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This paper proposes novel multi-energy inertia support for simultaneous frequency and voltage control of an isolated hybrid power system (IHPS). Multi-energy storage (gas inertia – hydrogen storage, thermal inertia – solar thermal storage, hydro inertia – gravity hydro storage, chemical inertia – battery energy storage) supported by demand side management (DSM) for simultaneous voltage and frequency regulation and backed by biodiesel generators, are the essential elements of IHPS. A novel control strategy of concurrent virtual droop control, virtual damping control, virtual inertia control, and virtual negative inertia control is proposed to utilise multiple inertia sources and to improve LFC and AVR performance effectively. The effective coordination of inertia sources in eradicating oscillations in IHPS, is aided by a developed cascaded proportional integral-tilt-integral-sliding mode (PI-TISMC) controller. The performance of PI-TISMC is compared with PID, PI-PID, and PI-SMC controllers. A maiden attempt has been done by training five diverse classes of optimization techniques to optimize the parameters of controllers in the present work. The results are evaluated in MATLAB and it is evident from the results that the performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). The performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). Similarly, the performance of voltage control is improved by 6.7%, 4.8% and 2.3 s (over shoot, undershoot, and settling time) by employing developed PI-TISMC controller and proposed concurrent inertia control. The combination exhibits superior performance in minimizing oscillations in IHPS due to variations in loading and solar insolation. [ABSTRACT FROM AUTHOR]

Cylindrical Roller Bearings (CRB) are subjected to dynamic loads rotating at high speeds causing fatigue, which demands high dynamic load capacity. The complexity of its internal geometry and the design constraints needs suitable optimization techniques. This article proposes a new approach to design optimization with minimal constraints. Optimal geometric parameters, such as bearing pitch diameter, mean roller diameter of the bearing, effective roller length, etc., offer 90% more high dynamic capacity than those in the catalogue of SKF rolling bearings. The method of approach followed here minimizes the computational time and improves the existing design. The adequacy of the optimized design is verified utilizing the MATLAB Genetic algorithm (GA). [ABSTRACT FROM AUTHOR]

Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management. [ABSTRACT FROM AUTHOR]

Amid escalating tension between environmental conservation and economic development, the imperative to enhance air quality has become increasingly urgent. This study elucidates a sophisticated approach for the assessment and remediation of air pollution issues through the integration of an enhanced particle swarm optimization algorithm and a differential gravitational fireworks algorithm-optimized support vector machine (SVM). In the initial phase of this research, a series of intricate data preprocessing and augmentation procedures were conducted, and the differential evolution algorithm played a pivotal role. The differential gravitational fireworks algorithm was subsequently introduced to optimize the SVM parameter settings, thereby bolstering classification accuracy and mitigating issues such as overfitting. Through rigorous and meticulous empirical testing, the augmented SVM model demonstrated notable performance in terms of classification accuracy and sequential and nonsequential data fusion, surpassing conventional SVM techniques. Notably, our sequential fusion method achieved an accuracy of up to 91%, at least 3% higher than that of nonsequential techniques. In conclusion, this study reveals an innovative and enhanced technological approach that is highly effective for the precise measurement and control of air pollution levels. [ABSTRACT FROM AUTHOR]

The shortest path problem is fundamental in graph theory and has been studied extensively due to its practical importance. Despite this aspect, finding the shortest path between two nodes remains a significant challenge in many applications, as it often becomes complex and time consuming. This complexity becomes even more challenging when constraints make the problem non-additive, thereby increasing the difficulty of finding the optimal path. The objective of this paper is to present a broad perspective on the conventional shortest path problem. It introduces a new method to classify cost functions associated with graphs by defining distinct sets of cost functions. This classification facilitates the exploration of line graphs and an understanding of the upper bounds on the transformation sizes for these types of graphs. Based on these foundations, the paper proposes a practical methodology for solving non-additive shortest path problems. It also provides a proof of optimality and establishes an upper bound on the algorithmic cost of the proposed methodology. This study not only expands the scope of traditional shortest path problems but also highlights their computational complexity and potential solutions. [ABSTRACT FROM AUTHOR]

This thesis explores the application of ray tracing algorithms in creating photorealistic scenes and their integration into virtual reality (VR) environments. Ray tracing, an advanced rendering technique, simulates the behavior of light rays as they travel through a scene, resulting in realistic effects such as reflections, shadows, and refractions. The practical part focuses on implementing key elements of ray tracing, specifically the calculation of ray-object intersections, performance optimization, and integration with global illumination in Unity. The study demonstrates how ray tracing can enhance the visual fidelity of VR, but also highlights the technical challenges, particularly in maintaining performance in real-time applications. Through a case study, the key benefits and limitations of ray tracing in VR are examined, providing valuable insights into its potential to improve image quality and user experience. [ABSTRACT FROM AUTHOR]

Satellite image enhancement is the method, which is commonly used in the field of satellite image processing for the betterment of feature visualization and for clear visibility to properly identify those features. These satellite images have many atmospheric barriers like distortion and noise because they are captured from a very high and far distance. After the image capturing, some geometric corrections and radiometric corrections are carried out on those images, but these corrections are not enough for all the applications. So, it is needed to enhance the image restored before using it for feature identification. Nowadays, there are so many approaches for assuring effective image enhancement. However, there are so many drawbacks that need to be addressed in future. Hence, this article illustrates a clear and elaborate survey of various research articles presenting satellite image enhancement methods, such as machine learning-based techniques, optimization-based techniques, wavelet-based techniques, filter-based techniques and Histogram equalization-based techniques. Finally, the analysis is given in the survey based on the classification of research methods, publication year, utilized datasets and performance measures towards enhancement of satellite images. At last, issues and research gaps in methods are given in such a way that the motivation for developing an effective method for enabling effective enhancement is revealed. [ABSTRACT FROM AUTHOR]

The current study uses Field-Programmable Gate Array (FPGA) hardware to advance smart home technology through a self-learning system. The proposed intelligent three-hidden layer system outperformed prior systems with 99.21% accuracy using real-world data from the MavPad dataset. The research shows that FPGA solutions can do difficult computations in seconds. The study also examines the difficulties of maximizing performance with limited resources when incorporating deep learning technologies into FPGAs. Despite these challenges, the research shows that FPGA-based solutions improve home technology. It promotes the integration of sophisticated learning algorithms into ordinary electronics to boost their intelligence. [ABSTRACT FROM AUTHOR]

Cloud computing is an emerging technology composed of several key components that work together to create a seamless network of interconnected devices. These interconnected devices, such as sensors, routers, smartphones, and smart appliances, are the foundation of the Internet of Everything (IoE). Huge volumes of data generated by IoE devices are processed and accumulated in the cloud, allowing for real-time analysis and insights. As a result, there is a dire need for load-balancing and task-scheduling techniques in cloud computing. The primary objective of these techniques is to divide the workload evenly across all available resources and handle other issues like reducing execution time and response time, increasing throughput and fault detection. This systematic literature review (SLR) aims to analyze various technologies comprising optimization and machine learning algorithms used for load balancing and task-scheduling problems in a cloud computing environment. To analyze the load-balancing patterns and task-scheduling techniques, we opted for a representative set of 63 research articles written in English from 2014 to 2024 that has been selected using suitable exclusion-inclusion criteria. The SLR aims to minimize bias and increase objectivity by designing research questions about the topic. We have focused on the technologies used, the merits-demerits of diverse technologies, gaps within the research, insights into tools, forthcoming opportunities, performance metrics, and an in-depth investigation into ML-based optimization techniques. [ABSTRACT FROM AUTHOR]

A major aspect of distributed systems is task scheduling. How significant is it to adequately allocate tasks to each one of the computer's processors for the purpose of achieving better performance? The indicated approaches attempt to reduce costs and improve performance while maximizing the utilization of the central processing unit (CPU) in such a situation. The main flaw in such methods is that they require a lot of scheduling and, as a result, a lot of time. To address such a flaw, a modified CPU job-scheduling method was suggested, which used the Round Robin technique (R.R.) and dual synchronized time-slices to reduce jobs' waiting time to be executed. Therefore, the present study proves that the suggested approach has optimization efficiency with an average percentage of (12.4%–62.07%), according to an original R.R. algorithm. [ABSTRACT FROM AUTHOR]

Abstract This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

Abstract Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.

Abstract Amid escalating tension between environmental conservation and economic development, the imperative to enhance air quality has become increasingly urgent. This study elucidates a sophisticated approach for the assessment and remediation of air pollution issues through the integration of an enhanced particle swarm optimization algorithm and a differential gravitational fireworks algorithm-optimized support vector machine (SVM). In the initial phase of this research, a series of intricate data preprocessing and augmentation procedures were conducted, and the differential evolution algorithm played a pivotal role. The differential gravitational fireworks algorithm was subsequently introduced to optimize the SVM parameter settings, thereby bolstering classification accuracy and mitigating issues such as overfitting. Through rigorous and meticulous empirical testing, the augmented SVM model demonstrated notable performance in terms of classification accuracy and sequential and nonsequential data fusion, surpassing conventional SVM techniques. Notably, our sequential fusion method achieved an accuracy of up to 91%, at least 3% higher than that of nonsequential techniques. In conclusion, this study reveals an innovative and enhanced technological approach that is highly effective for the precise measurement and control of air pollution levels.

The present investigation studies performance modeling and analysis of an M/M/1 queue with a hybrid vacation policy and Bernoulli service interruption. When the system becomes empty, the server has the choice to switch over to a complete vacation state or a working vacation state. During working vacations, the server also serves customers through a Bernoulli service process at a slower rate. After completing the service, the server either transitions to a normal busy state or remains in the working vacation state. To analyze the performance of the system, we have developed a Markovian queueing model by formulating the Chapman–Kolmogorov governing equations for the system states. Through an iterative and difference-differential equation approach, we derived the stationary probability distribution of the queue size, along with other key queueing metrics such as average queue length, average waiting time, and throughput. To deal with a more practical scenario of imprecise information about the system descriptors, the proposed crisp queueing is transformed into a fuzzy model by retaining the features. The soft computing approach based on adaptive neuro-fuzzy inference system (ANFIS), -cut, and parametric nonlinear programming (PNLP) is employed to obtain various fuzzified indices. Moreover, to determine the optimal service rate, the differential evolution (DE) and golden section search (GSS) methods are used. By taking illustration, the proposed optimization techniques are implemented to develop a cost-effective service system and to examine the sensitivity of the key descriptors. [ABSTRACT FROM AUTHOR]

In the field of optimization algorithms, nature-inspired techniques have garnered attention for their adaptability and problem-solving prowess. This research introduces the Arctic Fox Algorithm (AFA), an innovative optimization technique inspired by the adaptive survival strategies of the Arctic fox, designed to excel in dynamic and complex optimization landscapes. Incorporating gradient flow dynamics, stochastic differential equations, and probability distributions, AFA is equipped to adjust its search strategies dynamically, enhancing both exploration and exploitation capabilities. Through rigorous evaluation on a set of 25 benchmark functions, AFA consistently outperformed established algorithms particularly in scenarios involving high-dimensional and multi-modal problems, demonstrating faster convergence and improved solution quality. Application of AFA to real-world problems, including wind farm layout optimization and financial portfolio optimization, highlighted its ability to increase energy outputs by up to 15% and improve portfolio Sharpe ratios by 20% compared to conventional methods. These results showcase AFA's potential as a robust tool for complex optimization tasks, paving the way for future research focused on refining its adaptive mechanisms and exploring broader applications. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution, in short Industry 4.0 is the next stage in the digitization of the industrial sector and is being driven by disruptive trends including the growth of data and connectivity, analytics, human-machine interaction, and advancements in robotics. For the past ten years, Industry 4.0 consistently addressed both the industry’s strengths and weaknesses. Industry 4.0 is limited by the higher productivity that smart manufacturing systems provide. The new version of Industry 4.0 is termed Industry 5.0 and it refers to the fifth industrial revolution and is an emerging concept that builds on the advancements of Industry 4.0. Thus, industry 5.0 is an extension and further advancement of Industry 4.0, which focuses on automation, connectivity, and data exchange in manufacturing processes. Industry 5.0 introduces a new level of human-machine collaboration and emphasizes the importance of human creativity and skills alongside advanced technologies. This study discusses the opportunities and directions of future mathematical research towards Industry 5.0.

Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market’s bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits.

In recent years, the integration of sustainable principles into the Vehicle Routing Problem (VRP) for efficient waste management has gained significant attention. This paper provides an overview of advancements in multi-objective mathematical programming (MP) models and the optimization techniques for sustainable waste management practices within the VRP framework. Sustainable waste management practices in the context of the VRP involve optimizing routes and schedules aim to reduce the environmental impact of waste collection. It investigates the integration of multi-objectives with sustainability criteria, including social, economic, and environmental considerations. Furthermore, it surveys various optimization methodologies applied in waste management to enhance efficiency and sustainability practices. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was employed as a formal systematic review guideline for data gathering. Data was obtained from studies conducted between 2017 and 2023. The analysis included a total of 18 papers from online sources and databases. Based on the literature review, it has been observed that the majority of the research focused on employing Mixed-Integer Linear Programming (MILP) models to tackle the optimization problem for waste management. The literature survey demonstrated significant contributions in computational performance, cost-effectiveness, operational efficiency, and utilization of real-world data by improving the solution approaches, aiming to prioritize economic, social, and environmentally friendly solutions. The paper aims to serve as a convenient reference for researchers and practitioners seeking information on using the MP model and solution approaches to optimize VRP for waste management. [ABSTRACT FROM AUTHOR]

AbstractAdhesive bonding is a versatile joining method gaining popularity due to its ability to join variety of metals, nonmetals, and composite materials to achieve lightweight, high-strength structures. Unlike mechanical fasteners that require holes, adhesive bonding creates a continuous bond, resulting in a more uniform stress distribution. It also allows bonding thin materials and dissimilar materials, and creates airtight seals. However, even with these advantages, adhesive joints lack structural redundancy and sometimes strength compared to their mechanical counterparts. This has driven extensive research and development efforts to improve their strength, reliability, and overall performance. The present paper aims to explore both historical and recent advancements in techniques for enhancing the strength of adhesively bonded joints, organizing them in a coherent and systematic manner that can be easily quantified. The paper also presents a comprehensive comparison of the techniques, their dependency on various factors, and guidelines for the optimal choice. This examination could pave the way for novel optimization techniques and data-driven approaches for achieving superior joint performance. [ABSTRACT FROM AUTHOR]

Navigating the stock market's unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market's bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits. [ABSTRACT FROM AUTHOR]

Timing risk refers to a situation in which the timing of an economically important event is unknown (risky) from the perspective of an economic decision maker. While this special class of dynamic stochastic control problems has many applications in economics, the methods used to solve them are not easily accessible within a single, comprehensive survey. We provide a survey of dynamic optimization methods under comprehensive assumptions about the nature of timing risk. We also relax the assumption of full information and summarize optimization with limited information, ambiguity, imperfect hedging, and dynamic inconsistency. Our goal is to provide a concise user guide for specialists and nonspecialists alike. [ABSTRACT FROM AUTHOR]

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This study explores the increasing global interest in the preservation and management of cultural heritage, emphasizing the need for innovative tools in urban planning and design to address this critical issue. As cultural heritage gains international attention, particularly through the concept of cultural routes, there is a growing demand for urban planning strategies that are both flexible and forward-thinking. The shift from traditional planning approaches to advanced digital systems, particularly those driven by artificial intelligence, marks a significant transformation in the field. This study aims to introduce an innovative application recommendation, designed to optimize urban environments by leveraging the dynamic potential of cultural routes. Central to this approach is the Ant Colony Optimization (ACO) technique, enhanced with the 2-Opt algorithm based on the Traveling Salesman Problem (TSP), a highly effective method widely used for solving complex routing problems. By integrating this improved ACO, the recommendation not only generates optimal routes tailored to user preferences but also enhances efficiency in both time and budget management. Beyond its technical merits, this optimization-based solution offers a holistic approach to urban planning by integrating cultural heritage management with contemporary technological advancements. By doing so, the recommendation is expected to contribute to the sustainable development of cities, ensuring that cultural heritage is preserved while also addressing the practical needs of urban environments. This research presents a forward-thinking proposal that aligns with global trends in cultural preservation, offering a functional and adaptable tool for the challenges faced by the user. The findings of this study show that the proposed cultural route planning and design model can create specific routes according to individual interests, thus helping to personalize travel experiences and determine the most appropriate routes. [ABSTRACT FROM AUTHOR]

As a consequence of human activity, waste generation is unavoidable, and its volume and complexity escalate with urbanization, economic progress, and the elevation of living standards in cities. Annually, the world produces about 2.01 billion tons of municipal solid waste, which often lacks environmentally safe management. The importance of solid waste management lies in its role in sustainable development, aimed at reducing the environmental harms from waste creation and disposal. With the expansion of urban populations, waste management systems grow increasingly complex, necessitating more sophisticated optimization strategies. This analysis thoroughly examines the optimization techniques used in solid waste management, assessing their application, benefits, and limitations by using PRISMA 2020. This study, reviewing the literature from 2010 to 2023, divides these techniques into three key areas: waste collection and transportation, waste treatment and disposal, and resource recovery, using tools like mathematical modeling, simulation, and artificial intelligence. It evaluates these strategies against criteria such as cost-efficiency, environmental footprint, energy usage, and social acceptability. Significant progress has been noted in optimizing waste collection and transportation through innovations in routing, bin placement, and the scheduling of vehicles. The paper also explores advancements in waste treatment and disposal, like selecting landfill sites and converting waste to energy, alongside newer methods for resource recovery, including sorting and recycling materials. In conclusion, this review identifies research gaps and suggests directions for future optimization efforts in solid waste management, emphasizing the need for cross-disciplinary collaboration, leveraging new technologies, and adopting tailored approaches to tackle the intricate challenges of managing waste. These insights offer valuable guidance for policymakers, waste management professionals, and researchers involved in crafting sustainable waste strategies. [ABSTRACT FROM AUTHOR]

This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence's role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles' energy management systems is discussed. The findings underscore artificial intelligence's potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications. [ABSTRACT FROM AUTHOR]

This review provides a comprehensive exploration of distributed generation (DG) in the context of radial distribution systems, encompassing various DG technologies, current advancements, and their integration challenges. It evaluates the impacts of DG on distribution networks, highlighting diverse applications, drivers, and benefits. A significant focus is on the optimal integration of DG, assessing various optimization techniques, algorithms, and objective functions used to address this challenge. Through a detailed analysis of existing literature, this study identifies that no single optimization method stands out as universally superior. Instead, the research concludes that hybrid optimization techniques, particularly those combining heuristic methods such as genetic algorithms with other approaches, offer the most effective and reliable solutions for the complex task of DG integration. These hybrid methods leverage the strengths and mitigate the weaknesses of individual techniques, providing more robust and dependable outcomes. This conclusion underscores the need for innovative, multi-faceted approaches to optimize DG integration in evolving distribution systems. [ABSTRACT FROM AUTHOR]

One of the DC-DC conversion systems is boost converters which are used in commonly with solar systems to convert low DC voltage levels to higher ones. This is particularly useful in solar systems because the voltage generated by solar panels can vary widely depending on factors such as the amount of sunlight and the temperature of the panels. The duty cycle of the boost must be controlled to have the maximum output power. Using the Grey Wolf Optimizer to control the duty cycle of a boost converter is one of the ways to have this maximum power. The optimization problem can be stated as minimizing the voltage error of the boost converter output by optimizing the duty cycle. The objective function can be defined as the difference between the desired output voltage and the actual output voltage of the boost converter. The duty cycle can be optimized by adjusting the PWM signal's pulse width that controls the boost converter switch. [ABSTRACT FROM AUTHOR]

The process of software development is complex, and one of its most critical aspects is estimating the amount of work required for various projects. However, accurately defining the exact amount of work needed in the early stages of production can be challenging. Researchers have been working on creating different machine and deep learning models to address this issue. These models, including single-approach models and multi-model ensembles, utilize optimization strategies to provide precise predictions. We propose a hybrid particle swarm optimization (PSO) based artificial neural networks (ANNs) model for software effort estimation (SEE), which has shown to outperform existing models. This model was tested on various datasets such as Albrecht, China Desharnais, Kemerer Kitchenham Maxwell, and Cocomo81. The hybrid PSO-optimized ANNs model has exhibited exceptional accuracy, as evidenced by consistently high R-squared (R2) values across multiple datasets. Additionally, the model has displayed low root mean square error (RMSE) and mean absolute error (MAE) values, indicating precise predictions. These outcomes affirm the model's precision and effectiveness. The model's small MAE further confirms its accuracy in predicting the required work during software development. With these remarkable results, the hybrid PSOoptimized ANNs model will undoubtedly play a crucial role in software development processes, providing accurate and precise predictions of the required work. [ABSTRACT FROM AUTHOR]

Power quality is a critical concern in modern electrical grids, particularly with the increasing integration of grid-connected photovoltaic (PV) systems. Voltage sags and swells, which are common disturbances in such systems, can significantly impact the stability and efficiency of power supply. This paper introduces an advanced approach to enhancing power quality in grid-connected PV systems by employing a Type II Fuzzy Logic Controller (FLC) with Gaussian membership functions and integrating a Dynamic Voltage Restorer (DVR). The Type II FLC is specifically designed to handle the inherent uncertainties and nonlinearities in PV systems more effectively than traditional controllers. By utilizing Gaussian membership functions, the controller offers precise and adaptive control, addressing the variations in solar power output and grid conditions. The DVR complements the FLC by injecting compensating voltage to mitigate disturbances such as voltage sags and swells, ensuring a stable voltage profile at the point of common coupling (PCC). Simulation results demonstrate a significant enhancement in power quality, with a reduction in total harmonic distortion (THD) by approximately 35% and voltage sags and swells mitigated by up to 45%. This study provides valuable insights into advanced control strategies, showing that the proposed system significantly improves the overall performance and reliability of PV systems, thereby contributing to the maintenance of optimal power quality in renewable energy grids. [ABSTRACT FROM AUTHOR]

The research paper represents a novel approach to the design and optimization of a compiler for a domain-specific language (DSL) focused on geometric shape creation and manipulation. The primary objective is to develop a compiler capable of generating efficient machine code while offering users a high level of abstraction. The paper begins with an overview of DSLs and compilers, emphasizing their importance in software development. Next, it outlines the specific requirements of the geometric shape DSL and proposes a compiler design that addresses them. This innovative approach considers DSL's unique features, such as shape creation and manipulation, and aims to generate high-quality machine code. The paper also discusses optimization techniques to enhance the generated code's quality and performance, including loop unrolling and instruction scheduling. These optimizations are particularly suited to the DSL, which focuses on geometric shape creation and manipulation and are integral to achieving efficient machine code generation. In conclusion, the paper emphasizes the novelty of this approach to DSL compiler design and anticipates exciting results from testing the compiler developed for the geometric shape DSL. [ABSTRACT FROM AUTHOR]

This study proposes a synergistic approach that aligns environmental sustainability with economic viability to meet the energy trilemma of energy security, affordability, and sustainability. Employing two qualitative methods-bibliometric analysis and systematic literature review- the research focused on Scopus-based articles related to renewable energy optimization using the VOSviewer software. The analysis revealed prevalent environmental tradeoffs in renewable energy schemes, including habitat loss, fragmentation, sediment transportation, and deforestation. Similarly, economic tradeoffs such as high initial costs, intermittency, reliability challenges, job security, resource scarcity, and supply chain risks were identified. Following a thorough examination, the study suggests solutions to manage these tradeoffs viz. strategic planning, continuous monitoring and adaptation, life cycle evaluation, policy and regulation implementation, and diversification of renewable energy sources. In conclusion, the study advocates a three-tiered recommendation for a smooth and predictable transition toward a secure energy future viz. integrated policy frameworks, and technological innovation.

ABSTRACTData-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

The integration of artificial intelligence (AI) and statistical methods has revolutionized civil engineering by enhancing accuracy, efficiency, and reliability in various processes. This review systematically examines how advanced optimization techniques, including artificial neural networks (ANNs), Design of Experiments (DOE), and fuzzy logic (FL), are transforming civil engineering practices. It emphasizes the significant roles these methods play in addressing modern challenges such as structural health monitoring, damage detection, seismic design optimization, and concrete condition assessment. The review delves into case studies and real-world applications, showcasing the potential of these methods to create more resilient, sustainable, and cost-effective infrastructures. It critically examines the limitations and scalability of these techniques, identifying gaps in current research and practical challenges in real-world applications. The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. By addressing these issues, the review not only shows advancements in optimization techniques but also outlines future research directions, aiming to bridge the gap between theoretical developments and practical applications in civil engineering. This review serves as an essential resource for researchers, professionals, and policymakers interested in leveraging optimization techniques to advance civil engineering practices.

This paper proposes novel multi-energy inertia support for simultaneous frequency and voltage control of an isolated hybrid power system (IHPS). Multi-energy storage (gas inertia – hydrogen storage, thermal inertia – solar thermal storage, hydro inertia – gravity hydro storage, chemical inertia – battery energy storage) supported by demand side management (DSM) for simultaneous voltage and frequency regulation and backed by biodiesel generators, are the essential elements of IHPS. A novel control strategy of concurrent virtual droop control, virtual damping control, virtual inertia control, and virtual negative inertia control is proposed to utilise multiple inertia sources and to improve LFC and AVR performance effectively. The effective coordination of inertia sources in eradicating oscillations in IHPS, is aided by a developed cascaded proportional integral-tilt-integral-sliding mode (PI-TISMC) controller. The performance of PI-TISMC is compared with PID, PI-PID, and PI-SMC controllers. A maiden attempt has been done by training five diverse classes of optimization techniques to optimize the parameters of controllers in the present work. The results are evaluated in MATLAB and it is evident from the results that the performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). The performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). Similarly, the performance of voltage control is improved by 6.7%, 4.8% and 2.3 s (over shoot, undershoot, and settling time) by employing developed PI-TISMC controller and proposed concurrent inertia control. The combination exhibits superior performance in minimizing oscillations in IHPS due to variations in loading and solar insolation.