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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]

In this paper, we discuss the quaternion matrix equation A X ε + B X δ = 0 , where ε ∈ { I , C } , δ ∈ { † , ⁎ } and I , C , †, ⁎ denote the identity, involutive automorphism, involutive anti-automorphism and transpose, involutive automorphism and anti-automorphism and transpose, respectively. Firstly, we transform the given matrix equation into the matrix equation A ˜ Y ε + B ˜ Y δ = 0 with complex coefficient matrices and quaternion target matrix Y by utilizing the regularity of (A , B) , where A ˜ = P A Q and B ˜ = P B Q are two complex matrices with P , Q being two invertible quaternion matrices. Secondly, we show that the solution can be obtained in terms of Q , the Kronecker canonical form of (A ˜ , B ˜) and one of the two invertible quaternion matrices which transform (A ˜ , B ˜) into its Kronecker canonical form. Meanwhile, we also decouple the transformed equation into some systems of small-scale equations in terms of Kronecker canonical form of (A ˜ , B ˜). Thirdly, we determine the dimension of the solution space of the equation in terms of the sizes of the blocks arising in the Kronecker canonical form. Finally, we give the necessary and sufficient condition for the existence of the unique solution. [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]

We review the solution space for the field equations of Einstein's General Relativity for various static, spherically symmetric spacetimes. We consider the vacuum case, represented by the Schwarzschild black hole; the de Sitter-Schwarzschild geometry, which includes a cosmological constant; the Reissner-Nordström geometry, which accounts for the presence of charge. Additionally we consider the homogenenous and anisotropic locally rotational Bianchi II spacetime in the vacuum. Our analysis reveals that the field equations for these scenarios share a common three-dimensional group of point transformations, with the generators being the elements of the D ⊗ s T 2 Lie algebra, known as the semidirect product of dilations and translations in the plane. Due to this algebraic property the field equations for the aforementioned gravitational models can be expressed in the equivalent form of the null geodesic equations for conformally flat geometries. Consequently, the solution space for the field equations is common, and it is the solution space for the free particle in a flat space. This approach open new directions on the construction of analytic solutions in gravitational physics and cosmology. [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]

Sea-level rise is unstoppable. Communities worldwide are facing difficult choices in responding to changing coastlines and estuaries. Understandably, there is little attention on the potential for repurposing inundated areas because retreat and adaptation take precedence. Repurposing may be infeasible for newly-claimed seascapes in exposed and high energy coasts. Nevertheless, for sheltered coastal areas, shallow estuaries and harbours, there may be potential for repurposing some areas for aquaculture, fisheries, wetlands, and/or blue carbon. For example, abandoned and decontaminated structures may provide fish nursery habitat as artificial reefs. Here, we present the results of a systematic literature review of potential options, along with identified benefits and implementation barriers. Our purpose is not to examine the feasibility of such options because these will be place- and context-specific; rather, we explore whether the solution space can be extended beyond the point of impact. We suggest that repurposing could be added to the PARA management framework.

Adaptation to increasing impacts of climate change will require societies to design portfolios of risk management solutions by selecting from an array of available adaptation options. A major step on the way towards a strong social contract for adaptation is understanding how different actors view different adaptation options. Here we adopt an actor-oriented multi-dimensional framework to understand how different actors evaluate their perceived adaptation solution space. The aim is to understand who identifies which adaptation options and how they are assessed. We build on the feasibility assessment used in IPCC’s sixth assessment report and explore the framework by applying it to the solution space for flood risk in Mumbai. We draw on empirical data collected through key informant interviews with state, civil society and academic actors. Our findings show that, overall, actors see most need for adaptation measures in relation to institutional changes, e.g. in relation to planning procedures and a shift in risk management paradigms. While actors show consensus on the importance of green infrastructure, we find stark differences in their perception of grey infrastructure measures. In terms of the factors perceived to enable or constrain adaptation, actors agree on the pivotal role of institutional aspects as even more important than financial or technical factors. This study makes an empirical and analytical contribution to advance feasibility assessments by including actor perceptions and their evaluation of options in real world settings.

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]

This study explores an approach to generative design solution space reduction by offering a flexible, efficient, and accessible method by leveraging clustering techniques. The studied method of generative design solution space reduction uses clustering analysis with a combination of the Gower distance matrix and partitioning around medoids in an iterative process. This iterative generative design solution space reduction method retains the originality of unique design solutions, while simultaneously reducing the quantity of design solutions presented to the user, theoretically improving cognitive function during the design process. Design originality is maintained since the clustering process groups similar designs into clusters, from which a systematic reduction of similar designs can be achieved, thereby leaving novel solutions from the design envelope. This paper presents this clustering approach in the context of an aircraft engine loading bracket with multiple nominal and continuous variables, however, this approach be transferred to other applications with similar variables. Further work can explore the relationship between reduced generative design solution spaces and human cognitive function.

Adaptation of port infrastructures to climate change and sea level rise effects is highlighted as a key field among transportation systems' lines of action for adaptation, given their highly exposed location in coastal areas and position as critical nodes in logistic chains and local, regional and national economies. The present work proposes an adaptation assessment framework that, based on a high-resolution compound climate risk assessment, identifies the main threats that climate change may pose to port performance, defines a set of optimized adaptation measures and characterizes constraints for implementation, and finally evaluates the applicability and effectiveness of these measures under diverse climate scenarios and different time frames. The framework is applied in a study case located in the northern coast of Spain. It is shown that the proposed methodology enables port managers and planners to develop tailor-fitted adaptation plans, providing tools to make them coherent with actual and future uncertain climate conditions. • The paper develops a novel framework to prepare climate change adaptation plans for port and coastal infrastructures. • It identifies the main needs and limitations for adaptation of port infrastructures based on a compound CC risk assessment. • It identifies and optimizes a set of adaptation measures, considering measure combinations, based on risk-reduction criteria. • It assesses the adaptation options' effectiveness in reducing climate risk for different scenarios and time horizons. • The framework has been applied and validated for a case study located on the Atlantic coast in Spain. [ABSTRACT FROM AUTHOR]

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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]

Conspicuous interdisciplinary effort has been spent on addressing the consequences of climate change in a forward-looking way. The concept of solution space is a recent contribution to help decision-makers identify feasible and effective adaptation solutions and to provide guidance on when they should be implemented and by whom. Although the current conceptualization of solution space already considers multiple disciplines, it remains dominated by biophysical ones and has not yet fully integrated legal and governance dimensions. This article first reflects on the current solution space framework through the lenses of law and governance and then proposes approaches to enrich legal and governance dimensions in the solution space concept. We argue that the legal and governance dimensions of the current concept of solution space can be improved by taking into account four aspects: 1) understanding the institutional and legal systems in a context-specific way; 2) embracing the dynamics and reflexivity of law and governance in the episteme of path dependency; 3) applying more diverse analytical methods (qualitative, qualitative/ quantitative, value-oriented) and/or assessments on a case-by-case basis; and 4) adding a normative perspective that includes the principles of legitimacy, transparency, accountability, equity, and distributive justice to measure the appropriateness of a certain adaptation strategy. The article concludes with suggestions for future research on how to implement the enriched solution space concept. • Conceptualization of solution space for climate adaptation has not yet fully integrated legal and governance dimensions. • Understanding the institutional and legal systems in a context-specific way can help to enrich solution space concept. • Embracing the dynamics and reflexivity of law and governance can help to enrich solution space concept. • Applying more diverse analytical and case by case assessments can enrich solution space concept. • Normative perspectives can help to measure the appropriateness of adaptation strategies. [ABSTRACT FROM AUTHOR]

The concept of "solution spaces" is used to explore the potential future of forestry under climate change for different types of forestry management structures. We base the analysis in New Zealand, where forestry plays an increasingly critical role in the nation's climate policy, but the concept could be applied to any region. Understanding solution spaces and the ways in which they can be influenced at different levels of ownership is a critical step towards effective climate change adaptation. Building on the base of existing climate projections, scenarios, and economic and social science literature, we form an assessment of the capacity of each forest owner typology to influence their solution spaces into the future. Different management structures have strengths in different areas – while industrial forest managers may be able to utilise emerging technologies better than their smaller scale counterparts for example, they may be less agile and flexible. The sector as a whole may benefit from working collectively to draw on the respective strengths of each typology. Critically, planning now to expand the space into the future will be essential. • The solution space for forestry under climate change is influenced by diverse drivers that will change over time. • Proactive adaptation actions can expand the solution space into the future. • Different forest owner typologies have differing abilities to influence the solution space. • Collaboration between forest owner typologies could improve the overall solution space. [ABSTRACT FROM AUTHOR]

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]

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.

We consider linear systems of ordinary differential equations (ODE) with rectangular matrices of coefficients, including the case when the matrix before the derivative of the desired vector function is not full rank for all argument values from the domain. Systems of this type are usually called differential-algebraic equations (DAEs). We obtained criteria for the existence of nonsingular transformations splitting the system into subsystems, whose solution can be written down analytically using generalized inverse matrices. The resulting solution formula is called a generalized split form of a DAE and can be viewed as a certain analogue of the Weierstrass-Kronecker canonical form. In particular, it is shown that arbitrary DAEs with rectangular coefficient matrices are locally reducible to a generalized split form. The structure of these forms (if it is defined on the integration segment) completely determines the structure of general solutions to the systems. DAEs are commonly characterizes by an integer number called index, as well as by the solution space dimension. The dimension of the solution space determines arbitrariness of the the general solution manifold. The index determines how many times we should differentiate the entries on which the solution to the problem depends. We show the ways of calculating these main characteristics.