Optimization of truss structures with two archive-boosted MOHO algorithm

Alexandria Engineering Journal, Год журнала: 2025, Номер 120, С. 296 - 317

Опубликована: Фев. 18, 2025

Язык: Английский

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A standardised comparison of chest and percutaneous drainage catheters to evaluate the applicability of the ‘French’ sizing units

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 10, 2025

A variety of medical specialities undertake percutaneous drainage but understanding device performance outside radiology is often limited. Furthermore, the current catheter sizing using "French" measurement outer diameter unhelpful; it does not reflect internal and gives no information on flow rate. To illustrate this to improve selection, notably for chest drainage, we assessed variation drain under standardised conditions. Internal rates 6Fr.-12Fr. catheters from 8 manufacturers were tested ISO 10555-1 standard: diameters measured with Meyer calibrated pin-gauges. Flow calculated over a period 30s after achieving steady state. Evaluation demonstrated wide range 6Fr., 8Fr., 10Fr. 12Fr. catheters. Mean measurements 1.49 mm (SD:0.07), 1.90 (SD:0.10), 2.43 (SD:0.11) 2.64 (SD:0.03) respectively. 128 mL/min (SD:37.6), 207 (SD: 55.1), 291 (SD:36.7) 303 (SD:20.2) There was such variance that there overlap between different size: thin-walled drains performed better than "Seldinger" drains. Better characteristics declaration data by are required allow optimum choice individual patients outcomes.

Язык: Английский

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Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 30, 2025

Abstract The classification of chronic diseases has long been a prominent research focus in the field public health, with widespread application machine learning algorithms. Diabetes is one high prevalence worldwide and considered disease its own right. Given nature this condition, numerous researchers are striving to develop robust algorithms for accurate classification. This study introduces revolutionary approach accurately classifying diabetes, aiming provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) proposed combination Kernel Extreme Learning Machine (KELM) diabetes prediction model. First, (SBOA) enhanced by integrating particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, quantum computing-based t-distribution variations. performance QHSBOA validated using CEC2017 benchmark suite. Subsequently, used optimize kernel penalty parameter $$\:C$$ bandwidth $$\:c$$ KELM. Comparative experiments other models conducted datasets. experimental results indicate that QHSBOA-KELM model outperforms comparative four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, specificity. offers an effective method early diagnosis diabetes.

Язык: Английский

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A fuzzy system based self-adaptive memetic algorithm using population diversity control for evolutionary multi-objective optimization

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 17, 2025

Abstract Simulated by nature’s evolution, numerous evolutionary algorithms had been proposed. These perform better for a particular problem domain and extensive parameter fine tuning adaptations are required in optimizing problems of varied domain. This paper aims to develop robust self-adaptive memetic algorithm combining Differential Evolution based algorithm, popular population global search method with the Controlled Local procedure solve multi-objective optimization problems. Memetic Algorithm is an enhanced it combines local techniques faster convergence. improves both exploration exploitation, preventing premature convergence also refines current best solutions efficiently. Proposed named as Fuzzy using Diversity control (F-MAD). In F-MAD, diversity controlled through parameters self-adaptation (DE) such as, crossover rate scaling factor two fuzzy systems. A adapted guiding process thus balancing explore-exploit cycle. The selection aid decision space attaining optimal uniform distribution terms metrics objective space. characteristics help proposed suitable be extended different application without need trial-and-error parameters. performance tested standard benchmark test problems-CEC 2009 DTLZ further validated statistical test. It compared experiment results indicate that F-MAD well than State of-The-Art (SOTA) taken comparison. attains 8 out 10 CEC (UF1-UF10) when 20 other For problems, ALL 7 (DTLZ 1-DTLZ7) SOTA algorithms. evaluated Friedman rank significantly outperformed

Язык: Английский

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A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 8, 2025

Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, finance. The Network generates unstructured information about brands, political issues, cryptocurrencies, global pandemics. major challenge is translating this into reliable consumer opinion as it contains jargon, abbreviations, reference links with previous content. Several ensemble models have been introduced mine the enormous noisy range on social platforms. Still, these need more predictability are less-generalized sentiment analysis. Hence, an optimized stacked-Long Short-Term Memory (LSTM)-based analysis model proposed cryptocurrency price prediction. can find relationships of latent contextual semantic co-occurrence statistical features between phrases in a sentence. Additionally, comprises multiple LSTM layers, each layer Particle Swarm Optimization (PSO) technique learn based best hyperparameters. model's efficiency measured terms confusion matrix, weighted f1-Score, Precision, Recall, training accuracy, testing accuracy. Moreover, comparative results reveal that stacked outperformed. objective introduce benchmark predicting prices, which will be helpful other predictions. A pretty significant thing presented process multilingual cross-platform data. This could achieved by combining LSTMs embeddings, fine-tuning, effective preprocessing providing accurate robust across diverse languages, platforms, communication styles.

Язык: Английский

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Method for reconstructing safety and arming motion process by integrating Kalman filter and KCF

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 11, 2025

This paper addresses the challenge of reconstructing motion process safety and arming (S&A) mechanism in fuze by transforming problem into a target detection tracking problem. A novel method, which fuses an improved Kalman filter with temporal scale-adaptive KCF (AKF-CF), is proposed. The methodology introduces key innovations: (1) Extraction grayscale images directional gradient histogram (HOG) features target, followed use Adaptive Wave PCA-Autoencoder (AWPA) method to accurately capture multi-modal multi-scale target; (2) Application bilinear interpolation hybrid filtering techniques generate spatial bounding box for filtered enabling dynamic adjustment size; (3) Integration occlusion-aware using average peak correlation energy (APCE) trigger Kalman-based position prediction when occluded, thus mitigating drift. Finally, curve plotted, facilitating reconstruction S&A mechanism's trajectory. Experimental results from five datasets indicate effectiveness proposed method. Compared ACSRCF algorithm on OTB50 dataset, achieves accuracy success rate improvements 0.8 0.6%, respectively. On OTB100 it attains 92.50% 68.10% rate, outperforming other related algorithms. These highlight significant demonstrating algorithm's robustness handling challenging scenarios. Additionally, reconstructed curves effectively replicate mechanical trajectories, showcasing strong performance complex occlusion environments.

Язык: Английский

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Dynamic path planning of UAV with least inflection point based on adaptive neighborhood A* algorithm and multi-strategy fusion

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 12, 2025

Planning a safe and efficient global path in complex three-dimensional environment is challenging optimization task. Existing planning algorithms are faced with problems such as lengthy path, too many inflection points insufficient dynamic obstacle avoidance performance. In order to solve these challenges, this paper proposes algorithm (MSF-MTPO) multi-strategy fusion achieve the least point optimization. Initially, an adaptive extended neighborhood A* designed, which dynamically adjusts extension range according distribution of obstacles around current location, selecting optimal travel direction step size each time reduce redundant paths unnecessary nodes. Then, combined two-way search mechanism, starting from original end point, opposite node searched target respectively, so number nodes time. further improve efficiency, trajectory correction method designed eliminate on premise ensuring safety. Fourthly, problem deviation or excessive softening caused by limited control existing smoothing methods, local tangent circle proposed, effectively improves smoothness basis retaining superiority path. Finally, used guiding artificial potential field avoid falling into optimum realize avoidance. addition, performance compared several advanced different environments, MSF-MTPO has lowest cost scenarios, proves effectiveness stability UAV 3D planning.

Язык: Английский

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The role of generative AI tools in shaping mechanical engineering education from an undergraduate perspective

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 17, 2025

Abstract This study evaluates the effectiveness of three leading generative AI tools-ChatGPT, Gemini, and Copilot-in undergraduate mechanical engineering education using a mixed-methods approach. The performance these tools was assessed on 800 questions spanning seven core subjects, covering multiple-choice, numerical, theory-based formats. While all demonstrated strong in questions, they struggled with numerical problem-solving, particularly areas requiring deep conceptual understanding complex calculations. Among them, Copilot achieved highest accuracy (60.38%), followed by Gemini (57.13%) ChatGPT (46.63%). To complement findings, survey 172 students interviews 20 participants provided insights into user experiences, challenges, perceptions academic settings. Thematic analysis revealed concerns regarding AI’s reliability tasks its potential impact students’ problem-solving abilities. Based results, this offers strategic recommendations for integrating curricula, ensuring responsible use to enhance learning without fostering dependency. Additionally, we propose instructional strategies help educators adapt assessment methods era AI-assisted learning. These findings contribute broader discussion role implications future methodologies.

Язык: Английский

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Predicting the chemical equilibrium point of reacting components in gaseous mixtures through a novel Hierarchical Manta-Ray Foraging Optimization Algorithm

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 1, 2025

Abstract This study proposes a Hierarchical Manta-Ray Foraging Optimization (HMRFO) algorithm for calculating the equilibrium points of chemical reactions. To improve solution diversity in trial population and enhance general optimization effectivity algorithm, an ordered hierarchy is integrated into original taking account efficient search strategies Elite-Opposition learning, Dynamic Opposition Learning, Quantum operator. Within this proposed concept, Manta-ray divided three main sub-populations: Elite Oppositional learning scheme manipulates top elite individuals, equations update average members, quantum-based process worst members. The improved MRFO applied to hundred 30D 500D benchmark functions, results have been compared those obtained from state-of-art metaheuristic optimizers. Then, optimizer solved twenty-eight test problems previously employed CEC-2013 competitions, corresponding were benchmarked against well-reputed metaheuristics. research also suggests novel mathematical model solving ideal gas mixtures. Four challenging case studies related performed by HMRFO varying conditions, it observed that can effectively cope with tedious nonlinearities complexities governing thermodynamic models associated gaseous reacting mixture components.

Язык: Английский

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Multi objective elk herd optimization for efficient structural design

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 6, 2025

This research presents an advancement of the Elk Herd Optimization targeting specific real-world multi-objective optimization problems, this algorithm is stated as (MOEHO). MOEHO exploits reproductive behaviour among elk herds for balancing exploration and exploitation within procedure toward diversification convergence. The performed better over set small-to-medium scale structural design problems thus widely applicable in engineering design. Further, when compared with eight benchmark truss structures against five well-established algorithms has outperformed them perspective performance parameters like Spacing (SP), Hypervolume (HV) Inverted Generational Distance (IGD). More concrete statistical analysis through Friedman rank test also ascertains robustness efficiency algorithm, especially at high complexities optimization. attracts attention to ability such which maintains a balance between exploitation. Computational qualitatively diversifying solutions along Pareto front, makes it complex applications. Further into extension applicability on more dimensional applied even energy systems

Язык: Английский

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