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Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

This work presents an effective way to remove ammonium from compost using corn wastes. The study examined the role of experimental factors on the removal of ammonium using a full factor experimental design. The experimental factors and their related levels selected were as follows: Material type (corn cob and corn husk), ratio (10 and 25%), moisture (50 and 70%), and finally duration (30 and 60 days). The results were evaluated with an ANOVA test. A regression model that took into account the significant main and interaction effects was suggested. According to the optimization algorithm, the optimum levels were corn cob, 25% ratio, 50% moisture, and 60-day duration, with a removal efficiency of 91.84%. The proposed procedure is simple to implement and cost-effective. [ABSTRACT FROM AUTHOR]

With a focus on the coupling problems of multivariable control for the paper cross‐directional (CD) basis weight caused by a hydraulic headbox, a novel approach of multivariable decoupling control based on sparse decomposition is proposed. The model function of the CD basis weight control for a single dilution water branch valve is used as the basis of a sparse atomic library. Then, according to the deviation curve of the CD basis weight, the particle swarm optimization (PSO) algorithm is used to obtain atoms corresponding to each control unit (the multiple dilution water branch valves) to form a sparse atomic library. As each atom in the atomic library contains the control information of the corresponding dilution water branch valve, the decoupling control of paper CD basis weight can be realized via mining control information. The essence of this method is to solve the coupling problems of multivariable control by using optimization theory. Experimental results demonstrate that this method achieves a remarkable decoupling control effect. [ABSTRACT FROM AUTHOR]

Conference organizers often face the following problem: Given a collection of submitted papers, select a subset to be presented at the conference. The bulk of the work often amounts to assembling a pool of reviewers and then sending each submitted paper to several reviewers. We present in this paper a technique for finding a good assignment of papers to reviewers. An important feature of the solution we find is that each paper is sent to at least one reviewer who is "as expert as possible" for that paper. A major component of the problem is modeled as a bottleneck version of a capacitated transshipment problem. [ABSTRACT FROM AUTHOR]

To fulfil the potential of Li–S batteries (LSBs) with high energy density and low cost, multiple challenges need to be addressed simultaneously. Most research in LSBs has been focused on the sulfur cathode design, although the performance is also known to be sensitive to other parameters such as binder, current collector, separator, lithium anode, and electrolyte. Here, an integrated LSB system based on the understanding of the different roles of binder, current collector, and separator is developed. By using the cross‐linked carboxymethyl cellulose–citric acid (CMC‐CA) binder, Toray carbon paper current collector, and reduced graphene oxide (rGO) coated separator, LSBs achieve a high capacity of 960 mAh g−1 after 200 cycles (2.5 mg cm−2) and 930 mAh g−1 after 50 cycles (5 mg cm−2) at 0.1 C. Moreover, the failure mechanism at a high sulfur loading with characteristics of fast capacity decay and infinite charging is discussed. This work highlights the synergistic effect of different components and the challenges towards more reliable LSBs with high sulfur loading. Lithium–sulfur batteries: The practical application of lithium–sulfur batteries depends on systematic optimization of the complicated system. A facile integrated approach based on the synergy between the current collector, binder, and separator is developed to improve the performance of batteries. The synergistic effect of different components and the failure mechanism of batteries with high sulfur loading are discussed (see scheme). [ABSTRACT FROM AUTHOR]

Sugarcane is a product of great economic relevance and, as well as its use for the production of different types of sugar, it is also a renewable source for the production of bio‐fuels, other bio‐products, and electricity. In this context, efficient planning and technology changes are necessary to ensure high productivity, competitiveness and harmony with economic, social, and environmental issues. Consequently, this sector needs technical and scientific support. Mathematical optimization models can be useful to build efficient planning tools for the activities involved in the sugarcane supply chain, helping to address these challenges. This paper describes the sugarcane supply chain and presents a literature review of mathematical optimization models that have been proposed to represent its main stages: planting, harvesting, transporting, industrial processing, and marketing. The findings show that several models have been proposed to represent these five stages, however, most of them have not been thoroughly explored from the point of view of strength of the models and solution methods. Moreover, the harvesting stage is the one that has received more attention than the other stages. In the integration of two or more stages, there are many challenges yet to be addressed by mathematical optimization models. This review contributes to a better understanding of the sugar‐energy sector and opens up new perspectives for future investigations. [ABSTRACT FROM AUTHOR]

In the three‐phase grid‐connected current‐source inverters (CSIs), the resonance result from the AC‐side CL filter and the quality of the grid‐current waveform under the unbalanced and harmonic grid voltage conditions are two issues deserving attention. To solve the two problems, a continuous control set‐model predictive control (CCS‐MPC) method based on the optimization theory is proposed in the two‐phase synchronous coordinate frame in this paper. The constructed cost function takes grid‐connected current and output current of the inverter as control target, and obtains its optimal solution under the least‐squares method, where the optimal inverter‐side current reference is composed of inductor‐current proportional feedback, capacitor‐voltage proportional feedback, and inverter‐side current steady‐state value feedforward. The proposed method can easily realize the normal operation of three‐phase grid‐connected CSIs because the method can be determined by only one parameter under the unbalanced and distorted grids. Furthermore, in the proposed method, the proportional feedback of capacitor voltage can effectively suppress the CL filter resonance. Finally, simulation and experimental results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Activated carbons (ACs) were prepared from waste Kraft lignin (KL) obtained from pulp and paper industry by chemical activation with H2SO4. The KL was characterized by elemental analysis and thermal gravimetric analysis. The suitable process conditions were optimized by an orthogonal design test [L9 (34)], factors including the activation temperature, concentration of H2SO4, activation time, and N2 flow rate. The morphology and pore structure of ACs were investigated by means of scanning electron microscopy, cryogenic N2 adsorption isotherm. The orthogonal test results suggested that the optimal activation parameters were as follows: 600°C of activation temperature, 35% of H2SO4 concentration, 120 min of activation time, and 100 cm3/min of N2 flow rate. Under the selected conditions, a mesoporous ACs with BET surface area of 873.9 m2/g and total pore volume of 0.485 cm3/g was obtained. The ACs obtained from KL by H2SO4 activation were also compared with ACs prepared by H3PO4 and KOH activation. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 1158-1163, 2013 [ABSTRACT FROM AUTHOR]

Summary: In recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of the biggest problems faced by WSNs. The traditional localization techniques may not be able to handle the issues during the scenario to estimate the location of sensor nodes due to anchor mobility, mobile WSNs, latency, energy harvesting, unfavorable environmental states, and many more issues. However, these issues open the door for the amalgamation of machine learning (ML) and optimization techniques with localization techniques. Motivated by the earlier discussion, we explored various ML and optimization techniques to estimate the location coordinates in a sensor network in this paper. Finally, a comparison of existing ML algorithms concerning optimization techniques has been presented, highlighting their improved outcomes. This research offers a detailed survey by exploring the various parameters for location estimation through tabular forms by incorporating ML and optimized localization techniques. A survey of surveys is also presented to identify the key limitations of existing surveys and to introduce the novelty in the comprehensive study done in this paper. A year‐wise evaluation of ML Techniques with localization (2011–2022) is also discussed and presented over various performance parameters, including energy‐efficiency, accuracy, error, and complexity. This discussion concluded that Hybrid Techniques are least explored for using optimized localization machine learning. Further, a summarized discussion of the various comparison tables paves the path for future research in the area of localization in WSN. [ABSTRACT FROM AUTHOR]

With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants' benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. [ABSTRACT FROM AUTHOR]

In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles' tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions. [ABSTRACT FROM AUTHOR]

Summary Location‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes. [ABSTRACT FROM AUTHOR]

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]

In the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety. [ABSTRACT FROM AUTHOR]

van Lenthe, Broer, and Rashid made comments on our 2009 paper [Song et al., J. Comput. Chem. 2009, 30, 399] by criticizing that we did not properly reference the work by Broer and Nieuwpoort in 1988 [Broer and Nieuwpoort, Theor. Chim. Acta. 1988, 73, 405], and we favorably compared our valence bond self-consistent field (VBSCF) algorithm with theirs. However, both criticisms are unjustified insignificant. The Broer-Nieuwpoort algorithm, properly cited in our paper, is for the evaluations of matrix elements between determinants of nonorthogonal orbitals. Stating that this algorithm 'can be used for an orbital optimization' afterwards [van Lenthe et al., submitted] is not a plausible way to require more credits or even criticize others. While we stand by our statement that our algorithms scales at O( m4) and van Lenthe et al.'s approximate Newton Raphson algorithm scales at O( mN 5) (here m and N are the numbers of basis functions and electrons), as we discussed in our original paper, it becomes obvious that any strict comparison among different algorithms is difficult, unproductive, and counteractive. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We report on the potential of today's LED technologies to approximate Spectral Power Distributions (SPDs) aimed at optimizing specific performance goals. We compare numerically optimal SPDs to actual SPDs produced by a 16-channel LED light source, aiming at optimizing performance for six different applications. These theoretical and practical limits of the potential of current LED technologies can serve as guidelines for the design of future LED sources for different applications. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to analyze the adoption and outcomes of the ISO 14001 standard through a systematic review of the main studies on this issue published in peer‐reviewed journals between 1996 and 2015. The 94 papers analyzed make it possible to paint a comprehensive picture of the effectiveness of ISO 14001 in environmental management practices, performance in this area and social aspects such as employee awareness. The systematic review also sheds more light on the main pitfalls and success factors of the standard. Nevertheless, the similarities and even redundancies of the literature in terms of objectives, approaches and methods used tend to produce quite predictable and optimistic results, which do not reflect the complexity of the impact of ISO 14001. The paper highlights the importance of more diverse and critical approaches that might challenge the successful rhetoric of the dominant literature, which tends to focus on positive aspects and be limited to a few countries that are not representative of the wide international distribution of certification. The findings of this systematic review can also help managers in making decisions on the adoption and renewal of certification. [ABSTRACT FROM AUTHOR]

A battery energy storage system (BESS), due to its very fast dynamic response, plays an essential role in improving the transient frequency stability of a grid. The performance of the BESS varies with the system's installation site. Hence, the optimal location of the BESS is of utmost importance for improving transient frequency stability. Therefore, this paper presents a hierarchical approach for optimizing the BESS placement to improve a grid's transient frequency stability. In most research, frequency nadir and rate of change of frequency (ROCOF) have been considered for studying frequency stability. This paper considers two more parameters, along with frequency nadir and ROCOF, to study the transient frequency stability, settling time, and decay ratio. A novel frequency stability index (FSI) using the four transient frequency parameters has been developed. After a significant disturbance in a benchmarked test system, the FSI was used to identify the optimal location of the BESS for stabilizing the frequency. It has been observed that, after a sudden generator outage, the ROCOF and the frequency nadir improve the best when the BESS is located at the bus closest to the generator experiencing the outage. However, considering the other two parameters as well, the value of the FSI is the minimum; that is, the optimum solution is when the BESS is located at the bus that is the second closest to the generator experiencing the outage. Results of similar studies validate the proposed FSI in indicating the optimal location of the BESS in improving the transient frequency behavior of the system. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

Summary: Multi‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results. [ABSTRACT FROM AUTHOR]

This paper evaluates experimentally a novel strategy for solving a variant of the differential game of target defense in presence of obstacles. The game is widely applied in the areas of military defense for protecting important equipment such as a ship, an aircraft, a moving vehicle, or a sensitive installation from a malicious attacker. The state‐of‐the‐art approaches mostly employ an offline optimization strategy that is only applicable to holonomic robots. Moreover, most of the approaches could not autonomously avoid obstacles or take into account uncertainties. As a consequence, this paper presents an online optimization technique, by designing a trade‐off parameter that integrates game theory with the model predictive control, which allows a nonholonomic defender to intercept the attacker while simultaneously defending the target. Simulations under different conditions as well as several indoor laboratory experiments validate the proposed approach. Moreover, performance is compared with a standard model predictive control approach. [ABSTRACT FROM AUTHOR]

The adoption of electrical vehicles (EVs) is nowadays a center of attraction, which reduces the carbon footprint and adds to the economic growth of a country. However, providing a proper charging facility to the customers is still a challenging task. This paper presents the mix (AC‐DC) charging station (CS) integrated with the PV array and the smart distribution grid. The acceleration of the EV causes the power quality burden on the grid and this can be taken care of by using advanced adaptive controls. In this paper, the control strategy used for the CS is the immune feedback‐based adaptive (IFA) control technique. It reduces the ripples in the fundamental load current extraction under steady‐state and produces fast response under dynamic conditions and helps to synchronize CS with the grid. The multi‐functional capabilities of the CS are presented with different voltage levels DC fast off‐board chargers and AC onboard slow chargers. Moreover, the Rao optimization technique is utilized to select the gains for the cascaded proportional‐integral (PI) controllers which are used to regulate the DC bus voltage and battery charging/discharging currents. The optimized fitness values of integral time square error (ITSE) by using Rao1, Rao2, and Rao3 techniques are 297.341, 285.023, and 282.924 respectively. The simulated and test results depict the efficacy of the CS. Moreover, the mixed charging facilities with multi DC fast chargers avoid the rush and burden on the grid, which reduces the power outage across the load and rapidly rising energy cost. [ABSTRACT FROM AUTHOR]

Optimization of system architectures can help deal with finding better system architectures in a large design space plagued by combinatorial explosion of alternatives. To enable architecture optimization, the design space should therefore be formalized into a numerical optimization problem, and it should be possible to quantitatively evaluate architecture alternatives. This paper presents a methodology for generating and modeling architecture design spaces using the Architecture Design Space Graph (ADSG), and using collaborative Multidisciplinary Design Analysis and Optimization (MDAO) techniques to evaluate architectures. Collaborative MDAO leverages disciplinary expertise while ensuring that analysis tools exchange data consistently and correctly using a central data schema. The problem solved in this paper is the missing link between architecture optimization and collaborative MDAO: the reflection of generated architectures in the central data schema. It is solved by the authors by mapping architecture components and Quantities of Interest (QOIs) to the central data schema using Data Schema Operations (DSOs). Such a mapping also assists the user in identifying missing or unnecessary disciplinary analysis tools. Three web‐based software tools implementing the methodology are presented. Finally, the methodology and tools are demonstrated using the design of a supersonic business jet as an example. [ABSTRACT FROM AUTHOR]

Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study. [ABSTRACT FROM AUTHOR]

This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances. [ABSTRACT FROM AUTHOR]

This note contains ramifications of results of Delbaen et al. (2002). Assuming that the price process is locally bounded and admits an equivalent local martingale measure with finite entropy, we show, without further assumption, that in the case of exponential utility the optimal portfolio process is a martingale with respect to each local martingale measure with finite entropy. Moreover, the optimal value always can be attained on a sequence of uniformly bounded portfolios. [ABSTRACT FROM AUTHOR]

There is a drastic increase in urbanization over the past few years, which requires energy‐efficient and optimized solutions for transportation, governance, quality of life in a smart city among all the citizens. The Internet‐of‐Energy (IoE) ecosystem offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoE applications is increased while IoE devices continue to grow. Therefore, smart city solutions must have the ability to utilize energy and handle the associated challenges efficiently. Moreover, energy Optimization (EO) techniques can be used to reduce energy consumption to meet the sustainability goals in IoE. Different techniques have been proposed for EO in various fields by researchers worldwide. Computing systems also need energy optimization. The energy consumption in the data center, clouds, and blockchain (BC)‐based architectures are a point of concern at the current time. Due to the enormous energy demands of these systems, we cannot take advantage of the latest technologies to their fullest. Due to the emergence of new technologies and some limitations of proposed techniques, we can still not optimize energy usage more than some extent. There is minimal exploration done in energy optimization in BC‐based systems. In this paper, we have proposed a survey on the energy optimization techniques in various systems, including the optimization techniques in BC‐based systems. We have proposed a taxonomy that classifies energy optimization techniques. We have also proposed an energy‐efficient consensus mechanism, Proof‐of‐High Performance optimization (named as PoHPo), for High‐Performance Computing (HPC) based ecosystems. The open issues and challenges are then discussed in EO. The survey intends to propose future directions for industry professionals, green‐energy stakeholders, and researchers worldwide to explore this topic further. [ABSTRACT FROM AUTHOR]

Software resource allocation is an significant factor of system configuration which plays a critical role in guaranteeing the performance of multitier web service systems. Computing the optimal allocation of different software resources in order to meet performance requirements under dynamic workloads conditions is in highly challenging. Existing approaches mostly rely on translating domain knowledge from experts into computational solutions through heuristics‐based optimization techniques. While such techniques are useful, they cannot leverage actual usage data generated by system users which may contain allocation strategies that are not captured by domain experts' knowledge. In this paper, we propose an iterative feedback mechanism which solves the problem to some extent by optimizing software resource allocation of multitier web systems through imitating system users who have achieved excellent performance. Specifically, we propose a deep Q‐learning network‐based approach for performance prediction to deal with the dynamic changes of complex workloads. The performance prediction method involves the reinforcement learning method for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the multitier web benchmark system, and the experimental results demonstrated significant improvement compared to models built based on domain knowledge. [ABSTRACT FROM AUTHOR]

As it tackles electrical and non‐electrical losses, the triple‐diode model (TDM) of photovoltaic (PV) cells is highly exact. This paper employs a novel optimization method known as the innovative optimization algorithm (INFO) technique to correctly estimate the electrical characteristics of such TDM. To shift agents towards a better position, the INFO algorithm exploits the concept of weighted mean. The primary goal of INFO is to stress its performance features to solve some optimization difficulties that other approaches cannot effectively solve. In this paper, the objective function based on a combination of the absolute value of the current error, its squared value, and its quadrable value is employed, which the INFO optimizer minimizes to predict the optimum parameters of such TDM precisely. The proposed INFO algorithm is carried out on multi‐ and mono‐crystalline varieties, such as the Kyocera KC200GT and the Canadian Solar CS6K‐280 M. The simulation outcomes demonstrate the INFO's ability to extract the model parameters precisely. The INFO achieved the lowest ideal fitness values of 9.0738 × 10−06 and 5.7356 × 10−05 for the KC200GT and Canadian Solar CS6K‐280 M, respectively, throughout the optimization procedure. Under various environmental circumstances, experimental validation of the calculated parameters using the (INFO) optimizer is carried out, and the results are compared to the observed values from the laboratory experiments. The simulation results demonstrate the INFO's convergence time and accuracy advantage over competing optimization techniques. Additionally, statistical analysis shows that the INFO optimizer is resilient. [ABSTRACT FROM AUTHOR]

Summary: Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two‐stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step‐by‐step calculation method that is not feasible under real‐time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a broadband superheterodyne receiver (RX) front‐end for millimeter‐wave (mm‐Wave) imaging radar is presented. Realized in 28‐nm CMOS technology, the proposed RX incorporates a wideband low‐noise amplifier (LNA), double‐balanced passive mixers for the dual down‐conversion, a differential power divider, a differential intermediate frequency filters, a current‐mode‐logic divider‐by‐2, and a local oscillator buffers. Wideband technique and layout optimization are taken into consideration in the RX. A four‐stage LNA is devised to realize the high gain and low noise figure (NF) with a common‐gate input stage and three pseudodifferential common‐source stages. Optimized transistor layout and capacitor neutralization technique are developed to improve gain and noise performance. Transformer‐based matching networks with the peak‐staggered technique are adopted in the LNA to achieve a flatter gain response with a wider bandwidth (BW). The passive double‐balanced mixers are implemented with a highly symmetrical layout for broadband down‐conversion. Simulation results show that the mm‐Wave RX front‐end exhibits a BW ranging from 90 to 115 GHz. The simulated NF of our RX is 4.7 dB at 86 GHz. With a supply voltage of 1 V, the presented RX consumes 88.2‐mW power and occupies a chip size of 1.6 × 0.5 mm2 including all the testing pads. The proposed RX is suitable for W‐band imaging systems. [ABSTRACT FROM AUTHOR]

This paper presents a panoramic view of research on economic and financial networks in the journal Networks, since its inception half a century ago. This paper focuses on both the breadth and depth of the journal articles, and within the context of earlier contributions, as well as more recent related ones in other scientific publications. From network optimization to game theory and a plethora of equilibrium concepts, along with novel dynamical systems frameworks, the journal has led the way in advancing economic and financial network models, algorithms, and applications. Moreover, Networks has helped to attract researchers in a variety of disciplines to the science of networks and the formulation and solution of associated problems drawn from the real world. [ABSTRACT FROM AUTHOR]

We have developed an outcoupling solution for OLED based on scattering high index layer. We demonstrate an increase of the efficiency by a factor 2 together with low dark currents on large, white, standard OLED. These results have been obtained thanks to an optimization of the scattering properties of the layer and a careful work on the surface quality. [ABSTRACT FROM AUTHOR]

The new technologies, such as soft open points (SOPs) and demand response (DR), offer original approaches to stabilize the system operation and accommodate the high penetration of distributed generators (DGs). However, the applied effects of these technologies are closely affected by the uncertainties of the consumer's willingness and the outputs of DGs. To solve the problems caused by the uncertainty of the resources in active distribution system, this paper proposes a distributionally robust co‐optimization model for the demand side resources and SOPs, which realizes the combination of investment economy and operation robustness. Next, using equivalent substitution and polyhedron linearization technique, this paper proposes the mathematical method which transforms the original non‐linear programming model into a mixed‐integer linear programming model, and obtains the solution to the distributionally robust model with column and constraint generation (CCG) algorithm. Finally, the effectiveness of the proposed model is verified with the sample from a power distribution system in northern China. The result demonstrates that the co‐optimization model can realize complementary advantages of the demand side resources and SOPs, which can not only guarantee the economy of scheme, but also improve the accommodation of DGs. [ABSTRACT FROM AUTHOR]

This paper proposes a filter‐integrated switched boost grid‐connected inverter by coupling switched boost network between inverter bridge and power grid, which can effectively solve the inherent resonance peak problem of LCL filter. Meanwhile, the proposed topology can realize boost function at AC side, which can effectively avoid the problem of excessive voltage stress caused by the boost at the DC side. Due to the design of the parameters in the control system will affect the effect of resonance spike damping, it will also affect the harmonic content of the grid‐connected current. Based on the stability margin and steady‐state error of the system, combined with the characteristics of the system regulator, the parameter selection area that meets the system requirements is drawn in this paper, and the optimization of the system parameter design method is realized. Finally, the function of the circuit is verified by building an experimental platform. [ABSTRACT FROM AUTHOR]

This paper introduces a real-time rendering method for single-bounce glossy caustics created by GGX microsurfaces. Our method is based on stochastic light culling of virtual point lights (VPLs), which is an unbiased culling method that randomly determines the range of influence of light for each VPL. While the original stochastic light culling method uses a bounding sphere defined by that light range for coarse culling (e.g., tiled culling), we have further extended the method by calculating a tighter bounding ellipsoid for glossy VPLs. Such bounding ellipsoids can be calculated analytically under the classic Phong reflection model which cannot be applied to physically plausible materials used in modern computer graphics productions. In order to use stochastic light culling for such modern materials, this paper derives a simple analytical solution to generate a tighter bounding ellipsoid for VPLs on GGX microsurfaces. This paper also presents an efficient implementation for culling bounding ellipsoids in the context of tiled culling. When stochastic light culling is combined with interleaved sampling for a scene with tens of thousands of VPLs, this tiled culling is faster than conservative rasterization-based clustered shading which is a state-of-the-art culling technique that supports bounding ellipsoids. Using these techniques, VPLs are culled efficiently for completely dynamic single-bounce glossy caustics reflected from GGX microsurfaces. [ABSTRACT FROM AUTHOR]

The traditional LCD display is not suitable for the application of transparent displays because of its low transmittance which results in poor transparency. In this paper, we proposed a 28‐inch transparent LCD display with novel pixel arrangement and multiple optimization algorithms. We achieved high transparency characteristics without the sacrifice of color gamut by adopting multiple white sub‐pixels. And at the same time design of specific algorithms improved and enhanced the visual effects of transparent display. The simulation and experimental results verify that the new processing method greatly balanced the transparency and clarity of the display. [ABSTRACT FROM AUTHOR]

In this paper, a system for LTE Cell Outage Compensation (COC) based on hybrid Genetic Algorithms (GA) and Artificial Neural Networks (ANN) has been proposed. COC aims to minimize the impact of cell outage which leads to decrease in operator revenue and/or the customer satisfaction. The proposed system adopts an optimization module to search for an optimal setting of a set of LTE operational parameters to achieve a targeted set of key performance indicators. The optimization process always leads to good enough solutions, but it also requires a huge number of trials. So, in the proposed system, a huge set of outage scenarios is collected along with their optimal argument settings that are acquired by the optimization module and they are used to train an artificial neural network (ANN) module, which acts as an expert that can optimally act on the different situations in real‐time mode. Simulation environment is set to evaluate different LTE measures and Key Performance Indicators (KPIs) on different outage scenarios. Simulation results proved the capability and robustness of the proposed system to minimize the number of users experiencing outage. Simulation results also show that the proposed system achieves optimal parameter settings without violating the overall system performance and with minimal processing time, while introducing significant impact on the performance of LTE. [ABSTRACT FROM AUTHOR]

In order to address the issue of keeping the converter operating at a high efficiency level under different conditions, this paper suggests an adaptive step frequency adjustment online optimization search algorithm, which achieves the goal of fast adjustment and real‐time response to parameter changes. The converter is quickly adjusted to operate at a high efficiency by sampling the input and output current values and modulating the switching frequency utilizing the algorithm. Additionally, the algorithm shifts between two working models to finish the search for the best switching frequency while the converter's characteristics do not change. Moreover, it accomplishes the objective of instantly resynchronizing the algorithm after a change in the converter's parameters and starting a search for the best switching frequency in the new converter state. For various input voltages and output loads, the operating principle, optimal frequency, and efficiency are given. In addition, the principle analysis when the converter parameters are changed is provided, and the choice of two algorithm models is compared. Finally, a prototype buck converter with adjustable input voltage and load is constructed to test the algorithm's efficiency. [ABSTRACT FROM AUTHOR]

Nowadays, with the increasing penetration rate of wind turbine generators (WTGs) in power distribution networks, possible network‐related problems should be considered. Distribution network reconfiguration (DNR) can reduce the effect of wind power curtailment by transferring loads to the other feeders and compensating for wind forecast error. In this paper, a two‐stage policy is presented to deal with the network configuration and wind power uncertainty. In the first stage, the scheduled output of WTGs is determined, and the wind power uncertainty is modeled using a scenario‐based approach. In the second stage, a modified firework (MFW) algorithm is considered for the network reconfiguration to minimize the network losses. In the MFW algorithm, a new local search method is implemented to enhance the performance of the FW algorithm by increasing the direction of the explosion. The impact of wind power curtailment in the network operation is minimized using the proposed policy that changes the network configuration. The proposed methodology is tested on a 70‐bus IEEE distribution test system. The simulation results demonstrate the efficiency of the proposed algorithm in solving the DNR problem in the presence of WTGs, considering the uncertainty of wind speed. [ABSTRACT FROM AUTHOR]

This paper mainly discusses distributed constrained optimization problem for second‐order multi‐agent system under undirected communication network. The task of all agents is to minimize the sum of the local convex functions, where each agent is individual and only accesses to one objective function. Different from the most existing results, where the objective functions are assumed to be time‐invariable, this paper considers the situation of time‐varying objective function. Besides, we don't require that the Hessian matrices are identical and the gradients are bounded. First, a novel time‐varying optimization algorithm is proposed based on the projection algorithm. Second, by using convex analysis and Lyapunov theory, it is shown that the states of all agents can reach consensus and asymptotically converge to the neighborhood of the optimal solution. Finally, some numerical examples are given to verify the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

Real‐time monitoring and accurate prediction of photovoltaic (PV) power generation operation parameters are essential to ensure stable operation. In this paper, a set of online PV power generation parameter measurement and monitoring devices characterized by simple structure, high sampling accuracy, small data fluctuations, and ease of measurement, are designed. Sensors based on the zero‐flux principle are employed in the real‐time collection of the output electrical signals in the process of PV power generation, realizing the accurate collection of electric parameter signals. Next, the basic structure and working principle of PV cells are analyzed, a mathematical model of PV cells for engineering purposes is established, a wavelet neural network is selected to predict the short‐term PV power generation, and particle swarm optimization and adding momentum are used to optimize the weight of wavelet neural network (WNN) as well as the parameters of the wavelet basis function. Finally, the historical power generation data and meteorological data of the power station are taken as the training samples to train and simulate the prediction sub‐models of different weather types to verify the effectiveness and accuracy of the PV power generation short‐term prediction model for optimizing the WNN based on the particle swarm optimization algorithm. The research results of this paper can realize real‐time monitoring of the output parameters and accurate prediction and evaluation of power generation during the operation of the PV‐power‐generation system. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive study is presented whereby genetic algorithm optimization (GAO) is used to find the best arrangement of three‐phase high‐voltage transmission line (TL) conductors, that minimizes emitted electric and magnetic fields (EMFs), maximizes power capacity, and avoids corona. Moreover, a financial cost study is conducted in order to show the feasibility of implementing such rearrangement, where construction costs are compared with the impact on a populated area in close proximity to the line. To facilitate applying the procedure of this paper, a detailed description of each step is provided. Both single circuit as well as double circuit transmission lines are considered, and bundled conductors are analyzed. To demonstrate the validity and usefulness of the mathematical formulation and MATLAB computer codes, computed results are compared with measured data where good agreement is observed. To show that using GAO for solving high voltage line problems is an alternative to other robust optimization techniques such as particle swarm optimization (PSO), GAO solutions are compared with PSO solutions. The GAO results are also compared with the unoptimized results, where significant improvements can be seen. Since according to this algorithm, line conductors move freely throughout the solution space in an attempt to find the best solution, it is important to restrict this movement in order to adhere to the separation limits of the transmission line international standards IEC‐71. [ABSTRACT FROM AUTHOR]

One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]

In the design of antenna arrays which require fast and robust flat‐top beam synthesis, computationally efficient methods are preferred. This feature is usually met by analytical techniques or simple optimisation procedures. On the other hand, in the flat‐top beam synthesis, a common requirement is the ability to control beamwidth or sidelobe level. However, this can result in a high dynamic range ratio (DRR) of array's excitation coefficients. In this paper, a straightforward method for the design of symmetrical flat‐top arrays with controllable sidelobe level or DRR is proposed. The method is based on quadratic and cubic transforms of Gaussian excitations. In addition, the method utilises zero coefficients whose positions are used to control the DRR, including the ability to achieve its minimum. Compared to other flat‐top arrays with analytically shaped beams, the proposed arrays have lower DRRs for the same sidelobe level. [ABSTRACT FROM AUTHOR]

Summary: Even with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (P‐V) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package. [ABSTRACT FROM AUTHOR]

Summary: In a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA. [ABSTRACT FROM AUTHOR]