Search Results (381)

IEA Wind Task 43 seeks to “unlock the full value of wind energy through digital transformation”. One mechanism to realize value is through enhanced data-driven decision-making and, while many areas in the wind sector can benefit from improved decision support, this case study focusses on a well-defined wind energy maintenance scenario involving blade inspection and repair. The solution concentrates on the specific damage category of blade leading edge erosion (LEE) and the optimum action to be taken for a given level of damage detected during periodic inspections. The key decision is whether to initiate repairs immediately or continue operating the turbine until the next inspection—and, if so, when that next inspection should take place. Even for such a specific damage type and decision option, the overall solution draws on multiple data types, ranging from damage classifications to cost drivers, and integrates a number of components including damage propagation, performance, and cost models. The core of the solution is a risk-based decision model using heuristic strategies, and Bayesian networks for optimized decision-making. This paper outlines the overall solution, expands on the data and modelling implementations, and discusses the results and conclusions arising from the investigation. Full article

Accurate volumetric modeling of indoor spaces is essential for emerging 3D cadastral systems, yet existing workflows often rely on manual intervention or produce surface-only models, limiting precision and scalability. This study proposes and validates an integrated, largely automated workflow (named VERTICAL) that converts classified indoor point clouds into topologically consistent 3D solids served as materials for land surveyor’s cadastral analysis. The approach sequentially combines RANSAC-based plane detection, polygonal mesh reconstruction, mesh optimization stage that merges coplanar faces, repairs non-manifold edges, and regularizes boundaries and planar faces prior to CAD-based solid generation, ensuring closed and geometrically valid solids. These modules are linked through a modular prototype (called P2M) with a web-based interface and parameterized batch processing. The workflow was tested on two condominium datasets representing a range of spatial complexities, from simple orthogonal rooms to irregular interiors with multiple ceiling levels, sloped roofs, and internal columns. Qualitative evaluation ensured visual plausibility, while quantitative assessment against survey-grade reference models measured geometric fidelity. Across eight representative rooms, models meeting qualitative criteria achieved accuracies exceeding 97% for key metrics including surface area, volume, and ceiling geometry, with a height RMSE around 0.01 m. Compared with existing automated modeling solutions, the proposed workflow has the ability of dealing with complex geometries and has comparable accuracy results. These results demonstrate the workflow’s capability to produce topologically consistent solids with high geometric accuracy, supporting both boundary delineation and volume calculation. The modular, interoperable design enables integration with CAD environments, offering a practical pathway toward an automated and reliable core of 3D modeling for cadastre applications. Full article

Total Soluble Solids (TSS) in tomatoes is a core indicator for evaluating fruit quality and processing characteristics. Its composition mainly consists of soluble sugars (such as fructose and glucose) and organic acids (such as citric acid and malic acid). The contents of sugars and acids and their ratio directly affect the flavor and nutritional value. Cultivated tomatoes have a TSS of 4–6%, compared with 10–15% in wild varieties. In recent years, with the advancement of molecular biology and genomics technologies, significant progress has been made in the research on the regulatory mechanisms of tomato fruit TSS and major sugars and acids, including the identification of major quantitative trait locus (QTLs) (Lin5, SlALMT9), functional characterization via CRISPR/Cas9 and elucidation of the transporter network. Breaking the negative correlation between TSS and yield remains a major bottleneck in breeding. Analyzing the mechanism by which environmental factors regulate the TSS and optimizing cultivation measures are crucial for increasing the TSS content in tomatoes. The deep integration of cutting-edge technologies (such as Genome-wide association studies (GWAS), metabolome-wide association studies (mGWAS), Genomic selection (GS), genome editing, and crop modeling) with design breeding is expected to accelerate the development of high-TSS tomato varieties. This paper reviews the current research status from the following four aspects: QTL mapping related to tomato TSS and mining of major genes, metabolic and transport mechanisms of major sugars and acids and key genes, the influence of environmental factors on TSS, and application of genetic improvement strategies and technologies. Full article

Time-sensitive networking (TSN), a cutting-edge technology enabling efficient real-time communication and control, provides strong support for traditional Ethernet in terms of real-time performance, reliability, and deterministic transmission. In TSN systems, although time-triggered (TT) flows enjoy deterministic delay guarantees, audio video bridging (AVB) and best effort (BE) traffic still share link bandwidth through statistical multiplexing, a process that remains nondeterministic. This competition in shared memory switches adversely affects data transmission performance. In this paper, a priority queue threshold control policy is proposed and analyzed for mixed-critical traffic in time-sensitive networks. The core of this policy is to set independent queues for different types of traffic in the shared memory queuing system. To prevent low-priority traffic from monopolizing the shared buffer, its entry into the queue is blocked when buffer usage exceeds a preset threshold. A two-dimensional Markov chain is introduced to accurately construct the system’s queuing model. Through detailed analysis of the queuing model, the truncated chain method is used to decompose the two-dimensional state space into solvable one-dimensional sub-problems, and the approximate solution of the system’s steady-state distribution is derived. Based on this, the blocking probability, average queue length, and average queuing delay of different priority queues are accurately calculated. Finally, according to the optimization goal of the overall blocking probability of the system, the optimal threshold value is determined to achieve better system performance. Numerical results show that this strategy can effectively allocate the shared buffer space in multi-priority traffic scenarios. Compared with the conventional schemes, the queue blocking probability is reduced by approximately 40% to 60%. Full article

Current research on heritage corridors predominantly focuses on linear heritage in Europe and America, while studies in Asia urgently need to be expanded. This study investigates China’s linear heritage. Based on the minimum cumulative resistance (MCR) model, it conducts heritage corridor construction for the Henan section of the Sui–Tang Grand Canal, and reveals the following: (1) A total of 252 heritage sites were classified into three categories: canal hydraulic heritage (13.5%), canal settlement heritage (21.4%) and related heritage (65.1%), exhibiting a “local clustering under global dispersion” pattern with a core–secondary–edge structure. (2) The influence of natural–social resistance factors was ranked as follows: elevation > roads > land use > slope. Interwoven corridors were simulated by GIS and optimized to four primary corridors with multiple secondary corridors. (3) The transverse zone of the primary corridors was stratified into core area (0–10 km from the centerline), buffer area (10–25 km), and influence area (>25 km) with a total width of 25–30 km. The longitudinal section was partitioned into four subsections based on hydrological continuity and heritage density. Then, a tripartite conservation framework characterized by “heritage clusters–holistic corridor–transverse stratification and longitudinal section” was proposed. It aimed to provide insights into methodologies and content structuring for transnational linear heritage (e.g., the Silk Road and the Inca Trail). Full article

The state of charge (SOC) serves as a critical indicator for evaluating the remaining driving range of electric vehicles (EVs), and its prediction is of significance for alleviating range anxiety and promoting the development of the EVs industry. This study addresses two key challenges in current SOC prediction technologies: (1) the scarcity of multi-step prediction research based on real driving conditions and (2) the poor performance in multi-scale temporal feature extraction. We innovatively propose the Wavelet-Guided Temporal Feature Enhanced Informer (WG-TFE-Informer) prediction model with two core innovations: a wavelet-guided convolutional embedding layer that significantly enhances anti-interference capability through joint time-frequency analysis and a temporal edge enhancement (TEE) module that achieves the collaborative modeling of local microscopic features and macroscopic temporal evolution patterns based on sparse attention mechanisms. Building upon this model, we establish a multidimensional SOC energy consumption prediction system incorporating battery characteristics, driving behavior, and environmental terrain factors. Experimental validation with real-world operating data demonstrates outstanding performance: 1-min SOC prediction accuracy achieves a mean relative error (MRE) of 0.21% and 20-min SOC prediction exhibits merely 0.62% error fluctuation. Ablation experiments confirm model effectiveness with a 72.1% performance improvement over baseline (MRE of 3.06%) at 20-min SOC prediction, achieving a final MRE of 0.89%. Full article

The rapid evolution of 5G and emerging 6G networks has increased system complexity, data volume, and security risks, making anomaly detection vital for ensuring reliability and resilience. However, existing machine learning (ML)-based approaches still face challenges related to poor generalization, weak temporal modeling, and degraded accuracy under heterogeneous and imbalanced real-world conditions. To overcome these limitations, a hybrid time series transformer–deep belief network (HTST-DBN) is introduced, integrating the sequential modeling strength of TST with the hierarchical feature representation of DBN, while an improved orchard algorithm (IOA) performs adaptive hyper-parameter optimization. The framework also embodies the concept of symmetry and asymmetry. The IOA introduces controlled symmetry-breaking between exploration and exploitation, while the TST captures symmetric temporal patterns in network traffic whose asymmetric deviations often indicate anomalies. The proposed method is evaluated across four benchmark datasets (ToN-IoT, 5G-NIDD, CICDDoS2019, and Edge-IoTset) that capture diverse network environments, including 5G core traffic, IoT telemetry, mobile edge computing, and DDoS attacks. Experimental evaluation is conducted by benchmarking HTST-DBN against several state-of-the-art models, including TST, bidirectional encoder representations from transformers (BERT), DBN, deep reinforcement learning (DRL), convolutional neural network (CNN), and random forest (RF) classifiers. The proposed HTST-DBN achieves outstanding performance, with the highest accuracy reaching 99.61%, alongside strong recall and area under the curve (AUC) scores. The HTST-DBN framework presents a scalable and reliable solution for anomaly detection in next-generation mobile networks. Its hybrid architecture, reinforced by hyper-parameter optimization, enables effective learning in complex, dynamic, and heterogeneous environments, making it suitable for real-world deployment in future 5G/6G infrastructures. Full article

This article presents the results of an experimental study on the hydrodynamics of the coolant at the inlet of the fuel assembly in the RITM reactor core. The importance of these studies stems from the significant impact that inlet flow conditions have on the flow structure within a fuel assembly. A significant variation in axial velocity and local flow rates can greatly affect the heat exchange processes within the fuel assembly, potentially compromising the safety of the core operation. The aim of this work was to investigate the effect of different designs of orifice inlet devices and integrated absorber grids on the flow pattern of the coolant in the rod bundle of the fuel assembly. To achieve this goal, experiments were conducted on a scaled model of the inlet section of the fuel assembly, which included all the structural components of the actual fuel assembly, from the orifice inlet device to the second spacer grids. The test model was scaled down by a factor of 5.8 from the original fuel assembly. Two methods were used to study the hydrodynamics: dynamic pressure probe measurements and the tracer injection technique. The studies were conducted in several sections along the length of the test model, covering its entire cross-section. The choice of measurement locations was determined by the design features of the test model. The loss coefficient (K) of the orifice inlet device in fully open and maximally closed positions was experimentally determined. The features of the coolant flow at the inlet of the fuel assembly were visualized using axial velocity plots in cross-sections, as well as concentration distribution plots for the injected tracer. The geometry of the inlet orifice device at the fuel assembly has a significant impact on the pattern of axial flow velocity up to the center of the fuel bundle, between the first and second spacing grids. Two zones of low axial velocity are created at the edges of the fuel element cover, parallel to the mounting plates, at the entrance to the fuel bundle. These unevennesses in the axial speed are evened out before reaching the second grid. The attachment plates of the fuel elements to the diffuser greatly influence the intensity and direction of flow mixing. A comparative analysis of the effectiveness of two types of integrated absorber grids was performed. The experimental results were used to justify design modifications of individual elements of the fuel assembly and to validate the hydraulic performance of new core designs. Additionally, the experimental data can be used to validate CFD codes. Full article

After rough planing, defects such as wormholes and small patches of green bark residue and decay are often overlooked and misclassified. Strip-like defects, including splinters and chipped edges, are easily confused with the natural bamboo grain, and a single elongated defect is frequently fragmented into multiple detection boxes. This study proposes a modified TASNet-YOLO model, an improved detector built on YOLO11n. Unlike prior YOLO-based bamboo defect detectors, TASNet-YOLO is a mechanism-guided redesign that jointly targets two persistent failure modes—limited visibility of small, low-contrast defects and fragmentation of elongated defects—while remaining feasible for real-time production settings. In the backbone, a newly designed TriMAD_Conv module is introduced as the core unit, enhancing the detection of wormholes as well as small-area defects such as green bark residue and decay. The additive-gated C3k2_AddCGLU is further integrated at selected C3k2 stages. The combination of additive interaction and CGLU improves channel selection and detail retention, highlighting differences between splinters and chipped edges and bamboo grain strips, thereby reducing false positives and improving precision. In the neck, the neck replaces nearest-neighbor upsampling and CBS with SNI-GSNeck to improve cross-scale alignment and fusion. Under an acceptable real-time budget, predictions for splinters and chipped edges become more contiguous and better aligned to edges, while wormholes predictions are more circular and less noisy. Experiments on our in-house dataset (8445 bamboo-strip defect images) show that, compared with YOLO11n, the proposed model improves detection accuracy by 5.1%, achieves 106.4 FPS, and reduces computational costs by 0.4 GFLOPs per forward pass. These properties meet the throughput demand of 2 m/s conveyor lines, and the compact model size and compute footprint make edge deployment straightforward for fast online screening and preliminary quality grading in industrial production. Full article

Background/Objectives: Gestational Diabetes Mellitus (GDM) is a global health issue with immediate and long-term maternal–fetal complications. Current diagnostic approaches, such as the Oral Glucose Tolerance Test (OGTT), have limitations in accessibility, sensitivity, and timing. This study aimed to identify key nodes and structural interactions associated with GDM using graph theory and network analysis to improve early predictive strategies. Methods: A literature review inspired by PRISMA guidelines (2004–2025) identified 44 clinically relevant factors. A directed graph was constructed using Python (version 3.10.12), and centrality metrics (closeness, betweenness, eigenvector), k-core decomposition, and a Minimum Dominating Set (MDS) were computed. The MDS, derived using an integer linear programming model, was used to determine the smallest subset of nodes with systemic dominance across the network. Results: The MDS included 20 nodes, with seven showing a high out-degree (≥4), notably Apo A1, vitamin D, vitamin D deficiency, and sedentary lifestyle. Vitamin D exhibited 15 outgoing edges, connecting directly to protective factors like HDL and inversely to risk factors such as smoking and obesity. Sedentary behavior also showed high structural influence. Closeness centrality highlighted triglycerides, insulin resistance, uric acid, fasting plasma glucose, and HDL as nodes with strong predictive potential, based on their high closeness and multiple incoming connections. Conclusions: Vitamin D and sedentary behavior emerged as structurally dominant nodes in the GDM network. Alongside metabolically relevant nodes with high closeness centrality, these findings support the utility of graph-based network analysis for early detection and targeted clinical interventions in maternal health. Full article

A smart device unlocking scheme based on ultrasonic gesture recognition is proposed, allowing users to unlock their devices by customizing the unlock code through gesture movements. This method utilizes ultrasound to detect multiple consecutive gestures, identifying micro-features within these gestures for authentication. To enhance recognition accuracy, an unsupervised segmentation algorithm is employed to accurately segment the gesture feature region and extract the time-frequency domain data of the gestures. Additionally, two-stage data enhancement techniques are applied to generate user-specific data based on a small sample size. Finally, the user-specific model is deployed to mobile devices via transfer learning for on-device, real-time inference. Experimental validation on a commercial smartphone (Redmi K50) demonstrates that the entire authentication pipeline, from signal acquisition to decision, processes 8 types of gestures in a sequence in sequence in approximately 1.2 s, with the core model inference taking less than 50 milliseconds. This ensures that the raw biometric data (ultrasonic echoes) and the recognition results never leave the user’s device during authentication, thereby safeguarding privacy. It is important to note that while model training is performed offline on a server to leverage greater computational resources for personalization, the deployed system operates fully in real time on the edge device. Experimental results demonstrate that our system achieves accurate and robust identity verification, with an average five-fold cross-validation accuracy rate of up to $93.56\%$, and it shows robustness across different environments. Full article

With the increasing emphasis on energy-efficient computing, edge devices accelerated by graphics processing units (GPUs) are gaining attention for their potential in scientific workloads. These platforms support compute-intensive simulations under strict energy and resource constraints, yet their computational efficiency across architectures remains an open question. This study evaluates the performance of GPU-based edge platforms for executing the stochastic simulation algorithm (SSA), a widely used and inherently compute-intensive method for modeling biochemical and physical systems. Execution time, floating point throughput, and the trade-offs between cost and power consumption are analyzed, with a focus on how variations in core count, clock speed, and architectural features impact SSA scalability. Experimental results show that the Jetson Orin NX consistently outperforms Xavier NX and Orin Nano in both speed and efficiency, reaching up to 4.86 million iterations per second while operating under a 20 W power envelope. At the largest workload scale, it achieves 2102.7 ms/W in energy efficiency and 105.3 ms/USD in cost-performance—substantially better than the other Jetson devices. These findings highlight the architectural considerations necessary for selecting edge GPUs for scientific computing and offer practical guidance for deploying compute-intensive workloads beyond artificial intelligence (AI) applications. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies. Full article

Ecological restoration of the Loess Plateau plays a pivotal role in mitigating land degradation and promoting regional sustainability. In this study, landscape pattern metrics were integrated into the MaxEnt model to evaluate the influence of landscape configuration on restoration planning. Nine representative species from three vegetation strata—herbs, shrubs, and trees—were selected based on ecological suitability. A comprehensive set of variables, including environmental, anthropogenic, and landscape metrics, was constructed for modeling. Results demonstrate that incorporating landscape metrics significantly enhanced the spatial explanatory power, providing a robust supplement to traditional ecological restoration assessments. Distinct responses to landscape structure were observed among vegetation types: herb species were more sensitive to patch aggregation and connectivity, shrubs preferred regular edges and larger patch size, while tree species favored extensive, low-fragmentation core habitats. Vertical structure optimization revealed that while large areas were suitable for single vegetation layers, composite vegetation configurations were more appropriate in certain central and southern subregions. These findings underscore the importance of landscape structure in guiding restoration strategies and serve as a basis for designing ecologically coherent and spatially targeted vegetation restoration plans on the Loess Plateau. Full article