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The vehicle routing problem with time windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of routes to deliver goods, using a fleet of identical vehicles with restricted capacity, to customers with service time windows. However, there are other objectives, and having a range of solutions representing the trade-offs between objectives is crucial for many applications. Although previous research has used evolutionary methods for solving this problem, it has rarely concentrated on the optimization of more than one objective, and hardly ever explicitly considered the diversity of solutions. This paper proposes and analyzes a novel multi-objective evolutionary algorithm, which incorporates methods for measuring the similarity of solutions, to solve the multi-objective problem. The algorithm is applied to a standard benchmark problem set, showing that when the similarity measure is used appropriately, the diversity and quality of solutions is higher than when it is not used, and the algorithm achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-objective optimizer.

Science, Technology, Engineering, and Mathematics (STEM) education has become a key focus in the U.S. government's public education agenda. Many STEM degrees require the successful completion of undergraduate introductory organic chemistry (O-Chem), which is notorious for its difficulty and high attrition rate. Concept Maps (CM) have been used as a tool to improve teaching and learning by providing feedback to teacher and students. Although numerous studies have examined the use of concept maps (CMs) as an assessment tool in science classes, none to date has examined such applications of CMs in O-Chem. Furthermore, studies investigating the validity of CMs in post-secondary science courses are rare. Thus, the present study investigated the validity of CMs as an assessment tool and their diagnostic uses in O-Chem by examining the relationship between CM scores and other key performance measures in O-Chem. Results indicated that CM scores were significantly correlated with problem set scores and final course grade. In addition, a mediation analysis revealed that problem solving scores partially mediated the relationship between problem solving and final course grade, confirming the role CMs are expected to play in O-Chem achievement. Implications for using CMs as diagnostic and formative tools in instruction are discussed.

Problem definition studies focus on the ways in which problems are characterized in the political domain, and how they can be used strategically to limit or expand policy participation. Nanotechnology entrepreneurs are vying for resources in the political domain while strategically linking their nano-solution to multiple and ambiguous problems. This article considers the evolution of nanotechnology as a solution, and the linked problems from a problem definition perspective. We consider how nanotechnology has been defined over time, in the scientific community and in the media, through development of a database of problem and solution definitional change. We find that, over time, advocates have defined the solution from a more narrow perspective while maintaining the overall ambiguity of the problem set. We suggest that the problem definition perspective is a viable framework for understanding the fluid and complex dynamics of science and technology issues and offer several suggestions for further research.

Imagine a typical student taking an average set of courses. She has to complete a laboratory write-up for chemistry, write a research paper for linguistics, finish a problem set for mathematics, cr...

The purpose of this dedication is to overcome the problems at SMP Al Inayah and Islamic Junior High School PGRI Tutur. The problem in both schools is the lack of learning media in the form of props or other innovative media. Students learn math more through direct explanation by teachers who are dominated by giving lectures. Considering the students' lack of understanding in the set matter it is necessary to hold a manipulative media training activity at SMP Al Inayah Purwosari and Islamic Junior High School PGRI Tutur on the set material. Students in this area desperately need a refresher related to instructional design, in order to develop their conceptual comprehension skills which leads to improving students' mathematics learning outcomes on the subject matter using Venn Smart Diagram media. Benefits of this devotion is the benefits gained among students obtaining learning with manipulative media that are rarely implemented so that students' understanding of the material set increases. Increased understanding of concepts and student learning outcomes in the set material that is marked with 90% of junior high school students Al Inayah and 88% of students of Islamic Junior High School PGRI Tutur can solve the problem set well.

This paper connects two independent fields of research - system-of-systems (SoS) and P-systems. P-systems, originally inspired by basic features of biological membranes, where objects are placed into compartments defined by a membrane structure, and evolve according to reaction rules. Systems that belong to a particular level in the hierarchy of a SoS are can be understood to be within a membrane of certain depth. Also, objects in P-systems evolve in a maximally parallel, non-deterministic manner, a preferred method of evolution for many intractable SoS problems. Transitions of individual systems to other systems, or to alternative configurations can be easily realised using the format of P-systems. By casting typical SoS problems into P-system representations, we demonstrate the ease of problem set up, and the possibility of solving commonly intractable SoS modelling and simulation problems in feasible time. An example problem involving communication between aircraft is studied to illustrate the applicability of P-systems theory to SoS.

The purpose of the study is to explore the anxiety and stress effects on usage of social media (SM) among library professionals in India. The study explores that most users preferred the SM to gather information, including demographic information, frequency of login, time spent, friends/followers usage, location, devices, etc. Most popular SM tools have stress effects if constant or often connected to tools. The study also elaborates vital issues such as SM affect constant checkers and non-constant checkers, collaborative along with SM worries, facing difficulty and problem set with using SM. Finally, most respondents may be having addiction of SM, and prevent of anxiety and stress on usage of SM technology management strategies and marks the level of anxiety and stress.

Homogenization or density-based topology optimization methods work by distributing a fixed amount of material to the most effective areas of the design domain so as to create an optimal structural configuration that meets the minimum compliance criteria. These topology optimization methods generally cannot control the maximum stress levels of the structure; therefore, the smoothened optimum structure is not guaranteed to be ready for immediate use. This can be because it is either unsafe if the maximum stress at this structure exceeds the strength limit, or over designed if the maximum stress is far below the stress limit. Difficult and complex shape optimization must then be done to obtain a minimum-weight structure that meets the maximum stress constraint. This paper proposes an adaptive volume constraint (AVC) algorithm, a heuristic approach, in place of traditional topology optimization methods so that the maximum stress in the optimal structural configuration will be below the predefined stress limit and the smoothened structure will be directly applicable. In order to test the applicability and robustness of the AVC algorithm, topology optimization using both a traditional fixed volume constraint and an AVC are tested on a number of configuration design problems. To further illustrate the usefulness of the AVC algorithm, shape optimizations at the maximum stress constraint are also conducted on the smooth structural models by both optimization approaches on an identical problem set.

During product platform life cycle, innovation problem identification and decision-making are regarded as vital issues in product platform evolution process. The Comprehensive Disturbance Degree is proposed and analyzed considering customer demand, technology status and production capacities, then existing problems of product platform are identified. According to Innovation Problem Selecting Principles, the innovation problem set Q is defined. The value of modules in product platform is calculated using Value Engineering, the module set M needed to be improved is determined. Then based upon the correlation degree analysis of the innovation problem set and the module set, Fuzzy Clustering Algorithms is advanced to classify innovation problems. Finally, a case study is given to illustrate the validity of the methodology.

This study considers the scheduling problem of multistage hybrid flowshops with multiprocessor tasks, which is a core topic for numerous industrial applications. An effective and efficient heuristic, namely the heuristic of multistage hybrid flowshops (HMHF) is proposed to solve this problem. To verify the developed heuristic, computational experiments are conducted on a well-known benchmark problem set. The results are compared with 10 constructive heuristics and a tabu search (TS) based meta-heuristic from the relevant literature. These computational results show that the proposed HMHF heuristic is highly effective when compared to these algorithms for this problem on the same benchmark instances.

This paper focuses on the development of a neural network (NN) scheduler for scheduling job-shops. In this hybrid intelligent system, genetic algorithms (GA) are used to generate optimal schedules to a known benchmark problem. In each optimal solution, every individually scheduled operation of a job is treated as a decision which contains knowledge. Each decision is modeled as a function of a set of job characteristics (e.g., processing time), which are divided into classes using domain knowledge from common dispatching rules (e.g., shortest processing time). A NN is used to capture the predictive knowledge regarding the assignment of operation’s position in a sequence. The trained NN could successfully replicate the performance of the GA on the benchmark problem. The developed NN scheduler was then tested against the GA, Attribute-Oriented Induction data mining methodology and common dispatching rules on a test set of randomly generated problems. The better performance of the NN scheduler on the test problem set compared to other methods proves the feasibility of NN-based scheduling. The scalability of the NN scheduler on larger problem sizes was also found to be satisfactory in replicating the performance of the GA.

Negative perceptions of political risk can prevent capital from being committed to support cross‐border investment. Information about risks that impact infra‐structure projects is often vague, imprecise, subjective, or ambiguous. Political risks in developing countries also often lack meaningful historical and numerical data. A novel fuzzy set approach for quantifying qualitative information on risks (QQIR) in structured finance transactions that bridges the gap between qualitative and quantitative risk assessment methods has been developed. The QQIR Method is validated empirically through the results of an international survey to determine the impact of perceived political risk on Asian infrastructure projects. The impact is measured by the effect on financial project criteria. The impact was assessed across 14 Asian countries and 14 infrastructure sectors. The survey findings are validated by triangulation of three data sets and employing non‐parametric statistics. The validation shows that in 77.5% of all observations the QQIR Method produces mean results that are within 0.85 standard deviations of the absolute values, without elimination of any seemingly unusual or unreasonable responses or data. The validation also shows that with increasing perceived risks, the costs of equity investment, debt finance, and insurance also increase. The QQIR Method is thus a valid tool to quantify perceptions on risks. In this case it has been applied to political risks, but the Method is generic and may be applied to any problem set in which perceptions can be structured and assessed with opinions.

Many optimization problems from the industrial engineering world, in particular manufacturing systems, are very complex in nature and are quite hard to solve by conventional optimization techniques. There has been increasing interest to apply metaheuristic methods to solve such kinds of hard optimization problems. In this work, a novel metaheuristic approach called scatter search (SS) is applied for the n/m/P/C max problem, an NP-hard sequencing problem, which is used to find a processing order of n different jobs to be processed on m machines in the same sequence with minimizing the makespan. SS contrasts with other evolutionary procedures by providing a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions and they exploit the adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. In this paper, various metaheuristic methods and best heuristics from the literature are used for solving the well-known benchmark problem set of Taillard (Eur J Oper Res 64:278–285, 1993). The results available for the various existing metaheuristic and heuristic methods are compared with the results obtained by the SS method. The proposed framework achieves better results for 4 of 12 benchmark problems and also achieves an average deviation of 1.003% from the benchmark problem set of Taillard (Eur J Oper Res 64:278–285, 1993). The computational results show that SS is a more effective metaheuristic for the n/m/P/C max problem.

Flowshop scheduling problems have received considerable research attention over the last five decades. This study proposes an iterated greedy heuristic for non-permutation flowshop scheduling problems. To validate and verify the proposed heuristic, computational experiments have been conducted on a well-known benchmark problem set, and the results are compared with 20 simple constructive heuristics and a state-of-the-art meta-heuristic performed on the same benchmark instances. In terms of both solution quality and computational expense, this study successfully develops an efficient and effective approach for non-permutation flowshop scheduling problems.

The emergence of metamodels as approximate objective function representations offers the ability to ‘design’ metamodels with favourable optimization characteristics without compromising the accurate representational capabilities of arbitrary function topologies and modalities. With non-uniform rational B-splines (NURBs) as a metamodel basis, favourable optimization properties can be obtained which allow the intelligent selection of starting points for multistart optimization algorithms and which constrain optimization searches to metamodel regions containing the global metamodel optimum. In this article NURBs-based metamodels are used to define an optimization algorithm (HyPerOp) which guarantees the discovery of the global metamodel optimum with known computational effort. Emphasis is placed on demonstrating how NURBs’ properties contribute to a favourable objective function approximation. Through a large non-linear optimization trial problem set, the claim that HyPerOp is guaranteed to find the global m...

Hyper-heuristics seek solution methods instead of solutions and thus provides a higher level of generality compared to bespoke metaheuristics and traditional heuristic approaches. In this paper, a hyper-heuristic is proposed to solve the ski-lodge problem which involves allocating shared-time apartments to customers during a skiing season in a way that achieves a certain objective while respecting the constraints of the problem. Prior approaches to the problem include simulated annealing and genetic algorithm. To the best of our knowledge, this is the first time the ski-lodge problem is approached from a hyper-heuristic perspective. Although the aim of hyper-heuristics is to provide good results over problem sets rather than producing best results for certain problem instances, for completeness and to get an idea of the quality of solutions, the results of the proposed hyper-heuristic are compared to that of genetic algorithm and simulated annealing. The hyper-heuristic was found to perform better than simulated annealing and comparatively to the genetic algorithm, producing better results for some of the instances. Furthermore, the hyper-heuristic has better overall performance over the problem set being considered.

Most homework sets in statistics courses are constructed so that students concentrate or “mass” their practice on a certain topic in one problem set. Distributed practice homework sets include review problems in each set so that practice on a topic is distributed across problem sets. There is a body of research that points to the efficacy of distributed practice for developing a variety of skills from word recall to surgical techniques. A trial was conducted in several sections of a business statistics course where students were randomly assigned to either have massed practice homework sets or distributed practice homework sets. The two groups were then compared on the course assessments. The results show some evidence for the efficacy of distributed practice homework sets, although this effect may be modified significantly by the instructor or by a Hawthorne effect.

The bilevel optimisation problem (BLP) is a subclass of optimisation problems in which one of the constraints of an optimisation problem is another optimisation problem. BLP is widely used to model hierarchical decision making where the leader and the follower correspond to the upper level and lower level optimisation problem, respectively. In BLP, the optimal solutions to the lower level optimisation problem are the feasible solutions to the upper level problem, which makes it particularly difficult to solve. This paper proposes a novel hardware architecture known as Recursive Pipelined Genetic Propagation (RPGP), to solve BLP efficiently on FPGA. RPGP features a graph of genetic operation nodes which can be scaled to exploit hardware resources. In addition, the topology of the RPGP graph can be changed at run-time to escape from local optima. We evaluate the proposed architecture on an Altera Stratix-V FPGA, using a benchmark bilevel optimisation problem set. Our experimental results show that RPGP can achieve a significant speed-up against previous work.

2D Bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem. Multiobjective versions of this well-known industrial engineering problem can occur frequently in real world application. Recently, Hybrid Evolutionary Algorithms have appear as a new area of research with their ability to combine alternative heuristics and local search mechanisms together for higher quality solutions. In this study, we propose a set of novel multiobjective hybrid genetic and memetic algorithms that make use of the state-of-the-art metaheuristics and local search techniques for minimizing the number of bins while also maintaining the load balance. We analyze the optimization time and the resulting solution quality of the proposed algorithms on an offline 2DBPP benchmark problem set with 500 instances. Using these results of exhaustive experiments, we conclude that the proposed hybrid algorithms are robust with their ability to obtain a high percentage of the optimal solutions.

Learning analytics (LA) has invested much effort in the investigation of students' behavior and performance within learning systems. This paper expands the influence of LA to students' behavior outside of learning systems and describes a novel machine learning model which automatically detects students' off-task behavior as students interact with a learning system, ASSISTments, based solely on log file data. We first operationalize social cognitive theory to introduce two new variables, affect states and problem set, both of which can be automatically derived from the logs, and can be considered to have a major influence on students' behavior. These two variables further work as the feature vector data for a K-means clustering algorithm in order to quantify students' different behavioral characteristics. This quantified variable representing student behavior type expands the feature space and contributes to the improvement of the various model performance compared with only time- and performance-related features. In addition, an advanced Hidden Naive Bayes (HNB) algorithm is coded for off-task behavior detection and to show the best performance compared with traditional modeling techniques. Implications of the study are then discussed.

Eye tracking allows us to identify visual strategies through gaze behavior, which can help us understand how students process content. Furthermore, understanding which visual strategies are successful can help us improve educational materials that foster successful use of these visual strategies. Previous studies have demonstrated the predictive value of eye tracking for student performance. Chemistry is a highly visual domain, making it particularly appropriate to study visual strategies. Eye tracking also provides measures of pupil dilation that correlate with cognitive processes important to learning, but have not yet been assessed in any realistic learning environments. We examined the gaze behavior and pupil dilation of undergraduate students working with a specialized ITS for chemistry: Chem Tutor. Chem Tutor emphasizes visual learning by focusing specifically on graphical representations. We assessed the value of over 40 high-level gaze features along with measures of pupil diameter to predict student performance and learning gains across an entire chemistry problem set. We found that certain gaze features are strong predictors of performance, but less so of learning gains, while pupil diameter is marginally predictive of learning gains, but not performance. Further studies that assess pupil dilation with higher temporal precision will be necessary to draw conclusions about the limits of its predictive power.

This paper describes the proposed software product, an application tool for teaching preparation, called Widya Pustaka. It includes planning the teaching activity and composing the teaching materials into a package ready to deliver to the students in class. The lesson plan will be built using a standardized template. It includes the topic covered, the objectives, the teaching scenarios, and the problem set. According to the newest curriculum, the lesson plan should cover several subjects, such as Math, Science, etc., that form an integrated scenario. The teaching scenarios will be incorporated with related teaching materials to form a package ready to deliver to the students in class. Widya Pustaka will be used by a teacher, to help preparing one session of teaching activity. The tool should be designed as simple as possible to increase the ease of use. The user will be guided step by step to build the lesson plan and the teaching-ware package. In the past three years, an elementary teacher community in West Java has been developed, as a part of the result of our research in digital learning. Several teachers from elementary school in Bandung, Subang, and Cianjur are involved in this community. This research intended to improve the quality of learning activities, especially at the elementary level. ICT-based learning activities were conducted such as ICT-based lesson study. Widya Pustaka is one of several ICT-based tools that will support all community member activities defined in several program. This tool will be validated by this community.

While adaptive tutoring systems have improved classroom education through individualization, few platforms offer students preference in regard to their education. In the present study, a randomized controlled trial is used to investigate the effects of student choice within ASSISTments. A problem set featuring either text feedback or matched content video feedback was assigned to a sample of 82 middle school students. Those who were able to choose their feedback medium at the start of the assignment outperformed those who were randomly assigned a medium. Results suggest that even if feedback is not ultimately observed, students average significantly higher assignment scores after voicing a choice. Findings offer evidence for enhancing intrinsic motivation through the provision of choice within adaptive tutoring systems.

The availability of quantum annealing devices with hundreds of qubits has made the experimental demonstration of a quantum speedup for optimization problems a coveted, albeit elusive goal. Going beyond earlier studies of random Ising problems, here we introduce a method to construct a set of frustrated Ising-model optimization problems with tunable hardness. We study the performance of a D-Wave Two device (DW2) with up to 503 qubits on these problems and compare it to a suite of classical algorithms, including a highly optimized algorithm designed to compete directly with the DW2. The problems are generated around predetermined ground-state configurations, called planted solutions, which makes them particularly suitable for benchmarking purposes. The problem set exhibits properties familiar from constraint satisfaction (SAT) problems, such as a peak in the typical hardness of the problems, determined by a tunable clause density parameter. We bound the hardness regime where the DW2 device either does not or might exhibit a quantum speedup for our problem set. While we do not find evidence for a speedup for the hardest and most frustrated problems in our problem set, we cannot rule out that a speedup might exist for some of the easier, less frustrated problems. Our empirical findings pertain to the specific D-Wave processor and problem set we studied and leave open the possibility that future processors might exhibit a quantum speedup on the same problem set., Comment: 24 pages, 25 figures. v2: new version replaces erroneously posted incomplete version. v3: updated to published version

The advancement of wireless communications and integrated circuit technology has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (disaster recovery, health, military, homeland security, environment, home, etc.). For each application area, there are different technical issues that researchers are currently resolving. However, many of them are trying to tackle the limitations of this field from a network perspective. Sometimes, the effectiveness of some proposed approaches must be complemented by the supports of hardware design. This article points out the possibilities of overcoming the same problem set from a device perspective by taking advantage of the merits of nanotechnologies. At the same time, open research issues and challenges are identified to spark new interests and developments in this field

Ant colony optimization (ACO) is a novel intelligent meta-heuristic originating from the foraging behavior of ants. An efficient heuristic of ACO is the ant colony system (ACS). This study presents a multi-heuristic desirability ACS heuristic for the non-permutation flowshop scheduling problem, and verifies the effectiveness of the proposed heuristic by performing computational experiments on a well-known non-permutation flowshop benchmark problem set. Over three-quarters of the solutions to these experiments are superior to the current best solutions in relevant literature. Since the proposed heuristic is comprehensible and effective, this study successfully explores the excellent potential of ACO for solving non-permutation flowshop scheduling problems.

Research in the analysis of discourse as such dates from the 1960s. Studying texts is, however, a much earlier practice. At first, the analysts of discourse were mainly concerned with corpuses that had not been studied previously: familiar conversations, mediated discourses, utterances linked to administrative, political, legal institutions, and so forth. They have thus allowed the traditional modes of analyzing philosophical, religious, or literary texts to endure. Still, I find it neces sary to use the concepts and methods of discourse analysis with these corpuses as well; this is what I have been trying to do with philosophical dialogue since the 1980s by developing concepts adapted to this type of discourse (Cossutta 1998-2001) and applying them to the works of philosophers, in a methodologi cal context bearing most especially upon the theories of linguistic enunciation (Benveniste 1966; Culioli 1990). In this article, I shall consider the problem set by dialogue in philosophical discourse. Research on conversations is probably the most developed area of study, and I would like to show that the representation of the verbal interactions takes place within a very different framework when it comes to philosophical texts: not only because, like in theater, the texts are produced by an author (and are not real interactions), but also because the "self-constituting" character of philosophy decisively shapes the use of this genre.

Parallel programming environments provide a way for programmers to reap the benefits of parallelism, while reducing the effort required to create parallel applications. The CO"2P"3S parallel programming system is one such tool that uses a pattern-based approach to express concurrency. Using the Cowichan Problems, we demonstrate that CO"2P"3S contains a rich set of parallel patterns for implementing a wide variety of applications running on shared-memory or distributed-memory hardware. An example of these parallel patterns, the Search-Tree pattern, is described and it is shown how the pattern was used to solve the Fifteen Puzzle problem. Code metrics and performance results are presented for the Cowichan applications to show the usability of the CO"2P"3S system and its ability to reduce programming effort, while producing programs with reasonable performance.

The revised simplex method is often the method of choice when solving large scale sparse linear programming problems, particularly when a family of closely-related problems is to be solved. Each iteration of the revised simplex method requires the solution of two linear systems and a matrix vector product. For a significant number of practical problems the result of one or more of these operations is usually sparse, a property we call hyper-sparsity. Analysis of the commonly-used techniques for implementing each step of the revised simplex method shows them to be inefficient when hyper-sparsity is present. Techniques to exploit hyper-sparsity are developed and their performance is compared with the standard techniques. For the subset of our test problems that exhibits hyper-sparsity, the average speedup in solution time is 5.2 when these techniques are used. For this problem set our implementation of the revised simplex method which exploits hyper-sparsity is shown to be competitive with the leading commercial solver and significantly faster than the leading public-domain solver.

Recently, time-switch constraints have been introduced in the literature by Yang and Chen [Eur. J. Operat. Res. 120 (2000) 603]. Basically, these constraints impose a specified starting time on the project activities and force them to be inactive during specified time periods. This type of constraints have been incorporated into the well-known discrete time/cost trade-off problem in order to cope with day, night and weekend shifts. In this paper, we propose a new branch-and-bound algorithm which outperforms the previous one by Vanhoucke et al. [J. Operat. Res. Soc. 53 (2002) 1]. The procedure makes use of a lower bound calculation for the discrete time/cost trade-off problem (without time-switch constraints). The procedure has been coded in Visual C++, version 6.0 under Windows 2000 and has been validated on a randomly generated problem set.

The IMMEX (Interactive Multi-Media Exercises) Web-based problem set platform enables the online delivery of complex, multimedia simulations, the rapid collection of student performance data, and has already been used in several genetic simulations. The next step is the use of these data to understand and improve student learning in a formative manner. This article describes the development of probabilistic models of undergraduate student problem solving in molecular genetics that detailed the spectrum of strategies students used when problem solving, and how the strategic approaches evolved with experience. The actions of 776 university sophomore biology majors from three molecular biology lecture courses were recorded and analyzed. Each of six simulations were first grouped by artificial neural network clustering to provide individual performance measures, and then sequences of these performances were probabilistically modeled by hidden Markov modeling to provide measures of progress. The models showed that students with different initial problem-solving abilities choose different strategies. Initial and final strategies varied across different sections of the same course and were not strongly correlated with other achievement measures. In contrast to previous studies, we observed no significant gender differences. We suggest that instructor interventions based on early student performances with these simulations may assist students to recognize effective and efficient problem-solving strategies and enhance learning.

We have developed models of how problem spaces are navigated as male and female secondary school, university, and medical students engage in repetitive complex problem solving. The strategies that students used when solving problem-solving simulations were first classified with self-organizing artificial neural networks resulting in problem solving strategy maps. Next, learning trajectories were developed from sequences of performances by Hidden Markov Modeling that stochastically described students' progress in understanding different domains. Across middle school to medical school there were few gender differences in the proportion of cases solved; however, six of the seven problem sets showed significant gender differences in both the strategies used (ANN classifications) as well as the in the HMM models of progress. These results were extended through a detailed analysis of one problem set. For this high school / university problem set, gender differences were seen in how the problems were encoded consolidated and retrieved. These studies suggest that strategic problem solving differences are common across gender, and would be masked by simply looking at the outcome of the problem solving event.

We propose a relaxation scheme for mathematical programs with equilibrium constraints (MPECs). In contrast to previous approaches, our relaxation is two-sided: both the complementarity and the nonnegativity constraints are relaxed. The proposed relaxation update rule guarantees (under certain conditions) that the sequence of relaxed subproblems will maintain a strictly feasible interior---even in the limit. We show how the relaxation scheme can be used in combination with a standard interior-point method to achieve superlinear convergence. Numerical results on the MacMPEC test problem set demonstrate the fast local convergence properties of the approach.

In this paper, a class of implicit advanced step-point methods that possesses a parallel feature is presented. Their accuracy and stability characteristics are examined in some detail and an experimental nonparallel code has been developed in order to give us a fair indication of their capabilities. Our aim is not to discuss a ''parallel implementation'' but, to demonstrate the worthiness of the new methods. The experimental code is compared with the powerful MEBDF code on the stiff DETEST problem set and the statistics displayed are obtained from the DETEST evaluation package. Some initial conclusions concerning the efficiency of the new methods are drawn from the analysis of the numerical results.

The aberrancy of a plane curve is a property of the curve that is invariant under both translation and rotation. It provides a numerical measure for the non-circularity of the curve at each point of the curve. (Recall that curvature gives an invariant numerical measure of nonlinearity.) The concept of aberrancy has been around for two centuries, but it has received very little attention. We hope to stir a little interest in the concept by presenting four different derivations of the formula for aberrancy. This may appear to be redundant, but there are some good reasons for doing so. First of all, the only derivation for aberrancy in the literature is a bit confusing and makes a few unjustified assumptions. Secondly, the derivations we present are all significantly different from each other and involve some interesting ideas in elementary real analysis. Finally, although the basic idea behind each derivation is simple, the details can become extremely messy unless a proper path is chosen. We encourage the reader to find one or two approaches of particular interest and use the concept of aberrancy as an extended problem set in an undergraduate analysis course or even a further mathematics class in an English school. The primary source of information, both mathematical and historical, on the topic of aberrancy is the article by Schot [1]. We refer the reader to that article for a history of the concept, one method of derivation of the formula, and the relationship between aberrancy and the rotated conic sections that best approximate a curve. Other references to this concept include Transon [2], Salmon [3], Walker [4], and Boyer [5]. Transon's derivation of aberrancy is rather difficult to follow due to the time period and style in which it was written. The derivation given there relies heavily on geometry and infinitesimals. In his article, Schot cleans up and modernises Transon's argument, but (in my opinion) makes some unjustified assumptions in his proof. Salmon [3] merely gives the definition, then states that the formula follows easily. He gives no indication of his method of derivation for aberrancy. Walker presents a short derivation that is also similar in style to Transon's argument. Boyer's article is primarily historical, but he clearly indicates the original motivation for the development of this concept.

A “minimally complex problem set” forab initio protein structure prediction has been proposed. As well as consisting of non-redundant and crystallographically determined high-resolution protein structures, without disulphide bonds, modified residues, unusual connectivities and heteromolecules, it is more importantly a collection of protein structures, with a high probability of being the same in the crystal form as in solution. To our knowledge, this is the first attempt at this kind of dataset. Considering the lattice constraint in crystals, and the possible flexibility in solution of crystallographically determined protein structures, our dataset is thought to be the safest starting points for anab initio protein structure prediction study.

Engineers and scientists are increasingly required to design, test, and validate new complex systems in simulation environments and/or with limited experimental results due to international and/or budgetary restrictions. Dealing with complex systems requires assessing knowledge and information by critically evaluating them in terms relevance, completeness, non-distortion, coherence, and other key measures. Using the concepts and definitions from evolutionary knowledge and epistemology, ignorance is examined and classified in the paper. Two ignorance states for a knowledge agent are identified: (1) non-reflective (or blind ) state , i.e. the person does not know of self-ignorance, a case of ignorance of ignorance; and (2) reflective state , i.e. the person knows and recognizes self-ignorance. Ignorance can be viewed to have a hierarchal classification based on its sources and nature as provided in the paper. The paper also explores limits on knowledge construction, closed and open world assumptions, and fundamentals of evidential reasoning using belief revision and diagnostics within the framework of ignorance analysis for knowledge construction. The paper also examines an algebraic problem set as identified by Sandia National Laboratories to be a basic building block for uncertainty propagation in computational mechanics. Solution algorithms are provided for the problem set for various assumptions about the state of knowledge about its parameters.

Confronting plagiarism is, for many faculty, an important rite of passage, marking the first of many difficult decisions concerning a just response to academic misconduct. But seeking justice by recommending disciplinary actions is very different from grading a problem set or critiquing an essay. Discretionary sanctions compel faculty to confront questions that are more moral than scientific in nature. As a result, it is especially difficult to feel confident in a disciplinary decision that is made based on an ethereal sense of right and wrong. In an effort to bring some clarity to an especially complex moral question, I argue that faculty members have an ethical responsibility to severely discipline students who overtly engage in academic plagiarism.' By relying on policies that emphasize leniency, faculty members actually promote rather than discourage plagiarism. Borrowing from the rational choice literature and ex

Results are presented for Part 2 (density) and Part 3 (viscosity) of the First Industrial Fluid Properties Simulation Challenge (FIFPSC). In both cases, the physical properties were calculated using existing published force fields not specifically tuned to the problem at hand. No assessment of the accuracy of our predictions was made until the experimental values for each problem set were announced at the end of the competition. Liquid densities were computed for the Part 2 problem set using the Towhee Monte Carlo molecular simulation program and the Amber96, Charmm22, Compass, and OPLS-aa force fields. No single force field was able to provide parameters for all the molecules in the problem set, but the Amber96 force field had the best results of the four tested and a reasonable coverage of the problem set. Viscosities were computed for the Part 3 problem set using the LAMMPS molecular dynamics code. The Towhee program was used to generate equilibrium starting configurations. Only one force field, OPLS-aa, was used. The predicted viscosities showed average deviation of about 35% from the experimental values. In cases where the experimental density is known, substantially better accuracy can be expected.

We consider in this work a variant of the Steiner Problem in Graphs (SPG) with the additional restriction that terminal nodes must have degree 1. We customize the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, testing its performance on a large problem set obtained by modifying 182 SPG instances from the SteinLib repository. The GRASP obtained good results, with low average gaps with respect to known lower bounds in most of the problem classes, and attaining the optimum in 44 cases (24% of the problem set).

This paper reports four experiments investigating whether model construction of linear reasoning problems is open to strategic decisions. A reversed choice/nochoice paradigm was used in which reasoners first had to apply two model construction strategies (acronym and rehearsal strategy) to two problem sets. Next, they could choose freely among the two strategies to apply to a new problem set. Experiment 1 showed that reasoners selected the strategy that they experienced as the most accurate one in the no-choice phase. Moreover, in Experiment 2, it was found that reasoners adapted their strategy choice to changing problem features, to use the most suitable strategy for premise encoding. Experiments 3 and 4 generalised these findings to more complex linear reasoning problems with a mixed sentence frame and a semi-continuous presentation of the premises, and to two-model problems. On the basis of these results, we argue that strategic decisions influence model construction in linear reasoning.

The efficiency of AI planning systems is usually evaluated empirically. For the validity of conclusions drawn from such empirical data, the problem set used for evaluation is of critical importance. In planning, this problem set usually, or at least often, consists of tasks from the various planning domains used in the first two international planning competitions, hosted at the 1998 and 2000 AIPS conferences. It is thus surprising that comparatively little is known about the properties of these benchmark domains, with the exception of Blocksworld, which has been studied extensively by several research groups.In this contribution, we try to remedy this fact by providing a map of the computational complexity of non-optimal and optimal planning for the set of domains used in the competitions. We identify a common transportation theme shared by the majority of the benchmarks and use this observation to define and analyze a general transportation problem that generalizes planning in several classical domains such as Logistics, Mystery and Gripper. We then apply the results of that analysis to the actual transportation domains from the competitions. We next examine the remaining benchmarks, which do not exhibit a strong transportation feature, namely Schedule and FreeCell.Relating the results of our analysis to empirical work on the behavior of the recently very successful FF planning system, we observe that our theoretical results coincide well with data obtained from empirical investigations.

Public involvement in transportation planning and design has a problematic history. Professionals lack access to a coherent, organized method for communicating with the public, and some important principles of public involvement known to community design professionals are still being discovered by transportation professionals. A protocol, structured public involvement (SPI), is proposed. SPI was designed to ensure that public involvement is meaningful to the professional and the public. Principles of SPI are presented, and a series of steps useful for engaging the general public in a complex design or planning problem is given. SPI is intended to be transparent, accountable, democratic, and efficient. SPI places the use of technology within a public involvement framework built on community design experience. While technology can be useful, it must be placed in a social context. That is, various technologies are used because they can address such problems as lack of access to information, inconvenient and time-consuming meetings, confusing terms and graphics, and one-way communication. Highlights and examples are drawn from practical experience, where SPI protocols have been designed and used to solve problems of route planning, highway design, and transit-oriented development. While each problem set called for a different mix of technical tools, the protocol within which those tools were used was the same, with similar encouraging results. With SPI, public participation is less contentious and more informed, and the professional has information of high quality with which to begin the design process.

Over the past 50 years, researchers have developed many simple constructive heuristics for the scheduling problem. A major defect of these heuristics is the non-robustness of their solutions. An ant colony system (ACS) approach is presented to continuously improve the constructive heuristics. To verify the developed ACS approach, a computational study is conducted on the single machine total weighted tardiness problem. The results show that the proposed approach can effectively improve the robustness of various constructive heuristics, and outperform the existing heuristics for a well-known benchmark problem set. From the viewpoints of both the solution quality and computational expenses, the proposed ACS approach is an efficient and effective method for scheduling problems.

This article collates results from a number of applications of interactive evolution as a sound designer's tool for exploring the parameter spaces of synthesis algorithms. Experiments consider reverberation algorithms, wavetable synthesis, synthesis of percussive sounds and an analytical solution of the stiff string. These projects share the property of being difficult to probe by trial and error sampling of the parameter space. Interactive evolution formed the guidance principle for what quickly proved a more effective search through the multitude of parameter settings.The research was supported by building an interactive genetic algorithm library in the audio programming language SuperCollider. This library provided reusable code for the user interfaces and the underlying genetic algorithm itself, whilst preserving enough generality to support the framework of each individual investigation.Whilst there is nothing new in the use of genetic algorithms in sound synthesis tasks, the experiments conducted here investigate new applications such as reverb design and an analytical stiff string model not previously encountered in the literature. Further, the focus of this work is now shifting more into algorithmic composition research, where the generative algorithms are less clear-cut than those of these experiments. Lessons learned from the deployment of interactive evolution in sound design problems are very useful as a reference for the extension of the problem set.

We have developed a bioresponse system concept to respond to large-scale medical disasters, including overwhelming, contagious bioagent events such as smallpox, plague, and influenza. This system, called Cybercare, aims to substantially increase the surge capacity of the healthcare system by allowing healthcare systems throughout the country to link together to provide specialists and other resources to augment those available at a disaster site. Key to the concept of Cybercare is the notion of a robust, distributed command and control system that will enable rapid and sustained responses to large-scale medical disasters. The Cybercare information infrastructure can also be utilized to support training and analysis of response procedures as well as a wide range of medical and disaster applications that occur on a frequent basis, such as home healthcare monitoring and rural and remote medical treatment. The purposes of this article are to: 1) define the problem set addressed by the Cybercare concept, 2) provide the reader with a fundamental knowledge of command and control and an emerging paradigm shift in command structure for the medical and disaster management domain, 3) define technologies that can be used to create a command and control system, and 4) illustrate how the technologies can be integrated into a disaster response system using the command and control paradigm.

A major problem with substrate crosstalk extraction methods in the literature is their lengthy computational time. This practically renders them useless for large circuits. This paper presents efficient substrate parasitic extraction techniques employing closed-form models derived using a quasi-static approach. We develop an integral equation model based on the Green's theorem utilizing a novel fast-convergent Green's function. In addition, we propose another modeling technique based on point approximation of the elements of the impedance matrix of the system. Although the proposed techniques speed the parasitic extraction process up, we show that it is unnecessary to run a full substrate parasitic extraction among all the devices for a system-on-a-chip. As such, by applying the new modeling technique to a multi-contact substrate structure, we develop a methodology that is a large-scale solution for substrate parasitic extraction in VLSI and mixed-signal circuits. Moreover, we demonstrate that the coupling among the contacts in a large system decreases faster than an exponential function of the distance among them. Using the proposed methodology, we introduce a decomposed two-contact problem set, associated with the original complex problem. Then, the total crosstalk of a device in the original problem is simply computed from the associated decomposed problem set.