Search Results (57,686)

This study addresses the growing need for structured, measurable organizational development (OD) models amid digital transformation, geopolitical uncertainty, and increasing managerial complexity. Contemporary middle- and top-level managers are expected to ensure productivity, strategic clarity, resilience, and data-driven decision-making; however, existing leadership methodologies are often examined separately and lack integrated evaluation frameworks. The research analyses two prominent approaches: the American Action Science methodology and the Scandinavian (particularly Finnish) consensus-based leadership concept. While Action Science emphasizes explicit reasoning, double-loop learning, accountability, and measurable performance outcomes, the Finnish consensus model prioritizes trust, participation, and relational cohesion. The aim of the study is to develop and empirically test the original digital model LeadWinO (LEADership for WINning Organizations) for evaluating the organizational development activities of middle- and top-level managers. The model was empirically tested on managers in Lithuania. The novelty of the research lies in combining management and informatics perspectives by embedding organizational development evaluation into a digital, indicator-based, and potentially predictive framework. The type of study is quantitative research integrating questionnaire analysis in the case of multi-profile sections. Analytical tool used for data simulation is Feedforward Neural Network for constructing sufficient gapless sets of digitalized data. Research results showed that the American Action Science methodology is most effectively used by managers working in very small and small enterprises in the service and maintenance sectors. The findings are expected to contribute to the operationalization of leadership effectiveness under uncertainty and provide organizations with an auditable structure linking managerial behaviour, decision-making processes, and organizational performance outcomes. Full article

Partial discharge (PD) measurements are crucial for evaluating the condition of the insulation systems of medium-voltage (MV) cables and their accessories. However, identifying PD defect types from phase-resolved partial discharge (PRPD) patterns still largely relies on expert knowledge. In this paper, the authors critically evaluate lightweight deep neural network architectures for automated classification of insulation defects from PRPD patterns: YOLOv8n, the MobileNetV2–YOLO hybrid network, and a compact SqueezeNet-based model. PD measurements were performed in a controlled environment in a factory laboratory for MV power cables in order to better evaluate the capability of the investigated models. The results demonstrate that lightweight deep neural architectures can effectively classify PRPD patterns and be deployed in a real measurement environment. The proposed approach has been integrated with the OMICRON MPD Suite measurement system, enabling automated defect recognition and visualisation during routine testing of MV cable. Full article

RUL prediction methods do not fully account for the uncertainties caused by data scarcity and inherent noise, and they also suffer from low reliability of RUL point estimates. To tackle these challenges, this paper proposes a Bayesian convolutional neural network with dual-output units for RUL interval predictions. The network employs the negative log-likelihood as the loss function. Thanks to its dual-output structure, it not only provides point estimates, but also quantifies the aleatoric uncertainty inherent in the data. During the training process, the CNN is reformulated using Bayesian principles, and the Bayes-by-backprop method is applied to train the network. This transformation converts model parameters from fixed values into random variables. As a result, epistemic uncertainty caused by model inaccuracies and limited data can be quantified. Experimental validation on the IEEE PHM Challenge 2012 dataset demonstrated that the proposed method achieved a higher prediction accuracy than state-of-the-art uncertainty-aware prediction approaches, demonstrating a better applicability in engineering practice. Full article

The pitot tube is the core sensor for aircraft to obtain external atmospheric data, and its failure has a very important impact on flight safety. However, as its structure and principle are relatively simple, all manufacturers have not adopted available monitoring methods for its health status due to the perspective of cost and complexity reduction. The pitot tube fault warning method is conducted in this paper with a fully connected neural network (FCNN) method based on the data collected by the pitot tube itself. By constructing and selecting parameters and extracting fault features from flight record data, a pitot tube fault warning model based on an FCNN is constructed. The effectiveness of the proposed method is verified through pitot tube fault warning experiments based on actual flight record data, which can provide technical reference for pitot tube fault warning during aircraft route operation in the future. Full article

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis. Full article

The aim of this work is to explore a load frequency control (LFC) strategy in micro hydro power plants (MHPPs). Using MATLAB/Simulink, we examined several variants of genetic algorithms (GAs), including Roulette, Tournament, and Uniform, which are utilized to optimize tuning proportional integral derivative (PID) parameters by addressing the problem of instability caused by load variations. The performances are compared with conventional PID methods and other advanced techniques like particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) algorithms for both single and dual-area MHPP systems. The results show that the GA-optimized PID controller with the roulette wheel achieves the fastest settling time of 0.3 s and the smallest undershoot of 0.015 pu in the single area. Also, optimizing GA demonstrates superior performance in the dual area, with the fastest settling times of 2.5 s for both Roulette and Uniform. In contrast, PSO is slower than GA, and conventional PID requires a much longer settling time of 19.8 s, a similar result occurring in the dual area. These findings confirm the effectiveness of the GA-optimized PID controller, especially the Roulette variant, as a reliable and fast solution for maintaining frequency stability in MHPPs. Full article

Autonomous task assignment in multi-unmanned aerial vehicle (UAV) systems operating in dynamic and safety-critical airspace environments is highly challenging due to complex spatial interactions and rapidly changing relative geometries. This paper proposes a hierarchical decision-making framework that bridges individual maneuvering behaviors with cooperative task allocation in multi-agent aerial systems. First, a high-fidelity single-agent maneuver model is learned using a physics-consistent simulation environment, where spatial advantage is evaluated based on relative distance and angular relationships within a kinematically feasible interaction zone (KIZ). Subsequently, a Geometry-Aware Graph Attention Network (GA-GAT) is developed to address scalable multi-agent assignment problems. Unlike conventional approaches that rely on flat feature representations, the proposed method explicitly incorporates kinematic feasibility constraints into the attention mechanism via a novel gating module, enabling efficient relational reasoning under dynamic conditions. The proposed framework is applicable to a range of civilian and safety-oriented scenarios, including UAV swarm coordination, emergency response monitoring, infrastructure inspection, and autonomous airspace management. Simulation results demonstrate that the GA-GAT-based approach significantly outperforms heuristic baselines in terms of coordination efficiency and overall system performance in complex multi-agent environments. This study highlights that decoupling maneuver-level control from high-level coordination provides a scalable and computationally efficient solution for real-time multi-UAV decision-making in safety-critical applications. The proposed framework is designed for general multi-agent coordination problems in civilian aerial applications. Full article

The high accuracy achieved by deep learning techniques has made them indispensable in computer vision applications. However, their substantial memory demands and high computational complexity limit their deployment in resource-constrained environments. To address this challenge, this study introduces a Feature Enhancement Module (FEM) as part of a unified hybrid compression framework that combines mixed-precision quantization and structured pruning to improve model efficiency. Experimental results on the Tiny ImageNet dataset using ResNet50 and MobileNetV3 architectures demonstrate the strong adaptability and scalability of the proposed approach. Compared with state-of-the-art compression methods, the proposed FEM-based framework achieves up to 6% improvement in Top-1 accuracy, while reducing memory usage by 32.26% and improving inference speed by 66%. Furthermore, the ablation study demonstrates that incorporating the FEM module leads to up to 24% improvement over the baseline model, highlighting its effectiveness. The results further show that FEM effectively preserves inter-channel feature representation stability even under aggressive compression, making it well suited for real-time processing and practical Artificial Intelligence (AI) applications. By maintaining semantic richness while significantly reducing computational cost, the proposed method bridges the gap between high-performance deep models and lightweight, deployable solutions. Overall, the FEM-based hybrid compression framework establishes a scalable and architecture-independent foundation for sustainable deep learning in resource-limited environments. Full article

Aiming to address the problems of low accuracy in coal–rock identification during coal mining, which lead to energy waste and safety hazards, a high-precision coal–rock medium identification method combining terahertz time-domain spectroscopy technology and multiple machine learning algorithms is proposed. By preparing coal–rock samples with a gradient change in coal content, terahertz time-domain spectroscopy data of coal–rock mixed media are collected, and optical parameters such as the refractive index and absorption coefficient are extracted. Principal component analysis is used to reduce the dimensionality of the terahertz data, and machine learning algorithms such as support vector machine, least squares support vector machine, artificial neural networks, and random forests are adopted for classification and identification. The study found that terahertz waves are more sensitive to coal–rock media in the 0.7–1.3 THz frequency band, and that the refractive index and absorption coefficient of coal–rock mixed media are significantly positively correlated with coal content within the range of 0–30%. After feature extraction and K-fold cross-validation, the random forest model achieved a coal–rock classification accuracy of over 96% on the test set, significantly outperforming other comparison algorithms. The research verifies the efficiency and practicality of terahertz technology combined with multiple machine learning algorithms in coal–rock identification, providing a new method for fields such as mineral separation. This method has, to a certain extent, broken through the accuracy bottleneck of traditional coal–rock identification technologies within its applicable range, providing a new solution for real-time detection of coal–rock interfaces and is expected to further reduce the risks of ineffective mining and roof accidents in the future. Full article

As a vital part of urban public transportation system, subway passenger flow prediction plays a crucial role in alleviating traffic congestion, improving transportation infrastructure, and optimizing travel experience. Existing subway passenger flow prediction mainly focuses on short-term predictions of inbound/outbound passenger flow and origin-destination (O-D) demand. Subway section passenger flow prediction can provide a more direct reflection of passenger fluctuations across different line segments, and offer robust support for management and resource allocation. We propose a subway section passenger flow generation model and a prediction method based on LTiT (LSTM-TSSA-iTransformer). This model is based on the overall architecture of the iTransformer encoder, and an LSTM (Long Short-Term Memory) network is employed to capture the temporal characteristics of subway section passenger flow. This is combined with the TSSA (Token Statistics Self-Attention) to adaptively weight the information at key time points. Efficient performance of the model was evaluated by comparing its predictions with other models, including SARIMA (Seasonal Auto-Regressive integrated moving average), BP neural networks, LightGBM (Light Gradient Boosting Machine) and LSTM (Long Short-Term Memory). Experimental results show that the proposed model outperforms traditional baseline models in evaluation metrics such as R², MAE, MSE, and MAPE. Finally, we further investigate the selection of input window length and prediction step size, and perform robustness analysis under different noise conditions. Full article

Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications. Full article

The TWC Sigma model, part of the Topological Weighted Centroid (TWC) family, is introduced as a spatial framework for source localization in systems where network information is incomplete or unavailable. Its architecture relies on two alternative approaches: one based on nonlinear correlation, capable of capturing complex spatial dependencies among observed signals, and another based on supervised neural networks, which use adaptive learning on a discretized spatial grid to estimate the probability of hidden source localization. In both cases, TWC Sigma provides a robust and consistent mechanism to estimate the probable positions of hidden sources using only spatial coordinates and signal intensity. Applications on both synthetic and real-world datasets—such as those collected by Minna-no Data Site on post-Fukushima radiocesium contamination—confirm the model’s ability to identify both primary and secondary emission zones with strong spatial coherence. These results highlight TWC Sigma as an efficient and interpretable model that can be used both independently and as a complementary tool to more complex network-based frameworks, offering rapid and reliable localization even in the presence of sparse, noisy, or heterogeneous data. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

The widespread deployment of unmanned aerial vehicles (UAVs) in civil and commercial airspace has raised significant safety concerns, driving the demand for reliable and real-time Anti-UAV visual detection systems. However, existing deep learning-based detectors face substantial challenges in complex low-altitude environments, including drastic scale variations, severe background clutter, and weak feature representation of small UAV targets. Moreover, handcrafted Transformer-based architectures often lack adaptability across diverse scenarios and struggle to balance detection accuracy with computational efficiency. To address these limitations, this paper proposes AutoUAVFormer, a super-resolution guided neural architecture search framework for Anti-UAV detection. In contrast to conventional manually designed approaches, AutoUAVFormer leverages joint optimization of a Transformer-based detection objective and a super-resolution reconstruction objective to automatically identify a task-specific optimal network architecture for detecting UAV targets. Specifically, a unified search space is formulated by jointly embedding Transformer hyperparameters and Feature Pyramid Network (FPN) structures, facilitating end-to-end co-optimization of multi-scale feature fusion and global context modeling. To efficiently locate architectures that balance accuracy and computational cost, a three-stage pipeline, combining supernetwork training with evolutionary search, is employed. Additionally, we design a super-resolution auxiliary branch that operates only during training to enhance the model’s ability to learn fine-grained textures and sharpen edge representations of small targets, without introducing any inference overhead. Extensive experiments on three challenging Anti-UAV detection benchmarks, namely DetFly, DUT Anti-UAV, and UAV Swarm, confirm the superiority of AutoUAVFormer over current state-of-the-art methods, with mAP@0.5 scores reaching 98.6%, 95.5%, and 89.9% on the respective datasets while sustaining real-time inference speed. These results demonstrate that AutoUAVFormer achieves strong generalization and maintains robust Anti-UAV detection performance under challenging low-altitude conditions. Full article

This work proposes solving the Traveling Salesman Problem by applying combined heuristic global and local search methods. The proposed method is divided into three phases: first, it evaluates an initial route and chooses the minimum value of rows in a distance matrix. The next phase seeks to improve the route’s cost globally and with a 2-opt local search method, remove the crossings, and further minimize the cost of departure. Finally, the last phase evaluates and conserves each cost using tabu search, proposing a parameter $\beta$ that describes the algorithm convergence factor. This paper assessed 29 TSPLIB instances and compared them with other algorithms: the ant colony optimization algorithm (ACO), artificial neural network (ANN), particle swarm optimization (PSO), and genetic algorithm (GA). With the proposed algorithm, results close to the optimal ones are obtained, and the proposed algorithm is assessed on 29 TSPLIB instances. Based on 30 independent runs per instance, the method achieves a mean absolute percentage error (MAPE) of 1.4484% relative to the known optima, demonstrating its accuracy. Furthermore, statistical comparisons using the coefficient of variation (CV) for runtime and the Wilcoxon signed-rank test confirm that the proposed hybrid algorithm is significantly faster than traditional ant colony optimization (T-ACO) and a new ant colony optimization algorithm (N-ACO) while maintaining competitive solution quality. Full article