Optimization Techniques Research Articles

Reliability-based topology optimization of imperfect structures considering uncertainty of load position

In this paper, a novel optimization technique is implemented to explore the effects of considering uncertain load positions. Therefore, the integration of reliability-based design into structural topology optimization, while considering imperfect geometrically nonlinear analysis, is proposed. By comparing the results obtained from perfect and imperfect geometrically and materially nonlinear analyses, this study examines the impact of nonlinearity on probabilistic and deterministic analyses. Concerning probabilistic analysis, the originality of this research lies in its incorporation of the position of the applied load as a stochastic variable. This distinctive approach complements the consideration of other relevant parameters, including volume fraction, material properties, and geometrical imperfections, with the overarching goal of capturing the variability arising from real-world conditions. For the assessment of uncertainties, normal distribution is assumed for all these parameters. Normal distributions are chosen due to their advantages in terms of simplicity, ease of implementation, and computational efficiency. These characteristics are particularly beneficial when dealing with complex optimization algorithms and extensive analyses, as is the case in our research. The proposed algorithm is validated according to the results of benchmark problems. Structural examples like cantilever beam, pinned-shell, and L-shaped beam problems are further explored within the context of imperfect geometrically nonlinear reliability-based topology optimization, with specific regard to the probabilistic aspect of the location of the externally applied loads. Moreover, the results of the suggested approach suggest that the inclusion of a probabilistic design strategy has influenced topology optimization. The reliability index acts as a controlling constraint for the resulting optimized configurations, including the mean stress values associated with the resulting topologies.

Exploring the optimal indwelling pleural catheter drainage technique in India.

Exploring the optimal indwelling pleural catheter drainage technique in India.

Effect of solids concentration and operational variables on the performance of a geometrically optimized concentrator hydrocyclone employing a pseudoplastic fluid

The employment of hydrocyclones in thickening operations is an attractive option when compared to centrifuges and filters due to their low operating, maintenance, and acquisition costs. However, the performance of hydrocyclone separation is impaired as the concentration of solids and the viscosity of the suspension increases. Using geometric optimization techniques, the Federal University of Uberlândia developed one concentrator hydrocyclone named HC. When working with diluted and Newtonian slurries, the HC could generate a stream 45 times more concentrated than the one fed into it. In this study, the HC performance was evaluated when operating pseudoplastic fluids containing up to 10% solids by volume. The combination of the underflow diameter and vortex finder length with an adequate supply of pressure energy maintained the thickening potential of the HC even when the rheology of the fluid was changed. For different working suspensions, the HC hydrocyclone achieved a minimum water split to underflow of 5%, a maximum concentration ratio of 7.0, and a maximum efficiency of 49%. The encouraging results obtained by the HC validated the benefits of the geometric optimization, as they point to a significant advance in the thickening operation of non–Newtonian sludges with a flow behavior index greater than 0.5.

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

Advancing AutoML: A Deep Dive into Neural Architecture Search (NAS) and Its Optimization Techniques

Abstract: Neural Architecture Search (NAS) is a pivotal technique in the field of automated machine learning (AutoML), enabling the automatic design of optimal neural network architectures. As deep learning models grow in complexity, NAS offers a scalable approach to improving model performance by exploring vast search spaces of potential architectures. In our research, we investigate the mathematical foundations and algorithms underpinning NAS, focusing on reinforcement learning-based, evolutionary, and gradient-based approaches. We provide mathematical proofs of convergence and efficiency for each method and analyze real-world applications, such as image classification and natural language processing (NLP). Through a comprehensive exploration of NAS, we aim to highlight its impact on AutoML and its potential to automate neural network design effectively while addressing challenges in computational cost and generalization.

PID-based control strategies for enhancing stability and precision in electro-hydraulic actuation systems

Electro-hydraulic actuation systems are essential in industrial applications but often face challenges related to nonlinearity, leading to instability and reduced precision. This paper explores the application of Proportional-Integral-Derivative (PID) control strategies to enhance the stability and precision of these systems. A detailed system model is developed, and the PID controller is designed and tuned using optimization techniques. Simulation results demonstrate improvements in performance metrics, such as reduced overshoot, settling time, and steady-state error. The proposed control strategy is validated experimentally on a physical setup, confirming its effectiveness in real-world applications. The findings show that PID-based control significantly enhances both stability and precision in electro-hydraulic actuation systems, offering a practical solution for industrial control challenges.

AI-driven predictive maintenance and optimization of renewable energy systems for enhanced operational efficiency and longevity

The rapid growth of renewable energy systems necessitates advanced strategies for maintenance and optimization to ensure long-term operational efficiency and sustainability. Traditional approaches often fall short in predicting failures and optimizing performance across diverse and dynamic renewable energy infrastructures. This study investigates the application of artificial intelligence (AI) techniques for predictive maintenance and optimization of renewable energy systems, with the aim of enhancing operational efficiency and extending system longevity. We employ a combination of machine learning algorithms, including deep neural networks and reinforcement learning, to develop predictive models and optimization strategies. These models are trained on large-scale datasets collected from operational wind farms, solar installations, and hydroelectric plants. Our results demonstrate that AI-driven approaches can predict equipment failures with 92% accuracy, reducing unplanned downtime by 35% compared to traditional methods. Moreover, AI-optimized operational parameters improved overall energy output by 8.5% across the studied systems. The proposed framework also showed adaptability to various environmental conditions and system configurations, suggesting broad applicability across the renewable energy sector. This research underscores the significant potential of AI in revolutionizing maintenance practices and operational strategies in renewable energy systems, paving the way for more reliable, efficient, and sustainable clean energy production.

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

Experimental investigation on tailoring compressive properties and energy absorption of 3D printed gradient star re-entrant hybrid auxetic structure

Purpose Fused filament fabrication (FFF) is a widely used 3D printing technique for the fabrication of mechanical metamaterials with intricate geometries. Gradient strategy is applied to geometric parameters of gradient star re-entrant hybrid auxetic (GSRA) structure. Deformation behaviour is studied under compressive loading. The purpose of this study is to investigate the influence of gradient geometric parameters on mechanical properties, namely, specific strength (SS), specific modulus (SM) and specific energy absorption (SEA). Design/methodology/approach Response surface methodology (RSM) is implemented for the design of experiments of gradient geometric parameters to minimize the number of experimental tests. Acrylonitrile butadiene styrene material is used for the fabrication of GSRA structures by FFF technique. The best set of gradient parameters has been optimized maximizing all three responses using RSM and artificial neural network optimization technique. Findings During compressive testing, row-wise deformation is observed with two-stage plateau regions, which results in increase in SEA of the structure. Furthermore, based on analysis of variance and 3D response plots, it is found that height gradient is the most influencing gradient geometric parameter on SS and SM, whereas the wall thickness gradient has maximum influence on SEA. Meanwhile, the interaction effect of wall thickness gradient and height gradient has maximum influence on SS, SM and SEA. Research limitations/implications This study of applying gradient strategy to geometric parameters is limited to GSRA structure under compressive loading. In addition, findings are valid within the selected range of gradient geometric parameters. These findings are useful for the selection of gradient geometric parameters to maximize SS, SM and SEA of GSRA structure simultaneously. These outcomes pave the way for designing light-weight gradient hybrid auxetic structures in the field of construction, aerospace, automobile and biomedical engineering. Originality/value Limited experimental study is available on investigating the influence of gradient geometric parameters on mechanical properties, namely, SS, SM and SEA, and deformation behaviours of hybrid auxetic structures. This study directly addresses the above research gaps.

Strengthening urban deer management with structured decision making

Urban deer management (UDM) decision‐makers face social, ecological, regulatory, and economic pressures when creating an agreeable deer management plan for stakeholders. Historically, decision making techniques (e.g. consensus‐based analyses) have not effectively balanced UDM elements leading to short‐lived management progress. Structured decision making (SDM) is a formal, values‐based approach to identifying an optimal management solution. Although SDM has been applied to other wildlife management decisions, it has not been applied in UDM. We provide the first case‐study of SDM‐based UDM and streamline the process for wildlife managers. We focused on one suburban and one semi‐rural community near Atlanta, Georgia, USA. We established a problem statement to capture what decision‐makers must address in UDM programs and reviewed the primary literature and UDM plans from five states to establish four fundamental objectives. We then utilized a support–effectiveness analysis to identify acceptable UDM alternatives and gathered expert insights to calculate the consequences of alternatives on objectives. Finally, we asked each community's decision‐makers to weigh objectives against each other. Using this framework, education was the most optimal UDM technique to implement in the suburban community, and sharpshooting was the most optimal UDM technique to implement in the semi‐rural community. This paper positions SDM as a transparent, defensible, inclusive, and adaptive approach to UDM. Furthermore, our SDM framework provides managers with the means and justification to create an optimal plan of action for communities in need of UDM.

Short-term outcomes and quality of life of esophagogastrostomy versus the double-tract reconstruction after laparoscopic proximal gastrectomy

BackgroundThere is no optimal reconstruction technique after proximal gastrectomy. The esophagogastrostomy (EG) is a rather simple procedure technically, but the incidences of reflux esophagitis and anastomotic stricture are higher. While the double-tract reconstruction (DTR) can lessen postoperative reflux esophagitis, it is technically complex with a long operation time. The purpose of this study was to evaluate the quality of life (QoL) and short-term outcomes of the two reconstruction techniques.MethodsWe retrospectively collected consecutive patients with upper-third gastric adenocarcinoma and adenocarcinoma of the esophagogastric junction (AEG) at our center between 2019 June and 2023 May. Patients who underwent laparoscopic proximal gastrectomy (LPG) with EG or DTR were included in this study. A comparison was made between the clinical and pathological characteristics of patients and their surgical parameters, postoperative complications, and its 1-year QoL in two groups. The QoL of the two groups was assessed by Visick grading, the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 and EORTC QLQ-STO22 scales at 1 year after operation. The nutritional status of the two groups was evaluated by BMI, hemoglobin and serum albumin.ResultsAII the qualified patients were divided EG group (n = 63) and DTR group (n = 93). Compared to the DTR group, the blood loss volume of EG group was more (p = 0.001). There were no significant differences in operation duration, number of lymph nodes dissected, and postoperative length of stay between the two groups(p > 0.05). No statistical differences were observed in terms of the incidence of early complications and Clavien-Dindo classification as well(p > 0.05). After one year, the Visick grade of the DTR group was better than EG group (p = 0.040). The multivariable logistic regression analysis showed the only independent risk factor for reflux esophagitis was the reconstruction method. According to the EORTC QLQ-C30 questionnaire, patients in the DTR group had a better global health status(p = 0.001) and complained less about nausea and vomiting(p = 0.033), and appetite loss (p = 0.022). Patients in the DTR group complained less about reflux (p = 0.030) based on the EORTC QLQ-STO22 questionnaire. The multiple linear regression analysis revealed that the reconstruction method, reflux esophagitis and age had a linear relationship with the global health status score. Regarding nutritional status, BMI of the two groups both decreased 1 year after operation, and BMI decline value of the DTR group was lower than EG group (p = 0.001). There is no statistically significant difference between the two groups as for postoperative change in hemoglobin and serum albumin.ConclusionOur findings suggest that it is possible for skilled surgeons to achieve minimal blood loss volume without significantly increasing operation duration when performing DRT, which does not raise risk. In terms of anti-reflux, postoperative QoL and BMI maintenance, 1-year postoperative follow-up outcomes reveal the DTR is superior to EG, which deserve further research and promotion.

MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature

Alzheimer’s disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely detection and prompt treatment can substantially slow its progression. This study presented a framework for automated AD detection using brain MRIs. Firstly, the deep network information (i.e., features) were extracted using various deep-learning networks. The information extracted from the best deep networks (EfficientNet-b0 and MobileNet-v2) were merged using the canonical correlation approach (CCA). The CCA-based fused features resulted in an enhanced classification performance of 94.7% with a large feature vector size (i.e., 2532). To remove the redundant features from the CCA-based fused feature vector, the binary-enhanced WOA was utilized for optimal feature selection, which yielded an average accuracy of 98.12 ± 0.52 (mean ± standard deviation) with only 953 features. The results were compared with other optimal feature selection techniques, showing that the binary-enhanced WOA results are statistically significant (p < 0.01). The ablation study was also performed to show the significance of each step of the proposed methodology. Furthermore, the comparison shows the superiority and high classification performance of the proposed automated AD detection approach, suggesting that the hybrid approach may help doctors with dementia detection and staging.

Mitral regurgitation improvement following left bundle branch pacing versus right ventricular septum pacing: a dedication to postoperative amelioration of mitral regurgitation

Abstract Background Mitral regurgitation (MR) is correlated with left ventricular (LV) remodeling and LV function in patients. Left bundle branch pacing (LBBP) exhibits a significant decline in MR in left bundle branch block (LBBB) and cardiomyopathy patients. This research aims to evaluate the influence of LBBP on MR in comparison to right ventricular septal pacing (RVSP) in patients with atrioventricular block (AVB) or sick sinus syndrome (SSS). Methods Sixty consecutive patients with SSS or AVB were enrolled if they underwent pacemaker implantation via LBBP or RVSP. Patients with a history of mitral valve surgery were excluded. Echocardiogram parameters were assessed to measure MR and LV remodeling as well as LV function at baseline and follow-up. Results Thirty-six patients (60 %; mean age: 70.39 ± 14.80 years; 61.1 % male) underwent successful LBBP, and 24 patients (40 %; mean age: 71.58 ± 10.91 years; 66.7 % male) underwent RVSP. The average follow-up duration was relatively comparable between the two groups. In comparison to RVSP, LBBP demonstrated significant changes in MR area, and severity at baseline and follow-up (P<0.05). LBBP independently predicted MR amelioration in patients who underwent pacemaker implantation (HR=0.289, CI=0.087–0.9961; P=0.043). At follow-up, the LBBP subgroup with moderate/severe MR exhibited significantly shorter lead-TVA-dist, higher left atrium (LA) diameters, and mitral E/A ratio (P<0.05) compared to the none/mild MR subgroup. In LBBP with MR amelioration, LV remodeling (ΔLVEDD) was significantly enhanced compared to baseline (P=0.016). Additionally, a non-significant trend toward better improvement in LV function (ΔLVEF and ΔE/A) was observed in patients with MR amelioration. Conclusion LBBP, considered an optimal pacing technique for SSS or AVB patients, may substantially reduce MR and subsequently enhance LV remodeling and function compared to RVSP. This open, observational pilot study warrants confirmation through further investigations.Comparison between LBBP and RVSPKaplan-Meier curves for MR amelioration

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

This paper explores innovative strategies for enhancing system efficiency in modern infrastructure by integrating artificial intelligence (AI), edge computing, and resource optimization techniques. As the complexity of infrastructure systems increases, traditional methods often fall short in addressing the evolving demands of operational efficiency and reliability. By leveraging AI algorithms for predictive analytics and resource allocation, and utilizing edge computing for real-time data processing, organizations can significantly improve performance and responsiveness. The study examines case studies that highlight successful implementations of these technologies across various sectors, including infrastructure monitoring, and grid maintenance. Insights from this research provide a framework for practitioners to adopt these advanced methodologies, ultimately leading to more resilient and efficient infrastructure systems.

Multiple Function Merging for Code Size Reduction

Resource-constrained environments, such as embedded devices, have limited amounts of memory and storage. Practical programming languages such as C++ and Rust tend to output multiple similar functions by monomorphizing polymorphic functions. An optimization technique called Function Merging, which merges similar functions into a single function, has been studied. However, in the state-of-the-art approach, the number of functions that can be merged at once is limited to two; thus, efficiently merging three or more functions, which are often generated from polymorphic functions, has been impossible. In this study, we propose Multiple Function Merging optimization, which targets merging three or more similar functions into a single function using a multiple sequence alignment algorithm. With multiple aligned information, Multiple Function Merging can increase merge opportunities and reduce extra branching overheads at the code generation stage. We evaluated it using the SPEC CPU benchmark suite and some large-scale C/C++ programs, and the results show that it reduces code size by as much as 7.61% compared with the state-of-the-art approach.

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

The Impact of Server-Side Rendering on UI Performance and SEO

This article explores the impact of Server-Side Rendering (SSR) on UI performance and Search Engine Optimization (SEO) in modern web development. It examines how SSR addresses the challenges faced by Single-Page Applications (SPAs) and content-rich websites by improving initial page load times, enhancing crawlability, and facilitating faster indexing. The article delves into the technical aspects of SSR implementation, discussing popular frameworks like Next.js, Nuxt.js, and Angular Universal. It also analyzes the performance benefits of SSR, including reduced Time to First Contentful Paint (FCP) and improved SEO scores across various web application types and network conditions. The article further explores optimization techniques such as code splitting, caching strategies, and streaming SSR, providing a comprehensive overview of SSR's role in creating faster, more discoverable, and user-friendly web applications.

Optimal control design for power system stabilizer using a novel crow search algorithm dynamic approach

This paper proposes the first application of a novel improved metaheuristic optimization technique, dynamic crow search algorithm (DCSA) to find the optimal design of the power system stabilizer (PSS). Using the novel DCSA adaptive approach provides a global search capability as well as fastness and effectiveness to get the PSS best parameters under the search space constraints and local optimums. Moreover, PSS parameters has a crucial effect on power system stability that is a major nowadays engineering concern, where its best parameters could make a difference by damping small-signal stability oscillations effectively and in the smallest amount of time if they are obtained through a robust algorithm. As a result, PSS will prevent large types of instabilities which could reach up to some generation stations shedding. The novel proposed algorithm is for first time used and tested under different power system instabilities scenarios by using single-machine infinity bus model over multiple operating conditions to damp out perturbation signals effectively by means of getting optimal PSS design parameters, where Integral absolute error (IAE) performance index is used as an objective function to be minimized. Additionally, results obtained by the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version and other comparison methods. It is revealed that the proposed adaptive method is able to improve the power system stability dynamics and damp out perturbation oscillations successfully.