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Synthesis of engineering solutions for energy storage systems is of great importance for the development of the economy, where renewable energy sources are becoming more and more in demand. Therefore, research, development, and application of renewable sources constitute one of the most important tasks for environmental protection and creation of perspective transportation and energy systems. To increase efficiency, it is necessary constantly to improve technical solutions and introduce new technologies. The development of energy storage systems, with a special focus on mechanical storage systems, is considered. An advanced morphological approach is used for the analysis. A number of engineering solutions are considered, and a hybrid device combining a hydraulic accumulator and a gravity energy storage system is investigated in detail. Conclusions are drawn about possible applications of the engineering solutions found. [ABSTRACT FROM AUTHOR]

Current approaches for identifying valuable content among the multitude of solutions in crowdsourcing contests are resource‐intensive and constrained by human processing capacity. As idea convergence processes usually focus on filtering out single ideas, the potential of solution‐related knowledge among the heterogeneous ideas is not exploited in a sustainable manner. Transformer‐based language models can process large sets of idea descriptions into digestible structures, with unprecedented capabilities for understanding and manipulating text. This study explores how they can help organizations and decision‐makers navigate crowdsourced solution spaces efficiently and comprehensively. Inspired by theoretical concepts around problem‐solving and innovation search, we conceptualize three related search practices—direct search, cluster exploration and pattern discovery—and illustrate them on 289 crowdsourced ideas for future mobility and energy services. Direct search can assist in identifying solutions that match pressing needs or subproblems. Cluster exploration enables aggregating semantically similar ideas into clusters to identify relevant needs. Pattern discovery synthesizes themes and interrelations to build a holistic understanding of potential solutions. The study contributes to the application of AI‐assisted idea convergence by adding a new perspective beyond filtering out a few promising ideas. [ABSTRACT FROM AUTHOR]

We prove, under an assumption on the critical points of a real‐valued function, that the symmetric Ising perceptron exhibits the 'frozen 1‐RSB' structure conjectured by Krauth and Mézard in the physics literature; that is, typical solutions of the model lie in clusters of vanishing entropy density. Moreover, we prove this in a very strong form conjectured by Huang, Wong, and Kabashima: a typical solution of the model is isolated with high probability and the Hamming distance to all other solutions is linear in the dimension. The frozen 1‐RSB scenario is part of a recent and intriguing explanation of the performance of learning algorithms by Baldassi, Ingrosso, Lucibello, Saglietti, and Zecchina. We prove this structural result by comparing the symmetric Ising perceptron model to a planted model and proving a comparison result between the two models. Our main technical tool towards this comparison is an inductive argument for the concentration of the logarithm of number of solutions in the model. [ABSTRACT FROM AUTHOR]

Home health care problems consist in scheduling visits to home patients by health professionals while following a series of requirements. This paper studies the Home Health Care Routing and Scheduling Problem, which comprises a multi‐attribute vehicle routing problem with soft time windows. Additional route inter‐dependency constraints apply for patients requesting multiple visits, either by simultaneous visits or visits with precedence. We apply a mathematical programming solver to obtain lower bounds for the problem. We also propose a biased random‐key genetic algorithm, and we study the effects of additional state‐of‐the‐art components recently proposed in the literature for this genetic algorithm. We perform computational experiment using a publicly available benchmark dataset. Regarding the previous local search‐based methods, we find results up to 26.1% better than those of the literature. We find improvements from around 0.4% to 6.36% compared to previous results from a similar genetic algorithm. [ABSTRACT FROM AUTHOR]

The creation of new innovative technical and technological systems based on the morphological approach is considered. The research refers to the area of decision-making at the stage of conceptual design. Particular attention is paid to the procedures for taking into account the opinion of experts. The features of the proposed approach consist in taking into account all potential variants of solutions synthesized by experts. Subsequently, the set of solutions is clustered and compared. Introduction of average normalized estimates makes it possible to analyze both experts individually and the whole group. Examples of the use of the morphological analysis for a number of technological processes are given. The combinatorial set of possible solutions amounted to tens of thousands of variants. The use of cluster analysis, set theory, and modeling makes it possible to find new innovative technologies. [ABSTRACT FROM AUTHOR]

The paper consideres linear systems of ordinary differential equations of arbitrary order with a matrix identically degenerate in the domain of definition at the highest derivative of the desired vector function and with loads in the form of Volterra and Fredholm integral operators. The initial value problems are formulated using projections onto admissible sets of initial vectors. Special attention is paid to systems having singular points on the interval of integration. The concept of a singular point is formalized. Their classification in the case of differential equations is given. A number of examples illustrating the theoretical results are presented. [ABSTRACT FROM AUTHOR]

Feature Selection in High Dimensional Space is a combinatory optimization problem with an NP-hard nature. Meta-heuristic searching with embedding information theory-based criteria in the fitness function for selecting the relevant features is used widely in current feature selection algorithms. However, the increase in the dimension of the solution space leads to a high computational cost and risk of convergence. In addition, sub-optimality might occur due to the assumption of a certain length of the optimal number of features. Alternatively, variable length searching enables searching within the variable length of the solution space, which leads to more optimality and less computational load. The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. However, an uncertainty in their performance exists. In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. For this purpose, we implemented four types of variable length meta-heuristic searching algorithms, namely VLBHO-Fitness, VLBHO-Position, variable length particle swarm optimization (VLPSO) and genetic variable length (GAVL), and we compared them in terms of classification metrics. The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower fitness values when optimizing mathematical functions of the variable length type. [ABSTRACT FROM AUTHOR]

Many technology-intensive (TI) firms find it challenging to leverage customisation and achieve sustainable innovation. Although some firms use modularity to tackle this challenge, mixed effects on sustainable innovation have been reported. This study uses organisational learning and ambidexterity theory to provide insights into how TI firms can achieve ‘win-win’ situations where sustainable innovation is increased through customisation. First, we argue that customisation should be viewed two-dimensionally and identify both modularity and solution space freedom as important dimensions. We argue that modularity reflects knowledge specialisation and solution space freedom reflects knowledge variety. Both of these dimensions affect organisational learning and, in turn, sustainable innovation. Second, we argue that the relationship between customisation and organisational learning is affected by supplier characteristics, specifically supplier sophistication. Survey data from 166 managers were used to empirically test the conceptual model and hypotheses. Polynomial response surface analysis confirms that customising by balancing high degrees of both modularity and solution space freedom results in superior organisational learning. High levels of supplier sophistication do not strengthen these effects. Rather, our results show that combining high degrees of modularity with constrained solution spaces increases learning for TI firms working with less sophisticated suppliers. In addition, organisational learning fully mediates the effect of customisation on sustainable product and process innovation. [ABSTRACT FROM AUTHOR]

Seeking inspiration from other perspectives is a prominent mechanism to support ideation. AI-based language models can help overcome information processing limits and efficiently structure large solution spaces spanned by prior ideas. However, it remains unclear how the search through a solution space affects the subsequent idea generation. This study explores the influence of different sets of prior idea stimuli pre-structured by an AI-supported clustering on ideation outcomes. The sets varied in quantity and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively interacts with domain-specific knowledge. However, it negatively affects idea feasibility and specificity. These findings encourage innovators seeking particularly novel ideas to complement their current processes with AI-supported clustering tools while taking steps to avoid vagueness. [ABSTRACT FROM AUTHOR]

Restoring the health of urban streams has many of the characteristics of a wicked problem. Addressing a wicked problem requires managers, academics, practitioners, and community members to make negotiated tradeoffs and compromises to satisfy the values and perspectives of diverse stakeholders involved in setting restoration project goals and objectives. We conducted a gap analysis on 11 urban stream restoration projects to identify disconnections, underperformance issues, and missing processes in the project structures used to develop restoration project goals and objectives. We examined the gap analysis results to investigate whether managers appropriately identified problem statements and met stated objectives. Projects that aimed to restore overall stream health commonly fell short for various reasons, including limited stakeholder and community input and buy-in, revealing potential limitations in the breadth of objectives, values, and stakeholder perspectives and knowledge types. Projects that emphasized integrating community values and diverse knowledge types tended to meet the expected outcomes of restoring stream processes through incremental solutions. Managers implementing more holistic solutions and values-driven approaches are more likely to consider diverse viewpoints from a variety of community local institutions. Based on these and other results, we propose a conceptual framework that integrates diverse perspectives and knowledge to enhance social and ecological outcomes of urban stream restoration. The framework also emphasizes the importance of setting objectives that support incremental solutions to foster more realistic expectations amongst stakeholders. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes an automated scanning process of a structured light system for objects without overhangs. The processes for scanning those objects need to plan scanning directions that minimise the missing area on a three-dimensional surface during the scanning process. Thus, the processes require an approach that finds the next scanning direction efficiently in terms of computational costs. This paper develops a scanning simulation approach to meet this requirement. In order to apply the developed approach, the proposed process generates asolution spacefor candidate-scanning directions, and represents an intermediate 3D model. The developed approach traverses the solution space in a virtual environment and executes virtual scanning for the intermediate 3D model. The virtual scanning result of each candidate-scanning direction is analysed in order to evaluate the contribution for filling missing area. The proposed process defines key scanning directions in the solution space through the iterative execution of the developed approach. The proposed process has been implemented, and applied to the scanning experiments of dental impressions. [ABSTRACT FROM PUBLISHER]

One of the main directions in the development of modern mechanical engineering consists in the use of novel materials and technological processes for the production of parts and products, in particular additive technologies. Nowadays, dozens of technological processes and additive manufacturing plants based on a number of physical and chemical effects have been developed and implemented. A brief history, the basic principles, and the features and advantages of additive technologies are considered, and a system for their classification, based on a generalized morphological approach, is proposed. All the known and promising technologies are placed in a morphological matrix; further a space of possible technical solutions is analyzed and formed. The number of potential variants amounts to 276 420. The proposed classification of additive technologies is the most complete at the moment. [ABSTRACT FROM AUTHOR]

Purpose: Product configurators are expert systems that support product customization by defining how predefined entities and their properties may be combined. Developers of configuration systems act as designers, although they do not often recognize that they are performing as such. Moreover, exploring solution spaces is typically not integral to configuration projects, as this task is typically perceived as mapping existing knowledge to the configurator. This article argues that developing configurators may be understood by distinguishing between the problem and solution spaces using design thinking (DT). Design/methodology/approach: A multiple-case-study approach with four configuration projects is adopted to study two projects involving DT and compare them to two similar projects not involving DT. Data collection depended on multiple data sources via workshops and semi-structured interviews. Findings: First, DT methods and concept–knowledge (C-K) theory are integrated into configuration projects. Second, the application of DT during configurator development is presented through workshops and interviews, which demonstrates the benefits of DT in overcoming existing challenges. Research limitations/implications: The case studies demonstrate the successful implementation of DT in developing configurators. However, a limited number of cases in only one company limits the generalizability of the results. Practical implications: The framework's individual steps create a structured approach to supporting industrial companies with a toolbox of DT techniques and methods for configuration projects. Originality/value: The results show that the application of DT to configuration projects can improve user motivation, stakeholder satisfaction and knowledge acquisition. [ABSTRACT FROM AUTHOR]

Copyright of Exacta is the property of Exacta - Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this brief, a technique using the co-design of three matching sub-networks is proposed to solve the contradiction between bandwidth and extended the back-off region of symmetrical Doherty power amplifier (DPA). The phase-shifting solution space of DPA output matching networks is calculated, and its relationship with the input impedance of the post-matching network is analyzed with and without the frequency variable. Configurable back-off region and adjustable operating center frequency can also be realized in the design by using the proposed method. The design flow is given, and then a detailed example under 9-dB back-off region is presented. To verify the proposed theory, a DPA is designed and fabricated across 1.9-2.4 GHz. The experimental results show drain efficiencies of 44.2-49.7% at 8.5-9 dB output power back-off levels across 1.9-2.4 GHz. The fabricated DPA achieves output powers of 44.1-44.8 dBm. [ABSTRACT FROM AUTHOR]

In the presence of alternative routings, the manufacturing cell design involves the solution of a large number of (simple) cell-formation problems. We present a bounding scheme which examines all combinations of alternative routings and solves only a few cell-formation problems, thereby limiting the solution space which is searched heuristically. This leads to increased reliability for the solutions obtained. The resulting algorithm is shown to be applicable for problems arising in practice frequently, when the initial design includes relatively few exceptional elements, and machine duplication and/or genuine multiple process plans are used correctively, to eliminate the remaining intercellular traffic. [ABSTRACT FROM AUTHOR]

We investigate the connection between divisibility of full-rank square matrices of linear scalar differential operators over some differential field K, and the solution spaces of these matrices over the universal Picard–Vessiot extension of K. We establish some properties of the solution spaces of the greatest common right divisor and the least common left multiple of such matrices. [ABSTRACT FROM AUTHOR]

This paper presents an advanced morphological approach supporting designers and developers in their search for as well as synthesis and analysis of new engineering solutions during the conceptual design stage. The proposed method is based on the cluster analysis and the set theory, the set of rules and engineering implementations maximizing the gain of the products potential. The number of possible combinations using the standard morphological technology is extremely large. We present a mathematical framework that handles this problem. The method was evaluated with case studies of new engineering solutions in aerospace, ecology and adaptive soundproofing system. The case studies verified the significant potential of the proposed approach in comparison with the methods presently in use. [ABSTRACT FROM AUTHOR]

In electrical impedance tomography, the primary task of image reconstruction process is to solve a discrete ill‐posed inverse problem. The estimated solution is commonly obtained under a regularisation framework so that the noise amplified solution, which occurs during the matrix inversion process, can be avoided. The regularisation framework aims at balancing the model‐data fitness while simultaneously constraining the solution space with additional prior (commonly prescribed through the regularisation matrix and solution‐norm). In this study, a relationship between two robust regularisation matrices namely Newton one‐step error reconstruction and fidelity‐embedded regularisation is explicitly highlighted in both spatial and singular value decomposition domains. A hybrid regularisation matrix which encompasses the two prior knowledge, non‐uniform sensitivity distribution and array response correlation, is then proposed as an alternative prior. Experimental results along with several evaluated performance parameters highlight the ability of the proposed prior to achieve a well‐balanced and robust performance. [ABSTRACT FROM AUTHOR]

Lack of knowledge or epistemic uncertainty in technical systems can be treated with so-called Solution Spaces. They are sets of good designs that reach by definition all design goals. Considering sets rather than one single design allows for unintended variations of component properties that are typical in the early stages of systems design. Box-shaped Solution Spaces can be expressed as the Cartesian product of permissible intervals for design variables. These intervals serve as independent target regions and can be interpreted as component requirements. Existing algorithms optimize the size of box-shaped Solution Spaces. Unfortunately, the size of the permissible intervals for crucial design variables is often not large enough to encompass all uncertainty and to ensure feasibility. A new approach is introduced where the design variables are divided into a set of early- and a set of late-decision variables. Early-decision variables are associated with permissible intervals on which they may assume any value to encompass uncertainty due to limited controllability. Late-decision variables are controllable and therefore associated with intervals where they can be adjusted to any specific value. The Cartesian product of these intervals is called a Solution-Compensation Space. It has the property that for all values of early-decision variables from their permissible intervals there exists at least one set of late-decision variable values from their intervals such that the resulting design reaches all design goals. The approach is applied to a design problem from vehicle driving dynamics. It is shown that the permissible intervals for the early-design variables can be increased significantly. [ABSTRACT FROM AUTHOR]

This article presents and discusses a perspective on the concept of "solution space" in physiotherapy. The model is illustrated with a subjective assessment of the way movements are performed and an objective quantification of the dynamics of the recovery process for a patient with a knee injury. Based on insights from the domain of human motor control, solution space is a key concept in our recovery model that explains the emergence of a variety of adaptive changes that may occur in the movement system recovering from an injury. The three dimensions that span the solution space are: (1) information and control processes; (2) time; and (3) degrees of freedom. Each dimension is discussed within the context of feasible physiotherapeutic assessments to identify and facilitate desirable behavioral patterns or bypass emerging but undesirable behavioral patterns that could impede both short- and long-term recovery. Central to this article is our view on the relationship between the recovery process and the three dimensions of the solution space, which determines the model's usefulness as a motor-rehabilitation monitoring tool. [ABSTRACT FROM AUTHOR]

A differential operator with constant coefficients is hypoelliptic if and only if its solution space is of finite functional dimension. We extend this property to operators with variable coefficient. We prove that an equally strong differential operator with variable coefficients has the same property. In addition, we extend the Zielezny's result to operators with variable coefficients; prove that an operator with analytic coefficients on Rn is elliptic if and only if locally the functional dimension of its solution space is the same as the Euclidean dimension n. [ABSTRACT FROM AUTHOR]

This study explored the use of heuristics in the design space by novice and expert engineers in the initial ideation of a design solution. Verbal protocol analyses were conducted with four engineering students and four professional engineers as they generated ideas to solve a design problem. Overall, both experts and novices used various types of heuristics. Although novices' heuristics tend to focus on improving the function of the design, experts' heuristics tend to focus on improving both function and form. The implication is that the deliberate teaching of design heuristics, along with other strategies, will help in the development of generative skills of students, stimulating more creative and innovative designs. Validated design heuristics can be integrated within engineering design content at appropriate grade levels to aid in building the repertoire of heuristics used by engineering and technology education students. [ABSTRACT FROM AUTHOR]

We settle the complexity of the Independent Set Reconfiguration problem on bipartite graphs under all three commonly studied reconfiguration models. We show that under the token jumping or token addition/removal model, the problem is NP-complete. For the token sliding model, we show that the problem remains PSPACE-complete. [ABSTRACT FROM AUTHOR]

We explore the nature of the solution space for the Golomb nested recursion . On this solution space, we define a natural equivalence relation and restrict our attention to non-equivalent solutions. We describe and prove an algorithm that determines whether a given set of initial conditions generates a solution. Up to equivalence, there is a unique solution whose forward differences are eventually either 0 or 1, namely, the Golomb sequence , generated by the initial condition . This sequence is asymptotic to ; we conjecture that this is true of every solution. We further conjecture that each solution has what we call a generational structure that abstracts combinatorial properties of . It appears that for any given solution, its generations are composed of only a finite number of building blocks. [ABSTRACT FROM AUTHOR]

In the Vertex Cover Reconfiguration (VCR) problem, given a graph G, positive integers k and ι two vertex covers S and T of G of size at most k, we determine whether S can be transformed into T by a sequence of at mostι vertex additions or removals such that every operation results in a vertex cover of size at most k. Motivated by results establishing the W[1]-hardness of VCR when parameterized by ι we delineate the complexity of the problem restricted to various graph classes. In particular, we show that VCR remains W[1]-hard on bipartite graphs, is NP-hard, but fixed-parameter tractable on (regular) graphs of bounded degree and more generally on nowhere dense graphs and is solvable in polynomial time on trees and (with some additional restrictions) on cactus graphs. [ABSTRACT FROM AUTHOR]

One of the main challenges for solving complex water-resources optimization is choosing an appropriate solution method. An important feature of optimization problems is the convexity and extent of their solution spaces. The solution space is the set whose elements are all the reservoir releases that meet the optimization problem's constraints and are thus feasible. The solution space of optimization problems can be convex or nonconvex. This study presents a method for determining the convexity or nonconvexity of the optimization problem solution space. The convexity and the extent of the solution space for a water-supply and a hydropower-production reservoir operation problem are evaluated by the proposed method. It is shown that the solution spaces of the former and latter problems are convex and nonconvex, respectively. The dependence of the solution spaces of the two reservoir operation problems on changes in evaporation, water demand for the water-supply reservoir, power plant capacity (PPC) for the hydropower reservoir, dead storage, reservoir capacity, and reservoir inflow is evaluated. The results demonstrate that the generalized reduced gradient (GRG) method finds an optimal value faster and more accurately than does the genetic algorithm (GA) when solving the water-supply problem, and that the GRG search is trapped in a local optimum when solving the hydropower-production problem. [ABSTRACT FROM AUTHOR]

This paper proposes a genetic algorithm $$GA\_JS$$ for solving distributed and flexible job-shop scheduling (DFJS) problems. A DFJS problem involves three scheduling decisions: (1) job-to-cell assignment, (2) operation-sequencing, and (3) operation-to-machine assignment. Therefore, solving a DFJS problem is essentially a 3-dimensional solution space search problem; each dimension represents a type of decision. The $$GA\_JS$$ algorithm is developed by proposing a new and concise chromosome representation $${\varvec{S}}_{{\varvec{JOB}}}$$ , which models a 3-dimensional scheduling solution by a 1-dimensional scheme (i.e., a sequence of all jobs to be scheduled). That is, the chromosome space is 1-dimensional (1D) and the solution space is 3-dimensional (3D). In $$GA\_JS$$ , we develop a 1D-to-3D decoding method to convert a 1D chromosome into a 3D solution. In addition, given a 3D solution, we use a refinement method to improve the scheduling performance and subsequently use a 3D-to-1D encoding method to convert the refined 3D solution into a 1D chromosome. The 1D-to-3D decoding method is designed to obtain a 'good' 3D solution which tends to be load-balanced. In contrast, the refinement and 3D-to-1D encoding methods of a 3D solution provides a novel way (rather than by genetic operators) to generate new chromosomes, which are herein called shadow chromosomes. Numerical experiments indicate that $$GA\_JS$$ outperforms the IGA developed by De Giovanni and Pezzella (Eur J Oper Res 200:395-408, 2010), which is the up-to-date best-performing genetic algorithm in solving DFJS problems. [ABSTRACT FROM AUTHOR]

Given a Boolean formula and a satisfying assignment, a flip is an operation that changes the value of a variable in the assignment so that the resulting assignment remains satisfying. We study the problem of computing the shortest sequence of flips (if one exists) that transforms a given satisfying assignment s to another satisfying assignment t of an input Boolean formula. Earlier work characterized the complexity of deciding the existence of a sequence of flips between two given satisfying assignments using Schaefer's framework for classification of Boolean formulas. We build on it to provide a trichotomy for the complexity of finding the shortest sequence of flips and show that it is either in P, NP-complete, or PSPACE-complete. Our result adds to the growing set of complexity results known for shortest reconfiguration sequence problems by providing an example where the shortest sequence can be found in polynomial time even though the sequence flips variables that have the same value in both s and t. This is in contrast to most reconfiguration problems studied so far, where polynomial-time algorithms for computing the shortest path were known only for cases where the path modified no more than the symmetric difference of s and t. Our proof uses Birkhoff's representation theorem on a set system that we show to be a distributive lattice. The technique provides insights and can perhaps be used for other reconfiguration problems as well. [ABSTRACT FROM AUTHOR]

We present the first results on the parameterized complexity of reconfiguration problems, where a reconfiguration variant of an optimization problem $$\mathcal {Q}$$ takes as input two feasible solutions S and T and determines if there is a sequence of reconfiguration steps, i.e. a reconfiguration sequence, that can be applied to transform S into T such that each step results in a feasible solution to $$\mathcal {Q}$$ . For most of the results in this paper, S and T are sets of vertices of a given graph and a reconfiguration step adds or removes a vertex. Our study is motivated by results establishing that for many NP-hard problems, the classical complexity of reconfiguration is PSPACE-complete. We address the question for several important graph properties under two natural parameterizations: k, a bound on the size of solutions, and $$\ell $$ , a bound on the length of reconfiguration sequences. Our first general result is an algorithmic paradigm, the reconfiguration kernel, used to obtain fixed-parameter tractable algorithms for reconfiguration variants of Vertex Cover and, more generally, Bounded Hitting Set and Feedback Vertex Set, all parameterized by k. In contrast, we show that reconfiguring Unbounded Hitting Set is W[2]-hard when parameterized by $$k+\ell $$ . We also demonstrate the W[1]-hardness of reconfiguration variants of a large class of maximization problems parameterized by $$k+\ell $$ , and of their corresponding deletion problems parameterized by $$\ell $$ ; in doing so, we show that there exist problems in FPT when parameterized by k, but whose reconfiguration variants are W[1]-hard when parameterized by $$k+\ell $$ . [ABSTRACT FROM AUTHOR]

In engineering, it is often desirable to find a subset of the set of feasible designs, a solution space, rather than a single solution. A feasible design is defined as a design which does not violate any constraints and has a performance value below a desired threshold. Performance measure, threshold value and constraints depend on the specific problem. For evaluation of a design with respect to feasibility, a model is required which maps the design parameters from the input space onto the performance measures in the output space. In state-of-the-art methodology, iterative sampling is used to generate an estimate of the frontier between feasible and infeasible regions in the input space. By evaluating each sample point with respect to feasibility, areas which contain a large fraction of feasible designs are identified and subsequently resampled. The largest hypercube containing only feasible designs is sought, because this results in independent intervals for each design parameter. Estimating this hypercube with sufficient precision may require a large number of model evaluations, depending on the dimensionality of the input space. In this paper, a novel approach is proposed for modeling the inequality constraints and an objective function in a way for which a linear formulation can be used, independently of the dimensionality of the problem. Thereby the exact solution for the largest feasible hypercube can be calculated at much lower cost than with stochastic sampling as described above, as the problem is reduced to solving a linear system of equations. The method is applied to structural design with respect to the US-NCAP frontal impact. The obtained solution is compared to numerical solutions of an identical system, which are computed using reduced order models and stochastic methods. By this example, the high potential of the new direct method for solution space computation is shown. [ABSTRACT FROM AUTHOR]

To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not occurred in the data before. We provide a detailed description of the system's implementation and perform a technical evaluation on a small set of data to determine the effectiveness of the component algorithms and ITAP's potential for self-improvement. The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time. [ABSTRACT FROM AUTHOR]

Vessel traffic is changing due to changing transport demands, larger ships, and new use of sea space, such as windmill parks. This may have an effect on the risks at sea. This paper uses ship state information provided by automatic identification system-messages to analyze the traffic on the North Sea. From the ship-state information conflict situations are selected, i.e., situations where the ships need to maneuver to avoid collision. In addition, situations of near-miss collisions are calculated. It was found that complex conflicts lead to more near misses. It was also found that near misses are not spread evenly over the sea but are concentrated in a number of specific locations. These findings may be important for the design of route structures for ships, as well as for investigations into methods to resolve complex conflict situations. [ABSTRACT FROM PUBLISHER]

A non‐alternating (NA) form of K‐means is proposed to improve the performance for the most fundamental, yet highly non‐convex clustering problem. The motivation of this Letter is that the non‐convex nature of K‐means can be better handled by stochastic optimisation. However, the alternating update of the Lloyd's algorithm prohibits an effective stochastic optimisation. In order to fully realise the idea, a probabilistic representation is provided for the solution space of K‐means, which leads to a simple yet efficient NA update. Experiments show that the proposed method outperforms the existing variants, especially for large numbers of clusters, with reasonable time complexity. [ABSTRACT FROM AUTHOR]

Oftentimes multivariate data are not available as sets of equally multivariate tuples, but only as sets of projections into subspaces spanned by subsets of these attributes. For example, one may find data with five attributes stored in six tables of two attributes each, instead of a single table of five attributes. This prohibits the visualization of these data with standard high-dimensional methods, such as parallel coordinates or MDS, and there is hence the need to reconstruct the full multivariate (joint) distribution from these marginal ones. Most of the existing methods designed for this purpose use an iterative procedure to estimate the joint distribution. With insufficient marginal distributions and domain knowledge, they lead to results whose joint errors can be large. Moreover, enforcing smoothness for regularizations in the joint space is not applicable if the attributes are not numerical but categorical. We propose a visual analytics approach that integrates both anecdotal data and human experts to iteratively narrow down a large set of plausible solutions. The solution space is populated using a Monte Carlo procedure which uniformly samples the solution space. A level-of-detail high dimensional visualization system helps the user understand the patterns and the uncertainties. Constraints that narrow the solution space can then be added by the user interactively during the iterative exploration, and eventually a subset of solutions with narrow uncertainty intervals emerges. [ABSTRACT FROM AUTHOR]

We explore a reconfiguration version of the dominating set problem, where a dominating set in a graph G is a set S of vertices such that each vertex is either in S or has a neighbour in S. In a reconfiguration problem, the goal is to determine whether there exists a sequence of feasible solutions connecting given feasible solutions s and t such that each pair of consecutive solutions is adjacent according to a specified adjacency relation. Two dominating sets are adjacent if one can be formed from the other by the addition or deletion of a single vertex. For various values of k, we consider properties of $$D_k(G)$$ , the graph consisting of a node for each dominating set of size at most k and edges specified by the adjacency relation. Addressing an open question posed by Haas and Seyffarth, we demonstrate that $$D_{\varGamma (G)+1}(G)$$ is not necessarily connected, for $$\varGamma (G)$$ the maximum cardinality of a minimal dominating set in G. The result holds even when graphs are constrained to be planar, of bounded tree-width, or b-partite for $$b \ge 3$$ . Moreover, we construct an infinite family of graphs such that $$D_{\gamma (G)+1}(G)$$ has exponential diameter, for $$\gamma (G)$$ the minimum size of a dominating set. On the positive side, we show that $$D_{n-\mu }(G)$$ is connected and of linear diameter for any graph G on n vertices with a matching of size at least $$\mu +1$$ . [ABSTRACT FROM AUTHOR]

Vulnerability and resilience are two crucial attributes of social-ecological systems that are used for analyzing the response to disturbances. We assess these properties in relation to agroecosystem buffer capacity and adaptive capacity, which depend on the ‘window of opportunities' of possible changes in terms of selected performance indicators, i.e., the solution space. The vulnerability of the system was quantified as the distance of performance indicators between original and disturbed systems. The buffer capacity was derived from the size of the solution space that could be obtained after reconfiguration of farm components (crops, animals, fertilizers, etc.) that were present on the original farm, whereas the assessment of adaptive capacity was derived in a similar way, but after allowing innovation by introducing new components to the farm. To illustrate the approach, we applied these concepts to two dairy farms in Northwest Michoacán, Mexico. After a disturbance resulting in a fodder maize yield decline, both economic profitability and soil organic matter inputs were reduced. The scope for recovery was different between the farms, but the projected improvements in profitability and organic matter inputs would require considerable changes in the farm configurations, and thus flexibility in farm management. High resilience requires a farmer with the managerial ability to make the required changes to move through the proposed solution space. The approach we present here offers a generic quantitative assessment of vulnerability and resilience concepts, based on a combined assessment of the social and ecological dimensions of agroecosystems. [ABSTRACT FROM AUTHOR]

A stochastic algorithm that computes box-shaped solution spaces for nonlinear, high-dimensional and noisy problems with uncertain input parameters has been proposed in Zimmermann and von Hoessle (Int J Numer Methods Eng 94(3):290-307, 2013). This paper studies in detail the quality of the results and the efficiency of the algorithm. Appropriate benchmark problems are specified and compared with exact solutions that were derived analytically. The speed of convergence decreases as the number of dimensions increases. Relevant mechanisms are identified that explain how the number of dimensions affects the performance. The optimal number of sample points per iteration is determined in dependence of the preference for fast convergence or a large volume. [ABSTRACT FROM AUTHOR]

Design methods practiced in the modern industrial era tend to override the joy of designing. As the understanding of design as science emerged in the 1980s, prescriptive design methods have gradually replaced the descriptive design methods traditionally practiced by designers. In response to the world's demand for cheap products, prescriptive design methods have also led to labor exploitation through mass production processes, which have ultimately changed the work-life experiences of those involved. This phenomenon has resulted in an "exotelic" work-life experience that is driven by fulfilling external goals as opposed to an "autotelic " work-life experience driven by fulfilling self-sustaining goals. Autotelic experience is a vital component of "flow" that makes life more meaningful, thus resulting in happier citizens and a better society. In this light, this study aims to explore the theoretical framework of practices of "problem space" and "solution space" in "descriptive" and "prescriptive" models of design methods. Moreover, this study seeks to determine how practices are related to levels of creativity and "flow, " which is an important factor in a model of an alternative design method focused on autotelic approaches to work, thus rendering work-life experience more pleasant and effective. [ABSTRACT FROM AUTHOR]

Two new linear operators determining automorphisms of the solution space of a special double-confluent Heun equation in the general case are obtained. This equation has two singular points, both of which are irregular. The obtained result is applied to solve the nonlinear equation of the resistively shunted junction model for an overdamped Josephson junction in superconductors. The new operators are explicitly expressed in terms of structural polynomials, for which recursive computational algorithms are constructed. Two functional equations for the solutions of the special double-confluent Heun equation are found. [ABSTRACT FROM AUTHOR]

One of most important stages of metabolic engineering is organism design. One of methods that allows process design is modeling or in silico approach. Application of biological models instead of biological experiments is cheaper and does not require specific resources. sAnalyzer tool uses reactions data to construct new metabolic pathways and predict its feasibility to transform one metabolite to another one using particular chassis organism represented by a model. sAnalyzer searches possible reactions connections in full solution space of all accessible reactions. One of reaction parameters used to predict its flow direction is reaction thermodynamic data - Gibbs free energy (ΔrG) value, which shows how much energy is needed for reaction to flow in specific direction. sAnalyzer tool demonstrates how thermodynamic thresholds of ΔrG value impacts possible solution space of metabolites transformation pathways on the example of using glycerol as substrate for bacteria Z.mobilis model. The solutions are ranked by flux over the pathway of interest, biomass production ability and integrated parameters derived from involved metabolites and reactions. [ABSTRACT FROM AUTHOR]

The solution space of a multi-product newsvendor problem (MPNP) with two constraints is analyzed and divided by four cases. One constraint is a loss constraint, which is nonlinear and has the integral symbol, while the other is a budget constraint. The threshold values of different solution regions are calculated and the corresponding solving methods for each case are provided, reducing the complexity of problem solving. The paper suggests the loss-based marginal utility deleting method solves non-negative constraint problems, and the linear approximate approach deals with nonlinear constraint optimal problems with an integral symbol in the model. Numerical examples are given, showing that the solving approach is more effective. [ABSTRACT FROM AUTHOR]

We establish properties common to dynamical systems of various types using their mathematical description by state evolution difference equations which are constructed and corrected according to measurement results. [ABSTRACT FROM AUTHOR]