Search Results (4,128)

With the development of manufacturing towards stricter precision requirements and increasingly complex geometric shapes, dimensional accuracy has become a key factor affecting precision engineering components used in many industries. Effective cooling and lubrication methods have always been a meaningful way to improve the surface quality of cutting materials. Minimum-quantity lubrication technology mixes compressed air with cutting fluid, produces a spray at ambient temperature, and guides these droplets to the cutting area under the action of high-pressure air to promote penetration into the contact area between the tool, workpiece, and chip. Minimum-quantity lubrication can be used to increase cutting speed, cool workpieces, improve workpiece quality, and significantly reduce the pollution caused by cutting fluid to the environment. However, minimum-quantity lubrication technology still cannot meet the requirements of sustainable machining in cutting processes. A test device platform for milling 6030 aluminum alloy with minimal quantity lubrication was established, and different cooling methods were used to analyze the effect on surface roughness. The spindle speed n, feed rate f, and cutting depth a_p are selected as optimization variables, with surface roughness as the optimization objective. Single-factor experiments were conducted to determine the optimal range for these variables. Subsequently, a model was constructed using the response surface methodology and solved using Design-Expert software. The interaction effects of spindle speed, feed rate, and depth of cut on surface roughness were analyzed. Additionally, genetic algorithms were employed to optimize cutting process parameters for the best combination. The results demonstrated that by combining Response Surface Methodology (RSM)and genetic algorithms, when the spindle speed n was 2520 r/min, the feed rate f was 48 mm/min, and the depth of cut a_p was 0.08 mm, the actual surface roughness after milling reached 0.148 µm, representing a 74.57% reduction compared to the initial surface roughness. This research method provides a theoretical foundation and technical support for optimizing minimal quantity lubrication (MQL) cutting processes. Full article

Parameter self-tuning of servo control systems is crucial for optimizing automation processes, especially in complex systems such as permanent magnet synchronous motors. In this paper, a nonlinear adaptive whale optimization algorithm (NAWOA) is proposed and applied to parameter self-tuning, which improves the traditional whale optimization algorithm (WOA) by nonlinearly adaptively adjusting two parameters during optimization to enhance fast convergence and global search capabilities. A servo control system with three parameters to be tuned is constructed using both simulation and physical methods. Simulation and experimental results show that the NAWOA outperforms the genetic algorithm, particle swarm optimization, and WOA in parameter self-tuning of the servo control system with lower error indicators and fast convergence speed. Although it still faces the challenge of initial condition dependency, the proposed NAWOA provides a powerful solution for real-time industrial applications. Full article

With the rapid development of genomic big data and genome-wide association study technologies, massive genomic data are available for the genetic dissection, development and utilization of important economic traits. Various GWAS algorithms have become increasingly efficient, enabling high-performance processing of these massive datasets. This has made it possible to conduct genetic dissection of economic traits based on big data and advanced statistical methods, which will provide accurate target loci for future trait improvement and genetic manipulation, greatly accelerating the process of genetic breeding. In this study, genotyping of 426 fish was performed using the T7 sequencing platform and 555,242 SNPs distributed across all the chromosomes were screened by data cleaning. We compared the performance of two GWAS methods, GCTA and GEMMA, in both single-trait and multi-trait frameworks. Twenty-nine SNPs significantly associated with seven traits were identified through single and multi-trait combined GWAS. Single-trait GWAS analysis using GCTA identified 1047 and 1452 significant loci for six growth traits and one sex trait (phenotypic sex, male or female) respectively, ultimately revealing 10 candidate genes, including slc48a1a, filip1L, nedd9, Crebbpa, LOC134024622, zbtb18, LOC117378376, LOC131530706, syde2, and col24a1. Similarly, 671 and 642 significant SNPs were detected with GEMMA for single-trait GWAS associated with six growth traits and the sex trait, respectively. In total, 16 candidate genes were mapped for these seven traits. Multi-trait GWAS was also performed using GEMMA for the six growth traits (sex was included as a covariate). The traits were grouped into five combinations based on their genetic correlations. A total of 37 SNPs were identified, corresponding to 10 candidate genes: LOC131530706, LOC134022516, abat, maml3, cica, LOC124013321, slc25a12, dnah10, syt9a, and LOC136932979. Notably, five overlapping candidate genes (LOC131530706, LOC134022516, abat, slc25a12 and dnah10) were also identified in both single- and multi-trait GWAS methods of GEMMA, highlighting their genetic stability and significance. The two GWAS methods, GCTA and GEMMA, identified two genes that were the same. The results of this study provide molecular markers and genetic resources for the improvement of growth traits in Plecoglossus altivelis. Full article

The present numerical study simultaneously investigates the aerodynamic performance, shape optimization, and aeroacoustic characteristics of modified leading-edge wavy wings for the NACA 0020 airfoil. Unlike conventional passive flow-control approaches, the present study proposes a collaborative vortex–slot control strategy, where streamwise vortices induced by a wavy leading edge interact constructively with momentum injection from upper-surface slot channels. Flow field is analyzed at a Reynolds number of 290,000 and various angles of attack (AoA) utilizing Computational Fluid Dynamics (CFD). Three leading-edge wavy wing configurations, namely A3L11, A3L40 and A11L40, are examined and further modified by introducing streamwise slots near the leading edge on the upper surface of the wing. Three slot diameters (0.07c, 0.10c, and 0.13c) are examined at a constant draft angle of 7.5°, which represents the inclination of the slot relative to the wing surface. The numerical results are validated against experimental data available in the literature. The findings indicate that the A3L11 configuration with a 0.07c slot diameter, as well as the A11L40 configuration at high angles of attack, outperform the baseline wavy wing. This improvement is attributed to the slotting mechanism, which enhances surface suction and streamwise momentum, thereby improving boundary-layer behavior. An increase in aerodynamic efficiency, quantified by the lift-to-drag ratio, is observed at 20° AoA for all configurations. To further enhance performance, shape optimization is performed by optimizing the slot diameter and the distance between the chord line and the slot center using a Genetic Algorithm (GA), with the A11L40 configuration at 20° AoA identified as the optimal design. The optimized configuration yields an overall aerodynamic performance improvement of approximately 27.76% compared to the smooth wing, while broadband aeroacoustic source modeling indicates a relative reduction in predicted noise-source intensity relative to the baseline modified wing. The results are presented through combined quantitative metrics and qualitative flow analyses, demonstrating the potential applicability of the proposed optimization framework to low-Reynolds-number aerodynamic and aeroacoustic design problems, such as those encountered in small-scale air vehicles, bio-inspired wings, and noise-sensitive systems. Full article

Rice blast disease is one of the most contagious and destructive diseases affecting rice, posing a serious threat to global rice production and the agricultural economy. To enable accurate early detection under field conditions, this study proposes an integrated feature sorting algorithm (IFSA). The algorithm integrates five spectral feature selection methods—partial least squares, successive projections algorithm (SPA), principal component analysis loading (PCA-Loading), genetic algorithm (GA), and random forest (RF)—and employs the Borda count method for comprehensive feature ranking and selection. Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47. A total of 4893 hyperspectral samples were collected under natural field conditions. The results demonstrate that IFSA effectively identifies key spectral wavelengths for the early diagnosis of rice blast disease, achieving significantly higher detection accuracy than conventional single-method dimensionality reduction approaches. Based on the IFSA-selected wavelengths, an early detection model (Inception-CBAM) was further developed by integrating a multi-channel convolutional neural network with a convolutional block attention module, thereby enhancing the extraction and recognition of early disease-related features. Compared with six baseline models (InceptionV4, ResNet, BiGRU, RF, support vector machine, and extreme learning machine), Inception-CBAM achieved an overall accuracy of 95.44 ± 0.50% and a Kappa coefficient of 93.92 ± 0.67% for early rice blast disease detection, outperforming all competing methods. This study confirms the effectiveness of IFSA for hyperspectral feature selection and demonstrates that the proposed Inception-CBAM model provides strong capability for early disease detection. Nevertheless, the data were collected from a single cultivar and a single region; therefore, the model’s generalization performance across broader environments requires further improvement. Future work will extend the evaluation to multi-cultivar and multi-region scenarios to facilitate practical deployment for real-time field diagnosis. Full article

Improving energy efficiency in electric drive systems is vital for sustainable manufacturing. Despite their potential, metaheuristic optimizations are rarely used in industrial virtual commissioning (VC) due to high computational demands and lack of reliability metrics. This paper presents a statistically validated genetic algorithm (GA) framework designed to bridge this gap, enabling high-speed deployment within practical VC workflows. The framework optimizes target speed, acceleration, and deceleration across three objectives: minimizing energy consumption, maximizing efficiency, and reducing losses. Comparative evaluations show that the GA achieves near-optimal solutions with a significant speedup, reducing optimization time from hours to minutes. Extensive statistical validation (100 independent runs) confirms high reliability and feasibility for multi-run industrial strategies. Furthermore, the study introduces a novel post hoc normalized analysis to quantify trade-offs between competing objectives. Validated on two physical testbeds (belt and strap conveyors), the methodology demonstrates cross-system generalizability and significant energy improvements. Implemented via IEC 61131-3 compliant programming, this eco-efficient automation system directly supports global sustainable development goals (SDGs 7 and 9) by enabling sustainable-by-design industrial automation. Full article

The efficient operation of the maintenance service system is key to achieving sustainable operations, with its core lying in the coordinated scheduling of spare parts production and maintenance personnel, as well as the holistic management of in-warranty and out-of-warranty demands. This approach optimizes resource allocation and enhances long-term service value. This paper investigates the integrated scheduling of distributed spare parts production and maintenance personnel with differentiated in-warranty and out-of-warranty demands (ISSPD). To solve the ISSPD, an improved non-dominated sorting genetic algorithm-II that uses Q-learning to adaptively select local search strategies (QLNSGA) is proposed, which incorporates a decoding strategy for differentiated order types, eight knowledge-driven local search strategies, and a Q-learning mechanism for the adaptive selection of key local search operators. Compared to random local search operators, the Q-learning mechanism achieves a 55% decrease in IGD metric and a 65% increase in HV metric. Through comparative experiments with four mainstream algorithms, QLNSGA outperforms RIPG by 58% in terms of the IGD index, and its HV index is generally superior to that of comparative algorithms such as MOEA/D. This indicates that QLNSGA exhibits superior performance in both computational efficiency and solution quality, effectively enhancing service levels and significantly reducing operational costs, thereby providing scientific decision support for service-oriented manufacturing enterprises. Full article

When a fixed-wing Unmanned Aerial Vehicle (UAV) conducts All-Weather Post-Disaster Coverage Path Planning (PDCPP), the commonly used Sequential Path Coverage (SPC) method tends to generate redundant flight distance during turning transitions between adjacent coverage paths, which in turn increases the UAV’s flight energy consumption and thereby compromises the timeliness of rescue information acquisition. To address these challenges, this paper proposes a Multi-Selector Genetic Algorithm with Reinforcement Learning (MSGA-RL). It enhances population diversity through a distance-priority heuristic greedy initialization strategy, employs a multi-selector crossover operator to improve both solution diversity and convergence speed, and integrates a reinforcement learning-based individual retention mechanism with an elite pool protection strategy to prevent premature convergence. To simulate post-disaster scenarios, the disaster-affected area is modeled as a convex polygonal region with obstacles, while the flight energy consumption and stability of MSGA-RL are evaluated under different numbers of coverage paths. Simulation results indicate that, across all coverage path settings, MSGA-RL consistently achieves lower flight energy consumption than SPC, the Genetic Algorithm (GA), and the Dubins-based Enhanced Genetic Algorithm (DEGA), while exhibiting superior stability. In particular, in the convex quadrilateral scenario with 50 coverage paths, the flight energy consumption of MSGA-RL is reduced by 52.80%, 32.06%, and 15.96% compared with SPC, GA, and DEGA, respectively. Full article

The rapid growth of air traffic demand and limited airspace resources have made efficient coordination in multi-airport systems a critical challenge. This paper develops a bi-level air–ground collaborative scheduling model for the Beijing-Tianjin-Airport cluster, integrating terminal-area departure sequencing (upper level) with airport surface taxi and pushback scheduling (lower level), where the upper-level model minimizes departure delays, maximizes airport satisfaction, and reduces fairness deviation, while the lower-level model optimizes taxi routing and pushback timing. To solve the model, NSGA-II is applied to the upper-level sequencing problem and a Genetic-Simulated Annealing algorithm is used for surface scheduling. Empirical evaluation using operational data from Beijing Capital, Beijing Daxing, and Tianjin Binhai airports shows that the proposed approach reduces total departure delay by 49.4%, lowers average taxi time by up to 40.4%, and improves overall airport satisfaction by 5.2%, while reducing fairness deviation by 52.6%. These results demonstrate that the framework effectively enhances efficiency and equity in multi-airport departure operations. Full article

With the increasing demand for personalized services in the market, manufacturing enterprises are facing frequent emergency order insertion and equipment resource shortages, and traditional scheduling methods lack flexibility. This article focuses on the workshop scheduling problem under emergency insertion disturbance, and constructs a dynamic scheduling optimization method considering equipment occupancy status. Firstly, a dynamic scheduling framework is proposed, and a real-time status model is established to monitor emergency insertion and equipment occupancy status in real time. An event-driven dynamic scheduling mechanism is also constructed. Secondly, with the optimization objective of minimizing the maximum completion time, a mixed integer programming model is established, and an improved genetic simulated annealing algorithm is proposed to solve the proposed model. Finally, the proposed method was validated using a standard case set and real production scenarios. The experimental results showed that the solution of the proposed method was better than similar algorithms under three different problem scales. In three emergency insertion scenarios, the proposed method can reduce the disturbance of insertion on the original plan while ensuring equipment utilization, verifying the practicality and effectiveness of the proposed dynamic scheduling method. Full article

Purpose: The aim of this narrative review is to update current knowledge on Moyamoya vasculopathy (MMV) by addressing key diagnostic debates—including laterality; genetic subtypes; regional epidemiology; and features distinguishing Moyamoya Disease (MMD), Moyamoya Syndrome (MMS) and their mimics. Methods: Key and representative studies were identified through PubMed/MEDLINE and Scopus, focusing on publications from 2014–2025 while also considering earlier seminal works. Results: MMD typically presents with bilateral steno-occlusion of the terminal internal carotid arteries (ICAs) and proximal middle and anterior cerebral arteries (MCAs/ACAs) due to concentric vascular thickening, accompanied by characteristic ‘puff-of-smoke’ collaterals, whereas MMS shows a similar but more often unilateral pattern with fewer collaterals, influenced by the underlying condition. However, this distinction often fails to reflect the full clinical and radiological variability of the Moyamoya spectrum. Atypical moyamoya-like patterns, often confined to M1 or A1 segments, further complicate diagnosis. Clinical manifestations ranged from asymptomatic cases to ischemic or hemorrhagic strokes, and occasionally seizures. Diagnosis relied on multimodal imaging (DSA, MRA, CTA), but genetic mutations, contributing to radiological variability, often complicate differentiation between MMD, MMS, and mimics. Management is pattern-specific: MMS and atypical forms are generally managed conservatively, whereas MMD frequently requires surgical revascularization, particularly in children and symptomatic adults. Nevertheless, variability within diagnostic categories limits the applicability of rigid treatment protocols. Conclusions: Current diagnostic algorithms remain limited. Integrating advanced imaging findings with clinical, genetic, and epidemiological data is essential to define the full disease spectrum, improve diagnostic accuracy, and inform patient management and outcome assessment. Full article

Beef freshness is a critical indicator of meat quality and safety, and its rapid, non-destructive detection is of significant importance for ensuring consumer health and enhancing quality control throughout the meat industry chain. This study developed a novel methodology for non-destructive beef freshness assessment using visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning, explainable artificial intelligence (xAI) techniques, and the SHapley Additive exPlanations (SHAP) framework. An improved hybrid heuristic method, particle swarm optimization–genetic algorithm (PSOGA), was used for feature selection, optimizing the wavelength subset for predicting beef quality indicators, including total volatile basic nitrogen (TVB-N) and color parameters (L*, a*, and b*). The eXtreme Gradient Boosting (XGBoost) was employed for regression modeling, and the results showed that PSOGA significantly outperforms traditional methods, with the PSOGA-XGBoost model achieving a satisfactory prediction accuracy (R²p values of 0.9504 for TVB-N, 0.9540 for L*, 0.8939 for a*, and 0.9416 for b*). The SHAP framework identified the key wavelengths as 1236 nm and 1316 nm for TVB-N, 728 nm for L*, 576 nm for a*, and 604 nm for b*, providing valuable insights into the determination of key wavelengths and enhancing the interpretability of the model. The results demonstrated the effectiveness of PSOGA and SHAP, providing a promising analytical method for monitoring beef freshness. Full article

Bronchopulmonary dysplasia (BPD) remains a major long-term morbidity among preterm infants. As lung-protective strategies advance and survival of extremely premature neonates improves, BPD has evolved from a ventilator-induced inflammatory and fibrotic process to a disease marked by arrested pulmonary vascular and alveolar development—pulmonary vascular disease. Within this evolving phenotype, pulmonary hypertension (PH) has emerged as a critical yet underrecognized complication. BPD-associated pulmonary hypertension (BPD-PH) is increasingly linked to higher mortality and worse clinical outcomes, but its pathophysiology, screening strategies to detect early changes, and optimal management remain incompletely understood. This review delineates the pathophysiology of BPD-PH, linking impaired pulmonary vascular development with subsequent maladaptation influenced by genetic, prenatal, and postnatal factors. The phenotypic and hemodynamic spectrum of BPD-PH is further subclassified using echocardiographic markers to support a physiology-based approach to diagnosis and management. We also propose a pragmatic algorithm for screening, evaluation, and longitudinal follow-up. Collectively, this review highlights the need for physiology-driven strategies and clinical studies to improve outcomes in these neonates. Full article

This paper addresses the challenge of simultaneous arrival for UAV clusters and proposes a route planning method based on an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III). Initially, the paper defines the simultaneous arrival problem and formulates the corresponding mathematical model, considering the complexity of multi-objective optimization in UAV clusters. A novel path generation framework is introduced, which incorporates multiple optimization objectives—such as time coordination, threat mitigation, and resource consumption—aimed at improving flight safety, efficiency, and resource management. To enhance the algorithm’s search performance, a hybrid approach combining the Artificial Bee Colony (ABC) algorithm with NSGA-III is proposed. This improved NSGA-III strategy overcomes the limitations of the original algorithm in managing complex constraints and multi-objective optimization problems, resulting in significant improvements in search accuracy and convergence speed. Finally, the performance of the improved algorithm is evaluated through simulations and compared with traditional methods. The results show that the proposed approach optimizes flight time, reduces resource consumption, and effectively mitigates threats, all while ensuring the simultaneous arrival of UAV clusters. Full article

Magnetorheological (MR) dampers exhibit significant hysteretic nonlinearities, particularly under dynamic operating conditions, where accurately modeling the complex coupling between magnetic flux density and excitation current remains challenging. To overcome the limitations of the conventional static Jiles–Atherton (JA) model in capturing dynamic hysteresis responses, a dynamic JA model incorporating multiple loss mechanisms (LS-DJAM) is proposed for MR dampers. Building on loss separation theory, the model integrates eddy current and excess loss mechanisms to more accurately represent the dynamic hysteresis behavior of MR dampers. By coupling the Bingham mechanical model, a magneto-mechanical constitutive relation for MR dampers is established. Furthermore, to enhance the accuracy of LS-DJAM parameter identification, a hybrid particle swarm optimization–genetic algorithm (PSO–GA) is developed. Genetic operators are embedded within the PSO framework to strengthen the global search capability and mitigate premature convergence, thereby enabling efficient LS-DJAM parameter identification. The proposed LS-DJAM, identified via the PSO–GA, significantly enhances the modeling of MR damper output forces. PSO–GA parameter estimation improves accuracy by over 60%, and the LS-DJAM reduces the maximum modeling error by 87.5% compared with the conventional JA model. It accurately captures the dynamic hysteresis characteristics of MR dampers, providing a robust theoretical basis and practical framework for high-performance control and engineering optimization. Full article