Search Technique Research Articles

Cost-utility analysis of once-weekly insulin icodec and once-daily insulin glargine in patients with type 2 diabetes receiving basal-bolus insulin therapy in China.

The purpose of this study is to explore the rational pricing range for the once-weekly administration of insulin icodec in the treatment of type 2 diabetes patients in China who have already received basal insulin therapy. The data foundation of this study originates from the ONWARDS 4 clinical trial and research materials on Chinese type 2 diabetes patients. By comprehensively applying cost-utility analysis methods and binary search techniques, the appropriate price positioning of insulin icodec was determined from the perspective of China's healthcare system. In the long-term treatment simulation, we found that insulin icodec and insulin glargine performed similarly in terms of quality-adjusted life years (QALYs), with 10.15 and 10.07 years, respectively. Although the annual cost of insulin icodec was initially assumed to be equivalent to that of insulin glargine, in-depth analysis revealed that insulin icodec may have higher cost-effectiveness potential. Further price sensitivity analysis indicated that the reasonable cost range of insulin icodec lies between $851.95 and $1358.25. After fine-tuning through univariate sensitivity analysis, this cost range was revised to $784.90 to $1145.96, a conclusion that was robustly validated in subsequent probabilistic sensitivity analysis and scenario simulations. The conclusion drawn from this study is that, with insulin glargine as the cost reference, the economic cost of insulin icodec for Chinese type 2 diabetes patients is expected to range from $784.90 to $1145.96, providing a reference basis for clinical decision-making and healthcare policy formulation.

Effectiveness of gait analysis in management of osteoarthritis knee at primary care settings in Asia: a systematic review and meta-analysis protocol

Background: This systematic review will focus on the effectiveness of gait analysis on osteoarthritis knee management. The objectives of this systematic review and meta-analysis are to synthesize the clinical effectiveness and to compare the effectiveness of gait analysis in the osteoarthritis knee management in Asian population. Methods: This review will be conducted and reported according to PRISMA guidelines. To discover relevant papers, the search technique will use MeSH terms such as osteoarthritis knee, gait analysis, osteoarthritis knee management, primary healthcare, effectiveness, and Asia. Other synonymous words will be searched using the Boolean operator "and"/"or" on PubMed, Embase, Scopus, and Cochrane. Randomized controlled trials assessing the efficacy of gait analysis in the therapy of osteoarthritis knee patients in primary care settings in Asia will be considered. A random effects meta-analysis will be carried out using the REVMAN software version 5.3.5. The STATA software version SE16 will be used for both cumulative and comparative meta-analysis. Conclusions: This review and meta-analysis will be among the first to give an extensive purview on the effectiveness of gait analysis in osteoarthritis knee management at primary care settings in Asian population. This study will also acknowledge the effects of different gait analysis strategies with an attempt to compare the same. Trial Registration: The protocol has been registered at the International Prospective Register of Systematic Reviews (PROSPERO registration number: CRD42024540444).

Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality.

Reflecting on a Decade of Evolution: MapReduce‐Based Advances in Partitioning‐Based, Hierarchical‐Based, and Density‐Based Clustering (2013–2023)

ABSTRACTThe traditional clustering algorithms are not appropriate for large real‐world datasets or big data, which is attributable to computational expensiveness and scalability issues. As a solution, the last decade's research headed towards distributed clustering using the MapReduce framework. This study conducts a bibliometric review to assess, establish, and measure the patterns and trends of the MapReduce‐based partitioning, hierarchical, and density clustering algorithms over the past decade (2013–2023). A digital text‐mining‐based comprehensive search technique with multiple field‐specific keywords, inclusion measures, and exclusion criteria is employed to obtain the research landscape from the Scopus database. The Scopus‐obtained data is analyzed using the VOSViewer software tool and coded using the R statistical analysis tool. The analysis identifies the numbers of scholarly articles, diversities of article sources, their impact and growth patterns, details of most influential authors and co‐authors, most cited articles, most contributing affiliations and countries, and their collaborations, use of different keywords and their impact, and so forth. The study further explores the articles and reports the methodologies employed for designing MapReduce‐based counterparts of traditional partitioning, hierarchical, and density clustering algorithms and their optimizations and hybridizations. Finally, the study lists the main research challenges encountered in the past decade for MapReduce‐based partitioning, hierarchical, and density clustering. It suggests possible areas for future research to contribute further in this field.

Driving Under Cognitive Control: The Impact of Executive Functions in Driving

This review will explore the role of executive functions and the impact they have in facilitating the skills of vehicle operation. Executive functions are critical for the decision-making process, problem-solving, and multitasking. They are considered the primary factors in driving cases that demand drivers to react quickly and adapt to certain situations. Based on the PRISMA 2020 guidelines, this study aims to investigate, analyze, and categorize higher mental skills and their qualities directly related to driving. The literature review was performed in the following databases: PubMed, Web of Science, Scopus, and Google Scholar, using the article collections’ snowball search technique. The results suggest that key executive functions like working memory and inhibitory control are closely related to risky behavior and driving errors that lead to accidents. This review adds valuable insight by highlighting the significance of their contribution to future research, driver educational programs, and technology for improving driver safety. Consequently, collecting recent data will contribute to understanding new parameters that influence driving behavior, creating the possibility for appropriate proposals for future research.

A cloud 15kV-HDPE insulator leakage current classification based improved particle swarm optimization and LSTM-CNN deep learning approach

Real-time insulator leakage current classification is crucial in preventing the pollution flashover phenomenon and providing appropriate maintenance schedules in high-voltage transmission towers. However, current methodologies only utilize traditional artificial neural networks, which have limitations when performing big data analysis. This research developed a novel cloud 15kV-HDPE insulator leakage current classified framework, utilizing a long short-term memory convolutional neural network (LSTM-CNN). The hybrid model structure is optimized through hyperparameter fine-tuning based on improved particle swarm optimization (IPSO), which reduces human effort and considerable time compared with PSO and random search (RS) techniques. The IPSO-LSTM-CNN model can productively identify correlations between selected weather features and target leakage current levels of 15kV-HDPE insulators. LSTM efficiently captures long-term patterns in sequential data, while CNN layers competently extract high-level dependency in time-invariant information. Four 15kV-HDPE insulators’ datasets, collected in high-voltage transmission lines in the coastal area of Taiwan for more than one year, are deployed for analyzing and comparing classified performance. Other conventional models are developed to evaluate and compare classified performance with the proposed IPSO-LSTM-CNN approach, which acquires the most significant enhancement of 48.08 % loss, 45.91 % validating loss, 52.57 % MAE, 35.47 % validating MAE, 47.34 % MSE, 27.02 % validating MSE, 9.15 % PRE, 3.40 % validating PRE, 4.76 % REC, and 6.17 % validating REC. The experiment outcomes demonstrate that the developed IPSO-LSTM-CNN model acquires improved robustness and accuracy in the leakage current classified capability of 15kV-HDPE insulators.

Hybrid Mobile Robot Path Planning Using Safe JBS-A*B Algorithm and Improved DWA Based on Monocular Camera

This paper addresses the formidable challenge of enabling autonomous navigation in Mobile Robots (MRs), focusing on the development of advanced path planning strategies. Despite their pivotal role in diverse applications, they face challenges in dynamic settings due to limitation in existing Global Path Planning (GPP) and Local Path Planning (LPP) techniques. In response to this, we propose an innovative hybrid path planning approach that enhances the A* algorithm with a risk-aware heuristic function and integrates the Jump Point Search (JPS) technique for route optimization. Additionally, B-spline smoothing is employed for perceptually global trajectory refinement. Our approach also includes an innovative improvement to the Dynamic Window Approach (DWA) to align with the proposed enhanced A* algorithm for effective local navigation. Acknowledging the importance of high-quality input in path planning, we present substantial improvements to the IRDC-Net, a monocular-image semantic-segmentation model that we studied previously. Novel improvements include the integration of quantization and the Adam optimizer, along with the implementation of the Balanced Cross-Entropy loss function. These enhancements not only elevate the output quality of IRDC-Net but also reduce the model’s training parameters. The experimental results demonstrate the performance and viability of the proposed algorithm. Ultimately, the hybrid MR’s path planning algorithm exhibits proficiency across various tasks, particularly in addressing the challenge of evading moving obstacles to ensure the robot’s safety while adhering to the global path.

Real-time reactive task allocation and planning of large heterogeneous multi-robot systems with temporal logic specifications

Existing methods for the task allocation and planning (TAP) of multi-robot systems with temporal logic specifications mainly rely on optimization-based approaches or graph search techniques applied to the product automaton. However, these methods suffer from high computational cost and scale poorly with the number of robots and the complexity of temporal logic tasks, thus limiting the applicability in real-time implementation, especially for large multi-robot systems. To address these challenges, this work develops a novel TAP framework that can solve reactive temporal logic planning problems for large-scale heterogeneous multi-robot systems (HMRS) in real time. Specifically, we develop a planning decision tree (PDT) to represent the task progression and task allocation specialized for HMRS with temporal logic specifications. Based on the PDT, we develop two key search algorithms—the planning decision tree search (PDTS) and the interactive planning decision tree search (IPDTS)—where PDTS generates an offline plan which will be modified online by PDTS and IPDTS jointly to enable fast reactive planning if environmental changes or temporary tasks occur. Such a design can generate satisfying plan for HMRS with multiple orders of magnitude more robots than those that existing methods can manipulate. Rigorous analysis shows that the PDT-based planning is feasible (i.e., the generated plan is applicable) and complete (i.e., a feasible plan, if exits, is guaranteed to be found). The algorithm complexity further indicates that the solution time is only linearly proportional to the robot numbers and types. Simulation and experiment results demonstrate that reactive plan can be generated for large HMRS in real-time, which outperforms the state-of-the-art methods.

System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson’s disease: a network pharmacology and in-silico evaluation

IMPHY000797 derivatives have been well known for their efficacy in various diseases. Moreover, IMPHY000797 derivatives have been found to modulate such genes involved in multiple neurological disorders. Hence, this study seeks to identify such genes and the probable molecular mechanism that could be involved in the pathogenesis of Parkinson’s disease. The study utilized various biological tools such as DisGeNET, STRING, Swiss target predictor, Cytoscape, AutoDock 4.2, Schrodinger suite, ClueGo, and GUSAR. All the reported genes were obtained using DisGeNET, and further, the common genes were incorporated into the STRING to get the KEGG pathway, and all the data was converted to a protein/pathway network via Cytoscape. The clustering of the genes was performed for the gene-enriched data using two-sided hypergeometrics (p-value). The binding affinity of the IMPHY000797 was verified with the highest regulated 25 proteins via utilizing the “Monte Carlo iterated search technique” and the “Emodel and Glide score” function. Three thousand five hundred eighty-three genes were identified for Parkinson’s disease and 31 genes for IMPHY000797 compound, among which 25 common genes were identified. Further, the “FOXO-signaling pathway” was identified to be a modulated pathway. Among the 25 proteins, the highest modulated genes and highest binding affinity were exhibited by SIRT3, FOXO1, and PPARGC1A with the compound IMPHY000797. Further, rat toxicity analysis provided the efficacy and safety of the compound. The study was required to identify the probable molecular mechanism, which needs more confirmation from other studies, which is still a significant hit-back.

Odonata assemblages at two urban wetlands in West Bengal, India

Adult Odonata represent valuable indicators of natural habitat quality, ecosystem integrity, and pollution status in freshwater ecosystems. This study aimed to investigate how varying climatic condition, sub-habitat types, and physicochemical ambience of wetlands impact Odonata diversity. We compared the abundance and richness of Odonata in two urban wetlands and their surrounding areas: Purulia Sahebbandh (PS) located amidst Purulia town and faced higher anthropogenic threats, and Adra Sahebbandh (AS) located in a slightly remote location. Sampling was conducted over a period of one year (December 2018 to November 2019) using the Pollard walk method and direct search technique. A total of 47 Odonata species were recorded, including 33 species of dragonflies (Anisoptera) and 14 species of damselflies (Zygoptera). AS exhibited significant higher overall abundance compared to PS, with the monsoon season showing the highest abundance in both wetlands. Water-associated vegetation zones harbored the highest species richness. Species richness and abundance significantly differed among most of the sub-habitat types. Physicochemical factors such as pH, electrical conductivity, salinity, nitrate and phosphate concentrations, and dissolved oxygen significantly influenced Odonata abundance and species richness. The urban location, pollution load, and lack of natural vegetation in PS’s surrounding areas likely contributed to the lower Odonata abundance. Implementing sustainable management measures for wetlands and their surroundings is crucial to enhancing ecosystem services and supporting Odonata populations.

Drivers and barriers of voluntary sustainability initiatives in mining raw materials for batteries

The increasing adoption of electric vehicles (EV) is growing demand for battery raw materials including lithium, cobalt, nickel, and manganese. The potential for Voluntary Sustainability Initiatives (VSI) to mitigate the social and environmental impacts of mine development and operation is now in focus as resource supply scales rapidly. This study examines and synthesizes the drivers and barriers that influence extractive companies to voluntarily adopt sustainability initiatives, including certification and reporting, to mitigate social and environmental impacts. The methodology involved a thematic analysis of articles, initially identified through a systematic keyword search and further expanded with a snowball search technique. Thematic insights were classified and mapped against actors operating within the lithium-ion battery value-chain. The research found that drivers for adopting voluntary sustainability initiatives include maintaining market access, and addressing the increased need for frameworks to facilitate communication between companies and local communities. Barriers encompass short-term greenwashing undermining VSI legitimacy, and the lack of comprehensiveness of such initiatives regarding risk identification and risk mitigation for responsibly sourced commodities.

An embedded differential evolution approach to adaptive covariance inflation with the ensemble Kalman filter

ABSTRACT The limited ensemble sizes and the neglected model errors in ensemble-based Data Assimilation (DA) usually lead to underestimation of the error variance, and covariance inflation ‘inflates’ the covariance of a forecast or analysis ensemble by some positive factor in each assimilation cycle. In practice, the value of the inflation factor is often set by trial and error. Thus, a variety of adaptive covariance inflation (ACI) algorithms have been proposed to improve assimilation performance by adjusting the optimal parameters. Most of these used traditional optimization algorithms, such as gradient methods and relaxation methods. However, Evolutionary Algorithms(EAs), which encompass a range of population-based stochastic search techniques widely used in various engineering optimization problems, are rarely seen in DA. In this study, we developed a new framework for adaptive inflation factors, introducing differential evolution (DE) algorithm into local ensemble Transform Kalman filter (LETKF) that can explore the most suitable/optimal inflation factors online. A ‘micro-DE’ based data assimilation method can obtain better assimilation results step by step without taking a much longer time. The feasibility and effectiveness of the algorithm is demonstrated in an idealized Lorenz-96 model with simulated observations. Under both perfect and imperfect-model scenarios, it is found that the ‘micro-DE’LETKF method is capable of outperforming the original LETKF with a considerable lower computational cost. The adaptive inflation factor is optimally updated at each assimilation cycle either around the upper bound or around the lower bound, which means that only an adaptive factor helps mitigate the ensemble collapse due to a lack of spread. However, the further study will consider the application of the methodology to more complex atmospheric models.

Vitamin D Deficiency Prevalence in Pakistan: Common, Important, and Neglected: A Comprehensive Meta-Analysis

Abstract Vitamin D is crucial for normal body metabolism. Two important vitamin D sources are synthesis in the lower layers of the skin (90%) and diet (10%). This systematic review and meta-analysis attempted to determine the incidence of vitamin D deficiency in Pakistan by using the findings of numerous cross-sectional studies. For subgroup analysis, a population-based subgroup analysis and a geographical location-based subgroup analysis were done. Different search techniques were utilized on various databases using the terms “vitamin D deficiency,” “Pakistan,” and “prevalence.” The article names and abstracts were scrutinized, and associated complete texts were evaluated. The articles that satisfied the inclusion requirements were chosen for the meta-analysis. The I 2 test was used to determine heterogeneity. They were combined with the random-effect method. Out of 429 articles, 37 studies, with a total sample size of 19,338, were selected for the final analysis. The highest and lowest reported percentages observed were 100% and 15%, respectively. The overall pooled prevalence reported was 78% (95% CI = 74.2–82%). In subgroup analysis, children/adolescents showed a higher pooled prevalence of 84% (95% CI = 76.1–93.6%), and among province-wise subgroup analysis, Punjab province has shown a higher pooled prevalence of 84% (95% CI: 74–94%). Meta-analysis of the data showed a higher proportion of the population with deficient states. Interventions in public health are essential to address the high incidence of vitamin D insufficiency. Vitamin D levels can be increased, and related health concerns can be minimized through dietary supplementation, recommended sun exposure, and awareness initiatives.

Novel Particle Swarm Optimization Algorithm to Solve the Internet Shopping Optimization Problem with Shipping Costs

In this paper, we approach the Internet Shopping Optimization Problem with Shipping Costs (IShOP), an NP-hard-relevant problem in the current e-commerce environment. To our knowledge, several solution metaheuristic algorithms have been reported in the literature. In this paper, we propose a novel Particle Swarm Optimization algorithm (PSO) to solve the problem. PSO incorporates a neighborhood diversification technique (NDT), a local search (LS) technique, and an adaptive parameter tuning (APT) method. The proposed algorithm (NDTLSAA-PSO) includes two techniques at the end of each iteration to avoid premature convergence in the search process, balancing exploration and exploitation in selecting candidate solutions. A comparison of the performance of the proposed algorithm has been made against the performance of the best state-of-the-art memetic algorithm solution of the IShOP. A total of 90 instances classified into three groups of small, medium, and large sizes were resolved. The Wilcoxon and Holm non-parametric tests were applied to validate the significance of the differences observed between these two algorithms. The results show that the proposed NDTLSAA-PSO algorithm is superior to the memetic algorithm. In addition, the proposed algorithm obtains the best results in all the in-stances evaluated in terms of quality.

Eighteen new fast radio bursts in the High Time Resolution Universe survey

Context. Current observational evidence reveals that fast radio bursts (FRBs) exhibit bandwidths ranging from a few dozen MHz to several GHz. Traditional FRB searches primarily employ matched filter methods on time series collapsed across the entire observational bandwidth. However, with modern ultrawideband receivers featuring gigahertz-scale observational bandwidths, this approach may overlook a significant number of events. Aims. We investigate the efficacy of sub-banded searches for FRBs, whereby we look for bursts within limited portions of the bandwidth. The aim of these searches is to enhance the significance of FRB detections by mitigating the impact of noise outside the targeted frequency range, thereby improving signal-to-noise ratios. Methods. We conducted a series of Monte Carlo simulations for the 400-MHz bandwidth Parkes 21-cm multi-beam (PMB) receiver system and the Parkes Ultra-Wideband Low (UWL) receiver, simulating bursts down to frequency widths of about 100 MHz. Additionally, we performed a complete reprocessing of the high-latitude segment of the High Time Resolution Universe South survey (HTRU-S) of the Parkes-Murriyang telescope using sub-banded search techniques. Results. Simulations reveal that a sub-banded search can enhance the burst search efficiency by 67−42+133% for the PMB system and 1433−126+143% for the UWL receiver. Furthermore, the reprocessing of HTRU led to the confident detection of 18 new bursts, nearly tripling the count of FRBs found in this survey. Conclusions. These results underscore the importance of employing sub-banded search methodologies to effectively address the often modest spectral occupancy of these signals.

Wind power prediction based on deep learning models: The case of Adama Wind farm

Wind is a renewable energy source that is used to generate electricity. Wind power is one of the suitable solutions for global warming since it is free from pollution, doesn’t cause greenhouse effects, and it is a natural source of energy. However, Wind power generation highly depends on weather conditions. It is very difficult to easily predict the amount of power generated from wind at a particular instant in time. Adama wind power farm is one of the wind farms in Ethiopia. There is no accurate and reliable forecasting model for the Adama wind farm that enables the forecasting of the power generated from the farm. The main objective of this research is to develop a wind power forecasting model for the Adama wind farm using deep learning techniques. Forecasting of wind power generation capacity involves appropriate modeling techniques that use past wind power generation data. The experiments have been conducted using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). To achieve the highest forecasting accuracy, four years of data (from 2016-2019), with five-minute intervals, have been collected with a total of 163,802 rows. For hyperparameter optimization grid search and random search techniques have been utilized. The performances of the proposed deep learning models were investigated error metrics, including Mean Absolute Errors (MAE) and the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R2). Bi-LSTM outperforms the other two algorithms, scoring 0.644, 0.388, 0.769 and 0.978 MAE, MAPE, RMSE and R2 values respectively. Such wind power forecasting helps energy planners and regional power providers to compute power production and energy generated from other sources.

APPLYING SOFT COMPUTING METHODS IN BUSINESS ANALYTICS USING HYBRID GENETIC ALGORITHMS

In the era of big data and advanced analytics, the application of soft computing techniques has emerged as a powerful tool in solving complex business problems. This paper presents the use of hybrid genetic algorithms (HGAs) in business analytics to address challenges related to optimization, prediction, and decision-making processes. Traditional algorithms often struggle with large, nonlinear, and dynamic datasets typical of business environments. The incorporation of soft computing techniques such as genetic algorithms (GAs) and their hybridization with other methods like fuzzy logic and neural networks can help overcome these limitations. The problem addressed in this research is optimizing decision-making in marketing strategies, focusing on maximizing return on investment (ROI). Standard methods face difficulties in navigating through vast datasets and discovering optimal solutions. The hybrid genetic algorithm proposed in this study combines the exploration strength of GAs with the exploitative precision of local search techniques. The model was tested using a real-world dataset of marketing expenditures and revenues from a retail company. The HGA achieved an ROI improvement of 25%, significantly outperforming standard GAs and traditional optimization methods, which yielded only a 12% improvement. The flexibility and efficiency of this approach make it ideal for various business applications, including supply chain optimization, customer segmentation, and product pricing.

Legal Responsibility For Operational Failures Through Robotic Telesurgery

Technological developments, especially in the health sector, are currently growing very rapidly. Robotic Telesurgery or remote surgical robots are expected to become a technology that can realize health equality in Indonesia, but it is a shame that technological progress is not comparable to the relevant regulations that regulate it, it is feared that this will not guarantee legal certainty which could lead to malpractice. The method used in this research is a normative legal research method because the focus of the study departs from the vagueness of norms using the approach: statute approach, conceptual approach, as well as analytical approach. The legal material search technique uses document study techniques, and study analysis uses qualitative analysis. The results of this research are that there is a void in norms in Law Number 17 of 2023 concerning Health which does not regulate legal responsibility for failed operations through Robotic Telesurgery So it is necessary to create a law that specifically regulates the responsibilities of doctors.

OStr-DARTS: Differentiable Neural Architecture Search Based on Operation Strength.

Differentiable architecture search (DARTS) has emerged as a promising technique for effective neural architecture search, and it mainly contains two steps to find the high-performance architecture. First, the DARTS supernet that consists of mixed operations will be optimized via gradient descent. Second, the final architecture will be built by the selected operations that contribute the most to the supernet. Although DARTS improves the efficiency of neural architecture search (NAS), it suffers from the well-known degeneration issue which can lead to deteriorating architectures. Existing works mainly attribute the degeneration issue to the failure of its supernet optimization, while little attention has been paid to the selection method. In this article, we cease to apply the widely-used magnitude-based selection method and propose a novel criterion based on operation strength that estimates the importance of an operation by its effect on the final loss. We show that the degeneration issue can be effectively addressed by using the proposed criterion without any modification of supernet optimization, indicating that the magnitude-based selection method can be a critical reason for the instability of DARTS. The experiments on NAS-Bench-201 and DARTS search spaces show the effectiveness of our method.

Solar PV focused LFC studies utilizing an SFS-optimized PID with fractional derivative (PIDDμ), and incorporating BESS and FESS applications

This study investigates the application of a PID controller with a fractional derivative component (PIDDμ), optimized using the stochastic fractional search (SFS) technique, for load frequency control (LFC) in power systems. Additionally, solar PV and battery energy storage systems (BESS) and flywheel energy storage systems (FESS) are integrated as backup sources to enhance the LFC performance. To assess the effectiveness of the proposed approach, three error metrics—Integral Time Absolute Error (ITAE), Integral Time Multiplied Square Error (ITSE), and Integral of Absolute Error (IAE)—are evaluated. The results are compared with recent optimization-based LFC techniques across various scenarios. It is found that the SFS-PIDDμ achieves IAE=0.006786, ITSE=1.07e-05, and ITAE=0.01853, which are significantly lower than the error values produced by other LFC techniques. Furthermore, integrating solar PV, along with BESS and FESS, further reduces these error values. The graphical LFC responses of the SFS-PIDDμ outperform other optimization-based LFC techniques. The robustness of the approach is also validated against random load changes of varying magnitudes and for broader parametric variations.