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Margins are defined as the difference between a design parameter's minimum required value to ensure functionality, and its actual capability. Margins allow engineers to mitigate uncertainties of various kinds. While some margins are intentionally allocated, some others may get included inadvertently in designs or arise from changes to requirements. Although common in use, the concept of margins has not been formalised systematically. This paper offers the first systematic literature review of margins. Concepts related to margins can be found in various interrelated domains with similar underlying principles. However, these concepts have developed in isolation, leading to a divergent and fragmented understanding. This paper brings these strands together by differentiating between margins which may be deliberately added or discovered during a typical product lifecycle and relates this to various domains such as safety, manufacturing etc. The paper discusses approaches to model, size and allocate margins. The thematic analysis presents insights into the importance of systematic use and management of margins and also raises currently observable gaps in the literature. [ABSTRACT FROM AUTHOR]

The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro–mechanical devices that are constructed in the semblance of humans and have the ability to sense their environment and take actions accordingly. The control of humanoids is broken down to the following: sensing and perception, path planning, decision making, joint driving, stability and balance. In order to establish and develop control strategies for joint driving, stability and balance, the triple inverted pendulum is used as a benchmark. As the presence of uncertainty is inevitable in this system, the need to develop a robust controller arises. The robustness is often achieved at the expense of performance. Hence, the controller design has to be optimized based on the resultant control system's performance and the required torque. Particle Swarm Optimization (PSO) is an excellent algorithm in finding global optima, and it can be of great help in automatic tuning of the controller design. This paper presents a hybrid H∞/sliding mode controller optimized by the PSO algorithm to control the triple inverted pendulum system. The developed control system is tested by applying it to the nominal, perturbed by parameter variation, perturbed by external disturbance, and perturbed by measurement noise system. The average error in all cases is 0.053 deg and the steady controller effort range is from 0.13 to 0.621 N.m with respect to amplitude. The system's robustness is provided by the hybrid H∞/sliding mode controller and the system's performance and efficiency enhancement are provided by optimization. [ABSTRACT FROM AUTHOR]

The shape of the connecting section of the shaft spillway directly affects the flow pattern of the tunnel, the vibration characteristics of the shaft and the energy dissipation effect. At present, the research on the shape of connecting section is mostly single factor, and few multi-factor studies. In engineering design practice, the optimization of shaft shape mostly refers to similar engineering experience, and there is no more systematic optimization method. In this paper, the numerical orthogonal test is used to analyze the sensitivity of the length-width ratio, the contraction ratio of the exit pressure slope and the compression slope ratio of the connecting section. The results show that the length-width ratio has the most significant effect on the outlet turbulent kinetic energy, followed by the compression slope ratio and the lowest contraction ratio. The research results can provide some reference for the optimization of the joint shape of the shaft spillway tunnel in engineering design. [ABSTRACT FROM AUTHOR]

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]

Finned-tube heat exchanger (FTHE) is often used as an evaporator in commercial products of separated heat pipe (SHP). The working conditions of FTHE in gravity-assisted SHP are significantly different from those working in refrigerators and air conditioners. Although FTHE is widely used in commercial products of SHP, previous research on its characteristics is very limited. In this paper, a mathematical model for a SHP with FTHE as the evaporator and plate heat exchanger as the condenser is established and verified with experiments. Parametric analyses are carried out to investigate the influences of evaporator design parameters: air inlet velocity, number of tube rows, tube diameter, and fin pitch. With the increasing of air velocity, number of tube rows and tube diameter, and the decreasing of fin pitch, the heat transfer rate increases, while the energy efficiency ratio (EER) decreases monotonically. Using the total cost of the ten-year life cycle as the performance index, the structure parameters of the evaporator with a given heat transfer rate are optimized by the method of orthogonal experimental design. It is found that the total cost can differ as large as nearly ten times between groups. Among the three factors investigated, the number of tube rows has a significant impact on the total cost of the evaporator. With more tube rows, the total cost will be less. The impacts of fin pitch and tube diameter are insignificant. These results are of practical importance for the engineering design of FTHE in gravity-assisted SHP. [ABSTRACT FROM AUTHOR]

ABSTRACT The scuffing load capacity of gear is closely related to the meshing temperature rise of tooth surface. The key to predict the temperature rise is to establish an accurate meshing temperature rise model. In the paper, a tribo‐dynamic model of helical gear is established through coupling of tooth surface lubrication parameters, and the influence of temperature rise on ambient temperature during meshing process is considered. Then, the effects of oil supply temperature, input speed and torque on tooth surface temperature rise, film thickness, friction excitation and gear dynamic characteristics are discussed. The results show that the temperature rise of the gear is higher during the engaging‐in and engaging‐out regions. Meanwhile, there is local high temperature at the end of the contact line due to the end effect. The vibration of gear along the off‐line‐of‐action direction is mainly determined by friction excitation. With the increase of oil supply temperature, input speed and torque, the risk of scuffing failure increases and the influence of oil supply temperature and input load is more significant. The conclusions of this paper may provide some valuable suggestions for the anti‐gluing failure design of gear in engineering. [ABSTRACT FROM AUTHOR]

Springs are fundamental components in mechanical systems, crucial for ensuring the safety and functionality of mechanisms. Timoshenko's stiffness formula accounts for both bending and torsional energy effects, providing accurate results for small deformations. However, when the deformation becomes large, the spring stiffness becomes a nonlinear problem due to the changing inclination angle and radius during deformation. In this study, we derive a formula for the cylindrical spring stiffness under nonlinear large deformation by considering two assumptions: the invariability of the polar angle at any point and spring wire length during deformation. This formula incorporates the effects of inclination and radius changes on the spring wire. We analyze the stiffness of the cylindrical spring with different initial inclinations using the finite element method (FEM). FEM results were compared with those obtained from Timoshenko's formula, Hiroyuki's formula, and the derived formula. For small deformations, the FEM results matched well with all three formulas. However, for nonlinear large deformations, the calculated results from Timoshenko's formula showed a discrepancy of up to 32.58% compared to the FEM results. The modified Hiroyuki formula also exhibited slightly poorer agreement with the FEM results than the formula proposed in this paper. On the other hand, our derived formula demonstrated excellent agreement with the FEM results for nonlinear large deformations. Therefore, our stiffness formula for cylindrical springs is recommended for mechanical engineering spring design applications involving nonlinear large deformations. [ABSTRACT FROM AUTHOR]

Postoperative tissue flap vitality monitoring enables early detection of clinical complications, allowing for intervention. Timely re-operation can prevent the need for extensive correction procedures, thus reducing healthcare costs and hospitalization time. Statistics show that monitoring can increase the success rate of flap survival to 95% or higher. However, despite the significant progress in monitoring techniques, major and minor complications, leading to the loss of the flap, still occur. This clinical application review aims to provide a comprehensive overview of the recent advancements and findings in flap surgery reconstructions, transplants, and systems for their postoperative assessment. The literature from the years 1925 to 2024 has been reviewed to capture previous and current solutions for monitoring flap vitality. Clinically acclaimed methods and experimental techniques were classified and reviewed from a technical and clinical standpoint. Physical examination, metabolism change, ultrasound method, and electromagnetic (EM) radiation-based measurement methods were carefully evaluated from the perspective of their considered applications. Guidelines aiding engineers in the future design and development process of monitoring systems were proposed. This paper provides a comprehensive overview of the monitoring techniques used in postoperative flap vitality monitoring. It also gives an overview of each approach and potential ways for future development. [ABSTRACT FROM AUTHOR]

In this paper, we present the development and evaluation of a contextually relevant, cost-effective, multihop cluster-based agricultural Internet of Things (MCA-IoT) network. This network utilizes commercial off-the-shelf (COTS) Bluetooth Low-Energy (BLE) and LoRa communication technologies, along with the Raspberry Pi 3 Model B+ (RPi 3 B+), to address the challenges of climate change-induced global food insecurity in smart farming applications. Employing the lean engineering design approach, we initially implemented a centralized cluster-based agricultural IoT (CA-IoT) hardware testbed incorporating BLE, RPi 3 B+, STEMMA soil moisture sensors, UM25 m, and LoPy low-power Wi-Fi modules. This system was subsequently adapted and refined to assess the performance of the MCA-IoT network. This study offers a comprehensive reference on the novel, location-independent MCA-IoT technology, including detailed design and deployment insights for the agricultural IoT (Agri-IoT) community. The proposed solution demonstrated favorable performance in indoor and outdoor environments, particularly in water-stressed regions of Northern Ghana. Performance evaluations revealed that the MCA-IoT technology is easy to deploy and manage by users with limited expertise, is location-independent, robust, energy-efficient for battery operation, and scalable in terms of task and size, thereby providing a versatile range of measurements for future applications. Our results further demonstrated that the most effective approach to utilizing existing IoT-based communication technologies within a typical farming context in sub-Saharan Africa is to integrate them. [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]

In this paper, a novel Snail Homing and Mating Search (SHMS) algorithm is proposed. It is inspired from the biological behaviour of the snails. Snails continuously travel to find food and a mate, leaving behind a trail of mucus that serves as a guide for their return. Snails tend to navigate by following the available trails on the ground and responding to cues from nearby shelter homes. The proposed SHMS algorithm is investigated by solving several unimodal and multimodal functions. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The solutions obtained from the SHMS algorithm exhibited superior robustness as well as search space exploration capabilities with less computational cost. The real-world application of the SHMS algorithm is successfully demonstrated in the engineering design domain by solving three cases of design and economic optimization Shell and Tube Heat Exchanger (STHE) problem. The objective function value and other statistical results obtained using SHMS algorithm are compared with other well-known metaheuristic algorithms. For Solving STHE Case 1 the SHMS algorithm achieved 0.5–35% minimization of the total cost. For Case 2, 0.6–29% minimization of the total cost has been attained. Furthermore, for Case 3, 0.3%, 0.4% and 52% minimization of total cost is achieved when compared with the ARGA & CI, GA, and original study, respectively. The analysis regarding the convergence of the SHMS algorithm is discussed in detail. The contributions in this paper have opened up several avenues for further applicability of the algorithm for solving complex real-world problems. [ABSTRACT FROM AUTHOR]

In this paper, Numerical simulation analysis was conducted on the ear pressure of a certain vehicle model during door closing process. Firstly, relevant airflow channel parameters are formulated based on the benchmark vehicle data, and transient closing ear pressure simulation calculation is carried out using overset grid technology. Then, the impact of key structural parameters on the interior ear pressure of the car was analyzed. Finally, based on the requirements of actual engineering design, the design scheme for the peak pressure of the car was determined, and the results will be a reference for the development and design of later vehicle. [ABSTRACT FROM AUTHOR]

Complex and nonlinear optimization challenges pose significant difficulties for traditional optimizers, which often struggle to consistently locate the global optimum within intricate problem spaces. To address these challenges, the development of hybrid methodologies is essential for solving complex, real-world, and engineering design problems. This paper introduces FVIMDE, a novel hybrid optimization algorithm that synergizes the Four Vector Intelligent Metaheuristic (FVIM) with Differential Evolution (DE). The FVIMDE algorithm is rigorously tested and evaluated across two well-known benchmark suites (i.e., CEC2017, CEC2022) and an additional set of 50 challenging benchmark functions. Comprehensive statistical analyses, including mean, standard deviation, and the Wilcoxon rank-sum test, are conducted to assess its performance. Moreover, FVIMDE is benchmarked against state-of-the-art optimizers, revealing its superior adaptability and robustness. The algorithm is also applied to solve five structural engineering challenges. The results highlight FVIMDE's ability to outperform existing techniques across a diverse range of optimization problems, confirming its potential as a powerful tool for complex optimization tasks. [ABSTRACT FROM AUTHOR]

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This paper provides the results and the analysis of a study conducted on the processes and technologies currently used by European Union shipyards, and their potential interest in new advanced composite construction technologies, including Adaptive Moulds, Automated Tape Laying, Automated Fibre Placement, Curved Pultruded Profiles, Additive Manufacturing, Hot Stamping, Modular and Serialised Shipbuilding and Digitisation of Production. A new set of indexes are also introduced with the objective of facilitating the evaluation of the technological level of the shipyard, thereby allowing the understanding of the shipyard's interest in new developments. The analysis of the survey conducted shows that almost the 95% of shipyards either currently use or plan to use composite materials, being the composites made of fibreglass and polyester resin the most used. These composites are mainly manufactured by manual lamination and vacuum infusion techniques. The survey has also shown that there is a high interest in the shipyard digitisation and the use of new technologies in the shipbuilding industry, especially for new construction shipyards. The study shows that despite shipyards want to adopt digitisation in engineering and design processes, implementation of new technologies and concepts is being held back by financial cost and uncertainty regarding outcomes such as improved operational efficiency of vessels. The shipyards that do not use composites are less technologically advanced than those that use them, according to the analysis of the technological indexes. This analysis has also shown that the shipyards with a high technological index have a more varied potential market offer. [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]

The osprey optimization algorithm (OOA) is a metaheuristic algorithm with a simple framework, which is inspired by the hunting process of ospreys. To enhance its searching capabilities and overcome the drawbacks of susceptibility to local optima and slow convergence speed, this paper proposes a modified osprey optimization algorithm (MOOA) by integrating multiple advanced strategies, including a Lévy flight strategy, a Brownian motion strategy and an RFDB selection method. The Lévy flight strategy and Brownian motion strategy are used to enhance the algorithm's exploration ability. The RFDB selection method is conducive to search for the global optimal solution, which is a symmetrical strategy. Two sets of benchmark functions from CEC2017 and CEC2022 are employed to evaluate the optimization performance of the proposed method. By comparing with eight other optimization algorithms, the experimental results show that the MOOA has significant improvements in solution accuracy, stability, and convergence speed. Moreover, the efficacy of the MOOA in tackling real-world optimization problems is demonstrated using five engineering optimization design problems. Therefore, the MOOA has the potential to solve real-world complex optimization problems more effectively. [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 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]

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]

Consumer behavior has changed due to digitization. Online shoppers now refer to user reviews containing comprehensive data produced in real-time, which can be used to determine users' needs. This paper combines Kansei engineering and natural language processing techniques to extract information on users' needs from online reviews and provide guidance for subsequent product improvements and development. A crawler tool was used to collect a large number of online reviews for a target product. Frequency analysis was then applied to the text to filter out the product components worth analyzing. The results were categorized and aggregated by experts before sentiment analysis was performed on statements containing the selected adjectives. Finally, the user needs identified could be inputted to Kansei engineering for further product design. This paper verifies the merit of the above method when applied to the mountain bike product category on Amazon. The method proved to be a quick and efficient way to attain accurate product evaluations from end-users and thus represents a feasible approach to intelligently determining user preferences. [ABSTRACT FROM AUTHOR]

This paper investigates the strengthening of non‐ductile reinforced concrete (RC) columns using partial steel‐plate jacketing to enhance ductile capacity. Quasi‐static cyclic loading tests were conducted on three rectangular RC columns with varying shear‐span‐to‐depth ratios, evaluating the effectiveness of steel‐plate jacketing. Results show improved ductile flexural response, high drift capacity, and maintained gravity‐loading capacity under high lateral displacements. A numerical investigation, adopting a multi‐spring fiber section model, predicted cyclic lateral strength, drift ratio, and failure mode, accurately simulating reinforcement behavior and capturing the shear‐strength enhancement due to steel‐plate jacketing. Numerical results demonstrated good agreement with experimental findings. Based on these outcomes, a simple design procedure for retrofitted concrete columns, applicable to design engineers, has been developed and reported in this paper. This study contributes valuable insights into the practical applications of steel‐plate jacketing for non‐ductile RC column retrofitting, offering a comprehensive approach to predicting their seismic performance. [ABSTRACT FROM AUTHOR]

In this paper, an experimental, numerical and analytical study is conducted on a 2 × 1 bored pile group on saturated sand under combined loading. Initially, a single-degree-of-freedom system founded on the pile group is tested at the ETH Zurich drum centrifuge under vertical, lateral and pushover loading, gaining insights and deriving benchmark results for validation of finite-element (FE) models. The latter account for non-linear soil–pile interaction, using hypoplasticity for sand and appropriate modelling of interfaces and pile response. Combining centrifuge and FE modelling, the governing resistance mechanisms are identified and quantified. The transition from model to prototype scale is achieved after careful consideration of scale effects. The concrete damaged plasticity model is employed to model the non-linear response of the reinforced concrete (RC) piles, accounting for axial load dependency of bending moment capacity. The prototype problem is studied parametrically, deriving failure envelopes for different levels of vertical loading. Distinguishable failure modes are identified, and the contribution of different resistance mechanisms is quantified. Finally, analytical failure envelopes are derived based on limit equilibrium, expanding Broms' theory to pile groups under combined loading. Accounting for the axial load dependency of RC bending moment capacity, the proposed closed-form solutions provide a useful design tool for engineering practice. [ABSTRACT FROM AUTHOR]

In optimization, metaheuristic algorithms have received extensive attention and research due to their excellent performance. The slime mould algorithm (SMA) is a newly proposed metaheuristic algorithm. It has the characteristics of fewer parameters and strong optimization ability. However, with the increasing difficulty of optimization problems, SMA has some shortcomings in complex problems. For example, the main concerns are low convergence accuracy and prematurely falling into local optimal solutions. To overcome these problems, this paper has developed a variant of SMA called CCSMA. It is an improved SMA based on horizontal crossover (HC) and covariance matrix adaptive evolutionary strategy (CMAES). First, HC can enhance the exploitation of the algorithm by crossing the information between different individuals to promote communication within the population. Finally, CMAES facilitates algorithm exploration and exploitation to reach a balanced state by dynamically adjusting the size of the search range. This benefits the algorithm by allowing it to go beyond the local space to explore other solutions with better quality. To verify the superiority of the proposed algorithm, we select some new original and improved algorithms as competitors. CCSMA is compared with these competitors in 40 benchmark functions of IEEE CEC2017 and CEC2020. The results demonstrate that our work outperforms the competitors in terms of optimization accuracy and jumping out of the local space. In addition, CCSMA is applied to tackle three typical engineering optimization problems. These three problems include multiple disk clutch brake design, pressure vessel design, and speed reducer design. The results showed that CCSMA achieved the lowest optimization cost. This also proves that it is an effective tool for solving realistic optimization problems. [ABSTRACT FROM AUTHOR]

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

Rubber-foamed concrete demonstrates exceptional toughness, a low elastic modulus, and significant sensitivity to density. It is necessary to parameterize the density mix of rubber-foaming concrete to meet engineering design requirements. Density-mix design methods for foaming concrete rely mainly on empirical knowledge or trial-and-error approaches. In this paper, with numerous parametric tests and regression analysis based on general principles for density-mix designs applicable to both foamed and rubber-foamed concretes, the key design parameters, such as volume correction coefficient, rubber size effect coefficient, and water-reducing agent effect coefficient, have been proposed in order to optimize their respective densities more accurately. The tests demonstrated an optimal water-to-cement ratio of 0.45, corresponding to a volume correction factor of 1.027. Incorporating rubber particles and water-reducing agents has a more significant effect on the cement-paste volume. Controlling fluidity in the 200 to 300 mm range is crucial when designing foamed concrete with varying densities. The regression equation accurately predicts the paste's measured volume and wetting density by incorporating volume corrections, size, and water reduction effect coefficients. By employing a foam excess coefficient of 1.1 and a mass coefficient of 1.25, the dry and wet density error of foam concrete is less than 5%. A comprehensive framework for optimizing mix design in terms of density is provided for applications in foamed concrete and rubber-foamed concrete, facilitating researchers in designing mix ratios for additional novel mixture-based foamed-concrete applications. [ABSTRACT FROM AUTHOR]

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

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

This paper introduces a novel stability metric specifically developed for IQRF wireless mesh sensor networks, emphasizing flooding routing and data collection methodologies, particularly IQRF's Fast Response Command (FRC) technique. A key feature of this metric is its ability to ensure network resilience against disruptions by effectively utilizing redundant paths in the network. This makes the metric an indispensable tool for field engineers in both the design and deployment of wireless sensor networks. Our findings provide valuable insights, demonstrating the metric's efficacy in achieving robust and reliable network operations, especially in data collection tasks. The inclusion of redundant paths as a factor in the stability metric significantly enhances its practicality and relevance. Furthermore, this research offers practical ideas for enhancing the design and management of wireless mesh sensor networks. The stability metric uniquely assesses the resilience of data collection activities within these networks, with a focus on the benefits of redundant paths, underscoring the significance of stability in network evaluation. [ABSTRACT FROM AUTHOR]

Biologically inspired design (BID) in engineering is a convergent, systematic approach that uses analogies from biological organisms to develop solutions for human engineering and design problems. Based on outcomes from prior studies of integrating BID in higher education, incorporating BID into pre-college education is a logical evolution. For effective BID instruction of these convergent concepts in pre-college education, teachers need to be well-equipped with biological, engineering, and pedagogical knowledge, both in general and those unique to the convergent, still evolving discipline. In this paper, we investigate the Professional Learning (professional learning) environment designed to foster engineering teachers' understanding of BID integration in engineering and to determine to what extent the evolving professional learning environment fostered engineering teachers' conceptual knowledge of BID across the three-year project. This design study applies conjecture mapping with design-based research (DBR) to examine a professional learning environment that changed over three summers and its impact on teachers' conceptual understanding of BID integration in engineering. The analysis indicates that a combination of experiential and informal learning experiences along with engagement in a formal design challenge promoted teacher enthusiasm and a conceptual understanding of BID across the three years. Professional learning fostered teachers' understanding of BID integration in engineering and enabled them to integrate BID into their engineering teaching practice. [ABSTRACT FROM AUTHOR]

Assessing the dynamic properties of rocks remains a foundational pursuit in the field of rock engineering, providing crucial insights into their mechanical behaviors across a spectrum of loading conditions, including static, cyclic, and dynamic scenarios. This paper expounds upon the utilization of the nonresonance (NR) torsional shear test and its implications for understanding rock responses, particularly in the context of low and medium loading rates. The NR method serves as a pivotal tool for investigating model rock materials subjected to loading conditions characterized by low frequencies and amplitudes. Renowned for its efficacy, this method allows the simultaneous determination of two critical dynamic parameters: shear modulus (G) and damping ratio (D), all at a specific loading frequency. It has been ascertained that the loading rate increased as the loading frequency and applied amplitude of loading increased. With increasing loading rate, the shear modulus consequently increased while the damping ratio decreased. It is observed that the dynamic responses of both ramp and sinusoidal loading waveforms increase concurrently with the amplitudes of the applied torque and loading frequencies. The sinusoidal waveform exhibits greater dynamicity than the ramp waveform at a certain loading rate. Furthermore, this study delves into the intricate analysis of the nonlinear viscoelastic dynamic response exhibited by rocks, utilizing the modified hyperbolic (MH) model and the Ramberg–Osgood (RO) model as analytical tools. The findings derived from curve fitting exercises unequivocally underscore the superior applicability of the Ramberg–Osgood model, particularly in characterizing modulus reduction behavior. Conversely, the modified hyperbolic model emerges as the preferred choice for comprehensive damping ratio analyses. This study enhances the comprehension of rock dynamics and responses under diverse loading conditions, contributing valuable understanding to rock engineering. Insights into loading and strain rate effects aid informed decisions and preventive measures for rock deformation and collapse risks. Practical Applications: This research suggests vital findings regarding the response of intact model materials to various dynamic loading conditions, providing significant insights for comprehending the mechanical response of rock structures exposed to cyclic loading conditions, which have the potential to create weaknesses in rocks resulting in untimely failures. The research can be utilized to assess the response of rocks in the context of seismic incidents, specifically those characterized by shear waves at particular frequencies. This study evaluates the response of rocks during earthquakes by establishing a correlation between the amplitude of torsional shear loading and the peak ground displacement linked to seismic events. In addition to its seismic implications, this research aids in advancing accurate predictive models and instruments that assess the stability and integrity of rock formations under different loading rates—a consequence of the symbiosis between frequency and amplitude. Professionals may ensure efficient risk mitigation, make well-informed decisions, and execute preventative measures concerning rock collapse and deformation by virtue of their comprehensive awareness of the delicate relationship between loading rate and dynamic rock properties. Incorporating the findings into engineering design standards and codes can bring about substantial improvements, augmenting the overall reliability and protection of rock structures. [ABSTRACT FROM AUTHOR]

The goal of this paper is to describe the motivation, methodology and results of introducing Active Learning Techniques in a Digital Design course. Digital Design is a four-credit junior level course for electrical and computer-engineering technology majors at Farmingdale State College, State University of New York. The students enrolled in this course have a large range of skills in term of experience with laboratory equipment, computer-based tools, and programming. The course introduces students to VHDL Hardware Description Language as the design entry method for digital circuits and to Field Programmable Gate Arrays (FPGA) platforms for the implementation of the digital circuits. Active learning techniques implemented in the course offer students more learning opportunities, potentially improving students' knowledge and skills in digital design. [ABSTRACT FROM AUTHOR]

We explore the concept of parameter design applied to the production of glass beads in the manufacture of metalencapsulated transistors. The main motivation is to complete the analysis hinted at in the original publication by Jim Morrison in 1957, which was an early example of discussing the idea of transmitted variation in engineering design, and an influential paper in the development of analytic parameter design as a data-centric engineering activity. Parameter design is a secondary design activity focused on selecting the nominals of the design variables to achieve the required target performance and to simultaneously reduce the variance around the target. Although the 1957 paper is not recent, its approach to engineering design is modern. [ABSTRACT FROM AUTHOR]

Highlights from the article: Electronic Engineering and Design Innovations are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Since the improved DC-AC converter system has significant advantages in tracking accuracy and solution quality over classical terminal sliding mode DC-AC converter systems, this paper will be applicable to designers of relevant robust control and optimal control techniques. Specifically, a deep long short-term memory recurrent neural network and a deep-gated recurrent unit-recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling.

Bayesian Optimization (BO) is an efficient global optimization algorithm, which is widely used in the field of engineering design. The probabilistic surrogate model and acquisition function are the two keys to the algorithm. Building an efficient probabilistic surrogate model and designing a collection function with excellent exploring capabilities can improve the performance of BO algorithm, allowing it to find the optimal value of the objective function with fewer iterations. Due to the characteristics of small samples and non-parametric derivation of the Gaussian Process (GP), traditional BO algorithms usually use the GP as a surrogate model. Compared with the GP, the Student's T Process (STP) retains the excellent properties of GP, and has more flexible posterior variance and stronger robustness. In this paper, STP is used as the surrogate model in BO algorithm, the hyperparameters of the model are optimized by STP, and the estimation strategy function (EST) is improved based on the posterior output of the optimized STP, thus realizing the improved BO algorithm based on the STP. To verify the performance of the proposed algorithm, numerical experiments are designed to compare the performances of the traditional BO algorithm, which includes the lower confidence bound function (LCB) and EST as acquisition function respectively and GP as the surrogate model, and the proposed BO algorithm with STP as the surrogate model and LCB, expected improvement function (EI), expected regret minimization function (ERM) as acquisition function respectively. The results show that the proposed algorithm in this paper performs well when finding the global minimum of multimodal functions. Based on the developed algorithm in this paper, the resonant frequency of printed dipole antenna and E-shaped antenna is modeled and optimized, which further confirms the good design ability and design accuracy of the BO algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Quan semblava que la crisi ens empenyia a formats digitals en comptes del paper amb olor de tinta, quan els e-books i els arxius PDF començaven a substituir a més velocitat de la prevista els llibres i catàlegs de paper, quan semblava que arriabava la fi de les arts gràfiques... algunes empreses del sector no han volgut llençar la tovallola i ens sorprenen amb noves propostes sensorials en impresos que, a més de ser atractius a la vista, aporten experiències olfactives i tàctils i fins i tot reaccionen a la temperatura corporal. Innovació i interacció en estat pur.

This paper proposes a modular modeling towards a health system integration of fuel cells by considering not only the dynamics of the gases but also fault models that affect the PEMFC performances. The main goal is to simulate the faulty state in order to overcome data scarcity, since running a fuel cell to generate a database under faulty conditions is a costly process in time and resources. The degradation processes detailed in this paper allow to introduce a classification of faults that can occur, giving a better understanding of the performance losses necessary to simulate them. The faults that are detailed and modeled are the flooding, drying and aging processes. This modeling is based on a system approach, so it runs faster than real-time degradation tests, allowing the training and validation of online supervisors, such as the energy management strategy (EMS) method or diagnosis. The faults are reproduced according to the study requirements to be a very effective support tool to help design engineers to include faulty conditions in early design stages toward a diagnosis process and health-conscious energy management strategies. [ABSTRACT FROM AUTHOR]