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Summary This paper presents computationally optimized 2‐element and 4‐element Multiple‐Input, Multiple‐Output (MIMO) antennas for WLAN/Wi‐Fi, 5G, and UWB applications. The antenna configuration is constructed with the orthogonal placement of sawtooth‐shaped circular monopole radiating elements. The Particle Swarm Optimization (PSO) and Covariance Matrix Adaptation Evolution Strategy (CMA‐ES) optimization techniques are employed to achieve the best performance and size of the proposed antenna. Among these optimization techniques, CMA‐ES is identified as the better approach. The final optimized geometry of the 2‐element and 4‐element MIMO antennas is 49.40 mm × 24.22 mm and 49.44 mm × 45 mm, respectively. Both optimized antennas are fabricated and experimentally verified. The fractional bandwidth of the antenna is more than 110.3%, and more than −20 dB isolation is attained without employing any decoupling method. The Envelop Correlation Coefficient (ECC), Directivity Gain (DG), Total Active Reflection Coefficient (TARC), and Channel Capacity Limit (CCL) are 0.0001, 9.99, < −15 dB, and 0.1 bits/s/Hz, respectively. The proposed antenna is a good candidate for numerous current wireless applications due to its size and performance. [ABSTRACT FROM AUTHOR]

Foxtail millet is a type of minor millet that has nutritional‐rich characteristics. The bran layer in millets contains a considerable amount of antinutritional properties, and grain/flour/product spoilage occurs due to fat in the bran layer. Therefore, the removal of bran from millets can enhance their shelf life. Bran removal from foxtail millet is challenging because of its tiny size. Hence, the study targeted optimizing polishing process parameters for foxtail millet. The effect of process variables such as grain moisture content (8%–16% w.b.), emery roller speed (800–2400 rpm), and polishing time (1–8 min) on polishing characteristics (degree of polishing, head yield, middling yield, and broken content) and proximate composition (protein, fat, crude fiber, ash, and carbohydrate contents) of foxtail millets was studied using the central composite rotatable design (CCRD). Emery roller speed and polishing time significantly affected all responses (p < 0.01). In contrast, grain moisture content had a significant effect only on the degree of polishing (p < 0.05), head, and middling yields (p < 0.01). A numerical optimization technique was adopted to optimize the process parameters of the polisher. The optimum operating conditions were 10% grain moisture content, 1650 rpm of emery roller speed, and 2 min of polishing time. A validation study conducted at optimized conditions revealed no significant difference between the predicted and actual values of responses and considerably less broken content (9.26%). The results of the present study indicate that machinery modified and processes optimized during the study are fairly accepted and can be used to upscale the machine for industrial use. [ABSTRACT FROM AUTHOR]

This article addresses the challenges of load curve variability in integrated energy systems (IES) and emphasizes carbon neutralization in response to global environmental concerns. It introduces a game-theoretic approach to understand the collaborative dynamics within an integrated energy-hydrogen system (HGESS). The study develops a collaborative game model for the IES-HGESS partnership, focusing on carbon neutralization considerations. Using game theory, it investigates the determinants shaping this partnership and extends to create a hybrid model for day-ahead and real-time markets. To foster enduring cooperation, the study introduces a cooperation profit coefficient and devises an income distribution strategy for HGESS-IES interactions. A load management program is incorporated to reduce costs and enhance profitability. Addressing complexity and constraints, the article introduces an advanced multi-objective particle swarm optimization method. Tested on a sample system under various conditions, the results demonstrate that the alliance between IES and HGESS can significantly enhance net profits, achieving mutual benefit and a win-win outcome while advancing sustainability in energy management. • "IES load curve challenges and carbon neutralization". • "Game theory in IES-HGESS dynamics and cooperative models". • "Cooperation profit coefficient for enduring IES-HGESS collaboration". • "Load management program to reduce costs and enhance profitability". • "Multi-objective particle swarm optimization for IES-HGESS alliance profitability". [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Economic dispatch (ED) problem is a popular optimization problem in electrical engineering. Many studies that handled ED problem employed metaheuristics as the optimization technique. Unfortunately, these metaheuristics are old or the existing ones. Meanwhile, ED problem is not the favourite constrained case in studies that introduced a novel metaheuristic. This research introduces a novel metaheuristic called quadratic time optimization (QTO). QTO comprises three sequential searches where both diversification and intensification are blended in every search. But the portion of both orientations changes by following the quadratic time. The appraisal of QTO is performed by employing it to solve both unconstrained and constrained problems. The 23 traditional functions represent the unconstrained problem while economic load dispatch (ELD) problem with valve point effect and spinning reserve problem represents the constrained problem. In both appraisals, there are five novel metaheuristics represent the benchmarks, including: walrus optimization algorithm (WaOA), coati optimization algorithm (COA), total interaction algorithm (TIA), addax optimization algorithm (AOA), and language education optimization (LEO). The result shows the competitiveness of the proposed QTO in handling both unconstrained and constrained problems. QTO becomes the first best in 8 functions out of 23 functions including five high dimension unimodal functions, two high dimension multimodal functions, and one fixed dimension multimodal functions. The result also shows that QTO is competitive in three use cases in ELD problems as the ratio between the range and average total cost among metaheuristics for the first, second, and third cases is 0.72, 0.04, and 0.02 percent consecutively. [ABSTRACT FROM AUTHOR]

The optimization of process systems within the field of Chemical Engineering often confronts uncertainties that can exert significant influences on the performance and dependability of the obtained solutions. This research endeavors to investigate the application of Bayesian optimization in the realm of constrained mixed integer nonlinear problems. A comparative analysis was conducted, exploring different surrogate models, and evaluating diverse kernel functions and acquisition functions. Furthermore, a sampling strategy was devised to assess the enhancement achieved by the acquisition function. The findings of this article reveal the superior performance of sparse Gaussian processes in conjunction with computationally inexpensive acquisition functions, thereby highlighting their suitability for addressing mixed integer nonlinear programming problems characterized by noisy functions and stochastic behavior. Consequently, this article presents a computationally efficient approach to effectively tackle the challenges associated with data‐driven mixed integer nonlinear programming problems within the domain of Process System Engineering. [ABSTRACT FROM AUTHOR]

Operational water management is a critical global challenge, and decision making can be improved by using mathematical optimization. This paper provides an overview of optimization techniques, both exact and heuristic, used in water management. It focuses on the use of optimization techniques in the short term: operational planning in reservoir management, control of open channels, hydropower scheduling, and operation of polder drainage pumps. Principles of model predictive control, methods for optimization under forecast uncertainty, and approaches for conflict resolution are explained with the help of educational examples and practical cases. Challenges and research questions to be addressed in the future are presented as an outlook.This article is categorized under: Engineering Water > Methods Science of Water > Water and Environmental Change Water and Life > Conservation, Management, and Awareness [ABSTRACT FROM AUTHOR]

The designs of first- and second-order digital low-pass filters with infinite impulse response (IIR) are presented in this letter, utilizing a meta-heuristic optimization technique. Firstly, the analog transfer functions of the first and second- order filters are considered, followed by the application of an L1-norm-based multi-verse optimization algorithm to directly emulate their magnitude-frequency response in the digital domain. The obtained magnitude-frequency response shows superior matching with the analog counterpart for different cut-off frequencies of the first- and second-order filters, as well as varying quality factors for the second-order filter. In comparison to the filter's magnitude-frequency response obtained through traditional bilinear transform and advanced operators, the proposed technique accurately manifests the analog magnitude-frequency response in the digital domain. [ABSTRACT FROM AUTHOR]

The pursuit of energy efficiency in machining processes is a critical aspect of sustainable manufacturing. A significant portion of global energy consumption is by the industrial sector; thus, improving the energy efficiency of machining processes can lead to substantial environmental and economic benefits. The present study reviews the recent advancement made for improving the energy efficiency of machining processes. First the energy consumption of the machining processes was explored and then the key areas and developments in their energy consumption modeling were identified. Following this, the review explores various strategies for achieving energy savings in machining. These strategies include energy-efficient machine tools, the accurate modeling of the energy consumption of machining processes, the implementation of optimization techniques and the application of artificial intelligence (AI). Additionally, the review highlights the potential of AI in further reducing energy consumption within machining operations and achieving energy efficiency. A review of these energy-saving strategies in machining processes reveals impressive potential for significant reductions in energy consumption: energy-efficient design can achieve up to a 45% reduction, optimizing cutting parameters may minimize consumption by around 40%, optimizing tool paths can reduce consumption by approximately 50%, optimizing non-cutting energy consumption and sequencing can lead to savings of about 30% and employing AI shows promising energy efficiency improvements of around 20%. Overall, the present review offers valuable insights into recent advancements in making machining processes more energy-efficient. It identifies key areas where significant energy savings can be achieved. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive comparative analysis of optimization techniques for achieving the optimal design of cellular beams. The incorporation of gaps within cellular beams reduces the weight of the beam and increases section height, resulting in the production of lighter and stronger sections. The Taguchi method is employed to fine-tune the parameters of the Grey Wolf Optimizer, enabling the achievement of a robust design. The performance of each algorithm is evaluated through three design examples, facilitating comprehensive comparisons among the seven algorithms. Moreover, the study encompasses the modeling and analysis of optimally designed cellular beams using finite element software. [ABSTRACT FROM AUTHOR]

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system’s Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system’s robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques. [ABSTRACT FROM AUTHOR]

We evolved our past Portfolio Yield Reactive (PYR) model to provide a competitive system with infiltration of categorical information and fundamentals into advanced higher-order moments that support more objective portfolio selection aided by intelligent computing. The system of the PYR model searches for hidden corporate performance prototypes in big data from accounting and financial statements. The PYR model restricts malicious patterns, such as hoaxes, noise, and manipulation, incorporated into a novel optimal portfolio selection method. [ABSTRACT FROM AUTHOR]

This study presents a technology used for the prediction of economic losses to facilities in a given area during an earthquake, thereby enabling the efficient application of performance-based maintenance and seismic strengthening. We also propose an algorithm for the establishment of a reinforcement plan that minimizes earthquake-induced economic losses within a constrained budget. The algorithm incorporates fragility functions from prior research and utilizes an optimization technique for budget allocation, leveraging the target damage ratio concept and constrained optimization. Based on the fragility curve, the probability of occurrence for each damage state for a specific PGA value and the damage rate for each damage state are calculated. From these values, the expected damage ratio (EDR) is estimated. An optimization-based budget allocation algorithm is developed to find the elements that would result in the lowest damage rate for a limited cost. To validate the applicability of the model, we created a hypothetical city with a 30 km × 30 km area containing bridges, embankments, and buildings. The estimated pre- and post-reinforcement damage was assessed in two earthquake scenarios, allowing us to test the effectiveness of the optimization-based budget allocation model in reducing damage. These results suggest that the proposed model offers a viable strategy for efficient seismic strengthening within budgetary constraints. [ABSTRACT FROM AUTHOR]

Over the last few decades, researches related to the impact of production and industrial manufacturing on environmental sustainability, coupled with multi-dimensional multi-criteria decision analysis and performance evaluation has consistently gained importance. However, no significant work in which an effort to optimize the total production process with the consideration of the positive and negative impacts of each input and output on Environmental sustainability has been noticed. The main aim of our paper is to address this gap. In this paper, an effort has been made to develop a realistic optimization model wherein a manufacturing firm has been considered to function with several decision-making units taking into account the effects of factors like profit, employment generation, social development, environmental pollution and sustainability. The various inputs employed and outputs produced by the firm are responsible for having negative impacts on the environment, besides creative value generation. With due consideration to both these values instead of mere profit, the model determines and explores the firm's decision considering different objectives. The optimization problem has been solved by a GA developed by us and demonstrated with numerical examples. The solutions of the numerical examples point towards some interesting revelations and indicate some prospective future research directions which contributes to this field of research. [ABSTRACT FROM AUTHOR]

This directive proposes an efficiency optimization process in which the flying capacitor multilevel flyback converter (FCMFC) will be designed for the highest efficiency based on component selection, the number of flying capacitor stages, with isolation. The application of interest is a front-end voltage-boosting converter that is part of a solar microinverter. The converter will need high gain and high efficiency over a large range due to the variable input voltage supplied by the output of a solar panel. The electrical specifications are 40 V to 400 V conversion for a 200 W load; however, the input voltage and load power are subject to variability. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as solar are becoming increasingly popular worldwide. Mathematical derivation is used to show the structural framework of PV cells and their models with a single-diode configuration. This paper proposed a two-step optimization technique for extracting the unknown parameters of solar PV cells and modules. The implementation of the proposed techniques is to find the unknown parameters of the PV module from Kyocera (KC200GT). First, we configured the single-diode configuration of the PV equation in terms of five unknown parameters ( I ph , I 0 , V t , R s , and R sh ) and in terms of two unknown parameters ( R s and V t ). After that, we implemented the proposed two-step optimization techniques for extracting the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module Kyocera (KC200GT). Also, we used the genetic algorithm (GA) and particle swarm optimization (PSO) techniques to find the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module from Kyocera (KC200GT). The performance of the proposed two-step optimization techniques was compared with the traditional single-stage optimization techniques: particle swarm optimization (PSO), genetic algorithms (GAs), grey wolf optimization (GWO), Villalva's method, Accarino's method, Iterative method, and Silva's method. The test results and the output P–V waveform indicate that the proposed method is more efficient and has a greater impact than the standard techniques. [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]

Nature inspired meta heuristics like swarm intelligence (SI), Artificial neural networks (ANN), evolutionary computing (EC) etc. have been used by researchers to solve single and multi-objective optimization problems of different fields. This work uses a novel a-SOS (Adaptive symbiotic organisms search) algorithm for cost optimization of shell and tube heat exchanger. This algorithm is implemented for cost optimization of two benchmark STHX problem which are used by several researchers. Validation of the results is presented by comparing the geometric, flow and operational parameters of the same design problems when solved using particle swarm optimization (PSO), Alpha tuned elephant herding optimization technique (a-EHO) and Gravitation search algorithm (GSA). Result indicates a 4.73% and 11.3% reduction in cost for both the case study respectively when compared to same problems solved using PSO. Although when comparing with a-EHO, results does not indicate any substantial difference. Furthermore, operational, and geometric dimensions are also calculated. This algorithm can be eventually applied to real world design engineering problems. [ABSTRACT FROM AUTHOR]

Nowadays, various deep learning (DL) approaches have been devised for image inpainting, which provided a substantial improvement in image quality. However, these approaches have failed to reconstruct the accurate structure of the original image. Hence, this research devised a novel and effective image inpainting approach, namely Autoregressive Flower Pollination Student Psychology Optimization (ArFPSPO). The image inpainting is carried out based on a newly devised hybrid context DL with the hybrid optimization scheme. The designed hybrid context DL approach adopts three DL techniques, such as Context encoder (CE), Context-Conditional Generative Adversarial Networks (CC-GAN), and Partial convolutional layer to complete the image inpainting process so that the outcomes are fused using probabilistic fusion with maximum entropy, thereby the final inpainted image is attained. Each of these three DL techniques is separately trained using a developed hybrid optimization technique. The experimental outcome reveals that the devised model gives optimal performance. [ABSTRACT FROM AUTHOR]

Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify critical nodes in the three-level network and upgrade them as transshipment facilities in the two-level one. However, traditional optimization models seldom combine empirical business data, composite metrics, and objective evaluation rules. We proposed an approach integrating empirical data, multi-criteria decision-making methods based on the real-world application of the SF Express Chengdu branch. We also developed a mathematical optimization model using statistical and operations management techniques combined with logistics expertise for a location decision. First, the appropriateness of each service point as a candidate transshipment facility is evaluated from internal and external perspectives by applying multiple centrality assessment from complex network theory and fuzzy Technique for Order Preference by Similarity to an Ideal Solution, respectively. Second, 16 candidate transshipment facilities are selected by combining these two ways. Then, a multi-objective integer programming model is built to obtain the optimal number, locations of transshipment facilities, and the corresponding service points covered by each transshipment facility. Using this multi-methodologic approach, we show that the optimized two-level network is economically feasible and simply applicable, with the total cost and average delivery time reduced by 18.41% and 6 h, respectively. This article is of practical significance and provides an important reference for optimizing ground express service networks for other large cities. [ABSTRACT FROM AUTHOR]

The healthcare industry has evolved with the advent of artificial intelligence (AI), which uses advanced computational methods and algorithms, leading to quicker inspection, forecasting, evaluation and treatment. In the context of healthcare, artificial intelligence (AI) uses sophisticated computational methods to evaluate, decipher and draw conclusions from patient data. AI has the potential to revolutionize the healthcare industry in several ways, including better managerial effectiveness, individualized treatment regimens and diagnostic improvements. In this research, the ECG signals are preprocessed for noise elimination and heartbeat segmentation. Multi-feature extraction is employed to extract features from preprocessed data, and an optimization technique is used to choose the most feasible features. The i-AlexNet classifier, which is an improved version of the AlexNet model, is used to classify between normal and anomalous signals. For experimental evaluation, the proposed approach is applied to PTB and MIT_BIH databases, and it is observed that the suggested method achieves a higher accuracy of 98.8% compared to other works in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents a novel approach for solving the economic dispatch (ED) problem in groups of generating units communicating through a communication network. The suggested strategy is a consensus-based dynamic event-triggered (ET) distributed optimization method. Our methodology considers the sharing of the local information between generators and their convex cost functions to address the total cost function and offers a decentralized optimization solution over a network. The proposed distributed method addresses the ED problem by considering the criterion of optimal cost and by offering efficient communication. Generating units are grouped according to their generation operational limits, that is, total capacity and dynamic ET distributed protocols are developed to ensure the consensus of cost variables among generating units, operating under normal capacity conditions. The remaining generating agents work on their operating limits, which are segregated through the sharing of flag information through a switching mechanism. Consequently, in contrast to the existing methods, the recommended protocol allows nodes to function in groups, based on the power supply, for ED with geographical clustering and capacity restrictions, in addition to handling the system constraints. Furthermore, the proposed technique employs a dynamic triggering method to manage bandwidth and guarantee the elimination of Zeno behavior. The simulation results validate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years. [ABSTRACT FROM AUTHOR]

Purpose. Justification of the criterion for automatic optimization of the ore grinding process in self-grinding drum mills by compiling and researching mathematical models of material movement inside the drum of a rotating mill. Methodology. Methods of mathematical modeling of the internal mechanics of drum mills in combination with experimental studies of the spectral density of the variable component of the active power consumed by the drive motor of the mills are used. Findings. As a result of the simulation, it was established that the loose material placed on the inner surface of the rotating drum undergoes periodic oscillations under certain conditions. The parameters of these oscillations depend on the radius of the drum, the amount of material and the coefficient of friction. It is theoretically justified that in the case of load fluctuations, the work of friction forces and, therefore, the process of material destruction intensifies. The theoretically obtained conclusions are confirmed by the results of experimental studies of the active power signal spectrum of the drive motor of the mills in the characteristic low-frequency range. It is found that the most intense fluctuations occur at a certain degree of filling of the drum of the mill in the working range of fillings and their intensity correlates with the productivity of the mill according to the newly created finished class. Originality. The mechanism of occurrence of fluctuations in the ore load of drum mills is revealed and the connection of this phenomenon with indicators of the technological efficiency of the grinding process is substantiated. Practical value. It is advisable to use the intensity of fluctuations of the ore load as a criterion for automatic control of mill loading, since this parameter characterizes the technological efficiency of the mill and can be measured quickly. Using the intensity of ore loading fluctuations as a control criterion allows implementing a search system for extreme control of mill loading. [ABSTRACT FROM AUTHOR]

Background: Uncertainty poses a pervasive challenge in decision analysis and risk management. When the problem is poorly understood, probabilistic estimation exhibits high variability and bias. Analysts then utilize various strategies to find satisficing solutions, and these strategies can sometimes adequately address even highly complex problems. Previous literature proposed a hierarchy of uncertainty, but did not develop a quantitative score of analytical complexity. Methods: In order to develop such a score, this study reviewed over 90 strategies to cope with uncertainty, including methods utilized by expert decision-makers such as engineers, military planners and others. Results: It found that many decision problems have pivotal properties that enable their solution despite uncertainty, including small action space, reversibility and others. The analytical complexity score of a problem could then be defined based on the availability of these properties. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are essential elements for the prosperity and development of societies around the globe. However, over time, the pipeline network starts to age and deteriorate, which results in an increasing rate of breaks and water loss due to leakages. Many countries have started government-funded plans to upgrade and rehabilitate their WDS network components to overcome these challenges. This study proposes an optimization framework that addresses these issues and offers potential benefits. It aims to achieve the optimal upgrading strategies considering network operation (hydraulic) performance and upgrading cost, including investment and non-revenue water costs. The upgrade of the WDS network in the model consists of replacing pipes and controlling the pressure-reducing valve (PRV) settings to reduce leakages. The proposed framework is demonstrated using a small-sized benchmark WDS. The study's outcomes provide the utilities' operators and municipalities' decision-makers with a guiding tool to choose the optimal upgrading strategy for their WDS networks at the lowest cost and optimum operation performance. The methodology involves simulating various leakage scenarios and applying optimization techniques to find the best combination of pipe replacements and PRV settings. This approach ensures a balance between minimizing leakage rates and controlling upgrading costs. The framework achieved a reduction of leakage up to 20% from the original leakage with a 70% probability for the tested benchmark network. The optimization framework can also offer a range of upgrading strategies, with a trade-off between the WDS network leakage reduction and the required cost of the upgrading strategy. [ABSTRACT FROM AUTHOR]

The SO2 absorption ability of an aqueous suspension of red mud, which is waste from the Bayer process for bauxite, and the possibility of dissolution of metals using sulfurous acid are studied. It is found out that red mud can be used to neutralize sulfur dioxide of any concentration to an environmentally safe level. A statistical mathematical model is developed, and the absorption process is optimized. Under the optimal conditions, the mud is totally neutralized, and the sulfurous acid produced in the presence of an excess amount of SO2 dissolves 35% of all the aluminum in one step. Since iron precipitates as FeSO3, aluminum readily precipitates as hydroxide from solutions of iron-free aluminum salts. [ABSTRACT FROM AUTHOR]

Turning biomass waste into briquettes using densification techniques is one of the most promising steps toward mitigating biomass waste pollution and fuel issues in developing countries. Despite the continuous growth of scientific output over the past few decades, only a limited amount of information is available in the literature on biomass briquette optimization and mathematical modeling, as well as the physiochemical characterization of biomass feedstocks and briquette operating variables. In light of this gap in the current literature, this study summarizes the current state of the art and recent advances in biomass-based briquettes generated from agro-residues as an alternative source of clean energy. The primary research method for this study is literature review and conceptual modeling. First, many densification processes, such as piston press, screw press, roller press, hydraulic press, and quality variables such as ash content, calorific value, moisture content, density, compressive strength, shatter index, etc., are thoroughly discussed and compared. Then characteristics of different biomass wastes are studied, together with process parameters, including temperature, type of binder used, particle size, and influence on densification process choice. The current evaluation concentrated on the mathematical modeling and optimization of the briquetting technology and the usefulness of briquettes in applications for heating, cooking, and energy production. Overall, this manuscript will help new researchers understand the basic methodology, classification, limitations, and future perspective of briquetting technology in the production of solid biofuels. [ABSTRACT FROM AUTHOR]

The area of the brain affected by a brain tumour can be identified using the tumour's shape, size, location, and border. This study seeks to develop a novel system of classification for brain tumours through pre-processing, segmentation, feature extraction, and tumour classification. An improved median filter will be applied to the input image in this initial phase to improve it. In this step, the image is segmented using a U-net model. Then, characteristics based on the Median Binary Pattern (MBP), the loop, the modified Local Gabor Directional Pattern (LGDiP), and the tumour size are retrieved. A hybrid model that fuses DBN and Bi-LSTM is presented to classify cancers. The optimal weights for both classifiers will be tuned during training to improve the classification performance. For this, BMEBEO (Blue Monkey Extended Bald Eagle Optimization) is proposed, which is a hybrid optimization technique. The suggested model obtains the maximum F-measure of 96.16%%. [ABSTRACT FROM AUTHOR]

Assessment of energetic renovations for residential buildings by optimization The energetic renovation of existing buildings plays a crucial role in the decarbonization of the building sector. For the best allocation of available resources, a holistic quantification and optimization of renovation decisions is necessary. In this context optimization methods offer potential that has so far hardly been used, especially in German‐speaking countries. A new methodology is presented that allows the optimization of variable target functions. The methodology uses a mathematical replacement model based on selective results from dynamic building simulations. The strength of the proposed method lies in the analysis of a very large number of renovation combinations with minimal computational effort. The applicability to real objects is verified using two different exemplary residential building types. The resulting renovation strategies from the analysis are presented for 3 different target cost functions which include investment and operating costs as well as CO2 emissions under different restrictions. The method can currently be used to assess and optimize the heating demands of residential buildings. For the future different extensions of the methodology are planned. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the impact of incorporating rubber tire waste and brick fillers as a substitute for sand in cement mortar samples. The evaluation includes measurements of the bending strength, dynamic modulus of elasticity, and displacement. The replacement of sand by rubber waste (RW) and brick waste (BW) at 0%, 10%, 20% and 30% for rubber waste and 0%, 2.5%, 5.0%, 7.5%, and 10% for brick waste by volume was experimentally conducted. According to the findings, the introduction of rubber waste leads to a significant reduction in the flexural strength, dynamic modulus of elasticity, and displacement. The brick waste can be considered a suitable filler, which minimizes the negative effect of rubber tires and tends to result in suitable mixtures. The optimal values (10.10% for the RW and 10% for the BW) showed good agreement with the experimental results, with differences of 1.85%, -4.73%, and -4.48% for the displacement, flexural strength, and dynamic elastic modulus, respectively. [ABSTRACT FROM AUTHOR]

In the last decade, micro-architected structures have gained significant attention in academia and industry for their lightweight, strong, and thermally efficient properties. Inspired by biomimicry design, this paper presents a novel ribbed family of additively manufactured Micro-Architected Domes (MAD). The design incorporates tetrapod pyramid unit cells, golden ratio-based fractal patterns, Schoen's Minimal Gyroid, and spherical geometry. The study focuses on dome radius, height, and azimuth/elevation partitioning as input variables, with the main output being ribbed micro-cell diameter. The relationships between unit-cells' diameter and input variables were established through problem-solving and numerical computations: linear dependency with the dome radius and hyperbolic dependency with the azimuth and elevation partitioning. The proposed design successfully adhered to the Surface-to-Volume ratio of Schoen's Minimal Gyroid, achieving an average volume relative density of 2.5%, confirming its lightweight nature. The feasibility of the design was further supported by fabricating three specimens using Filament Fused Fabrication. This research showcases the potential of biomimicry-inspired micro-architected structures, paving the way for innovative applications in various fields. [ABSTRACT FROM AUTHOR]

This research is based on the hypothesis that optimization techniques can significantly improve the performance of complex visualizations in web browsers. The aim of the former was to determine to which extent the optimization can be achieved. Optimizations were coded to improve visualization, reduce the need for visual rendering, and decrease script execution time as well as the needed resources. To test the hypothesis, various optimization methods and algorithms were implemented on the initial visualization script and were tested. The main goal of this implementation was to assess how optimization methods, including quadtrees, spatial hashing, binning, LOD adjustments, and the use of the map data structure, affect the performance of web visualization. The obtained results confirmed the hypothesis and the original animation was significantly improved. The implementation of optimizations had a positive effect on the performance of visualizations. The conducted tests gave concrete evidence confirming the validity of the initial hypothesis. This led to certain conclusions regarding which methods provide the best results when optimizing complex visualizations. Key recommendations for code optimization, which can be used in the development of complex visualizations in web browsers, were derived. [ABSTRACT FROM AUTHOR]

The Indonesian Waqf Board (BWI) explains that there is a gap between the potential for cash waqf and its realization. To optimize this potential, the government supports the Cash Waqf Linked Sukuk (CWLS) instrument as an alternative financing for state development. This research aims to provide suggestions for channeling CWLS funds so that they are directed toward sustainable investment. The research approach used is a qualitative approach which focuses on analyzing the content of literature (literature research). The data used is secondary data originating from various reading sources in journals, books, and other libraries. The results of this research confirm that sustainable investment is in line with the urgency of Sharia economics, including achieving SDGs targets in Indonesia. The distribution of CWLS funds can be optimized by channeling funds to the procurement of equipment and technology to reduce air pollution. Apart from being able to fulfill social and environmental aspects, the distribution of these projects becomes an investment forum that is beneficial for the world and the hereafter and is sustainable. [ABSTRACT FROM AUTHOR]

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In order to improve the hydraulic efficiency and internal flow pattern of a multi-stage pump under multiple flow conditions, an intelligent optimization design was proposed for its hydraulic components. Sensitivity analysis was used to select the key parameters influencing the hydraulic efficiency of a multi-stage pump. The optimal Latin hypercube sampling and non-dominated sorting genetic algorithm Ⅱ were employed to build a multi-objective optimization system. Moreover, a radial basis function neural network was adopted as the surrogate model of hydraulic efficiency. The research results showed that the impeller outlet width, impeller blade wrap angle, impeller outlet blade angle, and diffuser inlet width were the key factors affecting the hydraulic efficiency. The efficiency of the optimized model increased by 4.35% under the design condition and the matching of the internal flow between the optimized impeller and diffuser was significantly enhanced under the nominal condition. The improved flow pattern could be clearly observed in the flow passage of both the pump impeller and the diffuser. After optimization, the wear performance of the model was also improved compared to the original design. The wear area decreased in size and was distributed more evenly, resulting in a noticeable decrease in the maximum amount of wear. [ABSTRACT FROM AUTHOR]

This paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on. One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future. [ABSTRACT FROM AUTHOR]

Many new metaheuristic algorithms prioritize their search strategy phase, often neglecting equally critical stages like initialization. Latin hypercube sampling (LHS) is one technique that stands out in this context. LHS selects representative samples through permutations in a multidimensional space, effectively preventing the clustering of points in specific areas. However, its limitations become apparent in high-dimensional problems, as it fails to provide crucial search space information and struggles to determine samples across all dimensions. Addressing these challenges, this paper introduces an innovative population initialization approach, combining the strengths of LHS with evolutionary behaviors. This technique is divided into two main sections: spatial and quality. The spatial section divides the search space into equal intervals across each dimension to establish initial solutions. Meanwhile, the quality section employs evolutionary strategies like mutation and crossover. These strategies serve a dual purpose: they explore the search space thoroughly and refine solutions, bringing them closer to the objective function. To validate the effectiveness of this method, these principles have been integrated it into the classic Differential Evolution algorithm. We conducted extensive tests using 30 representative benchmark functions to assess its performance. The experimental results are encouraging; our methodology not only speeds up convergence but also enhances solution quality, outperforming other similar techniques. [ABSTRACT FROM AUTHOR]

The current research investigation aims to use and compare three different population-based optimization approaches to optimize the operating factors of drilling process to reduce thrust force (TF) and subsequently delamination in flax/PP laminates. Five different tool point geometries and three levels of spindle speed (SS) and feed rate (FR) were considered. Analysis of variance indicated that drill point geometry was most significant parameter affecting the TF, whereas SS had the most negligible impact. Moreover, the lowest and maximum TF and delamination factor (DF) were recorded for the U-shape drill bit and 135°- point angle twist drill bit, respectively. The outcome of the experimental investigation revealed that a low FR value (0.05 mm/rev) and medium-range SS value (1500–1800 RPM) were optimum parameters for each drill geometry. Although all optimization techniques provided almost the same optimum values, but TLBO (teaching learning-based optimization) technique's convergence rate was fast compared to others. [ABSTRACT FROM AUTHOR]

A model of a system of wireless power transfer (WPT) pads is developed, where each WPT pad is modeled as a node and the coupling between pads is modeled as graph edges. This modeling approach is generalized to admit primary, secondary, and booster coils, where power can flow among the pads and a pad can fill multiple roles. An excitation in one pad induces voltage and current in all neighboring pads, causing each pad to act as both a booster coil and either a transmitter or a receiver. Power flow through the entire system can be modeled with the graph structure; the power flow can then be optimized by alternating the phases of the WPT excitations to maximize power transfer. An example is shown where exploiting the graph-based WPT system modeling increases total energy transfer by 25% compared to another method. This increase occurs without altering the geometry of the pads or the magnitude of the pad excitations. [ABSTRACT FROM AUTHOR]

Featured Application: Parametric optimization design study of low-pressure turbine vane by means of artificial immune system. One requirement posed by customers is to achieve adequate durability levels as described in technical requirement documents. Modal analysis is one of the design assessments aimed at identifying the risks of high cycle fatigue (HCF). This article presents a novel application of an artificial immune system (AIS) in the optimization of a nozzle guide vane's modal characteristics. The aim is to optimize the system's natural frequencies in the vibration vane and adjacent hardware (turbine casing). The geometrical characteristics accounted for in the optimization process include the shell thicknesses on the turbine casing side and the nozzle outer band features (hook thickness, leaning and position). The optimization process is based on a representative model established from FEM analysis results. The framework is robust because of the applied metamodel and does not require time-consuming FEM analysis in order to evaluate the fitness function. The aim is to minimize the model area (a derivative of the system weight) with constraints imposed on the frequency (a penalty function). The optimum design is given as the solution with an increased shell thickness in the turbine casing and leaning nozzle outer band hooks to obtain the maximum stiffness of the system. The results obtained by means of the artificial immune system (AIS) and a novel variant based on an additional costimulation procedure (CAIS) are compared with the solution obtained by means of a genetic algorithm implemented in the commercial CAE software (Ansys version 19.2). [ABSTRACT FROM AUTHOR]

This study evaluated the potential of a biocoagulant produced from prickly pear peel waste valorization and its use as a biocoagulant aid mixed with aluminum sulfate to remove turbidity in domestic wastewater. A central composite design (CCD) and a simplex lattice design (SLD) of two components (biocoagulant and aluminum sulfate) were developed to determine the optimal doses and pH of the biocoagulant and optimal mixing proportions. Both designs optimized the coagulation process from an analysis of variance to fit the experimental data to mathematical models and an optimization analysis to obtain the highest percentage of turbidity removal. The results showed that a water pH of 4 and a biocoagulant dose of 100 mg/L are optimal conditions for a turbidity removal of 76.1%. The potential decreases to 51.7% when the wastewater pH is maintained at 7.8 and a dose of 250 mg/L is used. This efficiency could be increased to 58.2% by using a mixture with optimal proportions of 30% biocoagulant and 70% aluminum sulfate. The experimental data were fitted to two quadratic models, estimating model prediction errors of 0.42% and 2.34%, respectively. Therefore, these results support the valorization of prickly pear peel waste to produce a biocoagulant, which could be used in acid and alkaline wastewater or as a biocoagulant aid mixed with aluminum sulfate. [ABSTRACT FROM AUTHOR]

Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental & Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9%, improve miners efficiency by 7.4%, reduce number of redundant computations by 3.5%, and reduce energy required for mining by 7.1% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios. [ABSTRACT FROM AUTHOR]

Objective: Due to the alarming rise in COVID-19 cases every day in India, numerous academics are already developing a variety of math-based estimation models to forecast the pandemic future course. This paper makes use of publicly available data to anticipate certain COVID-19 trajectories in India. Methods: We used the Auto-Regressive Integrated Moving Average Model, a time series model, to anticipate the number of COVID-19 infected cases every day soon. People need to consume nutritious food that is well-balanced and contains the right number of calories, nutrients, as well as vitamins for strong development, bearing in mind that sustaining and repairing bodily tissues is the goal while preventing unfavorable illnesses and disease. Results: Recent studies have shown that healthy eating can help lower the possibilities of developing cancer, cardiovascular disease and for COVID patients. Therefore, an optimization strategy and LINDO software was used to solve the model. This study offers an exhaustive daily meal plan created especially for hospital patients, acting as a helpful resource for school administrators. With six different food choices, the diet plan makes sure that you get all the nutrients you need each day at a reasonable price. Conclusions: The model is solved using LINDO software in the study, which shows how effective it is when compared to other heuristic techniques like biological algorithms. It is determined through thorough investigation that the chosen meals are both financially and nutritionally feasible to serve in ho spital settings. Hospital patients make up the study's participants, and each day's total cost comes to Rs109.34. This cost makes it possible to deliver meals that are minimally more costly but of higher quality, improving the patients' overall nutritional value. [ABSTRACT FROM AUTHOR]

Project managers often face some challenges resulting from the scarcity of resources in construction management. Levelling the used resources in multiple projects is a frequently encountered problem in construction areas and manufacturing sectors. In this regard, the current study proposes a robust Forensic-Based Investigation (FBI) algorithm for resource leveling in multiple projects, considering diferent objective functions of the resource graphs. To this end, Fuzzy C-Means (FCM) clustering approach was fused into the main operation of the FBI to enhance the convergence rate using the population information. The proposed scheduling examines diferent objective functions to effciently optimize the resource Profile selection. Two case studies were taken into account in this research to elaborate on the performance of the improved optimization algorithm while dealing with the resource-leveling problem in multiple projects. The experimental findings and statistical comparisons revealed that the developed Fuzzy clustering Forensic-Based Investigation (FFBI) could acquire solutions of high quality and outperform the compared optimization algorithms [ABSTRACT FROM AUTHOR]

The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method's main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam's inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam's outflow in the shortest amount of time. This is achieved by accounting for the environment's changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir's optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model's performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time. [ABSTRACT FROM AUTHOR]