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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 paper explores the artificial intelligence (AI) concept for complex engineering design processes in the shipping industry. It is driven by the computer technologies advancement for fast and concurrent tasks processing, machine learnability, and data-centric approach. While AI has been adopted in many industries, it is still lacking the structured approaches for practical implementation. This is especially on the generality of the methodologies and explaining AI to the non-technical members and their preparedness. Therefore, this work proposed a conceptual framework to systematically extract, represent and visualize the ship design knowledge, to develop and deploy the machine learning (ML) models, and to demonstrate the AI-based ship design processes. Comparisons to the generic ship design model were made and discussed to highlight the improvements observed. It is found that while the conventional algorithmic approach procedures were faster in terms of execution time, the stepwise empirical models were often limited by the dataset and the design assumptions with restricted estimation capabilities for solving the nonlinear ship design problems. The findings presented the impact in improving the existing processes and effectively reducing its cycle. Additionally, the approach emphasised on the validated ship design data thus its generalization for fast and wide adoptions at scales. [ABSTRACT FROM AUTHOR]

The Garden in Motion is a concept that was developed by Gilles Clément in the early 1980s as the result of the experimentation conducted in his garden-house, La Vallée. Clément’s interest in promoting this garden archetype based on managing neglected land led him to write a number of works in order to explain the conceptual underpinnings of his model, with the ultimate aim of having them published. From the writing of the first manuscripts, dating from 1984, to the first edition of “Le jardin en movement” in 1991, he produced a series of different documents that would form the basis of the text that was finally published. It was a long process in which Clément not only had to fully develop his ideas but also to overcome the difficulties involved in finding a company to publish what he called his ‘petit livre’. The aim of this article is to show the creative process behind Clément’s book and its relationship with the creative process of building his garden, while endeavouring to reply to the question of whether it is possible to establish an equivalence between both processes. This is made possible through the analysis of documents kept by Clément in his personal archive, some of which have never been published.

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]

Engineered solutions are becoming more complex and multi‐disciplinary in nature. This evolution requires new techniques to enhance design and analysis tasks that incorporate data integration and interoperability across various engineering tool suites spanning multiple domains at different abstraction levels. Semantic web technologies (SWT) offer data integration and interoperability benefits as well as other opportunities to enhance reasoning across knowledge represented in multiple disparate models. This paper introduces the digital engineering framework for integration and interoperability (DEFII) for incorporating SWT into engineering design and analysis tasks. The framework includes three notional interfaces for interacting with ontology‐aligned data. It also introduces a novel model interface specification diagram (MISD) that provides a tool‐agnostic model representation enabled by SWT that exposes data stored for use by external users through standards‐based interfaces. Use of the framework results in a tool‐agnostic authoritative source of truth spanning the entire project, system, or mission. [ABSTRACT FROM AUTHOR]

The article discusses ways to teach engineering fundamentals with simple materials. Topics discussed include tips and tricks to use paper effectively to teach engineering principles to children, thousands of STEM/STEAM challenges available in books and online and working of Marina Bernasuli, Technology Coordinator at the Browning School, by using engineering with paper projects throughout the 2020-21 school year.

By far most PDA clients like to scrutinize the news through internet-based media over the web. The news locales are dispersing the data and giving the wellspring of approval. The request is the method for approving the news and articles which are flowed among online media like WhatsApp social occasions, Facebook Pages, Twitter, and other smaller than expected web diaries and relational connection areas. It is harmful for the overall population to acknowledge the pieces of tattle and claim to be data. The need for an hour is to stop the stories, especially in the arising countries like India, and focus on the right, checked reports. This paper shows a model and the methodology for fake news areas. With the help of Machine learning and standard language taking care of, it is endeavored to add up to the news and later choose if the news is certified or fake using Different Algorithms. The eventual outcomes of the proposed model are differentiated and existing models. The proposed model is working honorably and portraying the rightness of results up to extremely fine accuracy. [ABSTRACT FROM AUTHOR]

Digital transformation – the pervasive incorporation of digital technology into virtually every process and system – is arguably the greatest force of change within the naval engineering community. Digital innovation is disrupting traditional ship design paradigms and giving rise to new ways of modernising and sustaining a future integrated force structure. This paper highlights digital transformation as a core strategic initiative that is aligned with the U.S. Department of Defense Digital Engineering Strategy. Recognising the need to expand the U.S. Navy's advantage over its competitors, this paper identifies foundational elements required to transform current engineering design and development processes. Next, a multi-factor framework is introduced to align traditional processes employed for physical system design and development with a new digital paradigm in recognition of the highly integrated nature of Model Based Systems Engineering (MBSE). Finally, discussion will focus on efforts underway to leverage advanced computational models and highlight the digital engineering use case for Naval Power and Energy Systems (NPES). [ABSTRACT FROM AUTHOR]

[Display omitted] • Robust probe is designed by engineering biomimic MOFs with specific pockets. • The probe gives "key-lock" identification and sensitive quantitative information. • Innovatively integrating the probe with filter paper could avoid ring-like deposit. • The well-distributed interface is harvested to reduce colorimetric signal fluctuation. • Self-adaptive signal model allows for monitoring pesticide in colored matrices. Excessive residual pesticides have raised overall environmental damage and health hazard issues, thus the development of facile and sensitive means for pesticide monitoring in different environmental field is desperately needed but challenging. In this work, a flexible paper-based colorimetry beating coffee-ring effect is achieved toward sensing environmental pesticide exposure in user-friendly mode. First, to circumvent poor performance of biomolecules, robust probe is designed by imprinting metal-organic frameworks (MOFs) with specific pockets, enabling to provide "key-lock" identification and sensitive quantitative information under harsh environment. Innovatively, integrating the MOF-mediated probe with filter paper, a well-distributed sensing interface is harvested to reduce colorimetric signal fluctuation for reliable detection properties, which is ascribed to superior wetting behavior of filter paper for avoiding ring-like deposit formation. Additionally, the uniform profile is simulated by Fluent numerical software. Further exploiting smartphone-assisted image acquisition and self-adaptive signal model, the simple-to-perform system exhibits good accuracy toward ppb-level thiacloprid free of colored background interference. A low limit of detection of 0.04 μM thiacloprid (corresponding to 0.01 μg g−1 by weight) is achieved. Moreover, it allows for in-filed monitoring thiacloprid exposure in agricultural wastewater, soil, fresh vegetables with satisfactory recoveries ranging from 92.7 % to 109.0 %, verifying the broad potential applications for point-of-use tracking of environmental pesticide and eventually bringing benefits to ecosystem protection in resource-deficient settings. [ABSTRACT FROM AUTHOR]

This study is the first step of a research program organized to contribute to the dream of using recycled paper as a structural element for sustainable design in Civil Engineering. One of the important tasks is to understand the behavior of connections in paper materials. This study presents the results of experimental research carried out to investigate the behavior of paperboard laminate connections consisting of a single bolt in double shear. A simple and effective method to predict the bearing strength in bolted connection for paperboard laminates has been proposed. A total of 96 bolted connections were fabricated from paperboards oriented in both machine and cross direction. The effect of end distance and edge distance of bolt hole are investigated to address the limiting value for both variables. It was found that the failure is governed by both shear and bearing stresses developed around the hole. Finally, a straightforward design equation has been proposed for such connections. • Single bolt connections can be implemented on paperboard laminates. • Minimum end distance, and edge distance values should be 1.5 times the hole diameter. • Mixed-mode failure which is the combination of shear and bearing mode will govern. • Equation with max. radial compressive stress can be used conservatively for design. [ABSTRACT FROM AUTHOR]

Origami/kirigami, the ancient art of paper folding and cutting techniques, has provided considerable inspiration for structural design routes in the engineering and medical fields over the last few decades. The practicability of the methods and concepts of origami/kirigami has been demonstrated in several emerging classes of technologies, e.g., stretchable electronics, deformable devices, self‐assembly fabrication, etc. More and more related products are produced pursuing a folding form for better storage, deformation capacity, and multifunction realization. Herein, the innovative creased patterns of origami/kirigami designs are distinguished and discussed, and the four most widely used creased pattern types are introduced, which may potentially provide origami/kirigami related inspiration and additional solutions toward many research fields. [ABSTRACT FROM AUTHOR]

Digital twin technology assists physical entities better to achieve functional perfection through the interactive cointegration of physical entities and virtual spaces. In this view, this paper proposes a digital twin inspired intelligent diagnosis method for bearing faults based on adaptive correlation filtering and an improved stack autoencoder (SAE) classification model. First, the adaptive correlation filtering algorithm is designed to filter the engineering signal in physical space, in which the noise-free ideal bearing fault signal spectrum in the virtual space is used as the comparison. In this filtering, the optimal cut-off frequency is determined adaptively with the condition that the similarity between the simulated spectrum and the engineering spectrum is maximized. Second, the Tan activation function is used in the SAE classification model to enhance the signal differentiation and avoid the problem of large computation of the traditional sigmoid function. Finally, the accuracy of the method in this study is verified on the basis of engineering experiments and Case Western Reserve University bearing fault dataset. Experimental results show that the method can achieve 100% correct diagnosis rate of bearing faults. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

The conference was organised by European Technology Development (ETD Consulting), chaired by Dr Ahmed Shibli and co-chaired by Dr David Allen. Paper 1-4: B Experiences in the Manufacture of Large MARBN Steel Castings to the UK "IBN1" Specification b S. Roberts Goodwin Group, UK Goodwin Steel Castings have been a mainstay of the UK MARBN development programme over the past decade, and are now actively engaged in pursuing potential business opportunities worldwide. Paper 2-3: B The Development of Electromagnetic (EM) Sensor Technique for Creep Damage Detection and Assessment b J. Wilson I et al. i University of Manchester, UK; ETD, UK: Nippon Steel Technology Corporation, Japan The aim of this work was to establish correlations between "crept" and "aberrant" Grade 91 material and magnetic properties using laboratory measurement systems utilising Barkhausen Noise; which is connected with dislocation density. Paper 1-2: B Development of UK MARBN steel "IBN1" for advanced power plant applications b D. Allen ETD, UK This paper followed on from Prof. Abe's introduction by providing a more practical perspective and summarising the achievements of a decade of UK development of MARBN steel through a sequence of collaborative projects part-funded by Innovate UK. [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]

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 the past decade, flexible electronics have attracted significant research attention due to their distinct features and emerging applications in numerous fields such as, flexible displays, implantable sensors, and energy storage systems, among other applications. Due to the development of flexible electronics, this paper details the substrates employed to produce flexible electronic devices, given that substrates generally govern the overall device properties. The increase in research attention can be attributed to the use of films as flexible substrates, which enable the implementation of numerous design strategies and engineering methodologies, thus leading to extensive advances in the manufacturing quality and prospect of flexible electronics in various applications. This paper provides a comprehensive review of the significance of substrates in flexible wearable electronics over the past decade, such as, the substrate properties requirements, processing classification, important flexible devices, and applications, including sensing, energy storage, and other electronic devices. [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]

Engineering design has proven to be a rich context for applying artificial intelligence (AI) methods, but a categorization of such methods applied in AI-based design research works seems to be lacking. This paper presents a focused literature review of AI-based methods mapped to the different stages of the engineering design process and describes how these methods assist the design process. We surveyed 108 AI-based engineering design papers from peer-reviewed journals and conference proceedings and mapped their contribution to five stages of the engineering design process. We categorized seven AI-based methods in our dataset. Our literature study indicated that most AI-based design research works are targeted at the conceptual and preliminary design stages. Given the open-ended, ambiguous nature of these early stages, these results are unexpected. We conjecture that this is likely a result of several factors, including the iterative nature of design tasks in these stages, the availability of open design data repositories, and the inclination to use AI for processing computationally intensive tasks, like those in these stages. Our study also indicated that these methods support designers by synthesizing and/or analyzing design data, concepts, and models in the design stages. This literature review aims to provide readers with an informative mapping of different AI tools to engineering design stages and to potentially motivate engineers, design researchers, and students to understand the current state-of-the-art and identify opportunities for applying AI applications in engineering design. [ABSTRACT FROM AUTHOR]

This article discusses the importance of systems approaches in addressing complex real-world problems. The author highlights the tension between rigor and relevance in these approaches, emphasizing the need for a "systems stance" that balances principles such as sufficiency and requisite detail. The article presents five papers that exemplify different frameworks of rigorous inquiry, including the use of causal loop diagrams and fault trees. It also explores the combination of soft (social) and hard (technical) systems, as well as the analysis of interactions and feedback loops. The author concludes by emphasizing the participatory nature of systems approaches and their relevance to engineering practice. [Extracted from the article]

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]