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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]

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]

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]

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]

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]

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]

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]

Metal additive manufacturing is an active field of innovation. However, for laser power bed fusion (LPBF), supports removal is a major constraint. In this technology, supports are strongly welded to the part to tightly maintain it, avoid distortion, and evacuate thermal load. Although supports are usually optimized for manual removal, machining is often necessary, which can affect post-processing productivity. This paper proposes a comprehensive methodological approach to optimize the selection of cutting parameters, cutting tools, and support structure for LPBF. The aim is to help additive manufacturers find supports that reduce machining costs in terms of time and cutting tool degradation, from among the numerous support designs available. This approach can also optimize the design of lattice structures used inside parts. Our results show that among the 11 designs tested, honeycomb and squared pattern grid supports are the most efficiently machined using the 8-teeth tangential milling of the 3 tools tested, with a good post-machined surface roughness and tools' health. The method considers low magnification optical analysis and an accelerometer sensor, which is easy to use even for small- and medium-sized enterprises. This paper also proposes and analyzes a new kind of porous support using this method. [ABSTRACT FROM AUTHOR]

Background: There is a soar in the figure of companies aiming to achieve efficiency in undergoing experimental processes. Therefore, instead of deploying one-factor-at-a-time, design of experiments is becoming rampantly utilized in order to reduce the resources outflow. There are a copious of different smart designs which could be employed as design of experiments tools. Central composite and d-optimal designs were investigated in this paper. The purpose of this investigation was to compare the two designs and identify the most accurate design at analyzing, interpreting and making predictions with regards to the data offered. The aforementioned purpose was achieved by applying both designs to a preexisting study which sought to prolong the gastrointestinal retention of repaglinide tablets through deploying a full factorial design. Further optimization was performed using Design-Expert software after inducing an outlier point. Results: R-squared, adjusted R-squared, predicted R-squared and adequate precision were computed in addition to acquiring diagnostics figures such as predicted versus actual, residual versus run, Box–Cox, contour plot and 3D surface plots. Model equations were also produced for each design. Results showed that both designs were successful at modeling the data both scoring r-squared values > 0.7 and adequate precision > 4 implying high fitting, prediction power and ability to navigate the experimental space using a reduced number of experimental runs. The d-optimal design obtained the least relative error of only 3.81%. Conclusions: In conclusion, the d-optimal design provides a great tool for reduction of experimental testing which in turn diminishes resources consumption. Therefore, this design is favored to be enforced in the pharmaceutical sector. [ABSTRACT FROM AUTHOR]

The remarkable progress of machine learning has had a significant impact on decision-making, thus fairness is an important topic. Existing fair recommendation methods generally design a constraint framework between consumer satisfaction or item exposure in the optimization model. However, these methods only focus on the fairness of consumers and items, but fail to ensure the fairness of platforms. Ignoring platform fairness may lead to an imbalance between consumer satisfaction and platform profit. In this paper, we propose a novel recommendation method based on fairness called CPG-FairRec, which consists of three modules: Data division module, Global fairness-aware module and Local fairness-aware module. Data division module divides the consumers and items into two groups based on spendings and prices, respectively. Global fairness-aware module learns the difference_score of the item group and consumer group based on the consumer's historical behavior, and continuously balance the satisfaction of consumer group and the exposure of item group. Local fairness-aware module aims to optimize the exposure of individual item through the greedy algorithm based on the item price to achieve higher platform profit. We conduct experiments on two real-world datasets and results demonstrate the superiority of the CPG-FairRec in recommendation quality and profitability. [ABSTRACT FROM AUTHOR]

This paper deals with the determination of the optimal values to be given for the seven unknown parameters of the proton exchange membrane fuel cell (PEMFC). To this end, the weighted mean of vectors optimizer (INFO) metaheuristic algorithm is applied to estimate these parameters by minimizing the sum of squared errors (SSEs) between the measured and calculated voltages of the PEMFC. Three commercial types of PEMFCs are investigated: (i) BCS 500 W Stack, (ii) NedStack PS6 Stack, and (iii) Horizon 500 W Stack. The accuracy of the applied INFO algorithm is verified by comparing the estimated voltage–current (I - V) characteristics with the measured data. Furthermore, the estimated parameters of electrical PEMFCs, the minimum reached SSE, and the standard deviation Std values achieved by INFO are compared with the results obtained using other competitive metaheuristic optimization algorithms such as Honey badger algorithm, Gradient-based optimizer, Harris hawks optimization, and others. From the obtained results, the convergence curves show that the unknown parameters of the three PEMFCs are better estimated using the proposed INFO than other algorithms. [ABSTRACT FROM AUTHOR]

The processing of scientific workflow (SW) in geo-distributed cloud computing holds significant importance in the placement of massive data between various tasks. However, data movement across storage services is a main concern in the geo-distributed data centers, which entails issues related to the cost and energy consumption of both storage services and network infrastructure. Aiming to optimize data placement for SW, this paper proposes EQGSA-DPW a novel algorithm leveraging quantum computing and swarm intelligence optimization to intelligently reduce costs and energy consumption when a SW is processed in multi-cloud. EQGSA-DPW considers multiple objectives (e.g., transmission bandwidth, cost and energy consumption of both service and communication) and improves the GSA algorithm by using the log-sigmoid transfer function as a gravitational constant G and updating agent position by quantum rotation angle amplitude for more diversification. Moreover, to assist EQGSA-DPW in finding the optima, an initial guess is proposed. The performance of our EQGSA-DPW algorithm is evaluated via extensive experiments, which show that our data placement method achieves significantly better performance in terms of cost, energy, and data transfer than competing algorithms. For instance, in terms of energy consumption, EQGSA-DPW can on average achieve up to 25 % , 14 % , and 40 % reduction over that of GSA, PSO, and ACO-DPDGW algorithms, respectively. As for the storage services cost, EQGSA-DPW values are the lowest. [ABSTRACT FROM AUTHOR]

Availability contracting is a prominent procurement strategy among many defense and other capital equipment suppliers and their customers. Under availability contracting, the revenue generated for a service provider is a function of equipment/system availability over the predetermined contract period. A major challenge in implementing availability contracting is predicting the availability of equipment, defining the target availability, and determining the best contract duration based on the target availability. In this paper, we extend the classical operational availability model for estimating equipment availability under hierarchical maintenance, which is frequently used for complex equipment such as aircraft. Under an availability contract, bounds have been proposed on system-level operational availability. The current study also develops optimization models for finding the optimal availability contract duration under different scenarios: stochastic and non-stochastic contract durations; contract written at parts level, system level; minimizing total maintenance costs with minimum level of target operational availability as a constraint etc. The study also shows that the optimal contract duration is a non-decreasing function of spares on hand, and the inherent availability of system part. The results from the study have been validated using numerical study with both simulated and real data. [ABSTRACT FROM AUTHOR]

Introduction: Energy system simulation modeling plays an important role in understanding, analyzing, optimizing, and guiding the change to sustainable energy systems. Objectives: This review aims to examine energy system simulation modeling, emphasizing its role in analyzing and optimizing energy systems for sustainable development. Methods: The paper explores four key simulation methodologies; Agent-Based Modeling (ABM), System Dynamics (SD), Discrete-Event Simulation (DES), and Integrated Energy Models (IEMs). Practical applications of these methodologies are illustrated through specific case studies. Results: The analysis covers key components of energy systems, including generation, transmission, distribution, consumption, storage, and renewable integration. ABM models consumer behavior in renewable energy adoption, SD assesses long-term policy impacts, DES optimizes energy scheduling, and IEMs provide comprehensive sector integration. Case studies demonstrate the practical relevance and effectiveness of these models in addressing challenges such as data quality, model complexity, and validation processes. Conclusions: Simulation modeling is essential for addressing energy challenges, driving innovation, and informing policy. The review identifies critical areas for improvement, including enhancing data quality, refining modeling techniques, and strengthening validation processes. Future directions emphasize the continued importance of simulation modeling in achieving sustainable energy systems. [ABSTRACT FROM AUTHOR]

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat's position change while moving towards the prey and (ii) exploitation based on simulating the bobcat's position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender's side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times. [ABSTRACT FROM AUTHOR]

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic (α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering. [ABSTRACT FROM AUTHOR]

Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance. [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]

This paper presents an optimization study of spent coffee grounds (SCG) as cement mortar additives to enhance mortar strength. In recent years, sustainable materials have begun finding their way into cement mortar, with SCG being one. There is limited optimization study on the SCG addition in mortars, hence this study was performed to optimize the curing time and SCG addition in cement mortar to achieve the highest compressive strength through response surface methodology. Scanning Electron Microscopy (SEM) characterization was carried out on the SCG particles to identify their physical properties. An Energy Dispersive X-ray (EDX) analysis was carried out to identify its chemical properties. Simultaneously, a workability test, the flow table test, is conducted to study the effect of SCG on the flowability of the cement mortar mixes. The synergistic effect between SCG content in cement mortar mixes and the curing period was statistically studied and analyzed. Both parameters were then optimized to obtain the best performance mix of SCG in cement mortar. It was found that 1.1% SCG and a curing day of 68 days produced the highest compressive strength (33.4MPa) of cement mortar. The Response Surface Methodology (RSM)-optimized cement mortar mix presented at least a 12.62% improvement in compressive strength from control cement mortar without SCG additives (28.77MPa). Experimental validation of the optimum condition showed a good agreement with a deviation of 3.12% in three replicates, thus indicating that the optimum model in this work can be used to model the compressive strength of the SCG-cement mortar mixture.Article Highlights: The addition of spent coffee grounds (SCG) in cement mortar enhanced its strength by 12.6%. The optimum SCG addition that corresponds to the highest mortar strength is 1.1%. The flowability of cement mortar mix decreases as the percentage of SCG addition increases. [ABSTRACT FROM AUTHOR]

Amidst the industrial transformation and upgrade, the new energy vehicle industry is at a crucial juncture. Power batteries, a vital component of new energy vehicles, are currently at the forefront of industry competition with a focus on technological innovation and performance enhancement. The operational temperature of a battery significantly impacts its efficiency, making the design of a reliable Thermal Management System (TMS) essential to ensure battery safety and stability. Cylindrical power batteries are widely utilized in the industry. This article outlines the four main structures and their drawbacks of TMS for cylindrical power batteries. Among these structures, air cooling falls short in meeting high heat dissipation requirements. Liquid cooling is expensive, intricate, and adds considerable weight. Phase Change Materials (PCM) are not yet prevalent in practical applications. Similarly, heat pipes are relatively uncommon in large high-power battery packs. To better align with the new energy vehicle industry's demands for top-notch performance, cost-effectiveness, eco-friendliness, and reliability, this paper strongly recommends delving deeper into composite cooling solutions. The construction of an economically viable and fully optimized composite cooling method is poised to become a significant scientific challenge for future research endeavors. [ABSTRACT FROM AUTHOR]

Quantum-behaved Particle Swarm Optimization (QPSO) is a meta-heuristic optimization algorithm, which is widely used in many research fields and practical problems due to its flexibility and low computational cost. However, the existing QPSO algorithms and their variants still have problems such as insufficient search capabilities, lack of adaptivity, and prone to stagnation. This paper proposes a novel ensemble algorithm, ACQPSOW-HGS, based on Quantum-behaved Particle Swarm Optimization (QPSO) and Hunger Games Search (HGS). By combining three improvements and introducing three hybrid strategies, our algorithm has made a comprehensive development, effectively improving the stability and solution accuracy in a large number of test functions and the parameter identification application, which is superior compared with many existing algorithms. First, we design the Weibull distribution random number generation operator, the distance-guided adaptive control technique, and the chaotic update mechanism to deal with the weak randomness, insufficient adaptability, and susceptibility to stagnation of the original QPSO algorithm, respectively. Integrating the above improvements, ACQPSOW is proposed as an improved variant of QPSO. Second, the proposed ensemble algorithm ACQPSOW-HGS is built on ACQPSOW and HGS and combined with specific hybrid strategies to add population diversity and improve search efficiency, including the Selection-Crossover-Mutation mechanism, the elite local search mechanism, and the information exchange mechanism. Finally, the experiments, on 23 benchmark functions and the IEEE CEC 2017 test suite, demonstrate that ACQPSOW-HGS outperforms comparison algorithms in terms of convergence speed and solution accuracy through non-parametric statistical tests. Moreover, ACQPSOW-HGS was applied to the fractional-order hyper-chaotic financial system for parameter identification to illustrate the applicability and robustness in solving real-world problems. [ABSTRACT FROM AUTHOR]

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model's performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model. [ABSTRACT FROM AUTHOR]

Numerous studies revealed optimization techniques' applicability in minimizing the costs of reinforced concrete buildings. However, the existing literature has narrowly focused on optimizing buildings with a single function, such as residential or office buildings, hindering the generalization of the results. This paper aims to bridge the gap between optimization and structural engineering by obtaining the minimum-cost design of flat slab buildings with different intended functions. In this context, the optimal designs of 120 alternatives were obtained, considering various spans (4–8 m), live loads (2–10 kPa), and concrete compressive strength (25–40 MPa). The optimization was executed using the evolutionary algorithm provided in Microsoft Excel's Solver tool. The optimization model permits the utilization of drop panels to resist punching stresses developed from the slab-column interaction. The objective function is the cost of materials and labor involved in constructing floors and columns. The decision variables are the floor dimensions and column configurations in dimensions and reinforcement. The structural constraints were applied per the Egyptian design code (ECP203-2020). Eventually, guidelines were developed to help the designers choose the economic floor system and quantities of materials based on the building's intended function. [ABSTRACT FROM AUTHOR]

With the transformation of transmission and distribution grids into smart grids that are more dominated by renewable energy, power electronics-based inverters that can improve power quality are becoming more visible. In order to maximize the output voltage quality and reduce the total harmonic distortion (THD), efficient operation of inverters is required. Therefore, in this paper, the problem of harmonic elimination in multilevel inverters is solved by using an adaptive grey wolf optimizer with local search. We have performed a grid search-based landscape analysis of the seven-level inverter to understand the behaviour of the proposed algorithm. For verification, the numerical results of the proposed adaptive grey wolf optimizer are compared with those of the original grey wolf optimization algorithm, a modified version of the grey wolf optimization algorithm, the particle swarm optimization algorithm, multi-verse optimization algorithm, and salp swarm algorithm. In the simulations, we solved the optimization model for three different structures of multilevel inverters (7, 11, and 15 levels) by changing the modulation indexes. It is found that the adaptive grey wolf optimization provides lower total harmonic distortion for different modulation indexes. [ABSTRACT FROM AUTHOR]

Model orbits have been fitted to 27 physical double stars listed in a 1922 catalog. A Markov Chain Monte Carlo technique was applied to estimate best-fitting values and associated uncertainties for the orbital parameters. Dynamical masses were calculated using parallaxes from the Hipparcos mission and are presented in this paper with the estimates of the orbital parameters for the 27 systems. The resulting mass estimates of the current study are in good agreement with a recently published study, as are comparisons with the orbital parameters listed by the Washington Double Star catalog, confirming the validity of the optimization methodology. [ABSTRACT FROM AUTHOR]

In this paper, we enrich the model of cooperative cost games by introducing the notion of selfishness levels for players. With this notion, we introduce a new value called the generalized CIS value. To reveal the fairness of the generalized CIS value, we characterize this value through three perspectives. The first one is to provide a procedural implementation for this value, and show that the outcome of this implementation process coincides with the generalized CIS value. Secondly, by applying the optimization theory, we show that the generalized CIS value may minimize the players' complaints from the perspective of average complaint. Lastly, we approach the generalized CIS value axiomatically by using the efficiency property and the equal maximal complaint property. [ABSTRACT FROM AUTHOR]

Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is imperative to evaluate the predictive performance it attains for diverse datasets. This is done in order to stress test the strengths and weaknesses of the novel algorithms and provide evidence for situations in which they are most useful. A common practice is to gather some datasets from public benchmark repositories for such an evaluation. But little or no specific criteria are used in the selection of these datasets, which is often ad-hoc. In this paper, the importance of gathering a diverse benchmark of datasets in order to properly evaluate ML models and really understand their capabilities is investigated. Leveraging from meta-learning studies evaluating the diversity of public repositories of datasets, this paper introduces an optimization method to choose varied classification and regression datasets from a pool of candidate datasets. The method is based on maximum coverage, circular packing, and the meta-heuristic Lichtenberg Algorithm for ensuring that diverse datasets able to challenge the ML algorithms more broadly are chosen. The selections were compared experimentally with a random selection of datasets and with clustering by k-medoids and proved to be more effective regarding the diversity of the chosen benchmarks and the ability to challenge the ML algorithms at different levels. [ABSTRACT FROM AUTHOR]

A wind power forecast is an useful support tool for planning and operating wind farm production, facilitating decisions regarding maintenance and load share. This paper presents an evaluation of a cooperative method, which uses a time series pre-processing strategy, artificial neural networks, and multi-objective optimization to forecast wind power generation. The proposed approach also evaluates the accuracy of the hybridization of variational mode decomposition (VMD) with bootstrap aggregation and extreme learning machine model for forecasting very short and short-term wind power generation. Multi-objective strategy aggregates the VMD-based components and obtains the final forecasting. The results imply that the presented algorithm has better forecasting performance compared to bootstrap stacking, other machine learning approaches, and statistical models, with a reduction of root mean squared error of approximately 12.76%, 25.25%, 31.91%, and 34.76%, respectively, for out-of-sample predictions. The forecasting results indicate that the presented approach can improve generalizability and accuracy in cases of very short and short-term wind energy generation. [ABSTRACT FROM AUTHOR]

In this paper we analyze the links between the agent's reservation utility, bargaining power, and risk aversion in terms of their simultaneous effects on the structure of optimal static contracts. We compare the following principal-agent models in the symmetric and asymmetric information environments: the standard approach, which includes a participation constraint, and a multi-objective (MO) optimization approach in which the objective function is a convex combination of the expected utilities of the principal and the agent. The MO model does not include a participation constraint, but it includes a parameter for the agent's bargaining power. We also study an Evolutionary Algorithm implementation of the static principal-agent model to support and extend our analytical results. We show that the numerical solution approximated by our implementation of an evolutionary algorithm is in line with the analytical solutions mentioned before. That is, for every admissible value of the agent's reservation utility, there is a corresponding admissible value of the agent's bargaining parameter, both in the MO approach and the EA implementation. [ABSTRACT FROM AUTHOR]

The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization. It is also characterized by multiple objectives and constraints, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities and vendors but hand-designed solutions continue to be the prevalent method in the industry. To improve the state-of-the-art, Deep Reinforcement Learning (RL), in particular, Proximal Policy Optimization is leveraged. This work presents a first-of-a-kind approach to utilize deep RL to solve the loading pattern problem and could be leveraged for any engineering design optimization. This paper is also to our knowledge the first to propose a study of the behavior of several hyper-parameters that influence the RL algorithm. The algorithm is highly dependent on multiple factors such as the shape of the objective function derived for the core design that behaves as a fudge factor that affects the stability of the learning. But also an exploration/exploitation trade-off that manifests through different parameters such as the number of loading patterns seen by the agents per episode, the number of samples collected before a policy update , and an entropy factor that increases the randomness of the policy during training. We found that RL must be applied similarly to a Gaussian Process in which the acquisition function is replaced by a parametrized policy. Then, once an initial set of hyper-parameters is found, reducing and until no more learning is observed will result in the highest sample efficiency robustly and stably. This resulted in an economic benefit of 535,000 - 642,000 $/year/plant. Future work must extend this research to multi-objective settings and comparing them to state-of-the-art implementation of stochastic optimization methods. [ABSTRACT FROM AUTHOR]

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a robust optimization of eight chaotic maps: Logistics, Sine, Gauss, Circle, Tent, Chebyshev, Singer, and Piecewise Maps, for superior image encryption. The proposed model consists of two main processes: chaotic confusion and pixel diffusion. In the chaotic confusion process, the positions of the image's pixels are permuted with the chaotic maps, where the initial condition and the control parameters represent the confusion key. Firstly, the confusion process was performed using the eight chaotic maps without optimization. Then nine metaheuristic optimizers, which are the genetic algorithm, particle swarm optimizations, whale optimization algorithm, dragonfly algorithm, grey wolf optimizer, moth-flame optimizer, sine cosine algorithm, multi-verse optimizer, and ant-lion optimizer, have been used to fine-tune the control parameters of the eight chaotic maps. Then the image's pixel values are changed using the diffusion function in the pixel diffusion process. Multiple performance metrics, such as entropy, histogram, cross-correlation, computation time analysis, the number of pixels change rate (NPCR), unified average changing intensity (UACI), noise attack, data loss, and key analysis metrics, are utilized to evaluate the proposed model. The results demonstrate that the encryption algorithms based on the eight optimized chaotic maps are more resistant to differential attacks than those without optimization. Furthermore, the optimized Gauss chaotic map is the most computationally efficient, while the chaotic circle map has the most robust key. The careful adjustment of initial conditions and control parameters empowers the chaotic maps to create encryption keys with greater randomness and complexity, thereby increasing the security level of the encryption scheme. Experimental analysis indicates that the correlation coefficient values of images encrypted with the proposed scheme are nearly zero, the histogram of the encrypted images is uniform, the execution time of 0.1556 s, the key space of 10^80, NPCR of 99.63%, UACI of 32.92%, and entropy of 7.997. Moreover, the analysis of noise and cropping attacks, along with the comparison with other algorithms, demonstrate the efficiency and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multidimensional scaling (MDS) is an often-used method to reduce the dimensionality of multidimensional data nonlinearly and to present the data visually. MDS minimizes some stress function which variables are coordinates of points in the projected lower-dimensional space. Recently, the so-called Geometric MDS has been developed, where the stress function and multidimensional scaling, in general, are considered from the geometric point of view. Using ideas of Geometric MDS, it is possible to construct the iterative procedure of minimization of the stress where coordinates of a separate point of the projected space are moved to the new position defined analytically. In this paper, we discover and prove the main advantage of Geometric MDS theoretically: changing the position of all the points of the projected space simultaneously (independently of each other) in the directions and with steps, defined analytically by Geometric MDS strategy for a separate point, decreases the MDS stress. Moreover, the analytical updating of coordinates of projected points in each iteration has a simple geometric interpretation. New properties of Geometric MDS have been discovered. The obtained results allow us for the future development of a class of new both sequential and parallel algorithms. Ideas for global optimization of the stress are highlighted. [ABSTRACT FROM AUTHOR]

Classic multidimensional scaling (MDS) and scaling by majorizing a complex function (SMACOF) are well-known centralized algorithms that are used to solve MDS problem. In this paper, we present a distributed algorithm for solving MDS problem. Estimations of coordinates are performed concurrently under the assumption that each item knows only its own position and its distances from its neighbors and their approximated present locations. The update process is done by calculating the average of the current coordinate of each object and its projections on the solution spaces allocated to it by its neighbors. We apply the method to the problem of sensor localization and obtain numerical results that demonstrate the efficacy of our suggested strategy. [ABSTRACT FROM AUTHOR]

Neural network applications, as an emerging machine learning technology, are increasingly being integrated into pharmaceutical manufacturing technologies, offering significant improvement opportunities for performance, efficiency and sustainability. This review paper utilizes a systematic methodology to establish key literature trends and themes. The state-of-the-art body of knowledge in this hot research area is analyzed in descriptive (e.g. neural network technologies studied, sustainability indicators considered, manufacturing process addressed) and thematic synthesis components. Process analysis and improvement, quality control and additive manufacturing were identified as the three focal research themes, and research lines within these themes were further studied and discussed. To guide future research, potential paths and research questions are proposed against the gaps identified. The originality of this work lies in its methodology (adoption of a systematic review approach, highly limited in the current literature), its inclusion of sustainability (as an imperative concept for manufacturing technology research) and its specific focus on neural network applications in the context of pharmaceutical manufacturing technologies (a perspective, either has been missing or addressed too widely by extant contributions). [ABSTRACT FROM AUTHOR]

Robot technology is considered one of the most promising technologies to achieve intelligent production, with picking robots being the most common type. Picking robots are highly integrated mechatronic systems, which autonomously complete tasks including picking, carrying, and sorting. The application of picking robots enhances the efficiency of production across various environments. In this paper, a classification-design-optimization-application integrated framework of picking robots is addressed, contributing to theoretical research and application of picking robots. Classification of picking robot is established and analyzed considering the differences of overall form and end-effector to guide the development of research strategies and approaches of picking robot. Design of picking robot is described from different aspects of the target, structure, monitoring, and control design. Additionally, the commonly used optimization methods for picking robots, including structural parameters, kinematics and dynamics, and energy consumption, are discussed. Finally, the application of picking robots under different environments is expounded, and the challenges and prospects of picking robots are highlighted. This study could present theoretical support and application measures for picking robot study and technology development. [ABSTRACT FROM AUTHOR]

The sixth generation (6G) networks demand high security, low latency, and highly dependable standards and capacity. One of the essential components of 6G networks is flexible wireless network slicing. In this paper, we propose a hybrid model that combines a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). The hybrid model is applied to the Unicauca IP Flow Version2 dataset. The CNN handles the automated feature section, while the BiLSTM is utilized for categorizing the suitable network slices. This hybrid model is capable of offering a reliable and effective network slice to the end user. The proposed hybrid model has an overall recognition rate of 97.21%, which reflects the applicability of the proposed approach. A stratified 10-fold cross-validation is used to assess the applicability of the proposed model. The main challenge for network service providers is to assign slices correctly. A clever method is needed to make a standard for accurately assigning network slices to an unidentified device when it asks for them. For each incoming request for new traffic, the proposed model forecasts the suitable network slice [ABSTRACT FROM AUTHOR]

The main goal of this paper is to develop a new class of sixth- and fourth-order implicit Runge–Kutta methods to treat linear and nonlinear initial value ordinary differential equations (ODEs) in applied science and engineering. In this regard, a completely unconventional approach is taken to derive the new formulations. They are derived through a computational approach without involving in the rigorous theory of order conditions. The new formulation has two distinctive parts, namely integration and interpolation. In the first part, we introduce a form of multi-stage single-step numerical integrator with unknown weights. These weights are determined by four simple interconnectivity rules that relate them to minimize the error of the integrator. Some of these rules are based on our expertise gathered from the available prominent quadratures. However, the most important one, the so-called fundamental weighting rule (FWR), is obtained from evolutionary and statistical computations. The second part of this research focuses on developing efficient tools for evaluating the internal stages of the integrator. Accordingly, a series of strong Hermite interpolators has been extended to approximate the solution values at the internal stages. They effectively collaborate with the integrator in several numerical algorithms, the so-called α I I - (q + r) P algorithms, to solve applied ODEs. Development of the integrator, its weighting rules, and the interpolators constitutes the central contribution of this research. Numerical studies demonstrate that the presented algorithms exhibit a high level of accuracy when approximating high-frequency problems as well as long-term solutions of linear and nonlinear ODEs. [ABSTRACT FROM AUTHOR]

The stability of the injection molding process is critical to efficient production. However, environment and production disturbances pose challenges to maintaining a stable molding process. A two-stage dynamic adjustment strategy is proposed in this paper to increase weight consistency, considering fluctuation of density and fluidity based on products' types and structure. This study investigates the factors influencing the weight stability of injection molding products during the injection and holding stages. Experiments show that the injection stage is affected by density fluctuations, while melt fluidity fluctuations influence the holding stage. Simulation experiments prove the efficacy of dynamic adjustment strategies for injection-sensitive products (ISP) and holding-sensitive products (HSP). Thinner products as ISPs benefit from adjustments of the injection stage parameters, while thicker products as HSPs require optimization of holding stage parameters. To prove its validity, this strategy was tested on two molds and improved weight stability by 60.0% and 34.8%, respectively. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles is expected to substantially raise electricity demand. This could require significant grid investment to maintain secure electricity supply, which has traditionally been provided through infrastructure upgrades. The potential of smart technologies like Vehicle-to-Grid (V2G) to contribute to security of supply has prompted the need to quantify their impact. We hypothesize that the F-Factor methodology can effectively quantify V2G's security of supply contribution. Applying F-Factor analysis to V2G through optimization modeling and sensitivity studies, we find that key parameters like V2G charger ratings, EV battery capacities, and load profile peakiness significantly influence the results. We conclude that the F-Factor provides a valuable tool for assessing V2G's potential to enhance security of supply, with implications for more efficient grid planning in the context of transport electrification. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important approaches used to extend the lifetime of Wireless Sensor Networks (WSN). The fundamental metric taken by clustering algorithms is energy enhancement. Moreover, network coverage and load balance are two important approaches that play crucial roles in improving network lifetime and delivery since the former focuses on maximizing the use of all network resources, while the second is based on distributing the load between the nodes to enhance the energy consumption. As the challenge of clustering nodes in an energy-efficient way is an NP-Hard problem, firefly optimization algorithm is used to address this challenge. However, the proposed solutions focus on centralized processing of the algorithm, which makes them unsuitable for large-scale WSN. In this paper, a double firefly based efficient clustering solution is proposed for large-scale WSN which is implemented in a decentralized fashion to improve the lifetime and packet delivery. The first firefly algorithm is used by each node to move to the best initial Cluster Head (CH) by performing a balance of belonging between the clusters, while the second algorithm is used only between the initial CHs to eliminate membership redundancy and optimally construct balanced clusters. The simulation results show that our proposed solution significantly improves the network lifetime as well as the delivery rate. [ABSTRACT FROM AUTHOR]

This research focuses on designing a real-time, flexible gait planner for lower limb exoskeleton robots to assist patients with lower limb disabilities. Given the dynamic nature of gait parameters, which vary according to ground conditions and user intent, the challenge lies in developing a gait planner capable of adapting to these changes in real-time. To avoid planning complications in the cartesian space and to comply with the speed constraints of joint motors, this paper proposes planning in joint space. Furthermore, the approach also considers the maximum speed capabilities of the joint motors, aiming to generate an executable gait pattern and simultaneously enhance the robot’s walking speed by determining the minimum time required for implementation. The introduced gait planner optimizes joint trajectories for minimal angular acceleration. To provide flexibility, generalized boundary conditions suitable for different scenarios are defined. The effectiveness of the proposed planner is validated through comprehensive performance analysis, simulations, and successful implementation trials on the Exoped® robot in various scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of nano-dome anti-reflective coatings (ND-ARC) and nano-cone anti-reflective coatings (NC-ARC) on the optical characteristics of organic light-emitting diodes (OLEDs). By applying anti-reflective coatings on an OLED substrate surface, the glass–air interface reduces reflections, thus boosting the light intensity. The finite-difference time-domain (FDTD) simulation approach and Fresnel theory were leveraged to model the OLEDs and to study the effects of ND-ARC and NC-ARC on the glass–air interface. To enhance parameters such as thickness, refractive indices, pitch, and anti-reflective coating layer height, the Fresnel principle was used. The numerical calculations show that the coupling efficiency of the ND-ARC-coated OLED is improved compared to that of the NC-ARC-coated OLED. The analysis was conducted to observe the effect of various wavelengths on far-field intensities. [ABSTRACT FROM AUTHOR]

Solutions of uranium salts can be used as targets for the production of medical radionuclides to exclude the most troublesome stages of solid target processing and make the targets reusable. An industrial introduction of this technology for the production of 99Mo requires further development of the technological process of molybdenum extraction using a stable selective sorbent, such as titanium dioxide. Solutions of uranium sulfate in sulfuric acid have proven themselves well at the stage of irradiation, but the adsorption behavior of molybdenum in this media has not been studied sufficiently. A negative effect of the background concentration of the sulfate ion on the efficiency of extraction is shown in this paper, and a kinetic analysis is carried out using Morris-Weber model of intraparticle diffusion. Experiments show that the sorption kinetics can be both positively and negatively affected by an increase in pH from 1.3 to 2.0 when using sorbents of similar composition. The proposed explanation considers the nature of functional groups existing on the surface of the sorbent to be a determining parameter. The negative Y-intercept of the kinetic plot is considered to be a result of relatively slow activation of adsorption centers by protonation. Based on these assumptions, the possibility of existence of active centers with high activation rate, strongly affected by pH, and active centers with low activation rate, less affected by pH, on titanium dioxide surface was postulated. [ABSTRACT FROM AUTHOR]

In this paper, a novel power management strategy (PMS) is proposed for optimal real-time power distribution between battery and supercapacitor hybrid energy storage system in a DC microgrid. The DC-bus voltage regulation and battery life expansion are the main control objectives. Contrary to the previous works that tried to reduce the battery current magnitude and its fluctuation to increase the battery life, the battery thermal considerations, which are a key factor in battery life, are also considered as an objective function in the proposed PMS. Also, the quick charging capability of the battery is another objective in the optimization problem. To solve this multi-objective problem, the Karush–Kuhn–Tucker conditions have been used to make the PMS suitable for real-time operation. In addition, one of the important inputs of conventional PMS is the amount of grid excess/deficiency power. Due to the use of the adaptive Kalman filter estimator, another superior of the proposed PMS is using local data and no need for communication and knowledge of the load or generation power. Using simulation, the performance of the proposed method is compared with three traditional methods filter-based, droop control, and fuzzy logic in terms of controlling the battery current, battery temperature, and battery health, and its superiority has been shown. Finally, the validation of the proposed PMS is experimentally tested on a real setup. [ABSTRACT FROM AUTHOR]