Search Results (3,883)

Rolling bearings, as core components of rotating machinery, are prone to failure under harsh working conditions, and their fault diagnosis is crucial for the safe operation of industrial systems. Aiming at resolving the problems of weak fault feature representation, poor model generalization ability and high dependence on manual preprocessing in traditional bearing fault diagnosis methods, an end-to-end fault diagnosis model named KanMSConv is proposed for one-dimensional raw vibration signals. The model abandons complex time–frequency transformation and manual feature engineering, and constructs a multi-scale feature extraction module based on depthwise separable convolution to capture local impulsive components and global modulation characteristics of fault signals simultaneously. The SE channel attention mechanism is integrated to adaptively enhance fault-related critical features and reduce redundant channel responses. Residual connection is introduced to alleviate the gradient degradation problem of deep networks and improve feature reuse capability. On this basis, the Kolmogorov–Arnold Network (KAN) is used to replace the traditional fully connected layer, which enhances the model’s ability to fit complex nonlinear mapping relationships and distinguish fault classification boundaries. Experimental verification is carried out on three representative rolling bearing datasets (CWRU, PU, SDUST) under multi-load, multi-class and cross-platform conditions. The results show that the KanMSConv model achieves 100% accuracy on the CWRU dataset, 99.93% on the PU dataset and 99.80% on the SDUST dataset, which is significantly superior to the existing mainstream fault diagnosis models in terms of Accuracy, Precision, Recall and F1-Score. And the ablation and computational cost analyses further support this conclusion. Full article

Centered on lean production, this study integrates operational technologies (OT), communication technologies (CT), and information technologies (IT) within an open-system software architecture. Under stochastic customer demand, reliance on static data and experience-based decision-making constrains firms’ responsiveness to market. The integration of lean management with a data-driven database enhances operational flexibility and decision quality, enabling small and medium-sized enterprises (SMEs) in the bicycle industry to develop responsive digital factory environments with real-time monitoring and improved operational transparency. The proposed platform is applicable to both manufacturing processes and operational management, improving overall equipment effectiveness (OEE), production efficiency, process optimization, and reducing quality losses, inventory levels, and workforce misallocation. This study investigates the application of the Analytic Hierarchy Process (AHP) and multi-criteria decision-making (MCDM) within a performance framework integrating ESG indicators and a balanced scorecard to identify key success factors for digital lean improvement in the bicycle industry. A case study of a bicycle manufacturer was conducted using questionnaire surveys and expert interviews with exporters. The results indicate that the five most critical success factors are: enhancing return on invested capital, strengthening digital capabilities, improving product quality, minimizing inventory waste, and reducing lead time. These findings provide practical guidance for decision-makers in designing more effective lean management strategies in highly competitive digital markets. Furthermore, by facilitating the adoption of appropriate digital technologies under a reasonable return on investment, this approach supports the systematic implementation of Industry 4.0 initiatives and transforms traditional lean practices into more efficient and sustainable digital lean operations. Full article

The increasing demand for lithium (Li), cobalt (Co), and rare earth elements (REEs) driven by battery technologies, electrification and renewable energy systems has intensified the interest in recovery pathways as an alternative to conventional mining. High-salinity mine waters—including lithium brines, geothermal fluids, and metallurgical effluents—represent both an environmental liability and a significant secondary resource for metal recovery. However, extreme ionic strength, complex metal speciation, and strong competition from major ions severely limit the efficiency and selectivity of traditional extraction technologies. Microalgae and cyanobacteria are promising biological agents for metal recovery via biosorption, bioaccumulation, and extracellular polymeric substance (EPS)-mediated binding, especially in saline and hypersaline systems. This review synthesises current knowledge on microalgal-based recovery of Li, Co, and REEs from high-salinity waters, emphasising co-design principles that integrate strain physiology, their adaptation to the extreme operating conditions, water chemistry, and process engineering. Halotolerant and extremophilic taxa—Nannochloropsis oceanica, Galdieria sulphuraria, and Synechococcus elongatus—are examined as representative models for complementary metal-binding mechanisms and operational niches. Limitations such as weak affinity for lithium, competitive ion suppression, desorption inefficiencies, and scale-up challenges are discussed. Emerging strategies such as modular multi-strain systems, hybrid bio-physicochemical platforms, and biomass valorisation are also addressed. The review concludes that microalgal systems, when co-designed for selectivity and resilience, can contribute to the strategic recovery of critical materials that align with EU, UK and US policies. Full article

This study conducts a multi-objective optimization of an impact pruner for coniferous plantation trees, using Prince Rupprecht’s larch (Larix principis-rupprechtii Mayr) in North China as a case study. The objective is to establish an impact cutting mechanics model and to construct an impact cutting platform. This study utilizes the Box–Behnken principle, with the cutting speed (v), cutter wedge angle (β), and cutting clearance (L) as influencing factors and the cutting energy consumption (Y₁), total equipment energy consumption (Y₂), and specific cutting area (S) as evaluation indexes. The cutting parameters were optimized using a mathematical model for multi-objective optimization. The experimental results indicate that the factors influencing target Y₁ were ranked as β, L, and v, while the factors influencing target Y₂ were ranked as β, v, and L, and the factors influencing target S were ranked as L, β, and v. Field tests demonstrated that the optimization reduced the cutting energy consumption by up to 16.90% and improved the cutting quality by up to 19.28%. These gains directly translate to improved operational efficiency and economic value in forestry management. The optimal parameters corresponding to these improvements are v = 2.15 m·s⁻¹, β = 20°, and L = 5 mm, resulting in Y₁ = 36.10 J, Y₂ = 3351.01 J, and S = 3.45. These results demonstrate the feasibility and efficiency of the impact pruning method for Larix principis-rupprechtii in coniferous plantation forests. By combing mechanism analysis with multi-objective optimization, this study proposes a solution that can improve the pruning quality of coniferous plantation trees, reduce the energy consumption of impact pruning machines, enhance tree health, and serve as a measure to prevent pests and diseases, contributing to the advancement of artificial forest plant protection technology. Full article

The growth of advanced cyber threats has inspired organisations to start using powerful cybersecurity platforms, but the process of selection is analytically challenging due to the multidimensional, uncertain, and conflicting character of the evaluation criteria. The prevailing culture of decision-support frameworks is based on unyielding numerical evaluations that cannot reflect the underlying vagueness of expert judgment and the dynamic interplay of macro-environmental factors. This paper presents a combined Fuzzy Multi-Criteria Decision-Making (FMCDM) system, which uses polygonal fuzzy numbers, in particular pentagonal fuzzy representation, and four other complementary methods of MCDM (Fuzzy AHP, Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS), integrated by a Borda Count consensus system. Sixteen assessment sub-criteria are logically obtained through an analysis of PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) and weighted using the Fuzzy Analytic Hierarchy Process. The model is used to compare six cybersecurity platforms, including Microsoft Security Framework, CrowdStrike Falcon, Cisco Cybersecurity Portfolio, Palo Alto Networks Cortex, Fortinet Security Fabric, and Sophos Central. In this study, Fuzzy AHP demonstrates that the aggregate weight of political factors is the highest (0.4181), followed by cross-border data management, regulatory compliance, and government incentives as the most popular sub-criteria. According to the results from the Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS methods, Microsoft Security Framework ranks consistently in the first place, and CrowdStrike Falcon and Cisco Cybersecurity Portfolio were ranked second and third, respectively. The framework presented in the study provides decision-makers with a reproducible, uncertainty-conscious basis for cybersecurity platform selection. Full article

Accurate and rapid detection of Volvariella volvacea (straw mushroom) fruiting bodies at harvestable maturity is a critical prerequisite for automated industrial cultivation. However, existing detection methods often yield high false-negative and false-positive rates when processing a small-scale, densely distributed, and heavily occluded targets against complex straw substrate backgrounds. Furthermore, these methods frequently struggle to balance the competing requirements of architectural efficiency (such as parameter volume and computational complexity) and real-time performance for edge computing. To address these challenges, this study proposes a YOLO11n-CPDM, a lightweight detection model based on an improved YOLO11n architecture. The model incorporates synergistic optimizations across feature extraction, fusion, and reconstruction. First, a Dual Coordinate Attention Feature Extraction mechanism is integrated into the C3k2 bottleneck blocks of the backbone network. This enhances target perception in complex, occluded environments by concurrently modeling global context and local salient features. Second, within the neck network, the standard attention module is replaced with the PnPNystraAttention module, coupled with the DySample dynamic upsampling operator. This modification strengthens contextual relationships among multi-scale features and improves spatial consistency during reconstruction while preserving linear computational complexity. Finally, the detection head is optimized using MBConv blocks based on an inverted residual structure to minimize parameter volume. Experimental results on a custom V. volvacea dataset demonstrate that the proposed YOLO11n-CPDM model achieves significant performance gains, with Precision (P), Recall (R), and Mean Average Precision (mAP50) reaching 86.8%, 87.5%, and 88.4%, respectively. These figures represent improvements of 2.7, 3.0, and 3.2 percentage points over the baseline YOLO11n model. Additionally, the model size is reduced to 4.8 MB (a 12.7% decrease), while achieving inference speeds of 42.7 FPS on Jetson AGX Orin and 21.2 FPS on Jetson Nano, outperforming the baseline model on both embedded platforms. Consequently, the proposed model effectively enhances detection performance in complex environments while maintaining excellent lightweight characteristics and deployment flexibility, providing a solid technical foundation for intelligent perception and automated harvesting of V. volvacea. Full article

Climate change and soil degradation pose a challenge to grape quality, motivating the development of integrated monitoring approaches combining soil analysis with remote sensing techniques. However, harmonized information addressing this multidisciplinary challenge remains scarce. Therefore, this systematic review synthesizes the scientific literature published since 2020 with the aim of (i) identifying key soil properties and techniques applied, (ii) evaluating remote sensing approaches and their integration with soil data, and (iii) highlighting knowledge gaps and challenges for sustainable precision viticulture. A search in Scopus yielded 197 full-text articles classified into three thematic groups and analyzed using a standardized extraction protocol. Our synthesis reveals that pH, electrical conductivity, soil organic matter, and cation exchange capacity are the most consistently reported physicochemical parameters across the reviewed studies, while next-generation sequencing and multi-omics approaches are increasingly adopted in microbiological research to characterize rhizosphere communities and their links to terroir expression. In remote sensing, multispectral UAV platforms and satellite missions (Sentinel-2, Landsat) combined with vegetation indices, principally NDVI, dominate the toolset for monitoring vine vigor and water status. Nevertheless, genuine integration of remote-sensing outputs with root-zone soil measurements remains uncommon, with most studies treating both data streams independently. The principal knowledge gaps identified concern the absence of standardized sustainability assessment frameworks, limited cross-terroir transferability of predictive models, and insufficient long-term multi-site datasets to underpin climate change adaptation in vineyard management. Full article

Maritime search and rescue is an important component of emergency response frameworks and primarily relies on Unmanned Aerial Vehicles (UAVs) for maritime object detection. However, maritime accidents frequently occur in low-visibility environments, such as foggy or low-light conditions, which lead to low contrast, blurred object boundaries, and degraded texture representations. Most existing maritime object detection algorithms are developed for natural light scenes, and their performance deteriorates markedly when deployed directly in low-visibility environments, primarily due to reduced image quality that hinders feature extraction and semantic information aggregation. Although several studies incorporate image enhancement techniques prior to detection to improve image quality, these approaches often introduce significant additional computational overhead, limiting their practical deployment on UAV platforms. To tackle these challenges, this paper proposes a lightweight model built upon a recent YOLO framework, termed Multi-Scale Adaptive YOLO (MSA-YOLO), for maritime detection using UAVs in low-visibility environments. The proposed model systematically optimizes the backbone, neck, and detection head networks. Specifically, an improved StarNet backbone is designed by integrating Efficient Channel Attention (ECA) mechanisms and multi-scale convolutional kernels, which strengthen feature extraction capability while maintaining low computational overhead. In the neck network, a high-frequency enhanced residual block branch is inserted into the C3k2 module to capture richer detailed information, while depthwise separable convolution is utilized to further reduce computational cost. Moreover, a non-parametric attention module is incorporated into the detection head to adaptively optimize features in the classification and regression branches. Finally, a joint loss function that combines bounding box regression, classification, and distribution focal losses is utilized to improve detection accuracy and training stability. Experimental results on the constructed AFO, Zhoushan Island, and Shandong Province datasets demonstrate that, relative to YOLOv11-s, MSA-YOLO reduces model parameters and FLOPs by 52.07% and 41.36%, respectively, while achieving improvements of 1.11% and 1.33% in mAP@0.5:0.95 and mAP@0.5. These results indicate that the proposed method effectively balances computational efficiency and detection accuracy, rendering it suitable for practical maritime search and rescue applications in low-visibility environments. Full article

Quantum Secure Multi-party Summation (QSMS) is a fundamental primitive of Quantum Secure Multi-party Computation (QSMC), enabling multiple participants to jointly compute the sum of their private inputs without disclosing individual data. However, most existing QSMS protocols require all participants to possess full quantum capabilities and often rely on pre-shared keys, auxiliary mask transmission, or multiple trusted third parties, resulting in high communication overhead and limited practicality. To address these limitations, we propose an efficient Semi-Quantum Secure Multi-party Summation (SQSMS) protocol based on d-dimensional n-particle entangled states. By exploiting the global correlation properties of high-dimensional entangled states, the proposed protocol generates correlated random masks directly from quantum measurement outcomes. These masks cancel automatically during the aggregation process, eliminating the need for additional mask distribution and transmission. Compared with existing QSMS schemes, the proposed protocol reduces communication overhead, improves quantum efficiency, and avoids reliance on pre-shared keys or multiple trusted third parties. Moreover, only simple measurement operations are required from classical participants, making the protocol more practical for semi-quantum environments. We further provide formal correctness and security analyses of the proposed protocol and conduct quantum circuit simulations using the IBM Qiskit platform to demonstrate its feasibility. Moreover, based on the proposed summation protocol, we design several extended application protocols, including anonymous voting, anonymous auction, and anonymous ranking, which further illustrate the scalability and practical applicability of the proposed scheme. Full article

Fusing spatial observations from sensors with heterogeneous error characteristics is a persistent challenge in geostatistics. Classical kriging assumes a Gaussian likelihood for all observations, an assumption that fails when sensors exhibit one-sided or asymmetric noise. We present a Variable Rank Kriging (VRK) formulation that supports per-observation heterogeneous likelihoods where each observation may define its own likelihood function, thus enabling principled fusion of sensors whose noise structures are significantly different in terms of distribution family and magnitude. Within this framework, we use the exponential (one-sided) likelihood as a case study to demonstrate the method and compare it with sampling-based numerical alternatives for general likelihoods without closed forms. A non-collocated RTK calibration workflow uses kriging predictions from a sparse high-accuracy reference to characterise sensor-specific likelihood parameters without requiring co-located paired observations. Synthetic 1-D and 2-D experiments show that correct per-point likelihood specification reduces RMSE by up to 92% (1-D) and 57% (2-D) relative to a misspecified Gaussian model while also eliminating systematic positive bias. A demonstration using NEON Airborne Observation Platform lidar data at Harvard Forest confirms these findings in a practical, real-world scenario. Across multiple subsamples of the lidar dataset, the exponential likelihood reduces vegetated-zone RMSE by $20.6\%$ (open zone: $18.6\%$) and mean absolute bias by $26.5\%$ relative to a heteroscedastic Gaussian baseline. The open-source vrk Python ($>=$3.10) package provides a reproducible implementation that can be applied to any spatial domain that requires multi-sensor spatial fusion with heterogeneous error structures. Full article

Manual data storage methods on various mobile devices, IoT devices, and traditional computing platforms still lack sufficient security governance due to the absence of a unified security framework. Unlike application controlled environments, manual storage locations such as file systems, removable media, and IoT devices are highly susceptible to unauthorized access, misuse, and exfiltration. To address this problem, the paper proposes a security framework for manual storage systems using metadata, and the proposed framework includes three different algorithms, namely MARShield, ThreshGuard, and SentinelScheduler. These three algorithms operate together to ensure security for manual storage systems. MARShield is used for enforcing immutable metadata, multi-access rights based on tokens, and persistent source tracking by cryptographically securing provenance logs. ThreshGuard, on the other hand, enables the use of adaptive threshold-based misuse regulation and bottleneck-controlled serialized execution. SentinelScheduler optimizes the use of cryptography by incorporating trust-based application profiling and idle-time scheduling for heavy security operations. The proposed methodology is evaluated using a hybrid approach combining real-world datasets (CIC-IoT2023, TON-IoT, Bot-IoT and ISCX VPN non-VPN) and dataset-driven synthetic access pattern generation. Real datasets are used to model realistic IoT traffic behaviors, while additional synthetic scenarios are introduced to evaluate adaptability against evolving and previously unseen attack patterns. Network level features from these datasets are systematically transformed into storage-level access behaviors to evaluate metadata-driven access control. The experimental results indicate improved detection accuracy (94.6%), reduced false positive rate (4.3%), improved misuse control efficiency (92%) and scalability (94%). The proposed methodology for securing manual storage domains is scalable, adaptive, and portable, extending the security of applications and their associated domains. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

Lane-changing and overtaking constitute a typical complex driving manoeuvre for intelligent vehicles operating on structured roads; this task demands that the vehicle not only plan a safe and smooth lane-change trajectory but also requires the control system to maintain high tracking accuracy and lateral stability. Addressing the challenges of real-time path planning and stable tracking control inherent in lane-changing and overtaking scenarios, this paper proposes a trajectory planning and control method that integrates an improved artificial potential field (APF) approach with a lateral–longitudinal cooperative controller. Regarding path planning, the proposed method constructs attractive and repulsive fields based on the APF framework, while introducing virtual target points, elliptical obstacle models, and velocity-dependent repulsive fields to mitigate the risk of local minima and enhance dynamic obstacle avoidance capabilities. To ensure trajectory continuity and trackability, a fifth-order polynomial is employed to smooth the planned path. Regarding control, the method utilises a Linear Quadratic Regulator (LQR)—optimised via a genetic algorithm—for lateral control; this is coupled with a dual-PID longitudinal controller that generates throttle and braking commands based on vehicle speed errors, thereby establishing a cooperative lateral–longitudinal tracking control strategy. The proposed method is validated using a CarSim–MATLAB/Simulink co-simulation platform. Simulation results demonstrate that the proposed method significantly improves trajectory-tracking accuracy and vehicle stability during lane-changing and overtaking manoeuvres. In a single lane-change scenario, the maximum lateral error is reduced from approximately 0.62 m to 0.22 m, and the heading angle error decreases from about 0.058 rad to 0.01 rad; in a continuous lane-changing scenario, the maximum lateral error drops from approximately 0.30 m to 0.04 m, while the heading angle error falls from about 0.016 rad to 0.005 rad. Furthermore, the yaw rate, sideslip angle, and lateral acceleration are reduced by 39.1%, 22.2%, and 28.9%, respectively. These results confirm that, under the specified simulation conditions, the proposed method exhibits superior tracking performance and stability. Future research could further explore more complex driving scenarios, such as curved roads, multi-vehicle interactions, sensor uncertainties, actuator delays, and real-vehicle field experiments. Full article

Background: Stabilization of surgical guides in fully edentulous patients remains a clinical challenge due to mucosal resilience and potential micromovement, even when fixation pins are used. Guide instability may affect drilling accuracy and overall workflow predictability. This proof-of-concept case series describes a stabilization approach based on pre-placed tripod reference implants with multi-unit coupling, designed to create a mechanically defined prosthetic docking platform for fully guided implant surgery. Methods: Three fully edentulous patients requiring implant-supported rehabilitation were treated using a two-stage protocol. Three temporary reference implants were inserted in a tripod configuration 7–10 days prior to definitive surgery. Multi-unit abutments were mounted on the reference implants, and intraoral scanning was performed to design a surgical guide indexed to the prosthetic interfaces. During implant placement, the guide was screw-retained to the reference implants via the multi-unit connections. Postoperative implant positions were evaluated radiographically by superimposing postoperative datasets onto the preoperative planning model. Intraoperative guide stability, surgical events, and early postoperative outcomes were recorded. Results: Stable guide fixation was achieved in all three cases without detectable intraoperative displacement. Implant placement was completed as planned in each patient, and removal of the temporary reference implants was uneventful. No intraoperative or early postoperative complications were observed. Mean coronal, apical, and angular deviations between planned and achieved implant positions were 0.70 ± 0.16 mm, 0.39 ± 0.13 mm, and 3.30 ± 0.59°, respectively. These preliminary findings, derived from four treated arches, were comparable to ranges reported in selected studies on fully guided implant surgery; however, no direct statistical comparison with previously published datasets was performed. Conclusions: Within the limitations of this proof-of-concept case series, temporary reference implants arranged in a tripod configuration provided a stable and reproducible prosthetic indexing platform for guided implant surgery in fully edentulous patients. Further prospective studies involving larger patient cohorts and controlled comparative designs with conventional mucosa-supported or fixation-pin-supported surgical guides are required to evaluate the reproducibility, clinical performance, and long-term applicability of this stabilization concept. Full article

Autonomous navigation of unmanned surface vehicles (USVs) in complex obstacle scenarios remains challenging due to redundant perception inputs, unstable value estimation, and inefficient policy convergence. To address these problems, this paper proposes LDA-D3QN, an improved deep reinforcement learning method for USV autonomous navigation. The proposed method constructs a compact navigation state representation by combining target-related information with local obstacle features, allowing the agent to retain key decision-making information while reducing unnecessary environmental redundancy. Based on this representation, an enhanced value-learning framework is developed to improve the stability of navigation decisions in cluttered environments. Moreover, a reward-guided and staged training strategy is introduced to help the agent gradually adapt to increasingly complex navigation tasks. The proposed method was evaluated on a Unity–ROS–MATLAB integrated simulation platform. Experimental results show that LDA-D3QN achieves superior overall navigation performance compared with several representative reinforcement learning algorithms. Specifically, the proposed method achieves a final training success rate of 91.4%, outperforming PPO (82.3%), Dueling DQN (78.5%), Double DQN (79.8%), and Rainbow DQN (86.5%). Additional tests in complex multi-obstacle and multi-target scenarios further demonstrate that the learned policy can generate safe, stable, and effective navigation behaviors. Preliminary validation using real-USV sensor data also confirms the feasibility of the LiDAR and GPS data processing procedures, providing a basis for future closed-loop autonomous navigation experiments and multi-sensor fusion deployment. Full article