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Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE‐BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE‐BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process. [ABSTRACT FROM AUTHOR]

This paper addresses optimization problems subject to an objective function's evaluation‐number constraint arbitrarily given in advance. These problems are practical for black‐box objective functions which cost much money and/or time per evaluation because money and/or time constraints are typically applicable in real‐world projects. This paper suggests a new solution strategy that can be used to adapt metaheuristics to evaluation‐number constraints arbitrarily given in advance. The proposed strategy updates the setting parameters of a target metaheuristic algorithm for every generation by solving a fixed small‐size optimization problem related to the evaluation‐number constraint. It has the advantage of being insensitive to the effects of dimensionality increases in principle. The effectiveness of the proposed strategy is confirmed by numerical experiments with two well‐known metaheuristics, i.e., the particle swarm optimization algorithm and the differential evolution algorithm. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary Intrustion Detection System (IDS) refers to the gear or software that monitors a network or system for malicious activity or policy violations. Periodically, the system records any intrusion action or breach, which frequently modifies the administrator. Cyber Physical System (CPS) is particularly called as networked connected system, in which the system components are spatially distributed and integrated via the communication network. The control mechanism ensures computation significance; however, the system does affect attacks. Researchers are trying to handle this issue via the existing anomaly datasets. In this way, this paper follows an intrusion detection system under three major stages including extraction of features, selection of feature, and detection. The primary stage is the extraction of Statistical features like standard deviation, mean, mode, variance, and median, as well as higher‐order statistical features like moment, percentile, improved correlation, kurtosis, mutual information, skewness, flow‐based features, and information gain‐based features. The curse of dimensionality becomes a significant problem in this scenario, so it is crucial to choose the right features. Improved Linear Discriminant Analysis (LDA) is utilized to choose the right features. The selected features are subjected to a Hybrid classifier for final detection. Here, models like CNN (Convolutional Neural Network) and Bi‐GRU (Bidirectional Gated Recurrent Unit) are combined. A new Bernoulli Map Estimated Arithmetic Optimization Algorithm (BMEAOA) is added to train the system by adjusting the ideal weights of the two classifiers, leading to improved detection outcomes. Ultimately, the effectiveness is assessed in comparison to the other traditional techniques. [ABSTRACT FROM AUTHOR]

This paper calculates the crashworthiness capability of glass‐reinforced epoxy composites over wrapped polyvinyl chloride (PVC) circular tubes with a triggering mechanism in the form of cutouts. The intended tubes were prepared by a wet wrapping method; after which they were subjected to quasi‐static axial compression. To compute crashworthiness indications, three design parameters, each at three levels, were used. The design parameters are the hole diameter (d), the hole's number (n), and the hole position (L). The Taguchi technique has been employed in the design of experiments (DOE) tactic to obtain the best crashworthiness parameters. With the maximum specific energy absorbed (SEA) and crashing force efficiency (CFE), the optimal parameters were found. Furthermore, the main effect, signal‐to‐noise ratio (S/N), as well as the analysis of variance (ANOVA), have been studied using the commercial software programme MINITAB 18. A few experiments were accompanied by the L9 orthogonal array. According to the results, "N" has the largest impact on the value of SEA with a contribution percent of 29.27%. While "d" has the main influence on the value of CFE with a contribution percent of 67.29%. Lastly, tests for confirmation were performed. to verify the predicted values in light of the experimental results. The optimized SEA developed tube is 74.72%, which is higher than the intact PVC tube and 12.29% lower than the intact hybrid tube. However, the optimum CFE was also 48 and 29.82% higher than the intact PVC tube and the intact hybrid tube, respectively. Highlights: The number of holes has the highest impact on SEA value by 29%.The ideal specimen enhances SEA by 75% compared to the intact PVC tube.Hole diameter has the most influence on CFE value by 67%.The best specimen improves the CFE by 48% related to the intact PVC tube. [ABSTRACT FROM AUTHOR]

In this paper, the seeded batch cooling crystallization of sodium phosphite (SP) is simulated and optimized through a coupled method of the genetic algorithm and nonlinear programming. At first, the modeling and simulation test methods of the crystallization process are applied for the crystallization of SP, which expands the relevant study of SP from the experiment to the simulation. A comprehensive model is established in MATLAB/Simulink, and based on this model, the results of the common cooling strategy (linear cooling) on the process are investigated. Meanwhile, the process sensitivity to the change of seeding conditions is analyzed. Then, the coupled optimization method based on the genetic algorithm and nonlinear programming is applied to optimize the crystallization process for the first time, and the obtained optimized cooling strategy is compared to the result of the traditional nonlinear programming method (NLPM). The traditional NLPM has more significant effects on large seeding mass and small mean size, while the coupled method has better adaptability. When the coefficient of variation is almost fixed, the cooling strategy obtained by the coupled method could produce more crystals with large mean size. In addition, the end of the process can be reached earlier. The results show that the coupled method is more suitable for the optimization of the batch cooling crystallization of SP. [ABSTRACT FROM AUTHOR]

The minorization–maximization (MM) algorithm is an optimization technique for iteratively calculating the maximizer of a concave target function rather than a root–finding tool. In this paper, we in the first time develop the MM algorithm as a new method for seeking the root x∗$$ {x}^{\ast } $$ of a univariate nonlinear equation g(x)=0$$ g(x)=0 $$. The key idea is to transfer the root–finding issue to iteratively calculate the maximizer of a concave target function by designing a new MM algorithm. According to the ascent property of the MM algorithm, we know that the proposed algorithm converges to the root x∗$$ {x}^{\ast } $$ and does not depend on any initial values, in contrast to Newton's method. Several statistical examples are provided to demonstrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

In today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain. [ABSTRACT FROM AUTHOR]

Highlights Industrial development has led to an increase in demand for friction materials, resulting in increasingly prominent environmental issues throughout their entire life cycle, and developing green and eco‐friendly brake friction materials has become a focus in the braking field. This paper explores the mechanical and tribological properties of fly ash (industrial waste) and sisal fiber (natural fiber) as eco‐friendly components to enhance brake friction materials. Four samples containing different ratios of fly ash and sisal fiber were prepared and tested with a variable‐speed friction testing machine. The results show that the eco‐friendly alternative combination can effectively improve the friction coefficient, and reduce friction fluctuations and thermal degradation, but the wear rate will also increase accordingly. In addition, the worn morphology reveals the formation of the contact platform and wear mechanism. A hybrid integration of analytic hierarchy process (AHP) and multi‐objective optimization by ratio analysis (MOORA) was used to weigh various evaluation indicators objectively and rank the samples. The sample with 8% fly ash and 6% sisal fiber exhibited the best comprehensive tribological performance. Development of eco‐friendly brake friction composites with natural and waste additives. Investigation of the effect of the natural wastes' ratio on prepared composites' physical, mechanical, and tribological properties. Evaluation of brake friction composites with AHP‐MOORA. Sample S6F8 exhibits the best comprehensive tribological performance. [ABSTRACT FROM AUTHOR]

Summary: This paper presents Relay node selection scheme and Deep sleep period for power management in Energy Harvesting Wireless Sensor Networks (RD‐EHWSN), a new energy‐saving scheme founded on asynchronous duty cycling. RD‐EHWSN reduces sensor node energy consumption and guarantees equilibrium energy use between sensor nodes in WSN with the energy harvesting capacity by adjusting these sensor nodes duty cycles more drastically and deeply by according to the estimated value of its residual energy on the basis of future‐presented harvested energy, and this is done through the use of a new proposed energy threshold policy. RD‐EHWSN also grips the benefit of transmitter initiated using the low power listening (LPL) technique with short preamble messages and uses a new relay node selection procedure to achieve the load balancing in WSN. We implemented RD‐EHWSN by using OMNeT++/MiXiM. For evaluation, we compared it with PS‐EHWSN, under multiple concurrent multihop traffic flows scenarios and scenarios in which nodes can harvest different energy harvesting rate. In all experiments, RD‐EHWSN significantly outperformed the PS‐EHWSN scheme; the results of simulation demonstrate that our scheme enhances the general yielding of WSN thru lessening the energy consumption and the mean latency, as well as raising the packet delivery ratio and the throughput. Moreover, RD‐EHWSN improves the WSN lifetime and ensures it operates in good condition in the case where the energy harvesting rate is lower by comparing it with the PS‐EHWSN scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimization problem of networked Euler–Lagrange (EL) systems with actuator faults and unknown model parameters. In order to solve this issue, some event‐triggered fault‐tolerant (ETFT) optimal coordination algorithms with the mechanism of intermittent communication and intermittent controller update are designed for the first time to compensate for the impact of physical faults on the system, and reduce communication costs and control resource consumption. For practical applications, it is difficult for the system to have a secure environment if there exist malicious attacks blocking network channels among agents. Therefore, the resilient ETFT optimal coordination with intermittent communication subject to asynchronous denial‐of‐service (DoS) attacks is considered. Especially, auxiliary systems with adaptive gains are introduced in the algorithms due to the unavailability of the EL model parameters, which also enable virtual data exchange between agents and protect actual state information. Note that the algorithms are fully distributed as the global topology information is not required. Finally, the feasibility of the proposed algorithms is verified through numerical simulation of examples. [ABSTRACT FROM AUTHOR]

In the realm of evolutionary game theory, the majority of scenarios involve players with incomplete knowledge, specially regarding their opponents' actions and payoffs compounded by the ever‐shifting landscape of players' interactions. These dynamics present formidable challenges in both the analysis and optimization of game evolution. To address this, a novel model named the networked evolutionary game (NEG) is proposed based on incomplete information with switched topologies. This model captures situations where players possess limited insight into their opponents' benefits, yet make decisions based on their own payoffs while adapting to different networks and new players. To bridge the gap between incomplete and complete information games, R. Selten's transformation method is leveraged, a renowned approach that converts an incomplete information game into an interim agent game, thereby establishing the equivalence of pure Nash equilibria (NE) in both scenarios. Employing the semi‐tensor product (STP) of matrices, a powerful tool in logistic system, the evolution of the model is articulated through algebraic relationships. This enables to unravel the patterns of game evolution and identify the corresponding pure Nash equilibria. By introducing control players, strategically positioned within the game, optimized control is facilitated over the evolutionary trajectory, ultimately leading to convergence towards an optimal outcome. Finally, these concepts are illustrated with a practical example within the paper. [ABSTRACT FROM AUTHOR]

Contemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology. [ABSTRACT FROM AUTHOR]

Glued laminated timber made from hardwood is a sustainable structural product that is currently gaining momentum due to climatic changes and afforestation policy. Diversities in hardwood species have led to a lack of a complete standard for the production of glued laminated timber from hardwood in Europe. This paper addresses hardwood species available in Croatian forests, namely Carpinus Betulus L. (European hornbeam), Quercus Cerris L. (Turkey oak), and Acer Campestre L. (Maple), in terms of their suitability for the production of glued laminated timber. The suitability is assessed by a multi‐criteria analysis with seven criteria. Visual and physical properties include density, the occurrence of knots, and grain deviation in lamellas. Mechanical properties include the bending strength, modulus of elasticity, and shear strength of glue lines. In order to obtain physical and mechanical properties, corresponding experimental research has been carried out. Production costs have been evaluated in collaboration with the manufacturer. Results indicate a very high potential of European hornbeam and Turkey oak for the production of glued laminated timber. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for evaluating the resistance factor used for the estimation of lateral pile capacity in cohesive soil. The developed method uses limit equilibrium and mobilized strength principles, which are implemented on the half‐symmetric problem using a modification of the traditional slice method. The soil medium is discretized into a polar numerical grid, allowing sliplines investigation independently of geometrical shape assumptions. The paper presents the development of the SLIP (Snake Locomotion Iterative Procedure) optimization method, which enables an effective and efficient search of the critical slipline out of all slipline geometries that can be generated within the numerical grid. The SLIP iteratively updates the evaluated optimal slipline, according to the criterion of minimizing the pile resistance factor, N. The obtained solutions are consistent with previous plasticity results, with special attention to the smooth pile solution which is bounded between the best lower‐ and upper‐bounds solutions to date. The work utilizes safety map principles to prove the uniqueness of the solution. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for evaluating the resistance factor used for the estimation of lateral pile capacity in cohesive soil. The developed method uses limit equilibrium and mobilized strength principles, which are implemented on the half‐symmetric problem using a modification of the traditional slice method. The soil medium is discretized into a polar numerical grid, allowing sliplines investigation independently of geometrical shape assumptions. The paper presents the development of the SLIP (Snake Locomotion Iterative Procedure) optimization method, which enables an effective and efficient search of the critical slipline out of all slipline geometries that can be generated within the numerical grid. The SLIP iteratively updates the evaluated optimal slipline, according to the criterion of minimizing the pile resistance factor, N. The obtained solutions are consistent with previous plasticity results, with special attention to the smooth pile solution which is bounded between the best lower‐ and upper‐bounds solutions to date. The work utilizes safety map principles to prove the uniqueness of the solution. [ABSTRACT FROM AUTHOR]

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%. [ABSTRACT FROM AUTHOR]

Baseflow was proven to be the most unpredictable component of streamflow through various research. However, the recent method for estimating baseflow is due to the development of theoretical and computational techniques. This paper attempted to develop a fully automated baseflow separation system based on a recursive digital filter with an optimization algorithm for the single separation. Most of the previous baseflow separation methods use a single set of a parameter and BFImax (the maximum value of baseflow index), which can underestimate or overestimate the baseflow; however, the system developed in this study estimates multiple optimized a parameters using seasonality and flow conditions and uses them for BFImax calculation and baseflow separation. This system derived baseflow results in better understanding of watershed and streamflow tendency characteristics. This study developed a Web‐based Hydrograph Analysis Tool 2020 with a user‐friendly interface and new separation method regarding the seasonality and flow conditions with a fully automated python module to optimize a parameters and BFImax. The application to the two area show diverse parameter sets and different baseflow according to seasonality and flow conditions representing the flow characteristics. This study could be a fundamental tool for detailed watershed management decisions regarding water security in the dry season or environmental water for aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Glass sagging is a subsequent process to the CVD process used for large‐size and high‐purity silica glass synthesis. Physical phenomena taking place in this process are complicated which need an in‐depth understanding for better control. In this paper, a comprehensive study is conducted for the sagging process using a level‐set and enthalpy‐porosity coupled model. With this model, the deforming behavior of glass ingot and evolution of OH uniformly distributed region are well predicted. Then, two performance indices (the effective yield rate and maximum extension radius of OH uniformly distributed region) are proposed based on different applications, and important factors, including geometrical parameters (the ingot initial length, crucible diameter and pedestal height) and operating parameter (the heater power allocation scheme), are explored for their effects on the two indices. The orthogonal test design method is adopted to further determine the collective effects of the four factors. According to the range analysis results, the initial ingot length has the greatest effect, while the crucible diameter has the least effect on the effective yield rate; and for the maximum extension radius, the crucible diameter becomes the major factor, while the pedestal height is the most insensitive factor. The corresponding optimal schemes are proposed for the two indices finally, which are believed to provide useful guidance for improving the sagging process. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) is a strategy that tries to save energy consumption by managing consumers' voltage. On the other hand, enhancing reactive power compensation in the power system gives rise to CVR implementation more effectively. This paper addresses how the power system planner could encourage investment to establish the above‐mentioned topics. Here, a model for encouraging industrial loads to dynamically invest in implementing the CVR strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. Industrial loads benefit from saving in their electricity bills and incentive payments that might be paid by utilities (if necessary). The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. The proposed planning problem is modelled as a non‐linear optimization problem that can be solved by off‐the‐shelf software, for example, GAMS. The proposed model is applied to the Ontario transmission system. The numerical results show the proposed model's effectiveness in designing an investment plan for industrial loads to implement the CVR strategy and upgrade PEVs' chargers. Here, a model for encouraging industrial loads to dynamically invest in implementing the conservation voltage reduction (CVR) strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to the fact that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. [ABSTRACT FROM AUTHOR]

Microgrid has attracted more and more attention to provide backup power for customers in the case of power grid outages. Microgrid expansion planning is significant to handle the increasing customer demand and to enhance power resilience. Current research about long‐term microgrid expansion planning rarely if ever considered the uncertainties associated with energy storage and power generation units, for example, battery cycle degradation. These factors have important influence on the performance of microgrid expansion planning in reality. In this paper, a long‐term microgrid expansion planning model with multiple energy resource is presented. Deep reinforcement learning method is used to obtain the cost‐effective microgrid expansion policies to enhance power resilience. In the case study, optimal microgrid expansion planning is achieved based on the proposed model. The impacts of battery degradation and resilience constraint on microgrid expansion policy optimization are also investigated. The simulation results prove the effectiveness of the proposed method on economic and resilient microgrid expansion planning. [ABSTRACT FROM AUTHOR]

Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

Existing methods for nonlinear robust control often use scenario‐based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the control problems include time‐varying uncertainty. This paper draws from local reduction methods used in semi‐infinite optimization to solve robust optimal control problems with parametric and time‐varying uncertainty. By iteratively adding interim worst‐case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. We show that the local reduction method for optimal control problems consists of solving a series of simplified optimal control problems to find worst‐case constraint violations. In particular, we present examples where local reduction methods find worst‐case scenarios that are not on the boundary of the uncertainty set. We also provide bounds on the error if local solvers are used. The proposed approach is illustrated with two case studies with parametric and additive time‐varying uncertainty. In the first case study, the number of scenarios obtained from local reduction is 101, smaller than in the case when all 214+3×192$$ {2}^{14+3\times 192} $$ extreme scenarios are considered. In the second case study, the number of scenarios obtained from the local reduction is two compared to 512 extreme scenarios. Our approach was able to satisfy the constraints both for parametric uncertainty and time‐varying disturbances, whereas approaches from literature either violated the constraints or became computationally expensive. [ABSTRACT FROM AUTHOR]

The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm "LWaOA," making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA. [ABSTRACT FROM AUTHOR]

The energy consumption of a battery‐powered train in an interstation depends on the running time and state of energy (SOE) at departure. In this paper, we develop an optimization method of train timetables to minimize energy consumption in line with several stations. The variables in this proposed optimization model are running, dwell, and charging times as real numbers and places of charging facilities as binaries. Additionally, we conducted a case study using the real‐world light rail transit (LRT) route, vehicle, and onboard battery model to confirm the effectiveness. In the case study, the proposed method can optimize the timetable and placement of charging facilities by considering the track gradient and battery SOE. [ABSTRACT FROM AUTHOR]

The restoration control of the modern alternating current–direct current (AC–DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the line‐commutated converter high‐voltage direct current (LCC‐HVDC) and the AC power grid, this paper proposes a novel optimization method of restoration path to reconfigure the skeleton network for the blackout power grid. Based on the system strength, the supporting capability of the AC power grid for the LCC‐HVDC is first analysed from the aspects of start‐up and operation of LCC‐HVDCs. Subsequently, the quantitative relationship between the restoration path and the restoration characteristic of LCC‐HVDC is derived in detail based on the system strength indices of the short‐circuit capacity and the frequency regulation capability. Then, an optimization model of restoration path considering non‐tree paths is formulated and a feasible optimization algorithm is proposed to achieve the optimal path restoration scheme. A modified IEEE 39‐bus system and a partial power grid of Southwest China are simulated to show that the proposed method is suitable for the restoration of AC–DC power grids and can improve restoration efficiency. This research can be an important guidance for operators to rapidly restore the AC–DC power grid. [ABSTRACT FROM AUTHOR]

Vehicular Adhoc Networks (VANET) facilitate inter‐vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing‐based Secure Protocol for VANET Environment (SC‐SPVE) method, which aims to tackle security challenges. The SC‐SPVE technique integrates an adaptive neuro‐fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC‐SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2. [ABSTRACT FROM AUTHOR]

In this paper, the delay margin problem of a linear system with multiple delays is investigated. Estimations for allowable delay bounds of the system are provided in the form of matrix measure. By means of the weighted matrix measure and optimization technologies, the conservatism of estimated delay bounds is reduced. Numerical examples show that the weighted matrix measure has better performance in estimating the delay margin of the system than usual matrix measures. [ABSTRACT FROM AUTHOR]

In the power system, optimal sizing of hybrid energy systems (HESs) is a vital and challenging issue. Optimal sizing leads to designing a cost‐effective and reliable power generation system. In such a problem, modelling the load uncertainty helps the planner to make appropriate decisions for optimizing the performance of HESs against possible changes of the electrical load. In this paper, techno‐economic and optimal sizing of an off‐grid photovoltaic‐diesel generator‐fuel cell (PV‐diesel‐FC) HES is investigated in two frameworks, risk‐neutral strategy and risk‐averse strategy, where the load uncertainty is modelled by information gap decision theory. To size the HES, objective function is defined as the total net present cost and with respect to a desirable loss of power supply probability, size of the system components is optimally determined. In the risk‐averse framework, the sizing problem is solved with different critical tolerance levels of the objective function and the results are evaluated. Over the case study, simulation results show that at risk‐averse framework, optimal combination of PV, diesel generator and FC leads to a cost‐effective and reliable HES. [ABSTRACT FROM AUTHOR]

This paper focuses on enhancing the energy collection efficiency of an integrated collector storage solar water heater (ICS SWH) by vee‐corrugating the absorber and optimizing the design for the vee‐included angle through simulation and experimental study. This paper presents an efficient algorithm for analyzing a vee‐corrugated absorber ICS SWH using Engineering Equation Solver Software. For validating this algorithm, two models of ICS SWH systems have been fabricated: one with a flat absorber and the other with a 60° vee‐included angle corrugated absorber with eight corrugations. The basic purpose of the fabrication of the flat absorber ICS SWH system was to approximate the absorptivity of the absorber. A typical value of 0.68 was estimated for the absorptivity of the aluminum absorber coated with nonselective black paint. After the experimental investigation of the 60° vee‐included angle ICS SWH system, it was found that the real‐time readings were in close agreement with the numerical model readings. For comparison with the previous work, a five‐corrugation system with an approximate 90° vee‐included angle with the same projected dimensions was modeled and the efficiencies of both the models for the time from 7 a.m. to 3 p.m. were calculated. The efficiency of the eight‐corrugation model was 42.56%, which was better than the previous work of the five‐corrugation model, with a 38.86% efficiency for the same ambient conditions. Also, it was theoretically deduced that we had an optimized system at 18 corrugations and a vee‐included angle of 28.78°. [ABSTRACT FROM AUTHOR]

Assembly and testing are the last stages in the design phase to obtain proof of concept for a certain product. Although it is time and cost consuming, assembly fit and test are crucial for the future development of any complex product, unveiling potential consequences in the future product life cycle. The current paper addresses the main assembly stages, testing strategies, feedback implementation, and redesign decisions involved in the development of a programmable equipment in personalized football training. Demountable subassemblies are used for the manufacture of the final prototype, encouraging interchangeability of parts, and modularity of the final product. Coupling, gear, key, and bearing assemblies are mainly used to physically obtain the programmable equipment prototype. Since some components are bespoke and are 3D printed specifically for this product, fit tests are undertaken to ensure proper functioning of all mechanical and electronic components. Software and electronic testing are also done to both calibrate and ensure proper code functioning. Optimization of the prototype is implemented in all three main systems that make up the personalized football training equipment: mechanical, electronic, and software. The optimized functional prototype of the equipment will be subjected to endurance tests in order to evaluate the life expectancy of individual components, subassemblies, and, finally, of the general product. [ABSTRACT FROM AUTHOR]

Summary: The number of internet users and connected devices has dramatically expanded due to the recent technological boom and the benefits that the internet of things offers to ease our lives. Network scheduling, quality of service, resource allocation, and security issues are now being addressed via software‐defined networking (SDN). SDN has several benefits over traditional networks, including global centralized control, managing network traffic, and separating the forwarding and control plane. The work done in this paper aims to design and implement a traffic‐aware routing framework based on routing optimization presented as an integer linear programming (ILP) to improve heterogeneous traffic flows' quality of service (QoS) in a simulated SDN environment. With the knowledge that the routing problem is a nondeterministic polynomial‐time‐hard problem, the proposed scheme aims to decrease the computational routing time to make the ILP‐based routing system more suitable for real‐time processing. The simulation results illustrate that the proposed framework reduces the computational time by 23% and 49% for Abilene and Goodnet topology, respectively. Additionally, with 1000 flows in the network, the suggested scheme reduces the number of network flows that violate the QoS by 9% and 22% (with Abilene topology) and 16% and 51% (with Goodnet topology) as compared to the existing shortest path delay and sway methods, respectively. [ABSTRACT FROM AUTHOR]

As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers. [ABSTRACT FROM AUTHOR]

Plant leaf classification is a crucial task in the field of computer vision and pattern recognition, with various applications such as plant species identification and disease diagnosis. In this paper, the authors introduce the Pyramid Blurred Shape Model (PBSM) as a new descriptor for plant leaf classification. The PBSM extracts both shape and texture features from plant leaf images, which are combined to define a probability density function for leaf shape. Our experimental results show that the proposed PBSM achieves high classification accuracy, F1‐score, and precision‐recall results, demonstrating its effectiveness for leaf classification. However, extracting all available features from leaf images can lead to redundant and inessential features, which can degrade the classification performance and computational efficiency. To address this issue, the authors implement grey wolf optimization (GWO)‐based feature selection to identify the most informative features for classification. The final set of features is then classified using a list of selected classifiers, further enhancing the performance of the authors' approach. The authors evaluate their proposed method on three publicly available datasets, namely the Middle European Woods (MEW), Swedish, and Flavia leaf datasets, and achieve high classification accuracies of 96.34%, 96.89%, and 92.41% for the Flavia, Swedish, and MEW leaf datasets, respectively. The authors' approach outperforms state‐of‐the‐art descriptors in terms of classification accuracy and robustness, demonstrating its potential for real‐world applications. Overall, the authors' proposed PBSM descriptor with feature selection provides a reliable and efficient solution for plant leaf classification. It can contribute to the development of automated plant species identification systems and disease diagnosis, thereby facilitating the conservation and protection of plant species. [ABSTRACT FROM AUTHOR]

In the present paper, a dynamic optimization problem regarding grade transitions in bulk poly(propylene) polymerization processes is formulated and solved for the first time. Initially, a detailed dynamic process model is presented and implemented, comprising mass and energy balances, some of the polymer properties (such as the melting flow index and the xylene solubles) and regulatory control loops. Additionally, the effects of cocatalyst and electron donor on the propagation rate constant are taken into account. Then, the dynamic optimization problem is formulated and an evolutionary algorithm is used to solve the resulting nonlinear programming problem. It is shown that there is significant coupling among the manipulated variables and the controlled performance and polymer property variables, which adds complexity to the grade transition problem and demands the simultaneous manipulation of multiple variables during transitions. Despite the inherent open‐loop unstable nature of the analyzed process, it is shown that smooth grade transition trajectories can be accomplished through proper adjustment of the objective function weights. Finally, it is shown that the obtained optimum trajectories can significantly diminish the transition time, which can be of paramount importance for the plant economics. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an efficient optimization strategy based on the Stoyan and Yaskov algorithm to maximize the tube density of randomly distributed mixed carbon nanotube bundles (RMCBs) for configuring optimum performance of on‐chip interconnects. Optimizing tube density has proposed eight different RMCB (RMCB1–RMCB8) structures of a varying number of carbon nanotubes (CNTs), where RMCB1 has the maximum CNTs. Moreover, the ABCD technique adequately analyzes the temperature‐dependent frequency and stability characteristics of a three‐line RMCB interconnect placed on smooth and rough substrates (SiC, BN, and SiO2). The smooth substrate material has the best 3‐dB bandwidth compared with rough SiC substrate, followed by BN and SiO2 for all RMCB structures, and among all structures, the RMCB8 has the best 3‐dB bandwidth. The relative stability of RMCB1 is improved by 134.2% and 22.7% w.r.t. RMCB8 placed on a smooth and rough SiC substrate, respectively. Whereas bandwidth of the RMCB1 is reduced by 26% and 88.5% w.r.t. RMCB8 placed on a smooth and rough substrate, respectively. Hence, there is a tradeoff between the bandwidth and the relative stability as the number of CNT increases. Therefore, RMCB3 placed on a smooth substrate and RMCB4 placed on a rough SiC substrate are considered the best‐optimized RMCB interconnects to maintain a balance between the two. [ABSTRACT FROM AUTHOR]

The regulation and stabilization of a system's output to achieve desired performance and ensure system reliability require the use of different controllers, but selecting appropriate control parameters presents a challenge in ensuring robustness and stability. Proportional–integral–derivative (PID) controllers are popularly used due to their simplicity and effectiveness in addressing these challenges. In this paper, a simple, effective, and efficient, novel graphical technique is proposed to design PID and its variants (PI/PD) controllers, which addresses the challenges in selecting appropriate control parameters. The method involves creating three equispaced vectors for controller parameters (Kp,Ki,Kd)$(K_{\text{p}}, K_{\text{i}}, K_{\text{d}})$, and obtaining a 3D (2D in PI/PD) Cartesian grid of these vectors. All nodes in the grid provide several possible controllers, and integral time squared error (ITSE) is calculated for each controller from the closed‐loop step response of the system. The obtained ITSE is plotted in a 4D (PID) or 3D (PI/PD) graph, and controller parameters corresponding to the minimum value of ITSE are identified. Furthermore, the proposed graphical technique aids in choosing the lower and upper bounds (LB and UB) if the controller is designed using optimization techniques. The better selection of LB and UB reduces the search space resulting in lesser execution time and fewer iterations. To validate the proposed graphical technique, we designed various controllers for widely‐used brush‐less DC and switch reluctance motors in electric vehicles. Additionally, by choosing the LB and UB with the proposed technique, controllers are also designed using three optimization techniques: particle swarm optimization, black widow optimization algorithm, and honey badger algorithm. The obtained controllers using the graphical technique outperformed the optimization techniques in terms of time and frequency domain specifications, and the proposed selection of lower and upper bounds resulted in improved performance in terms of iterations and execution time. [ABSTRACT FROM AUTHOR]

Energy is essential to sustain life and development and to fulfill daily requirements. A clean source of energy like the Solar Chimney Power Plant (SCPP) is essential or hybridized with conventional sources to meet the demand. Also, it will provide energy during the night and it will reduce the need and expense of physical batteries in comparison to the photovoltaic energy storage system. In this paper, a mathematical study has been done for the generation of 10 MW power from the SCPP. This paper optimized the core parameter of SCPP such as chimney height and collector radius under different operating conditions. It has been evaluated on the basis of data produced by the developed mathematical model. The current research work highlights the modern application of state‐of‐the‐art Deep Learning (DL) techniques that is, Functional Link Convolutional Neural Network (FLCNN) to map developed mathematical models. The proposed FLCNN is compared with various other Machine Learning algorithms including Support Vector Regression, K‐Nearest Neighbors, Random Forest, Naïve Bayes, and Multilayer Perceptron. Proposed FLCNN performed efficiently and achieved 91% and 93% of Adjusted R2 score for predicting values of collector radius and chimney height, respectively, which is comparable to other ML algorithms. [ABSTRACT FROM AUTHOR]

Summary: The analysis of spectrum sensing performance of energy detection (ED)‐based wireless cognitive radio sensor network (ED‐WCRSN) with hard‐decision combining (HDC) is presented in this paper. Particularly, several network parameters are derived to estimate the performance of ED‐WCRSN, considering channel errors, noise, and generalized α−μ fading. In the considered network, first, cognitive radio sensor (CRS) senses a primary user (PU), gets sensing data, and then uses an ED to make a local binary decisions about PU's active or inactive status. In both sensing and reporting channels, the channel error probability is also taken into account. Next, HDC technique is used at control center (CC) to combine the locally obtained decisions, and a final decision about the status of the PU is made. To do so, first, the expression for the novel and analytical, which incorporates noise and α−μ fading, detection probability in a CRS is derived and validated using Monte Carlo simulations in MATLAB and using an experimental setup. Then, utilizing derived mathematical expressions, closed‐form expressions of an average error rate (AER), optimal numbers of CRSs, and detection thresholds under noisy and α−μ fading conditions are developed. The substantial influence of channel and network factors is assessed using receiver operating characteristics (ROC), complementary ROC, and AER. Finally, the impacts of channel and network parameters on ED‐WCRSN performance are explored. For numerous parameters of the considered network, the optimal values detection threshold and number of CRSs are also found. [ABSTRACT FROM AUTHOR]

Antenna array synthesis is quite a popular topic in electromagnetics and finding suitable array parameter values is a challenging nonlinear engineering problem in this field. In recent years, it has been realized that optimization algorithms can be used to achieve desired solutions in this area. Besides, improvements in the optimization algorithms make it possible to perform synthesis processes on different array structures and to obtain better results. In this paper, a method based on a combination of ant lion optimizer (ALO) and sequential quadratic programming (SQP) is proposed for the concentric circular antenna array (CCAA) synthesis. Excitation amplitudes of array elements are optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). The results of proposed ALO‐SQP method are compared with those of 11 different algorithms: ALO, moth flame optimizer (MFO), SQP, symbiotic organisms search (SOS), biogeography based optimization (BBO), opposition‐based gravitational search algorithm (OGSA), cat swarm optimization (CSO), firefly algorithm (FA), evolutionary programming (EP), backtracking search optimization algorithm (BSA), and seeker optimization algorithm (SOA). The comparisons indicate that the CCAAs synthesis using the proposed method presents good MSLs, FNBWs, and DRRs. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Bio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack. Although the Grey Wolf Optimizer has been successfully applied to solve different optimization problems, it may suffer from premature convergence and get stuck in local optima. In order to overcome these drawbacks, this paper proposes a chaotic version of the Grey Wolf Optimizer that differs from the original algorithm and previously published modified versions because it does not add a chaotic variable in the parameters that control the execution of the algorithm. Instead, the proposed model uses a chaotic variable to define the wolves in the pack that will be used to guide the hunting process in each iteration of the algorithm. Numerical experiments using 20 benchmark functions are carried out. The performance of the proposed model is compared with the performance of the original Grey Wolf Optimizer and other well‐known algorithms, namely the Particle Swarm Optimization, the Genetic Algorithm, the Symbiotic Organisms Search, and the Teaching‐Learning Based Optimization. Nine chaotic maps reported in the literature are tested. The results show that the proposed algorithm has a very competitive performance, and the Chebyshev map presented the best performance among the chaotic maps simulated. The proposed algorithm can be integrated into other modified versions of the Grey Wolf Optimizer in a straightforward way. [ABSTRACT FROM AUTHOR]

In recent years, nonlocal strain gradient theory (NSGT) has been widely applied by researchers for the analysis of nanostructures. In this theory, the appropriate selection of small‐scale parameters and the type of high‐order boundary conditions is of great importance. In the current paper, free vibrations of carbon nanotubes are studied using a nonlocal strain gradient Timoshenko beam model, and the small‐scale parameters are calibrated based on the molecular dynamics (MD) results. The calibration process is conducted by defining an optimization problem, and the genetic algorithm is applied for obtaining the best values of the small‐scale parameters. The governing equations are derived based on Hamilton's principle and solved analytically using the modified Fourier series method. The variational consistent high‐order boundary conditions are obtained applying the weighted residual approach. The small‐scale parameters are calibrated for the first three natural frequencies considering two different boundary conditions. Moreover, the type of high‐order boundary conditions that is more consistent with the MD results is determined. The outcomes of this paper provide an appropriate reference for researchers who are using the NSGT for the analysis of micro/nanobeams. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the effect of the structural parameters of louvered fin radiators on its thermal‐hydraulic performance, optimize its structural parameters, and improve the performance of louvered fin radiators in negative gauge pressure condition. The thermal calculation model of louvered fin radiator in negative gauge pressure is established. Then, the effects of design variables on thermal‐hydraulic characteristics are investigated. The second generation of nondominated sorting genetic algorithm (NSGA‐II) is applied to optimize the louvered fin heat exchanger in negative gauge pressure environment. Technique for order preference by similarity to an ideal solution is applied to choose the final optimal solution. Numerical simulation of the solution is conducted and the result is compared with literature in order to verify the optimization method. The optimal structural parameters are obtained under constraints. Compared with the original test piece, the heat transfer rate of optimal solution is increased by 7.7%, and total annual cost is reduced by 24.8%. The present paper establishes optimization method of louvered fin radiators in negative gauge pressure condition, the optimization result is superior to the original radiator. The optimization method can be used in the design of radiators operating in negative gauge pressure conditions. [ABSTRACT FROM AUTHOR]

Summary: Multiple‐input multiple‐output (MIMO) systems perform well from the energy efficiency (EE) and the spectral efficiency (SE) points of view in fifth generation (5G) communication systems. This paper considers the operation of a MIMO system with a relay. The optimization problem relates to the EE maximization. This problem has two types of limitations, which provide a maximum transmission power and a minimum data rate for users. The encountered objective function is in a fractional form and thus it is a non‐convex function. Besides, the problem is constrained. We utilize a lower bound analysis for the data rates, some properties of the linear programming, and the maximum ratio transmission (MRT) precoding scheme to obtain a convex objective function. Using the Lagrange dual function, we obliterate the constraints of the problem and then it is easy to solve. To improve system performance, users are divided into two groups based on their channel gains, and the maximum transmission power is reasonably divided between them. Two iterative algorithms are proposed to solve the optimization problem numerically, and finally we investigate performance of the proposed method. [ABSTRACT FROM AUTHOR]

Most of the concrete volume in multistorey buildings is cast in solid slabs, which are frequently flat slabs supported on columns. By using two‐way spanning ribbed slabs, concrete consumption could be significantly reduced. However, due to the high costs associated with formwork, such a complex rib configuration is rarely used nowadays. With the advent of technologies for automated formwork fabrication, the material‐saving potential inherent in this structural system could again be exploited. This paper investigates the feasibility of material‐efficient ribbed concrete slabs on a building scale using conventional concrete and steel reinforcing bars cast inside a three‐dimensional‐printed plastic‐based formwork. To that end, the code‐compliant design of ribbed slabs is first discussed, followed by the introduction of a concept for an automated design‐to‐production workflow. The sustainability of this slab system is compared to a solution using conventional formwork in a case study consisting of a multibay office building with slabs spanning 8 m in both directions, revealing that ribbed slabs use 40% less concrete than solid slabs. Several representative structural elements of the case study (ribs, slab‐column transition) were produced at full‐scale and tested until failure to investigate the feasibility of production and structural performance. Three T‐beams with various rib shapes (straight, kinked with diaphragms, curved) were tested in a three‐point bending configuration, showing a ductile behavior with longitudinal reinforcement yielding and indicating the relevance of torsional effects in curved ribs. Punching tests on two slab‐column connections (ribbed, solid) revealed that the optimized ribbed slab could prevent brittle punching failures and achieve an ultimate load 105% higher than the solid reference slab. All specimens' load‐bearing behavior could be predicted using established design formulas, showing the feasibility of producing code‐compliant ribbed slabs with the applied technology. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is a well‐developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error‐free data and run an energy‐efficient network. In this paper, we propose a sling‐shot spider optimization (S2SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)‐based networks. The S2SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1‐hop, 2‐hop, and multi‐hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio‐inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%. [ABSTRACT FROM AUTHOR]

Energy‐efficient train operation (EETO) in high‐speed railways (HSRs) is an extra cost‐effective and flexible means to promote energy‐saving. This paper first examines the energy consumption sources and energy‐saving measures of high‐speed trains (HSTs). Then presents the EETO in HSRs, including three categories: energy‐efficient train control, energy‐efficient train timetabling, and EETO considering train timetabling and driving strategy. Next, the current research status and progress on three aspects of EETO in HSRs, namely optimization algorithm, constraint condition, and oriented scenario, are sorted out. Finally, the research on EETO in HSRs has prospected and proposed directions that can study in depth in the future. By summarizing and reviewing the research on EETO in HSRs, it is hoped to furnish a reference for the railway sector to enhance quality, efficiency, and green, low‐carbon development. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]