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3D, 1-particle systems

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Some 1D infinite well stuff

Blackbody

A little energy and momentum practice (and units) Problems 1-2 deal with “rest” energy and relativity.

A bit of stat mech Problems 1-3 refer to: N identical, noninteracting, and distinguishable spin-1/2 particles (i.e., their separation is much greater than their de Broglie wavelength) are placed in an external magnetic field. Assume the ground state energy of one such particle is 0 and the excited state energy is ε , and the system is in thermal equilibrium at temperature T.

The first year optics curriculum at the Arizona College of Optometry aims to provide students with an understanding of geometrical, physical, and visual optics principals that will be the foundation of their clinical understanding of the optics of the eye and its correction in advanced courses such as ophthalmic optics and contact lenses. Although the optics of the eye are a fantastic model to use in optics education, the clinical applications may not become apparent until later in the course of study. Successful strategies are needed to engage students and facilitate the understanding of optical principals and the growth of process skills including problem solving, analysis, and critical thinking that will help in their future as health care providers. These include the implementation of ophthalmic applications as early as possible, encouragement of group work including open office hours, and the use of video problem set solutions to supplement traditional static solutions.

Most academic institutions have policies around academic integrity, and students who are found to have violated the policies are disciplined. In spite of this, a number of students cheat. The most common and easily detectable form is plagiarism, where someone else’s work is copied across and claimed as one’s own.Experience suggests that about 30% of the class might be plagiarizing, though some research point to as much as 70% cheating in various forms. Dealing with plagiarism is a highly time-consuming affair. Prior research observed high value low frequent assignments as the most plagiarized as opposed to low value high frequent ones. It is therefore desirable to have low value high frequent assignments so as to reduce plagiarism incidents, thereby reducing the time spent on dealing with detected plagiarism cases.This paper discusses the implementation of an automated assignment generation and marking framework that is able to deliver high frequent assignments and automatically grade the submitted solutions. More importantly, the framework supports personalized assignments so that every student gets a different problem set to solve. This means that blindly copying answers from another student will not help gain any mark.The paper briefly shares some of the experience using the framework in engineering and science, where staff and students felt positively about the system and observed a huge reduction in plagiarism incidents. The reduction in the incidents resulted in saving a large amount of time that would have otherwise been spent on dealing with the incidents.

Design and redesign of construction works is a formal and informal specification relating to the actions of the individual, including both structural and interpersonal aspects of the work, taking into account the needs and demands of both the organization and the individual. The term “redesign of construction work” necessarily implies such operational approaches and methods as change of work, growing responsibilities, ergonomic factors, enrichment of work, the design of socio-technical systems, formation of teams performance/productivity, streamline production operations, etc. The systematic redesign of works usually use either methods related to the solution of employment problems on an individual basis, from time to time, or methods of diagnosis, evaluation and improvement of work organization in the framework of a larger program aimed at improving productivity and quality of working life. When “appropriate” task or problem set before “according” by a group or individuals and used “appropriate” group process, the results can be exceptional and serve as an effective source of productivity growth and efficiency of construction companies.

This Article outlines the past model of legal research and writing at Wake Forest School of Law, the problems it presented and the redesign methods implemented over the course of two academic years. It details student evaluations and evidence of better student learning when legal writing and research is taught together, in an integrated fashion, rather than in two paralleling courses. Most importantly, this Article provides evidence to support an argument that legal research should never be taught outside the context of a legal writing, or a doctrinal, problem set. Collaboration among faculty is the key to successful student learning.

Groups of agents in multi-agent systems may have to cooperate to solve tasks efficiently, and coordinating such groups is an important problem in the field of artificial intelligence. In this paper, we consider the problem of forming disjoint coalitions and assigning them to independent tasks simultaneously, and present an anytime algorithm that efficiently solves the simultaneous coalition structure generation and task assignment problem. This NP-complete combinatorial optimization problem has many real-world applications, including forming cross-functional teams aimed at solving tasks. To evaluate the algorithm’s performance, we extend established methods for synthetic problem set generation, and benchmark the algorithm using randomized data sets of varying distribution and complexity. Our results show that the presented algorithm efficiently finds optimal solutions, and generates high quality solutions when interrupted prior to finishing an exhaustive search. Additionally, we apply the algorithm to solve the problem of assigning agents to regions in a commercial computer-based strategy game, and empirically show that our algorithm can significantly improve the coordination and computational efficiency of agents in a real-time multi-agent system.

Pencasts are videos of problem solving with narration by the problem solver. Pedagogically, students can create pencasts to illustrate their own problem solving to the instructor or to their peers. Pencasts have implications for teaching at multiple levels from elementary grades through university courses. In this article, we describe the use of pencasts in a university level upperdivision Electromagnetic Fields course usually taken by junior and senior physics majors. For each homework assignment, students created and submitted pencasts of ordinary problems several days before the problem set was due. We compare students' performance in the class (grades for pencast submission excluded) with the pencast submission rate. Students who submitted more pencasts tend to do better in the course. We conclude with some practical suggestions for implementing pencasts in other courses., Comment: Accepted at The Physics Teacher

The paper presented the analyses of one of the enigmas as set forth by the managerial economics. In analysing it, the paper formulated ways of resolving the enigma. The paper developed the theorem, which uncovered the essence of the two-dimensional measuring the indicators of the balanced economic growth in a developing economy. The outcomes of the analyses, carried out by the study, are expected to put a halt to the ineffective and inefficient use of natural material, capital and labour resources in any given developing economy. To that end, the results of the analyses will help open up the path leading to the effective green economy and will help maximize on the cost saving in the use of natural resources. The findings of the analyses will contribute to the development of the managerial economics and will be essential assets in measuring the balanced economic growth in developing countries. The novice of the paper is that the solution to the problem set as explained in this paper has ever been uncovered before.

In this study, we investigate the problem of maximum frequent time-window selection (MFTWS) that appears in the process of discovering association rules time-windows (ARTW). We formulate the problem as a mathematical model using integer programming that is a typical combination problem with a solution space exponentially related to the problem size. A variable neighbourhood search (VNS) algorithm is developed to solve the problem with near-optimal solutions. Computational experiments are performed to test the VNS algorithm against a benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering many large-one frequent itemset with time-windows (FITW) with a larger time-coverage rate than the lower bounds, thus laying a good foundation for mining ARTW.

For practical purposes, rules have to be defined to use hierarchical membrane structures of P-systems or membrane computing, the latest distributed and parallel paradigm in natural computing. The objective of this paper is to investigate and report the comparative implementation of five membrane optimization algorithms of P-systems based on evolution rules of Particle Swarm Optimization, on the latest CEC 2014 benchmark problem set, as has been asked for in Liang et al. (2013). The results, on this set of 30 problems of varying difficulty levels, is presented as per the specifications and evaluation criteria asked for, therein. The results of our paper show that our results are better than the results presented in the two papers appeared till now. Further, it is concluded that, using membrane optimization algorithms of P-systems based on PSO rules, improves the performance especially for higher dimensions. This can be achieved by minimum inter-communication and load-balancing amongst membranes.

This article describes the development and implementation of an open-access organic chemistry question bank for online tutorials and assessments at University College Cork and Dublin Institute of Technology. SOCOT (structure-based organic chemistry online tutorials) may be used to supplement traditional small-group tutorials, thereby allowing students to develop essential problem-solving skills in organic chemistry. This online approach may be used for both formative and summative assessment. Students complete one problem set weekly or fortnightly, which consists of a number of questions of varying difficulty. A wide range of question types is possible; for example, prediction of reaction products, identification of reaction intermediates or reagents, and retrosynthetic analyses. Questions involving stereochemistry may be also be incorporated. The implementation is described, along with several sample questions and advice for creating questions. This approach is suitable for all levels of undergraduates, from introductory nonmajors to final-year chemistry students. Student feedback was overwhelmingly positive, and in particular, students found SOCOT to be a quite useful tool for review purposes. Our approach uses MarvinSketch, which is free for academic purposes, and the SMILES algorithm, which converts chemical structures into a text string and is compatible with any learning management system.

The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The platform, hosted by Worcester Polytechnic Institute, allows teachers to write individual ASSISTments (composed of questions with answers and associated hints, solutions, web-based videos, etc.) or to use pre-built ASSISTments, bundle them together in a problem set, and assign these to students. The system gives immediate feedback to students while they are working and provides student-level data to teachers on any assignment. The word “ASSISTments” blends tutoring “assistance” with “assessment” reporting to teachers and students. While originally focused on mathematics, the platform now has content from many other subjects (e.g., science, English, Statistics, etc.). Due to the large library of mathematics content, however, it is mostly used by math teachers. Over 50,000 students used ASSISTments last school year (2013–4) and this number has been doubling each year for the last 8 years. The platform allows any user, mostly researchers, to create randomized controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies. The data collected by the system has also been used in a few dozen peer-reviewed data mining publications. This paper will not seek to review these publications, but instead we will share why ASSISTments has been successful and what lessons were learned along the way. The first lesson learned was to build a platform for learning sciences, not a product that focused on a math topic. That is, ASSISTments is a tool, not a curriculum. A second lesson learned is expressed by the mantra “Put the teacher in charge, not the computer.” This second lesson is about building a flexible system that allows teachers to use the tool in concert with the classroom routine. Once teachers are using the tool they are more likely to want to participate in research studies. These lessons were born from the design decisions about what the platform supports and does not support. In conclusion, goals for the future will be presented.

The present research tested the hypothesis that interest functions, in part, to optimize performance while also optimizing self-regulatory resources, and that this occurs when both affect- and value-related interest are high. Study 1 provided evidence that both affect- and value-related interest support task performance such that undergraduates (N = 153) in the high task importance condition, who also reported high affect-related interest, demonstrated relatively superior performance on a word-forming problem set. Study 2 (N = 88) provided further evidence that affect- and value-related interest were associated with superior anagram performance. A subsequent task demonstrated that self-regulatory resources were optimized for participants with both high affect- and value-related interest. The present studies provide evidence that high levels of performance can be achieved while maintaining optimal self-regulatory resources, depending on affect-related interest and the task's personal significance. Implications for goal pursuit and self-regulation are discussed.

На основании постановки вариационной задачи Больца рассмотрено движение “идеальной” ракеты в вертикальной плоскости, которое есть модификациею классической задачи о брахистохроне. Получена аналитическая зависимость, которая характеризирует кинематические характеристики движения “идеальной” ракеты в вертикальной плоскости; выявлено особенности пути наименьшего времени., На основі постановки варіаційної задачі Больца розглянуто рух “ідеальної” ракети у вертикальній площині, яка є модифікацією класичної задачі про брахістохрону. Отримано аналітична залежність, яка характеризує кінематичні характеристики руху “ідеальної” ракети у вертикальній площині; виявлено особливості шляху найменшого часу., Movement of the “ideal” rocket in the vertical plane on the basis of Bolz variational problem set up has been considered which is modification of classical problem of brachistichrone. Analytical dependence which characterizes kinematics of “ideal” rocket in the vertical plane movement has been obtained; peculiarities of the basic time way have been discovered.

Manufacturing facilities are subjected to many uncertainties such as variability in demand, queuing delays, variable task times, rejects and machine breakdown. These volatilities have a large impact on leap time, inventory cost and delivery performance of a manufacturing unit. To operate efficiently the manufacturing facilities should adapt to these variations. The paper explores the way uncertainties are addressed in designing of flexible optimal layout. Such facility layout problem is known as stochastic dynamic facility layout problem (SDFLP). SDFLP is an NP-hard combinatorial optimization problem, which means the time taken to solve increases exponentially with problem size. To solve SDFLP, the paper presents an adaptation of simulated annealing (SA) meta-heuristic. Various SA cooling schemas are discussed, computed and evaluated for generating the optimal flexible layout. An optimal layout is one that minimises the distance travelled by materials taking into account uncertain product demand (material handling cost). A computer-based tool was developed and analysis was conducted on small to large size problem set. The results showed that SA with exponential cooling schedule provides better solution in terms of layout efficiency and gave better solution as compared to literature.

Positioning as the beginning place of new lessons, the One Assignment per Lesson is not only a teaching link, but also an assignment or a group of assignments. It has two basic functions: The first function is to fully expose and solve problems. Problems found on the last course can be shown by the form of exercises (exercise set). Students exposed problems, and then these problems can be analyzed through the communication between students and teachers. The sticking problems made from all aspects of analysis will be solved completely and implemented into the following teaching. The second is to guide the teaching of new lessons with discipline thoughts and scientific principles. Find out the principles and rules from the knowledge of new lessons, and then design problems (problem set) or exercises (exercise set) by the principles and same knowledge that students have mastered. In doing that, it will inspire student to open mind, reason evidences, extract principles, form opinions, compose model, form model awareness, design experiment, practice innovation, study scientifically and form quality. Students can make a self-regulated learning, cooperation and communication, and pursuit and respect science on the platform of One Assignment per Lesson, and they will further form true value concepts, essential quality and key ability, achieving the education goal of teaching knowledge and cultivating people.

Minimizing makespan in permutation flowshops is one of the most frequently investigated problems in scheduling theory. The NEH heuristic is commonly regarded as the best current constructive heuristic for solving this NP-hard problem. In this paper, we propose a novel constructive heuristic with an effective tie-breaking strategy to improve the scheduling quality of this problem. Experimental results reveal that the proposed approach outperforms NEH for all problem sizes on the standard benchmark problem set of Taillard. This study thus appears to successfully demonstrate a high-performing constructive heuristic that can serve as a new foundation for future research on this extremely challenging scheduling problem.

Antenna selection (AS) is poised to provide solutions to hardware complexity issues while guaranteeing untappable information flow. We investigate the AS problem set up in the context of linear precoder design for secrecy rate maximization of a multiple-input single-output system. The original problem is both non-tractable and nonconvex. We propose a provably convergent iterative procedure that sequentially solves a series of convex programs. The proposed algorithm is then refined to yield a precoder of desired sparsity. Finally, numerical experiments are performed to study the execution of AS algorithm from various different aspects.

The distributed permutation flowshop scheduling problem (DPFSP) is a newly proposed topic in the shop scheduling field, which has important application in globalised and multi-plant environments. This study presents a modified iterated greedy (MIG) algorithm for this problem to minimise the maximum completion time among all the factories. Compared with previous approaches, the proposed algorithm is simpler yet more effective, more efficient, and more robust in solving the DPFSP. To validate the performance of the proposed MIG algorithm, computational experiments and comparisons are conducted on an extended benchmark problem set of Taillard. Despite its simplicity, the computational results show that the proposed MIG algorithm outperforms all existing algorithms, and the best-known solutions for almost half of instances are updated. This study can be offered as a contribution to the growing body of work on both theoretically and practically useful approaches to the DPFSP.

The blocking flow shop scheduling problem has found many applications in manufacturing systems. There are a few exact methods for solving this problem with different criteria. In this paper, efforts will be made to optimize the total completion time criterion for this problem. We present two mixed binary integer programming models, one of which is based on the departure times of jobs from machines, and the other is based on the idle and blocking times of jobs. An initial upper bound generator and some lower bounds and dominance rules are also developed to be used in a branch and bound algorithm. The algorithm solves 17 instances of the Taillard's benchmark problem set in less than 20min.

This paper presents a hybrid discrete differential evolution HDDE algorithm to solve the Pm// problem. The coding and decoding scheme based on job permutation is applied in the algorithm. The algorithm employs several efficient heuristics in the initialisation phase and incorporates an insertion-based local search. To validate the proposed algorithm, computational experiments are conducted on a benchmark problem set from the literature. Computational results show that the HDDE algorithm outperforms the existing state-of-the-art algorithms for the Pm// problem.

This study investigated the promotion of children's understanding and acquisition of arithmetic concepts and the effects of inhibitory skills. Children in Grades 3, 4, and 5 solved two sets of three-term addition and subtraction problems (e.g., 3 + 24 − 24, 3 + 24 − 22) and completed an inhibition task. Half of the participants received a demonstration of conceptually-based shortcuts between problem sets. All participants increased their use of the inversion shortcut (stating that the answer to a problem of the form a + b − b was the first number without any calculations) across a problem set but only the participants who received the demonstration increased their associativity shortcut use (subtracting first and then adding on a problem of the form a + b − c ), particularly if they evaluated the shortcut as being better than a traditional left-to-right computational algorithm (adding then subtracting). Four clusters of participants using varying degrees of conceptually-based shortcuts were identified. Participants with weak inhibition skills were more likely to use a strategy that mixed conceptual knowledge and a computational algorithm suggesting that although they had conceptual knowledge, they had difficulties inhibiting a well-learned computational algorithm.

During the last decade, an exponential growth of web services is observed over the Internet. This offers a big challenge for the web service based systems to make the optimal selection of the desired web service. In this work, we have used a two layer architecture for web service selection, prefiltering followed by selection. The use of K-Means clustering technique for grouping the web services with similar Quality of Service (QoS) under a common umbrella is explored. This act as prefiltering step for candidate web services to filter out unrelated web services. From the set of filtered web services, a non-dominated set of web services is obtained using skyline technique. The first step ensures to include only those web services, which are related based on QoS information. The second step operates on the reduced problem set and identifies the best web service among the group. The real world web service dataset is used to test the approach and an improvement in the web service selection is observed.

Plagiarism is a common concern for coursework in many situations, particularly where electronic solutions can be provided e.g. computer programs, and leads to unreliability of assessment. Written exams are often used to try to deal with this, and to increase reliability, but at the expense of validity. One solution, outlined in this paper, is to randomise the work that is set for students so that it is very unlikely that any two students will be working on exactly the same problem set. This also helps to address the issue of students trying to outsource their work by paying external people to complete their assignments for them. We examine the effectiveness of this approach and others (including blended assessment) by analysing the spread of similarity scores across four different introductory programming assignments to find the natural similarity i.e. the level of similarity that could reasonably occur without plagiarism. The results of the study indicate that divergent assessment (having more than one possible solution) as opposed to convergent assessment (only one solution) is the dominant factor in natural similarity. A key area for further work is to apply the analysis to a larger sample of programming assignments to better understand the impact of different features of the assignment design on natural similarity and hence the detection of plagiarism.

In our days, “Electric Power System Kit” is the only producer of power and secondary equipment scale models. The production laboratory is established on the basis of «Automated Power Systems» department, Ural Power Engineering Institute, UrFU. In the laboratory, the following processes are organized: designing, 3D printing of models and 3D laser scanning of power equipment installed at substations. Laser scanning of power facilities allows to achieve the priority goal, namely to obtain three-dimensional primary digital CAD-models and drawings of high accuracy, which are necessary in order to meet the challenges of designing, construction, quality control, diagnostics, and other engineering tasks. Applied to electric power system kit, laser scanning is used to improve the quality of basic data needed for the creation of 3D drawings of equipment and creation of printed models on the basis of CAD-scale models. At the present moment, the problem set is to accomplish the reverse engineering of 110/20 kV step-down transformer, using up-to-date methods of 3D laser scanning. On the basis of the data obtained, the transformer CAD-model of high accuracy is crested in accordance with the initial dimensions.

In a multi-criteria system, it is often required to optimize the decision according to the given problem set. In this paper, a reversible decision support system has been utilized in a human-machine system to select the best decision among the selected alternatives. A reversible decision support system is used to interchange the alternatives between inputs and outputs. It also examines the dissonance level while taking decisions and ranked alternatives according to the attributes and sub-attributes of each criterion. These decisions have been classified into two segments such as reversible and irreversible. This paper highlights the reversible aspects of the alternatives and how it can be performed better with minimum dissonance. Fuzzy analytic hierarchy process has been applied especially triangular membership function to evaluate interim judgments. Triangular fuzzy number is implemented to form the perception of alternatives with different weights for each factor and sub-factor. Moreover, this study reveals that cognitive dissonance can be reduced in a reversible environment to resolve multi-constraint. Finally, performance of the proposed reversible decision support system has been analysed through an experiment to demonstrate its merits and capabilities. The result shows that the final decision has less dissonance level and more satisfaction when users get the chance to reverse the decision.

Learning classifier systems (LCSs) originated from artificial cognitive systems research, but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extract building blocks of knowledge in order to solve more difficult problems in the same or a related domain. The past work showed that the reuse of knowledge through the adoption of code fragments, GP-like sub-trees, into the XCS learning classifier system framework could provide advances in scaling. However, unless the pattern underlying the complete domain can be described by the selected LCS representation of the problem, a limit of scaling will eventually be reached. This is due to LCSs' 'divide and conquer' approach utilizing rule-based solutions, which entails an increasing number of rules (subclauses) to describe a problem as it scales. Inspired by human problem solving abilities, the novel work in this paper seeks to reuse learned knowledge and learned functionality to scale to complex problems by transferring them from simpler problems. Progress is demonstrated on the benchmark Multiplexer (Mux) domain, albeit the developed approach is applicable to other scalable domains. The fundamental axioms necessary for learning are proposed. The methods for transfer learning in LCSs are developed. Also, learning is recast as a decomposition into a series of sub-problems. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, it is possible to learn a general solution to any n-bit Mux problem for the first time. This is verified by tests on the 264, 521 and 1034 bit Mux problems.

Selecting a proper set of test problems is essential for fair performance comparison of evolutionary multi-objective optimization (EMO) algorithms. This is because the comparison results strongly depend on the choice of test problems. Test problems are also very important for examining the behavior of each algorithm. In general, it is advisable to prepare a set of various test problems including both easy and difficult ones for each algorithm. Our idea is to use a meta-optimization technique for generating such a set of test problems. More specifically, we use a two-level meta-optimization model. In the upper level, test problems are optimized. That is, test problems are handled as solutions. In the lower level, each test problem is evaluated using multiple EMO algorithms. The point of our idea is high flexibility in the definition of an objective function in the upper level. For example, when we want to design a difficult test problem only for a particular EMO algorithm, the minimization of its relative performance can be used as an objective function. By maximizing its relative performance, we can also design an easy test problem only for that algorithm. By generating both easy and difficult problems for each algorithm in this manner, we can prepare an appropriate test problem set for fair performance comparison. Through computational experiments, we demonstrate that we can generate a wide variety of test problems, each of which is difficult for a different type of EMO algorithms.

This book covers the material of a one year course in real analysis. It includes an original axiomatic approach to Lebesgue integration which the authors have found to be effective in the classroom. Each chapter contains numerous examples and an extensive problem set which expands considerably the breadth of the material covered in the text. Hints are included for some of the more difficult problems

It has become increasingly clear that the current design paradigm for mobile broadband systems is not a scalable and economically feasible way to solve the expected future 'capacity crunch', in particular in indoor locations with large user densities. 'Moore's law', e.g. state-of-the art signal processing and advanced antenna techniques now being researched, as well as more millimeter wave spectrum indeed provide more capacity, but are not the answer to the 3-4 orders of magnitude more capacity at today's cost, that is needed.We argue that solving the engineering problem of providing high data rates alone is not sufficient. Instead we need to solve the techno-economic problem to find both business models and scalable technical solutions that provide extreme area capacity for a given cost and energy consumption. In this paper we will show that achieving very high capacities is indeed feasible in indoor environments., Comment: Submitted to IEEE Communications Magazine

Extending the application of intelligent tutoring beyond the desktop and into the physical world is a sought after capability. If implemented correctly, Artificial Intelligence AI tools and methods can be applied to support personalized and adaptive on-the-job training experiences as well as assist in the development of knowledge, skills and abilities KSAs across athletics and psychomotor domain spaces. While intelligent tutoring in a physical world is not a traditional application of such technologies, it still operates in much the same fashion as all Intelligent Tutoring Systems ITS in existence. It takes raw system interaction data and applies modeling techniques to infer performance and competency while a learner executes tasks within a scenario or defined problem set. While a traditional ITS observes learner interaction and performance to infer cognitive understanding of a concept and procedure, a physical ITS will observe interaction and performance to infer additional components of behavioral understanding and technique. A question the authors address in this paper is how physical interactions can be captured in an ITS friendly format and what technologies currently exist to monitor learner physiological signals and free-form behaviors? Answering the question involves a breakdown of the current state-of-the-art across technologies spanning wearable sensors, computer vision, and motion tracking that can be applied to model physical world components. The breakdown will include the pros and cons of each technology, an example of a domain model the data provided can inform, and the implications the derived models have on pedagogical decisions for coaching and reflection.

It has been shown that an important trend of psychometric testing cognitive abilities is evolving:- from the solution of problems with a closed beginning and closed end in conditions of maximum regulation of the participants’ activities (in intelligence tests);- toward greater freedom in choosing solutions while it is still necessary to solve the problem set from without (a problem with a closed beginning but an open end in creativity tests);- and toward situations that allow maximum freedom compared to preceding types of tests: problem situations of independent exploration and goal-setting with an open beginning and an open end.The “triangle” of problems related to intelligence, creativity and exploration has been introduced.Movement toward greater freedom of cognitive activity for participants means movement toward greater complexity and creativity for the persons designing and administering the test as well. This may throw up problems, contradictions and paradoxes as attempts are made to keep the balance between the freedom of unique and complicated cognitive activity which becomes the object of study and its assessment in the framework of standard procedures.

Models of learning in EDM and LAK are pushing the boundaries of what can be measured from large quantities of historical data. When controlled randomization is present in the learning platform, such as randomized ordering of problems within a problem set, natural quasi-randomized controlled studies can be conducted, post-hoc. Difficulty and learning gain attribution are among factors of interest that can be studied with secondary analyses under these conditions. However, much of the content that we might like to evaluate for learning value is not administered as a random stimulus to students but instead is being self-selected, such as a student choosing to seek help in the discussion forums, wiki pages, or other pedagogically relevant material in online courseware. Help seekers, by virtue of their motivation to seek help, tend to be the ones who have the least knowledge. When presented with a cohort of students with a bi-modal or uniform knowledge distribution, this can present problems with model interpretability when a single point estimation is used to represent cohort prior knowledge. Since resource access is indicative of a low knowledge student, a model can tend towards attributing the resources with low or negative learning gain in order to better explain performance given the higher average prior point estimate. In this paper we present several individualized prior strategies and demonstrate how learning efficacy attribution validity and prediction accuracy improve as a result. Level of education attained, relative past assessment performance, and the prior per student cold start heuristic were employed and compared as prior knowledge individualization strategies.

Students who successfully engage in self-regulated learning, are able to plan their own studying, monitoring their progress and make any necessary adjustments based upon the data and feedback they gather. In order to promote this type of independent learning, a recent introductory mechanics course was modified such that the homework and tests emphasized the planning, monitoring and adjusting of self-regulated learning. Students were able to choose many of their own out-of-class learning activities. Rather than collecting daily or weekly problem set solutions, assignments were mostly progress reports where students reported which activities they had attempted, self-assessment of their progress and plans for their next study session. Tests included wrappers where students were asked to reflect on their mistakes and plans for improvement. While many students only engaged superficially the independent aspects of the course, some did demonstrate evidence of self-regulation. Despite this lack of engagement, students performed as well as comparable student populations on course exam and better on the Force Concept Inventory.

In presenting the final chapter for this Research into Mathematics Education in Australasia (RiMEA) book, I first give consideration to the official curriculum and the operational curriculum as a basis for exploring how we might advance mathematics education research within our Science, Technology, Engineering and Mathematics (STEM) environment. Next, I present an overview of some of the core features of the current national and international spotlight on STEM education. From this basis, I argue that the roles and positioning of mathematics are in danger of being overlooked or diminished within the increased STEM framework. As one approach to lifting the profile of mathematics, I explore problem-solving and modelling across STEM contexts. In utilising findings from the chapter reviews together with my own research, I offer suggestions for (a) developing content and processes through idea-generating problems, (b) promoting in-depth content understanding, and (c) fostering general skills and processes . Next, I address the advancement of modelling across STEM contexts and illustrate this with a problem set within an environmental engineering context. I conclude by offering a few avenues for further research.

In this paper, we studied the problem of maximum frequent time-window selection (MFTWS) that appears in the process of discovering frequent itemset with time-windows (FITW). We formulated this problem as an integer programming mathematical model that is a typical combination problem with a solution space exponentially related to the problem size. A variable neighborhood search algorithm has been developed to solve the problem with near-optimal solutions. Computational experiments have been carried out to test the performances of the VNS algorithm against benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering quite many FITWs with larger time-coverage rate than lower bounds, and lay a base for future's further studies on this problem.

In this paper, two evolutionary heuristics are proposed to determine the cutting parameters in multipass turning operations. Optimal selection of cutting speed, feed rate, and depth of cut is important in machining operations due to significant influence of these parameters on machining quality and machining economics. This paper is about optimising these three parameters using heuristic methods. The first heuristic implemented in the paper is a simple Particle Swarm Optimisation (PSO) algorithm, and the second heuristic implemented is a hybrid of Genetic Algorithm and Artificial Immune System (GA-AIS). A multipass turning operation with rough machining and then finish machining is considered in this paper. The objective function considered is minimisation of unit production cost that optimises the machining parameters. The performance of the PSO and GA-AIS are evaluated by comparing with the heuristics reported in the literature using a problem set used by other researchers for this problem. The results ...

The need for efficient and effective optimization problem solving methods arouses nowadays the design and development of new heuristic algorithms. This paper present ideas that leads to a novel multiagent metaheuristic technique based on creative social systems suported on music composition concepts. This technique, called “Musical Composition Method” (MMC), which was proposed in Mora-Gutierrez et al. (Artif Intell Rev 2012) as well as a variant, are presented in this study. The performance of MMC is evaluated and analyzed over forty instances drawn from twenty-two benchmark global optimization problems. The solutions obtained by the MMC algorithm were compared with those of various versions of particle swarm optimizer and harmony search on the same problem set. The experimental results demonstrate that MMC significantly improves the global performances of the other tested metaheuristics on this set of multimodal functions.

Dynamic optimization of chemical processes can be carried out with evolutionary algorithms that involve many parameters. These parameters need to be given appropriate values for the algorithms to perform efficiently. This paper proposes parameter setting methods based on factorial experimentation and fuzzy logic, aimed at balancing convergence speed, robustness (consistent performance for each problem) and versatility (applicability to many different problems). The methods were tested on an existing dynamic optimisation method with at least nine tuneable parameters. The test problem set turned out to be quite demanding due to one particular problem behaving in opposite direction to the rest with respect to the most influential factor, population size. It is probable that no single tuning would be possible that will satisfy all problems. However, for the other problems, the Fuzzy Logic tuning method proposed in this paper proves to be a very promising approach.

The purpose of the study was to examine the role of problem sets on student learning and satisfaction in university microeconomics. 126 students participated in the study in consecutive years. Independent samples t-test shows that students who were not given answer keys outperformed students who were given answer keys. Multiple regression analysis demonstrates that, along with high school percentiles and student major, problem set with or without answer key significantly explains student learning in economics. The authors discuss the role of answer keys and their implications for teaching university economics courses.

In clinical and experimental settings, planning ability is typically assessed using the Tower of London (ToL) or one of its variants. For enhancing the comparability across studies, a common ToL problem set was recently suggested comprising a collection of 4- to 7-move problems. Based on previous theoretical and empirical analyses of problem space and task structure, development of the problem set accounted particularly for the influence of structural problem parameters on the detection of individual differences in planning ability. To assess its adequacy as a clinical and research instrument, the present study evaluated the psychometric properties of the suggested problem set. Results showed a clear and nearly perfect linear increase of task difficulty across minimum moves. Given a broad range of item difficulty, high- and low-achieving subjects could be well discriminated. The test scores' split-half reliability (r = .72) and internal consistency (α = .69) were satisfactory. Taken together, the ToL problem set evaluated here proved to have good psychometric qualities and constitutes a conceptually sound basis for diagnostic and research purposes.

The fields of science have undergone dramatic reorganizations as they have come to terms with the realities of the growing complexities of their problem set, the costs, and the breadth of skills needed to make major progress. A field such as particle physics transformed from principal investigator-driven research supported by an electron synchrotron in the basement of your physics building in the 1950s, to regional centers when costs became prohibitive to refresh technology everywhere, driving larger teams of scientists to cooperate in the 1970s, to international centers where multinational teams work together to achieve progress. The 2013 Nobel Prize winning discovery of the Higgs boson would have been unlikely without such team science.[1] Other fields such as the computational sciences are well on their way through such a transformation. Today, we see precision medicine as a field that will need to come to terms with new organizational principles in order to make major progress, including everyone from individual medical researchers to pharma. Interestingly, the Cancer Moonshot has helped move thinking in that direction for part of the community and now the initiative has been transformed into law.[2]

The last proposition of Diophantus’ De polygonis numeris , inquiring the number of ways that a number can be polygonal and apparently aiming at “simplifying” the definitory relation established by Diophantus himself, is incomplete. Past completions of this proposition are reported in detail and discussed, and a new route to a “simplified” relation is proposed, simpler, more transparent and more “Greek looking” than the others. The issue of the application of such a simplified relation to solving the problem set out by Diophantus is also discussed in full detail.

This work presents an iterative-deepening A? (IDA?) based approach to the traveling tournament problem (TTP). The TTP is a combinatorial optimization problem which abstracts the Major League Baseball schedule. IDA? is able to find optimal solutions to this problem, with performance improvements coming from the incorporation of various past concepts including disjoint pattern databases, symmetry breaking, and parallelization along with new ideas of subtree skipping, forced deepening, and elite paths to help to reduce the search space. The results of this work show that an IDA? based approach can find known optimal solutions to most TTP instances much faster than past approaches. More importantly, it has been able to optimally solve two larger instances that have been unsolved since the problem's introduction in 2001. In addition, a new problem set called GALAXY is introduced, using a 3D space to create a challenging problem set.

This paper discusses the damage identification using artificial neural network methods for the benchmark problem set up by IASC-ASCE Task Group on Health Monitoring. A three-stage damage identification strategy for building structures is proposed. The BP network and PNN are employed for damage localization and BP network for damage extent identification. Four damage patterns (patterns i~iv) in Cases 1-6 are discussed. The comparison between BP network and PNN are carried out. The results show that PNN performs better than BP network in damage localization. The damage extent identification using BPN is successful even in Cases 2 and 5&6 in which the modeling error is quite large.