Search

Misalignment of mechanical rolling elements such as belt and pulley causes catastrophic malfunctions of high speed paper feeding systems like automated teller machines (ATMs), printers, and copy machines. A combination of a crown pulley and a belt can be an effective self-alignment mechanism for the systems. In this paper, an equation of motion of the belt and pulley system is driven, verification tests are carried out, and a design improvement strategy is suggested for high speed paper media transport systems. [ABSTRACT FROM AUTHOR]

The recent rise of methods using Artificial Intelligence (AI) and Machine Learning (ML) has led to similar problems. Although the title was relatively narrow, the editorial addressed the general issue of papers that concentrated on the numerical method development without much relation to rock mechanics or rock engineering. [Extracted from the article]

In this article, we report on sixth-grade students' responses to a set of problem activities that required the application of mathematics, science, and engineering knowledge in designing and constructing a paper bridge that could withstand an optimal load. Increasing students' application and awareness of their disciplinary learning and how they are applying this in an integrated STEM activity remains a challenge for educators. In addressing this issue, we included a focus on knowledge reflection and knowledge scaffolding through thought-provoking student workbooks. Among the findings are students' capabilities in planning, designing, reflecting, constructing, and redesigning. Students' planning indicated that they could justify their proposed bridge type/s, which often included a combination of types, by referring to their STEM understandings. At the same time, students remained cognizant of the problem boundaries. Students' design sketches indicated an awareness of the problem constraints, an understanding of basic engineering principles, and an application of mathematics and science knowledge. Students' reflections on their actions helped them to improve their bridge constructions. Suggestions are presented for knowledge scaffolding to facilitate the flexible and innovative application of STEM learning to new problem situations. [ABSTRACT FROM AUTHOR]

Selected papers We classify the eleven papers included in this special section into two categories: (1) metamodeling and model transformations, and (2) domain and application specific MBE. The seven papers included in the category of domain and application specific MBE describe MBE solutions crossing various domains and applications: content management, automotive, avionics, civil engineering, critical software system, robotics. The IEEE/ACM International Conference on Model Driven Engineering Languages and Systems (MODELS) serves as a venue for researchers and practitioners to share recent advances and practices in the area of model-based software and system engineering (MBE including MBSE). [Extracted from the article]

As the complexity of optimization problems continues to rise, the demand for high-performance algorithms becomes increasingly urgent. This paper addresses the challenges faced by the Aquila Optimizer (AO), a novel swarm-based intelligent optimizer simulating the predatory behaviors of Aquila in North America. While AO has shown good performance in prior studies, it grapples with issues such as poor convergence accuracy and a tendency to fall into local optima when tackling complex optimization tasks. To overcome these challenges, this paper proposes a multi-strategy boosted AO algorithm (PGAO) aimed at providing enhanced reliability for global optimization. The proposed algorithm incorporates several key strategies. Initially, a chaotic map is employed to initialize the positions of all search agents, enriching population diversity and laying a solid foundation for global exploration. Subsequently, the pinhole imaging learning strategy is introduced to identify superior candidate solutions in the opposite direction of the search domain during each iteration, accelerating convergence and increasing the probability of obtaining the global optimal solution. To achieve a more effective balance between the exploration and development phases in AO, a nonlinear switching factor is designed to replace the original fixed switching mechanism. Finally, the golden sine operator is utilized to enhance the algorithm's local exploitation trends. Through these four improvement strategies, the optimization performance of AO is significantly enhanced. The proposed PGAO algorithm's effectiveness is validated across 23 classical, 29 IEEE CEC2017, and 10 IEEE CEC2019 benchmark functions. Additionally, six real-world engineering design problems are employed to assess the practicability of PGAO. Results demonstrate that PGAO exhibits better competitiveness and application prospects compared to the basic method and various advanced algorithms. In conclusion, this study contributes to addressing the challenges of complex optimization problems, significantly improving the performance of global optimization algorithms, and holds both theoretical and practical significance. [ABSTRACT FROM AUTHOR]

The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to "reward" or "punish" the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems. [ABSTRACT FROM AUTHOR]

Harris Hawks Optimization (HHO) is a novel meta-heuristic optimization algorithm. The algorithm is inspired by the behavior of Harris Hawks collaborating with each other to pursue prey in nature. The algorithm has the advantages of simple structure, fewer parameters, easy implementation, and excellent performance on high-dimensional problems. However, the algorithm also suffers from the inability to strike a good balance between exploration and exploitation, low convergence accuracy, and slow convergence speed in the early stage. In response to these defects, this paper will introduce three strategies to the HHO: a non-negative stochastic shrinkage exponential energy function, a Cauchy-Gaussian-based dynamic variance reduction selection strategy, and a greedy-difference-based selection strategy. The improved algorithm TSHHO is analyzed on the well-established 28 benchmark test functions, and four industrial engineering design problems. The experimental results show that the TSHHO algorithm proposed in this paper can achieve a better balance in the exploration and development stages,the strategies significantly improve the search efficiency, convergence accuracy, and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

The relation between scientific research and engineering design is fraught with controversy. While the number of academic PhD programs on design grows, because the discipline is in its infancy, there is no consolidated method for systematically approaching the generation of knowledge in this domain. This paper reviews recently published papers from four top-ranked journals in engineering design to analyse the research methods that are frequently used. The research questions consider the aim and contributions of the papers, as well as which experimental design and which sources of data are being used. Frequency tables show the high variety of approaches and aims of the papers, combining both qualitative and quantitative empirical approaches and analytical methods. Most of the papers focus on methodological concerns or on delving into a particular aspect of the design process. Data collection methods are also diverse without a clear relation between the type of method and the objective or strategy of the research. This paper aims to act as a valuable resource for academics, providing definitions related to research methods and referencing examples, and for researchers, shedding light on some of the trends and challenges for current research in the domain of engineering design. [ABSTRACT FROM AUTHOR]

Complexity is often regarded as a "problem" to solve. Instead of attempting to solve complexity, we follow systems engineering practices and switch back to the problem domain, where a major obstacle is the impossibility to universally define complexity. As a workaround, we explored complexity characterization and its existing shortcomings, including: lack of standardization, inconsistent semantics, system-centricity, insufficiently transparent reasoning, and lack of validation. To address these shortcomings, we proposed a compilatory framework to characterize complexity using the Five Ws information-gathering method. The answer to the WHO question proposed four complexity viewpoints; the answer to the WHY question proposed a two-dimensional structure for complexity drivers; and the answer to the WHAT question derived generalized complexity challenges. As a preliminary step to show the potential of the framework to characterize complexity, we used and validated it as a tool to structure general literature related to complexity. In general, our findings suggest that papers with complexity solutions do not frame their research within the complexity problem domain, hindering the contribution evaluation. Through the viewpoints, we identified general research gaps of six solution directions. From the drivers, we noted three observations in the discourse of complexity origins: (1) a system-driven tendency, (2) a preference for concreteness vs. abstraction, and (3) an unclear distinction between origins and effects. Through the challenges' findings we explored two hypotheses: (1) a system-centric preference; and (2) a solution-oriented vision, both of which were supported by the results (most challenges relate to the system viewpoint and challenges are defined based on solution directions). [ABSTRACT FROM AUTHOR]

In two recent papers, approximate solutions for compact non-axisymmetric contact problems of homogeneous and power-law graded elastic bodies have been suggested, which provide explicit analytical relations for the force–approach relation, the size and the shape of the contact area, as well as for the pressure distribution therein. These solutions were derived for profiles, which only slightly deviate from the axisymmetric shape. In the present paper, they undergo an extensive testing and validation by comparison of solutions with a great variety of profile shapes with numerical solutions obtained by the fast Fourier transform (FFT)-assisted boundary element method (BEM). Examples are given with quite significant deviations from axial symmetry and show surprisingly good agreement with numerical solutions. [ABSTRACT FROM AUTHOR]

Artificial intelligence invades our lives and professions at an ever-increasing pace and intensity. Architecture, engineering, construction, and operation of the real estate have been joining the trend only timidly and belatedly. The paper overviews the basic concepts, methods, general background, and results of artificial intelligence in architecture to date, discusses the achievements and prospects, and concludes the perspectives on the deployment of machine learning in the field. The record of some of the most recent "famous achievements" in the field is set straight and challenged, the flawed idea of a (truly) creative potential of the technology is debunked. Its roots equidistributed both in a farsighted vision of the next workflow of both productive and creative architectural and engineering designing, and construction and real estate management on the one hand and state-of-the-art machine learning on the other, an ambitious though realistic blueprint for R&D of AI-fostered architectural creativity, building design, planning, and operation is tabled for discussion. The attention turns to open-source patterns platforms, generative patterns processing, generative pre-design, parametric evaluation and optimization, latest achievements in machine learning building on reinforcement learning, imitation-based learning, learning a behavior policy from demonstration, and self-learning paradigms zooming in on the design-development processes instead of only on their results. Leveraging the objectivity of assessments and streamlining workflows, artificial intelligence promises to unleash true architectural creativity and leverage the productivity and efficiency of the design, planning, and operation processes. [ABSTRACT FROM AUTHOR]

In engineering design, the selection of the optimal design solution represents a critical phase for the development of successful products. In this paper, we present ELIGERE, an open source decision support system targeted at engineering design applications. It allows to rank multiple design solutions with respect to different evaluation criteria according to the evaluations provided by a group of experts. ELIGERE is composed by three main modules: (1) a distributed web application, for generation and participation to the decision making session; (2) a mathematical engine, based on the fuzzy analytical hierarchy process, to quantify the results of the decision making session according to the evaluation of the experts; (3) a relational database, to collect and store data. The most important contribution of this paper is introducing a practical and effective software tool that facilitates decision-making analysis based on the fuzzy analytical hierarchy process, thereby allowing better-informed choices on concept selection, as it has been designed with a specific focus on the engineering field. In this paper we describe the key concepts of ELIGERE and its modalities of use in several real use cases. Finally, we compare ELIGERE with the widely used general purpose decision support software based on the fuzzy analytical hierarchy process. [ABSTRACT FROM AUTHOR]

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified. [ABSTRACT FROM AUTHOR]

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat's position change while moving towards the prey and (ii) exploitation based on simulating the bobcat's position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

Golden jackal optimization (GJO) is inspired by mundane characteristics and collaborative hunting behaviour, which mimics foraging, trespassing and encompassing, and capturing prey to refresh a jackal's position. However, the GJO has several limitations, such as a slow convergence rate, low computational accuracy, premature convergence, poor solution efficiency, and weak exploration and exploitation. To enhance the global detection ability and solution accuracy, this paper proposes a novel complex-valued encoding golden jackal optimization (CGJO) to achieve function optimization and engineering design. The complex-valued encoding strategy deploys a dual-diploid organization to encode the real and imaginary portions of the golden jackal and converts the dual-dimensional encoding region to the single-dimensional manifestation region, which increases population diversity, restricts search stagnation, expands the exploration area, promotes information exchange, fosters collaboration efficiency and improves convergence accuracy. CGJO not only exhibits strong adaptability and robustness to achieve supplementary advantages and enhance optimization efficiency but also balances global exploration and local exploitation to promote computational precision and determine the best solution. The CEC 2022 test suite and six real-world engineering designs are utilized to evaluate the effectiveness and feasibility of CGJO. CGJO is compared with three categories of existing optimization algorithms: (1) WO, HO, NRBO and BKA are recently published algorithms; (2) SCSO, GJO, RGJO and SGJO are highly cited algorithms; and (3) L-SHADE, LSHADE-EpsSin and CMA-ES are highly performing algorithms. The experimental results reveal that the effectiveness and feasibility of CGJO are superior to those of other algorithms. The CGJO has strong superiority and reliability to achieve a quicker convergence rate, greater computation precision, and greater stability and robustness. [ABSTRACT FROM AUTHOR]

In recent years, state-of-the-art artificial intelligence systems have started to show signs of what might be seen as human level intelligence. More specifically, large language models such as OpenAI's GPT-3, and more recently Google's PaLM and DeepMind's GATO, are performing amazing feats involving the generation of texts. However, it is acknowledged by many researchers that contemporary language models, and more generally, learning systems, still lack important capabilities, such as understanding, reasoning and the ability to employ knowledge of the world and common sense in order to reach or at least advance toward general intelligence. Some believe that scaling will eventually bring about these capabilities; others think that a different architecture is needed. In this paper, we focus on the latter, with the purpose of integrating a theoretical–philosophical conception of understanding as knowledge of dependence relations, with the high-level requirements and engineering design of a robust AI system, which integrates machine learning and symbolic components. [ABSTRACT FROM AUTHOR]

Current practice in simulation and implementation of robot controllers is usually undertaken with guidance from high-level design diagrams and pseudocode. Thus, no rigorous connection between the design and the development of a robot controller is established. This paper presents a framework for designing robotic controllers with support for automatic generation of executable code and automatic property checking. A state-machine based notation, RoboChart, and a tool (RoboTool) that implements the automatic generation of code and mathematical models from the designed controllers are presented. We demonstrate the application of RoboChart and its related tool through a case study of a robot performing an exploration task. The automatically generated code is platform independent and is used in both simulation and two different physical robotic platforms. Properties are formally checked against the mathematical models generated by RoboTool, and further validated in the actual simulations and physical experiments. The tool not only provides engineers with a way of designing robotic controllers formally but also paves the way for correct implementation of robotic systems. [ABSTRACT FROM AUTHOR]

The rainwater tank is a critical component of the rainwater harvesting system and has a significant impact on the non-potable water saving efficiency and economic acceptability of the rainwater harvesting system. The choice of a suitable tank design to suit an existing catchment and local conditions is critical. In this paper, capacity is considered as a function of local rainfall data, unit price of tap water, roof area, non-potable water demand and an estimate of the cost of constructing a rainwater harvesting system. Analytical equations are derived that allow direct calculation of the desired cistern volume in terms of roof area and non-potable water demand. The validity of the analytical equations was demonstrated by comparing their results with the results of the sequential simulation, and the analytical equations proved to be adequate for the estimation of cisterns, but it was necessary to exclude some extreme scenarios due to the high deviation rate of the calculation results in the buildings with extremely high demand rates or extremely low roof areas. The proposed approach provides 'easy-to-use' design tools for engineers, architects, local authorities and rainwater tank manufacturers to estimate or recommend suitable sizes of rainwater cisterns. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of whether to optimize or satisfice in model-based engineering design. When dealing with operations research problems in the context of engineering design, one may encounter (i) nonlinear, nonconvex objectives and constraints, (ii) objectives with different units, and (iii) computational models that are abstractions of reality and fidelity, Seeking a single-point optimal solution that meets the necessary and sufficient Karush–Kuhn–Tucker (KKT) conditions makes it impossible to obtain a solution that satisfies all the targeted goals. Instead, a method to identify satisficing solutions that satisfies necessary KKT condition but not the sufficient condition is proposed. These solutions are relatively robust, easy to acquire, and often good enough. In this paper, we demonstrate the combined use of the compromise Decision Support Problems and the adaptive linear programming algorithm, as proposed by Mistree and co-authors. This method is appropriate in formulating design problems and obtaining solutions that satisfy only the necessary KKT condition. Further, the use of the proposed method circumvents complications associated with the use of gradient-based optimization algorithms typically used to solve optimization problems. We discuss the efficacy of our proposed method using four test problems to illustrate how the satisficing strategy outperforms the optimizing strategy in model-based engineering design. [ABSTRACT FROM AUTHOR]

In the ostensibly unceasing prioritization of motorized infrastructure, walking has remained a ubiquitous mode of mobility for a large proportion of Nairobi's urban commuters. Planning for motorized mobility has historically been at a higher level of consideration although a much larger percentage of the population travels on foot. The conspicuous pedestrian has been and continues to be masked under the spotlight of the motor vehicle with a discernible outcome of spatial injustices. Using secondary data, historical literature and expert interviews, this paper examines how walking as a mode of mobility has developed over time and the challenges experienced by pedestrians in Nairobi. Linking to the notion of justice, the paper attempts to assess the association between walking and spatial justice using three dimensions—spatial, modal and individual dimensions—that are used as a framework to assess how injustices unfold and are experienced by Nairobi's pedestrians. The historical path dependency that has restricted and attempted to replace walkability by prioritizing motor vehicle use as well as the technical engineering design that lacks integration of social aspects of mobility has presented challenges in provision of safe non-motorized infrastructure in the contemporary urban travel in Nairobi, enduringly dismissing walking as a valid mode of mobility. Advancing spatial justice in Nairobi's urban mobility will require more than a technical process of extending the side of the road by a metre or two but rather deliberate effort in understanding the pedestrians' mobility needs that can best be understood by attuning to the everyday realities of travelling on foot. [ABSTRACT FROM AUTHOR]

Aeroelastic effects, which affect the dynamic responses of low-rise roof structures to extreme wind conditions, are often neglected or oversimplified in current wind engineering design standards and applications. However, it is crucial to understand the details of those aeroelastic effects for performance-based wind engineering. This paper presents an isogeometric fluid–structure interaction (FSI) tool to investigate the aeroelastic effects of wind pressure distributions on roof structures under different turbulent wind conditions. A representative low-rise roof structure is simulated with an FSI model using an Arbitrary Lagrange-Eulerian-based variational multi-scale formulation coupled with isogeometric Kirchhoff-Love shells. The simulation results are compared to the quasi-steady approach and wind load provisions from ASCE 7-22. It shows that the quasi-steady approach and the design standard underestimate the pressure fluctuations, indicating the necessity of using FSI simulations to capture the aeroelastic effect for the roof of low-rise structures. This paper also studies the impacts of different roof configurations, e.g., the number of roof panels and inflow turbulent intensity, on the distribution of pressure coefficients and roof deflections. For the given mean wind speed, the mean pressure coefficient remains almost the same regardless of the turbulent intensity and roof configuration. However, the pressure fluctuation (standard deviation) varies significantly with the turbulence intensity and roof configuration. The aeroelastic effect also leads to complicated roof deflections at the crucial location having the maximum pressure coefficient. The paper first describes the mathematical details of the FSI model and simulation setup. Then, the pressure coefficients by the FSI simulation and design code are compared. Finally, the roof deflection with different inlet turbulence intensities and roof configurations are presented and discussed. [ABSTRACT FROM AUTHOR]

Tunicate Swarm Algorithm (TSA) is a new bio-based optimization technique that has proven not only to be able to compete with other methods but has also shown successful performance in classic design engineering problems/benchmark test problems. However, like some population-based methods, TSA tends to be trapped in local optima, converging to global optima in a long time, unbalanced exploitation/exploration, and the inability to effectively solve high-capacity engineering problems. In this paper, the M-TSA, which is a Modified version of the TSA, is proposed to overcome such problems. M-TSA was developed in three steps. The first is the new movement strategy that improves the movement of tunicates with a spiral movement, the second is the new herd strategy that improves the herd movement of tunics with the Levy movement, and the third is the consideration of the FAD effect. In this study, the efficiency and robustness of the M-TSA algorithm is tested on the CEC'17 test suite, six real-life design engineering problems, and two complex power system engineering problems. The test results were compared with other techniques reported in the literature and with the original TSA. Comparing the results from the M-TSA technique with other techniques proves the effectiveness of M-TSA with better exploration/exploitation balance and optimal solution finding. In this paper, MATLAB 2020b software is used for optimization problems simulation. [ABSTRACT FROM AUTHOR]

Transformative technologies and products can help solve some of society's most complex problems. To create these new products and increase the likelihood of desired impact, designers would benefit from understanding and improving a product's social impacts while still in development. Methods of predicting social impacts have been proposed that estimate a product's impacts on aggregated groups of people, but these approaches are inadequate at approximating the complexities of a society's socio-technical systems. In this paper, a new methodology is presented that utilizes information from individuals in a synthetic population to create social impact models for engineered products. Using this method, a product designer can make predictions about a product's social impacts during the product development process. Social impact predictions are made for individuals in the population, thereby giving product designers an understanding of a product's impact at the individual level. Once these individual impacts are aggregated, the product designer can also estimate the social impact of a product on sub-populations and communities. While this method can guide product designers in better understanding a product's social impacts, it is limited by the availability of high-quality individual-level data, the designer's understanding of the product's socio-technical system, and the existing complexity of social systems. This methodology is illustrated by predicting the social impact of a new cassava peeling machine being developed with a farming cooperative in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

The design for manufacturing and assembly (DFMA) is a family of methods belonging to the design for X (DfX) category which goal is to optimize the manufacturing and assembly phase of products. DFMA methods have been developed at the beginning of the 1980s and widely used in both academia and industries since then. However, to the best of the authors' knowledge, no systematic literature reviews or mapping has been proposed yet in the field of mechanical design. The goal of this paper is to provide a systematic review of DFMA methods applied to mechanical and electro-mechanical products with the aim to collect, analyse, and summarize the knowledge acquired until today and identify future research areas. The paper provides an overview of the DFMA topic in the last four decades (i.e., from 1980 to 2021) emphasizing operational perspectives such as the design phase in which methods are used, the type of products analysed, the adoption of quantitative or qualitative metrics, the tool adopted for the assessment, and the technologies involved. As a result, the paper addresses several aspects associated with the DFMA and different outcomes retrieved by the literature review have been highlighted. The first one concerns the fact that most of the DFMA methods have been used to analyse simple products made of few components (i.e., easy to manage with a short lead-time). Another important result is the lack of valuable DFMA methods applicable at early design phases (i.e., conceptual design) when information is not detailed and presents more qualitative than quantitative data. Both results lead to the evidence that the definition of a general DFMA method and metric adaptable for every type of product and/or design phase is a challenging goal that presents several issues. Finally, a bibliographic map was developed as a suitable tool to visualize results and identify future research trends on this topic. From the bibliometric analysis, it has been shown that the overall interest in DFMA methodologies decreased in the last decade. [ABSTRACT FROM AUTHOR]

With continuous improvement of lifting height and operation range for all-terrain crane in engineering construction, the buckling failure analysis of slender boom composite structure has become a hot research direction in order to ensure the safety and efficiency. In this paper, eigenvalue buckling analysis and nonlinear buckling analysis under multiple loads are carried out on the boom system of telescopic boom and truss auxiliary boom. The results show that the instability will occur in the amplitude plane for slender structure with super-lift system. Meanwhile, as the increase in the elevation angle for boom structure, the buckling load value increases and the stability is enhanced. Compared with geometric nonlinear and eigenvalue buckling load, when the elevation angle of the boom is 70°, the difference can reach 72.53%. Moreover, geometric initial defects are introduced. Compared displacement–load curves and buckling load values with and without geometric initial defects, the influence on slender composite structure is smaller. The results show that in order to prevent the buckling failure of structure, the hoisting load should not exceed rated load when the elevation angle is small, which provides a basis for engineering design. [ABSTRACT FROM AUTHOR]

Rock strength parameters such as uniaxial compressive strength and modulus of elasticity are crucial parameters in designing rock engineering structures. Owing to the importance of the aforementioned parameters, in this paper, image processing technique is coupled with artificial neural network (ANN) method for assessing the uniaxial compressive strength and modulus of elasticity of sandstones. For this reason, 102 core sandstone samples were prepared. Subsequently petrographic analyses and imaging operation for 102 images were performed. Principal component analysis was then conducted for feature reduction purposes. At last, an ANN model, which received its input data from image processing technique, was constructed for assessing the UCS and E of sandstone samples. Overall, the best performance of the network was obtained when 10 hidden nodes were used. The correlation coefficient (R) values of 0.9722 and 0.97062 for UCS and E, respectively, suggest the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

The Fennec Fox algorithm (FFA) is a new meta-heuristic algorithm that is primarily inspired by the Fennec fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. The "No free lunch" theorem shows that an algorithm has different effects in the face of different problems, such as: when solving high-dimensional or more complex applications, there are challenges such as easily falling into local optimal and slow convergence speed. To solve this problem with FFA, in this paper, an improved Fenna fox algorithm DEMFFA is proposed by adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, and differential evolution mutation strategies. Firstly, a sin chaotic mapping strategy is added in the initialization stage to make the population distribution more uniform, thus speeding up the algorithm convergence speed. Secondly, in order to expedite the convergence speed of the algorithm, adjustments are made to the factors of the formula whose position is updated in the first stage, resulting in faster convergence. Finally, in order to prevent the algorithm from getting into the local optimal too early and expand the search space of the population, the Cauchy operator mutation strategy and differential evolution mutation strategy are added after the first and second stages of the original algorithm update. In order to verify the performance of the proposed DEMFFA, qualitative analysis is carried out on different test sets, and the proposed algorithm is tested with the original FFA, other classical algorithms, improved algorithms, and newly proposed algorithms on three different test sets. And we also carried out a qualitative analysis of the CEC2020. In addition, DEMFFA is applied to 10 practical engineering design problems and a complex 24-bar truss topology optimization problem, and the results show that the DEMFFA algorithm has the potential to solve complex problems. [ABSTRACT FROM AUTHOR]

The dynamic response description of materials in high-speed impact fields is of practical significance to structural design and practical engineering application. In this paper, an electromagnetic impact (EI) loading process was proposed to acquire dynamic stress-strain relationships of 2A10 aluminum alloys. A modified Johnson-Cook (J-C) material model was obtained by combining with Quasi-static experiments and verified by numerical simulations. Comparing the J-C model obtained by a Split Hopkinson pressure bar, the simulative results about maximum deformation displacements showed the modified J-C model was more in line with actual experimental results. The accuracy under the discharge energy of 4 and 5 kJ was improved by 50% and 11%, respectively. In addition, electromagnetic impact loading characteristics and microstructure evolution of materials were studied. The discharge current with an attenuated sine wave caused that electromagnetic impact forces demonstrated a bimodal trend. The maximum impact velocities reached up to 4.7 m/s and 6.7 m/s under the discharge energy of 4 and 5 kJ, respectively (the maximum strain rates are 655.0 and 932.3 s−1, respectively). The high-speed impact effect led to the emergence of adiabatic shear bands (ASBs) during deformation microstructure evolution. Due to higher impact speed, the deformation concentration degree was more remarkable under the energy of 5 kJ. [ABSTRACT FROM AUTHOR]

This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored. [ABSTRACT FROM AUTHOR]

The article explains the review process for papers submitted to the "Research Engineering Design" journal. The two primary goals of the review process are managing the journal mission and maintaining and improving the journal quality. The main causes of rejecting research papers are failure to fit in the scope of the journal and the lack of soundness of the research.

In response to the demand for high-quality electronic information talents in the mobile network industry, in the situation of artificial intelligence (AI) to promote technological innovation, this paper conducts an overall design in the target system, curriculum system, teaching platform, teaching mode and teaching case. The practice education mode of teaching practice, engineering practice, innovation practice, and enterprise practice, which aims to improve students' ability to solve complex engineering problems, is constructed. The mode breaks geographical boundaries between schools and enterprises to build the through-through experimental teaching course system based on artificial intelligence and edge computing and design a medical image intelligent analysis system project case based on Mobile Edge Computing (MEC), which improves students' practical ability, engineering design ability, scientific research innovation ability, enterprise practice ability and mobile network application capabilities. At the same time, the hardware portability of the edge computing platform provides good conditions for long-distance education and the mobile network. This method is a beneficial attempt to cultivate high-level, diversified, and creative electronic information talents. [ABSTRACT FROM AUTHOR]

This paper illustrates the design-based learning (DBL) approach to promoting the deep learning of students and improving the quality of teaching in engineering design education. We performed three aspects of research with students in a typical educational activity. The first study investigated students' deep learning before and after the DBL approach, both in terms of deep learning status and deep learning ability. The second study examined the effectiveness of the DBL approach by comparative research of a control class (traditional teaching method) and an experimental class (DBL method). The third study examined students' evaluations of the DBL approach. It is approved that the DBL approach has distinctively stimulated the students' motivation to learn, making them more actively engaged in study. The students' higher-order thinking and higher-order capabilities are enhanced, such as critical thinking ability and problem-solving ability. At the same time, they are satisfied with the DBL approach. These findings suggest that the DBL approach is effective in promoting students' deep learning and improving the quality of teaching and learning. [ABSTRACT FROM AUTHOR]

Harris Hawks Optimization (HHO) is a newly proposed metaheuristic algorithm, which primarily works based on the cooperative system and chasing behavior of Harris' hawks. In this paper, an augmented modification called HHMV is proposed to alleviate the main shortcomings of the conventional HHO that converges tardily and slowly to the optimal solution. Further, it is easy to trap in the local optimum when solving multi-dimensional optimization problems. In the proposed method, the conventional HHO is hybridized with Multi-verse Optimizer to improve its convergence speed, the exploratory searching mechanism through the beginning steps, and the exploitative searching mechanism in the final steps. The effectiveness of the proposed HHMV is deeply analyzed and investigated by using classical and CEC2019 benchmark functions with several dimensions size. Moreover, to prove the ability of the proposed HHMV method in solving real-world problems, five engineering design problems are tested. The experimental results confirmed that the exploration and exploitation search mechanisms of conventional HHO and its convergence speed have been significantly augmented. The HHMV method proposed in this paper is a promising version of HHO, and it obtained better results compared to other state-of-the-art methods published in the literature. [ABSTRACT FROM AUTHOR]

Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican's natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. [ABSTRACT FROM AUTHOR]

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems. [ABSTRACT FROM AUTHOR]

This review aims to exploit a study on different benchmark test functions used to evaluate the performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH optimization techniques is evaluated with the different sets of mathematical benchmark test functions and various real-world engineering design problems. These benchmark test functions can help to identify the strengths and weaknesses of newly proposed MH optimization techniques. This review paper presents 215 mathematical test functions, including mathematical equations, characteristics, search space and global minima of the objective function and 57 real-world engineering design problems, including mathematical equations, constraints, and boundary conditions of the objective functions carried out from the literature. The MATLAB code references for mathematical benchmark test functions and real-world design problems, including the Congress of Evolutionary Computation (CEC) and Genetic and Evolutionary Computation Conference (GECCO) test suite, are presented in this paper. Also, the winners of CEC are highlighted with their reference papers. This paper also comprehensively reviews the literature related to benchmark test functions and real-world engineering design challenges using a bibliometric approach. This bibliometric analysis aims to analyze the number of publications, prolific authors, academic institutions, and country contributions to assess the field's growth and development. This paper will inspire researchers to innovate effective approaches for handling inequality and equality constraints. [ABSTRACT FROM AUTHOR]

Owing to the rapid increase in the demands of train speed and axle loads, the slab track has been introduced to replace the ballast in the classical ballasted track with reinforced concrete slab or asphalt-bearing layer to improve the track stability, strength, and durability. This paper aims to develop a new methodology for estimating the rail deformations for the most common slab track systems (BÖGL, Shinkansen, and RHEDA 2000. This methodology yielded the first design aid for slab track systems based on design equations and graphs for high-speed systems. Using a regression analysis of more than 300 finite element models which are validated by experimental tests, the relationship between the rail deflection, modulus of elasticity for subgrade and replacement, and the replacement thickness was determined for the most common slab tracks under the American (AREMA) and European (EN) loads. According to EN, it was found that the minimum modulus of elasticity for subgrade to fulfill the rail deflection criterion without a replacement soil ranges from 128 to 143 MPa for the most common slab track systems; meanwhile, for AREMA, it ranges from 59 to 70 MPa. Furthermore, for these slab track systems, one simple design chart was introduced to aid engineers with the design of the slab track replacement layer according to each design code. [ABSTRACT FROM AUTHOR]

Given the pervasiveness of AI systems and their potential negative effects on people's lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more "explainable" by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of "health", which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing "justificatory explanations" about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of "health", we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that "justificatory explanations" are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project's main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project. [ABSTRACT FROM AUTHOR]

It is essential to understand the in situ stresses underground for engineering design and ground stability control in mining, geotechnical and petroleum engineering. Determination of the complete 3D stresses is particularly difficult due to the complex conditions underground. Overcoring and some other methods have been used in the past. They are either very expensive to implement or are based on some pre-assumptions. A recently developed alternative method is based on differential-direction drilling (D3 method). By this method, the complete 3D in situ stresses can be estimated through measurements of diametrical borehole deformation in multiple non-parallel planes. The D3 method has overcome the theoretical hurdle of using 2D data to find a 3D solution to the in situ stresses. It has five mathematical models to fit the anticipated field conditions. This paper, as a follow-up, considers practical applications of the D3 method and analyzes a few key factors which may affect its application. The minimum space angle between measurement planes and the minimum number of measurement directions in each plane (or around a borehole) are determined. The effects of measurement errors and selection of a proper model for a specific site condition are also analyzed. A statistical approach is applied to help detect erroneous measurement data and search for the best-fit solution. Practical drilling patterns in mining and petroleum engineering are also suggested. [ABSTRACT FROM AUTHOR]

Recent reform initiatives in STEM disciplines inspired the development and implementation of integrated STEM approaches to science teaching and learning. Integrated STEM as an approach to science teaching and learning leverages engineering principles and practices to situate learning in an authentic and meaningful science learning environment. However, integrated STEM curricular activities can be cognitively challenging for learners, so it is essential that teachers employ scaffolding techniques to facilitate student understanding of the connections between concepts and practices of the integrated disciplines. In this paper, we describe Legitimation Code Theory as an analytical framework and provide an analysis of semantic patterns of an integrated STEM unit (written discourse) and a middle school teacher's enactment of that unit (oral discourse). Specifically, this analysis focused on the semantic gravity (SG), or level of context dependency, of the activities and dialogue present throughout the unit. Creating a semantic profile offers a snapshot of how abstract (weaker SG) or how specific (stronger SG) a concept is presented in relation to other concepts. Curriculum that presents ideas through the formation of semantic waves, or oscillations between areas of stronger and weaker semantic gravity, is linked to enhanced learning of complex ideas. The results of this study identify the areas in the curriculum unit and instruction that enable or constrain knowledge-building within the science classroom. We posit that the Legitimation Code Theory is a useful tool for developing and examining integrated STEM curriculum and its implementation. [ABSTRACT FROM AUTHOR]

Understanding the anomalies generated by various subsurface targets and their responses to different geophysical techniques in various subsoil types is critical to near-surface geophysical investigations. Geophysical test site (GTS) plays a vital role in near-surface geophysical investigations and related Earth sciences to adequately predict the geometries and anomalies generated by the subsurface targets. Therefore, developing a GTS on a site requires some technical efficiencies, mechanical procedures, engineering concepts and scientific approach, depending on the operating environment and the purpose of construction. This paper reviews the evolving roles of GTS in engineering, sciences, and technology via remarkable pedagogical and scientific research. The procedures for designing and installing GTS were also discussed. Every constructed GTS is unique and has its operating environment and sets of scientific requirements. As a result, the execution of GTS should be subjected to numerous factors that invariably affect the overall long time usage and performance. Comparative studies of GTS activities indicate that GTS is a vital geophysical research and academic platform to enrich the outcomes of the geophysical modelling for near-surface geophysical applications in engineering, science and technology. The evolving application of GTS has greatly impacted the field of science and engineering by enhancing the knowledge and understanding of the earth's interior, which invariably affects the engineers, geophysicists, archaeologists and geologists to be critical in the analysis, interpretation, and providing precise and accurate information of subsurface anomalies underlying the uppermost soil of the earth's crust. After a critical investigation, it was noted that the installation of GTSs is usually conceived to replicate situations often encountered in field investigation contexts. Examination shows that GTS can provide an ideal platform for young geoscientists, engineers and archaeologists to acquire the requisite skills, knowledge, technical know-how, and professional techniques for resolving near-surface challenges in real-life work situations. More also, a well-developed and equipped GTS could be a watershed in technological advancement, research development, and new scientific ideas. The GTS platform also indicates a promising pedagogical approach to geophysical educational usage, research mobilization, and development of new shallow geophysical techniques for various near-surface investigation and calibrating/testing geophysical equipment, which invariably catalyzed engineering designs, scientific concepts and technological advancement. [ABSTRACT FROM AUTHOR]

The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization. It is also characterized by multiple objectives and constraints, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities and vendors but hand-designed solutions continue to be the prevalent method in the industry. To improve the state-of-the-art, Deep Reinforcement Learning (RL), in particular, Proximal Policy Optimization is leveraged. This work presents a first-of-a-kind approach to utilize deep RL to solve the loading pattern problem and could be leveraged for any engineering design optimization. This paper is also to our knowledge the first to propose a study of the behavior of several hyper-parameters that influence the RL algorithm. The algorithm is highly dependent on multiple factors such as the shape of the objective function derived for the core design that behaves as a fudge factor that affects the stability of the learning. But also an exploration/exploitation trade-off that manifests through different parameters such as the number of loading patterns seen by the agents per episode, the number of samples collected before a policy update , and an entropy factor that increases the randomness of the policy during training. We found that RL must be applied similarly to a Gaussian Process in which the acquisition function is replaced by a parametrized policy. Then, once an initial set of hyper-parameters is found, reducing and until no more learning is observed will result in the highest sample efficiency robustly and stably. This resulted in an economic benefit of 535,000 - 642,000 $/year/plant. Future work must extend this research to multi-objective settings and comparing them to state-of-the-art implementation of stochastic optimization methods. [ABSTRACT FROM AUTHOR]

Currently, a standardized design and calculation specification for suction drum foundations has yet to exist in China. The engineering design currently depends mainly on the subjective understanding and engineering experience of the designers, which can be considered somewhat blind and subjective. In this paper, we utilize the offshore wind power project in Yangjiang City, Guangdong Province, as our case study. Building upon domestic and international research results, relevant investigations, design specifications, and engineering applications in related fields, we conduct a systematic study on the design calculation and construction control technology of the suction drum foundation. The document presents the design calculation and inspection of the suction drum foundation. Building on this foundation, we propose a sinking feasibility analysis method and a parameter value method for the suction drum foundation calculation. We also examine the suction drum foundation construction process, examining its control parameters, technology, and standards. Finally, based on the measured data from six four-barrel guided frame platform suction drum foundations that were successfully installed, the proposed design and control method are evaluated, and their effectiveness is verified. The results of this study can provide valuable references for the design and construction of similar suction drum foundation platforms. [ABSTRACT FROM AUTHOR]

Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods. [ABSTRACT FROM AUTHOR]

Background: Due to its counter-intuitive nature, understanding the dynamics of nonlinear systems has given rise to many challenges to overcome. This has brought about so many quests to answer because most engineering systems are ubiquitously nonlinear in nature. Vibration analysis is found to be one of the dominant and predictive maintenance techniques. It ranges from budding problems to catastrophic failure of the rotating machinery. It leads to acceptance testing, quality control, loose part detection, noise control, leak detection, machine design and engineering.For example, vibration analysis is found to be a significant approach in the industries for analyzing the performance of rotating machinery. Purpose: The study of vibrational resonance is important in many branches of engineering. For example, in mechanical and civil engineering design, vehicle design, the design of steam-turbine rotor-bearing systems, and bridge design, and the design of vibration controllers and isolators. Understanding vibrational resonance is important to ensure an appropriate running condition, increase the effiency of machines and a desired behavior of the systems. Method: In this work,vibrational resonance for a class of velocity-dependent potentials and nonpolynomial type oscillator is studied. By using the method of separation of motions (MSM) an analytical equation for the slow oscillations of the systems is obtained in terms of the parameter of the fast signal. The analytical computations are verified by fourth order Runge-Kutta method. Result: The analytical findings concure well with numerical studies. The response amplitude (Q) of the system depending on the amplitude of high-frequency drive forcing strength. The effects of damping and strength of nonlinearity on the emergence of vibrational resonances and resonant frequencies are analyzed. It is intresting to note that decreasing the strength of the nonlinearity significantly contributies to the emergence of VR in the lower values of amplitude of high-frequency drive forcing strength. When the damping term is increseaed the response amplitude (Q) is reduced. Conclusion: In this paper, we have proposed the new avenue in a broad class of velocity-dependent potential systems and nonpolynomial oscillators, which are typically found in physical and mechanical situations. The results are useful for the study of energy transfer phenomena for this class of nonlinear systems and for investigating the effects of damping on the nonlinear behaviour. These results are important for the design and fault diagnosis of mechanical systems and structures which can be described by this nonlinear model. As a result, the study of the dynamics of nonlinear systems has drawn the attention of researchers in various fields such as for instance, engineering and cognitive sciences. [ABSTRACT FROM AUTHOR]

Purpose: Two passive tuned mass damper inerter control configurations TMDI-I and TMDI-II are used in this paper to minimize the response of the primary structure under random force excitation. Methods: TMDIs are attached to an undamped primary structure under random force excitation; after obtaining the mathematical model via the Newton second law of motion, the dimensionless frequency response function of the primary structure is found considering harmonic input excitation; then, the H 2 optimization criterion is performed and the optimum design parameters for the proposed TMDIs are found based on the algebraic procedures and the computer softwares. Results and Conclusion: The results of this study reveals that, with respect to high-performance NTMDI-C4 and classic TMD, respectively, the proposed tuned mass dampers inerter (TMDIs), reduce significantly (1) the means square response of the primary structure: More than 52% and 61% improvement can be obtained from TMDI-II, and 47% and 57% from TMDI-I; in the case of harmonic excitation (2) the peaks resonance amplitude of the primary structure: A recorded improvement more than 38% and 54% can be obtained from TMDI-II, and 34% and 51% from TMDI-I. And (3) broadening the vibration suppression bandwidth: The maximum suppression bandwidth is found to be 49% and 64% from TMDI-I, and 47% and 63% from TMDI-II. These results provide the novel passive design of TMDIs for engineering practice. [ABSTRACT FROM AUTHOR]

Design inspiration comes from the continuous stimulation of external information and the continuous accumulation of knowledge. In order to obtain an ideal design inspiration from nature, researchers have proposed a large number of biological information retrieval and knowledge acquisition methods. But how to purposefully acquire valuable biological knowledge in order to effectively stimulate design inspiration and produce the novel and feasible designs idea is still an urgent problem to be solved. This paper proposes a method for acquiring valuable biological knowledge to efficiently stimulate inspiration and quickly conceive solutions in engineering design. First, keywords, such as the functional requirements and key components of design objects, are selected as the engineering terminologies. Next, biological keywords related to the engineering terminologies are searched from the biological dictionary and biology websites. Then in order to retrieve enough biological knowledge, these biological keywords are expanded manually and automatically respectively based on Thesaurus Webpage and WordNet database, and expanded keywords are filtered according to repeated words and different forms of the same words. Finally, in the biological knowledge base, biological keywords that had been filtered are used to obtain biological knowledge with Python web crawler programming. Through an example of application for ship equipment, the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

Flowcharts have broad applications in the fields of software development, engineering design, and scientific experimentation. Current flowchart data structure is mainly based on the adjacency list, cross-linked list, and adjacency matrix of the graph structure. Such design originated from the fact that any two nodes could have a connection relationship. But flowcharts have clear regularities, and their nodes have a certain inflow or outflow relationship. When graph structures such as an adjacency table or an adjacency matrix are used to store a flowchart, there is a large room for optimization in terms of traversal time and storage complexities, as well as usage convenience. In this paper we propose two hierarchical data structures for flowchart design. In the proposed structures, a flowchart is composed of levels, layers, and numbered nodes. The nodes between layers are connected according to a certain set of systematic design rules. Compared with the traditional graph data structures, the proposed schemes significantly reduce the storage space, improve the traversal efficiency, and resolve the problem of nesting between sub-charts. Experimental data based on flowchart examples used in this paper show that, compared with adjacency list, the hierarchical table data structure reduces the traversal time by 50% while their storage spaces are similar; compared with adjacency matrix, the hierarchical matrix data structure reduces the traversal time by nearly 70% and saves the storage space by about 50%. The proposed structures could have broad applications in flowchart-based software development, such as low-code engineering for smart industrial manufacturing. [ABSTRACT FROM AUTHOR]

Enterprises reach out for collaborations with other organizations in order to offer complex products and services to the market. Such collaboration and coordination between different organizations, for a good share, is facilitated by information technology. The BPMN process choreography is a modeling language for specifying the exchange of information and services between different organizations at the business level. Recently, there is a surging use of the REST architectural style for the provisioning of services on the web, but few systematic engineering approach to design their collaboration. In this paper, we address this gap in a comprehensive way by defining a semi-automatic method for the derivation of RESTful choreographies from process choreographies. The method is based on natural language analysis techniques to derive interactions from the textual information in process choreographies. The proposed method is evaluated in terms of effectiveness resulting in the intervention of a web engineer in only about 10% of all generated RESTful interactions. [ABSTRACT FROM AUTHOR]