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The current study explores the effects of physical spacing within mathematical expressions on student performance. A total of 2,152 students in 5th-12th grade were randomly assigned to one of four conditions within an online problem set, with terms in algebraic expressions spaced 1) neutrally, with no spaces in the expression, 2) congruent with the order of precedence through grouping terms, 3) incongruent with the order of precedence, or 4) mixed, a combination of the previous conditions. Results show that students who viewed incongruent problems made more errors and had to solve more problems to complete the assignment than those who viewed congruent or neutrally spaced problems. Additionally, students who viewed problems with mixed spacing had to solve more problems to complete the assignment than students who viewed congruent problems. These findings suggest that viewing expressions with spacing that is incongruent with the order of precedence presents challenges for students. Overall, these results replicate prior research in perceptual learning in a natural homework environment and support the claim that physical spacing between terms does influence student performance on order of precedence problems.

In this paper, a set of stylized numerical benchmark problems is developed. These problems are based on the Oak Ridge National Laboratory preconceptual design of a fluoride-salt-cooled small modula...

We analyze the parallelism particularities of finite volume based computational algorithms. We use a problem set from electrodynamics and parallelize its solution. It serves as an example and illustration of our findings. The tests are carried out on the Zhores supercomputer. The hardware used to run parallel algorithms includes Nvidia Tesla V100 GPUs and Intel Xeon CPUs. We find that finite volume method discretizations work well with parallelization on GPUs. The speedup can be increased by proper CUDA optimizations. However, it is very resource consuming to perform all computations on supercomputer GPUs if the problem size is exceptionally big. Hence, it is necessary to harness every processing power that a supercomputer can offer including CPUs. We offer ways to parallelize the posed problem in a hybrid fashion.

Biogeography can be broken down into bio and geography, which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology inspired algorithm is inclined towards providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, and some for the worse. This paper tries to mimic the natural influences in a habitat in a python environment and try to come up with a minimal objective value after iterating through the given metaheuristic algorithm.

With the advent of commercially available Mixed-Reality(MR)-headsets in recent years MR-assisted learning started to play a vital role in educational research, especially related to STEM (science, technology, engineering and mathematics) education. Along with these developments it seems viable to further frameworks and structured design processes for MR-based learning environments. Instead of a widely applicable framework for designing educational MR applications, we here consider the case of virtually enhancing physical hands-on experiments in STEM, where students are given a certain problem to solve, and how to design these. For this focused realm, we suggest an empirically driven problemand user-centred design process for MR applications to get novices to act more like experts and exemplify it for a specific experiment and problem set containing a non-trivial electric circuit with capacitors and coils., Comment: 15th International Technology, Education and Development Conference

Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly. In this work, we focus on a particular case - the box constraints, for which many boundary constraint handling methods (BCHMs) have been proposed. We call for the necessity of studying the impact of BCHMs on metaheuristics' performance and behavior, which receives seemingly little attention in the field. We target quantifying such impacts through systematic benchmarking by investigating 28 major variants of Differential Evolution (DE) taken from the modular DE framework (by combining different mutation and crossover operators) and $13$ commonly applied BCHMs, resulting in $28 \times 13 = 364$ algorithm instances after pairing DE variants with BCHMs. After executing the algorithm instances on the well-known BBOB/COCO problem set, we analyze the best-reached objective function value (performance-wise) and the percentage of repaired solutions (behavioral) using statistical ranking methods for each combination of mutation, crossover, and BBOB function group. Our results clearly show that the choice of BCHMs substantially affects the empirical performance as well as the number of generated infeasible solutions, which allows us to provide general guidelines for selecting an appropriate BCHM for a given scenario., 9 pages, 2 figures. To be published in the 2021 Genetic and Evolutionary Computation Conference Companion

When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems. One possible way of easing this difficult task is by using artificially generated problems. In this paper, one such single-objective continuous problem generation approach is analyzed and compared with the COCO benchmark problem set, a well know problem set for benchmarking numerical optimization algorithms. Using Exploratory Landscape Analysis and Singular Value Decomposition, we show that such representations allow us to further explore the relations between the problems by applying visualization and correlation analysis techniques, with the goal of decreasing the bias in benchmark problem assessment., To appear in the Proceedings of Genetic and Evolutionary Computation Conference Companion (GECCO 2021), ACM

The growing advancement of technology helps researchers a lot to confront new problems and making new solutions. Not only with the existing algorithm but also various ways can be found for making a solution. This could be either mathematically or logically. This work proposed a method for using deep learning for solving the combinatorial optimization problem which is the most talked about the topic now in the field of computer science. The combinatorial optimization problem is very much hard in nature and very much difficult to solve or compute mathematically. This proposed work help to find the optimization with the help of deep learning. This work mainly focuses on a problem that is considered as a data point and finds out the optimal solution with the help of deep learning for the given task. Such as for a problem set of mango leave disease, this proposed work at first by applying some Deep learning methods with Convolutional Neural Network models such as DenseNet201, and Inception V3. Also, the accuracy of these models is again justified by seven performance metrics like Accuracy, Precision, F1 Score, Sensitivity, Specificity, FNR, FPR. After getting the full model the optimal solution finds using the combinatorial optimization algorithm. Which gives us the best solution between the two models.

In this study, we focus on robust criteria for vehicle routing problems with soft time windows (VRPSTW). The main objective is to find a robust solution that provides the best for the worst case performance for VRPSTW under uncertain travel times. The robust criteria are used in this study such as absolute robustness, robust deviation, and relative robustness as a basis for comparison. The VRPSTW becomes complex when the travel times are uncertain. This uncertainty can be caused by traffic jams, accidents, or inclement weather conditions. The experiment uses benchmarking problems. The number of scenarios is generated randomly into intervals of travel time, equal to 4, 6, and 8 instances for each problem set. Each set of problem instances can be denoted by the percentage of uncertainty α, equal to 0.2, 0.4, 0.6, and 0.8. This study will demonstrate that the most indicated robust criteria for these situations are robust deviation and relative robustness. The most important part of the decision maker is to determine the uncertainty percentage to cover all uncertainties that need to be considered.

We empirically analyze equity premium predictions with “traditional” linear regression models and tree-based machine learning approaches. Based on a commonly used dataset of equity market predictors extended by additional fundamental, macroeconomic, sentiment and risk indicators, we find mixed results for machine learning algorithms for equity market predictions. In contrast to sophisticated linear regression models such as penalized least squares or principal component regressions, the analyzed machine learning algorithms fail to significantly outperform the historical average benchmark forecast. However, an investment strategy that uses machine learning predictions in a market timing strategy outperforms a passive buy-and-hold investment. Compared to sophisticated linear prediction models, machine learning algorithms do not improve forecast accuracy in our problem set.

The no-wait flowline manufacturing cell scheduling problem with sequence-dependent family setup times and the objective of minimizing makespan is NP-hard in the strong sense even in the two-machine case. This work proposes an efficient matheuristic for solving this problem to optimality in a reasonable computational time. To demonstrate the effectiveness and efficiency of the proposed matheuristic, computational experiments are performed using a well-known benchmark problem set. The computational results reveal that all test instances of the problem are solved to optimality using the proposed matheuristic, and that the computational times of the matheuristic are comparable with those of state-of-the-art meta-heuristics for finding approximate solutions. This work contributes significantly to the research into algorithms for solving this computationally intractable problem.

Efficient methods for optimising expensive black-box problems with multiple objectives can often themselves become prohibitively expensive as the number of objectives is increased. We propose an infill criterion based on the distance to the summary attainment front which does not rely on the expensive hypervolume or expected improvement computations, which are the principal causes of poor dimensional scaling in current state-of-the-art approaches. By evaluating performance on the well-known Walking Fish Group problem set, we show that our method delivers similar performance to the current state-of-the-art. We further show that methods based on surrogate mean predictions are more often than not superior to the widely used expected improvement, suggesting that the additional exploration produced by accounting for the uncertainty in the surrogate’s prediction of the optimisation landscape is often unnecessary and does not aid convergence towards the Pareto front.

Continuous black-box optimization problems are found in many real-world situations and scientific domains. The fitness landscape of an optimization problem represents the structural topology of the objective function over the space of variables. Optimization is a search problem over this landscape to find a maximum or minimum value. The performance of an optimization algorithm depends upon the landscape structure of the underlying problem. Therefore, characterization of the landscape is very important to understand the algorithmproblem relationship. Most of the research in black box optimization is focused on developing algorithms for solving problems rather than problem characterization. Problem characterization helps in identifying the (dis)similarities that can assist in choosing the most promising or suitable algorithm to solve the problem. Existing methods to characterize problem landscapes can be combined under the term, exploratory landscape analysis (ELA). These techniques are largely algorithm independent and based on data sampled from problem instances to compute features. To capture different characteristics of a landscape, a set of heterogeneous problem features has been developed. For further analysis, a set of selected features is used to generate a feature space in which problems are positioned depending upon their characteristics (i.e. similar problems are located close to each other). While the featurebased approach has shown some success, validating and evaluating the utility of problem features in practice presents some significant challenges. Machine learning models have been employed as part of the analysis process, but they have their own hyperparameters, biases and experimental variability. As a result, extra layers of uncertainty and complexity are added into the experimental evaluation process, making it difficult to clearly assess the role of the problem features. In regards to problem comparison using features, the selection of the most informative feature set is another difficult task.This thesis makes several contributions to exploratory landscape analysis. First, a novel method for the evaluation of problem features is proposed. The existing methods of feature evaluation are based on the collective performance of a set of features for classifying problems into pre-defined categories using machine learning techniques. However, the individual contribution/strength of a feature is not clear. In the proposed methodology, every feature is tested individually against different sets of problem transformations. In each set, a single problem is transformed into another problem gradually, by altering one characteristic (ruggedness, neutrality, ill-conditioning, linearity) of the landscape. This creates a ranking of similarity among problems. Analysis of variance (ANOVA) significance tests are then used to provide evidence about the feature’s ability to detect differences among problems in each transformation.The second major contribution of the thesis is a model-based framework that can be used to compare problems in terms of landscape similarity. The general idea is to fit a regression model to a problem landscape using a sample of candidate solutions and their corresponding fitness values. The (dis)similarity between models (measured using a Kullback-Leibler (KL) divergence) is used as a measure of the (dis)similarity between problem instances. The choice of regression model used here is Gaussian process (GP). GPs are used as a flexible regression model that can characterize a much wider range of problems as compared to the problems that can be characterized using individual features. A clear advantage of this approach is that the goodness of fit of the regression model can be used directly to validate how well the model approximates the original problem. In general, this is not possible with existing landscape features. In the experiments, different problem sets are compared using the model-based framework. The results show that the framework is very good at recovering induced similarities in artificial problem sets. The framework is then used to explore the black box optimization benchmark (BBOB) problem set in high dimensions using different sample sizes. Later, a set of clustering problems based on real world datasets is compared using the model based framework. The results show that the framework is very effective at identifying the differences between a diverse set of problems.The work in this thesis provides important practical techniques for characterizing and comparing black box optimization problems. It contributes a new method for capturing problem structure as well as improvements to the experimental methodology for applying and using exploratory landscape analysis. These contributions can lead to a better understanding of the problem-algorithm performance mapping and provide benefits for automated algorithm selection and configuration.

Mathematics word problems provide students with an opportunity to apply what they are learning in their mathematics classes to the world around them. However, students often neglect their knowledge of the world and provide nonsensical responses (e.g., they may answer that a school needs 12.5 buses for a field trip). This study examined if the question design of word problems affects students' mindset in ways that affect subsequent sense-making. The hypothesis was that rewriting standard word problems to introduce inherent uncertainty about the result would be beneficial to student performance and sense-making because it requires students to reason explicitly about the context described in the problem. Middle school students (N = 229) were randomly assigned to one of three conditions. In the standard textbook condition, students solved a set of six word problems taken from current textbooks. In the modified yes/no condition, students solved the same six problems rewritten so the solution helped answer a "yes" or "no" question. In the disfluency control condition, students solved the standard problems each rewritten in a variety of fonts to make them look unusual. After solving the six problems in their assigned condition, all students solved the same three "problematic" problems designed to assess sense-making. Contrary to predictions, results showed that students in the modified yes/no condition solved the fewest problems correctly in their assigned condition problem set. However, consistent with predictions, they subsequently demonstrated more sense-making on the three problematic problems. Results suggest that standard textbook word problems may be able to be rewritten in ways that mitigate a "senseless" mindset.

This chapter discusses the new conceptual approach to the relationship between humans and AI/ML systems in war by interrogating interactions between correlation and being, between machine learning and human heuristics, and between uncertainty and constraints. It shows that the human-centric model we propose mitigates concerns in techno-social development, ethical employment in combat contexts, and in the strategic environment facing the United States and other Western states. To hypothesize the ideal-typical relationships between humans and hardware, we build on the widely held view that humans are relatively more effective than hardware when operating at higher levels of complexity. Therefore, the relationship between humans and hardware should be defined by the level of complexity of the problem set, which can be linked in turn back to the steps of the OODA loop. The most important finding in all of these use cases is the relationship between humans and hardware in the context of the contemporary, forecasted future security environment.

Algorithm tuning, especially in the context of metaheuristic algorithms, is a growing area of research and practice, which concerns configuring the parameters of an algorithm on the basis of a ‘training set’ of problem instances. There is much interest (but little research so far) on the relationship between the training set and generalization performance. In general terms, we expect that, if an algorithm is targeted at problems of a certain type, then the training set of problem instances should represent a diverse collection of problems of that type. However, many questions around this area are still the topic of research. In this paper, we explore how certain structural aspects of the training set problem instances (in contrast, for example, to problem size) affect generalization performance. Specifically, we look at spaces of simple TSP instances where the number of cities is constant, but with different spatial distributions. We find that, when the training problem sets are suitably difficult, an algorithm tuned for a specific spatial distribution tends towards being the best algorithm on test sets following that distribution. Conversely, and significantly, if the target problem set is not from a ‘particularly difficult’ spatial distribution, a better optimizer for that distribution may be produced by choosing a different spatial distribution for the training set. This has implications for the development of real-world optimizers for specific problem classes, such as, for example, vehicle routing problems with particular depot and customer distributions.

The advanced route analysis concerns are addressed and implemented using the ArcGIS Network Analyst environment. The location analytics has continuously progressed with shortest path problems and origin-destination cost matrix-related problems at several stages of analysis. Dijkstra’s algorithm is highly useful for network analysis on small and relatively less complex networks, but the output performance degrades as the networks become more complex. In real life, the analysis problems are based on “real-time queries on large hierarchical road networks”. The shortest path algorithm is also sometimes known as the classical exact best route algorithm and is not suitable for the stated problem set. The hierarchical algorithms are used to solve the problem effectively and efficiently.

Portfolio approaches aim to combine different algorithms and take advantages of their strengths since it is challenging for a single algorithm to solve a problem set with all optimization problems. When there are many algorithms to choose from, there are various algorithm combinations of possibilities. Selecting a well-composed algorithm portfolio becomes essential to solve a problem set efficiently. In this paper, we propose a problem set dependent method to automatically and generically accomplish portfolio construction. The problem set can be composed of any problem, and come from any distribution or any benchmark. To decide which algorithm should be added in the portfolio, we utilize the average rank of results from solving problems to find the best-performing algorithm. Then we select complementary algorithms for the portfolio by applying Pearson correlation coefficient of fitness values. The method then iterates to compose more and more complex portfolios until there is no more improvement. This method is tested under three different problem sets. The experimental result shows the good ability of this approach to detect well-cooperated algorithms; and the composed portfolio is proved to have good adaptability to the problem set.

Massive Open Online Courses (MOOCs) are accessible to anyone with a device that can connect to the internet. MOOCs aim to increase the accessibility of higher-level knowledge and skills, such as programming. To understand how students are performing and struggling in the course, we investigate a popular MITx MOOC that teaches introductory programming. We look at problem set questions and examine students with different levels of pre-existing knowledge. Specifically, we study the number of attempts of each group per question and the mean final accuracy of each group per question. We find that for nearly all questions, students with no programming experience struggle more than students with prior programming experience. Moreover, we observe a potential turning point in the course where students of all experience levels begin to struggle. Our findings both show that two groups of MOOC students perform differently and inform question design in MOOCs by demonstrating which question types are particularly arduous.

We study demand-side participation in an electricity market for an industrial consumer of electricity, with some flexibility to reduce demand, and capable of offering interruptible load reserve. Our consumer is a price maker, and the impact of its actions in the market is modelled via a bi-level optimization problem. We have extended a standard model for optimal strategic consumption, to the case where reserve offer curves need to be optimized simultaneously with consumption curves; our models provide intuition into this interaction. Furthermore, we provide tailor-made solution strategies for the resulting problems under uncertainty, and report numerical results of our implementation on instances over the full New Zealand network yielding a realistic and large problem set.

This manuscript presents a benchmark problem for the simulation of single-phase flow, reactive transport, and solid geometry evolution at the pore scale. The problem is organized in three parts that focus on specific aspects: flow and reactive transport (part I), dissolution-driven geometry evolution in two dimensions (part II), and an experimental validation of three-dimensional dissolution-driven geometry evolution (part III). Five codes are used to obtain the solution to this benchmark problem, including Chombo-Crunch, OpenFOAM-DBS, a lattice Boltzman code, Vortex, and dissolFoam. These codes cover a good portion of the wide range of approaches typically employed for solving pore-scale problems in the literature, including discretization methods, characterization of the fluid-solid interfaces, and methods to move these interfaces as a result of fluid-solid reactions. A short review of these approaches is given in relation to selected published studies. Results from the simulations performed by the five codes show remarkable agreement both quantitatively—based on upscaled parameters such as surface area, solid volume, and effective reaction rate—and qualitatively—based on comparisons of shape evolution. This outcome is especially notable given the disparity of approaches used by the codes. Therefore, these results establish a strong benchmark for the validation and testing of pore-scale codes developed for the simulation of flow and reactive transport with evolving geometries. They also underscore the significant advances seen in the last decade in tools and approaches for simulating this type of problem.

This paper deals with a set of geometrical problems for mathematical problem-solving at different difficulty levels. All of these are presented as national flags and one has to investigate invarian...

An important research problem in artificial intelligence is how to organize multiple agents, and coordinate them, so that they can work together to solve problems. Coordinating agents in a multi-agent system can significantly affect the systems performance-the agents can, in many instances, be organized so that they can solve tasks more efficiently, and consequently benefit collectively and individually. Central to this endeavor is coalition formation-the process by which heterogeneous agents organize and form disjoint groups (coalitions). Coalition formation often involves finding a coalition structure (an exhaustive set of disjoint coalitions) that maximizes the systems potential performance (e.g., social welfare) through coalition structure generation. However, coalition structure generation typically has no notion of goals. In cooperative settings, where coordination of multiple coalitions is important, this may generate suboptimal teams for achieving and accomplishing the tasks and goals at hand. With this in mind, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent alternatives (e.g., tasks/goals), and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This combinatorial optimization problem hasmany real-world applications, including forming goal-oriented teams. To evaluate the presented algorithms performance, we present five methods for synthetic problem set generation, and benchmark the algorithm against the industry-grade solver CPLEXusing randomized data sets of varying distribution and complexity. To test its anytime-performance, we compare the quality of its interim solutions against those generated by a greedy algorithm and pure random search. Finally, we also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that it can be used in game-playing to coordinate smaller sets of agents in real-time. Funding Agencies|Linkoping University; Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests. Despite various successful approaches proposed to solve the NAS task, the landscape of it, along with its properties, are rarely investigated. In this paper, we argue for the necessity of studying the landscape property thereof and propose to use the so-called Exploratory Landscape Analysis (ELA) techniques for this goal. Taking a broad set of designs of the deep convolutional network, we conduct extensive experimentation to obtain their performance. Based on our analysis of the experimental results, we observed high similarities between well-performing architecture designs, which is then used to significantly narrow the search space to improve the efficiency of any NAS algorithm. Moreover, we extract the ELA features over the NAS landscapes on three common image classification data sets, MNIST, Fashion, and CIFAR-10, which shows that the NAS landscape can be distinguished for those three data sets. Also, when comparing to the ELA features of the well-known Black-Box optimization Benchmarking (BBOB) problem set, we found out that the NAS landscapes surprisingly form a new problem class on its own, which can be separated from all 24 BBOB problems. Given this interesting observation, we, therefore, state the importance of further investigation on selecting an efficient optimizer for the NAS landscape as well as the necessity of augmenting the current benchmark problem set.

In this paper, a novel multi-swarm particle swarm optimizer driven by delayed-activation (DA) strategy and repulsive mechanism, named as enhanced multi-swarm cooperative particle swarm optimizer (EMCPSO) is proposed. EMCPSO is designed to make use of the advantage of multi-swarm technique and overcome the problem of premature convergence of original PSO. In this algorithm, the whole population is partitioned into four identical sub-swarms. The best particle of each sub-swarm, sbest, is used to estimate the evolutionary state of the group. If the sbest can continuously improve its solution's quality, that sub-swarm evolves independently without communicating with other counterparts. Otherwise, based on a non-ascending sequence, a delayed-activation (DA) strategy will be triggered. With information sharing among multi-swarm, activating exemplar is constructed to promote the stagnant sub-swarm to search for better solutions again. On the other hand, a repulsive mechanism is introduced to prevent the whole population from gathering together prematurely. In this way, more potential regions of the search space can be explored by EMCPSO. The experiment results on CEC 2017 problem set demonstrate the superior performance of the proposed EMCPSO in terms of solution accuracy and convergence speed.

Characterizing black box optimization problems is critical for improved benchmarking and subsequent automation of algorithm selection and configuration. Existing approaches in exploratory landscape analysis are based on heterogeneous features that can create difficulties in applying the analysis and validating the results. In this paper, we propose a simple yet powerful method of comparing problems using regression models. Gaussian processes are used as a flexible regression model, providing an approximate characterization of the problem landscape. We use the difference between regression models of different problem instances as a measure of distance between the problems. The goodness of fit of the regression models allows us to validate the approximation that the model makes of the problem. This can subsequently be used to determine an appropriate sample size to provide a good representation of the problem. We apply our model-based framework to explore the problem relationships in the BBOB benchmark problem set. The results demonstrate that the framework is effective at representing the problem similarities in relatively high dimensions. We also evaluate the method on a set of clustering problems where a known ranking of similarity is imposed. The results show that the framework is very effective in recovering the problem similarities.

We consider a discrete time financial market with proportional transaction cost under model uncertainty, and study a super-replication problem. We recover the duality results that are well known in the classical dominated context. Our key argument consists in using a randomization technique together with the minimax theorem to convert the initial problem to a frictionless problem set on an enlarged space. This allows us to appeal to the techniques and results of Bouchard and Nutz (2015) to obtain the duality result.

Introduction U.S. military forces have engaged in combat in mature areas of operations (AOs) in Iraq and Afghanistan that allow for casualty evacuation to definitive surgical care within "The Golden Hour." Future combat casualty care will be complex and challenging. Facing the medical demand of the Multi-Domain Battlefield remains an uncertain problem set. What can be anticipated is that a near peer adversary will not allow freedom of movement, air superiority, or uninterrupted communications. Telemedicine is one solution that can aid in this environment because it can reduce the medical footprint in a theater of operation by bringing the remote expert's knowledge and experience to the point of need. Materials and methods Telemedicine can augment the capabilities of caregivers in austere, operational settings using synchronous or asynchronous technology to optimize the care of casualties who are delayed in evacuation to higher levels of care. These technologies have been implemented and tested over the past 30 yr. We reviewed the historical literature about military telemedicine and assembled current leaders in military telemedicine to write this review. Results This manuscript reviews the history of and current capabilities of military telemedicine. Conclusions Broad implementation of telemedicine in the operational setting is challenged by network limitations and cyber security concerns. Reliable, high bandwidth, low latency, secure communications that is necessary for advanced telemedicine capabilities (i.e., procedural telementoring) will not likely be available at all times during future engagements. The military must develop and train a full spectrum of telemedical support options that include low-to-high bandwidth solutions. Telemedicine is not a substitute for deploying anticipated medical resources or optimizing training: telemedicine is plan B where plan A is training, deployment, and casualty evacuation. Nevertheless, when network and communications resources are sufficient, telemedicine brings advanced expertise to austere, resource-limited contexts when timely evacuation is not possible.

Bin packing with fragmentable items is a variant of the classic bin packing problem where items may be cut into smaller fragments. The objective is to minimize the number of item fragments, or equivalently, to minimize the number of cuts, for a given number of bins. Models based on packing fragmentable items are useful for representing finite shared resources. In this article, we present improvements to approximation and metaheuristic algorithms to obtain an optimality-preserving optimization algorithm with polynomial complexity, worst-case performance guarantees and parametrizable running time. We also present a new family of fast lower bounds and prove their worst-case performance ratios. We evaluate the performance and quality of the algorithm and the best lower bound through a series of computational experiments on representative problem instances. For the studied problem sets, one consisting of 180 problems with up to 20 items and another consisting of 450 problems with up to 1024 items, the lower bound performs no worse than 5 / 6. For the first problem set, the algorithm found an optimal solution in 92 % of all 1800 runs. For the second problem set, the algorithm found an optimal solution in 99 % of all 4500 runs. No run lasted longer than 220 ms.

In an industrial scenario, one can find measured data that do not satisfy the mass and energy laws of conservation. This problem can be approached by applying data reconciliation (DR) and gross error detection and identification (GEDI) techniques, however, authors generally validate their methods using a reduced set of problems, restricting the application of the proposed methods to them. The objective of this work is to present a collection of benchmark problems for DR and GEDI to help the evaluation of these methods in different types of flowsheets. First, challenges issues related with DR and GED are presented with examples. Then, a general overview of the benchmark collection set is presented. In conclusion, it can be observed that this challenging research area needs a common problem set for validating DR and GEDI and this paper fills this gap, helping the validation of the methods.

Text understanding and reasoning are among the core areas of artificial intelligence. Even a total solution to automatic text understanding and reasoning is still beyond the current techniques, thus it is time to build the stepping stones to solve a more straightforward problem set like question-answering (QA) without ambiguous utterances in the contexts or questions. The reported state-of-the-art approaches to this kind of problem are nearly all connectionist models based on neural networks. Significant progress has even been made in this direction. It is still hard for the pure connectionist models to handle logical reasoning. They generally suffer from the longstanding drawback of poor explainability and sensitivity to data noise and distribution. In this paper, we propose a complementary symbolic approach, GMR (Graph Matching based Reasoner) to QA — it automatically generates reasoning rules in the form of graphs from the training set and uses the generated rules to infer answers to the questions in the test set via graph matching. By employing this symbolic approach, 20 tasks in bAbI are solved with an average accuracy of 99.38%, and it outperforms the state-of-the-art for a real-life QA dataset WikiTableQuestions. After analyzing the accuracy of the basic evaluation indicators, we studied the generalization ability of the model in the paper, including the anti-noise ability, the convergence of the model, the stability of the model, the complexity of the algorithm, and the uncertainty of the parameters. Through comprehensive analysis and comparison, our model is stronger than the neural network model regarding anti-noise interference. Compared with the neural network model, our model performs very well in multi-tasking, and the stability of the model is quite high. The diversity of tasks did not reduce the stability of the model. We have conducted a comprehensive analysis and comparison of the parameter uncertainties. Our model can optimally select parameter configurations and will not cause a sharp drop in performance due to the parameter uncertainties. Finally, we describe the complexity of the GMR method and the optimal configuration of its parameters.

This paper studies about the capacitated vehicle routing problem (CVRP). Since the problem is NP-hard, a local search based algorithm is used for the CVRP with the target to limit the aggregate visited distance and number of vehicles. This algorithm is proven to be effective and as efficient for CVRP by checking its comparability with existing best-known results. In this paper, this algorithm is used for CVRPSD by increasing the demands the customers by 10%, 15%, and 20% orderly. This algorithm is tested on the 5 problem set obtained from Augerat et. al CVRP benchmark problems which include instances range from 13 to 51 service nodes and a number of homogeneous vehicles are changed from 2 to 8 according to problem set. The empirical outcomes demonstrate that this algorithm gives better arrangements than understood benchmark problems contrasted with those detailed in the literature.

We consider a layout design of unequal area facilities.Harmony search method is applied with modification to improve its performance.An effective slicing tree representation for layout configuration is presented.New penalty scheme for solution selection is introduced.The proposed approach generates the favorable solutions for well-known problem set. An unequal area facility layout problem (UA-FLP) is a typical optimization problem that occurs when constructing an efficient layout within given areas. In this research, a harmony search (HS)-based heuristic algorithm is presented to solve UA-FLPs. In this study, the facility layout is represented as an allocation of blocks with restrictions in terms of an unequal area and rectangular shape. A more effective facility layout representation is proposed. This is done via a slicing tree representation as a form of layout structure, and via the HS-based algorithm, which generates a quality solution. Once the basic HS solution is generated, modifications are introduced to facilitate improvements. Specifically, the structure of the slicing tree representation is modified, and a re-adjustment operation is added to diversify the possible range of solutions. A penalty scheme is also proposed to improve the feasible region searching capabilities. The effects of the alterations are evaluated by testing well-known problems from previous studies. The proposed algorithm generates the solutions as proficiently as the best results provided by previous research. The proposed method is robust in terms of process, and it determines a favorable solution within a short amount of time.

This paper proposes a hybrid algorithm that combines a Tabu Search algorithm enhanced with a modified neighborhood structures to solve the Vehicle Routing Problem with Private fleet and common Carrier (VRPPC). We consider a variant of the well-known Vehicle Routing Problem in which a fixed internal fleet (Private fleet) is available at the warehouse in the presence of an external Transporter. In order to provide computer based decision support a numerical experiments were carried out on the standard instances taken from the literature which show that the proposed solution methodology based on ejections chains neighborhood structures provides competitive quality solutions compared to other heuristics proposed in the literature and it provides the best-known solutions to a well-known benchmark problem set.

Although every educational institution has a code of academic honesty, they still encounter incidents of plagiarism. These are difficult and time-consuming to detect and deal with. This paper explores the use of personalized assessments with the goal of reducing incidents of plagiarism, proposing a personalized assessment software framework through which each student receives a unique problem set. The framework not only auto-generates the problem set but also auto-marks the solutions when submitted. The experience of using this framework is discussed, from the perspective of both students and staff, particularly with respect to its ability to mitigate plagiarism. A comparison of personalized and traditional assignments in the same class confirms that the former had far fewer observed plagiarism incidents. Although personalized assessment may not be cost-effective in all courses (such as language courses), it still can be effective in areas such as mathematics, engineering, science, and computing. This paper concludes that personalized assessment is a promising approach to counter plagiarism.

The route planning of service vehicles is an important issue for large-scale enterprises which have hundreds of employee. Especially for the flexible shift system which brings about uncertainty for the drivers, the route plans have to be formed daily by the managers. This study addresses the route optimization problem of an automotive company which aims to minimize total transportation cost of the service fleet and proposes a mixed integer mathematical model to solve problem. The objective of the mathematical model is to minimize fixed and transportation cost of the fleet by considering vehicle capacities and travelling time constraints. The mathematical model is tested on a randomly generated problem set which consist of different sized instances for homogeneous service fleet. The computational results obtained by the Gurobi solver show that the proposed mathematical model is capable to find route plans for the real-life service vehicles routing decisions that can minimize total transportation cost of the fleet. Keywords: Vehicle routing, service systems, mathematical modelling

In today's cross-docking offers a significant cost benefits to companies because of the small orders and frequent deliveries in logistical environment. Cross-docking is a relatively new logistics strategy in which items are moved from suppliers to customers through cross-docking centers without putting them into long-term storage. One of the fundamental issues in cross-docking facility is to assign the incoming and outgoing trucks to the doors in order to minimize movement of the products. This paper addresses the truck-door assignment problem of cross-docking centers with product placement plans and proposes a mixed integer mathematical model to solve problem. The objective of the mathematical model is to find best truck-door assignment and product placement plans that minimize total travelling distance of the products. The proposed model is tested on a problem set which are created by using a real-life data of a logistics company. The computational results show that the proposed mathematical model can be carried out for the real-life operations to minimize movement costs of the products in cross-docking centers. Keywords: Cross-docking, truck-door assignment, product placement, mathematical modelling

Timetabling is one of the computationally difficult problems in scheduling and aims to find best time slots for a number of tasks which require limited resources. In this paper, we examine different solution approaches for the real-world examination timetabling problem (ETP) for university courses. The problem has unique hard and soft constraints, when compared to previous efforts, i.e. consecutive exams, sharing of rooms, room preferences, room capacity and number of empty slots. The aim of the problem is to achieve a timetable, which minimizes the total number of the examination slots without any conflicts. First, the real-world problem is formally defined and a mixed integer linear model is presented. Then, a constructive heuristic and a genetic algorithm based meta-heuristic are proposed in order to solve the ETP. Proposed approaches are tested on a problem set formed by using a real-life data. Results reveal that, proposed approaches are able to produce superior solutions in a limited time. Keywords: Timetabling, constructive heuristic, genetic algorithm

While Physics Education Research has a rich tradition of problem-solving scholarship, most of the work has focused on more traditional, well-defined problems. Less work has been done with ill-structured problems, problems that are better aligned with the engineering and design-based scenarios promoted by the Next Generation Science Standards. This study explored the relationship between physics content knowledge and ill-structured problem solving for two groups of high school students with different levels of content knowledge. Both groups of students completed an ill-structured problem set, using a talk-aloud procedure to narrate their thought process as they worked. Analysis of the data focused on identifying students’ solution pathways, as well as the obstacles that prevented them from reaching “reasonable” solutions. Students with more content knowledge were more successful reaching reasonable solutions for each of the problems, experiencing fewer obstacles. These students also employed a greater variety of solution pathways than those with less content knowledge. Results suggest that a student’s solution pathway choice may depend on how she perceives the problem.

Purpose Problem solving is a skill in mathematics which although always relevant has heightened priority due to the changes in the new mathematics GCSE (Department for Education, 2013). It has previously been a skill which is deemed underdeveloped within mathematics and therefore is a theme which teachers are seeking to improve and nurture in order to align with the new changes. The GCSE is the formal qualification that students take at the end of Key Stage 4 (KS4) in the UK. The paper aims to discuss these issues. Design/methodology/approach The focus of the enquiry was to explore, using lesson studies, the differences in students’ approaches to problem solving. Consequently, key themes relating to the mediation of gender, ability, and academic motivation surfaced. Considering these themes, the paper subsequently reflects upon pedagogical practices which might effectively develop students’ ability to problem solve. The study took part in a mixed gender comprehensive secondary school with students taking part in the observation lesson ranging in age from 11 to 12 years old. The authors are the teachers who took part in the lesson study. The teachers implemented observation techniques in the form of video and peer observation with the accompanying teacher. In addition, students provided feedback on how they approached the problem-solving tasks through a form of semi-structured interviews, conducted via the use of video diaries where no teachers were present to prevent power bias. Following this, a thematic analysis of both the observations and student video diaries generated conclusions regarding how said key themes shaped the students’ approaches to problem solving. Findings Students’ frustration and competitive need to find a specific answer inhibited their ability to thoroughly explore the problem posed thus overseeing vital aspects needed to solve the problem set. Many students expressed a passion for problem solving due to its freedom and un-rigid nature, which is something teachers should nurture. Originality/value Generally, teachers are led by a culture in which attainment is the key. However, an atmosphere should be developed where the answer is not the key and students can explore the vibrant diversity mathematics and problem solving can offer.

8th International Conference on Advances in Information Technology (IAIT) -- DEC 19-22, 2016 -- Macau, PEOPLES R CHINA, WOS: 000418465800008, Two dimensional strip-packing problem (2DSPP) consists of packing a set of rectangular items on one strip with a restriction of a maximal width and height. Because the conventional algorithms are still sub-optimal, the researchers tend towards searching for more successful alternative algorithms to solve 2DSPP. The fruit fly optimization algorithm (FOA), which is one of the recently proposed meta-heuristic algorithms, has been successfully applied on many engineering and mathematical problems. This study presents an implementation of FOA for solving non-oriented 2DSPP. The aim of the study is to find the optimal sequence of the rectangles in a strip, and then to place the rectangles by bottom left fill approach to have the optimal height within a fixed width box. The experiments are concluded on online available set of 2DSPP test problems. The preliminary results of the study are compared with the results of some conventional or heuristic approaches which use the same problem set. The experimental results show the promising results are obtained by FOA on solving 2DSPPs. (c) 2017 The Authors. Published by Elsevier B.V., Selcuk UniversitySelcuk University, This study is supported by the Coordinatorship of Scientific Research Projects at Selcuk University.

The purpose of this study is to analyze the problem set items to get information and feedback on critical thinking skills tests. This research develops a SGIL model that can improve students’ critical thinking learning. This study uses a research and development design to develop the SGIL model. The SGIL model is proven to be able to improve the critical understanding of PGSD students. The syntax of SGIL consists of 6 steps, namely: (1) discussion of problems and topic selection; (2) research planning; (3) implementation; (4) data integration; (5) analysis and synthesis; and (6) conclusions and communication. The participants were 114 Elementary Teacher Education Program (PGSD) students who were selected by using a random sampling technique. This research was conducted in 3 sample universities. The data of SGIL model effectiveness was measured by using instrument of critical thinking skills. This instrument has been analyzed for its validity, reliability, level of difficulty, and discrimination item. Tests to obtain data were carried out for 90 minutes. The instrument was calculated by using analysis of Quest and Lisrel.

To benefit from the location advantages provided from two main covering location problems, namely set covering location problem and maximal covering location problem, a new mathematical model is presented in this study. In this model, the main facilities are located gradually through the planning periods, providing full coverage for the incremental demand of nodes. In addition, a limited number of modules are assigned to the located facilities to maximize the covered demand nodes in operational periods. Combining the set covering and maximal covering location problems in one model, to take advantage of the merits of both models is studied. Some test problems are solved to investigate the proposed model. The numerical results confirm the applicability of the model and provide insights for the proposed model.

This paper focuses on a four-phase approach for describing the process whereby a school shooter realizes the activities leading up to the attack Desire to Act, Authority to Act, Means to Act, and Opportunity to Act. Greater emphasis is given to the first two phases, and the concept of ‘Authority to Act’ is examined closely. Confronted with the awful reality of a school shooting, one of the most difficult questions in everyone’s mind is, “How could they do such a thing”? The answer lies in the belief that they have the authority to act and a better understanding of this component may one day provide enhanced tools for prevention. A systems thinking approach is used to discover key relationships within the problem set that lead to new insights into the true nature of the problem. This paper will use this modern set of tools to investigate the causal factors which influence school shooters.

The aim of this study is to present how to implement the constructive learning theory and self-determination theory into a video game. This video game is designed as a new kind of teaching tool for chemistry concepts and as an assessment tool for domain-specific problem-solving competence, by applying a problem-solving model and the factors of self-determination theory through game elements. These types of video games are designed not only to have fun and motivate students, but also to help the teachers and educators to assess their students according to their weaknesses and strengths in each particular phase of the competence. Thus, educators could improve their teaching strategy or use the tools to improve weak areas. Based on this idea, we developed ALCHEMIST. ALCHEMIST targeted the 9th-grade students in the German chemistry curriculum. The scientific content is about acids, bases, and indicators, and is designed and based on the problem-solving model to access this competence of the students. The 3D game framework was chosen for this game since it makes the game efficient, interactive and drives it into more virtual reality. The designing process was complicated, as it includes multidisciplinary work across psychology, design, scientific content, development and programming, which makes it challenging. To cover these points, we followed a game design model, adding some steps to cover the educational needs and the aim of the game.In order to make the game more effective and interactive, and to drive it into a more virtual world, the 3D game framework was chosen for this game since for this age group a 3D educational game is expected to be more effective (Gunter et al, 2008; Terzidou et al, 2012) . Moreover, the use of avatars in the 3D virtual environment establishes non-verbal communication (NVC) features, which can foster collaboration interactions, and enhance the student’s ability to apply abstract knowledge later in reality (Dede, 1992; Tsiatsos and Terzidou, 2010). The log file provided shows the player points and gameplay path which indicates the performance levels for each problem set. Also, it can help teachers to evaluate each particular phase of problem-solving competence. After the success of the development of our game-based theoretical background, it will be tested and validated by experts in chemistry education. This validation process should examine if the problem-solving model is correctly applied and test the significance of the game design, scientific content and the game’s objective.It may also be of interest to develop such video games with scientific content and educational background in other fields. It also would be a success for the scientist to apply the scientific content through a fantasy 3D video game to the students at different ages to have fun, to learn, and to assess their competencies.

Manual evaluation of software program is always a daunting task as it is subjective, time-consuming and requires significant amount of effort. It may also involve human bias. Automated analysis and evaluation can address the above problems adequately. In our paper, we have proposed an automated performance evaluation framework for programming assignment tasks, maintained within a Case-based corpus. The corpus should mandatorily consist of different programming techniques utilized to solve the problem set in an assignment, each one saved as a case instance. Candidate programs are maintained in a separate corpus and then statistically measured to detect outlier performance with respect to the degree of novelty of the approach, quality based on time complexity and amount of plagiarism detected. All new approaches would automatically update the existing base.