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Innovative design of complex systems faces multi-contradiction (MC). The existing methods to solve MC are mainly based on the General Theory of Powerful Thinking (OTSM) which is independent of the classical Engineering Design Method (ED) to form the process model, they cannot inherit the accumulated knowledge and advantages of ED. This paper proposes a product innovation design process model for MC based on the function-structure model. It starts with an initial scenario of the system to build the function-structure model and MC network based on the root contradiction analysis. The network structure entropy and function importance decision methods are introduced for the trimming target confirming. The key contradiction-node is identified and solved by strategies of trimming based on internal and external resources of the system. The function-structure model in ED and trimming in TRIZ are combined to form an innovative design process for MC systems. The proposed method is evaluated in a case study of an innovative design of the girder bridge erector. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue in the journal Applied Sciences titled "Feature Review Papers in Mechanical Engineering." The issue covers various topics in mechanical engineering, including crank-slide actuating mechanisms, industrial robots, cellular manufacturing systems, shape memory alloys, and the application of Constructal theory. The articles explore the advancements, challenges, and potential solutions in these areas, aiming to enhance efficiency, adaptability, sustainability, and control in mechanical systems. The research presented in this special issue offers valuable insights and directions for further study in the field of mechanical engineering. [Extracted from the article]

This document discusses the development of digital twin (DT) technologies and their application in the field of vehicle system dynamics. It explains that digital twin technologies are closely related to concepts such as Industry 4.0, IoT, and Big Data, and have become increasingly important in engineering tasks such as design optimization and predictive maintenance. The document emphasizes the need to understand the transition from virtual prototyping to digital twin methodologies, as well as the combination of vehicle dynamics modeling and data-based approaches in vehicle engineering applications. It also highlights that more research is needed in this area to improve the system architecture and developmental framework of digital twin techniques. The document concludes by inviting researchers to contribute further innovations and research papers on digital twins in Vehicle System Dynamics. [Extracted from the article]

The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures. [ABSTRACT FROM AUTHOR]

With the rapid advancement of digital technology, three-dimensional designs of Grid Information Models (GIMs) are increasingly applied in the power industry. Addressing the challenges of extracting key parameters during the GIM's process for power grid equipment, this paper explores an innovative approach that integrates artificial intelligence with image recognition technologies into power design engineering. The traditional methods of "template matching, feature extraction and classification, and symbol recognition" have enabled the automated processing of electrical grid equipment engineering drawings, allowing for the extraction of key information related to grid equipment. However, these methods still rely on manually designed and selected feature regions, which limits their potential for achieving full automation. This study introduces an optimized algorithm that combines enhanced Convolutional Neural Networks (CNNs) with Depth-First Search (DFS) strategies, and is specifically designed for the automated extraction of crucial GIM parameters from power grid equipment. Implemented on the design schematics of power engineering projects, this algorithm utilizes an improved CNN to precisely identify component symbols on schematics, and continues to extract text data associated with these symbols. Utilizing a scene text detector, the text data are matched with corresponding component symbols. Finally, the DFS strategy is applied to identify connections between these component symbols in the diagram, thus facilitating the automatic extraction of key GIM parameters. Experimental results demonstrate that this optimized algorithm can accurately identify basic GIM parameters, providing technical support for the automated extraction of parameters using the GIM. This study's recognition accuracy is 91.31%, while a traditional CNN achieves 71.23% and a Faster R-CNN achieves 89.59%. Compared to existing research, the main innovation of this paper lies in the application of the combined enhanced CNN and DFS strategies for the extraction of GIM parameters in the power industry. This method not only improves the accuracy of parameter extraction but also significantly enhances processing speed, enabling the rapid and effective identification and extraction of critical information in complex power design environments. Moreover, the automated process reduces manual intervention, offering a novel solution in the field of power design. These features make this research broadly applicable and of significant practical value in the construction and maintenance of smart grids. [ABSTRACT FROM AUTHOR]

This paper provides a systematic review of the Genetic Algorithm (GA)s proposed to solve planning and scheduling problems in Engineer-To-Order (ETO) contexts. Our review focuses on how the key characteristics of ETO projects affect both the problem studied and the GA algorithmic features. Typical ETO projects consist of one-of-a-kind products with complex structures and uncertain designs. A deep analysis of the papers published between 2000 and 2022 enables identifying 10 main characteristics of ETO projects, six activity types, 10 decision types, eight groups of constraints, and 10 optimisation objectives. Our study shows that none of the reported papers integrates all 10 ETO characteristics. The less studied ETO characteristics are incorporating design and engineering information in the problem definition and the design uncertainty. Our review also identifies 10 recurrent encoding formats and emphasises the most frequently used genetic operators. We observed that most planning and scheduling problems consider objectives and decisions related to product customisation or supply chain configuration yielding multi-objective problems. Most multi-objective GAs use a weighted sum or are based on NSGAII. Diversity maintenance methods, adaptive and parameter tunning mechanisms, or hybridisation with machine learning models are still not used in this context. A systematic review of genetic algorithms dedicated to industrial planning and scheduling Analysis on how the characteristics of ETO projects impact the design of genetic representation and operators Recommendation on approaches employed to reach high-quality solutions [ABSTRACT FROM AUTHOR]

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]

In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model's receptive field is enhanced by introducing a multi-branch convolutional module, improving the extraction of shallow features for small targets. Next, the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks. Finally, the Convolutional Block Attention Module (CBAM) module is employed to achieve better target determination. The FDSC-YOLOv8s algorithm's mAP50 and mAP50-95 reach 96.5% and 66.4%, according to the testing data. [ABSTRACT FROM AUTHOR]

The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%. [ABSTRACT FROM AUTHOR]

Topology optimization focuses on the conceptual design of structures, characterized by a large optimization space and a significant impact on structural performance, and has been widely applied in industrial fields such as aviation and aerospace. However, most topology optimization methods prioritize structural stiffness and often overlook stress levels, which are critical factors in engineering design. In recent years, explicit topology optimization methods have been extensively developed due to their ability to produce clear boundaries and their compatibility with CAD/CAE systems. Nevertheless, research on incorporating stress constraints within the explicit topology optimization framework remains scarce. This paper is dedicated to investigating stress constraints within the explicit topology optimization framework. Due to the clear boundaries and absence of intermediate density elements in the explicit topology optimization framework, this approach avoids the challenge of stress calculation for intermediate density elements encountered in the traditional density method. This provides a natural advantage in solving topology optimization problems considering stress constraints, resulting in more accurate stress calculations. Compared with existing approaches, this paper proposes a novel component topology description function that enhances the deformability of components, improving the representation of geometric boundaries. The lower-bound Kreisselmeier–Steinhauser aggregation function is employed to manage the stress constraint, reducing the solution scale and computational burden. The effectiveness of the proposed method is demonstrated through two classic examples of topology optimization. [ABSTRACT FROM AUTHOR]

Soft robots present resilient and adaptable systems characterized by deformable bodies inspired by biological systems. In this paper, we comprehensively review existing design methods for soft robots. One unique feature of our review is that we first formulate criteria, which enables us to derive knowledge gaps and suggest future research directions to close these gaps and go further. Another distinctive feature of our review is that we pivot on the general engineering design process for soft robots. As such, we consider three criteria: (1) the availability of design requirements to start with the design of soft robots, (2) the availability of the so-called concept design or architecture design for soft robots, and (3) the systematic process that leads to the final design of soft robots. The review is conducted systematically, especially when searching for and selecting relevant publications in the literature. The main contribution of this review includes (i) identifying knowledge gaps and (ii) suggesting future research directions to close these gaps and go further. [ABSTRACT FROM AUTHOR]

Purpose: Assessing front end engineering design (FEED) accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions. A framework to measure FEED accuracy does not exist in the literature or in practice, not does systematic data directly linking FEED accuracy to project performance. This paper aims to focus first on gauging and quantifying FEED accuracy, and second on measuring its impact on project performance in terms of cost change, schedule change, change performance, financial performance and customer satisfaction. Design/methodology/approach: A novel measurement scheme was developed for FEED accuracy as a comprehensive assessment of factors related to the project leadership and execution teams, management processes and resources; to assess the environment surrounding FEED. The development of this framework built on a literature review and focus groups, and used the research charrettes methodology, guided by a research team of 20 industry professionals and input from 48 practitioners representing 31 organizations. Data were collected from 33 large industrial projects representing over $8.8 billion of installed cost, allowing for a statistical analysis of the framework's impact on performance. Findings: This paper describes: (1) twenty-seven critical FEED accuracy factors; (2) an objective and scalable method to measure FEED accuracy; and (3) data showing that projects with high FEED accuracy outperformed projects with low FEED accuracy by 20 percent in terms of cost growth in relation to their approved budgets. Practical implications: FEED accuracy is defined as the degree of confidence in the measured level of maturity of the FEED deliverables to serve as a basis of decision at the end of detailed scope, prior to detailed design. Assessing FEED accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions. Originality/value: FEED accuracy has not been assessed before, and it turned out to have considerable project performance implications. The new framework presented in this paper is the first of its kind, it has been tested rigorously, and it contributes to both the literature body of knowledge as well as to practice. As one industry leader recently stated, "it not only helped to assess the quality and adequacy of the technical documentation required, but also provided an opportunity to check the organization's readiness before making a capital investment decision." [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. The tool supports the emulation or the actual connectivity of a large number of IoT devices. The emulated devices can generate realistic correlated IoT data and stream it via the message queuing telemetry transport (MQTT) protocol to a big data platform. The tool allows students to design ML models with different algorithms for their chosen use cases and train them for decision-making based on the streamed data. Moreover, the paper proposes learning outcomes to be targeted when integrating the tool into an electrical engineering curriculum. The tool is evaluated using a comprehensive survey. The survey results show that the students gained significant knowledge about IoT concepts after using the tool, even though many of them already had prior knowledge of IoT. The results also indicate that the tool noticeably improved the students' practical skills in designing real-world use cases and helped them understand fundamental machine learning analytics with an intuitive user interface. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic modeling method of motor driven electromechanical system is presented, and the uncertainty quantification of mechanism motion is investigated based on this method. The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method, in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system. The advantages of this include establishing intuitive coupling between the mechanism and motor, facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism, and enabling dynamic simulation with controller to take the randomness of the electric load into account. Dynamic simulation considering feedback control of ammunition delivery system is carried out, and the feasibility of the model is verified experimentally. Based on probability density evolution theory, we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization. Our work can not only provide guidance for engineering design of ammunition delivery mechanism, but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system. [ABSTRACT FROM AUTHOR]

Nurses play a pivotal role in the healthcare sector; we are all well acquainted with this fact amidst the pandemic. A significant yet underestimated task they usually face is snapping off the glass ampoule necks in order to draw the medicine into the syringe during the course of treating patients. This paper ascertains that most of the nurses who encountered this problem were prone to cuts and injuries on their hands due to the sharp edges and medicine spills from the glass ampoules respectively. Thus, the present study aims to address this concern by employing the principles of design thinking as a part of a first- year engineering course; design thinking for social innovation. An active engagement was initiated with the nurses affiliated to a multispecialty hospital situated in Hubballi, Karnataka, India. A keychain glass ampoule opener capable of opening ampoules of 4 different sizes was built as a prototype taking into account the prevention of wounds caused by the uneven edges of the glass ampoules. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the bearing capacity of cross-bracing in steel transmission tower structures. Design guidelines (ASCE 10-15, BS EN 50341-1, GB 50017-2017, and DL/T 5486-2020) related to the buckling capacity of the cross-bracing are summarized and compared with the experimental results. The current design provisions obtained the bearing capacity from the equivalent slenderness ratio, and then the stability coefficient and buckling capacity were derived. The calculated bearing capacity based on the design code tends to be overly progressive for smaller slenderness ratios (particularly those below 100), except for EN 50341-1-2012. Conversely, for larger slenderness ratios, ASCE 10-15 and DL/T 5486-2020 Class A design codes lean towards being overly progressive, while GB 50017-2017 and EN 50341-1-2012 codes tend to be more conservative. The design standard appears to exhibit unsafe predictions for Class A and B connections with low slenderness ratios and Class C connections. It needs to be noted that the effects of torsional stiffness and joint connection type are not considered in the current design codes, which are proved to be nonnegligible by the test results. In this paper, the bearing capacity calculation formula is proposed by introducing a modified effective length coefficient (K), and both the torsional stiffness and joint connection type are taken into account. The modified bearing capacity is verified with the test results; the correlation coefficient is 0.997, and the coefficient of variation is 0.04. It can provide a reference for the engineering design of steel lattice transmission tower structures. [ABSTRACT FROM AUTHOR]

Today's industrial processes use a variety of smart structure systems technologies. Various construction devices can significantly enhance a structure's ability to adapt to environmental changes. Smart structures are essential to the development of building technology. These forms of technologies are developing intelligent structural systems that behave like living systems and can perceive their environment. The objective of the research is to examine the connection of these professional systems, which become increasingly dependent over time. The research acknowledges and give emphasis to the significant role played by different types of systems (passive, semi-active, active, and hybrid) in formative the scope of construction through the analysis of novel techniques and innovative approaches. The paper state the assorted classification of smart structure schemes that have a significant role to serve the cycle life of the structure by reducing severe subjected damage to it. The study aims to investigate how these schemes of callings are inseparably connected, creating ever more advantageously as time goes on. The think about state and recognize the significant part of different categories of schemes (inactive, semi-active, active, and hybrid schemes) within the development scope through examination, unused methods and inventive strategies. The paper displayed the different categories of shrewd structure schemes that have a critical part to serve the cycle life of the structure by diminishing serious subjected harm to it.t. Furthermore, the research also demonstrated the smart structure's useful behavior, which is taken into account at every stage of the civil engineering design process. [ABSTRACT FROM AUTHOR]

Variable camber wing technology stands out as the most promising morphing technology currently available in green aviation. Despite the ongoing advancements in smart materials and compliant structures, they still fall short in terms of driving force, power, and speed, rendering mechanical structures based on kinematics the preferred choice for large long-range civilian aircraft. In line with this principle, this paper introduces a linkage-based variable camber trailing edge design approach. Covering coordinated design, internal skeleton design, flexible skin design, and drive structure design, the method leverages a two-dimensional supercritical airfoil to craft a seamless, continuous two-dimensional wing full-size variable camber trailing edge structure, boasting a 2.7 m span and 4.3 m chord. Given the significant changes in aerodynamic load direction, ground tests under cruise load utilize a tracking-loading system based on tape and lever. Results indicate that the designed single-degree-of-freedom Watt I mechanism and Stephenson III drive mechanism adeptly accommodate the slender trailing edge of the supercritical airfoil. Under a maximum cruise vertical aerodynamic load of 17,072 N, the structure meets strength requirements when deflected to 5°. The research in this paper can provide some insights into the engineering design of variable camber wings. [ABSTRACT FROM AUTHOR]

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This document is an editorial published in the journal Frontiers in Physics, discussing advances in nonlinear systems and networks. Nonlinear systems have nonlinear relationships between input and output, and studying them is important for understanding natural phenomena and improving engineering design. The editorial highlights 10 articles in this research topic, covering various aspects of nonlinear systems and networks, including dynamic displacement estimation, complex propagation networks, representation learning, electricity scheduling, ternary logic gates, chaotic oscillators, vibration control, brain-like coupling synchronization, pinning synchronization, and medical image encryption. The authors emphasize the need for continuous advancement in the field and express gratitude to the authors, reviewers, editors, and staff involved in the publication. This document provides a valuable resource for researchers and library patrons interested in these areas of study. [Extracted from the article]

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 use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method. [ABSTRACT FROM AUTHOR]

An important step in the development of any process is evaluation. Evaluation ensures that the process affects effectiveness, efficiency, or other metrics as designed. Without evaluation, processes must stand solely on the theories, axioms, and/or heuristics that formed them. Outcome or objectives-based evaluations are especially useful in seeing if the expected impact is realized. Systems and design engineering present problems for outcome evaluations due to system design efforts being long in duration, expensive, organization/team specific, and environment/context specific. These characteristics make repeating the same effort under the same conditions using different processes difficult, if not impossible. While a comparison of processes for evaluation in a real application may not be feasible, a simulation of the processes with agents that capture system design behaviors may produce findings to support hypotheses. This paper examines the use of agent-based modeling and simulation to compare pseudo-waterfall and pseudo-agile engineering processes for a simple design problem. The Function–Behavior–Structure (FBS) model of design is used along with empirical data from FBS studies to examine the performance of a two-person design team using pseudo-waterfall and pseudo-agile engineering processes. The results of this exercise show possible advantages of agile-like processes in total time to complete the design. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a week-long engineering summer camp that engages pre-college learners in human-centered design principles. Students worked in small teams to create and present a viable prototype for a specified user group. The one-week camp engaged students in understanding, ideating, and low-fidelity prototyping. Takeaways from the camp design and implementation are described and possible adaptations to high school science classroom contexts are discussed. [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]

The impact of reservoirs on downstream river hydrological characteristics is always a focal point in relevant studies exploring the relationship between rivers and dams. Anticipating river runoff patterns following the construction of new dams is crucial for the design of riverine engineering projects, particularly during dry periods. This paper presents a semi-theoretical estimation method based on the correlation between hydrological alterations and reservoir operation. The method incorporates differences in runoff increment distribution and the discrepancy between theoretical and practical results. It was validated and applied in the sub-basins of the upper reaches of the Yangtze River, namely the Jinsha River and Min River. The runoff increment during the driest month for the Jinsha River and the Min River is 817 m3/s and 434 m3/s, respectively. The estimated prediction biases were within 30% of the practical runoff increments observed in the Jinsha River and Min River, which is an acceptable range considering the inherent variability in such studies. Since the construction of the Wudongde and Baihetan dams in 2021, the average runoff during the driest month and the navigation assurance runoff at a 95% probability were predicted to be 2866 m3/s and 2174 m3/s, respectively. Therefore, the method developed in this paper provides a reasonable and straightforward tool for researchers, which can help prevent future engineering invalidation and minimize resource costs. Moreover, in the application process, this method requires careful consideration of the characteristics of the studied river section and the operation of the reservoir group. It relies on measured data to determine the differences between theoretical and actual runoff rather than simply generalizing to all watersheds. [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]

Metal roof systems were widely utilized in various important buildings; however, cases of wind damage were often observed. In this paper, wind uplift tests of standing seam aluminum magnesium manganese and continuous welded stainless-steel roof systems were conducted, and the wind resistance bearing capacity and mechanical properties of key joints in the two roof systems were compared and analyzed. Strain gauges and displacement sensors were arranged at different structural layers and key nodes of the roof system to compare and analyze the stress and displacement changes. The results showed that the wind resistance capacity of the continuous welded stainless-steel roof system was more than 25% higher than that of the standing seam aluminum magnesium manganese roof system. The stress and displacement of the roof system gradually increased with the increase in wind load. Obvious differences in stress at different positions of the two roof systems were identified. The stress at the roof panel of the roof system was greater than that of other structural layers, and the maximum displacement of the roof panel in the elastic stage could reach more than 97.5 mm. The fitting coefficient between the test and the finite element was 0.976, and the ultimate bearing capacity of Specimen B was 479.64 MPa. The research results of this paper can provide some data support and reference for engineering design and applications. [ABSTRACT FROM AUTHOR]

Dealing with uncertainties is inherent in the engineering design process and often poses challenges for young learners. This necessitates providing learners with adequate support to navigate their uncertainties effectively. However, achieving this requires a deeper understanding of the factors influencing learners' uncertainty management processes. In this paper, we analyze learners' framing of uncertain situations in collaborative engineering design tasks. Using the case study method, we explore two framing dimensions—epistemological and positional—to understand their impact on collaborative uncertainty management processes. Specifically, we examine how learners' framing dynamically shifts during uncertainty management activities by analyzing two contrasting cases from a sixth-grade classroom where groups tackled an engineering design problem, employing interaction analysis. Learners' epistemological and positional framings are intertwined, collectively influencing their own and their team members' uncertainty management actions. Additionally, contextual factors such as mentor intervention, early success, affect induced by prolonged failure, and time constraints can prompt shifts in learners' framing, both in productive and unproductive manners. The paper provides nuanced insights into how learners' evolving epistemological orientations and positioning influence uncertainty management, offering practical insights into supporting learners' uncertainty management processes in problem-solving contexts such as engineering design. [ABSTRACT FROM AUTHOR]

Narratives about STEM (science, technology, engineering, and mathematics) education are strongly connected with conversations about developing learners' humanistic knowledge and their ability to listen with understanding and empathy. This is challenging because learners need to find resonance through first-hand contextual experiences with the issues at hand. In this paper, we describe and discuss an activity that was enacted to actively engage 74 teachers from Thailand in constructing a prototype cutting device for the blind to prepare food themselves. This activity underscores important considerations for inclusive design and offers affordances for teachers to develop their learners' inclusive mindsets. Findings were generated from voice recordings of reflections and written reflections collected after the activity. We highlight the importance of creating opportunities for learning to listen and resonate with others' experiences and argue that such STEM activities can offer a platform for learners to develop humanistic qualities such as social empathy. [ABSTRACT FROM AUTHOR]

The impact of the topological formation of wind farms upon the lightning induced overvoltages injected into the grid was not covered earlier in literature. However, this topic is highly important to be investigated to allow the usage of the most reliable topology against lightning strikes. For such reason, the paper investigates this point with consideration of most damaging cases as lightning strikes to multi-blades. The testing used ATP software for four main topologies, radial, single-sided ring SSR, double sided ring and star topology. The features defining the similarities in response and the variance range between these topologies were recorded and analyzed. The multi-blade strikes gave an expected increase of 15% to 100% in the injected overvoltage to the grid for all topologies. The star topology showed the most reliable performance by allowing the least injected overvoltage to the grid. The percentage of reduction in the magnitude of the injected overvoltages reached 50.78%, 66.07% and 89.04% for SSR, DSR and star topology respectively with respect to radial topology. Recommendation was provided for design engineers to consider star topology during design phase in terms of more reliable lightning protection. [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]

Lunar bases for long-term human habitation are a topic of growing interest to engineers. Structures to be built on the surface of the Moon must be designed to resist extreme environmental hazards such as hard vacuum/no atmosphere, low gravity, ionizing radiation, large temperature variations, moonquakes, and meteoroid impacts. Protective regolith covers may provide adequate shielding from some of these hazards. This paper presents a methodology that utilizes established physics-based models to determine the immediate effects of hypervelocity impacts (HVI), including crater geometry and shock pressure, for engineering design of lunar structures. First, empirical equations and nondimensional scaling laws for computing geometric features of HVI craters are discussed. These damage estimation tools are adopted from the fields of spacecraft design and planetary impact cratering. Next, the planar impact approximation model is discussed, which utilizes one-dimensional shock wave physics and impedance matching conditions to estimate the peak initial pressures generated in HVI. Comprehensive parametric analyses, encompassing a wide range of impact velocities and impactor masses as well as a variety of impactor and target materials, are performed to demonstrate applications of the models to both regolith-shielded and unshielded structures. Crater geometry descriptors and peak shock pressures in HVI scenarios involving various impactor-target combinations are computed with in-house programs and compared with computational results reported in the literature. The analyses indicate that for a given target, the main factors determining cratering damage are impactor mass, impactor density, and impact velocity, while the initial shock pressure is influenced mainly by impact velocity and impactor density. A comparison of impact pressures experienced by monolithic unshielded targets and granular dry sand layers highlights the potential importance of providing regolith-like shielding to attenuate HVI effects. Practical implications of the study are discussed from an engineering design perspective, underscoring the considerable value of such models to practitioners. Practical Applications: A practical methodology for determining cratering and peak shock pressure due to hypervelocity meteoroid impacts on unshielded and regolith-shielded lunar structures is proposed in this work. The methodology utilizes well-established physics-based models of hypervelocity impact effects. These models have several advantages that make them suitable for inclusion in lunar structural design guidelines: (1) the models presented are relatively simple yet realistic, empirical, and analytical models; (2) the models do not rely on computationally expensive numerical methods and have been successfully implemented using a commonly available scientific computing platform; and (3) the models consider several essential aspects of the complex physics of hypervelocity meteoroid impacts, which is essential to ensure accurate prediction of the immediate effects of such impacts for lunar structure design. Additionally, this work also includes a wide-ranging parametric analysis that explores and compares the predictions of the physics-based models from an engineering design perspective, encompassing a variety of impact scenarios and impactor-target combinations. The results highlight the practical importance of providing a protective shield, composed of a regolith-like granular material, to mitigate the peak shock pressure sustained by lunar structures in hypervelocity meteoroid impacts. The models, applications, and results presented herein are of immediate relevance to practicing engineers. [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 high-power photovoltaic systems, the inverter with an LCL filter is widely used to reduce the value of output inductance at which a lower switching frequency is required. However, the effect on the stability of the system caused by an LCL filter due to its resonance characteristic cannot be ignored. This paper studies the stability of a single-phase voltage source full-bridge inverter with an LCL filter through the bifurcation theory as it is a nonlinear system. The simulation results show that low-frequency oscillation appears when the proportional coefficient of the system controller increases or the damping resistance decreases to a certain extent. The average model is derived to analyze the low-frequency oscillation; the theoretical analysis demonstrates that low-frequency oscillation is essentially a period in which doubling bifurcation occurs, which indicates the intrinsic mechanism of the instability of the full-bridge inverter with an LCL filter. Additionally, the limitation of the existing damping resistor design standards, which only considers the main circuit parameters but ignores the influence of the controller on system stability, is identified. To solve this problem, the analytical expression of the system stability boundary is provided, which can not only provide convenience for engineering design to protect the system from low-frequency oscillation but also expand the selection range of damping resistance in practice. The experiments are performed to verify the results of the simulation and theoretical analysis, demonstrating that the analysis method can facilitate the design of the inverter with an LCL filter. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport equations. Based on the AFA-3G assembly model, a neutron transport equation solving model is established using deep neural networks, considering factors that influence the neutron transport process in real engineering scenarios, such as varying temperature, power, and boron concentration. Comparing the model's predicted values with reference values, the average error in the infinite multiplication factor kinf of the assembly is found to be 145.71 pcm (10−5), with a maximum error of 267.10 pcm. The maximum relative error is less than 3.5%, all within the engineering error standards of 500 pcm and 5%. This preliminary validation demonstrates the feasibility of using data-driven artificial intelligence methods to solve high-dimensional neutron transport equations, offering a new option for engineering design and practical engineering computations. [ABSTRACT FROM AUTHOR]

In this paper, the effect of size on the wind pressure coefficient on the surface of solar greenhouses is investigated using numerical simulations. The models were designed with consistent ratios of ridge height to span and north wall height to ridge height across different spans. To effectively understand the impact of dimensions on wind pressure distribution, two wind directions—0° (north wind) and 180° (south wind)—which have previously been shown to impose significant overall wind loads on solar greenhouses, are focused on. It was found that the wind load per unit area increases continuously with greenhouse size. Wind-induced suction is particularly concentrated along the ridge, with greater suction on the leeward side compared to the windward side. The wind suction near the ridge is more significantly affected by greenhouse size. This study provides valuable insights for the practical engineering design of solar greenhouses. [ABSTRACT FROM AUTHOR]