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This paper employs low-field nuclear magnetic resonance (LF-NMR) technology to meticulously analyze and explore the intricate soybean infiltration process. The methodology involves immersing soybeans in distilled water, with periodic implementation of Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence experiments conducted at intervals of 20 to 30 minutes to determine the relaxation time T2. Currently, magnetic resonance imaging (MRI) is conducted every 30 minutes. The analysis uncovers the existence of three distinct water phases during the soybean infiltration process: bound water denoted as T21, sub-bound water represented by T22, and free water indicated as T23. The evolution of these phases unfolds as follows: bound water T21 displays a steady oscillation within the timeframe of 0 to 400 minutes; sub-bound water T22 and free water T23 exhibit a progressive pattern characterized by a rise-stable-rise trajectory. Upon scrutinizing the magnetic resonance images, it is discerned that the soybean infiltration commences at a gradual pace from the seed umbilicus. The employment of LF-NMR technology contributes significantly by affording an expeditious, non-destructive, and dynamic vantage point to observe the intricate motion of water migration during soybean infiltration. This dynamic insight into the movement of water elucidates the intricate mass transfer pathway within the soybean-water system, thus furnishing a robust scientific foundation for the optimization of processing techniques. [ABSTRACT FROM AUTHOR]

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk. [ABSTRACT FROM AUTHOR]

This paper examines the optimal design for agri‐photovoltaic (agri‐PV) systems. Agri‐PV systems should be evaluated on the total crop yield and power generation, and the balance is essential. Specifically, the amount of solar irradiance reaching the farmland and power generation by PV systems varies by panel separation and tilt angle, affecting crop yield. Quantitative analysis is essential for balancing. This study investigated the balance between the output of Agri‐PV installed on farmland and photosynthetic photon flux density (PPFD), a crucial indicator for crop growth, and discussed the optimal solutions for Agri‐PV systems installed in various regions. Simulations were conducted for Miyazaki (semi‐tropical) and Nagano (inland climate) to understand the changes in different climate regions. Miyazaki has higher PPFD and PV output than Nagano because Miyazaki has more solar irradiance and longer sunshine duration than Nagano. When the normalized panel pitch is small, PPFD changes significantly by the tilt angle. When the normalized panel pitch is large, the profit may be maximized by setting the panel tilt angle to maximize the PV output. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A cascaded relativistic magnetron array with symmetric feeder structure was first proposed as a multi-port phase-coherent high-power microwave source, which is intrinsically equipped with high structural symmetry. Two symmetrically positioned slow-wave structures surround the feeder structure, which reduces axial electron drifting in each resonant system and ensures the phase-locking process. In this paper, a theory of structure-provided coupling coefficient and oscillator-required coupling coefficient is proposed as the phase-locking prerequisites. The method is evaluated by adopting two A6-type resonant systems. The symmetrically driven cascaded relativistic magnetron array employs a typical π-mode with an anode voltage of 450 kV and an axial magnetic field of 0.47 T. The phase-locked state was achieved in 17 ns with a jitter less than 5 deg. The total output power exceeds 2.3 GW at a frequency of 2.15 GHz, and the power flow in each output port exceeds 350 MW. The transversely opposed driven scheme can be combined with other phase-locking patterns for additional uses, and further optimization of resonant system could be applied for enhancing device performance. The theory of coupling prerequisites is also sufficient for analyzing other cascaded relativistic magnetrons. [ABSTRACT FROM AUTHOR]

Using of ultra-wideband (UWB) technology in radio transceiver systems has increased in recent years due to high-speed data transmission, low power dissipation, low cost, and low complexity. In particular, distributed amplifier (DA) is a critical component of transceiver in UWB technology. However, designing an ultra-wideband DA with high performance becomes challenging. The DA design suffers from the tight trade-offs between the amplifier parameters such as gain, noise, linearity, input/output impedance matching, and power dissipation. In this paper, a new approach for multi-objective optimization of the DA is introduced. In the proposed approach, the meta-heuristic optimization techniques are applied over the entire bandwidth of the UWB, while the most recent optimization approaches for amplifiers are performed at the center frequency and they can't achieve the proper design specifications for wideband amplifiers. The simultaneous optimization of power gain (S21), noise figure (NF), input and output return loss (S11 and S22) are conducted over the wide bandwidth using three multi-objective optimization algorithms including Multi-Objective Inclined Planes System Optimization (MOIPO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). The obtained results demonstrate the tapered matrix DA optimized by MOIPO exhibits better performance than others. The circuit simulations are performed in 0.18 µm TSMC RF-CMOS technology. Simulation results show that the optimized tapered matrix DA by MOIPO, compared to NSGA-II and MOPSO, exhibits a good performance over the frequency band of 0.1–28 GHz with maximum S21 of 12.9 dB, NF less than 5.9 dB, S11 and S22 below than − 10 dB over the whole frequency band. The DC power dissipation is 25 mW from a 1.5 V supply. [ABSTRACT FROM AUTHOR]

The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The system mainly completes operations through elevators and multiple rail-guided vehicles (RGVs). This paper focuses on the scheduling optimization of compound operations in the AVS/RS to improve system performance. Compound operations involve the coordinated execution of both single-command and double-command operations. A mathematical model with compound operations was proposed and effectively decomposed into a horizontal component for RGVs and a vertical counterpart for the elevator, which can represent the operations of one elevator cooperating with multiple RGVs. The goal of this model was to minimize the makespan for compound operations and to determine the optimal operation sequence and path for RGVs. An improved discrete particle swarm optimization (DPSO) algorithm called AGDPSO was proposed to solve the model. The algorithm combines DPSO and a genetic algorithm in an adaptive manner to prevent the algorithm from falling into local optima and relying solely on the initial solution. Through rigorous optimization, optimal parameters for the algorithm were identified. When assessing the performance of our improved algorithm against various counterparts, considering different task durations and racking configurations, our results showed that AGDPSO outperformed the alternatives, proving its effectiveness in enhancing system efficiency for the model. The findings of this study not only contribute to the optimization of AVS/RS but also offer valuable insights for designing more efficient warehouses. By streamlining scheduling, improving operations, and leveraging advanced optimization techniques, we can create a more robust and effective storage and retrieval system. [ABSTRACT FROM AUTHOR]

Since last few decades, a lot of work has been done on the evolutionary techniques to solve the optimisation problems. With the time passes, these techniques were modified, and also new algorithms were introduced to improve the performance. These techniques had been used by many researchers in synthesis of mechanisms to get the optimum results with minimum design errors. Improved harmony search (IHS) algorithm is developed from harmony search technique by improvising the harmony in HS algorithm. This technique is inspired by searching the best state of harmony in musical process in which a jazz musician make practice after practice to find the same. In this paper, IHS algorithm is utilized to synthesize four-bar path generation mechanism. A mathematical model was derived using vector loop closure equation, wherein the error function, representing positional error between actual and desired points, was considered for forming objective function. The penalty function was used to prevent violation of structural constraints corresponding to Grashof's criteria and input crank angle sequence. Three different cases were studied with ten, fifteen and eighteen precision points with and without prescribed timings. The results obtained from the study, compared with other evolutionary techniques from literature, and found significant competition. [ABSTRACT FROM AUTHOR]

Self-energy recycling cooperative communication, in which the relay nodes simultaneously harvest energy from the source and their own transmitted signal, has been demonstrated to significantly increase the total harvested energy. In this paper, we present an optimization framework for the resource allocation and relay selection with the objective of maximizing the sum-throughput in afull-duplex multi-relay multi-user multiple-input-multiple-output network by incorporating self-energy recycling at the relays, for the first time. The formulated problem is a mixed integer non-linear programming problem, which is difficult to solve for the global optimal solution in polynomial-time. As a solution strategy, we decompose the optimization problem into two sub-problems: resource allocation problem and relay selection problem. First, we solve the resource allocation problem for a predetermined relay selection by using Taylor series approximation and convex optimization techniques. For the relay selection problem, we propose a polynomial-time sub-optimal algorithm based on the idea of iteratively selecting the relay offering the maximum throughput at each time. Through simulations, we demonstrate that the proposed dynamic time FD-MIMO system increases the throughput by 30% as compared to the conventional static time protocol system, and by a factor of 2.4 over the system without self-energy recycling. [ABSTRACT FROM AUTHOR]

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess "better or worse" system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption. [ABSTRACT FROM AUTHOR]

Based on the scenario of high-penetration distributed photovoltaic connected to an AC/DC distribution network, this paper analyzes the dynamic characteristics of frequency and voltage in a distribution network after the blocking failure of the flexible interconnection channel. In order to enhance the transient stability of the system after the fault, this paper comprehensively considers the active regulation ability of photovoltaic units, and puts forward an emergency coordinated control strategy for a single-ended distribution network with flexible interconnection channel blocking. Firstly, the non-fault channel is overloaded for a short time, then the comprehensive influence of factors such as electrical distance, response time and adjustment cost on the frequency modulation effect of the system is quantitatively evaluated; according to the evaluation results, the photovoltaic and synchronous units are controlled by "control instead of tripping", and finally, the high-frequency tripping is carried out, based on the principle of "photovoltaics first". After the frequency control is completed, the reactive power optimization model of the system is established, and the improved tabu–particle swarm optimization algorithm is used to solve it, so as to optimize the voltage of the distribution network nodes. Finally, an equivalent simulation model is established to verify the coordinated control strategy. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of tracking a high-speed ballistic target in real time. Particle swarm optimization (PSO) can be a solution to overcome the motion of the ballistic target and the nonlinearity of the measurement model. However, in general, particle swarm optimization requires a great deal of computation time, so it is difficult to apply to realtime systems. In this paper, we propose a parallelized particle swarm optimization technique using field-programmable gate array (FPGA) to be accelerated for realtime ballistic target tracking. The realtime performance of the proposed method has been tested and analyzed on a well-known heterogeneous processing system with a field-programmable gate array. The proposed parallelized particle swarm optimization was successfully conducted on the heterogeneous processing system and produced similar tracking results. Also, compared to conventional particle swarm optimization, which is based on the only central processing unit, the computation time is significantly reduced by up to 3.89×. [ABSTRACT FROM AUTHOR]

The paper reflects the results of the ongoing discussion about sustainable development in Slovakia as an increasingly urgent topic resonating across scientific disciplines. Specifically, it deals with the participation processes, which were realized in 2022-2023 under the auspices of the Ministry of Investments, Regional Development and Informatization of the Slovak Republic (MIRRI), and the second part of which was implemented at the Institute of Management of the Slovak University of Technology in Bratislava using its expert and organizational capacities. The mentioned activities follow the strategic document entitled Vision and Development Strategy of Slovakia 2030 and are related to the preparation of the document Vision and Development Strategy of the Slovak Republic until 2050 - Slovakia 2050. In the light of the outputs from the participation processes, the paper deals, among other things, with the necessity of accelerating the transformation of regional economies from the industrial towards the post-industrial society, including sustainable innovation-oriented circular economy less dependent on raw materials and energy, with the simultaneous optimization of the system of decision-making and effective management, based on partnership and division of responsibilities between different levels and various actors of sustainable development. It also points out that discussions about this vision have the potential to significantly frame the creation of concepts of integrated territorial innovation-oriented clusters, the urgency of which - in parallel with the frustration of the ineffectiveness of fragmented development processes and activities - is felt at different levels by practically all participating subjects of the professional discourse analyzed in this paper. The results of the participation process fully confirm that Slovakia is not adequately prepared to respond to the challenges associated with all the necessary transformation processes. [ABSTRACT FROM AUTHOR]

Partially reusable launch vehicle design has raised a great interest in order to minimize the costs of space transportation by recovering and refurbishing the first stage. Several configurations such as toss-back architectures are now operational. These concepts require additional rocket propellant in order to carry the boost-back and landing maneuvers, that induce several losses in terms of payload mass performance for the ascent mission. In order to limit these losses, this paper focuses on the design of two types of architectures to carry out the return-to-launch-site mission. The first, named glide-back, uses the main propulsion system of the first stage to perform a boost-back burn and returns to the landing site by a gliding mode using additional lifting surfaces. The second, named, fly-back, performs the return-to-launch-site mission using several air-breathing engines located in the nose of the first stage combined with lifting surfaces. In this paper, the design of a reusability kit, allowing to provide the first stage with both expendable and reusable capabilities, is investigated. This kit is composed of the lifting surfaces, the nose including the air-breathing propulsive system (for fly-back configuration), landing gears and additional avionics. This paper addresses the design of such reusability kits and presents the mission specifications, the design process relying on Multidisciplinary Design Optimization techniques, the vehicle performance and the optimal trajectories. Different analyses (aero-propulsive, optimal control, operational considerations, etc.) are detailed and trade-offs between the studied configurations are assessed. [Display omitted] • Reusable launch vehicles. • Multidisciplinary design and optimization of reusability kit. • Inclusion of aeronautical technologies in launch vehicle design. [ABSTRACT FROM AUTHOR]

This research addresses the intricate challenges of fault estimation (FE) and fault-tolerant control (FTC) within a specific subset of T-S fuzzy systems. These systems are characterized by their localized nonlinear models, the presence of unknown inputs, actuator imperfections, and disruptive output disturbances, making them fertile ground for exploration in this study. The contributions of this paper can be summarized as follows: (1) First, we employ coordinate transformation matrices to convert the T-S fuzzy model. This transformation separates the unknown inputs and disturbances at the output. Subsequently, we equip the modified system with a T-S fuzzy adaptive sliding mode observer (ASMO) that serves the dual purpose of fortifying resilience against disruptions and adeptly deducing an extensive spectrum of fluctuating actuator failure signals. (2) In the next step, the insights gained from FE are harnessed to craft a dynamic fuzzy output feedback fault-tolerant controller (DOFFTC). This controller aims to mitigate the effects of actuator errors to maintain the stability of the closed-loop system. The article creates the necessary conditions for the presence of the required ASMO and DOFFTC using H-infinity filtering methods. To address the optimization issue posed by these criteria, we utilize linear matrix inequalities (LMIs) and calculate the required gains for implementation using convex optimization techniques. (3) The study concludes by illustrating the applicability of the proposed techniques with an example employing an inverted pendulum. This paper presents a comprehensive approach to overcoming the challenges of FE and FTC within T-S fuzzy systems. It leverages precise mathematical formulations and optimization strategies to achieve resilient and dependable control, even when confronted with intricate system dynamics. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy. Design/methodology/approach: First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly. Findings: A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control. Originality/value: To the best of the authors' knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review on optimizing feature extraction for palm and wrist multimodal biometrics. Identifying informative features across different modalities can be computationally expensive and time-consuming in such complex systems. Optimization techniques can streamline this process, making it more efficient thereby improving accuracy and reliability. The paper frames four research questions on input traits, approaches for feature extraction, classification approaches, and performance metrics of image data. The search query is generated based on the research questions that help retrieve the information on the above parameters. The focus of this paper is to provide the comprehensive and exhaustive gestalt of the appropriate input traits for image data from the information retrieved as well as optimal feature extraction and selection. However, the paper also intends to highlight the various classification approaches taken as well as the performance indicators against those classifiers. Further, the paper aims to analyze the effectiveness of various filtering techniques in eliminating image noise and improving overall system performance using MATLAB 2018. The paper concludes that a combination of palm and wrist biometrics could be a good input-trait combination. This work is novel as it covers multi-faceted processing, addressing various aspects of optimizing feature extraction and selection for palm and wrist multimodal biometrics. [ABSTRACT FROM AUTHOR]

As the global demand for energy continues to increase, floating photovoltaic (FPV) power is gaining more attention as a promising clean energy source. This paper summarizes the unique advantages of FPV, such as its freedom from land restrictions, higher energy output, and potential integration with other forms of energy. However, FPV also faces challenges due to complex and harsh environmental conditions such as wind, waves, corrosion, and biological fouling. Therefore, the integrated mechanism design and the use of lightweight hydrophobic materials are crucial for ensuring the safety and stability of FPV systems under such conditions. This article also highlights certain key design points and optimization techniques that can improve the structural reliability of FPV systems. With continuous technological advancements and the accumulation of experience, the production efficiency of FPV has improved, leading to the expansion of FPV projects and a reduction in investment costs. As a result, the development of FPV has been accelerating globally, with numerous high-capacity projects being constructed. Building upon the previous literature reviews, this paper provides a concise review of the latest FPV case studies, innovative technologies, challenges in marine environments, economic costs, and market prospects from various perspectives. The primary objective is to encourage further research and application in the field of FPV. [ABSTRACT FROM AUTHOR]

This paper deals with the use of a FOPID Controller for the direct current motor speed controlling process. FOPID Controller consists of fractional integral-derivative terms along with the integer order proportional terms. It is a specific controller in which orders of derivative and integral lie in between fractions of 0 and 1. Mathematical model of DC motor and controller is presented whose field has been excited by an external source. In this paper, the simulation part of a DC motor for controlling its speed using a FOPID Controller has been performed. There are five degrees of freedom in FOPID controller contrary to traditional PID controller which have only three. The values of the five parameters (Kp, Ki, Kd, λ, µ) of a FOPID Controller have been improved by reducing the ITAE (Integral Time Absolute Error) cost to best possible value using the ACO i.e. Ant Colony Optimization Technique. The closed loop ZNT (Ziegler-Nichols Tuning) method used for the tuning of DC motor. Simulink model of proposed system has been developed and simulated to find out the minimum cost. The intensification in the steady and transient behaviors of the system. The results also exhibit significant improvement in the rise time, settling time and peak overshoot as compared to the other optimization methods. [ABSTRACT FROM AUTHOR]

To meet increasing energy demands, reduce environmental impacts, and increase economic benefits, many water utilities have installed on-site renewable energy generation and storage facilities. These are commonly referred to as behind-the-meter (BTM) energy systems. This paper proposes a systematic optimization approach to the design and evaluation of water distribution systems (WDS) with BTM solar energy. The trade-offs between economic and environmental costs and the benefits these systems can have throughout their design life are demonstrated using a real-world pressurized irrigation system. Due to the large number of decision variables required during the optimization process and the associated high-computational cost, a hydraulic-power-based search-space reduction method has been developed. This method identifies common features of the pipes across a network based on the potential maximum flow and residual pressure head at the outlet of each pipe, so that pipes having similar hydraulic power capacity and thus similar diameters can be grouped together as a single decision variable. This effectively reduces the search space size and significantly increases the optimization efficiency for complex WDS optimization problems such as the optimal pipe design of WDS incorporating BTM solar energy in this study. In this paper, trade-offs between two objective function values [the total life-cycle cost and total life-cycle greenhouse gas (GHG) emissions] are investigated. A reduction in GHG emissions leads to an increase in the total cost and vice versa. It has also been found that the incorporation of BTM solar energy can significantly improve both objective function values. However, the optimal diameters of pipes and therefore the capital costs and GHG emissions are not sensitive to the increase in solar photovoltaic sizes. [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]

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells. [ABSTRACT FROM AUTHOR]

To ensure the safe operation of the power system, it is necessary to monitor and manage all kinds of power facilities. Real-time power monitoring can play a role in the early warning of geographical disasters. At the transmission end, the state detection and fault location of transmission lines are related to the normal operation of the power system. The precise time synchronization of the power system can be guaranteed through the satellite positioning time service, so that the time of various power operation actions is consistent. The accuracy can reach several microseconds, which provides a basis for the analysis of power grid operation. In this paper, by installing BeiDou functional modules in power equipment and developing the BeiDou operation platform for the distribution network, the precise management of power timing and positioning is realized by docking with the geographic information systems platform of the power grid. The three-dimensional sphere error probability (SEP) algorithm is selected to evaluate the positioning error data. Based on the three traditional SEP calculation methods, the maximum likelihood estimation algorithm is optimized and improved, which can achieve nanosecond synchronization accuracy. It can also be applied to other precision of synchronization scenarios of the power system to meet the development needs of the future clock system of the power system. Through the simulation of experimental data and comparison with other algorithms, the accuracy is verified and the reliable estimation of parameters is realized. Considering the information loss caused by dimension reduction evaluation, the simulation analysis in this paper is compared with other methods, and the accuracy is significantly improved. The power system positioning and timing module developed in this paper can not only be used in the power system but also has a certain reference and application value for other devices in the same industry that need timing and positioning services. [ABSTRACT FROM AUTHOR]

This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for the distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for the edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL. [ABSTRACT FROM AUTHOR]

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the "Dual-Carbon" target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a stochastic optimization technique that imitates the evolution process in nature. This paper uses an improved adaptive differential evolution to solve gravity inversion with multiplicative regularization. Compared with additive regularization, the advantage of multiplicative regularization is that it does not require the regularization parameter in the search process. The contributions in this paper mainly focus on two aspects: accelerating the convergence speed of adaptive DE and balancing the effect of model and data misfits in the objective function. The effectiveness of the proposed inversion method is verified by synthetic and field cases. For the synthetic cases, it is concluded that, based on the obtained results and analysis, the presented DE method is superior and competitive with its original version. Additionally, the designed parameter adaptation for multiplicative regularization is useful for trading off the effect of data and model misfits. For the field cases, two successful applications from China were conducted, and the obtained density source distributions were in accordance with those obtained from drilling wells. The synthetic and practical examples demonstrate that high-quality inversion results can be obtained using improved adaptive differential evolution and multiplicative regularization. [ABSTRACT FROM AUTHOR]

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model's overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. [ABSTRACT FROM AUTHOR]

The firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. The FA is used to solve various optimization problems, but it still has some deficiencies, such as high complexity, slow convergence rate, and low accuracy of the solution. This paper proposes a highly efficient quantum firefly algorithm with stochastic search strategies (QSSFA). In QSSFA, individuals are generated in the way of quantum angle coding by introducing the laws of quantum physics and quantum gates, and combined with the random neighborhood attraction model, an adaptive step size strategy is also introduced in the optimization. The complexity of the algorithm is greatly reduced, and the global search ability of the algorithm is optimized. The convergence speed of the algorithm, the ability to jump out of the local optimum, and the algorithm accuracy are improved. The proposed QSSFA's performance is tested on ten mathematical test functions. The obtained results show that the QSSFA algorithm is very competitive compared to the firefly algorithm and three other FA variants. [ABSTRACT FROM AUTHOR]

Modeling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual chemical identity and position of all atoms involved. Obtaining such information for macro-molecules, nano-particles, and clusters and for the surface, interface, and bulk phases of amorphous and solid materials represents a difficult high-dimensional global optimization problem. The rise of machine learning techniques in materials science has, however, led to many compelling developments that may speed up structure searches. The complexity of such new methods has prompted a need for an efficient way of assembling them into global optimization algorithms that can be experimented with. In this paper, we introduce the Atomistic Global Optimization X (AGOX) framework and code as a customizable approach that enables efficient building and testing of global optimization algorithms. A modular way of expressing global optimization algorithms is described, and modern programming practices are used to enable that modularity in the freely available AGOX Python package. A number of examples of global optimization approaches are implemented and analyzed. This ranges from random search and basin-hopping to machine learning aided approaches with on-the-fly learnt surrogate energy landscapes. The methods are applied to problems ranging from supported clusters over surface reconstructions to large carbon clusters and metal-nitride clusters incorporated into graphene sheets. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal Axioms titled "Optimisation Models and Applications." It explores the use of optimization models in various fields such as engineering, economics, health, and more. The issue includes several research papers that address different optimization problems and propose solutions using various algorithms and techniques. The papers cover topics such as control systems, traffic networks, image communication, sit-to-stand motion, and more. The authors of the papers have contributed equally to the writing, editing, and reviewing process. The document concludes with acknowledgments to the authors, reviewers, and editorial board, and declares no conflicts of interest. [Extracted from the article]

Data envelopment analysis (DEA) is a widely used mathematical programming approach for assessing the efficiency of decision-making units (DMUs) in various sectors. Inverse DEA is a post-DEA sensitivity analysis approach developed initially for solving resource allocation. The main objective of inverse DEA is to determine the optimal quantity of inputs and/or outputs for each DMU under input and/or output perturbation (s), which would allow them to reach a given efficiency target. Since the early 2000s, inverse DEA has been extended theoretically and applied successfully in different areas including banking, energy, education, sustainability and supply chain management. In recent years, research has demonstrated the potential of inverse DEA for solving novel inverse problems, such as estimating merger gains, minimizing production pollution, optimizing business partnerships and more. This paper provides a comprehensive survey of the latest theoretical and practical advancements in inverse DEA while also highlighting potential areas for future research and development in this field. One such area is exploring the use of heuristic algorithms and optimization techniques in conjunction with inverse DEA models to address issues of infeasibility and nonlinearity. Moreover, applying inverse DEA to new sectors such as healthcare, agriculture and environmental and climate change issues holds great promise for future research. Overall, this paper sets the stage for further advancements in this promising approach. [ABSTRACT FROM AUTHOR]

Childhood vaccines play a vital role in social welfare, but in hard-to-reach regions, poor transportation, and a weak cold chain limit vaccine availability. This opens the door for the use of vaccine delivery by drones (uncrewed aerial vehicles, or UAVs) with their fast transportation and reliance on little or no infrastructure. In this paper, we study the problem of strategic multimodal vaccine distribution, which simultaneously determines the locations of local distribution centers, drone bases, and drone relay stations, while obeying the cold chain time limit and drone range. Two mathematical optimization models with complementary strengths are developed. The first model considers the vaccine travel time at the aggregate level with a compact formulation, but it can be too conservative in meeting the cold chain time limit. The second model is based on the layered network framework to track the vaccine flow and travel time associated with each origin-destination (OD) pair. It allows the number of transshipments and the number of drone stops in a vaccine flow path to be limited, which reflects practical operations and can be computationally advantageous. Both models are applied for vaccine distribution network design with two types of drones in Vanuatu as a case study. Solutions with drones using our parameter settings are shown to generate large savings, with differentiated roles for large and small drones. To generalize the empirical findings and examine the performance of our models, we conduct comprehensive computational experiments to assess the sensitivity of optimal solutions and performance metrics to key problem parameters. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Association for Supply Chain Management (ASCM) and the University of Missouri Research Board (UMSL Award 0059109). Supplemental Material: The online supplement is available at https://doi.org/10.1287/trsc.2023.1205. [ABSTRACT FROM AUTHOR]

In the first part of this paper, we present a unified framework for analyzing the algorithmic complexity of any optimization problem, whether it be continuous or discrete in nature. This helps to formalize notions like "input", "size" and "complexity" in the context of general mathematical optimization, avoiding context dependent definitions which is one of the sources of difference in the treatment of complexity within continuous and discrete optimization. In the second part of the paper, we employ the language developed in the first part to study information theoretic and algorithmic complexity of mixed-integer convex optimization, which contains as a special case continuous convex optimization on the one hand and pure integer optimization on the other. We strive for the maximum possible generality in our exposition. We hope that this paper contains material that both continuous optimizers and discrete optimizers find new and interesting, even though almost all of the material presented is common knowledge in one or the other community. We see the main merit of this paper as bringing together all of this information under one unifying umbrella with the hope that this will act as yet another catalyst for more interaction across the continuous-discrete divide. In fact, our motivation behind Part I of the paper is to provide a common language for both communities. [ABSTRACT FROM AUTHOR]

A conventional oil and gas well does not have a natural production capacity, which necessitates a hydraulic fracturing operation. The effectiveness of the fracturing directly impacts the economic benefit of a single well. Among the various parameters, including fracture spacing, fracture width, and conductivity, fracture half-length is one of the main influencing factors on the productivity of horizontal wells. For conventional homogeneous reservoirs, research mainly focuses on fracture patterns with equal fracture lengths. However, in actual production processes, due to mutual interference and the superimposition of drainage areas between fractures, the production distribution of each fracture is non-uniform. Typical fracture distribution patterns mainly include uniform, staggered, dumbbell, and spindle. While many believe that the dumbbell-shaped fracture distribution pattern has the best effect, there has been no quantitative study on the length of each fracture under the dumbbell-shaped pattern. Based on this, this paper proposes a modeling approach for function-shaped fracture distribution that takes advantage of the high production of edge fractures and the low output of middle fractures in horizontal wells. The influence of this approach on production capacity is studied. Constant, linear, and parabolic functions are used to establish the relationship between fracture position and fracture half-length, optimizing the fracture distribution function to achieve the best production effect. This method can guide the horizontal well fracture distribution in the block to maximize productivity. The results show that the parabolic function-shaped model is better than the linear function-shaped model and the constant function-shaped model is the least effective. The research presented in this paper offers a new idea for optimizing on-site fracturing plans. It utilizes mathematical expressions to describe the parameters that affect productivity, which provides valuable guidance for designing multi-stage fractured horizontal wells in the field. In the future, this research will be extended by exploring the optimal fracture distribution function under different formation conditions. [ABSTRACT FROM AUTHOR]

Background: MathWorks has provided an invaluable tool for designing and implementing FPGAs. MATLAB HDL coder serves a dual purpose, providing a quick proof of concept on the one hand and providing the g an easy-to-use platform for testing and verification on the other. It has main drawbacks over these advantages; it generates a code that is not optimized for both area and frequency. Results: In this paper, we provide a technique for optimizing both area and frequency without losing the main advantages. The most affecting problem we found is loops. This paper classifies loop writing purposes into two types. The first one is preferable and introduces ease of writing a few lines instead of repeating the code. The second type is the problem that we intended to solve. Type II loop is appearing when the algorithm should perform these lines for several clock cycles. Writing it traditionally, force the synthesizer to implement all the repetitive clock cycles as repetitive hardware to be done in one clock cycle. This clock cycle is wide in time and is slow in frequency. This paper introduces an optimization technique for this problem. We compare before and after the implementation of our proposed technique. Conclusions: We used Xilinx Spartan 6 XC6SLX4-2CPG196 FPGA. Our proposed technique improves the number of slice LUTs (Look Up Tables) requirement from 366 to 72%. The frequency improved from: 26.574 to 185.355 MHz. Based on that, we now recommend using MATLAB HDL coder in FPGA Design. [ABSTRACT FROM AUTHOR]

This document lists some challenges that researchers face when implementing transactive energy in weak power grids. These challenges often include high voltage fluctuations, limited generation, high line loadability, and unbalanced grids. The operation of transactive energy, as well as optimization techniques, are also considered, highlighting the performance and functionalities depending on power grid characteristics and market topology. Some of the most used optimization techniques for market clearing, considering the characteristics and topology, are presented as part of the research work.In addition, this paper compares different market topologies and highlights their advantages and challenges. Furthermore, this paper contains a brief description of the interoperability frameworks applied to a smart grid.As a result, it was determined that interoperability is necessary for the proper functioning of the grid. Moreover, all methods were found to be effective for their purpose from the user's point of view as each technique has different characteristics relevant to the user and the grid. It was concluded that it is convenient to combine the optimization techniques to consider different constraints in the market clearing. [ABSTRACT FROM AUTHOR]

• Variational Quantum Circuits (VQCs) play a key role in several applications. • VQCs are parameterized quantum circuits trained by using classical optimizers. • The paper proposes to apply memetic algorithms to train VQCs. • The designed memetic algorithm outperforms the state-of-the-art classical optimizers. Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session. [ABSTRACT FROM AUTHOR]

A positively invariant set is an important concept in dynamical systems. The study of positively invariant set conditions for discrete-time systems is one interesting topic in both theoretical studies and practical applications research. Different methods for characterizing the invariance of different types of sets have been established. For example, the ellipsoidal and the Lorenz cone, which are quadratic convex sets, have different properties from a polyhedral set. This paper presents an optimization method and a dual optimization method to characterize the positive invariance of the ellipsoidal and the Lorenz cone. The proposed methods are applicable to both linear and nonlinear discrete-time systems. Using nonlinear programming and an induced norm, the positive invariance condition problems are transformed into optimization problems, and the dual optimization method is also used to give equivalent dual forms. Fewer results on the positive invariance condition of Lorenz cones can be found than for the other type of set; this paper fulfills the results of this problem. In addition, the proposed methods in this paper provide more options for checking the positive invariance of quadratic convex sets from the perspective of optimization and dual optimization. The effectiveness of this method is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

Data-centric is a newly explored concept, where the attention is given to data optimization methodologies and techniques to improve model performance, rather than focusing on machine learning models and hyperparameter tunning. This paper suggests an effective data optimization methodology for optimizing imbalanced small datasets that improves machine learning model performance. This paper is focused on providing an effective solution when the number of observations is not enough to construct a machine learning model with high values of the estimated magnitudes. For example, the majority of the observations are labeled as one class (majority class), and the rest as the other, commonly considered as the class of interest (minority class). The proposed methodology does not depend on the applied classification models, rather it is based on the properties of the data resampling approach to systematically enhance and optimize the training dataset. The paper examines numerical experiments applying the data centric optimization methodology, and compares with previously obtained results by other authors. [ABSTRACT FROM AUTHOR]

• A novel global optimization algorithm, rime optimization algorithm (RIME), is proposed. • RIME simulates the growth and crossover behavior of the rime-particle population. • The performance of RIME is adequately discussed based on benchmark functions. • RIME has better convergence accuracy and speed compared to other methods. • RIME is applied to solving five practical engineering optimization problems. This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice, called the RIME. The RIME algorithm implements the exploration and exploitation behaviors in the optimization methods by simulating the soft-rime and hard-rime growth process of rime-ice and constructing a soft-rime search strategy and a hard-rime puncture mechanism. Meanwhile, the greedy selection mechanism in the algorithm is improved, and the population is updated in the stage of selecting the optimal solution to enhance the exploitation capability of the RIME. In the experimental, this paper conducts qualitative analysis experiments on the RIME to clarify the characteristics of the algorithm in the process of finding the optimal solution. The performance of RIME is then tested on a total of 42 functions in the classic IEEE CEC2017 and the latest IEEE CEC2022 test sets. The proposed algorithm is compared with 10 well-established algorithms and 10 latest improved algorithms to verify its performance advantage. In addition, this paper designs experiments for the parametric analysis of RIME to discuss the potential of the algorithm in running different parameters and handling different problems. Finally, this paper applies RIME to five practical engineering problems to verify its effectiveness and superiority in real-world problems. The statistical and comparison results show that the RIME is a strong and competitive algorithm. The source code of the RIME 1 1 Source codes of RIME algorithm are also available at: https://codeocean.com/capsule/4472937/tree and https://www.mathworks.com/matlabcentral/fileexchange/124610-rime-a-physics-based-optimization and https://github.com/aliasgharheidaricom/RIME-A-physics-based-optimization. algorithm and associated files are publicly accessible at https://aliasgharheidari.com/RIME.html. [ABSTRACT FROM AUTHOR]