Search Results (800)

The growing emphasis on sustainable manufacturing has motivated the integration of environmental and social factors into traditional assembly line balancing problems (ALBPs). This study introduces a Sustainable Mixed-Model Assembly Line Balancing Problem (S-MMALBP) that jointly considers task precedence, machine selection, worker allocation, carbon-emission control, and green-rating incentives. An exact optimization model is formulated to minimize total operating cost while satisfying sustainability and capacity constraints. To address the problem’s combinatorial complexity, an Adaptive Large Neighborhood Search (ALNS) metaheuristic is developed, incorporating customized destroy and repair operators, adaptive penalty updating, and a simulated-annealing-based acceptance criterion. An analytical lower bound is derived to evaluate the algorithm’s performance, and an enhanced constructive method, Precedence-Driven Task Grouping (PDTG), is proposed to generate high-quality initial solutions. Computational experiments on benchmark instances confirm that the ALNS achieves near-optimal solutions with deviations below 5% from the lower bound, while solving large instances within seconds. A real-world case study on aircraft assembly involving 166 tasks further validates the model’s applicability, achieving a cost deviation below 4% from the theoretical bound under realistic sustainability constraints. The results demonstrate that the proposed model provides an effective and scalable decision-support tool for designing environmentally and socially responsible production systems. The study is the first to incorporate sustainability and worker–machine decisions into a mixed-model ALB framework solved by a tailored ALNS and lower bound. Full article

Accurate characterization of rail corrugation is essential for the operation and maintenance of urban rail transit. To enhance the representation capability for rail corrugation, this study proposes a sound–vibration feature fusion method based on Laplacian manifold learning. The method constructs a multidimensional feature space using real-world acoustic and vibration signals measured from metro vehicles, introduces a Laplacian manifold structure to capture local geometric relationships among samples, and incorporates inter-class separability into traditional intra-class compactness metrics. Based on this, a comprehensive feature evaluation index L_r is developed to achieve adaptive feature ranking. The final fusion indicator, LWVAF, is generated through weighted feature integration and used for rail corrugation characterization. Validation on in-service metro line data demonstrates that, after rail grinding, LWVAF exhibits a more pronounced reduction and higher sensitivity to changes compared with individual acoustic or vibration features, reliably reflecting improvements in rail corrugation. The results confirm that the proposed method maintains strong robustness and physical interpretability even under small-sample and weak-label conditions, offering a new approach for sound–vibration fusion analysis and corrugation evolution studies. Full article

Vacuolar H⁺-ATPases play crucial roles in plant ion homeostasis and stress adaptation, yet the functional characterization of their subunit genes in purslane remains limited. In this study, PoVHA-a3, encoding a tonoplast-localized V-ATPase a3 subunit, was identified as a key salt-responsive gene through transcriptomic analysis. Integrated bioinformatic analysis and molecular docking simulations predicted specific binding of NAC3, MYB1, and bHLH62 to the PoVHA-a3 promoter, suggesting their synergistic role in regulating PoVHA-a3 expression. Under salt stress, PoVHA-a3 transgenic Arabidopsis lines exhibited elevated endogenous abscisic acid levels and upregulation of signaling genes (AtNCED3, AtRD29A, AtCOR15A), while the brassinosteroid signaling pathway was suppressed, as indicated by the reduced expression of AtBZR1 and AtEXPA8. Meanwhile, the transgenic lines demonstrated enhanced ATP levels, respiratory rate, and V-ATPase activity. In addition, PoVHA-a3 expression led to greater accumulation of osmoprotectants (proline, soluble sugars and proteins), higher activities of antioxidant enzymes, and reduced levels of oxidative stress indices. Furthermore, a significantly lower shoot Na⁺/K⁺ ratio was observed in transgenic plants, indicating improved ion homeostasis. In conclusion, this study demonstrates that PoVHA-a3 acts as a pivotal positive regulator of salt tolerance in purslane, providing a valuable genetic resource for enhancing salt tolerance in crops through genetic engineering. Full article

Compared with the finite element method (FEM), the meshfree smoothed point interpolation method (SPIM) has a more accurate stiffness and is not sensitive to mesh distortion, which has high potential in solving engineering problems. In this study, an effective simulation program based on the face-based SPIM was developed and was applied to solve geomechanical problems. To enhance the reliability of the SPIM program when dealing with large-scale and nonlinear problems, the line search algorithm, the adaptive sub-step method, and the OpenMP parallel design were adopted to enhance the convergence, stability, and computational efficiency. The test results of the slope stability analysis show that the SPIM program is correct when compared with the Bishop method. Moreover, the SPIM program has an asymptotic quadratic convergence and satisfactory stability, even when the slope is in a critical state. In addition, for large-scale examples, the speedup ratio of the OpenMP parallel program can achieve a speedup ratio of 6~8 on a computing platform with 20 CPU cores, and the maximum speedup ratio for a single load step can reach 14.50. Finally, future work on the developing face-based SPIM simulation program is discussed. Full article

The safety of the deep-buried, long tunnel at the active fault is a crucial issue in the Yangtze River to Hanjiang River Water Diversion Project, which crosses the Tongcheng River Fault. This study presents the first systematic investigation into the behavior of large deep-buried water diversion tunnels crossing active faults. Based on an analysis of the geostress field, numerical simulations were conducted to evaluate the response of the lining without adaptive measures. Subsequently, a method for estimating hinged design parameters was proposed, and reasonable design values were determined. Furthermore, the effectiveness of the adaptive hinged structure in improving anti-dislocation performance was assessed using a self-developed evaluation framework for tunnel lining. The results show that (1) Geostresses include a 35° angle between horizontal principal stress and the tunnel axis, with horizontal stresses of 20 MPa (axial) and 21 MPa (perpendicular), and vertical stress of 18 MPa. (2) Without adaptive measures, tunnel deformation peaks in the fault zone, showing vault-floor convergence; maximum principal stresses and liner damage concentrate there. (3) The proposed hinge-type adaptive design suggests a 6 m segmented section length and 2–4 cm hinge width initially; sensitivity analysis recommends 6 m and 5 cm, respectively. (4) Adaptive measures reduce tensile stress in the fault zone, significantly mitigating deformation, stress, and liner damage, proving their efficacy in enhancing anti-fault-rupture performance. Full article

The exploration of marine minerals, essential for sustainable development, requires advanced techniques for accurate resource delineation. The self-potential (SP) method, sensitive to mineral polarization, has been increasingly deployed using autonomous underwater vehicles. This approach enables dense planar SP data acquisition, offering the potential to reduce inversion uncertainties through enhanced data volume. This study investigates the benefits of inverting planar SP datasets for improving the spatial delineation of subsurface deposits. An analytical solution was derived to describe SP responses of spherical polarization models under a planar measurement grid. An adaptive Markov chain Monte Carlo algorithm within the Bayesian framework was employed to quantitatively assess the constraints imposed by the enriched dataset. The proposed methodology was validated through two synthetic cases, along with a laboratory-scale experiment that monitored the redox process of a spherical iron–copper model. The results showed that, compared to single-line data, the planar data reduced the average error in parameter means from 10.9% and 6.4% to 4.1% and 1.7% for synthetic and experimental cases, respectively. In addition, the 95% credible intervals of model parameters narrowed by nearly 50% and 40%, respectively. Full article

At the current stage, indoor Ultra-Wideband (UWB) positioning systems often encounter challenges in achieving high localization accuracy under non-line-of-sight (NLOS) conditions within workshop environments when employing the Double-Sided Two-Way Ranging (DS-TWR) algorithm. To address this issue, a positioning optimization method based on the DS-TWR algorithm is proposed. By streamlining message exchanges between nodes, the method reduces node energy consumption and shortens ranging time, thereby enhancing system energy efficiency and response speed. Furthermore, to improve positioning accuracy in workshop NLOS environments, an Adaptive Kalman Filtering algorithm is introduced. This algorithm dynamically evaluates the influence of obstruction information caused by NLOS conditions on the covariance of observation noise and adaptively adjusts the filtering gain of the signals accordingly. Through this approach, the system can effectively eliminate invalid positioning information in signals, mitigate the adverse effects of NLOS conditions on positioning accuracy and achieve more precise localization. Experimental results demonstrate that the proposed optimization algorithm achieves substantial performance improvements in both static and dynamic positioning experiments under workshop NLOS conditions. Specifically, the algorithm not only enhances system positioning accuracy but also further strengthens the real-time ranging precision of the DS-TWR algorithm. Full article

In the scenario where the number of interceptors is equal to the number of target aircraft, and recognizing that the geometric configuration of interceptors during their maneuver towards targets affects detection effectiveness and guidance accuracy, we propose a Cooperative Detection and Guidance (CDG) method rooted in optimal control theory. This method optimizes detection by adjusting the line-of-sight (LOS) angle to minimize errors, and leverages the Fast Multiple Model Adaptive Estimation (Fast MMAE) algorithm to enhance interceptors’ ability to estimate the motion states and maneuver switching times of target aircraft, thereby boosting guidance accuracy. Results from 500 Monte Carlo simulations reveal that, compared to the Augmented Proportional Navigation (APN) guidance law, our integrated detection and guidance approach exhibits superior target recognition capabilities and achieves higher interception accuracy. Full article

Indoor positioning using commodity WiFi has gained significant attention; however, achieving sub-meter accuracy across diverse layouts remains challenging due to multipath fading and Non-Line-Of-Sight (NLOS) effects. In this work, we propose a hybrid Graph–Temporal Convolutional Network (GTCN) model that incorporates Access Point (AP) geometry through graph convolutions while capturing temporal signal dynamics via dilated temporal convolutional networks. The proposed model adaptively learns per-AP importance using a lightweight gating mechanism and jointly exploits WiFi Received Signal Strength (RSS) and Round-Trip Time (RTT) features for enhanced robustness. The model is evaluated across four experimental areas such as lecture theatre, office, corridor, and building floor covering areas from 15 m × 14.5 m to 92 m × 15 m. We further analyze the sensitivity of the model to AP density under both LOS and NLOS conditions, demonstrating that positioning accuracy systematically improves with denser AP deployment, especially in large-scale mixed environments. Despite its high accuracy, the proposed GTCN remains computationally lightweight, requiring fewer than ${10}^5$ trainable parameters and only tens of MFLOPs per inference, enabling real-time operation on embedded and edge devices. Full article

Background: Many biological drugs have a rival version produced from different cell lines by other manufacturers; these drugs are referred to as biosimilars. By providing accurate information, encouraging patient and medical community acceptance, and advocating for their appropriate usage, pharmacists can play a crucial role in supporting the uptake of biosimilar medicines. Aim: This study aimed to assess pharmacists’ knowledge and attitudes toward biosimilar medicines in the Aseer region in Saudi Arabia. Methods: The study employed a descriptive, cross-sectional design using an anonymous online self-administered questionnaire. The questionnaire was developed by adapting a previously validated instrument and consisted of three sections: demographic data, knowledge about biosimilars, and attitudes toward biosimilars. Two non-probability sampling approaches, i.e., convenience and snowball sampling, were using for data collection. Results: A total of 298 pharmacists participated in the current study. Overall, a total of 135 (45.3%) demonstrated good knowledge of biosimilar medicines, while 163 (54.7%) exhibited poor knowledge. The median knowledge score among the study participants was 5 (5–6). Only 26.2% of pharmacists in the current study correctly identified that biosimilars were not generics and not interchangeable with reference biologics. More experienced pharmacists and those working in industry-related sectors demonstrated greater knowledge of biosimilars (p < 0.05). Pharmacists in the current study demonstrated generally favorable attitudes toward biosimilar medicines. Conclusions: The current study revealed knowledge gaps regarding biosimilar medicines among pharmacists. Targeted educational initiatives, continuing professional development opportunities, and enhanced curricular content could be implemented to address these gaps. Full article

Plant diseases are currently a major threat to agricultural economies and food availability, having a negative environmental impact. Despite being a promising line of research, current approaches struggle with poor cross-site generalization, limited labels and dataset bias. Real-field complexities, such as environmental variability, heterogeneous varieties or temporal dynamics as are often overlooked. Numerous studies have been conducted to address these challenges, proposing advanced learning strategies and improved evaluation protocols. Synthetic data generation and self-supervised learning reduce dataset bias, while domain adaptation, hyperspectral, and thermal signals improve robustness across sites. However, a large portion of current methods are developed and validated mainly on clean laboratory datasets, which do not capture the variability of real-field conditions. Existing AI models often lead to imperfect detection results when dealing with field images complexities, such as dense vegetation, variable illumination or changing symptom expression. Although augmentation techniques can approximate real-world conditions, incorporating field data represents a substantial enhancement in model reliability. Federated transfer learning offers a promising approach to enhance plant disease detection, by enabling collaborative training of models across diverse agricultural environments, using in-field data but without disclosing the participants data to each others. In this study, we collaboratively trained a hybrid Graph–SNN model using federated learning (FL) to preserve data privacy, optimized for efficient use of participant resources. The model achieved an accuracy of 0.9445 on clean laboratory data and 0.6202 exclusively on field data, underscoring the considerable challenges posed by real-world conditions. Our findings demonstrate the potential of FL for privacy preserving and reliable plant disease detection under real field conditions. Full article

Unmanned underwater vehicles (UUVs) face significant challenges in achieving safe and efficient autonomous navigation in complex marine environments due to uncertain perception, dynamic obstacles, and nonlinear coupled motion control. This study proposes a hierarchical autonomous navigation framework that integrates improved particle swarm optimization (PSO) for 3D global route planning, and a deep deterministic policy gradient (DDPG) algorithm enhanced by noisy networks and proportional prioritized experience replay (PPER) for local collision avoidance. To address dynamic sideslip and current-induced deviations during execution, a novel 3D adaptive line-of-sight (ALOS) guidance method is developed, which decouples nonlinear motion in horizontal and vertical planes and ensures robust tracking. The global planner incorporates a multi-objective cost function that considers yaw and pitch adjustments, while the improved PSO employs nonlinearly synchronized adaptive weights to enhance convergence and avoid local minima. For local avoidance, the proposed DDPG framework incorporates a memory-enhanced state–action representation, GRU-based temporal processing, and stratified sample replay to enhance learning stability and exploration. Simulation results indicate that the proposed method reduces route length by 5.96% and planning time by 82.9% compared to baseline algorithms in dynamic scenarios, it achieves an up to 11% higher success rate and 10% better efficiency than SAC and standard DDPG. The 3D ALOS controller outperforms existing guidance strategies under time-varying currents, ensuring smoother tracking and reduced actuator effort. Full article

Unmanned Aerial Vehicles (UAVs) are integral components of future 6G networks, offering rapid deployment, enhanced line-of-sight communication, and flexible coverage extension. However, UAV communications in low-altitude environments face significant challenges, including rapid link variations due to attitude instability, severe signal blockage by urban obstacles, and critical sensitivity to transmitter–receiver alignment. While traditional planar reconfigurable intelligent surfaces (RIS) show promise for mitigating these issues, they exhibit inherent limitations such as angular sensitivity and beam squint in wideband scenarios, compromising reliability in dynamic UAV scenarios. To address these shortcomings, this paper proposes and evaluates a spherical-cap reflective intelligent surface (ScRIS) specifically designed for dynamic low-altitude communications. The intrinsic curvature of the ScRIS enables omnidirectional reflection capabilities, significantly reducing sensitivity to UAV attitude variations. A rigorous analytical model founded on Generalized Sheet Transition Conditions (GSTCs) is developed to characterize the electromagnetic scattering of the curved metasurface. Three distinct 1-bit RIS unit cell coding arrangements, namely alternate, chessboard, and random, are investigated via numerical simulations utilizing CST Microwave Studio and experimental validation within a mechanically stirred reverberation chamber. Our results demonstrate that all tested ScRIS coding patterns markedly enhance electromagnetic field uniformity within the chamber and reduce the lowest usable frequency (LUF) by approximately 20% compared to a conventional metallic spherical reflector. Notably, the random coding pattern maximizes phase entropy, achieves the most uniform scattering characteristics and substantially reduces spatial field autocorrelation. Furthermore, the combined curvature and coding functionality of the ScRIS facilitates simultaneous directional focusing and diffuse scattering, thereby improving multipath diversity and spatial coverage uniformity. This effectively mitigates communication blind spots commonly encountered in UAV applications, providing a resilient link environment despite UAV orientation changes. To validate these findings in a practical context, we conduct link-level simulations based on a reproducible system model at 3.5 GHz, utilizing electromagnetic scale invariance to bridge the fundamental scattering properties observed in the RC to the application band. The results confirm that the ScRIS architecture can enhance link throughput by nearly five-fold at a 10 km range compared to a baseline scenario without RIS. We also propose a practical deployment strategy for urban blind-spot compensation, discuss hybrid planar-curved architectures, and conduct an in-depth analysis of a DRL-based adaptive control framework with explicit convergence and complexity analysis. Our findings validate the significant potential of ScRIS as a passive, energy-efficient solution for enhancing communication stability and coverage in multi-band 6G networks. Full article

The development of improved, better-adapted purple-fleshed sweetpotato genotypes can enhance public health, diversify market opportunities, and increase incomes for Brazilian farmers while making biofortified foods more accessible and strengthening food security. Breeding programs should simultaneously target yield and quality traits to secure acceptance from both producers and consumers. This study aimed to identify promising purple-fleshed sweetpotato genotypes by evaluating multiple traits: root yield, postharvest quality, and anthocyanin content. We carried out two field trials, with predicted genetic gains of 127% for the number of marketable roots and 90.6% for total root yield in the first stage, and 13.1% for total yield, 14.5% for marketable yield, and 9.4% for dry matter of marketable roots in the second stage. Beginning with 1048 experimental genotypes, we preselected 21 promising lines. In the first trial (augmented block design), we chose 28 high-yielding genotypes. In the second trial, 12 genotypes from the breeding program were tested using an alpha-lattice design, with the cultivar SCS370 Luiza serving as a control in both experiments. We assessed traits including propagation potential, total root number, total and marketable yield, number of marketable roots, average mass and dry matter of marketable roots, resistance to insect damage, external appearance, pulp color, root spatial distribution in the soil, average root diameter, number of perforations, soluble solids, and anthocyanin content. Genotype selection was guided by the multi-trait genotype–ideotype distance index. In the final selection, 21 genotypes stood out as highly promising: U1-46, U1-145, U2-08, FA-08, U2-100, F06-32, B-77, U2-D, U2-47, FA-143, U1-123, U1-113, U2-49, F06-25, F06-199, FA-120, U1-55, LP-75, U2-74, F06-57, and U1-47, combining a mean total root yield of 27.392 t ha⁻¹ and anthocyanin levels between 0.174 and 0.804 mg 100 g⁻¹. These genotypes constitute promising candidates for incorporation into breeding pipelines targeting markets for purple-fleshed sweetpotato, with favorable implications for both producer income and nutritional outcomes. Full article

To address the challenge of bird species detection on transmission lines, this paper proposes a detection method based on dual data enhancement and an improved YOLOv8s model. The method aims to improve the accuracy of identifying small- and medium-sized targets in bird detection scenes on transmission lines, while also accounting for the impact of changing weather conditions. To address these issues, a dual data enhancement strategy is introduced. The model’s generalization ability in outdoor environments is enhanced by simulating various weather conditions, including sunny, cloudy, and foggy days, as well as halo effects. Additionally, an improved Mosaic augmentation technique is proposed, which incorporates target density calculation and adaptive scale stitching. Within the improved YOLOv8s architecture, the CBAM attention mechanism is embedded in the Backbone network, and BiFPN replaces the original Neck module to facilitate bidirectional feature extraction and fusion. Experimental results demonstrate that the proposed method achieves high detection accuracy for all bird species, with an average precision rate of 94.2%, a recall rate of 89.7%, and an mAP@50 of 94.2%. The model also maintains high inference speed, demonstrating potential for real-time detection requirements. Ablation and comparative experiments validate the effectiveness of the proposed model, confirming its suitability for edge deployment and its potential as an effective solution for bird species detection and identification on transmission lines. Full article