Search Results (484)

Introduction: Vitamin D deficiency [25(OH)D < 30 ng/mL] is widely prevalent globally and the efforts to tackle it have been rather unsuccessful to date. Despite different cutoffs used to define it, many clinicians adhere to the 2011 Endocrine Society definition. We present a special treat-to-target protocol aiming to restore and maintain vitamin D sufficiency. Methods: We reviewed the efficacy and safety of our vitamin D supplementation protocol over 5 years, and compared it to a group of patients who self-reported never taking vitamin D supplements. We recorded the baseline, 2-month, and annual 25(OH)D (D) measurements, along with subjects’ age, sex, BMI, history of osteoporosis, nephrolithiasis, nephrocalcinosis, and renal colics. According to our supplementation protocol, replenishment of vitamin D involves cholecalciferol dosing in two steps: a loading dose (LD) for 2 months and a maintenance dose (MD) thereafter. Please refer to the main text for loading and maintenance dose titration. Results: Of 8329 cases with vitamin D measurements, 2248 had adequate follow up data of 3524.5 patient-years and were included in the study: a total of 1575 intervention subjects and 673 controls, with an average follow-up of 18.8 months. Baseline vitamin D concentrations of 22.6 ng/mL (controls) did not change significantly (2 months: 22.2; 1 year: 21.7; 2 years: 22.0; 3 years: 23.8; 4 years: 21.8; and 5 years: 22.1 ng/mL), while concentrations of 21.9 ng/mL (intervention group) reached and remained 40 ng/mL (2 months: 41.0; 1 year: 39.4; 2 years: 39.0; 3 years: 39.3; 4 years: 40.4; and 5 years: 39.4 ng/mL). Vitamin D adequacy was achieved in 91.6% of patients in the intervention arm compared to only 16.9% in controls (p < 0.0001). Mean D and rates of adequacy were significantly higher over time in the intervention arm (p < 0.0001). The incidence of renal adverse events or hypervitaminosis did not differ between groups (p > 0.05). Conclusions: Our intervention protocol appears highly efficient in achieving and maintaining vitamin D adequacy over 5 years, with no increase in adverse events compared with controls, presenting it as an effective long-term strategy. Full article

Accurate detection of pressure-relief boreholes is crucial for evaluating drilling quality and monitoring safety in coal mine roadways. Nevertheless, the highly challenging underground environment—characterized by insufficient lighting, severe dust and water mist disturbances, and frequent occlusions—poses substantial difficulties for current object detection approaches, particularly in identifying small-scale and low-visibility targets. To effectively tackle these issues, a lightweight and robust detection framework, referred to as YOLO-DFBL, is developed using the YOLOv11n architecture. The proposed approach incorporates a DualConv-based lightweight convolution module to optimize the efficiency of feature extraction, a Frequency Spectrum Dynamic Aggregation (FSDA) module for noise-robust enhancement, and a Biformer (Bi-level Routing Transformer)-based routing attention mechanism for improved long-range dependency modeling. In addition, a Lightweight Shared Convolution Head (LSCH) is incorporated to effectively decrease the overall model complexity. Experimental results on a real coal mine roadway dataset demonstrate that YOLO-DFBL achieves an mAP@50:95 of 78.9%, with a compact model size of 1.94 M parameters, a computational complexity of 4.7 GFLOPs, and an inference speed of 157.3 FPS, demonstrating superior accuracy–efficiency trade-offs compared with representative lightweight YOLO variants and classical detectors. Field experiments under challenging low-illumination and occlusion environments confirm the robustness of the proposed approach in real mining scenarios. The developed method enables reliable visual perception for underground drilling equipment and facilitates safer and more intelligent operations in coal mine engineering. Full article

Ground autonomous mobile robots are increasingly critical for reconnaissance, patrol, and resupply tasks in public safety and national defense scenarios, where global path planning in 3D uneven terrains remains a major challenge. Traditional planners struggle with high dimensionality, while Deep Reinforcement Learning (DRL) is hindered by two key issues: (1) systematic overestimation of action values (Q-values) due to function approximation error, which leads to suboptimal policies and training instability; and (2) inefficient exploration under sparse reward signals. To address these limitations, we propose DSAC-ICM: a Distributional Soft Actor–Critic framework integrated with an Intrinsic Curiosity Module (ICM). Our method fundamentally shifts the learning paradigm from estimating scalar Q-values to learning the full probability distribution of state-action returns, which inherently mitigates value overestimation. We further integrate the ICM to generate dense intrinsic rewards, guiding the agent toward novel and unvisited states to tackle the exploration challenge. Comprehensive experiments conducted in a suite of realistic 3D uneven-terrain environments demonstrate that DSAC-ICM successfully enables the agent to learn effective navigation capabilities. Crucially, it achieves a superior trade-off between path quality and computational cost when compared to traditional path planning algorithms. Furthermore, DSAC-ICM significantly outperforms other RL baselines in terms of convergence speed and return. Full article

This study tackles the critical challenges of stress evolution and pillar optimization in underground potash mining, with a focus on the 351-stope of Kaiyuan Mining in Laos. Integrating theoretical calculations, large-scale 3D numerical modeling, and an AHP-Fuzzy comprehensive evaluation, we systematically analyze the complex mechanical behaviors of the mining environment. Applying key stratum theory, we reveal the unique mechanism by which overlying hard rock bends without fracturing in carnallite layers under room-and-pillar conditions. Comparative numerical simulations of four pillar-width schemes—involving 8 m rooms with 10 m, 8 m, 6 m, and 4 m pillars—show that reducing pillar width markedly increases vertical stress, triggers exponential roof subsidence, and expands pillar failure zones. Using an AHP-Fuzzy method that incorporates safety, technical, and economic factors, the Simultaneous Backfilling with 8 m Mining and 6 m Pillar Retention is identified as the optimal scheme. This configuration demonstrates superior stability, exhibiting an average pillar stress of 9.3 MPa and only limited plastic failure zones at pillar ends. These findings offer a robust scientific and technical foundation for enhancing the safety, efficiency, and sustainability of underground potash mining operations. Full article

Defect inspection of Printed Circuit Board (PCB) is essential for maintaining the safety and reliability of electronic products. With the continuous trend toward smaller components and higher integration levels, identifying tiny imperfections on densely packed PCB structures has become increasingly difficult and remains a major challenge for current inspection systems. To tackle this problem, this study proposes the Multi-scale Edge-Aware Enhanced Detection Transformer (MEE-DETR), a deep learning-based object detection method. Building upon the RT-DETR framework, which is grounded in Transformer-based machine learning, the proposed approach systematically introduces enhancements at three levels: backbone feature extraction, feature interaction, and multi-scale feature fusion. First, the proposed Edge-Strengthened Backbone Network (ESBN) constructs multi-scale edge extraction and semantic fusion pathways, effectively strengthening the structural representation of shallow defect edges. Second, the Entanglement Transformer Block (ETB), synergistically integrates frequency self-attention, spatial self-attention, and a frequency–spatial entangled feed-forward network, enabling deep cross-domain information interaction and consistent feature representation. Finally, the proposed Adaptive Enhancement Feature Pyramid Network (AEFPN), incorporating the Adaptive Cross-scale Fusion Module (ACFM) for cross-scale adaptive weighting and the Enhanced Feature Extraction C3 Module (EFEC3) for local nonlinear enhancement, substantially improves detail preservation and semantic balance during feature fusion. Experiments conducted on the PKU-Market-PCB dataset reveal that MEE-DETR delivers notable performance gains. Specifically, Precision, Recall, and mAP_50–95 improve by 2.5%, 9.4%, and 4.2%, respectively. In addition, the model’s parameter size is reduced by 40.7%. These results collectively indicate that MEE-DETR achieves excellent detection performance with a lightweight network architecture. Full article

To achieve the automation and intelligence of mining equipment, it is essential to address the challenge of autonomous driving, with the core task being how to navigate safely from the starting point to the mining area endpoint. This paper proposes a boundary-aware multi-point preview control algorithm to tackle the strong dependency on predefined paths and the lack of foresight in the autonomous driving of underground articulated mining vehicles in highly confined underground spaces. The algorithm determines the driving direction by calculating the vehicle’s real-time state and LiDAR data, previewing road conditions without relying on preset path planning. Experiments conducted in a ROS Noetic/GAZEBO 11 simulation environment compared the proposed method with single-point and two-point preview algorithms, validating the effectiveness of the boundary-aware multi-point preview control. The results show that the proposed control strategy yields the lowest lateral deviation and the highest steering smoothness compared to single-point and two-point preview algorithms; it also outperforms the standard multi-point preview algorithm. This demonstrates its superior performance. Specifically, the proposed boundary-aware multi-point preview algorithm outperformed other methods in terms of steering smoothness and stability, significantly enhancing the vehicle system’s adaptability, robustness, and safety. Full article

This study addresses the practical demand for facial recognition of pigs in the food safety and insurance industries, tackling the challenge of low recognition accuracy caused by complex farming environments, occlusions, and similar textures. To this end, we propose an enhanced model, DGS-YOLO, based on YOLOv11n, designed to achieve precise facial recognition of group-raised young pigs. The core improvements of the model include the following: (1) replacing standard convolutions with dynamic convolutions (DMConv) to enhance the network’s adaptive extraction capability for critical detail features; (2) designing a C3k2_GBC module with a bottleneck structure to replace the C3k2 neck, enabling more efficient capture of multi-scale contextual information; (3) introducing the SimAM parameter-free attention mechanism to optimize feature focusing; (4) employing the Shape-IoU loss function to mitigate the impact of bounding box geometry on regression accuracy. Experiments on self-built datasets demonstrate that DGS-YOLO achieves 4%, 2.1%, and 2.3% improvements in accuracy, recall, and mAP50, respectively, compared to the baseline model YOLOv11n. Furthermore, its overall performance surpasses that of Faster R-CNN and SSD in comprehensive evaluation metrics. Especially in limited sample scenarios, the model demonstrates strong generalization ability, with accuracy and mAP50 further increased by 20.1% and 10.3%. This study provides a highly accurate and robust solution for animal facial recognition in complex scenarios. Full article

Three-dimensional unmanned aerial vehicle (UAV) path planning presents a challenging optimization problem characterized by high dimensionality, strong nonlinearity, and multiple constraints. To address these complexities, this study proposes an Enhanced Protozoan Optimizer (EAPO) by refining the initialization, behavioral decision-making, environmental perception, and population diversity preservation mechanisms of the original Protozoan Optimizer. Specifically: Latin hypercube sampling enriches initial population diversity; a behavior adaptation mechanism based on historical success dynamically adjusts the exploration-exploitation balance; environmental structure modeling using perception fields enhances local exploitation capabilities; an adaptive hibernation-reconstruction strategy boosts global escape ability. Ablation experiment validates the effectiveness of each enhancement module, while exploration-exploitation analysis demonstrates EAPO maintains an optimal balance throughout the optimization process. Comprehensive evaluations using CEC2022 and CEC2020 benchmark datasets, ten real-world engineering design problems, and four drone path planning scenarios of varying scales and complexities further validate its excellent performance. Experimental results demonstrate that EAPO outperforms the baseline APO and twelve advanced optimizers in convergence accuracy, stability, and robustness. In UAV path planning applications, paths generated by EAPO satisfy all constraints and outperform APO-generated paths across multiple path quality evaluation metrics concerning safety, smoothness, and energy consumption. Compared to APO, EAPO achieved average fitness improvements of 14.0%, 4.5%, 8.7%, and 31.42% across the four maps, respectively, fully demonstrating its practical value and formidable capability in tackling complex engineering optimization problems. Full article

Railways play a pivotal role in advancing environmentally conscious and safe transportation systems, positioning them as a vital component of Europe’s future mobility strategy. This study tackles the complex dimensions of sustainability in railway transport by combining environmental impacts and safety considerations within a single, integrated analytical framework. We extend the variable intermediate slack-based measure (VSBM) model to incorporate undesirable outputs—specifically accidents and emissions—allowing for a joint evaluation of safety and environmental performance. The revised model is applied to assess the operational efficiency of 14 European railway operators between 2010 and 2018. Compared to conventional efficiency models, our enhanced VSBM approach provides improved discriminatory power and reveals significant changes in relative efficiency rankings. By integrating safety and environmental dimensions, this study contributes a new perspective on sustainable railway performance measurement. Full article

Railway track fasteners, serving as critical connecting components, have a reliability that directly impacts railway operational safety. To address the performance bottlenecks of existing detection methods in handling complex scenarios with diverse fastener types and co-occurring multiple defects, this paper proposes a Type-Guided Expert Model-based Fastener Detection and Diagnosis framework (TGEM-FDD) based on You Only Look Once (YOLO) v8. This framework follows a “type-identification-first, defect-diagnosis-second” paradigm, decoupling the complex task: the first stage employs an enhanced YOLOv8s with Deepstar, SPPF-attention, and DySample (YOLOv8s-DSD) detector integrating Deepstar Block, Spatial Pyramid Pooling Fast with Attention (SPPF-Attention), and Dynamic Sample (DySample) modules for precise fastener localization and type identification; the second stage dynamically invokes a specialized multi-label classification “expert model” based on the identified type to achieve accurate diagnosis of multiple defects. This study constructs a multi-label fastener image dataset containing 4800 samples to support model training and validation. Experimental results demonstrate that the proposed YOLOv8s-DSD model achieves a remarkable 98.5% mean average precision at an Intersection over Union threshold of 0.5 (mAP@0.5) in the first-stage task, outperforming the original YOLOv8s baseline and several mainstream detection models. In end-to-end system performance evaluation, the TGEM-FDD framework attains a comprehensive Task mean average precision (Task mAP) of 88.1% and a macro-average F1 score for defect diagnosis of 86.5%, significantly surpassing unified single-model detection and multi-task separate-head methods. This effectively validates the superiority of the proposed approach in tackling fastener type diversity and defect multi-label complexity, offering a viable solution for fine-grained component management in complex industrial scenarios. Full article

The process industry (PI) plays a vital role in the global economy and faces mounting pressure to enhance sustainability, operational agility, and resource efficiency amid tightening regulatory and market demands. Although artificial intelligence (AI) has been explored in this domain for decades, its adoption in industrial practice remains limited. Recently, machine learning (ML) has gained momentum, particularly when integrated with core PI systems such as process control, instrumentation, quality management, and enterprise platforms. Among ML techniques, reinforcement learning (RL) has emerged as a promising approach to tackle complex operational challenges. In contrast to conventional data-driven methods that focus on prediction or classification, RL directly addresses sequential decision making under uncertainty, a defining characteristic of dynamic process operations. Given RL’s growing relevance, this study conducts a systematic literature review to evaluate its current applications in the PI, assess methodological developments, and identify barriers to broader industrial adoption. The review follows the PRISMA methodology, a structured framework for identifying, screening, and selecting relevant publications. This approach ensures alignment with a clearly defined research question and minimizes bias, focusing on studies that demonstrate meaningful industrial applications of RL. The findings reveal that RL is transitioning from a theoretical construct to a practical tool, particularly in the chemical sector and for tasks such as process control and scheduling. Methodological maturity is improving, with algorithm selection increasingly tailored to problem-specific requirements and a trend toward hybrid models that integrate RL with established control strategies. However, most implementations remain confined to simulated environments, underscoring the need for real-world deployment, safety assurances, and improved interpretability. Overall, RL exhibits the potential to serve as a foundational component of next-generation smart manufacturing systems. Full article

Moving Target Defense (MTD) has been proposed as a dynamic defense strategy to address the static and isomorphic vulnerabilities of networks. Recent research in MTD has focused on enhancing its effectiveness by combining it with cyber deception techniques. However, there is limited research on evaluating and quantifying this hybrid defence framework. Existing studies on MTD evaluation often overlook the deployment of deception, which can expand the potential attack surface and introduce additional costs. Moreover, a unified model that simultaneously measures security, reliability, and defense cost is lacking. We propose a novel hybrid defense effectiveness evaluation method that integrates queuing and evolutionary game theories to tackle these challenges. The proposed method quantifies the safety, reliability, and defense cost. Additionally, we construct an evolutionary game model of MTD and deception, jointly optimizing triggering and deployment strategies to minimize the attack success rate. Furthermore, we introduce a hybrid strategy selection algorithm to evaluate the impact of various strategy combinations on security, resource consumption, and availability. Simulation and experimental results demonstrate that the proposed approach can accurately evaluate and guide the configuration of hybrid defenses. Demonstrating that hybrid defense can effectively reduce the attack success rate and unnecessary overhead while maintaining Quality of Service (QoS). Full article

Antimicrobial resistance (AMR) is one of the most significant global health threats of the 21st century, driving the urgent search for alternatives to conventional antibiotics. Copper nanoparticles (CuNPs) have gained attention due to their broad antimicrobial spectrum, cost-effectiveness, and versatile applications in medicine, agriculture, and the food industry. This review provides a systematic overview of the advances in CuNP synthesis, mechanisms of antimicrobial action, biomedical and industrial applications, and associated toxicity issues. A comprehensive literature review was conducted, covering chemical, physical, and biological synthesis strategies; mechanistic studies on microbial inhibition; and experimental reports on biomedical and environmental applications. A comparative analysis revealed opportunities, limitations, and knowledge gaps, with particular emphasis on cytotoxic and ecotoxicological aspects. CuNPs show strong antimicrobial activity against bacteria, fungi, viruses, and multidrug-resistant strains through mechanisms such as reactive oxygen species (ROS) generation, membrane disruption, and DNA/protein interactions. Their use in medical devices, wound dressings, textiles, and packaging materials underlines their application potential. However, cytotoxicity to mammalian cells, ecological risks, and the lack of standardized safety protocols remain critical challenges. Particle size, morphology, and surface chemistry strongly influence both efficacy and toxicity, underlining the importance of controlled synthesis and functionalization. Overall, CuNPs represent a promising strategy to tackle the AMR crisis. Future research should focus on environmentally friendly and surface-modified synthesis approaches, standardized toxicity assessments, and robust regulatory frameworks. By balancing antimicrobial efficacy with biosafety and sustainability, CuNPs could become a transformative platform for clinical, industrial, and environmental applications. Full article

This paper presents a novel algorithm for the dynamic analysis of fractional-order viscoelastic spinning disks in the time domain. The novelty mainly lies in the use of the shifted Legendre polynomial algorithm for the direct time-domain numerical analysis of displacement in two directions for a three-dimensional viscoelastic rotating disk, tackling a more complex and strongly coupled problem than those addressed in previous studies. By using the fractional-order Kelvin–Voigt model to describe the viscoelastic properties of the disk, a system of governing equations with three independent variables is established. For the two ternary unknown functions in the equations, a fractional-order differential operator matrix based on Shifted Legendre polynomials is derived, transforming the original equations into two sets of algebraic equations that are easier to solve. This paper presents an in-depth analysis of the convergence of the Legendre polynomial algorithm, complemented by an investigation of its error characteristics using numerical examples, thereby verifying the method’s accuracy and feasibility. This study can be applied to the dynamic analysis of viscoelastic rotating structures under body force density. The findings provide theoretical support for the optimization and safety assessment of load-bearing rotating components in engineering. And the algorithm demonstrates high accuracy and applicability in handling fractional-order equations in science and engineering. Full article

Industry 5.0 represents the next stage in the industrial evolution, with a growing impact in the shipbuilding sector. In response to its challenges, Navantia, a leading international player in the field, is transforming its shipyards towards the creation of a Shipyard 5.0 through the implementation of digital technologies that enable human-centered, resilient and sustainable processes. This approach gives rise to Cognitive Cyber-Physical Systems (CCPS) in which the system can learn and where the generated data are integrated into a digital platform that supports operators in decision-making. In this scenario, different smart elements (e.g., IoT-based tows, trucks) are used to transport key components of a ship like pipes or steel plates, which are present in a large number, representing a strategic opportunity to enhance traceability in shipbuilding operations. The accurate tracking of these elements, from manufacturing to assembly, helps to improve operational efficiency and enhances safety within the shipyard environment. Considering the previous context, this paper describes a CCPS that enables tracking and real-time data visualization through portable interfaces adapted to the shipyard operator needs. Following the Industry 5.0 foundations, the presented solution is focused in providing human-centric interfaces, tackling issues like information overload, poor visual organization and accessibility of the control panels. Thus, to address such issues, an iterative human-centered redesign process was performed. This approach incorporated hands-on testing with operators at each development stage and implemented specific adjustments to improve interface clarity and reaction speed. Full article