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Emergency general surgeons often provide care to severely ill patients requiring surgical interventions and intensive support. One of the primary drivers of morbidity and mortality is perioperative bleeding. In general, when addressing life threatening haemorrhage, blood transfusion can become an essential part of overall resuscitation. However, under all circumstances, indications for blood transfusion must be accurately evaluated. When patients decline blood transfusions, regardless of the reason, surgeons should aim to provide optimal care and respect and accommodate each patient's values and target the best outcome possible given the patient's desires and his/her clinical condition. The aim of this position paper was to perform a review of the existing literature and to provide comprehensive recommendations on organizational, surgical, anaesthetic, and haemostatic strategies that can be used to provide optimal peri-operative blood management, reduce, or avoid blood transfusions and ultimately improve patient outcomes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Optimization study of Photo-Fenton and PDS/UV processes for BOD and COD removal. • Photo-Fenton process was able to enhance biodegradability. • PDS/UV process on the best condition only could maintain the biodegradability. • The operational cost of Photo-Fenton processes is much cheaper than PDS/UV process. The aim of this study is to provide an Advanced Oxidation Process (AOP) based solution to enhance biological treatment efficiency by improving biodegradability of wastewater. Photo-Fenton and Peroxydisulfate (PDS)/UV processes were selected in this study as a representative of hydroxyl and sulfate radical based AOPs. A central composition design (CCD) employed to optimize operational cost and BOD/COD ratio to determine optimal conditions for enhancing wastewater treatment efficiency. The study achieved a maximum COD removal of 71.5 % and increased wastewater biodegradability. Optimal conditions for Photo-Fenton process were DOD (H 2 O 2) 0.28, [Fe2+] 106.26 mg/L, and pH 2.32, costing 2.27 USD per 1 kg COD removed. For PDS/UV process, 75.5 % COD removal was achieved at a cost of 10.6 USD/1 kg removed COD. The PDS/UV process had higher COD removal than Photo-Fenton but did not improve wastewater biodegradability. Optimal conditions were DOD (PDS) 0.19, pH 6.68, costing 10.6 USD/1 kg COD removed. [ABSTRACT FROM AUTHOR]

Despite their widespread applications in sectors such as pulp and paper, textile, food and beverage, pharmaceuticals, and biofuel production, laccases encounter challenges related to their activity and stability under varying reaction conditions. This review accumulates data on the complex interplay between laccase characteristics and reaction conditions for maximizing their efficacy in diverse biotechnological processes. Benefits of organic media such as improved substrate selectivity and reaction control, and their risks such as enzyme denaturation and reduced activity are reported. Additionally, the effect of reaction conditions such as pH and temperature on laccase activity and stability are gathered and reported. Sources like Bacillus pumilus, Alcaligenes faecalis, Bacillus clausii, and Bacillus tequilensis SN4 are producing laccases that are both thermo-active and alkali-active. Additionally, changes induced by the presence of various substances within reaction media such as metals, inhibitors, and organic solvents are also reported. Bacillus pumilus and Bacillus licheniformis LS04 produce the most resistant laccases in this case. Finally, the remarkable laccases have been highlighted and the proper laccase source for each industrial application is suggested. [ABSTRACT FROM AUTHOR]

Lignin extracted from black liquor in chemical pulp mills can potentially replace fossil carbon feedstocks in fuels and materials, thereby increasing the economic and environmental added values of woody biomass. However, since lignin extraction reduces the electricity generation of the mill, the added value depends on the characteristics of the electricity market in which the mill operates. In this study, a model mill is exposed to two different electricity price profiles: the low and steady prices of south-central Sweden in Year 2019; and the high and volatile prices of the same region in Year 2022. For the model mill, investments in lignin extraction designed to increase pulp production are economically viable and have low levels of sensitivity to electricity price levels and price volatility. The viability of lignin extraction without increased pulp production depends on the relationship between the electricity and lignin prices. With stable electricity prices, or steady mill operation, a rule-of-thumb holds that for lignin extraction to be viable, the lignin price (€/t) must be 1.8-times the average electricity price (€/MWh) plus 40 €/t for the supply of chemicals. With volatile electricity prices and flexible operation of the recovery boiler, the mill can shift the loss in electricity sales to low-price hours, thereby saving 15–70 % of the operational costs of lignin extraction, as compared to steady operation. This effect can be further enhanced by increasing the capacity of the lignin extraction process or extending the size of the black liquor storage tank. The proposed flexibility measures allow the market-integrated pulp mill to export lignin to replace fossil carbon supplies in other sectors, while supporting the electricity system during hours with high demand and low supply.

After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for decision-makers to obtain effective information from large amounts of textual data and make decisions that balance various factors. To address these issues, this paper presents an emergency assisted decision-making model based on a knowledge graph and many-objective optimization. First, we preprocess the information by extracting entities to construct a knowledge graph of emergencies to make the event information more structured and easier to use. A knowledge graph is also used to narrow the range of matching historical events. Second, we construct a many-objective model with four objectives: similarity, diversity, processing time, and resource cost. Finally, combining the model characteristics, we design genetic operations for duplicate location matching and substitution removal strategies to obtain the nonrepetitive VaEA algorithm. This algorithm is used to optimize the model and generate a list of reference cases in the reduced matching range to provide rescue strategy suggestions for the current situation. The experimental results show that the algorithm outperforms the comparison algorithms under all four evaluation metrics. This indicates that the method in this paper can match the higher-quality historical emergency solutions applicable to the current decision-making situation in the case base and provide support for decision-makers to respond reasonably in emergencies. [ABSTRACT FROM AUTHOR]

In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models. [ABSTRACT FROM AUTHOR]

With the improvement of people's living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products. [ABSTRACT FROM AUTHOR]

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In this paper, a new torque sharing function control strategy is proposed to reduce the torque ripple. The traditional torque ripple methods, such as exponential and linear torque allocation strategies, the torque generation capacity of each phase and the power limitation of the power converter is not taken into account, in this paper, the communication region is divided into three intervals by establishing a new partitioning standard. According to the re-established reference function of torque generation, the torque deviation caused by phase current tracking error is compensated according to the torque generation capacity. Both optimization of torque ripple suppression and copper loss minimization are taken into consideration in this paper. To verify the validity and performances of the proposed TSF methods, simulations and experiments have been implemented in a 12/8 structure Switched Reluctance Motor prototype. This method can be applied to other switched reluctance motors with different topologies. Result shows lower current tracking error and better performance of torque ripple minimization compared with the conventional two-interval compensation method. [ABSTRACT FROM AUTHOR]

One of the most important problems in directional sensor networks is coverage problem. The coverage can be measured in two ways: positional or temporal. In temporal coverage, the directional sensors rotate periodically round themselves in a repetitive process. Thus, in each time slot, those targets that are positioned within the sensor nodes radius receive their desired coverage. In this model, if a target is left uncovered, it is said that the target has remained in darkness. The main task defined for the temporal coverage model is the minimization of the total dark time for all the targets in the network. This problem has been solved by greedy‐based algorithms in last studies. Greedy‐based algorithms are able to solve the temporal coverage problem in real time. Remember that the performance of greedy algorithms is extremely dependent on the closeness of optimal solution and initial candidates. For this reason, greedy algorithms may obtain local minima due to heuristic search. As far as we know meta‐heuristic algorithms have not been used in past researches to solve such problems. For solving this problem, in this paper two algorithms were developed, GA‐based and hybridized model comprising genetic algorithms and tabu search. A new model was suggested for the chromosome in genetic algorithm. To evaluate the performance of the developed algorithms, they were compared with randomized scenario and greedy‐based algorithm presented in last studies. For better comparison, several parameters, including total dark time, number of sensors, number of targets, sector angle, sensing range were taken into account. The results obtained from the comparison of the algorithms indicated that the developed algorithms are effective in solving the temporal coverage problem in terms of minimizing the total dark time of the targets. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a fully working, easily usable algorithm for planning the placement of the trackside balise groups for ETCS level 1 applications to transmit infill information to the passing trains by minimizing the operational impact. This paper presents a method to determine the optimal positions of infill balise groups for applications of European Train Control System (ETCS) level 1. ETCS will be the only train protection system in the European Union of the future. Level 1 specifically enables a less complex system while retaining full compatibility to and interoperability of ETCS level 2. In level 1, the Movement Authority (MA), which contains information on how far and at what speed the train is authorized to run, is transmitted at fixed locations where switchable radio beacons—infill balise groups—are placed. Due to technical and economical constraints, their locations need to be optimized so that they provide the maximum operational benefit. Such a method with universal adaptability has not been available to date, so we developed an algorithm that determines the optimal locations of infill balise groups. The algorithm uses the basic laws of motion combined with domain knowledge about railways in general and specifically ETCS. All important parameters are considered while retaining simplicity in the application, making it convenient to use for planning new ETCS deployments and as a foundation for future research on ETCS level 1. The algorithm was implemented using Python and tested with real-world train characteristics. [ABSTRACT FROM AUTHOR]

In dietary assessment using a single-view food image, an object of known size, such as a checkerboard, is often placed manually in the camera's view as a scale reference to estimate food volume. This traditional scale reference is inconvenient to use because of the manual placement requirement. Consequently, utensils, such as plates and bowls, have been suggested as alternative references. Although these references do not need a manual placement procedure, there is a unique challenge when a dining bowl is used as a reference. Unlike a dining plate, whose shallow shape does not usually block the view of the food, a dining bowl does obscure the food view, and its shape may not be fully observable from the single-view food image. As a result, significant errors may occur in food volume estimation due to the unknown shape of the bowl. To address this challenge, we present a novel method to premeasure both the size and shape of the empty bowl before it is used in a dietary assessment study. In our method, an image is taken with a labeled paper ruler adhered to the interior surface of the bowl, a mathematical model is developed to describe its shape and size, and then an optimization method is used to determine the bowl parameters based on the locations of observed ruler makers from the bowl image. Experimental studies were performed using both simulated and actual bowls to assess the reliability and accuracy of our bowl measurement method. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to optimize the values of different parameters related to the image denoising algorithm involving Caputo Fabrizio fractional integral operator of non-singular type with the Mittag-Leffler function in generalized form. The algorithm aims to find the coefficients of a kernel to remove the noise from images. The optimization of kernel coefficients are done on the basis of different numerical parameters like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structure Similarity Index measure (SSIM) and Image Enhancement Factor (IEF). The performance of the proposed algorithm is investigated through above-mentioned numeric parameters and visual perception with the other prevailed algorithms. Experimental results demonstrate that the proposed optimized kernel based on generalized fractional operator performs favorably compared to state of the art methods. The uniqueness of the paper is to highlight the optimized values of performance parameters for different values of fractional order. The novelty of the presented work lies in the development of a kernel utilizing coefficients from a fractional integral operator, specifically involving the Mittag-Leffler function in a more generalized form. [ABSTRACT FROM AUTHOR]

This paper presents the theoretical fundamentals, prerequisites for creation, and peculiarities of modeling a new method for determining the refractive index of biological tissues. The method uses a mirror ellipsoid of revolution as an optical element to ensure total internal reflection phenomena. This paper thoroughly analyzes the differences in the refractive index of healthy and pathological tissues on a biometric diagnostic basis. The analysis is used to model the measurement setup's parameters. This paper also considers various methods of determining the refractive index of biological tissues based on different principles of physical optics, such as interferometry, refractometry, ellipsometry, and goniophotometry. It systematizes typical optical elements of total internal reflection that can be used in goniophotometry. It justifies the selection of the element base for the goniometric installation based on the ellipsoidal reflector method. A simulation of the installation operation was carried out for various parameters of the ellipsoidal reflector, ensuring the measurement of the biological tissue refractive index from 1.33 to 1.7. This paper also proposes a constructive solution for manufacturing an ellipsoidal reflector of the required configuration. [ABSTRACT FROM AUTHOR]

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. This paper presents the process of the optimization of linear ball bearings with four-point connection using a genetic algorithm and the finite element method. Currently, modern steering systems without an intermediate shaft—steer-by-wire systems—are being developed. The focus of this paper was on the optimization of linear ball bearings with four-point connection, embedded between the outer and inner columns tube in terms of the number of balls in the bearing and the clearance between balls. The aim of the research was to maximize the first two natural frequencies in the steering system, which is crucial for improving the stability and efficiency of the system. Various factors influencing natural vibration such as bearing geometry, raceway and ball materials, and operating conditions (preload) were taken into account in the research. Preload is a major factor affecting not only linear motion but also natural frequency. In order to speed up the calculations, the author's simplified model of a linear bearing with the use of a system of springs was proposed. The nonlinear properties of the spring were determined on the basis of Hertz's theory. A genetic optimization process resulted in a linear bearing structure that meets the natural frequency criteria. In addition, the full reference model was numerically compared with the simplified one, which showed convergent results of natural frequencies. [ABSTRACT FROM AUTHOR]

In this paper we consider a system of linear bars, spherically jointed at their ends. For each bar one end is linked to the origin. We discuss the equations from which one obtains the deviation of the origin, and some possible optimizations concerning the minimum displacement of the origin and the minimum force in one bar, which are the main goals of the paper. The optimization is performed considering that for two bars one end is unknown; that is, the angles between the bars and the axes are unknown. It is proved that it is difficult to obtain an analytical solution in the general case, and the problem can be discussed only by numerical methods. A numerical case is also studied and some comments concerning the results are given. [ABSTRACT FROM AUTHOR]

A thin shell model refers to a surface or structure, where the object's thickness is considered negligible. In the context of 3D printing, thin shell models are characterized by having lightweight, hollow structures, and reduced material usage. Their versatility and visual appeal make them popular in various fields, such as cloth simulation, character skinning, and for thin-walled structures like leaves, paper, or metal sheets. Nevertheless, optimization of thin shell models without external support remains a challenge due to their minimal interior operational space. For the same reasons, hollowing methods are also unsuitable for this task. In fact, thin shell modulation methods are required to preserve the visual appearance of a two-sided surface which further constrain the problem space. In this paper, we introduce a new visual disparity metric tailored for shell models, integrating local details and global shape attributes in terms of visual perception. Our method modulates thin shell models using global deformations and local thickening while accounting for visual saliency, stability, and structural integrity. Thereby, thin shell models such as bas-reliefs, hollow shapes, and cloth can be stabilized to stand in arbitrary orientations, making them ideal for 3D printing. [ABSTRACT FROM AUTHOR]

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users' requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi's production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry. [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]

Performance-based design optimization (PBDO) aims to design safe, resilient, and cost-effective structures. Methods used for PBDO have evolved by integrating numerical modeling, performance-based design principles, and optimization algorithms. The PBDO framework enables the design of structures with optimal performance and cost. This paper provides researchers with a comprehensive review of the rapidly growing field of PBDO. The evolution of PBDO methods is discussed with the goal of identifying the challenges that must be addressed in future studies. Knowledge gaps are brought to the forefront to emphasize the need for further investigation that expands on the application of PBDO in structural design. Various deterministic and probabilistic formulations for PBDO of structural systems under seismic and wind loading are reviewed. The formulations encompass one or more objective functions, including the upfront, life-cycle, and repair costs. Furthermore, the paper reviews retrofit design studies that have used PBDO methods. The high computational demand in performing PBDO is identified as the major challenge. Possible approaches to alleviate this and other challenges are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes an electric vehicle routing problem, considers the states of charging stations and suggests solution strategies. The charging of electric vehicles is a main issue in the field of electric vehicle routing. There are many studies that find the locations of charging stations, recharging functions for the batteries of vehicles, and so on. However, the state of charging stations significantly affects the routes of electric vehicles, which is not much explored. The states may include open or closed charging stations, occupied or empty charging slots, and so on. This paper investigates how the states of charging stations are estimated and how routing strategies are determined. We formulate a mixed integer programming model and suggest how to solve the problem with an exact method. Numerical examples provide the optimal routing strategies of electric vehicles for the changing environments regarding the states of charging stations. [ABSTRACT FROM AUTHOR]

Image processing has widespread uses practically in every branch of science and arts. Processing images is more difficult than processing sound or data as there are more bits in the high pixel quality image. It requires more space to store the image, more bandwidth to transmit it, and more time and resources to process. An image's complexity may decrease if its bit size is decreased. Sigma-delta modulation, or SDM for short, is an alternative method of minimizing data-word length to compression. Digital signal processing (DSP) systems can be made simpler by using the SDM approach, which was first created for analog to digital conversion (ADC). This paper suggests a novel way to use SDM in MATLAB for improved image processing. Consequently, the suggested single-bit SDM-based image arithmetic architecture is tested and compared with the traditional image arithmetic techniques. Additionally, to see the noisy channel influence on the traditional and proposed systems, some statistical metrics are also studied at different noise variance values, such as signal to noise ratio (SNR), mean square error (MSE), and Peak SNR value. The suggested architecture for the SDM-based image arithmetic precisely matches the addition and subtraction results of the conventional design, even yielding a higher SNR and the same Peak SNR as the traditional methods. In contrast, the outcomes of division and multiplication fall within an acceptable range. For better results the over-sampling ratio (OSR), an inherent characteristic of SDM must be increased at the cost of more processing cycles. Therefore, the trade-off between fewer resources, limited transmission bandwidth, and comparatively more cycles is provided by the SDM-based technique. [ABSTRACT FROM AUTHOR]

Automated tape placement (ATP) is an important automated process adopted for the fabrication of large composite components. Trajectory planning is the key link of ATP, which directly affects the precision and efficiency of the layup process and the quality of final products. Presently, most existing trajectory optimization methods for ATP focus on smooth surfaces. Nevertheless, as commercial CAD/CAM software generally uses NURBS (Non-uniform Rational B-Splines) for modelling, the difficulty of finding the solution and the low efficiency associated with the calculation process are inevitable. The discrete methods provide alternatives for designing layup trajectories, whereas their accuracy is seldom analysed. Furthermore, a path optimization algorithm for eliminating gap problems while preventing wrinkles on discrete models is rarely reported. In this paper, the adjustment of layup trajectories for ATP is considered on triangular meshes. First, the triangular mesh is reconstructed as a Nagata patch to recover the original geometry with good accuracy. Then, a numerical method for tracing desired paths on the Nagata patch set is provided and the computation efficiency is validated. Next, two optimization methodologies are proposed to improve the layup of composite tapes while avoiding wrinkles. Finally, the presented two strategies are examined on a discrete hyperbolic surface and a discrete freeform surface and some of the results are delivered. [ABSTRACT FROM AUTHOR]

This paper focuses on evaluating the roughness of heterogeneous surfaces, aiming to interpret data effectively for thorough assessment. Previous research highlights the significant impact of surface roughness on final coatings. Beam-cutting machining generates surfaces with position-dependent roughness parameter changes. However, there is inconsistency in the methods for investigating roughness in such surfaces, leading to the loss of crucial information and potentially inaccurate results. This could result in flawed coating preparation and subsequent defects. This paper proposes a suitable evaluation method involving an optical 3D profilometer and a stabilizing support system for reliable measurements. It provides a detailed description of the materials and methods used. The objective is to establish a more consistent and accurate approach to assessing roughness for coating preparation. Technical applications demonstrate up-to-fivefold fluctuations in surface topography parameters, as illustrated in this manuscript. Overall, this paper seeks to address these challenges and provide a robust framework for evaluating roughness in heterogeneous surfaces, thereby enhancing surface coating preparation processes. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available. [ABSTRACT FROM AUTHOR]

Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions. [ABSTRACT FROM AUTHOR]

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

Introduction: The global turkey market represents a sector of increasing growth in the previous decade, and projections for the next decade reflect the probable continuation of this growth. Industry trends also indicate the globalization of turkey meat production, as the loci of production has continually shifted from one dominated by the United States to one with an increasing number of production units globally. In contrast with other popular meat products, comparatively fewer resources have been devoted to academic research concerning the growth, production, distribution, and sale of turkey and turkey products. This lack of research is particularly notable in the area of supply chain management and environmental sustainability. Given the increasing volume of turkey production and lower volume of academic interest, it stands to reason that there remain many opportunities for improvement across the value chain in this industry. Methods: In this paper, we take a "green logistics" approach and use data provided by one of the largest turkey producers in the United States to formulate a mixed-integer program aimed at minimizing the environmental impact of turkey products in a segment of the product supply chain. Results: Implementation of the resulting brooder-finisher farm assignments developed by the model would yield an average 50% decrease (184 metric ton) in greenhouse gas emissions at the operation under investigation while also addressing other areas of significant vulnerability for the industry (production costs, biosecurity risk, and animal wellbeing). Discussion: As consolidated turkey meat production systems continue to expand globally, we argue that a similar approach could readily be deployed by these growing and emerging production systems. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions. [ABSTRACT FROM AUTHOR]

Represent Volume Element (RVE) is broadly used by investigators to control the properties of nano-cementitious materials. This paper focuses on analyzing a set of RVE data and proposes parametric equations for determining the compressive and flexural strength characteristics (σc and σf). Primarily, a parametric study is performed with RVE analysis. The essential design parameters are used to fit rational equations. The RVE data are also applied to train artificial neural networks of σc and σf. RVE, neural networks, and rational equations are correlated. Regression equations are validated with the experimental study. SEM, TEM, XRD, and FTIR results are carried out in the microscopic and mechanical analysis for carbon nanofiber cement composites, which can lift their strength, constancy, integrity, and density and reinforce the composite microstructure. Lastly, the Pareto-optimal design results are presented with a multiobjective optimization problem. [ABSTRACT FROM AUTHOR]

Tricubic interpolation, originally introduced by Lekien and Marsden (Int J Numer Methods Eng. 2005; 63(3): 455–471), has been a cornerstone in the field of interpolation, providing C1$$ {C}^1 $$ continuous interpolations within three‐dimensional spaces. However, real‐world applications often demand higher levels of smoothness within M$$ M $$‐dimensional spaces. This paper introduces LocalPoly interpolation, a novel generalization of tricubic interpolation that extends to Cn$$ {C}^n $$ continuity and M$$ M $$ dimensions. A key property is the use of solely local data for interpolation, allowing for on‐demand computation of interpolation polynomials, which is particularly advantageous in scenarios where a minor subset of the space is of interest. We rigorously prove the Cn$$ {C}^n $$ continuity achieved by the LocalPoly interpolation method; the proof features a numerically exact method for computing polynomial coefficients. The enhanced continuity is of great relevance in optimization algorithms, where efficient convergence often relies on the availability of C2$$ {C}^2 $$ information. The paper explores the use of LocalPoly interpolation applied to a squared distance field in ℝ3$$ {\mathbb{R}}^3 $$, offering insights into computational efficiency and practical implications. It also discusses future research directions to address the method's limitations in terms of dimensionality, making it a valuable addition to the toolbox of interpolation methods for various scientific and engineering applications. [ABSTRACT FROM AUTHOR]

Wearable robots are emerging as a viable and effective solution for assisting and enabling people who suffer from balance and mobility disorders. Virtual prototyping is a powerful tool to design robots, preventing the costly iterative physical prototyping and testing. Design of wearable robots through modelling, however, often involves computationally expensive and error-prone multi-body simulations wrapped in an optimization framework to simulate human–robot–environment interactions. This paper proposes a framework to make the human–robot link segment system statically determinate, allowing for the closed-form inverse dynamics formulation of the link–segment model to be solved directly in order to simulate human–robot dynamic interactions. The paper also uses a technique developed by the authors to estimate the walking ground reactions from reference kinematic data, avoiding the need to measure them. The proposed framework is (a) computationally efficient and (b) transparent and easy to interpret, and (c) eliminates the need for optimization, detailed musculoskeletal modelling and measuring ground reaction forces for normal walking simulations. It is used to optimise the position of hip and ankle joints and the actuator torque–velocity requirements for a seven segments of a lower-limb wearable robot that is attached to the user at the shoes and pelvis. Gait measurements were carried out on six healthy subjects, and the data were used for design optimization and validation. The new technique promises to offer a significant advance in the way in which wearable robots can be designed. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, the challenges of water scarcity and flood disasters become increasingly severe. This severity makes efficient reservoir scheduling management crucial for the rational utilization of water resources. Due to the diverse topological structures and varying objectives of different watersheds, existing optimization models and algorithms are typically applicable only to specific watershed environments. This specificity results in a "one watershed, one model" limitation. Consequently, optimization of different watersheds usually requires manual reconstruction of models and algorithms. This process is not only time-consuming but also limits the versatility and flexibility of the algorithms. To address this issue, this paper proposes a knowledge graph-driven method for reservoir optimization scheduling. By improving genetic algorithms, this method allows for the automatic construction of optimization models tailored to specific watershed characteristics based on knowledge graphs. This approach reduces the dependency of the optimization model on manual modeling. It also integrates hydrodynamic simulations within the watershed to ensure the effectiveness and practicality of the genetic algorithms. Furthermore, this paper has developed an algorithm that directly converts optimized reservoir outflow into actionable dispatch instructions. This method has been applied in the Pihe River Basin, optimizing flood control and resource management strategies according to different seasonal demands. It demonstrates high flexibility and effectiveness under varying hydrological conditions, significantly enhancing the operational efficiency of reservoir management. [ABSTRACT FROM AUTHOR]

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

Recent modern computing and trends in digital transformation provide a smart healthcare system for predicting diseases at an early stage. In healthcare services, Internet of Things (IoT) based models play a vital role in enhancing data processing and detection. As IoT grows, processing data requires more space. Transferring the patient reports takes too much time and energy, which causes high latency and energy. To overcome this, Edge computing is the solution. The data is analysed in the edge layer to improve the utilization. This paper proposed effective prediction of resource allocation and prediction models using IoT and Edge, which are suitable for healthcare applications. The proposed system consists of three modules: data preprocessing using filtering approaches, Resource allocation using the Deep Q network, and prediction phase using an optimised DL model called DBN-LSTM with frog leap optimization. The DL model is trained using the training health dataset, and the target field is predicted. It has been tested using the sensed data from the IoT layer, and the patient health status is expected to take appropriate actions. With timely prediction using edge devices, doctors and patients conveniently take necessary actions. The primary objective of this system is to secure low latency by improving the quality of service (QoS) metrics such as makespan, ARU, LBL, TAT, and accuracy. The deep reinforcement learning approach is employed due to its considerable acceptance for resource allocation. Compared to the state-of-the-art approaches, the proposed system obtained reduced makespan by increasing the average resource utilization and load balancing, which is suitable for accurate real-time analysis of patient health status. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs. [ABSTRACT FROM AUTHOR]

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies. [ABSTRACT FROM AUTHOR]

Consumers are increasingly seeking products with a higher degree of customization, and this has intensified the search for more flexible production systems. Unexpected phenomena, including those impacting the economy and public health, have shown the need for high reactivity by manufacturers. Reconfigurable Manufacturing Systems are one of the means to respond to new market needs. They are designed to be able to quickly change their structure and thus adapt their functionalities and capabilities. A central question with regard to these types of systems is when and how they should be reconfigured. It is, however, a question that needs to be addressed simultaneously alongside the classic resource allocation and scheduling problems, since the problems are interdependent. This paper proposes four models that address the problem sequentially, partially sequentially, and integrally. Our numerical analysis demonstrates the advantage and importance of treating these problems in an integrated manner. In addition, we provide and compare three exact methods for the integrated process planning, layout, and scheduling optimization problems. The exact methods are integer linear programming, constraint programming, and constraint programming with search strategies, which our numerical examples show to be greatly superior to the two others. Finally, we present a problem at an industrial scale. [ABSTRACT FROM AUTHOR]

Sectorization of a water distribution network (WDN) into district meter areas (DMAs) is considered a key strategy for an efficient management of WDNs. Typically, it involves a two-stage procedure: a clustering stage, in which the division of the WDN into clusters is performed, and a dividing stage, which involves the placement of valves and flow meters on the cluster's boundary pipes to define the DMAs. While recently published methods attempt to enhance both the clustering and dividing stages, they fail to provide decision-making flexibility. They also neglect to consider the presence of existing valves in the WDN, which can significantly affect the evaluated implementation cost, often considered the primary decision-driving factor. This paper presents improvements to the previously introduced DeNSE method for sectorization of WDNs, aiming to address these deficiencies. The methodology consists of a clustering stage, based on the network uniformity index, and a dividing stage, in which the originally used heuristic procedure is replaced with Genetic Algorithm (GA) optimization, minimizing implementation cost. Consideration of existing valves in WDN and criteria for water supply security are also included in the dividing stage to offer a better estimate of implementation costs and post-sectorized operational efficiency of the WDN. Finally, GIS visualization is implemented, and a hydraulic model of the sectorized WDN (EPANET file) is generated, providing practitioners with valuable insights and decision-making flexibility. The methodology is tested on a part of the Amsterdam WDN in the Netherlands, serving as a pilot for methodology evaluation. A range of feasible sectorization solutions is generated and compared based on implementation cost and three performance indicators (PIs). The paper provides an in-depth discussion on the selection of preferable sectorization solution. The reported results demonstrate the method's efficiency in optimizing sectorization solutions with minimum implementation cost whilst preserving the WDN operational efficiency and meeting the local design criteria. [ABSTRACT FROM AUTHOR]

Radio Frequency IDentification (RFID) Network Planning (RNP) is the problem of placing RFID readers in a working area where a tag is interrogated by at least one reader and at the same time satisfies some constraints such as minimum number of placed readers, minimal interference, and minimal outside coverage. The RNP optimization has been proven NP-hard; thus, natural-inspired approaches are often used to find the optimal solution. The paper proposes an energy-efficient RNP approach in which the positions of placed readers are optimized by a Hopfield network, and the energy efficiency is achieved through a placement area restriction technique. A mechanism of redundant reader elimination is also added to minimize the number of placed readers. Simulation results show that the Hopfield network-based energy-efficient RNP approach achieves the maximum tag coverage and energy efficiency by reducing interference, outside coverage, and the number of placed readers. [ABSTRACT FROM AUTHOR]

Contemporary quantum computers open up novel possibilities for tackling intricate problems, encompassing quantum system modeling and solving partial differential equations (PDEs). This paper explores the optimization of quantum algorithms aimed at resolving PDEs, presenting a significant challenge within the realm of computational science. The work delves into the application of the Variational Quantum Eigensolver (VQE) for addressing equations such as Poisson’s equation. It employs a Hamiltonian constructed using a modified Feynman-Kitaev formalism for a VQE, which represents a quantum system and encapsulates information pertaining to the classical system. By optimizing the parameters of the quantum circuit that implements this Hamiltonian, it becomes feasible to achieve minimization, which corresponds to the solution of the original classical system. The modification optimizes quantum circuits by minimizing the cost function associated with the VQE. The efficacy of this approach is demonstrated through the illustrative example of solving the Poisson equation. The prospects for its application to the integration of more generalized PDEs are discussed in detail. This study provides an in-depth analysis of the potential advantages of quantum algorithms in the domain of numerical solutions for the Poisson equation and emphasizes the significance of continued research in this direction. By leveraging quantum computing capabilities, the development of more efficient methodologies for solving these equations is possible, which could significantly transform current computational practices. The findings of this work underscore not only the practical advantages but also the transformative potential of quantum computing in addressing complex PDEs. Moreover, the results obtained highlight the critical need for ongoing research to refine these techniques and extend their applicability to a broader class of PDEs, ultimately paving the way for advancements in various scientific and engineering domains. [ABSTRACT FROM AUTHOR]

Material management is a critical component of any organization, as it encompasses the efficient and effective handling of materials throughout the entire supply chain process. Just-in-time (JIT) strategies play a vital role in streamlining the supply chain process and minimizing waste. The paper examines the optimization of JIT production management, quality, and maintenance planning in the context of material management. An integrated model is proposed based on optimal control theory to formulate a set of innovative systems dynamics that consider quality decline in a stochastic context. The model also includes random demand fluctuations through a stochastic diffusion process. The objective of the model is to enhance company competitiveness by satisfying a service level constraint and jointly optimizing the JIT production and maintenance control parameters to minimize inventory levels and reduce the total cost. The findings reveal significant interactions between costs and control parameters of both JIT production and maintenance strategies due to their close relationship, leading to the conclusion that the development of an integrative model is more cost-efficient than managing them independently. A comparative analysis further enhances the study by highlighting the potential cost savings in implementing the suggested collaborative control strategy. Overall, the paper contributes to the literature on material management by addressing the research gap in the optimization of JIT production management, quality, and maintenance planning under a stochastic context. [ABSTRACT FROM AUTHOR]

Growing concerns about seismic events enforced structural engineers and architects to embrace the hazardous effect of ground motion in design. To address this, researchers have developed various base isolation (BI) techniques. This study comprehensively reviews BI system types, techniques, and implementation. Exploring the dynamic response of three-dimensional BI devices and the mutual effects of isolation devices and soil-structure interaction during strong ground motion, the paper covers topics such as seismic isolation of nuclear power plants, cost analysis, and various optimization techniques. Furthermore, the paper investigates the behavior of isolation devices in beyond-design events, including blast and aircraft impact loading. In general, the seismic isolation and control device response is demonstrated through shaking table tests and computational analysis. The study sheds light on the functions of seismic isolation system by comparing them with fixed base structures. Additionally, the paper presents codal recommendations, recent advancements, and current practices, aligning them with historical developments and past reviews of different BI techniques, along with their advantages and disadvantages. In conclusion, the closing remarks emphasize the future research prospects in this field. [ABSTRACT FROM AUTHOR]

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013-22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi's L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decisionmaking tools. [ABSTRACT FROM AUTHOR]