problem scenarios and projects of different subjects of studies and with different characteristics uploaded and downloaded in the PBL repository.
Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua
Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random
Akhmetov, Dauren F.; Kotaki, Minoru
In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.
Deslauriers, Louis; Wieman, Carl
We measured mastery and retention of conceptual understanding of quantum mechanics in a modern physics course. This was studied for two equivalent cohorts of students taught with different pedagogical approaches using the Quantum Mechanics Conceptual Survey. We measured the impact of pedagogical approach both on the original conceptual learning and on long-term retention. The cohort of students who had a very highly rated traditional lecturer scored 19% lower than the equivalent cohort that was taught using interactive engagement methods. However, the amount of retention was very high for both cohorts, showing only a few percent decrease in scores when retested 6 and 18 months after completion of the course and with no exposure to the material in the interim period. This high level of retention is in striking contrast to the retention measured for more factual learning from university courses and argues for the value of emphasizing conceptual learning.
Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li
High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.
Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi
In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.
Samsudin; Nugraha, Bayu
This study aimed to know the difference between playing and learning methods of exploratory learning methods to learning outcomes throwing the ball. In addition, this study also aimed to determine the effect of nutritional status of these two learning methods mentioned above. This research was conducted at SDN Cipinang Besar Selatan 16 Pagi East…
Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima
To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.
Full Text Available To evaluate the use of ultrasound coupled with machine learning (ML and deep learning (DL techniques for automated or semi-automated classification of myositis.Eighty subjects comprised of 19 with inclusion body myositis (IBM, 14 with polymyositis (PM, 14 with dermatomyositis (DM, and 33 normal (N subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally were acquired. We considered three problems of classification including (A normal vs. affected (DM, PM, IBM; (B normal vs. IBM patients; and (C IBM vs. other types of myositis (DM or PM. We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A, 86.6% ± 2.4% for (B and 74.8% ± 3.9% for (C, while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A, 84.3% ± 2.3% for (B and 68.9% ± 2.5% for (C.This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.
Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.
da Costa Tavares, Ofelia Cizela; Suyoto; Pranowo
In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process) and SAW (simple additive Weighting) method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.
da Costa Tavares Ofelia Cizela
Full Text Available In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process and SAW (simple additive Weighting method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.
Finnegan, Alex; Song, Jun S
New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.
The aim of this study was to identify the influence of discovery learning method towards the mathematical analogical ability of junior high school's students. This is a research using factorial design 2x2 with ANOVA-Two ways. The population of this research included the entire students of SMPN 13 Jakarta (State Junior High School 13 of Jakarta)…
Gurpinar, Erol; Alimoglu, Mustafa Kemal; Mamakli, Sumer; Aktekin, Mehmet
The curriculum of our medical school has a hybrid structure including both traditional training (lectures) and problem-based learning (PBL) applications. The purpose of this study was to determine the learning styles of our medical students and investigate the relation of learning styles with each of satisfaction with different instruction methods and academic achievement in them. This study was carried out with the participation of 170 first-year medical students (the participation rate was 91.4%). The researchers prepared sociodemographic and satisfaction questionnaires to determine the characteristics of the participants and their satisfaction levels with traditional training and PBL. The Kolb learning styles inventory was used to explore the learning styles of the study group. The participants completed all forms at the end of the first year of medical education. Indicators of academic achievement were scores of five theoretical block exams and five PBL exams performed throughout the academic year of 2008-2009. The majority of the participants took part in the "diverging" (n = 84, 47.7%) and "assimilating" (n = 73, 41.5%) groups. Numbers of students in the "converging" and "accommodating" groups were 11 (6.3%) and 8 (4.5%), respectively. In all learning style groups, PBL satisfaction scores were significantly higher than those of traditional training. Exam scores for "PBL and traditional training" did not differ among the four learning styles. In logistic regression analysis, learning style (assimilating) predicted student satisfaction with traditional training and success in theoretical block exams. Nothing predicted PBL satisfaction and success. This is the first study conducted among medical students evaluating the relation of learning style with student satisfaction and academic achievement. More research with larger groups is needed to generalize our results. Some learning styles may relate to satisfaction with and achievement in some instruction methods.
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649
Olden, Julian D; Lawler, Joshua J; Poff, N LeRoy
Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.
Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad
Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.
Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.
PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator
Alipour, Sadaf; Moini, Ashraf; Jafari-Adli, Shahrzad; Gharaie, Nooshin; Mansouri, Khorshid
Mobile learning enables users to interact with educational resources while in variable locations. Medical students in residency positions need to assimilate considerable knowledge besides their practical training and we therefore aimed to evaluate the impact of using short message service via cell phone as a learning tool in residents of Obstetrics and Gynecology in our hospital. We sent short messages including data about breast cancer to the cell phones of 25 residents of gynecology and obstetrics and asked them to study a well-designed booklet containing another set of information about the disease in the same period. The rate of learning derived from the two methods was compared by pre- and post-tests and self-satisfaction assessed by a relevant questionnaire at the end of the program. The mobile learning method had a significantly better effect on learning and created more interest in the subject. Learning via receiving SMS can be an effective and appealing method of knowledge acquisition in higher levels of education.
Ketcheson, David I.
A course in numerical methods should teach both the mathematical theory of numerical analysis and the craft of implementing numerical algorithms. The IPython notebook provides a single medium in which mathematics, explanations, executable code, and visualizations can be combined, and with which the student can interact in order to learn both the theory and the craft of numerical methods. The use of notebooks also lends itself naturally to inquiry-based learning methods. I discuss the motivation and practice of teaching a course based on the use of IPython notebooks and inquiry-based learning, including some specific practical aspects. The discussion is based on my experience teaching a Masters-level course in numerical analysis at King Abdullah University of Science and Technology (KAUST), but is intended to be useful for those who teach at other levels or in industry.
Jeronen, Eila; Palmberg, Irmeli; Yli-Panula, Eija
There are very few studies concerning the importance of teaching methods in biology education and environmental education including outdoor education for promoting sustainability at the levels of primary and secondary schools and pre-service teacher education. The material was selected using special keywords from biology and sustainable education…
Cederkvist, Karin; Jensen, Marina B; Holm, Peter E
Stormwater treatment facilities (STFs) are becoming increasingly widespread but knowledge on their performance is limited. This is due to difficulties in obtaining representative samples during storm events and documenting removal of the broad range of contaminants found in stormwater runoff. This paper presents a method to evaluate STFs by addition of synthetic runoff with representative concentrations of contaminant species, including the use of tracer for correction of removal rates for losses not caused by the STF. A list of organic and inorganic contaminant species, including trace elements representative of runoff from roads is suggested, as well as relevant concentration ranges. The method was used for adding contaminants to three different STFs including a curbstone extension with filter soil, a dual porosity filter, and six different permeable pavements. Evaluation of the method showed that it is possible to add a well-defined mixture of contaminants despite different field conditions by having a flexibly system, mixing different stock-solutions on site, and use bromide tracer for correction of outlet concentrations. Bromide recovery ranged from only 12% in one of the permeable pavements to 97% in the dual porosity filter, stressing the importance of including a conservative tracer for correction of contaminant retention values. The method is considered useful in future treatment performance testing of STFs. The observed performance of the STFs is presented in coming papers. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available This study investigates the learners’ preference of academic, collaborative and social interaction towards interaction methods in e-learning portal. Academic interaction consists of interaction between learners and online learning resources such as online reading, online explanation, online examination and also online question answering. Collaborative interaction occurs when learners interact among themselves using online group discussion. Social interaction happens when learners and instructors participate in the session either via online text chatting or voice chatting. The study employed qualitative methodology where data were collected through questionnaire that was administered to 933 distance education students from Bachelor of Management, Bachelor of Science, Bachelor of Social Science and Bachelor of Art. The survey responses were tabulated in a 5-point Likert scale and analyzed using the Statistical Package for Social Science (SPSS Version 12.0 based on frequency and percentage distribution. The result of the study suggest that among three types of interaction, most of the student prefer academic interaction for their learning supports in e-learning portal compared to collaborative and social interaction. They wish to interact with learning content rather than interact with people. They prefer to read and learn from the resources rather than sharing knowledge among themselves and instructors via collaborative and social interaction.
Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan
In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People's Daily corpus to achieve a satisfactory accuracy.
Miller, Adam A.
Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..
Miller, Adam A.
Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..
Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun
For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
Full Text Available During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
Koponen, Jonna; Pyörälä, Eeva; Isotalus, Pekka
Despite numerous studies exploring medical students' attitudes to communication skills learning (CSL), there are apparently no studies comparing different experiential learning methods and their influence on students' attitudes. We compared medical students' attitudes to learning communication skills before and after a communication course in the data as a whole, by gender and when divided into three groups using different methods. Second-year medical students (n = 129) were randomly assigned to three groups. In group A (n = 42) the theatre in education method, in group B (n = 44) simulated patients and in group C (n = 43) role-play were used. The data were gathered before and after the course using Communication Skills Attitude Scale. Students' positive attitudes to learning communication skills (PAS; positive attitude scale) increased significantly and their negative attitudes (NAS; negative attitude scale) decreased significantly between the beginning and end of the course. Female students had more positive attitudes than the male students. There were no significant differences in the three groups in the mean scores for PAS or NAS measured before or after the course. The use of experiential methods and integrating communication skills training with visits to health centres may help medical students to appreciate the importance of CSL.
Full Text Available In this paper, a geometrical scheme is presented to show how to overcome an encountered problem arising from the use of generalized delta learning rule within competitive learning model. It is introduced a theoretical methodology for describing the quantization of data via rotating prototype vectors on hyper-spheres.The proposed learning algorithm is tested and verified on different multidimensional datasets including a binary class dataset and two multiclass datasets from the UCI repository, and a multiclass dataset constructed by us. The proposed method is compared with some baseline learning vector quantization variants in literature for all domains. Large number of experiments verify the performance of our proposed algorithm with acceptable accuracy and macro f1 scores.
Mu, Jingyi; Wu, Fang; Zhang, Aihua
In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing...
Kůrková, Věra; Sanguineti, M.
Roč. 21, č. 3 (2005), s. 350-367 ISSN 0885-064X R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : supervised learning * generalization * model complexity * kernel methods * minimization of regularized empirical errors * upper bounds on rates of approximate optimization Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2005
Hampton, Debra; Pearce, Patricia F; Moser, Debra K
Investigators have demonstrated that on-line courses result in effective learning outcomes, but limited information has been published related to preferred teaching strategies. Delivery of on-line courses requires various teaching methods to facilitate interaction between students, content, and technology. The purposes of this study were to understand student teaching/learning preferences in on-line courses to include (a) differences in preferred teaching/learning methods for on-line nursing students across generations and (b) which teaching strategies students found to be most engaging and effective. Participants were recruited from 2 accredited, private school nursing programs (N=944) that admit students from across the United States and deliver courses on-line. Participants provided implied consent, and 217 (23%) students completed the on-line survey. Thirty-two percent of the students were from the Baby Boomer generation (1946-1964), 48% from Generation X (1965-1980), and 20% from the Millennial Generation (born after 1980). The preferred teaching/learning methods for students were videos or narrated PowerPoint presentations, followed by synchronous Adobe Connect educations sessions, assigned journal article reading, and e-mail dialog with the instructor. The top 2 methods identified by participants as the most energizing/engaging and most effective for learning were videos or narrated PowerPoint presentations and case studies. The teaching/learning method least preferred by participants and that was the least energizing/engaging was group collaborative projects with other students; the method that was the least effective for learning was wikis. Baby Boomers and Generation X participants had a significantly greater preference for discussion board (PBaby Boomer and Generation X students and rated on-line games as significantly more energizing/engaging and more effective for learning (PBaby Boomer and Generation X students. In conclusion, the results of this
Full Text Available New qualitative research methods continue to emerge in response to factors such as renewed interest in mixed methods, better understanding of the importance of a researcher’s philosophical stance, as well as the increased use of technology in data collection and analysis, to name a few. As a result, those facilitating research methods courses must revisit content and instructional strategies in order to prepare well-informed researchers. Approaches range from paradigm to pragmatic emphasis. This descriptive case study of a doctoral seminar for novice qualitative researchers describes the intricacies of the syllabus of a pragmatic approach in a constructivist/social constructionist learning environment. The purpose was to document the delivery and faculty/student interactions and reactions. Noteworthy were the contradictions and frustrations in the delivery as well as in student experiences. In the end, student input led to seminal learning experiences. The confirmation of the effectiveness of a constructivist/social constructivist learning environment is applicable to higher education pedagogy in general.
Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.
Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.
This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.
Ponte, Pedro; Melko, Roger G.
Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.
Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna
The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.
Full Text Available For many qualitative researchers in the social sciences, learning about and teaching qualitative research methods and methodology raises a number of questions. This topic was the focus of a symposium held during the Second Berlin Summer School for Qualitative Research Methods in July 2006. In this contribution, some of the issues discussed during the symposium are taken up and extended, and some basic dimensions underlying these issues are summarized. How qualitative research methods and methodology are taught is closely linked to the ways in which qualitative researchers in the social sciences conceptualize themselves and their discipline. In the following, we distinguish between a paradigmatic and a pragmatic view. From a pragmatic point of view, qualitative research methods are considered research strategies or techniques and can be taught in the sense of recipes with specific steps to be carried out. According to a paradigmatic point of view (strongly inspired by constructivism, qualitative research methods and methodology are conceptualized as a craft to be practiced together by a "master" and an "apprentice." Moreover, the teaching of qualitative research methods also depends heavily on the institutional standing of qualitative compared to quantitative research method. Based on these considerations, five basic dimensions of learning about and teaching qualitative research methods are suggested: ways of teaching (ranging from the presentation of textbook knowledge to cognitive apprenticeship and instructors' experience with these; institutional contexts, including their development and the teaching of qualitative research methods in other than university contexts; the "fit" between personality and method, including relevant personal skills and talents; and, as a special type of instructional context that increasingly has gained importance, distance learning and its implications for learning about and teaching qualitative research methods
Tax, N.; Bockting, S.; Hiemstra, D.
Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered
Trivette, Carol M.; Dunst, Carl J.; Hamby, Deborah W.; O'Herin, Chainey E.
The effectiveness of four adult learning methods (accelerated learning, coaching, guided design, and just-in-time training) constituted the focus of this research synthesis. Findings reported in "How People Learn" (Bransford et al., 2000) were used to operationally define six adult learning method characteristics, and to code and analyze…
Ryberg, Thomas; Buus, Lillian; Nyvang, Tom
In this chapter, a specific learning design method is introduced and explained, namely the Collaborative E-learning Design method (CoED), which has been developed through various projects in “e-Learning Lab – Centre for User Driven Innovation, Learning and Design” (Nyvang & Georgsen, 2007). We br...
Mullins, Mary H.
Active learning approaches have shown to improve student learning outcomes and improve the experience of students in the classroom. This article compares a Process Oriented Guided Inquiry Learning style approach to a more traditional teaching method in an undergraduate research methods course. Moving from a more traditional learning environment to…
Sun, Dan; Garmory, Andrew; Page, Gary J.
For flows where the particle number density is low and the Stokes number is relatively high, as found when sand or ice is ingested into aircraft gas turbine engines, streams of particles can cross each other's path or bounce from a solid surface without being influenced by inter-particle collisions. The aim of this work is to develop an Eulerian method to simulate these types of flow. To this end, a two-node quadrature-based moment method using 13 moments is proposed. In the proposed algorithm thirteen moments of particle velocity, including cross-moments of second order, are used to determine the weights and abscissas of the two nodes and to set up the association between the velocity components in each node. Previous Quadrature Method of Moments (QMOM) algorithms either use more than two nodes, leading to increased computational expense, or are shown here to give incorrect results under some circumstances. This method gives the computational efficiency advantages of only needing two particle phase velocity fields whilst ensuring that a correct combination of weights and abscissas is returned for any arbitrary combination of particle trajectories without the need for any further assumptions. Particle crossing and wall bouncing with arbitrary combinations of angles are demonstrated using the method in a two-dimensional scheme. The ability of the scheme to include the presence of drag from a carrier phase is also demonstrated, as is bouncing off surfaces with inelastic collisions. The method is also applied to the Taylor-Green vortex flow test case and is found to give results superior to the existing two-node QMOM method and is in good agreement with results from Lagrangian modelling of this case.
Latisma D, L.; Kurniawan, W.; Seprima, S.; Nirbayani, E. S.; Ellizar, E.; Hardeli, H.
The purpose of this study was to see which method are well used with the Chemistry Triangle-oriented learning media. This quasi experimental research involves first grade of senior high school students in six schools namely each two SMA N in Solok city, in Pasaman and two SMKN in Pariaman. The sampling technique was done by Cluster Random Sampling. Data were collected by test and analyzed by one-way anova and Kruskall Wallish test. The results showed that the high school students in Solok learning taught by cooperative method is better than the results of student learning taught by conventional and Individual methods, both for students who have high initial ability and low-ability. Research in SMK showed that the overall student learning outcomes taught by conventional method is better than the student learning outcomes taught by cooperative and individual methods. Student learning outcomes that have high initial ability taught by individual method is better than student learning outcomes that are taught by cooperative method and for students who have low initial ability, there is no difference in student learning outcomes taught by cooperative, individual and conventional methods. Learning in high school in Pasaman showed no significant difference in learning outcomes of the three methods undertaken.
Richéal M. Burns
Full Text Available PurposeTo assess feasibility and health economic benefits and costs as part of a pilot study for a nurse-led, psychoeducational intervention (NPLI for prostate cancer in order to understand the potential for cost effectiveness as well as contribute to the design of a larger scale trial.MethodsMen with stable prostate cancer post-treatment were recruited from two cancer centres in the UK. Eighty-three men were randomised to the NLPI plus usual care or usual care alone (UCA (42 NLPI and 41 UCA; the NLPI plus usual care was delivered in the primary-care setting (the intervention and included an initial face-to-face consultation with a trained nurse, with follow-up tailored to individual needs. The study afforded the opportunity to undertake a short-term within pilot analysis. The primary outcome measure for the economic evaluation was quality of life, as measured by the EuroQol five dimensions questionnaire (EQ-5D (EQ-5D-5L instrument. Costs (£2014 assessed included health-service resource use, out-of-pocket expenses and losses from inability to undertake usual activities.ResultsTotal and incremental costs varied across the different scenarios assessed, with mean cost differences ranging from £173 to £346; incremental effect, as measured by the change in utility scores over the duration of follow-up, exhibited wide confidence intervals highlighting inconclusive effectiveness (95% CI: -0.0226; 0.0438. The cost per patient of delivery of the intervention would be reduced if rolled out to a larger patient cohort.ConclusionsThe NLPI is potentially cost saving depending on the scale of delivery; however, the results presented are not considered generalisable.
Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran
To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.
Yamaguchi, Tomohiro; Kinugasa, Yusuke; Shiomi, Akio; Sato, Sumito; Yamakawa, Yushi; Kagawa, Hiroyasu; Tomioka, Hiroyuki; Mori, Keita
Few data are available to assess the learning curve for robotic-assisted surgery for rectal cancer. The aim of the present study was to evaluate the learning curve for robotic-assisted surgery for rectal cancer by a surgeon at a single institute. From December 2011 to August 2013, a total of 80 consecutive patients who underwent robotic-assisted surgery for rectal cancer performed by the same surgeon were included in this study. The learning curve was analyzed using the cumulative sum method. This method was used for all 80 cases, taking into account operative time. Operative procedures included anterior resections in 6 patients, low anterior resections in 46 patients, intersphincteric resections in 22 patients, and abdominoperineal resections in 6 patients. Lateral lymph node dissection was performed in 28 patients. Median operative time was 280 min (range 135-683 min), and median blood loss was 17 mL (range 0-690 mL). No postoperative complications of Clavien-Dindo classification Grade III or IV were encountered. We arranged operative times and calculated cumulative sum values, allowing differentiation of three phases: phase I, Cases 1-25; phase II, Cases 26-50; and phase III, Cases 51-80. Our data suggested three phases of the learning curve in robotic-assisted surgery for rectal cancer. The first 25 cases formed the learning phase.
Koevesarki, Peter; Nuncio Quiroz, Adriana Elizabeth; Brock, Ian C. [Physikalisches Institut, Universitaet Bonn, Bonn (Germany)
High energy physics is a home for a variety of multivariate techniques, mainly due to the fundamentally probabilistic behaviour of nature. These methods generally require training based on some theory, in order to discriminate a known signal from a background. Nevertheless, new physics can show itself in ways that previously no one thought about, and in these cases conventional methods give little or no help. A possible way to discriminate between known processes (like vector bosons or top-quark production) or look for new physics is using unsupervised machine learning to extract the features of the data. A technique was developed, based on the combination of neural networks and the method of principal curves, to find a parametrisation of the non-linear correlations of the data. The feasibility of the method is shown on ATLAS data.
Vladimir S. Kublanov
Full Text Available The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.
Digitally delivered learning shows the promise of enhancing learner motivation and engagement, advancing critical thinking skills, encouraging reflection and knowledge sharing, and improving professional self-efficacy. Digital learning objects take many forms including interactive media, apps and games, video and other e-learning activities and…
Mohammed Abdallh Otair
Full Text Available Attempting to deliver a monolithic mobile learning system is too inflexible in view of the heterogeneous mixture of hardware and services available and the desirability of facility blended approaches to learning delivery, and how to build learning materials to run on all platforms. This paper proposes a framework of mobile learning system using an intelligent method (IP-MLI . A fuzzy matching method is used to find suitable learning material design. It will provide a best matching for each specific platform type for each learner. The main contribution of the proposed method is to use software layer to insulate learning materials from device-specific features. Consequently, many versions of learning materials can be designed to work on many platform types.
Wiebe, Nicholas J P; Meyer, Irmtraud M
The prediction of functional RNA structures has attracted increased interest, as it allows us to study the potential functional roles of many genes. RNA structure prediction methods, however, assume that there is a unique functional RNA structure and also do not predict functional features required for in vivo folding. In order to understand how functional RNA structures form in vivo, we require sophisticated experiments or reliable prediction methods. So far, there exist only a few, experimentally validated transient RNA structures. On the computational side, there exist several computer programs which aim to predict the co-transcriptional folding pathway in vivo, but these make a range of simplifying assumptions and do not capture all features known to influence RNA folding in vivo. We want to investigate if evolutionarily related RNA genes fold in a similar way in vivo. To this end, we have developed a new computational method, Transat, which detects conserved helices of high statistical significance. We introduce the method, present a comprehensive performance evaluation and show that Transat is able to predict the structural features of known reference structures including pseudo-knotted ones as well as those of known alternative structural configurations. Transat can also identify unstructured sub-sequences bound by other molecules and provides evidence for new helices which may define folding pathways, supporting the notion that homologous RNA sequence not only assume a similar reference RNA structure, but also fold similarly. Finally, we show that the structural features predicted by Transat differ from those assuming thermodynamic equilibrium. Unlike the existing methods for predicting folding pathways, our method works in a comparative way. This has the disadvantage of not being able to predict features as function of time, but has the considerable advantage of highlighting conserved features and of not requiring a detailed knowledge of the cellular
Malina, Mary A.; Nørreklit, Hanne; Selto, Frank H.
on the use and usefulness of a specialized balanced scorecard; and third, to encourage researchers to actually use multiple methods and sources of data to address the very many accounting phenomena that are not fully understood. Design/methodology/approach – This paper is an opinion piece based...... on the authors' experience conducting a series of longitudinal mixed method studies. Findings – The authors suggest that in many studies, using a mixed method approach provides the best opportunity for addressing research questions. Originality/value – This paper provides encouragement to those who may wish......Purpose – The purpose of this paper is first, to discuss the theoretical assumptions, qualities, problems and myopia of the dominating quantitative and qualitative approaches; second, to describe the methodological lessons that the authors learned while conducting a series of longitudinal studies...
Full Text Available Abstract Background Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. Results We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%. The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile to 0.5 CPU-hours (simplified 3D profile to seconds (machine learning. Conclusions We showed that the simplified profile generation method does not introduce an error with regard to the original method, while
Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd
Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset
Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin
Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.
Anderson, Lisa C; Krichbaum, Kathleen E
Physiology is a requisite course for many professional allied health programs and is a foundational science for learning pathophysiology, health assessment, and pharmacology. Given the demand for online learning in the health sciences, it is important to evaluate the efficacy of online and in-class teaching methods, especially as they are combined to form hybrid courses. The purpose of this study was to compare two hybrid physiology sections in which one section was offered mostly in-class (85% in-class), and the other section was offered mostly online (85% online). The two sections in 2 yr ( year 1 and year 2 ) were compared in terms of knowledge of physiology measured in exam scores and pretest-posttest improvement, and in measures of student satisfaction with teaching. In year 1 , there were some differences on individual exam scores between the two sections, but no significant differences in mean exam scores or in pretest-posttest improvements. However, in terms of student satisfaction, the mostly in-class students in year 1 rated the instructor significantly higher than did the mostly online students. Comparisons between in-class and online students in the year 2 cohort yielded data that showed that mean exam scores were not statistically different, but pre-post changes were significantly greater in the mostly online section; student satisfaction among mostly online students also improved significantly. Education researchers must investigate effective combinations of in-class and online methods for student learning outcomes, while maintaining the flexibility and convenience that online methods provide. Copyright © 2017 the American Physiological Society.
Aleksandr Vasilyevich Koshkarov
Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.
Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey
The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN. - Highlights: • Buildings consume huge amounts of energy for operation. • Envelope materials and insulation influence building energy consumption. • Extreme learning machine is used to estimate energy usage of a sample building. • The key effective factors in this study are insulation thickness and K-value.
Full Text Available Nowadays there are different evaluation methods focused in the assessment of the usability of telematic methods. The assessment of 3rd generation web environments evaluates the effectiveness and usability of application with regard to the user needs. Wireless usability and, specifically in mobile phones, is concentrated in the validation of the features and tools management using conventional interactive environments. There is not a specific and suitable criterion to evaluate created environments and m-learning platforms, where the restricted and sequential representation is a fundamental aspect to be considered.The present paper exposes the importance of the conventional usability methods to verify both: the employed contents in wireless formats, and the possible interfaces from the conception phases, to the validations of the platform with such characteristics.The development of usability adapted inspection could be complemented with the Remote’s techniques of usability testing, which are being carried out these days in the mobile devices field and which pointed out the need to apply common criteria in the validation of non-located learning scenarios.
There are many different methods that individuals use to learn languages like reading books or writing essays. Not all methods are equally successful for second language learners but nor do all successful learners of a second language show identical preferences for learning methods. Additionally, at the highest level of language learning various…
Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille
This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that…
Ban, Chunmei; Wu, Zhuangchun; Dillon, Anne C.
An electrode (110) is provided that may be used in an electrochemical device (100) such as an energy storage/discharge device, e.g., a lithium-ion battery, or an electrochromic device, e.g., a smart window. Hydrothermal techniques and vacuum filtration methods were applied to fabricate the electrode (110). The electrode (110) includes an active portion (140) that is made up of electrochemically active nanoparticles, with one embodiment utilizing 3d-transition metal oxides to provide the electrochemical capacity of the electrode (110). The active material (140) may include other electrochemical materials, such as silicon, tin, lithium manganese oxide, and lithium iron phosphate. The electrode (110) also includes a matrix or net (170) of electrically conductive nanomaterial that acts to connect and/or bind the active nanoparticles (140) such that no binder material is required in the electrode (110), which allows more active materials (140) to be included to improve energy density and other desirable characteristics of the electrode. The matrix material (170) may take the form of carbon nanotubes, such as single-wall, double-wall, and/or multi-wall nanotubes, and be provided as about 2 to 30 percent weight of the electrode (110) with the rest being the active material (140).
Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.
Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of
Barkeshli, Kasra; Volakis, John L.
The theoretical and computational aspects related to the application of the Conjugate Gradient FFT (CGFFT) method in computational electromagnetics are examined. The advantages of applying the CGFFT method to a class of large scale scattering and radiation problems are outlined. The main advantages of the method stem from its iterative nature which eliminates a need to form the system matrix (thus reducing the computer memory allocation requirements) and guarantees convergence to the true solution in a finite number of steps. Results are presented for various radiators and scatterers including thin cylindrical dipole antennas, thin conductive and resistive strips and plates, as well as dielectric cylinders. Solutions of integral equations derived on the basis of generalized impedance boundary conditions (GIBC) are also examined. The boundary conditions can be used to replace the profile of a material coating by an impedance sheet or insert, thus, eliminating the need to introduce unknown polarization currents within the volume of the layer. A general full wave analysis of 2-D and 3-D rectangular grooves and cavities is presented which will also serve as a reference for future work.
Nicolás Fernández Losa
Full Text Available This paper describes a teaching experience about experimental field work as practical learning method implemented in the subject of Organizational Behaviour. With this teaching experience we pretend to change the practical training, as well as in its evaluation process, in order to favour the development of transversal skills of students. For this purpose, the use of a practice plan, tackled through an experimental field work and carried out with the collaboration of a business organization within a work team (as organic unity of learning, arises as an alternative to the traditional method of practical teachings and allows the approach of business reality into the classroom, as well as actively promote the use of transversal skills. In particular, we develop the experience in three phases. Initially, the students, after forming a working group and define a field work project, should get the collaboration of a nearby business organization in which to obtain data on one or more functional areas of organizational behaviour. Subsequently, students carry out the field work with the realization of the scheduled visits and elaboration of a memory to establish a diagnosis of the strategy followed by the company in these functional areas in order to propose and justify alternative actions that improve existing ones. Finally, teachers assess the different field work memories and their public presentations according to evaluation rubrics, which try to objectify and unify to the maximum the evaluation criteria and serve to guide the learning process of students. The results of implementation of this teaching experience, measured through a Likert questionnaire, are very satisfactory for students.
We want to discuss the methods of efficient study habits and how they can be used by students to help them improve learning physics. In particular, we deal with the most efficient techniques needed to help students improve their study skills. We focus on topics such as the skills of how to develop long term memory, how to improve concentration power, how to take class notes, how to prepare for and take exams, how to study scientific subjects such as physics. We argue that the students who conscientiously use the methods of efficient study habits achieve higher results than those students who do not; moreover, a student equipped with the proper study skills will spend much less time to learn a subject than a student who has no good study habits. The underlying issue here is not the quantity of time allocated to the study efforts by the students, but the efficiency and quality of actions so that the student can function at peak efficiency. These ideas were developed as part of Project IMPACTSEED (IMproving Physics And Chemistry Teaching in SEcondary Education), an outreach grant funded by the Alabama Commission on Higher Education. This project is motivated by a major pressing local need: A large number of high school physics teachers teach out of field. )
Miller, Adam A.
Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..
When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.
Peci, Adriana; Winter, Anne-Luise; Gubbay, Jonathan B.
Legionella is a Gram-negative bacterium that can cause Pontiac fever, a mild upper respiratory infection and Legionnaire’s disease, a more severe illness. We aimed to compare the performance of urine antigen, culture, and polymerase chain reaction (PCR) test methods and to determine if sputum is an acceptable alternative to the use of more invasive bronchoalveolar lavage (BAL). Data for this study included specimens tested for Legionella at Public Health Ontario Laboratories from 1st January, 2010 to 30th April, 2014, as part of routine clinical testing. We found sensitivity of urinary antigen test (UAT) compared to culture to be 87%, specificity 94.7%, positive predictive value (PPV) 63.8%, and negative predictive value (NPV) 98.5%. Sensitivity of UAT compared to PCR was 74.7%, specificity 98.3%, PPV 77.7%, and NPV 98.1%. Out of 146 patients who had a Legionella-positive result by PCR, only 66 (45.2%) also had a positive result by culture. Sensitivity for culture was the same using either sputum or BAL (13.6%); sensitivity for PCR was 10.3% for sputum and 12.8% for BAL. Both sputum and BAL yield similar results regardless testing methods (Fisher Exact p-values = 1.0, for each test). In summary, all test methods have inherent weaknesses in identifying Legionella; therefore, more than one testing method should be used. Obtaining a single specimen type from patients with pneumonia limits the ability to diagnose Legionella, particularly when urine is the specimen type submitted. Given ease of collection and similar sensitivity to BAL, clinicians are encouraged to submit sputum in addition to urine when BAL submission is not practical from patients being tested for Legionella. PMID:27630979
Full Text Available Legionella is a gram-negative bacterium that can cause Pontiac fever, a mild upper respiratory infection and Legionnaire’s disease, a more severe illness. We aimed to compare the performance of urine antigen, culture and PCR test methods and to determine if sputum is an alternative to the use of more invasive bronchoalveolar lavage (BAL. Data for this study included specimens tested for Legionella at PHOL from January 1, 2010 to April 30, 2014, as part of routine clinical testing. We found sensitivity of UAT compared to culture to be 87%, specificity 94.7%, positive predictive value (PPV 63.8% and negative predictive value (NPV 98.5%. Sensitivity of UAT compared to PCR was 74.7%, specificity 98.3%, PPV 77.7% and NPV 98.1%. Of 146 patients who had a Legionella positive result by PCR, only 66(45.2% also had a positive result by culture. Sensitivity for culture was the same using either sputum or BAL (13.6%; sensitivity for PCR was 10.3% for sputum and 12.8% for BAL. Both sputum and BAL yield similar results despite testing methods (Fisher Exact p-values=1.0, for each test. In summary, all test methods have inherent weaknesses in identifying Legionella; thereforemore than one testing method should be used. Obtaining a single specimen type from patients with pneumonia limits the ability to diagnose Legionella, particularly when urine is the specimen type submitted. Given ease of collection, and similar sensitivity to BAL, clinicians are encouraged to submit sputum in addition to urine when BAL submission is not practical, from patients being tested for Legionella.
Hearn, Tristan A.
This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.
Nachmani, Eliya; Marciano, Elad; Lugosch, Loren; Gross, Warren J.; Burshtein, David; Be'ery, Yair
The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.
Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme
In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.
Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen
Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.
Full Text Available This paper considers the detection of spatial domain least significant bit (LSB matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature.
Wu, Wen; Mammone, Richard J.
The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.
McDermott, Hilary J.; Dovey, Terence M.
Research methods courses aim to equip students with the knowledge and skills required for research yet seldom include practical aspects of assessment. This reflective practitioner report describes and evaluates an innovative approach to teaching and assessing advanced qualitative research methods to final-year psychology undergraduate students. An…
Tan, Meng; Hew, Khe Foon
In this study, we investigated how the use of meaningful gamification affects student learning, engagement, and affective outcomes in a short, 3-day blended learning research methods class using a combination of experimental and qualitative research methods. Twenty-two postgraduates were randomly split into two groups taught by the same…
Mastel-Smith, Beth; Stanley-Hermanns, Melinda
In this qualitative descriptive study, we explored caregivers' educational needs and preferred methods of information delivery. Descriptions are based on five focus groups (N = 29) conducted with ethnically diverse, current and past family caregivers, including those who had previously attended a structured educational program. Themes arose from verbatim data transcriptions and coded themes. Four categories of educational needs were identified: (a) respite, (b) caregiving essentials, (c) self-care, and (d) the emotional aspects of caregiving. Advantages and disadvantages of learning methods are discussed, along with reasons for and outcomes of attending caregiver workshops. An informed caregiver model is proposed. Health care providers must assess educational needs and strive to provide appropriate information as dictated by the care recipient's condition and caregiver's expressed desires. Innovative methods of delivering information that are congruent with different caregiving circumstances and learning preferences must be developed and tested.
Freire, Luciana Lopes; Arezes, Pedro Miguel; Campos, José Creissac
The usability analysis of information systems has been the target of several research studies over the past thirty years. These studies have highlighted a great diversity of points of view, including researchers from different scientific areas such as Ergonomics, Computer Science, Design and Education. Within the domain of information ergonomics, the study of tools and methods used for usability evaluation dedicated to E-learning presents evidence that there is a continuous and dynamic evolution of E-learning systems, in many different contexts -academics and corporative. These systems, also known as LMS (Learning Management Systems), can be classified according to their educational goals and their technological features. However, in these systems the usability issues are related with the relationship/interactions between user and system in the user's context. This review is a synthesis of research project about Information Ergonomics and embraces three dimensions, namely the methods, models and frameworks that have been applied to evaluate LMS. The study also includes the main usability criteria and heuristics used. The obtained results show a notorious change in the paradigms of usability, with which it will be possible to discuss about the studies carried out by different researchers that were focused on usability ergonomic principles aimed at E-learning.
Full Text Available The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN, Decision Tree (DT and Bayesian Networks (BNs. A large sample of 62375 students was utilized in the procedures of model training and testing. The results of each model were presented in confusion matrix, and analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective in student dropout prediction, and DT presented a better performance. Finally, some suggestions were made for considerable future research.
Yang, Kai; Wu, Haifeng; Zeng, Yu
Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.
Дмитрий Васильевич Сенашенко
Full Text Available The article deals with modern methods of distance learning in the corporate sector. On the specifics of the application of the described methods is their classification and be subject to review their specific differences based on the features and applications of these techniques given the characteristics of the organization of teaching in higher education, a conclusion about their preferred sides, which can be used in distance education. Later in the article, taking into account the above factors, it is proposed an innovative method of formation of educational programs. In view of the similarity of the rendered appearance of the pyramids, this technique proposed name “pyramid”. Offered by the authors, this technique is best synthesis of the best features of the previously described in the article for the online teaching methods. In the future, we are given a detailed description and conducted a preliminary analysis of the applicability of this technique to the training process in the Russian Federation. The analysis describes the eight alleged authors of distance education problems of high school that this method can help to solve.
Liang, Faming; Carrol, Raymond J
This book provides comprehensive coverage of simulation of complex systems using Monte Carlo methods. Developing algorithms that are immune to the local trap problem has long been considered as the most important topic in MCMC research. Various advanced MCMC algorithms which address this problem have been developed include, the modified Gibbs sampler, the methods based on auxiliary variables and the methods making use of past samples. The focus of this book is on the algorithms that make use of past samples. This book includes the multicanonical algorithm, dynamic weighting, dynamically weight
Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin
This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study…
Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.
Pinson, Paul A.
A container for hazardous waste materials that includes air or other gas carrying dangerous particulate matter has incorporated in barrier material, preferably in the form of a flexible sheet, one or more filters for the dangerous particulate matter sealably attached to such barrier material. The filter is preferably a HEPA type filter and is preferably chemically bonded to the barrier materials. The filter or filters are preferably flexibly bonded to the barrier material marginally and peripherally of the filter or marginally and peripherally of air or other gas outlet openings in the barrier material, which may be a plastic bag. The filter may be provided with a backing panel of barrier material having an opening or openings for the passage of air or other gas into the filter or filters. Such backing panel is bonded marginally and peripherally thereof to the barrier material or to both it and the filter or filters. A coupling or couplings for deflating and inflating the container may be incorporated. Confining a hazardous waste material in such a container, rapidly deflating the container and disposing of the container, constitutes one aspect of the method of the invention. The chemical bonding procedure for producing the container constitutes another aspect of the method of the invention.
Radhakrishna, K.; Bowles, K.; Zettek-Sumner, A.
Summary Background Telehealth data overload through high alert generation is a significant barrier to sustained adoption of telehealth for managing HF patients. Objective To explore the factors contributing to frequent telehealth alerts including false alerts for Medicare heart failure (HF) patients admitted to a home health agency. Materials and Methods A mixed methods design that combined quantitative correlation analysis of patient characteristic data with number of telehealth alerts and qualitative analysis of telehealth and visiting nurses’ notes on follow-up actions to patients’ telehealth alerts was employed. All the quantitative and qualitative data was collected through retrospective review of electronic records of the home heath agency. Results Subjects in the study had a mean age of 83 (SD = 7.6); 56% were female. Patient co-morbidities (ppatient characteristics along with establishing patient-centered telehealth outcome goals may allow meaningful generation of telehealth alerts. Reducing avoidable telehealth alerts could vastly improve the efficiency and sustainability of telehealth programs for HF management. PMID:24454576
A container for hazardous waste materials that includes air or other gas carrying dangerous particulate matter has incorporated barrier material, preferably in the form of a flexible sheet, and one or more filters for the dangerous particulate matter sealably attached to such barrier material. The filter is preferably a HEPA type filter and is preferably chemically bonded to the barrier materials. The filter or filters are preferably flexibly bonded to the barrier material marginally and peripherally of the filter or marginally and peripherally of air or other gas outlet openings in the barrier material, which may be a plastic bag. The filter may be provided with a backing panel of barrier material having an opening or openings for the passage of air or other gas into the filter or filters. Such backing panel is bonded marginally and peripherally thereof to the barrier material or to both it and the filter or filters. A coupling or couplings for deflating and inflating the container may be incorporated. Confining a hazardous waste material in such a container, rapidly deflating the container and disposing of the container, constitutes one aspect of the method of the invention. The chemical bonding procedure for producing the container constitutes another aspect of the method of the invention. 3 figs
Hemphill, Geralyn M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to be an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.
Sloth Møller, Ditte; Knap, Marianne Marquard; Nyeng, Tine Bisballe; Khalil, Azza Ahmed; Holt, Marianne Ingerslev; Kandi, Maria; Hoffmann, Lone
Minimizing the planning target volume (PTV) while ensuring sufficient target coverage during the entire respiratory cycle is essential for free-breathing radiotherapy of lung cancer. Different methods are used to incorporate the respiratory motion into the PTV. Fifteen patients were analyzed. Respiration can be included in the target delineation process creating a respiratory GTV, denoted iGTV. Alternatively, the respiratory amplitude (A) can be measured based on the 4D-CT and A can be incorporated in the margin expansion. The GTV expanded by A yielded GTV + resp, which was compared to iGTV in terms of overlap. Three methods for PTV generation were compared. PTV del (delineated iGTV expanded to CTV plus PTV margin), PTV σ (GTV expanded to CTV and A was included as a random uncertainty in the CTV to PTV margin) and PTV ∑ (GTV expanded to CTV, succeeded by CTV linear expansion by A to CTV + resp, which was finally expanded to PTV ∑ ). Deformation of tumor and lymph nodes during respiration resulted in volume changes between the respiratory phases. The overlap between iGTV and GTV + resp showed that on average 7% of iGTV was outside the GTV + resp implying that GTV + resp did not capture the tumor during the full deformable respiration cycle. A comparison of the PTV volumes showed that PTV σ was smallest and PTV Σ largest for all patients. PTV σ was in mean 14% (31 cm 3 ) smaller than PTV del , while PTV del was 7% (20 cm 3 ) smaller than PTV Σ . PTV σ yields the smallest volumes but does not ensure coverage of tumor during the full respiratory motion due to tumor deformation. Incorporating the respiratory motion in the delineation (PTV del ) takes into account the entire respiratory cycle including deformation, but at the cost, however, of larger treatment volumes. PTV Σ should not be used, since it incorporates the disadvantages of both PTV del and PTV σ .
Guarino, Salvatore; Leopardi, Eleonora; Sorrenti, Salvatore; De Antoni, Enrico; Catania, Antonio; Alagaratnam, Swethan
The rapid and dramatic incursion of the Internet and social networks in everyday life has revolutionised the methods of exchanging data. Web 2.0 represents the evolution of the Internet as we know it. Internet users are no longer passive receivers, and actively participate in the delivery of information. Medical education cannot evade this process. Increasingly, students are using tablets and smartphones to instantly retrieve medical information on the web or are exchanging materials on their Facebook pages. Medical educators cannot ignore this continuing revolution, and therefore the traditional academic schedules and didactic schemes should be questioned. Analysing opinions collected from medical students regarding old and new teaching methods and tools has become mandatory, with a view towards renovating the process of medical education. A cross-sectional online survey was created with Google® docs and administrated to all students of our medical school. Students were asked to express their opinion on their favourite teaching methods, learning tools, Internet websites and Internet delivery devices. Data analysis was performed using spss. The online survey was completed by 368 students. Although textbooks remain a cornerstone for training, students also identified Internet websites, multimedia non-online material, such as the Encyclopaedia on CD-ROM, and other non-online computer resources as being useful. The Internet represented an important aid to support students' learning needs, but textbooks are still their resource of choice. Among the websites noted, Google and Wikipedia significantly surpassed the peer-reviewed medical databases, and access to the Internet was primarily through personal computers in preference to other Internet access devices, such as mobile phones and tablet computers. Increasingly, students are using tablets and smartphones to instantly retrieve medical information. © 2014 John Wiley & Sons Ltd.
Pressley, Michael; And Others
In five experiments, college-age students of differing foreign language-learning abilities were asked to learn Latin word translations to determine the effectiveness of the keyword method of foreign language vocabulary learning. The Latin words were the types for which it has been argued that the keyword method effects would be maximized (the…
Araya, S. N.; Ghezzehei, T. A.
Saturated hydraulic conductivity (Ks) is one of the fundamental hydraulic properties of soils. Its measurement, however, is cumbersome and instead pedotransfer functions (PTFs) are often used to estimate it. Despite a lot of progress over the years, generic PTFs that estimate hydraulic conductivity generally don't have a good performance. We develop significantly improved PTFs by applying state of the art machine learning techniques coupled with high-performance computing on a large database of over 20,000 soils—USKSAT and the Florida Soil Characterization databases. We compared the performance of four machine learning algorithms (k-nearest neighbors, gradient boosted model, support vector machine, and relevance vector machine) and evaluated the relative importance of several soil properties in explaining Ks. An attempt is also made to better account for soil structural properties; we evaluated the importance of variables derived from transformations of soil water retention characteristics and other soil properties. The gradient boosted models gave the best performance with root mean square errors less than 0.7 and mean errors in the order of 0.01 on a log scale of Ks [cm/h]. The effective particle size, D10, was found to be the single most important predictor. Other important predictors included percent clay, bulk density, organic carbon percent, coefficient of uniformity and values derived from water retention characteristics. Model performances were consistently better for Ks values greater than 10 cm/h. This study maximizes the extraction of information from a large database to develop generic machine learning based PTFs to estimate Ks. The study also evaluates the importance of various soil properties and their transformations in explaining Ks.
Makridakis, Spyros; Spiliotis, Evangelos; Assimakopoulos, Vassilios
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.
Makridakis, Spyros; Assimakopoulos, Vassilios
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784
Vick, Brianna M; Pollak, Adrianna; Welsh, Cynthia; Liang, Jennifer O
Here we describe projects that used GloFish, brightly colored, fluorescent, transgenic zebrafish, in experiments that enabled students to carry out all steps in the scientific method. In the first project, students in an undergraduate genetics laboratory course successfully tested hypotheses about the relationships between GloFish phenotypes and genotypes using PCR, fluorescence microscopy, and test crosses. In the second and third projects, students doing independent research carried out hypothesis-driven experiments that also developed new GloFish projects for future genetics laboratory students. Brianna Vick, an undergraduate student, identified causes of the different shades of color found in orange GloFish. Adrianna Pollak, as part of a high school science fair project, characterized the fluorescence emission patterns of all of the commercially available colors of GloFish (red, orange, yellow, green, blue, and purple). The genetics laboratory students carrying out the first project found that learning new techniques and applying their knowledge of genetics were valuable. However, assessments of their learning suggest that this project was not challenging to many of the students. Thus, the independent projects will be valuable as bases to widen the scope and range of difficulty of experiments available to future genetics laboratory students.
Full Text Available Article is describing process of creating and using of e-learning program for graphical solution of linear programming problems that is used in the Economic mathematical methods course on Faculty of Business and Economics, MZLU. The program was created within FRVŠ 788/2008 grant and is intended for practicing of graphical solution of LP problems and allows better understanding of the linear programming problems. In the article is on one hand described the way, how does the program work, it means how were the algorithms implemented, and on the other hand there is described way of use of that program. The program is constructed for working with integer and rational numbers. At the end of the article are shown basic statistics of programs use of students in the present form and the part-time form of study. It is mainly the number of programs downloads and comparison to another programs and students opinion on the e-learning support.
Cook, David A; Gelula, Mark H; Dupras, Denise M; Schwartz, Alan
Adapting web-based (WB) instruction to learners' individual differences may enhance learning. Objectives This study aimed to investigate aptitude-treatment interactions between learning and cognitive styles and WB instructional methods. We carried out a factorial, randomised, controlled, crossover, post-test-only trial involving 89 internal medicine residents, family practice residents and medical students at 2 US medical schools. Parallel versions of a WB course in complementary medicine used either active or reflective questions and different end-of-module review activities ('create and study a summary table' or 'study an instructor-created table'). Participants were matched or mismatched to question type based on active or reflective learning style. Participants used each review activity for 1 course module (crossover design). Outcome measurements included the Index of Learning Styles, the Cognitive Styles Analysis test, knowledge post-test, course rating and preference. Post-test scores were similar for matched (mean +/- standard error of the mean 77.4 +/- 1.7) and mismatched (76.9 +/- 1.7) learners (95% confidence interval [CI] for difference - 4.3 to 5.2l, P = 0.84), as were course ratings (P = 0.16). Post-test scores did not differ between active-type questions (77.1 +/- 2.1) and reflective-type questions (77.2 +/- 1.4; P = 0.97). Post-test scores correlated with course ratings (r = 0.45). There was no difference in post-test subscores for modules completed using the 'construct table' format (78.1 +/- 1.4) or the 'table provided' format (76.1 +/- 1.4; CI - 1.1 to 5.0, P = 0.21), and wholist and analytic styles had no interaction (P = 0.75) or main effect (P = 0.18). There was no association between activity preference and wholist or analytic scores (P = 0.37). Cognitive and learning styles had no apparent influence on learning outcomes. There were no differences in outcome between these instructional methods.
Jerez, José M; Molina, Ignacio; García-Laencina, Pedro J; Alba, Emilio; Ribelles, Nuria; Martín, Miguel; Franco, Leonardo
Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set. Imputation methods based on statistical techniques, e.g., mean, hot-deck and multiple imputation, and machine learning techniques, e.g., multi-layer perceptron (MLP), self-organisation maps (SOM) and k-nearest neighbour (KNN), were applied to data collected through the "El Álamo-I" project, and the results were then compared to those obtained from the listwise deletion (LD) imputation method. The database includes demographic, therapeutic and recurrence-survival information from 3679 women with operable invasive breast cancer diagnosed in 32 different hospitals belonging to the Spanish Breast Cancer Research Group (GEICAM). The accuracies of predictions on early cancer relapse were measured using artificial neural networks (ANNs), in which different ANNs were estimated using the data sets with imputed missing values. The imputation methods based on machine learning algorithms outperformed imputation statistical methods in the prediction of patient outcome. Friedman's test revealed a significant difference (p=0.0091) in the observed area under the ROC curve (AUC) values, and the pairwise comparison test showed that the AUCs for MLP, KNN and SOM were significantly higher (p=0.0053, p=0.0048 and p=0.0071, respectively) than the AUC from the LD-based prognosis model. The methods based on machine learning techniques were the most suited for the imputation of missing values and led to a significant enhancement of prognosis accuracy compared to imputation methods based on statistical procedures. Copyright © 2010 Elsevier B.V. All rights reserved.
Baker, Ryan S.J.d.; Gobert, Janice D.; van Joolingen, Wouter
This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students’ learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of
Full Text Available Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors.
Vast amounts of data exist in the astronomical data archives, and yet a large number of sources remain unclassified. We developed a multi-wavelength pipeline to classify infrared sources. The pipeline uses supervised machine learning methods to classify objects into the appropriate categories. The program is fed data that is already classified to train it, and is then applied to unknown catalogues. The primary use for such a pipeline is the rapid classification and cataloging of data that would take a much longer time to classify otherwise. While our primary goal is to study young stellar objects (YSOs), the applications extend beyond the scope of this project. We present preliminary results from our analysis and discuss future applications.
Full Text Available In Finland the Regional Fire and Rescue Services (RFRS are responsible for near shore oil spill response and shoreline cleanup operations. In addition, they assist in other types of maritime incidents, such as search and rescue operations and fire-fighting on board. These statutory assignments require the RFRS to have capability to act both on land and at sea. As maritime incidents occur infrequently, little routine has been established. In order to improve their performance in maritime operations, the RFRS are participating in a new oil spill training programme to be launched by South-Eastern Finland University of Applied Sciences. This training programme aims to utilize new educational methods; e-learning and simulator based training. In addition to fully exploiting the existing navigational bridge simulator, radio communication simulator and crisis management simulator, an entirely new simulator is developed. This simulator is designed to model the oil recovery process; recovery method, rate and volume in various conditions with different oil types. New simulator enables creation of a comprehensive training programme covering training tasks from a distress call to the completion of an oil spill response operation. Structure of the training programme, as well as the training objectives, are based on the findings from competence and education surveys conducted in spring 2016. In these results, a need for vessel maneuvering and navigation exercises together with actual response measures training were emphasized. Also additional training for maritime radio communication, GMDSS-emergency protocols and collaboration with maritime authorities were seemed important. This paper describes new approach to the maritime operations training designed for rescue authorities, a way of learning by doing, without mobilising the vessels at sea.
Li, Pan; Liu, Qiang; Zhao, Wentao; Wang, Dongxu; Wang, Siqi
In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning...
Cheryl J. Davis
Full Text Available It is common in college courses to test students on the required readings for that course. With a rise in online education it is often the case that students are required to provide evidence of reading the material. However, there is little empirical research stating the best written means to assess that students read the materials. This study experimentally compared the effect of assigned reading summaries or study questions on student test performance. The results revealed that study questions produced higher quiz scores and higher preparation for the quiz, based on student feedback. Limitations of the study included a small sample size and extraneous activities that may have affected general knowledge on a topic. Results suggest that study questions focusing students on critical information in the required readings improve student learning.
Brattain, Laura J; Telfer, Brian A; Dhyani, Manish; Grajo, Joseph R; Samir, Anthony E
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.
Wojciech M. Czarnecki
Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.
Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.
Beaver, Justin M [ORNL; Borges, Raymond Charles [ORNL; Buckner, Mark A [ORNL
Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems were designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. The centralization of control and the movement towards open systems and standards has improved the efficiency of industrial control, but has also exposed legacy SCADA systems to security threats that they were not designed to mitigate. This work explores the viability of machine learning methods in detecting the new threat scenarios of command and data injection. Similar to network intrusion detection systems in the cyber security domain, the command and control communications in a critical infrastructure setting are monitored, and vetted against examples of benign and malicious command traffic, in order to identify potential attack events. Multiple learning methods are evaluated using a dataset of Remote Terminal Unit communications, which included both normal operations and instances of command and data injection attack scenarios.
Ayse T. Daloglu; Musa Artar; Korhan Ozgan; Ali İ. Karakas
Optimum design of braced steel space frames including soil-structure interaction is studied by using harmony search (HS) and teaching-learning-based optimization (TLBO) algorithms. A three-parameter elastic foundation model is used to incorporate the soil-structure interaction effect. A 10-storey braced steel space frame example taken from literature is investigated according to four different bracing types for the cases with/without soil-structure interaction. X, V, Z, and eccentric V-shaped...
Gao, Qin; Yao, Sanxi; Widom, Michael
Density functional theory (DFT) provides an accurate and first-principles description of solid structures and total energies. However, it is highly time-consuming to calculate structures with hundreds of atoms in the unit cell and almost not possible to calculate thousands of atoms. We apply and adapt machine learning algorithms, including compressive sensing, support vector regression and artificial neural networks to fit the DFT total energies of substitutionally disordered boron carbide. The nonparametric kernel method is also included in our models. Our fitted total energy model reproduces the DFT energies with prediction error of around 1 meV/atom. The assumptions of these machine learning models and applications of the fitted total energies will also be discussed. Financial support from McWilliams Fellowship and the ONR-MURI under the Grant No. N00014-11-1-0678 is gratefully acknowledged.
Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric
Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.
Hano, Mitsuo; Hotta, Masashi
A new multigrid method based on high-order vector finite elements is proposed in this paper. Low level discretizations in this method are obtained by using low-order vector finite elements for the same mesh. Gauss-Seidel method is used as a smoother, and a linear equation of lowest level is solved by ICCG method. But it is often found that multigrid solutions do not converge into ICCG solutions. An elimination algolithm of constant term using a null space of the coefficient matrix is also described. In three dimensional magnetostatic field analysis, convergence time and number of iteration of this multigrid method are discussed with the convectional ICCG method.
Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho
Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.
Wang, Sheng; Sun, Siqi; Xu, Jinbo
Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L = length). A complete implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as a pixel-level image labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and coevolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both one-dimensional and two-dimensional deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method. © 2017 Wiley Periodicals, Inc.
Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu
Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.
Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.
Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…
Jewpanich, Chaiwat; Piriyasurawong, Pallop
This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student.…
Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.
Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert
using the labels provided by seven labelers. We also compared the performance of the passive and active learning models when using the consensus label. Results The AL methods produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p = 0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275 to 0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers’ different models during the training phase, compared to the variance of the induced models’ AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods. The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p = 0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p = 0.29), as was the difference between the Combination_XA and Exploitation methods (p = 0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired
Husebø, Anne Marie Lunde; Storm, Marianne; Våga, Bodil Bø; Rosenberg, Adriana; Akerjordet, Kristin
To give an overview of empirical studies investigating nursing homes as a learning environment during nursing students' clinical practice. A supportive clinical learning environment is crucial to students' learning and for their development into reflective and capable practitioners. Nursing students' experience with clinical practice can be decisive in future workplace choices. A competent workforce is needed for the future care of older people. Opportunities for maximum learning among nursing students during clinical practice studies in nursing homes should therefore be explored. Mixed-method systematic review using PRISMA guidelines, on learning environments in nursing homes, published in English between 2005-2015. Search of CINAHL with Full Text, Academic Search Premier, MEDLINE and SocINDEX with Full Text, in combination with journal hand searches. Three hundred and thirty-six titles were identified. Twenty studies met the review inclusion criteria. Assessment of methodological quality was based on the Mixed Methods Appraisal Tool. Data were extracted and synthesised using a data analysis method for integrative reviews. Twenty articles were included. The majority of the studies showed moderately high methodological quality. Four main themes emerged from data synthesis: "Student characteristic and earlier experience"; "Nursing home ward environment"; "Quality of mentoring relationship and learning methods"; and "Students' achieved nursing competencies." Nursing home learning environments may be optimised by a well-prepared academic-clinical partnership, supervision by encouraging mentors and high-quality nursing care of older people. Positive learning experiences may increase students' professional development through achievement of basic nursing skills and competencies and motivate them to choose the nursing home as their future workplace. An optimal learning environment can be ensured by thorough preplacement preparations in academia and in nursing home wards
Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas
Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Ramana, Jayashree; Gupta, Dinesh
Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred. PMID:20967128
Full Text Available Amphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination, position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.
Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao
Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.
Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin
This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study concerns strong constructions and framed structures. This article describes how this learning study was conducted and discusses reflections made du...
Wittman, Nora E.; And Others
This guide is planned to help the FLES teacher develop pleasurable language learning experiences in spoken German for children at the third-grade level. Experiences included in this guide present German in life situations, as well as insight into German culture. The guide offers suggestions for classroom procedures, and detailed directions are…
Full Text Available [english] The Free University’s Veterinary Clinic of Reproduction in the Department of Veterinary Medicine, Berlin, has been offering courses on alternative and complementary veterinary medicine to its students for several years. Due to time constraints and shortages in teaching staff, it has not been possible to satisfy student demand for instruction in these areas. To provide more detailed information as well as more opportunities for discussion and practica, subject area courses were modified in two steps. Initially, blended learning was implemented to include e-learning and in-class formats of instruction. Subsequently, an entire block of courses offered were transferred to e-learning format. Students may now voluntarily register for the e-learning course entitled “Introduction of alternative and complementary veterinary medicine” via the Internet and learn the basic principles of homoeopathy, herbal medicine, acupuncture and other alternative methods in veterinary medicine. After passing this basic course, blended learning courses enable advanced students to learn more about fundamentals of methods in greater detail as well as to perform practica with animal subjects. The evaluation of these courses showed that students rated e-learning to be a reasonable addendum to in-class instruction. More than two thirds of the students recommended an increased integration of e-learning into veterinary education. [german] Die Tierklinik für Fortpflanzung in Berlin bietet den Studierenden der Veterinärmedizin seit einigen Semestern Wahlpflichtkurse zu den Naturheilverfahren an. Der enormen Nachfrage seitens der Studierenden standen personelle und zeitliche Begrenzungen des Lehrpersonals gegenüber. Um den Interessenten dennoch umfangreiche Informationen zu bieten sowie Freiräume für Diskussionen und praktische Übungen zu schaffen, wurde das Ausbildungsangebot in zwei Projektphasen ausgebaut. Zunächst wurde dabei die Methode des Blended-Learning
Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert
labelers. We also compared the performance of the passive and active learning models when using the consensus label. The AL methods: produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p=0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275-0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers' different models during the training phase, compared to the variance of the induced models' AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p=0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p=0.29), as was the difference between the Combination_XA and Exploitation methods (p=0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired t-test, the difference between the intra-labeler AUC
Salehizadeh, M. Reza; Behin-Aein, Noureddin
In the Iranian higher education system, including engineering education, effective implementation of cooperative learning is difficult because classrooms are usually crowded and the students never had a formal group working background in their previous education. In order to achieve the benefits of cooperative learning in this condition, this…
Reynolds, Fiona; Stanistreet, Debbi; Elton, Peter
Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities), calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR) of 0.48 (Confidence Interval (CI) 0.38 – 0.58; X2: 72.227; p.value learning disabilities. Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities. PMID:18218106
Falk, Kristin; Falk, Hanna; Jakobsson Ung, Eva
A key area for consideration is determining how optimal conditions for learning can be created. Higher education in nursing aims to prepare students to develop their capabilities to become independent professionals. The aim of this study was to evaluate the effects of sequencing clinical practice prior to theoretical studies on student's experiences of self-directed learning readiness and students' approach to learning in the second year of a three-year undergraduate study program in nursing. 123 nursing students was included in the study and divided in two groups. In group A (n = 60) clinical practice preceded theoretical studies. In group (n = 63) theoretical studies preceded clinical practice. Learning readiness was measured using the Directed Learning Readiness Scale for Nursing Education (SDLRSNE), and learning process was measured using the revised two-factor version of the Study Process Questionnaire (R-SPQ-2F). Students were also asked to write down their personal reflections throughout the course. By using a mixed method design, the qualitative component focused on the students' personal experiences in relation to the sequencing of theoretical studies and clinical practice. The quantitative component provided information about learning readiness before and after the intervention. Our findings confirm that students are sensitive and adaptable to their learning contexts, and that the sequencing of courses is subordinate to a pedagogical style enhancing students' deep learning approaches, which needs to be incorporated in the development of undergraduate nursing programs. Copyright © 2015 Elsevier Ltd. All rights reserved.
Full Text Available Based on data from the observation of high school students grade XI that daily low student test scores due to a lack of role of students in the learning process. This classroom action research aims to improve learning outcomes and student motivation through discovery learning method in colloidal material. This study uses the approach developed by Lewin consisting of planning, action, observation, and reflection. Data collection techniques used the questionnaires and ability tests end. Based on the research that results for students received a positive influence on learning by discovery learning model by increasing the average value of 74 students from the first cycle to 90.3 in the second cycle and increased student motivation in the form of two statements based competence (KD categories (sometimes on the first cycle and the first statement KD category in the second cycle. Thus the results of this study can be used to improve learning outcomes and student motivation
Full Text Available Abstract Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method, short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.
Tonzar, Claudio; Lotto, Lorella; Job, Remo
In this study we investigated the effects of two learning methods (picture- or word-mediated learning) and of word status (cognates vs. noncognates) on the vocabulary acquisition of two foreign languages: English and German. We examined children from fourth and eighth grades in a school setting. After a learning phase during which L2 words were…
Pedrosa, Carlos Melgosa; Barbero, Basilio Ramos; Miguel, Arturo Román
This study compares an interactive learning manager for graphic engineering to develop spatial vision (ILMAGE_SV) to traditional methods. ILMAGE_SV is an asynchronous web-based learning tool that allows the manipulation of objects with a 3D viewer, self-evaluation, and continuous assessment. In addition, student learning may be monitored, which…
Cheng, Xusen; Li, Yuanyuan; Sun, Jianshan; Huang, Jianqing
Collaborative case studies and computer-supported collaborative learning (CSCL) play an important role in the modern education environment. A number of researchers have given significant attention to learning design in order to improve the satisfaction of collaborative learning. Although collaboration engineering (CE) is a mature method widely…
A study of 46 management students compared three methods for learning strategic management: cases, simulation, and action learning through consulting projects. Simulation was superior to action learning on all outcomes and equal or superior to cases on two. Simulation gave students a central role in management and greater control of the learning…
Kofoed, Lise B.; Jørgensen, Frances
This paper discusses how Problem-Based Learning (PBL) methods were used to support a Danish company in its efforts to become more of a 'learning organisation', characterized by sharing of knowledge and experiences. One of the central barriers to organisational learning in this company involved...
de Villiers, M. R.; Becker, Daphne
From the perspective of parallel mixed-methods research, this paper describes interactivity research that employed usability-testing technology to analyse cognitive learning processes; personal learning styles and times; and errors-and-recovery of learners using an interactive e-learning tutorial called "Relations." "Relations"…
Pires, Sara Monteiro
on the public health question being addressed, on the data requirements, on advantages and limitations of the method, and on the data availability of the country or region in question. Previous articles have described available methods for source attribution, but have focused only on foodborne microbiological...
Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric
This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…
Xu, Yan; Yang, Jing; Zhong, Shuiming
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.
darsih darsih darsih
Full Text Available ABSTRACT Assessing the quality of e-learning courses to measure the success of e-learning systems in online learning is essential. The system can be used to improve education. The study analyzes the quality of e-learning course on the web site www.kulon.undip.ac.id used a questionnaire with questions based on the variables of ISO 9126. Penilaiann Likert scale was used with a web app. Rule-base reasoning method is used to subject the quality of e-learningyang assessed. A case study conducted in four e-learning courses with 133 sample / respondents as users of the e-learning course. From the obtained results of research conducted both for the value of e-learning from each subject tested. In addition, each e-learning courses have different advantages depending on certain variables. Keywords : E-Learning, Rule-Base, Questionnaire, Likert, Measuring.
Mendoza Oropeza, Laura; Ortiz Sánchez, Ricardo; Ojeda Villagómez, Raúl
In the UNAM Faculty of Odontology, we use a stereoscopic 3D teaching method that has grown more common in the last year, which makes it important to know whether students can learn better with this strategy. The objective of the study is to know, if the 4th year students of the bachelor's degree in dentistry learn more effectively with the use of stereoscopic 3D than the traditional method in Orthodontics. first, we selected the course topics, to be used for both methods; the traditional method using projection of slides and for the stereoscopic third dimension, with the use of videos in digital stereo projection (seen through "passive" polarized 3D glasses). The main topic was supernumerary teeth, including and diverted from their guide eruption. Afterwards we performed an exam on students, containing 24 items, validated by expert judgment in Orthodontics teaching. The results of the data were compared between the two educational methods for determined effectiveness using the model before and after measurement with the statistical package SPSS 20 version. The results presented for the 9 groups of undergraduates in dentistry, were collected with a total of 218 students for 3D and traditional methods, we found in a traditional method a mean 4.91, SD 1.4752 in the pretest and X=6.96, SD 1.26622, St Error 0.12318 for the posttest. The 3D method had a mean 5.21, SD 1.996779 St Error 0.193036 for the pretest X= 7.82, SD =0.963963, St Error 0.09319 posttest; the analysis of Variance between groups F= 5.60 Prob > 0.0000 and Bartlett's test for equal variances 21.0640 Prob > chi2 = 0.007. These results show that the student's learning in 3D means a significant improvement as compared to the traditional teaching method and having a strong association between the two methods. The findings suggest that the stereoscopic 3D method lead to improved student learning compared to traditional teaching.
.... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...
.... Our research blends methods from several fields-statistics and probability, signal and image processing, mathematical physics, scientific computing, statistical learning theory, and differential...
Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina
Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.
Grey, Simon; Grey, David; Gordon, Neil; Purdy, Jon
This paper offers an approach to designing game-based learning experiences inspired by the Mechanics-Dynamics-Aesthetics (MDA) model (Hunicke et al., 2004) and the elemental tetrad model (Schell, 2008) for game design. A case for game based learning as an active and social learning experience is presented including arguments from both teachers and…
Gurpinar, Erol; Alimoglu, Mustafa Kemal; Mamakli, Sumer; Aktekin, Mehmet
The curriculum of our medical school has a hybrid structure including both traditional training (lectures) and problem-based learning (PBL) applications. The purpose of this study was to determine the learning styles of our medical students and investigate the relation of learning styles with each of satisfaction with different instruction methods…
D.Ed. The aim of this theses is to find out whether there is any relationship between learners' attitudes and learning difficulties in mathematics: To investigate whether learning difficulties in mathematics are associated with learners' gender. To establish the nature of teachers' perceptions of the learning problem areas in the mathematics curriculum. To find out about the teachers' views on the methods of teaching mathematics, resources, learning of mathematics, extra curricular activit...
Sim, Kevin; Hart, Emma; Paechter, Ben
We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.
Olden, Peter C
Organization theory (OT) provides a way of seeing, describing, analyzing, understanding, and improving organizations based on patterns of organizational design and behavior (Daft 2004). It gives managers models, principles, and methods with which to diagnose and fix organization structure, design, and process problems. Health care organizations (HCOs) face serious problems such as fatal medical errors, harmful treatment delays, misuse of scarce nurses, costly inefficiency, and service failures. Some of health care managers' most critical work involves designing and structuring their organizations so their missions, visions, and goals can be achieved-and in some cases so their organizations can survive. Thus, it is imperative that graduate healthcare management programs develop effective approaches for teaching OT to students who will manage HCOs. Guided by principles of education, three applied teaching/learning activities/assignments were created to teach OT in a graduate healthcare management program. These educationalmethods develop students' competency with OT applied to HCOs. The teaching techniques in this article may be useful to faculty teaching graduate courses in organization theory and related subjects such as leadership, quality, and operation management.
Naresh N. Vempala
Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.
Chen, Ye; Khashab, Niveen M.; Tao, Jing
Composition comprising at least one graphene material and at least one metal. The metal can be in the form of nanoparticles as well as microflakes, including single crystal microflakes. The metal can be intercalated in the graphene sheets
Rogowsky, Beth A.; Calhoun, Barbara M.; Tallal, Paula
While it is hypothesized that providing instruction based on individuals' preferred learning styles improves learning (i.e., reading for visual learners and listening for auditory learners, also referred to as the "meshing hypothesis"), after a critical review of the literature Pashler, McDaniel, Rohrer, and Bjork (2008) concluded that…
Hansen, Samantha Leigh
The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…
Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.
Williams van Rooij, Shahron
This study examined the impact of two Problem-Based Learning (PBL) approaches on knowledge transfer, problem-solving self-efficacy, and perceived learning gains among four intact classes of adult learners engaged in a group project in an online undergraduate business research methods course. With two of the classes using a text-only PBL workbook…
Garde, A H; Hansen, Åse Marie; Kristiansen, J
The aims of this study were to elucidate to what extent storage and repeated freezing and thawing influenced the concentration of creatinine in urine samples and to evaluate the method for determination of creatinine in urine. The creatinine method was based on the well-known Jaffe's reaction...... and measured on a COBAS Mira autoanalyser from Roche. The main findings were that samples for analysis of creatinine should be kept at a temperature of -20 degrees C or lower and frozen and thawed only once. The limit of detection, determined as 3 x SD of 20 determinations of a sample at a low concentration (6...
Hwang, Wonil; Sohn, Kwang Young; Cho, Chang Hwan; Kim, Sung Jong
The acceptance methods associated with commercial-grade dedication are the following: 1) Special tests and inspection (Method 1) 2) Commercial-grade surveys (Method 2) 3) Source verification (Method 3) 4) An acceptable item and supplier performance record (Method 4) Special tests and inspections, often referred to as Method 1, are performed by the dedicating entity after the item is received to verify selected critical characteristics. Conducting a commercial-grade survey of a supplier is often referred to as Method 2. Supplier audits to verify compliance with a nuclear QA program do not meet the intent of a commercial-grade survey. Source verification, often referred to as Method 3, entails verification of critical characteristics during manufacture and testing of the item being procured. The performance history (good or bad) of the item and supplier is a consideration when determining the use of the other acceptance methods and the rigor with which they are used on a case-by-case basis. Some digital equipment system has the delivery reference and its operating history for Nuclear Power Plant as far as surveyed. However it was found that there is difficulty in collecting this of supporting data sheet, so that supplier usually decide to conduct the CGID based on the Method-1 and Method-2 based on the initial qualification likely. It is conceived that the Method-4 might be a better approach for CGID(Commercial Grade Item Dedication) even if there are some difficulties in data package for justifying CGID from the vendor and operating organization. This paper present the lesson learned from the consulting for Method-1 and 2 for digital equipment dedication. Considering all the information above, there are a couple of issues to remind in order to perform the CGID for Method-2. In doing commercial grade survey based on Method 2, quality personnel as well as technical engineer shall be involved for integral dedication. Other than this, the review of critical
Guillemin, Ernst A
An eminent electrical engineer and authority on linear system theory presents this advanced treatise, which approaches the subject from the viewpoint of classical dynamics and covers Fourier methods. This volume will assist upper-level undergraduates and graduate students in moving from introductory courses toward an understanding of advanced network synthesis. 1963 edition.
Crandall, David Lynn
Sighting optics include a front sight and a rear sight positioned in a spaced-apart relation. The rear sight includes an optical element having a first focal length and a second focal length. The first focal length is selected so that it is about equal to a distance separating the optical element and the front sight and the second focal length is selected so that it is about equal to a target distance. The optical element thus brings into simultaneous focus for a user images of the front sight and the target.
Composition comprising at least one graphene material and at least one metal. The metal can be in the form of nanoparticles as well as microflakes, including single crystal microflakes. The metal can be intercalated in the graphene sheets. The composition has high conductivity and flexibility. The composition can be made by a one-pot synthesis in which a graphene material precursor is converted to the graphene material, and the metal precursor is converted to the metal. A reducing solvent or dispersant such as NMP can be used. Devices made from the composition include a pressure sensor which has high sensitivity. Two two- dimension materials can be combined to form a hybrid material.
Sloth Møller, Ditte; Knap, Marianne Marquard; Nyeng, Tine Bisballe
: PTVσ yields the smallest volumes but does not ensure coverage of tumor during the full respiratory motion due to tumor deformation. Incorporating the respiratory motion in the delineation (PTVdel) takes into account the entire respiratory cycle including deformation, but at the cost, however, of larger...
Yuk Chan, Cecilia Ka
Experiential learning pedagogy is taking a lead in the development of graduate attributes and educational aims as these are of prime importance for society. This paper shows a community service experiential project conducted in China. The project enabled students to serve the affected community in a post-earthquake area by applying their knowledge and skills. This paper documented the students' learning process from their project goals, pre-trip preparations, work progress, obstacles encountered to the final results and reflections. Using the data gathered from a focus group interview approach, the four components of Kolb's learning cycle, the concrete experience, reflection observation, abstract conceptualisation and active experimentation, have been shown to transform and internalise student's learning experience, achieving a variety of learning outcomes. The author will also explore how this community service type of experiential learning in the engineering discipline allowed students to experience deep learning and develop their graduate attributes.
Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup
current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown...... and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options...
Zhang, Ning; Rao, R Shyama Prasad; Salvato, Fernanda
-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently......, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino...
Saripalle, Sashi K; Vemulapalli, Spandana; King, Gregory W; Burgoon, Judee K; Derakhshani, Reza
This paper discusses the advantages of using posturographic signals from force plates for non-invasive credibility assessment. The contributions of our work are two fold: first, the proposed method is highly efficient and non invasive. Second, feasibility for creating an autonomous credibility assessment system using machine-learning algorithms is studied. This study employs an interview paradigm that includes subjects responding with truthful and deceptive intent while their center of pressure (COP) signal is being recorded. Classification models utilizing sets of COP features for deceptive responses are derived and best accuracy of 93.5% for test interval is reported.
Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed
Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.
Full Text Available Abstract Background Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. Results We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared our rule building machine learning program, DataSqueezer, with commonly used association rules and decision tree algorithms. All used machine learning algorithms produced similar results that were consistent with expected properties for a second gas phase mechanism at the second peptide bond. Conclusion The results provide compelling evidence that we have identified underlying chemical properties in the data that suggest the existence of an additional gas phase mechanism for the second peptide bond. Thus, the methods described in this study provide a valuable approach for analyses of this kind in the future.
Full Text Available The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.
Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.
Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal, Ginsburg, & Schau, 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof, Ceroni, Jeong, & Moghaddam, 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to...
Fischbach, Jens; Xander, Nina Carolin; Frohme, Marcus; Glökler, Jörn Felix
The need for simple and effective assays for detecting nucleic acids by isothermal amplification reactions has led to a great variety of end point and real-time monitoring methods. Here we tested direct and indirect methods to visualize the amplification of potato spindle tuber viroid (PSTVd) by loop-mediated isothermal amplification (LAMP) and compared features important for one-pot in-field applications. We compared the performance of magnesium pyrophosphate, hydroxynaphthol blue (HNB), calcein, SYBR Green I, EvaGreen, and berberine. All assays could be used to distinguish between positive and negative samples in visible or UV light. Precipitation of magnesium-pyrophosphate resulted in a turbid reaction solution. The use of HNB resulted in a color change from violet to blue, whereas calcein induced a change from orange to yellow-green. We also investigated berberine as a nucleic acid-specific dye that emits a fluorescence signal under UV light after a positive LAMP reaction. It has a comparable sensitivity to SYBR Green I and EvaGreen. Based on our results, an optimal detection method can be chosen easily for isothermal real-time or end point screening applications.
Beal, D. [BA-PIRC, Cocoa, FL (United States); McIlvaine, J. [BA-PIRC, Cocoa, FL (United States); Fonorow, K. [BA-PIRC, Cocoa, FL (United States); Martin, E. [BA-PIRC, Cocoa, FL (United States)
This document illustrates guidelines for the efficient installation of interior duct systems in new housing, including the fur-up chase method, the fur-down chase method, and interior ducts positioned in sealed attics or sealed crawl spaces.
Reng, Lars; Kofoed, Lise; Schoenau-Fog, Henrik
will focus on cases in which development of games did change the learning environments into production units where students or employees were producing games as part of the learning process. The cases indicate that the motivation as well as the learning curve became very high. The pedagogical theories......Game Based Learning has proven to have many possibilities for supporting better learning outcomes, when using educational or commercial games in the classroom. However, there is also a great potential in using game development as a motivator in other kinds of learning scenarios. This study...... and methods are based on Problem Based Learning (PBL), but are developed further by combining PBL with a production-oriented/design based approach. We illustrate the potential of using game production as a learning environment with investigation of three game productions. We can conclude that using game...
National Aeronautics and Space Administration — Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational...
Full Text Available This article reports on the use of Wiktionary, an open source online dictionary, as well as generic wiki pages within a university’s e-learning environment as teaching and learning resources in an Afrikaans sociolinguistics module. In a communal constructivist manner students learnt, but also constructed learning content. From the qualitative research conducted with students it is clear that wikis provide for effective facilitation of a blended learning approach to sociolinguistic research. The use of this medium was positively received, however, some students did prefer handing in assignments in hard copy. The issues of computer literacy and access to the internet were also raised by the respondents. The use of wikis and Wiktionary prompted useful unplanned discussions around reliability and quality of public wikis. The use of a public wiki such as Wiktionary served as encouragement for students as they were able to contribute to the promotion of Afrikaans in this way.
This paper reports on the learning designs, teaching methods and activities most commonly employed within the disciplines in six universities in Australia. The study sought to establish if there were significant differences between the disciplines in learning designs, teaching methods and teaching activities in the current Australian context, as…
Najafi, Mohammad; Motaghi, Zohre; Nasrabadi, Hassanali Bakhtiyar; Heshi, Kamal Nosrati
Regarding the importance of enhancement in learner's social skills, especially in learning process, this study tries to introduce one of the group learning programs entitled "debate" as a teaching method in Iran religious universities. It also considers the concept and the history of this method by qualitative and descriptive-analytical…
He, J.; de Rijke, M.
We describe our participation in the Link-the-Wiki track at INEX 2009. We apply machine learning methods to the anchor-to-best-entry-point task and explore the impact of the following aspects of our approaches: features, learning methods as well as the collection used for training the models. We
Tomcho, Thomas J.; Rice, Diana; Foels, Rob; Folmsbee, Leah; Vladescu, Jason; Lissman, Rachel; Matulewicz, Ryan; Bopp, Kara
Research methods and statistics courses constitute a core undergraduate psychology requirement. We analyzed course syllabi and faculty self-reported coverage of both research methods and statistics course learning objectives to assess the concordance with APA's learning objectives (American Psychological Association, 2007). We obtained a sample of…
Keenan, Kevin; Fontaine, Danielle
How undergraduate students learn research methods in geography has been understudied. Existing work has focused on course description from the instructor's perspective. This study, however, uses a grounded theory approach to allow students' voices to shape a new theory of how they themselves say that they learn research methods. Data from two…
Natland, Sidsel; Weissinger, Erika; Graaf, Genevieve; Carnochan, Sarah
The literature on teaching research methods to social work students identifies many challenges, such as dealing with the tensions related to producing research relevant to practice, access to data to teach practice-based research, and limited student interest in learning research methods. This is an exploratory study of the learning experiences of…
Bailey, Regina M.
In an information-saturated world, today's college students desire to be engaged both in and out of their college classrooms. This mixed-methods study sought to explore how replacing traditional teaching methods with engaged learning activities affects millennial college student attitudes and perceptions about learning. The sub-questions…
Lukman, Rebeka; Krajnc, Majda
This paper identifies the commonalities and differences within non-traditional learning methods regarding virtual and real-world environments. The non-traditional learning methods in real-world have been introduced within the following courses: Process Balances, Process Calculation, and Process Synthesis, and within the virtual environment through…
The purpose of this study was to investigate the preferred method of learning about heart disease by adult learners. This research study also investigated if there was a statistically significant difference between race/ethnicity, age, and gender of adult learners and their preferred method of learning preventative heart disease care. This…
Full Text Available A simulation model for 3D polydisperse bubble column flows in an Eulerian/Eulerian framework is presented. A computationally efficient and numerically stable algorithm is created by making use of quadrature method of moments (QMOM functionalities, in conjunction with appropriate breakup and coalescence models. To account for size dependent bubble motion, the constituent moments of the bubble size distribution function are transported with individual velocities. Validation of the simulation results against experimental and numerical data of Hansen  show the capability of the present model to accurately predict complex gas-liquid flows.
Ciere, Yvette; Jaarsma, Debbie; Visser, Annemieke; Sanderman, Robbert; Snippe, Evelien; Fleer, Joke
Quantitative diary methods are longitudinal approaches that involve the repeated measurement of aspects of peoples' experience of daily life. In this article, we outline the main characteristics and applications of quantitative diary methods and discuss how their use may further research in the field of medical education. Quantitative diary methods offer several methodological advantages, such as measuring aspects of learning with great detail, accuracy and authenticity. Moreover, they enable researchers to study how and under which conditions learning in the health care setting occurs and in which way learning can be promoted. Hence, quantitative diary methods may contribute to theory development and the optimization of teaching methods in medical education.
Chan, Aileen Wai-Kiu; Chair, Sek-Ying; Sit, Janet Wing-Hung; Wong, Eliza Mi-Ling; Lee, Diana Tze-Fun; Fung, Olivia Wai-Man
Case-based learning (CBL) is an effective educational method for improving the learning and clinical reasoning skills of students. Advances in e-learning technology have supported the development of the Web-based CBL approach to teaching as an alternative or supplement to the traditional classroom approach. This study aims to examine the CBL experience of Hong Kong students using both traditional classroom and Web-based approaches in undergraduate nursing education. This experience is examined in terms of the perceived self-learning ability, clinical reasoning ability, and satisfaction in learning of these students. A mixture of quantitative and qualitative approaches was adopted. All Year-3 undergraduate nursing students were recruited. CBL was conducted using the traditional classroom approach in Semester 1, and the Web-based approach was conducted in Semester 2. Student evaluations were collected at the end of each semester using a self-report questionnaire. In-depth, focus-group interviews were conducted at the end of Semester 2. One hundred twenty-two students returned their questionnaires. No difference between the face-to-face and Web-based approaches was found in terms of self-learning ability (p = .947), clinical reasoning ability (p = .721), and satisfaction (p = .083). Focus group interview findings complemented survey findings and revealed five themes that reflected the CBL learning experience of Hong Kong students. These themes were (a) the structure of CBL, (b) the learning environment of Web-based CBL, (c) critical thinking and problem solving, (d) cultural influence on CBL learning experience, and (e) student-centered and teacher-centered learning. The Web-based CBL approach was comparable but not superior to the traditional classroom CBL approach. The Web-based CBL experience of these students sheds light on the impact of Chinese culture on student learning behavior and preferences.
Ludwig, B; Bister, D; Schott, T C; Lisson, J A; Hourfar, J
Cephalometry is important for orthodontic diagnosis and treatment planning and is part of the core curriculum for training dentists. Training involves identifying anatomical landmarks. The aim of this investigation was to assess whether e-learning improves learning efficiency; a programme specifically designed for this purpose was compared to commercially available software. Thirty undergraduate students underwent traditional training of cephalometry consisting of lectures and tutorials. Tracing skills were tested immediately afterwards (T0). The students were then randomly allocated to three groups: 10 students served as control (CF); they were asked to improve their skills using the material provided so far. Ten students were given a program specifically designed for this study that was based on a power point presentation (PPT). The last group was given a commercially available program that included teaching elements (SW). The groups were tested at the end the six week training (T1). The test consisted of tracing 30 points on two radiographs and a point score improvement was calculated. The students were interviewed after the second test. Both e-learning groups improved more than the traditional group. Improvement scores were four for CF; 8.6 for PPT and 2.8 for SW. For PPT all participants improved and the student feedback was the best compared to the other groups. For the other groups some candidates worsened. Blended learning produced better learning outcomes compared to using a traditional teaching method alone. The easy to use Power Point based custom software produced better results than the commercially available software. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Noorafshan, Ali; Hoseini, Leila; Amini, Mitra; Dehghani, Mohammad-Reza; Kojuri, Javad; Bazrafkan, Leila
Learning by lecture is a passive experience. Many innovative techniques have been presented to stimulate students to assume a more active attitude toward learning. In this study, simultaneous sketch drawing, as an interactive learning technique was applied to teach anatomy to the medical students. We reconstructed a fun interactive model of teaching anatomy as simultaneous anatomic sketching. To test the model's instruction effectiveness, we conducted a quasi- experimental study and then the students were asked to write their learning experiences in their portfolio, also their view was evaluated by a questionnaire. The results of portfolio evaluation revealed that students believed that this method leads to deep learning and understanding anatomical subjects better. Evaluation of the students' views on this teaching approach was showed that, more than 80% of the students were agreed or completely agreed with this statement that leaning anatomy concepts are easier and the class is less boring with this method. More than 60% of the students were agreed or completely agreed to sketch anatomical figures with professor simultaneously. They also found the sketching make anatomy more attractive and it reduced the time for learning anatomy. These number of students were agree or completely agree that the method help them learning anatomical concept in anatomy laboratory. More than 80% of the students found the simultaneous sketching is a good method for learning anatomy overall. Sketch drawing, as an interactive learning technique, is an attractive for students to learn anatomy.
A great deal of thought has been given in recent years to the documentation of individual patients and their diseases, especially since the computerization of registry sytems facilitates the storage and retrieval of large amounts of data, but the documentation of radiation treatment methods has received surprisingly little attention. The guidelines which follow are intended for use both internally (within radiotherapy centres) and externally when a treatment method is reported in the literature or transferred from one centre to another. The amount of detail reported externally will, of course, depend on the circumstances: for example, a published paper will usually mention only the most important of the radiation and physical parameters, but it is important for the department of origin to list all parameters in a separate document, available on request. These guidelines apply specifically to the documentation of treatment by external radiation beams, although many of the suggestions would also apply to treatment by small sealed sources (brachytherapy) and by unsealed radionuclides. Treatment techniques which involve a combination of external and internal sources (e.g. Ca. cervix uteri treatd by intracavitary sources plus external beam therapy) require particularly careful documentation to indicate the relationship bwtween dose distribution (in both space and time) achieved by the two modalities
Tanioka, Yuichiro; Miranda, Greyving Jose Arguello; Gusman, Aditya Riadi; Fujii, Yushiro
Large earthquakes, such as the Mw 7.7 1992 Nicaragua earthquake, have occurred off the Pacific coasts of El Salvador and Nicaragua in Central America and have generated distractive tsunamis along these coasts. It is necessary to determine appropriate fault models before large tsunamis hit the coast. In this study, first, fault parameters were estimated from the W-phase inversion, and then an appropriate fault model was determined from the fault parameters and scaling relationships with a depth dependent rigidity. The method was tested for four large earthquakes, the 1992 Nicaragua tsunami earthquake (Mw7.7), the 2001 El Salvador earthquake (Mw7.7), the 2004 El Astillero earthquake (Mw7.0), and the 2012 El Salvador-Nicaragua earthquake (Mw7.3), which occurred off El Salvador and Nicaragua in Central America. The tsunami numerical simulations were carried out from the determined fault models. We found that the observed tsunami heights, run-up heights, and inundation areas were reasonably well explained by the computed ones. Therefore, our method for tsunami early warning purpose should work to estimate a fault model which reproduces tsunami heights near the coast of El Salvador and Nicaragua due to large earthquakes in the subduction zone.
home range maintenance or attraction to or avoidance of landscape features, including roads (Morales et al. 2004, McClintock et al. 2012). For example...radiotelemetry and extensive road survey data are used to generate the first density estimates available for the species. The results show that southern...secretive snakes that combines behavioral observations of snake road crossing speed, systematic road survey data, and simulations of spatial
Xu Chengjian, E-mail: firstname.lastname@example.org [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)
Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.
Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t
Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.
Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi
Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.
The purpose of this research was to determine if there were differences in academic performance between students who participated in traditional versus collaborative problem-based learning (PBL) instructional design approaches to physics curricula. This study utilized a quantitative quasi-experimental design methodology to determine the significance of differences in pre- and posttest introductory physics exam performance between students who participated in traditional (i.e., control group) versus collaborative problem solving (PBL) instructional design (i.e., experimental group) approaches to physics curricula over a college semester in 2008. There were 42 student participants (N = 42) enrolled in an introductory physics course at the research site in the Spring 2008 semester who agreed to participate in this study after reading and signing informed consent documents. A total of 22 participants were assigned to the experimental group (n = 22) who participated in a PBL based teaching methodology along with traditional lecture methods. The other 20 students were assigned to the control group (n = 20) who participated in the traditional lecture teaching methodology. Both the courses were taught by experienced professors who have qualifications at the doctoral level. The results indicated statistically significant differences (p traditional (i.e., lower physics posttest scores and lower differences between pre- and posttest scores) versus collaborative (i.e., higher physics posttest scores, and higher differences between pre- and posttest scores) instructional design approaches to physics curricula. Despite some slight differences in control group and experimental group demographic characteristics (gender, ethnicity, and age) there were statistically significant (p = .04) differences between female average academic improvement which was much higher than male average academic improvement (˜63%) in the control group which may indicate that traditional teaching methods
Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming
Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.
Roussel, Sophie; Felix, Benjamin; Vingadassalon, Noémie; Grout, Joël; Hennekinne, Jacques-Antoine; Guillier, Laurent; Brisabois, Anne; Auvray, Fréderic
Staphylococcal food poisoning outbreaks (SFPOs) are frequently reported in France. However, most of them remain unconfirmed, highlighting a need for a better characterization of isolated strains. Here we analyzed the genetic diversity of 112 Staphylococcus aureus strains isolated from 76 distinct SFPOs that occurred in France over the last 30 years. We used a recently developed multiple-locus variable-number tandem-repeat analysis (MLVA) protocol and compared this method with pulsed field gel electrophoresis (PFGE), spa-typing and carriage of genes (se genes) coding for 11 staphylococcal enterotoxins (i.e., SEA, SEB, SEC, SED, SEE, SEG, SEH, SEI, SEJ, SEP, SER). The strains known to have an epidemiological association with one another had identical MLVA types, PFGE profiles, spa-types or se gene carriage. MLVA, PFGE and spa-typing divided 103 epidemiologically unrelated strains into 84, 80, and 50 types respectively demonstrating the high genetic diversity of S. aureus strains involved in SFPOs. Each MLVA type shared by more than one strain corresponded to a single spa-type except for one MLVA type represented by four strains that showed two different-but closely related-spa-types. The 87 enterotoxigenic strains were distributed across 68 distinct MLVA types that correlated all with se gene carriage except for four MLVA types. The most frequent se gene detected was sea, followed by seg and sei and the most frequently associated se genes were sea-seh and sea-sed-sej-ser. The discriminatory ability of MLVA was similar to that of PFGE and higher than that of spa-typing. This MLVA protocol was found to be compatible with high throughput analysis, and was also faster and less labor-intensive than PFGE. MLVA holds promise as a suitable method for investigating SFPOs and tracking the source of contamination in food processing facilities in real time. PMID:26441849
Blockchain is a distributed database that maintains a dynamic list of data records, hardened to prevent tampering and revision. It is the framework for cryptocurrencies like Bitcoin.\\ud \\ud A Blockchain learning tool would provide a secure and verifiable learning transaction ledger. Its decentralised nature would ensure a learner, rather than institution-centred record of achievements that would be difficult to tamper with, enabling parties, such as employers or learning institutions, to revi...
Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.
Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147
Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M
Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.
Jannicke Madeleine Baalsrud Hauge
Full Text Available The challenge of delivering personalized learning experiences is often increased by the size of classrooms and online learning communities. Serious Games (SGs are increasingly recognized for their potential to improve education. However, the issues related to their development and their level of effectiveness can be seriously affected when brought too rapidly into growing online learning communities. Deeper insights into how the students are playing is needed to deliver a comprehensive and intelligent learning framework that facilitates better understanding of learners' knowledge, effective assessment of their progress and continuous evaluation and optimization of the environments in which they learn. This paper discusses current SOTA and aims to explore the potential in the use of games and learning analytics towards scaffolding and supporting teaching and learning experience. The conceptual model (ecosystem and architecture discussed in this paper aims to highlight the key considerations that may advance the current state of learning analytics, adaptive learning and SGs, by leveraging SGs as an suitable medium for gathering data and performing adaptations.
O Doherty, Diane; Mc Keague, Helena; Harney, Sarah; Browne, Gerard; McGrath, Deirdre
Problem-based learning (PBL) has been adopted by many medical schools as an innovative method to deliver an integrated medical curriculum since its inception at McMaster University (Dornan et al., Med Educ 39(2):163-170, 2005; Finucane et al., Med Educ 35(1):56-61, 2001; Barrows, Tutorials in problem-based learning: A new direction in teaching the health professions, 1984). The student experience in PBL has been explored in detail (Merriam, New Directions for Adult and Continuing Education 89: 3-13, 2001; Azer, Kaohsiung J Med Sci 25(5): 240-249, 2009; Boelens et al., BMC Med Ed 15(1): 84, 2015; Dolmans et al., Med Teach 24(2):173-180, 2002; Lee et al., Med Teach 35(2): e935-e942, 2013) but the tutors who facilitate PBL have valuable insight into how PBL functions and this aspect has not been extensively researched. The integrated curriculum for years 1 and 2 at the Graduate Entry Medical School at the University of Limerick is delivered though problem-based learning (PBL). This programme requires collaborative teamwork between students and the tutors who facilitate small-group tutorial sessions. All PBL tutors at GEMS are medically qualified, with the majority (68%) currently working in clinical practice. A mixed-methods approach was adopted, utilising two surveys and follow-up focus groups to fully understand the tutor experience. Thirty-three tutors took part in two online surveys with a response rate of 89%. Thirteen tutors participated in two focus groups. Descriptive analysis was completed on survey data and thematic analysis on focus group discussions which highlighted five main themes. Tutors reported challenges with managing group dynamics, development of confidence in tutoring with experience and a willingness to learn from peers to improve practice. Findings are in keeping with previously published work. Results also identified several less commonly discussed issues impacting student engagement in PBL including the use of mobile device technology
Our laboratory maintains standards for high doses in India. The glutamine powder dosimeter (spectrophotometric readout) is used for this purpose. Present studies show that 20 mg of unirradiated/irradiated glutamine dissolved in freshly prepared 10 ml of aerated aqueous acidic FX solution containing 2 x 10 -3 mol dm -3 ferrous ammonium sulphate and 10 -4 mol dm -3 xylenol orange in 0.033 mol dm -3 sulphuric acid is suitable for the dosimetry in the dose range of 0.1-100 kGy. Normally no corrections are required for the post-irradiation fading of the irradiated glutamine. The response of glutamine dosimeter is independent of irradiation temperature in the range of about 23-30 deg. C and at other temperatures, a correction is necessary. The dose intercomparison results for photon, electron and bremsstrahlung radiations show that glutamine can be used as a reference standard dosimeter. The use of flat polyethylene bags containing glutamine powder has proved very successful for electron dosimetry of wide energies. Several other amino acids like alanine, valine and threonine can also be used to cover wide range of doses using spectrophotometric readout method. (author)
Smith, D.R.; Luna, R.E.; Taylor, J.M.
Two studies were completed which evaluate the environmental impact of radioactive material transport. The first was a generic study which evaluated all radioactive materials and all transportation modes; the second addressed spent fuel and fuel-cycle wastes shipped by truck, rail and barge. A portion of each of those studies dealing with the change in impact resulting from alternative shipping methods is presented in this paper. Alternatives evaluated in each study were mode shifts, operational constraints, and, in generic case, changes in material properties and package capabilities. Data for the analyses were obtained from a shipper survey and from projections of shipments that would occur in an equilibrium fuel cycle supporting one hundred 1000-MW(e) reactors. Population exposures were deduced from point source radiation formulae using separation distances derived for scenarios appropriate to each shipping mode and to each exposed population group. Fourteen alternatives were investigated for the generic impact case. All showed relatively minor changes in the overall radiological impact. Since the radioactive material transport is estimated to be fewer than 3 latent cancer fatalities (LCF) for each shipment year (compared to some 300,000 yearly cancer fatalities or 5000 LCF's calculated for background radiation using the same radiological effects model), a 15% decrease caused by shifting from passenger air to cargo air is a relatively small effect. Eleven alternatives were considered for the fuel cycle/special train study, but only one produced a reduction in total special train baseline LCF's (.047) that was larger than 5%
Kim, Yun Goo; Seong, Poong Hyun
The Computerized Procedure System (CPS) is one of the primary operating support systems in the digital Main Control Room. The CPS displays procedure on the computer screen in the form of a flow chart, and displays plant operating information along with procedure instructions. It also supports operator decision making by providing a system decision. A procedure flow should be correct and reliable, as an error would lead to operator misjudgement and inadequate control. In this paper we present a modeling for the CPS that enables formal verification based on Petri nets. The proposed State Token Petri Nets (STPN) also support modeling of a procedure flow that has various interruptions by the operator, according to the plant condition. STPN modeling is compared with Coloured Petri net when they are applied to Emergency Operating Computerized Procedure. A converting program for Computerized Procedure (CP) to STPN has been also developed. The formal verification and validation methods of CP with STPN increase the safety of a nuclear power plant and provide digital quality assurance means that are needed when the role and function of the CPS is increasing.
Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A
To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright Â© 2012 Elsevier Inc. All rights reserved.
Dobie, Robert A; Wojcik, Nancy C
The US Occupational Safety and Health Administration (OSHA) Noise Standard provides the option for employers to apply age corrections to employee audiograms to consider the contribution of ageing when determining whether a standard threshold shift has occurred. Current OSHA age-correction tables are based on 40-year-old data, with small samples and an upper age limit of 60 years. By comparison, recent data (1999-2006) show that hearing thresholds in the US population have improved. Because hearing thresholds have improved, and because older people are increasingly represented in noisy occupations, the OSHA tables no longer represent the current US workforce. This paper presents 2 options for updating the age-correction tables and extending values to age 75 years using recent population-based hearing survey data from the US National Health and Nutrition Examination Survey (NHANES). Both options provide scientifically derived age-correction values that can be easily adopted by OSHA to expand their regulatory guidance to include older workers. Regression analysis was used to derive new age-correction values using audiometric data from the 1999-2006 US NHANES. Using the NHANES median, better-ear thresholds fit to simple polynomial equations, new age-correction values were generated for both men and women for ages 20-75 years. The new age-correction values are presented as 2 options. The preferred option is to replace the current OSHA tables with the values derived from the NHANES median better-ear thresholds for ages 20-75 years. The alternative option is to retain the current OSHA age-correction values up to age 60 years and use the NHANES-based values for ages 61-75 years. Recent NHANES data offer a simple solution to the need for updated, population-based, age-correction tables for OSHA. The options presented here provide scientifically valid and relevant age-correction values which can be easily adopted by OSHA to expand their regulatory guidance to
Vallila-Rohter, Sofia; Kiran, Swathi
Purpose The purpose of the current study was to explore non-linguistic learning ability in patients with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method Eighteen patients with aphasia and eight healthy controls participated in this study. All participants completed four computerized, non-linguistic category-learning tasks. We probed learning ability under two methods of instruction: feedback-based (FB) and paired-associate (PA). We also examined the impact of task complexity on learning ability, comparing two stimulus conditions: typical (Typ) and atypical (Atyp). Performance was compared between groups and across conditions. Results Results demonstrated that healthy controls were able to successfully learn categories under all conditions. For our patients with aphasia, two patterns of performance arose. One subgroup of patients was able to maintain learning across task manipulations and conditions. The other subgroup of patients demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions. Conclusions Results support the hypothesis that impairments of general learning are present in aphasia. Some patients demonstrated the ability to extract category information under complex training conditions, while others learned only under conditions that were simplified and emphasized salient category features. Overall, the typical training condition facilitated learning for all participants. Findings have implications for therapy, which are discussed. PMID:23695914
Moradabadi, Behnaz; Meybodi, Mohammad Reza
Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.
Guardiola, Carlos; Climent, Héctor; Pla, Benjamín; Reig, Alberto
Highlights: • Optimal Control is applied for heat release shaping in internal combustion engines. • Optimal Control allows to assess the engine performance with a realistic reference. • The proposed method gives a target heat release law to define control strategies. - Abstract: The present paper studies the optimal heat release law in a Diesel engine to maximise the indicated efficiency subject to different constraints, namely: maximum cylinder pressure, maximum cylinder pressure derivative, and NO_x emission restrictions. With this objective, a simple but also representative model of the combustion process has been implemented. The model consists of a 0D energy balance model aimed to provide the pressure and temperature evolutions in the high pressure loop of the engine thermodynamic cycle from the gas conditions at the intake valve closing and the heat release law. The gas pressure and temperature evolutions allow to compute the engine efficiency and NO_x emissions. The comparison between model and experimental results shows that despite the model simplicity, it is able to reproduce the engine efficiency and NO_x emissions. After the model identification and validation, the optimal control problem is posed and solved by means of Dynamic Programming (DP). Also, if only pressure constraints are considered, the paper proposes a solution that reduces the computation cost of the DP strategy in two orders of magnitude for the case being analysed. The solution provides a target heat release law to define injection strategies but also a more realistic maximum efficiency boundary than the ideal thermodynamic cycles usually employed to estimate the maximum engine efficiency.
Barkaoui, Abdelwahed; Chamekh, Abdessalem; Merzouki, Tarek; Hambli, Ridha; Mkaddem, Ali
The complexity and heterogeneity of bone tissue require a multiscale modeling to understand its mechanical behavior and its remodeling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network (NN) computation and homogenization equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained NN simulation. Finite element calculation is performed at nanoscopic levels to provide a database to train an in-house NN program; and (iii) in steps 2-10 from fibril to continuum cortical bone tissue, homogenization equations are used to perform the computation at the higher scales. The NN outputs (elastic properties of the microfibril) are used as inputs for the homogenization computation to determine the properties of mineralized collagen fibril. The mechanical and geometrical properties of bone constituents (mineral, collagen, and cross-links) as well as the porosity were taken in consideration. This paper aims to predict analytically the effective elastic constants of cortical bone by modeling its elastic response at these different scales, ranging from the nanostructural to mesostructural levels. Our findings of the lowest scale's output were well integrated with the other higher levels and serve as inputs for the next higher scale modeling. Good agreement was obtained between our predicted results and literature data. Copyright © 2013 John Wiley & Sons, Ltd.
A filmless X-ray imaging system includes at least one X-ray source, upper and lower collimators, and a solid-state detector array, and can provide three-dimensional imaging capability. The X-ray source plane is distance z.sub.1 above upper collimator plane, distance z.sub.2 above the lower collimator plane, and distance z.sub.3 above the plane of the detector array. The object to be X-rayed is located between the upper and lower collimator planes. The upper and lower collimators and the detector array are moved horizontally with scanning velocities v.sub.1, v.sub.2, v.sub.3 proportional to z.sub.1, z.sub.2 and z.sub.3, respectively. The pattern and size of openings in the collimators, and between detector positions is proportional such that similar triangles are always defined relative to the location of the X-ray source. X-rays that pass through openings in the upper collimator will always pass through corresponding and similar openings in the lower collimator, and thence to a corresponding detector in the underlying detector array. Substantially 100% of the X-rays irradiating the object (and neither absorbed nor scattered) pass through the lower collimator openings and are detected, which promotes enhanced sensitivity. A computer system coordinates repositioning of the collimators and detector array, and X-ray source locations. The computer system can store detector array output, and can associate a known X-ray source location with detector array output data, to provide three-dimensional imaging. Detector output may be viewed instantly, stored digitally, and/or transmitted electronically for image viewing at a remote site.
Llorens, Ariadna; Berbegal-Mirabent, Jasmina; Llinàs-Audet, Xavier
Engineering education is facing new challenges to effectively provide the appropriate skills to future engineering professionals according to market demands. This study proposes a model based on active learning methods, which is expected to facilitate the acquisition of the professional skills most highly valued in the information and communications technology (ICT) market. The theoretical foundations of the study are based on the specific literature on active learning methodologies. The Delphi method is used to establish the fit between learning methods and generic skills required by the ICT sector. An innovative proposition is therefore presented that groups the required skills in relation to the teaching method that best develops them. The qualitative research suggests that a combination of project-based learning and the learning contract is sufficient to ensure a satisfactory skills level for this profile of engineers.
Garwood, Janet K
The current longitudinal, descriptive, and correlational study explored which traditional teaching strategies can engage Millennial students and adequately prepare them for the ultimate test of nursing competence: the National Council Licensure Examination. The study comprised a convenience sample of 40 baccalaureate nursing students enrolled in a psychiatric nursing course. The students were exposed to a variety of traditional (e.g., PowerPoint(®)-guided lectures) and nontraditional (e.g., concept maps, group activities) teaching and learning strategies, and rated their effectiveness. The students' scores on the final examination demonstrated that student learning outcomes met or exceeded national benchmarks. Copyright 2015, SLACK Incorporated.
Peine, Arne; Kabino, Klaus; Spreckelsen, Cord
Modernised medical curricula in Germany (so called "reformed study programs") rely increasingly on alternative self-instructed learning forms such as e-learning and curriculum-guided self-study. However, there is a lack of evidence that these methods can outperform conventional teaching methods such as lectures and seminars. This study was conducted in order to compare extant traditional teaching methods with new instruction forms in terms of learning effect and student satisfaction. In a randomised trial, 244 students of medicine in their third academic year were assigned to one of four study branches representing self-instructed learning forms (e-learning and curriculum-based self-study) and instructed learning forms (lectures and seminars). All groups participated in their respective learning module with standardised materials and instructions. Learning effect was measured with pre-test and post-test multiple-choice questionnaires. Student satisfaction and learning style were examined via self-assessment. Of 244 initial participants, 223 completed the respective module and were included in the study. In the pre-test, the groups showed relatively homogenous scores. All students showed notable improvements compared with the pre-test results. Participants in the non-self-instructed learning groups reached scores of 14.71 (seminar) and 14.37 (lecture), while the groups of self-instructed learners reached higher scores with 17.23 (e-learning) and 15.81 (self-study). All groups improved significantly (p learning group, whose self-assessment improved by 2.36. The study shows that students in modern study curricula learn better through modern self-instructed methods than through conventional methods. These methods should be used more, as they also show good levels of student acceptance and higher scores in personal self-assessment of knowledge.
Carlisle, Caroline; Ibbotson, Tracy
The evidence base for the effectiveness of problem-based learning (PBL) has never been substantively established, although PBL is a generally accepted approach to learning in health care curricula. PBL is believed to encourage transferable skills, including problem-solving and team-working. PBL was used to deliver a postgraduate research methods module and a small evaluation study to explore its efficacy was conducted amongst the students (n = 51) and facilitators (n = 6). The study comprised of an evaluation questionnaire, distributed after each themed group of PBL sessions, and a group discussion conducted 4 weeks after the conclusion of the module, which was attended by student representatives and the facilitators. Questionnaire data was analysed using SPSS, and a transcript of the interview was subjected to content analysis. The results indicated that students felt that a PBL approach helped to make the subject matter more interesting to them and they believed that they would retain knowledge for a longer period than if their learning had used a more traditional lecture format. Students also perceived that PBL was effective in its ability to enhance students' understanding of the group process. All those involved in the PBL process reinforced the pivotal role of the facilitator. This study indicates that there is potential for PBL to be used beyond the more usual clinical scenarios constructed for health care professional education and further exploration of its use in areas such as building research capability should be undertaken.
Mauri Kalervo Åhlberg
Full Text Available Results and underpinning of over twenty years of research and development program of concept mapping is presented. Different graphical knowledge presentation tools, especially concept mapping and mind mapping, are compared. There are two main dimensions that differentiate graphical knowledge presentation methods: The first dimension is conceptual explicitness: from mere concepts to flexibly named links and clear propositions in concept maps. The second dimension in the classification system I am suggesting is whether there are pictures or not. Åhlbergʼs and his research groupʼs applications and developments of Novakian concept maps are compared to traditional Novakian concept maps. The main innovations include always using arrowheads to show direction of reading the concept map. Centrality of each concept is estimated from number of links to other concepts. In our empirical research over two decades, number of relevant concepts, and number of relevant propositions in studentsʼ concept maps, have been found to be the best indicators and predictors of meaningful learning. This is used in assessment of learning. Improved concept mapping is presented as a tool to analyze texts. The main innovation is numbering the links to show order of reading the concept map and to make it possible to transform concept map back to the original prose text as closely as possible. In Åhlberg and his research groupʼs research, concept mapping has been tested in all main phases of research, teaching and learning.
Siswanto; Yusiran; Asriyadin; Gumilar, S.; Subali, B.
The purpose of this research describes the effect of scientific methods designed by argumentation in maintaining retention of pre-service physics teachers (students) in mechanical concept. This learning consists of five stages including the first two stages namely observing and questioning. While the next three stages of reasoning, trying, and communicating are made of argumentation design. To know the effectiveness of treatment, students are given pre-test and post-test in one time. On the other hand, students were given advanced post-test to know the durability of retention as many as four times in four months. The results show that there was mean difference between pre-test and post-test based on the Wilcoxon test (z = -3.4, p=0.001). While the effectiveness of treatment is in the high category based on normalized gain values ( = 0.86). Meanwhile, mean difference of all post-test is significantly different based on Analysis of Varian (F = 365.63, p = 0.00). However, in the fourth month, students retention rates began to stabilize based on Tuckey’s HSD (p=0.074) for comparison of mean difference between fourth and fifth post-test. Overall, learning designed can maintain students retention within 4 months after the learning finish.
Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z
Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.
Full Text Available Learning plays an important role in developing nursing skills and right care-taking. The Present study aims to evaluate two learning methods based on team –based learning and lecture-based learning in learning care-taking of patients with diabetes in nursing students. In this quasi-experimental study, 64 students in term 4 in nursing college of Bukan and Miandoab were included in the study based on knowledge and performance questionnaire including 15 questions based on knowledge and 5 questions based on performance on care-taking in patients with diabetes were used as data collection tool whose reliability was confirmed by cronbach alpha (r=0.83 by the researcher. To compare the mean score of knowledge and performance in each group in pre-test step and post-test step, pair –t test and to compare mean of scores in two groups of control and intervention, the independent t- test was used. There was not significant statistical difference between two groups in pre terms of knowledge and performance score (p=0.784. There was significant difference between the mean of knowledge scores and diabetes performance in the post-test in the team-based learning group and lecture-based learning group (p=0.001. There was significant difference between the mean score of knowledge of diabetes care in pre-test and post-test in base learning groups (p=0.001. In both methods team-based and lecture-based learning approaches resulted in improvement in learning in students, but the rate of learning in the team-based learning approach is greater compared to that of lecturebased learning and it is recommended that this method be used as a higher education method in the education of students.
Fernando, Sithara Y. J. N.; Marikar, Faiz M. M. T.
Evidence for the teaching involves transmission of knowledge, superiority of guided transmission is explained in the context of our knowledge, but it is also much more that. In this study we have examined General Sir John Kotelawala Defence University's cadet and civilian students' response to constructivist learning theory and participatory…
The demands in higher education are on the rise. Charged with teaching more content, increased class sizes and engaging students, educators face numerous challenges. In design education, educators are often torn between the teaching of technology and the teaching of theory. Learning the formal concepts of hierarchy, contrast and space provide the…
Vidnerová, Petra; Neruda, Roman
submitted 25. 1. (2018) ISSN 1530-437X R&D Projects: GA ČR GA15-18108S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : machine learning * sensors * air pollution * deep neural networks * regularization networks Subject RIV: IN - Informatics, Computer Science Impact factor: 2.512, year: 2016
Moorthy, N. S.Hari Narayana; Kumar, Surendra; Poongavanam, Vasanthanathan
An accurate calculation of carcinogenicity of chemicals became a serious challenge for the health assessment authority around the globe because of not only increased cost for experiments but also various ethical issues exist using animal models. In this study, we provide machine learning...
Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao
In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.
This paper describes an alternative approachto the teaching of concepts related to theEnglish curriculum, namely literature, writing summaries and grammar. It combines ashift in the theory of school learning development by a combination with a psychologicaltheory of development. The research was conducted over the ...
Currin-Percival, Mary; Johnson, Martin
We investigate differences in what students learn about survey methodology in a class on public opinion presented in two critically different ways: with the inclusion or exclusion of an original research project using a random-digit-dial telephone survey. Using a quasi-experimental design and data obtained from pretests and posttests in two public…
Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to  empirically determine the signatures of this mechanism in solar image data and  use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.
海老澤, 賢史; Ebisawa, Satoshi
The educational methods with Learning Management System (LMS) are described, which are applied to two specialized courses for engineering education and a research guidance for graduation work at Niigata Institute of Technology.According to the analysis of LMS usage situation for graduation work, the LMS has provided an effect that learning time outside class hour is held and the convenience of students in learning is enhanced.In the specializedcourses, the rate of utilization of LMS has depen...
Full Text Available Background and purpose: This paper analyzes the interest of potential users for learning in the field of currency trading or foreign exchange (forex, FX. The purpose of our article is a to present currency trading, b to present different options, methods and learning approaches to educating in forex, c to present the research results discovering the interest of potential users for learning in the field of currency trading.
Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong
Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.
With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...
Full Text Available The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.
Bendinskaitė I. Perspective for applying traditional and innovative teaching and learning methods to nurse’s continuing education, magister thesis / supervisor Assoc. Prof. O. Riklikienė; Departament of Nursing and Care, Faculty of Nursing, Lithuanian University of Health Sciences. – Kaunas, 2015, – p. 92 The purpose of this study was to investigate traditional and innovative teaching and learning methods perspective to nurse’s continuing education. Material and methods. In a period fro...
Engum, Scott A; Jeffries, Pamela; Fisher, Lisa
Virtual reality simulators allow trainees to practice techniques without consequences, reduce potential risk associated with training, minimize animal use, and help to develop standards and optimize procedures. Current intravenous (IV) catheter placement training methods utilize plastic arms, however, the lack of variability can diminish the educational stimulus for the student. This study compares the effectiveness of an interactive, multimedia, virtual reality computer IV catheter simulator with a traditional laboratory experience of teaching IV venipuncture skills to both nursing and medical students. A randomized, pretest-posttest experimental design was employed. A total of 163 participants, 70 baccalaureate nursing students and 93 third-year medical students beginning their fundamental skills training were recruited. The students ranged in age from 20 to 55 years (mean 25). Fifty-eight percent were female and 68% percent perceived themselves as having average computer skills (25% declaring excellence). The methods of IV catheter education compared included a traditional method of instruction involving a scripted self-study module which involved a 10-minute videotape, instructor demonstration, and hands-on-experience using plastic mannequin arms. The second method involved an interactive multimedia, commercially made computer catheter simulator program utilizing virtual reality (CathSim). The pretest scores were similar between the computer and the traditional laboratory group. There was a significant improvement in cognitive gains, student satisfaction, and documentation of the procedure with the traditional laboratory group compared with the computer catheter simulator group. Both groups were similar in their ability to demonstrate the skill correctly. CONCLUSIONS; This evaluation and assessment was an initial effort to assess new teaching methodologies related to intravenous catheter placement and their effects on student learning outcomes and behaviors
Vittadini, Nicoletta; Carlo, Simone; Gilje, Øystein
One of the most significant challenges in researching the social aspects of contemporary societies is to adapt the methodological approach to complex digital media environments. Learning processes take place in this complex environment, and they include formal and informal experiences (learning...... in school, home, and real-virtual communities), peer cultures and inter-generational connections, production and creation as relevant activities, and personal interests as a focal point. Methods used in the study of learning and the social practices of young people must take into account four key issues......: boundaries between online and offline experiences are blurring; young people act performatively, knowingly, or reflexively; and their activities cannot be understood through the use of a single method, but require the use of multiple tools of investigation. The article discusses three methodological issues...
Vittadini, Nicoletta; Carlo, Simone; Gilje, Øystein
One of the most significant challenges in researching the social aspects of contemporary societies is to adapt the methodological approach to complex digital media environments. Learning processes take place in this complex environment, and they include formal and informal experiences (learning...... in school, home, and real-virtual communities), peer cultures and intergenerational connections, production and creation as relevant activities, and personal interests as a focal point. Methods used in the study of learning and the social practices of young people must take into account four key issues......: boundaries between online and offline experiences are blurring; young people act performatively; young people act knowingly or reflexively; and the activities of young people cannot be understood through the use of a single method but require the use of multiple tools of investigation. The article discusses...
Davis, Eric J.; Pauls, Steve; Dick, Jonathan
Presented is a project-based learning (PBL) laboratory approach for an upper-division environmental chemistry or quantitative analysis course. In this work, a combined laboratory class of 11 environmental chemistry students developed a method based on published EPA methods for the extraction of dichlorodiphenyltrichloroethane (DDT) and its…
Full Text Available Aims: In order to preserve its own progress, nursing training has to be utilized new training methods, in such a case that the teaching methods used by the nursing instructors enhance significant learning via preventing superficial learning in the students. Conceptual Map Method is one of the new training strategies playing important roles in the field. The aim of this study was to investigate the effectiveness of the designed software based on the mobile phone computer conceptual map on the learning level of the nursing students. Materials & Methods: In the semi-experimental study with pretest-posttest plan, 60 students, who were studying at the 5th semester, were studied at the 1st semester of 2015-16. Experimental group (n=30 from Meibod Nursing Faculty and control group (n=30 from Yazd Shahid Sadoughi Nursing Faculty were trained during the first 4 weeks of the semester, using computer conceptual map method and computer conceptual map method in mobile phone environment. Data was collected, using a researcher-made academic progress test including “knowledge” and “significant learning”. Data was analyzed in SPSS 21 software using Independent T, Paired T, and Fisher tests. Findings: There were significant increases in the mean scores of knowledge and significant learning in both groups before and after the intervention (p0.05. Nevertheless, the process of change of the scores of significant learning level between the groups was statistically significant (p<0.05. Conclusion: Presenting the course content as conceptual map in mobile phone environment positively affects the significant learning of the nursing students.
Esteban-Sánchez, Natalia; Pizarro, Celeste; Velázquez-Iturbide, J. Ángel
An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of…
Gillespie, Suzanne M; Olsan, Tobie; Liebel, Dianne; Cai, Xueya; Stewart, Reginald; Katz, Paul R; Karuza, Jurgis
To describe the development of a nursing home (NH) quality improvement learning collaborative (QILC) that provides Lean Six Sigma (LSS) training and infrastructure support for quality assurance performance improvement change efforts. Case report. Twenty-seven NHs located in the Greater Rochester, NY area. The learning collaborative approach in which interprofessional teams from different NHs work together to improve common clinical and organizational processes by sharing experiences and evidence-based practices to achieve measurable changes in resident outcomes and system efficiencies. NH participation, curriculum design, LSS projects. Over 6 years, 27 NHs from urban and rural settings joined the QILC as organizational members and sponsored 47 interprofessional teams to learn LSS techniques and tools, and to implement quality improvement projects. NHs, in both urban and rural settings, can benefit from participation in QILCs and are able to learn and apply LSS tools in their team-based quality improvement efforts. Published by Elsevier Inc.
Full Text Available The purpose of this research was determined the effect of application WhatsApp Messenger in the Group Investigation (GI method on learning achievement. The methods used experimental research with control group pretest-postest design. The sampling procedure used the purposive sampling technique that consists of 17 students as a control group and 17 students as an experimental group. The sample in this research is students in Electrical Engineering Education Study Program. The experimental group used the GI method that integrated with WhatsApp Messenger. The control group used lecture method without social media integration. The collecting data used observation, documentation, interview, questionnaire, and test. The researcher used a t-test for compared the control group and the experimental group’s learning outcomes at an alpha level of 0,05. The results showed differences between the experiment group and the control group. The study result of the experimental higher than the control groups. This learning was designed with start, grouping, planning, presenting, organizing, investigating, evaluating, ending’s stage. Integration of WhatsApp with group investigation method could cause the positive communication between student and lecturer. Discussion in this learning was well done, the student’s knowledge could appear in a group and the information could spread evenly and quickly.
Taufik, Nurshahira Alwani Mohd; Maat, Siti Mistima
Mathematics education is one of the branches to be mastered by students to help them compete with the upcoming challenges that are very challenging. As such, all parties should work together to help increase student achievement in Mathematics education in line with the Malaysian Education Blueprint (MEB) 2010-2025. Teaching methods play a very important role in attracting and fostering student understanding and interested in learning Mathematics. Therefore, this study was conducted to identify the perceptions of teachers in carrying out cooperative methods in the teaching and learning of mathematics. Participants of this study involving 4 teachers who teach Mathematics in primary schools around the state of Negeri Sembilan. Interviews are used as a method for gathering data. The findings indicate that cooperative methods help increasing interest and understanding in the teaching and learning of mathematics. In conclusion, the teaching methods affect the interest and understanding of students in the learning of Mathematics in the classroom.
Kidziński, Łukasz; Mohanty, Sharada Prasanna; Ong, Carmichael; Huang, Zhewei; Zhou, Shuchang; Pechenko, Anton; Stelmaszczyk, Adam; Jarosik, Piotr; Pavlov, Mikhail; Kolesnikov, Sergey; Plis, Sergey; Chen, Zhibo; Zhang, Zhizheng; Chen, Jiale; Shi, Jun
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar ...
Blended learning is a teaching technique that utilizes face-to-face teaching and online or technology-based practice in which the learner has the ability to exert some level of control over the pace, place, path, or time of learning. Schools that employ this method of teaching often demonstrate larger gains than traditional face-to-face programs…
In this study, the effect of the learning together technique, which is one of the cooperative learning methods, on the development of the listening comprehension and listening skills of the secondary school eighth grade students was investigated. Regarding the purpose of the research, experimental and control groups consisting of 75 students from,…
Discusses methods for analyzing case studies of failures of technological systems. Describes two distance learning courses that compare standard models of failure and success with the actuality of given scenarios. Provides teaching and learning materials and information sources for application to aspects of design, manufacture, inspection, use,…
Estébanez, Raquel Pérez
In the way of continuous improvement in teaching methods this paper explores the effects of Cooperative Learning (CL) against Traditional Learning (TL) in academic performance of students in higher education in two groups of the first course of Computer Science Degree at the university. The empirical study was conducted through an analysis of…
Reimann, Peter; Markauskaite, Lina; Bannert, Maria
This paper discusses the fundamental question of how data-intensive e-research methods could contribute to the development of learning theories. Using methodological developments in research on self-regulated learning as an example, it argues that current applications of data-driven analytical techniques, such as educational data mining and its…
Suprasegmental features are of paramount importance in spoken English. Yet, these pronunciation features are marginalised in EFL/ESL teaching-learning. This article reported a study that was aimed at improving the students' mastery of English suprasegmental features through the use of reflective learning method. The study adopted Kemmis and…
Vargas-Vargas, Manuel; Mondejar-Jimenez, Jose; Santamaria, Maria-Letica Meseguer; Alfaro-Navarro, Jose-Luis; Fernandez-Aviles, Gema
This document sets out a novel teaching methodology as used in subjects with statistical content, traditionally regarded by students as "difficult". In a virtual learning environment, instructional techniques little used in mathematical courses were employed, such as the Jigsaw cooperative learning method, which had to be adapted to the…
Martin, John F.
This mixed methods study exploring student outcomes of service learning experiences is inter-disciplinary, near the intersection of higher education research, moral development, and nursing. The specific problem examined in this study is that service learning among university students is utilized by educators, but largely without a full…
Toland, John; Boyle, Christopher
This study involves the use of methods derived from cognitive behavioral therapy (CBT) to change the attributions for success and failure of school children with regard to learning. Children with learning difficulties and/or motivational and self-esteem difficulties (n = 29) were identified by their schools. The children then took part in twelve…
Hunt, Emily M.; Lockwood-Cooke, Pamela; Kelley, Judy
Problem-Based Learning (PBL) is a problem-centered teaching method with exciting potential in engineering education for motivating and enhancing student learning. Implementation of PBL in engineering education has the potential to bridge the gap between theory and practice. Two common problems are encountered when attempting to integrate PBL into…
Full Text Available Abstract Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities, calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR of 0.48 (Confidence Interval (CI 0.38 – 0.58; X2: 72.227; p.value X2: 24.236; p.value X2: 286.341; p.value Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities.
Ivana Đurđević Babić
Full Text Available Academic motivation is closely related to academic performance. For educators, it is equally important to detect early students with a lack of academic motivation as it is to detect those with a high level of academic motivation. In endeavouring to develop a classification model for predicting student academic motivation based on their behaviour in learning management system (LMS courses, this paper intends to establish links between the predicted student academic motivation and their behaviour in the LMS course. Students from all years at the Faculty of Education in Osijek participated in this research. Three machine learning classifiers (neural networks, decision trees, and support vector machines were used. To establish whether a significant difference in the performance of models exists, a t-test of the difference in proportions was used. Although, all classifiers were successful, the neural network model was shown to be the most successful in detecting the student academic motivation based on their behaviour in LMS course.
Kamra, Ashish; Ber, Elisa
Application of machine learning techniques to database security is an emerging area of research. In this chapter, we present a survey of various approaches that use machine learning/data mining techniques to enhance the traditional security mechanisms of databases. There are two key database security areas in which these techniques have found applications, namely, detection of SQL Injection attacks and anomaly detection for defending against insider threats. Apart from the research prototypes and tools, various third-party commercial products are also available that provide database activity monitoring solutions by profiling database users and applications. We present a survey of such products. We end the chapter with a primer on mechanisms for responding to database anomalies.
How teaching and learning takes place in classrooms can be easily seen by the way classrooms are set up: Students' desks and chairs are arranged in rolls while teachers' desks are up front. Yet, why must teachers be the ones who lecture, why can't it be students? Would it be better or worse when teachers are the receivers and the students are the…
Policy flows are not quantifiable and calculating processes but part of the uneven movement of ideas and experiences that involves power and personalities. Processes of learning and policy circulation have thus proven difficult to study especially as the exchanges taking place between actors and localities rarely lead directly to uptake. This paper outlines a conceptual and methodological framework for conducting policy mobilities research by attending to the plethora of ordinary practices – ...
Sultan, A. Z.; Hamzah, N.; Rusdi, M.
The implementation of concept attainment method based on simulation was used to increase student’s interest in the subjects Engineering of Mechanics in second semester of academic year 2016/2017 in Manufacturing Engineering Program, Department of Mechanical PNUP. The result of the implementation of this learning method shows that there is an increase in the students’ learning interest towards the lecture material which is summarized in the form of interactive simulation CDs and teaching materials in the form of printed books and electronic books. From the implementation of achievement method of this simulation based concept, it is noted that the increase of student participation in the presentation and discussion as well as the deposit of individual assignment of significant student. With the implementation of this method of learning the average student participation reached 89%, which before the application of this learning method only reaches an average of 76%. And also with previous learning method, for exam achievement of A-grade under 5% and D-grade above 8%. After the implementation of the new learning method (simulation based-concept attainment method) the achievement of Agrade has reached more than 30% and D-grade below 1%.
Jeong, Yong Sun; Kim, Jin Sun
A blended learning can be a useful learning strategy to improve the quality of fever and fever management education for pediatric nurses. This study compared the effects of a blended and face-to-face learning program on pediatric nurses' childhood fever management, using theory of planned behavior. A nonequivalent control group pretest-posttest design was used. A fever management education program using blended learning (combining face-to-face and online learning components) was offered to 30 pediatric nurses, and 29 pediatric nurses received face-to-face education. Learning outcomes did not significantly differ between the two groups. However, learners' satisfaction was higher for the blended learning program than the face-to-face learning program. A blended learning pediatric fever management program was as effective as a traditional face-to-face learning program. Therefore, a blended learning pediatric fever management-learning program could be a useful and flexible learning method for pediatric nurses.
Roberts, Fiona; Cooper, Kay
The objective of this review is to identify if high fidelity simulated learning methods are effective in enhancing clinical/practical skills compared to usual, low fidelity simulated learning methods in pre-registration physiotherapy education.