Search Results (338)

The transformer–customer relationship is essential for the operation and management of low-voltage distribution systems. However, manual registration and verification of transformer–customer relationships are costly and prone to mismatches due to delayed archival updates. To address this issue, this paper proposes a data-driven method for transformer–customer relationship identification using smart meter voltage measurements. Specifically, raw voltage data obtained from Advanced Metering Infrastructure (AMI) are embedded into a low-dimensional feature space using t-distributed stochastic neighbor embedding (t-SNE). The key parameters of t-SNE are adaptively optimized using the proposed Dream Optimization Algorithm–Whale Optimization Algorithm (DOA-WOA), with the silhouette coefficient serving as the objective function. Subsequently, a Gaussian Mixture Model is applied to cluster the samples in the obtained low-dimensional feature space for transformer–customer relationship identification. The simulation results verify the effectiveness and robustness of the proposed method under different missing data rates and voltage measurement errors. Experiments conducted on real low-voltage distribution transformer datasets and incomplete data scenarios demonstrate that the proposed method achieves identification accuracies of 98.4% and 96.8%, respectively. Full article

Rapid and accurate multi-class fault diagnosis is essential for high-end equipment because different fault categories require different maintenance responses. This study aims to develop a lightweight and discriminative diagnostic framework that can identify multiple fault categories from non-stationary vibration signals while reducing redundant computation. High-frequency vibration signals provide direct condition information, but long sequences, noise, nonlinear dynamics, and non-stationary behavior make raw-signal classification unreliable. From an entropy-based information-processing perspective, the key issue is to separate informative fault modes from redundant fluctuations and enlarge inter-class distinctions in the probabilistic decision space. This study proposes Fastformer, an integrated framework for vibration-based fault identification. Empirical Mode Decomposition first converts each signal into Intrinsic Mode Functions to reduce modal mixing and preserve fault-related oscillatory components. The resulting components are processed by an encoder-oriented Q/K/V dot-product scoring mechanism, which constructs compact spatiotemporal embeddings without adopting a complete Transformer architecture. Validation-guided pruning removes low-contribution attention responses, while a Margin-Enhanced Fault Softmax classifier optimized with a cross-entropy-based objective strengthens category separation. By combining stable decomposition, lightweight attention scoring, pruning, and probabilistic margin learning, Fastformer achieves faster and more stable convergence. On the XJTU-SpurGear dataset, Fastformer obtains precision, recall, F1-score, and AUC values of 1.000. Additional validation on the HUST bearing dataset further shows that Fastformer achieves the best overall performance among the compared methods, with an AUC value of 0.9596. Full article

Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads to representations that overfit to frequent patterns, resulting in weak robustness and limited generalization to sparse or atypical trajectories. To address these issues, this paper presents a novel perspective, anchor-guided balanced learning, and instantiates it with a framework, AnchorTRL. AnchorTRL introduces anchors to proactively construct a balanced semantic space instead of passively fitting the empirical data distribution. Specifically, AnchorTRL designs a spatiotemporal anchor identification algorithm to recognize trajectory anchors that comprehensively cover the data manifold. And, it proposes a calculation method to measure all trajectories’ semantic similarity with anchors. Additionally, it develops an anchor-based balanced sampling strategy to mitigate the dominance of frequent patterns and steer the model towards learning a more balanced representation. Finally, it constructs a multi-task contrastive learning objective with adaptive constraints to enhance the aggregation of semantically similar trajectories. Experimental results show that AnchorTRL outperforms existing baseline methods in tasks such as travel time estimation and similar trajectory queries, demonstrating its effectiveness and robustness. This research provides methodological support for constructing more reliable trajectory representation learning models, and offers new insights for optimizing intelligent transportation applications under spatiotemporal biases. Full article

Coastal cities are highly sensitive and vulnerable to climate change risks. A scientifically grounded assessment of urban climate change risk and its driving mechanisms is essential for strengthening urban climate adaptation capacity and supporting sustainable development. Taking Ningbo as the study area, this paper constructs a risk assessment system comprising five categories of extreme climate indicators, namely heat, rainstorm, drought, humidity, and strong wind, based on the China Surface Climate Normals Dataset for 1981–2010 and meteorological observations from the National Centers for Environmental Information (NCEI) for 2015–2024. Using 30 m resolution land-use data for 2023, three land-use sensitivity indicators are extracted: the proportion of built-up land, the proportion of green space and forest land, and the proportion of water area. The CRITIC objective weighting method is then applied to construct an integrated climate change risk index and identify the spatial pattern of climate change risk in Ningbo. On this basis, the high-risk area identification performance of Logistic Regression, Random Forest, and XGBoost is compared. The optimal XGBoost model is selected and combined with the SHAP method to systematically reveal the direction, relative importance, and nonlinear threshold relationships through which land-use characteristics affect the formation of high-risk areas. The results show that urban climate change risk in Ningbo exhibits a pronounced spatial differentiation pattern, with higher risk in the northeastern coastal and central–eastern areas and lower risk in the western and southwestern areas. Insufficient green space and forest land buffering is the most important factor affecting the formation of high-risk areas. All three land-use variables have clear nonlinear thresholds. The critical turning points for identifying high-risk areas are 20.0% built-up land, 2.0% green space and forest land, and whether there is a water body or not. Significant interaction effects are observed among land-use variables, among which the interaction between built-up land and green space/forest land is the most prominent. These findings provide methodological support and empirical evidence for climate change risk assessment and climate-adaptive spatial planning regulation in coastal cities. Full article

The trans-Neptunian region (30–50 AU) is a dynamically structured reservoir of icy planetesimals whose orbital architecture reflects resonant dynamics, chaotic transport, and long-term gravitational sculpting by the giant planets. This review synthesizes recent developments in the dynamical investigation of trans-Neptunian objects (TNOs), with an emphasis on mean-motion and secular resonances, as well as chaotic diffusion, in a system whose growing observational census makes it an ideal testbed for chaos detection methods. Classical indicators, including Lyapunov exponents, MEGNO, SALI/GALI, and frequency map analysis, provide the quantitative backbone for mapping TNO phase space and are complemented by modern approaches such as Lagrangian descriptors, the FAIR resonance identification method, entropy-based chaos indicators, and recurrence plot divergence methods. An anomalous diffusion framework, in which mean squared displacement scales as a power law in time, further enables classification of sub- and superdiffusive orbital transport. Machine learning has emerged as a powerful complement to traditional dynamical methods: surrogate classifiers, deep neural network solvers, and hybrid physics–data-driven frameworks together extend reliable prediction horizons in chaotic regimes and open new routes for Bayesian inference of migration scenarios. The review concludes that the most promising path forward lies in hybrid dynamical–statistical frameworks anchored to Hamiltonian dynamics, enabling efficient exploration of high-dimensional parameter spaces informed by the expanding body of trans-Neptunian observations. Full article

Currently, the global manufacturing industry faces significant challenges due to increasingly competitive and constantly changing markets. Therefore, adapting to customer needs and improving efficiency and productivity are essential to compete internationally. Plant design and layout play a crucial role in production, material handling, time, and operational costs. The objective of this research was to implement the Systematic Layout Planning (SLP) methodology, supported by CAD and quality tools, to free up 280 m² for production processes in a horizontal blind manufacturing company. AutoCAD was used to model the facilities and visualize pre- and post-improvement scenarios, while ABC classification and root cause analysis supported problem identification in inventory areas. Results show a released expansion area of 340 m², corresponding to 21.5% above the initial space requirement, and a reduction in material travel distance from 317 m to 109 m, equivalent to 65.6%. These improvements enhanced workflow continuity and operational efficiency. The integration of SLP with CAD and quality tools provides a replicable framework for layout optimization in manufacturing environments, while future research should validate the approach under dynamic production conditions. Full article

Long-standing studies of historical cultural spaces have primarily focused on the preservation of heritage objects and landscapes, while insufficient attention has been paid to the structural relationships, land-use transformations, and cultural–ecological processes that sustain their long-term continuity. Taking the World Heritage site of Lijiang as a case, this study develops a three-dimensional structural decoding framework composed of spatial base, spatial network, and spatial entity, together with an analytical pathway of “Identification–Interpretation–Evaluation–Synthesis–Practice.” By integrating qualitative and quantitative approaches with multi-source data, the study establishes an evidence chain linking historical processes and contemporary conditions to examine the formation mechanisms, continuity, and contemporary deviations of Lijiang’s historical cultural space. The results show that terrain–habitat adaptability, water system coupling, and environmental risk avoidance shaped environmental adaptation; historical corridors, landscape perception, and core node associations organized spatial networks; and functional diversity, cultural capital agglomeration, and spatial-scale compatibility supported entity-based spatial practices. Although tourism development, urban expansion, and land-use transformation have not completely dismantled these historical relationships, they have caused localized deviations in ecological boundaries, path continuity, visual connections, functional vitality, and spatial scale. This study argues that the governance of historical cultural spaces should shift from preserving isolated heritage objects to sustaining cultural–ecological relationships that support memory, identity, spatial practice, and adaptive land governance. Full article

The Poisson multi-Bernoulli mixture (PMBM) filter is an effective approach for multi-object tracking in complex scenarios. However, its performance deteriorates when surviving objects spawn, as the PMBM filter only classifies detected objects as either new-born or surviving, thereby ignoring information from the surviving objects and preventing timely identification of spawning events. To address this limitation, this paper proposes the Dynamic Hybrid Detection-Gamma Gaussian inverse Wishart Spawning Poisson multi-Bernoulli mixture (DHD-GGIW-SPMBM) filter, which models spawning objects independently using a Bernoulli process to enhance tracking accuracy. The probability generating functional is employed to derive the recursive prediction and update equations of the proposed filter, and its conjugacy after prediction and update is formally proven. Additionally, a dynamic hybrid detection method is introduced to evaluate the consistency between measurements and theoretical samples, enabling the detection of spawning events. The detection results guide an evidential Gaussian mixture model (EGMM) for fuzzy partitioning of the spawning process, reducing errors under closely spaced and high-clutter conditions. Simulation results demonstrate that, compared with existing spawning-capable filters, the proposed DHD-GGIW-SPMBM filter achieves superior tracking performance, faster identification of spawned objects, and robust operation in complex scenarios. Full article

Crime surveillance in urban environments faces increasing challenges due to dynamic conditions and the demand for real-time monitoring. This paper investigates the use of video data from autonomous vehicles to enhance situational awareness in public spaces through deep learning models optimized for edge processing. High-resolution vehicle-mounted cameras serve as mobile surveillance units capable of real-time object detection, human action recognition, and anomaly detection, bridging the gap between autonomous mobility and urban monitoring. Building on this vision, we introduce ITS-Vision, a generic framework that operationalizes these use cases, enabling autonomous vehicles to function as mobile, context-aware sensing platforms. To validate this approach, we develop prototypes for key ITS-Vision components: a fight detection module using a fine-tuned X3D model, suspect identification via MediaPipe for detection combined with FaceNet for embedding extraction, and a dangerous items detection module using a fine-tuned YOLOv11n model. Due to the limited availability of real-world autonomous vehicle video datasets, experiments were conducted in controlled laboratory environments, demonstrating the feasibility of the proposed architecture and algorithms under simulated conditions. Future work will focus on collecting dedicated datasets and advancing the models toward deployment in real urban scenarios. Full article

Dynamic shading systems offer a promising approach to improving daylight and visual comfort in hot climates. The present work aims to enhance hourly daylighting performance by optimizing useful daylight illuminance (UDI_{500~1000 lx}), illuminance uniformity (Uo), and daylight glare probability (DGP). Radiance and SPEA-2 enable the identification of optimal solutions for corrugated, perforated, and separated trapezoid shading systems. The first multi-objective optimization selected a 15° slat angle, yielding an average illuminance of approximately 782.22 lx, a Uo of 0.7229, 100% coverage in the UDI_{500~1000 lx} range, and a DGP of 0.3324. Fully automatic control of the upper, central, and lower facade sections addressed issues of UDI_{300~500 lx} in June and UDI_{1000~2000 lx} in December. Corrugated shadings achieved over 90% for UDI_{500~1000 lx} during most investigated equinox and solstice conditions, with a minimum Uo of 0.79 in March and approximately 0.6 in December. Perforated inclined panels improved UDI_{500~1000 lx} for morning and afternoon. The fully automated trapezoid-separated shading system increased UDI_{500~1000 lx} coverage to at least 99.47% at 15:00 in March, 92.95% at 15:00 in June, and 92.06% at 12:00 in December, with DGP within imperceptible glare. The methodological scope was limited to a selected office space and point-in-time simulations from 10:00 to 15:00 on representative equinox and solstice days; therefore, the results should be interpreted as simulation-based design guidance rather than full annual or experimentally validated performance. Full article

Objectives: Local positioning systems (LPSs) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and load management. This study aimed to reduce the dimensionality of external load variables derived from LPS data and to identify data-driven external-load observation profiles using principal component analysis and clustering techniques. Methods: A total of 188 observations from indoor team sports (basketball, handball, and futsal) were analyzed. Continuous external load variables were standardized and subjected to principal component analysis (PCA), with component retention based on a ≥90% cumulative explained variance threshold. K-means clustering was applied in both the full standardized feature space and the PCA-reduced space. The optimal number of clusters was determined using silhouette analysis and the elbow method. Agreement between clustering solutions was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Cluster characteristics were further examined using descriptive statistics and variable separation analysis. Results: The first two principal components explained 53.7% of the total variance, representing high-intensity external load and neuromuscular load dimensions, while 12 components were required to exceed 90% cumulative explained variance. Clustering analysis consistently identified three moderately separated clusters in both the full and PCA-reduced spaces. The PCA-based solution demonstrated improved separation (silhouette = 0.362) compared to the full-space solution (silhouette = 0.319). Agreement between clustering approaches was high (ARI = 0.981; NMI = 0.971), indicating that dimensionality reduction largely preserved the main clustering structure within the analyzed dataset. The most discriminative variables included jump load, acceleration load, metabolic power, and anaerobic activity distance. Conclusions: A large set of external load variables can be reduced into interpretable latent dimensions that support exploratory external-load profile identification. The combination of PCA and clustering provides an exploratory and structure-preserving framework for summarizing complex external-load datasets and identifying latent load dimensions. These findings may assist future monitoring strategies; however, the practical utility of the identified profiles requires prospective validation before implementation in training-load management. Full article

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

This work presents a practical C++23 implementation of a physics-informed neural network (PINN) for a fractional-order damped oscillator. A fully connected network outputs displacement and velocity, so the governing dynamics are enforced through a compact state-space residual involving first and second time derivatives. Integer-order derivatives are obtained via automatic differentiation, which removes finite-difference noise and preserves smooth, consistent gradients during training. The history-dependent fractional damping term is incorporated using the classical L1 discretization on a uniform time grid, which makes each residual evaluation depend on the entire predicted solution history and naturally captures memory effects. The training objective combines the squared residual norms at collocation points with a strongly weighted initial-condition penalty to control drift and stabilize early iterations. Gradients of the complete objective with respect to all network parameters are computed using reverse-mode automatic differentiation in CppAD (20260000.0) by constructing a scalar loss function of a flat parameter vector, enabling efficient gradient-based optimization. Parameters are updated with the Adam algorithm using bias correction and double-precision moment accumulation for numerical robustness. This implementation includes deterministic parameter packing, explicit size checks, and lightweight diagnostics of boundary values during training, improving reproducibility and debuggability. Overall, the code provides an end-to-end baseline for PINN-based simulation of fractional-order oscillatory systems and can be readily extended to include external forcing, alternative loss weight schedules, and parameter identification from measurement data. Full article

The intensification and continuous evolution of dairy sheep and goat farming have played an essential role in the development and implementation of milking equipment. The increasing demand for time-efficient milking procedures, reduced labour costs, sustained milk production, and optimal mammary health have driven the widespread adoption and optimisation of machine milking technologies. The objectives of this article are (i) the review of milking systems and relevant technological developments in milking equipment and (ii) the evaluation and description of their impact on udder health, as applied on dairy small ruminant farms. Milking systems used on farms depend on the available space and number of animals on the farms. Appropriate settings in milking systems are important for ensuring good milk quality; among them, vacuum level, pulsation rate and ratio are important characteristics that must be monitored regularly. Further, use of appropriate teatcups specific to the animal species to be milked is significant. An important aspect of proper maintenance of the milking system is the cleaning procedure after completion of milking. Points for consideration are quality and temperature of the water used for cleaning, use of detergents and disinfectants, and maintenance schedule and teatcup replacement. Some technological features that are part of milking systems include automatic vacuum shut off, electronic milk recording, electronic identification of animals, automatic flushing of milking clusters and automatic pre-stimulators. Farms will benefit from applying precision technologies, which will use data from tools related to animal genetic background, animal behavioural indicators, environmental conditions and disease-related functions for more holistic and cost-effective farm management. In this context, integration of sensor-based technologies in milking systems will be able to provide real-time information regarding quality of milk produced at individual and farm levels. Moreover, the introduction of automatic system flushing in-between animals during the milking procedure can contribute to breaking chains of potential bacterial transfer and reducing animal infections during milking. Overall, although machine milking has certainly contributed to improved efficiency, milk quality and labour conditions, flaws in system function may adversely affect mammary health. Full article

Background/Objectives: In gas chromatography–mass spectrometry (GC-MS) library-based compound identification, spectrum preprocessing and associated tuning parameters critically influence identification performance. These parameters are conventionally optimized using grid search, which requires predefined parameter spaces and becomes computationally inefficient as dimensionality increases, often failing to identify optimal values because of discretization. Differential evolution (DE), a population-based metaheuristic optimization algorithm, provides a flexible alternative through efficient global exploration of the parameter space. This study compared the performance of DE and grid search for optimizing compound identification. Methods: Cosine similarity was applied to the NIST GC-MS library. DE was used to maximize either cross-validated accuracy or mean reciprocal rank (MRR). Results were compared with those from a grid search over five equally spaced parameter values. Identification performance was evaluated using accuracy, MRR, and area under the receiver operating characteristic curve (AUC). Results: When all four parameters were optimized simultaneously, DE achieved slightly higher cross-validated accuracy and MRR than grid search, although the absolute differences were modest. More pronounced differences were observed in specific unidimensional tuning scenarios, particularly for the intensity weight factor. Simultaneous multidimensional parameter optimization yielded better performance than isolated parameter tuning. Conclusions: Grid search may be computationally advantageous when the parameter space is known and limited, whereas DE provides a more flexible approach for unknown or high-dimensional search spaces. Overall, DE achieved comparable identification performance to grid search, with modest improvements observed in some optimization settings. A command line Julia-based tool, MSTune, was developed for spectrum preprocessing parameter optimization and is publicly available on GitHub. Full article