Search Results (12,646)

Active athletes represent a specific target for learning and development. Their schedules, including training sessions and competitions, leave little time for education. However, athletes still need skills beyond sports to ensure they are prepared for future employment. Our study approaches this issue by identifying appropriate settings for athletes’ learning and development. (1) Based on the background of current athletes’ education, it addresses the gap of not enough attention being paid to transferable practices from corporate attitudes to learning and development. (2) The study’s methodology primarily uses the case study concept because this conveys the video content we created for the athletes’ learning and development. This is combined with the method of content analysis of selected examples from corporate learning and development and the design thinking workshop, with the engagement of important stakeholder groups: athletes (2 participants), lecturers (2 participants), and representatives of sports organizations (1 participant). The other 9 workshop participants were master’s students in a managerial study programme because of their age similarities with the current athletes and the applicability of the courses they were studying to athletes’ education. (3) The designed process was created as a digital twin using haptic artefacts and the S2M technology (version 1.0) within the OMiLAB platform (version 1.6). Our results show that video content tailored to the athletes’ constraints is a viable solution that improves their career prospects. (4) The study’s practical implications are supported by the expert validation of the model provided by the inside of the large sports organizations’ management. Full article

Condition monitoring of rotating machinery is critical for ensuring industrial safety and operational reliability. As a core component of intelligent diagnostic systems, domain adaptation methods have achieved notable progress in mechanical fault diagnosis. However, most existing approaches presume a fully shared label space between source and target domains, limiting their effectiveness under partial domain adaptation scenarios commonly encountered in industrial practice. In addition, they often struggle with classification uncertainty near decision boundaries. To address these challenges, this paper proposes a Selective Adversarial Augmentation Network (SAAN) for cross-domain rolling bearing fault diagnosis with partial label space alignment. The proposed framework designs a multi-level feature extraction module to enhance transferable feature representation and a Balanced Augmentation Selective Adversarial Module (BASAM) to dynamically balance class distributions and selectively filter irrelevant source classes, thereby mitigating negative transfer and achieving fine-grained class alignment. Furthermore, an uncertainty suppression mechanism is put forth to reinforce classifier boundaries by minimizing the impact of ambiguous samples. Comprehensive experiments conducted on public and proprietary bearing datasets demonstrate that SAAN consistently surpasses state-of-the-art benchmarks in diagnostic accuracy and robustness, providing an effective solution for practical applications under class-imbalanced and variable operating conditions. Full article

Computed tomography and magnetic resonance imaging are two powerful modalities which can be used in the clinical setting to produce data for the creation of patient-specific finite element analysis (FEA) models and physical analogues—for instance, by using additive manufacturing (AM)—that mimic the properties of soft and hard tissues, both morphologically and mechanically. However, there remains a gap between creating a perfect biofidelic physical analogue and its computational counterpart. This gap exists because, firstly, in silico models are often too complex to realise, and secondly, real-life conditions are challenging to emulate both computationally and mechanically, as they involve multiscale situations that are inherently heterogeneous and patient specific. In this study, we applied a multi-scale approach to design and model porcine vertebral specimens. Our results identified critical design factors that affect the quality and accuracy of the models, specifically highlighting that scanning resolution/fidelity and the thresholding technique have a directly proportional impact on model accuracy. A small shift up and down the greyscale level by 20 units can affect the behaviour of the AM sample by as much as [−15% +47%]. Working up the levels for manufacturing, testing and modelling (i) cylindrical cores to (ii) whole vertebrae and then (iii) a whole spine motion segment, we observed that the fidelity of predictions reduces, and errors increase as the structure becomes more complicated and intricate (3.6%, 7.5% and 15%, respectively). We are confident that further material-level developments will provide solutions for the more intricate parts of spinal motion segments, such as the intervertebral discs and facets, which in their natural form are highly sophisticated structures. To the best of our knowledge, this is the first time a holistic multiscale approach has been implemented to produce AM biofidelic analogues of skeletal parts. Our data showed good agreement between the physical and in silico models, confirming that it is possible to model real-time objects and situations both physically and in silico. This ultimately will enable the development of accurate, patient-specific physical models for use in biomechanical testing and medicolegal applications. Full article

Functional data are nowadays routinely collected and stored in a wide variety of fields. Their adequate use and analysis are a challenge for the scientific community. Mathematically, each function can be understood as a sequence of infinite related numbers. Therefore, for statisticians, functional data can be read as a collection of a strongly correlated infinite-dimensional variable. Most existing statistical procedures have been adapted to functional data scenarios. In this manuscript, we are interested in understanding the use of functions for constructing adequate ROC curves and, therefore, for carrying out binary classifications. In particular, we consider the problem of studying the real capacity of functions derived from tissue doppler imaging (TDI) for identifying cardiac dysfunction related to cardiotoxicity therapy (CRTCD) in breast cancer women with high levels of the protein human epidermal growth factor receptor 2 (HER2). With this goal, we use public and freely available data that has been already used for illustrating the use of functional data in the binary classification problem with very different take-home messages. This variability in the conclusions made us question the reproducibility of the results. Here, we explore five different functional approaches, and we think about the clinical use of the provided solutions and their potential overfitting. The main aim of this manuscript is identifying whether published results are excessively optimistic or if they adequately capture the actual capacity of TDI for accurately diagnostic CRTCD. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for crop monitoring, but their widespread adoption is limited since they often rely on non-standard specialized cameras equipped with near-infrared (NIR) sensors. More affordable and scalable crop monitoring solutions would be enabled, however, if data could be collected using standard RGB sensors. We compared visible-band indices that incorporate blue spectral range (NDGBI and NDRBI) with traditional NIR-based indices (NDVI and GNDVI) for their effectiveness in monitoring maize growth and nitrogen status. UAV multispectral data capture at different maize growth stages was complemented by ground-based spectroradiometer measurements for calibration and validation. Various agronomic and yield variables (including cornstalk NO₃–N content, grain yield, grain moisture, number of corncobs, and grain test weight) were recorded to link spectral responses with plant performance and nutritional status. The results show that the overall performance of the RGB-based approach was comparable to that of the NIR-based approach, with the visible-band indices proving to be highly sensitive to physiological stress, chlorophyll degradation, and nitrogen variability in maize. Our findings highlight the potential of the RGB-based indices to complement or even replace specialized NIR-based indices, providing a cost-effective, high-resolution tool for precision agriculture. Full article

Open-source software is transforming visualization-oriented digital documentation and conceptual BIM by lowering financial and technical barriers, enabling broader participation in the digitalization of the AEC sector. This study develops and validates a cost-effective Scan-to-BIM workflow that combines low-cost hardware with freely available software for 3D data acquisition, processing, and modeling. Photogrammetry and SLAM-based techniques generate accurate point clouds, which, once verified against terrestrial laser scanning data, can be integrated into open-source BIM environments. The workflow leverages COLMAP for 3D reconstruction and BlenderBIM for parametric modeling, combining geometric and semantic information to produce fully interoperable models. While open-source tools offer accessibility and transparency, they require supplementary validation in precision-critical applications and may involve trade-offs in accuracy, stability, and automation compared to commercial solutions. Application to a case study shows how efficient and rapid the process is, representing the trend for the scientific community. Full article

Photosensitizers are susceptible to interference from the biological internal environment, which largely restricts the clinical application of photodynamic therapy. For instance, most existing photosensitizers tend to aggregate in the biological environment, resulting in a decrease in reactive oxygen species yield; their therapeutic efficacy is unsatisfactory in hypoxic tumor environments; they are difficult to accumulate effectively in tumor sites and cannot accurately distinguish between tumors and healthy tissues. To address these issues, this review systematically elaborates on a series of optimization strategies, including improving the intersystem crossing efficiency of photosensitizers through molecular engineering, endowing them with aggregation-induced emission properties, developing type I photosensitizers, and functionalizing photosensitizers by modifying biological proteins, targeting groups, or combining with nanoengineering, aiming to enhance the efficiency of photodynamic therapy. By summarizing the latest research breakthroughs, innovative methods, and emerging applications in this field, the review provides practical solutions and broad application prospects for photodynamic therapy, which is expected to promote the clinical translation and application of photosensitizers. Full article

This paper explores the role of metrology in the assessment of image quality in the field of radiomics. Image Quality Assessment (IQA) is central to ensuring the reliability and reproducibility of radiomic analyses, as it directly affects the accuracy of feature extraction and segmentation, ultimately impacting diagnostic outcomes. From the analysis of approximately 20,000 papers sourced from three databases (PubMed, Scopus, IEEE Xplore), last searched in December 2025, the need for standardized imaging protocols and quality control measures emerges as a critical theme. Studies were included if they involved radiomic feature extraction and evaluated the impact of image quality variations on feature robustness and no formal risk-of-bias assessment was performed. A total of 105 studies were included, covering different medical imaging modalities. Across the included studies, noise, motion, acquisition and reconstruction parameters, and other artifacts consistently emerged as major sources of radiomic feature instability. Indeed, in most papers, IQA is neglected, while the effect of poor-quality images is reported. This research identifies and discusses the relevant issues reported in clinical practice, as well as the main metrics adopted for image quality evaluation. Through a comprehensive review of current literature and an analysis of emerging trends, this paper highlights the urgent need for innovative solutions in image quality metrics tailored to radiomics applications. Full article

With the focus on the growing concern of voltage instability and its inherent risks connected to blackouts, this study addresses the importance of Volt/VAR control (VVC) in maintaining voltage stability, optimizing power factor, and reducing losses. As such, this scientific article presents a review of the methodologies used to estimate the quantity of reactive power required to restore voltage in power grids. Although reviews exist on classical methods, optimization, and machine learning, a study unifying these approaches is lacking. This gap hinders an integrated comparison of methodologies and constitutes the main motivation for this study in 2025. This absence of a consolidated and up-to-date review limits both academic progress and practical decision-making in modern power systems, especially as DER penetration accelerates. This research was conducted using the Scopus database through the selection of articles that address reactive power estimation methods. The results indicate that traditional numerical and optimization methods, although accurate, demonstrate high computational costs for real-time application. In contrast, techniques such as Deep Reinforcement Learning (DRL) and hybrid models show greater potential for dealing with uncertainties and dynamic topologies. The conclusion reached is that the solution for reactive power management lies in hybrid approaches, which combine machine learning with numerical methods, supported by an intelligent and robust data infrastructure. The comparative analysis shows that numerical methods offer high precision but are computationally expensive for real-time use; optimization techniques provide good robustness but depend on detailed models that are sensitive to system conditions; and machine learning-based approaches offer greater adaptability under uncertainty, although they require large datasets and careful training. Given these complementary limitations, hybrid approaches emerge as the most promising alternative, combining the reliability of classical methods with the flexibility of intelligent models, especially in smart grids with dynamic topologies and high penetration of Distributed Energy Resources (DERs). Full article

This work aims to demonstrate the successful, long-term human use of an automatic scheduling-manual operation (AS-MO) precision irrigation tool by farmers on a medium-scale Jordanian farm. Innovation in low-cost, accessible, and water-efficient irrigation technologies is critical as water resources become scarce, especially on resource-constrained farms in the drought-prone Middle East and North Africa (MENA) region. Prior work has shown that a proposed AS-MO decision support tool could bridge the gap between fully manual irrigation—a common practice on many MENA farms—and existing precision agriculture solutions, which are often too expensive or complex for medium-scale farmers to adopt. Recent developments have also demonstrated that the scheduling theory behind the proposed AS-MO tool uses up to 44% less water compared to fully manual irrigation. However, a functional design of the AS-MO tool has not been realized nor has it been demonstrated on a farm with farmer users. This work documents the detailed design of an AS-MO tool’s human–machine interaction (HMI) and validates the human execution of the tool in context. Through an 11-week case study conducted on a Jordanian farm, we show that farmers used a functional prototype of the AS-MO tool as intended. The functional tool prototype was designed to deliver a long-term AS-MO user experience to study participants. The prototype monitored local weather conditions, generated water-efficient schedules using an existing scheduling theory, and notified users’ phones when they should manually open or close valves. The irrigation practices of participants using the AS-MO prototype were measured, and participants demonstrated successful use of the tool. Users correctly confirmed 93% of the scheduled events using the tool’s HMI. Despite manual operation, a majority of confirmed irrigation event durations fell within 15% of the automatically scheduled durations; relative to the length of scheduled irrigation event durations, the medians of confirmed and scheduled durations were 102% and 88%, respectively. These results demonstrate the success of the tool’s decision support ability. Feedback from study participants can support the AS-MO tool’s next design iteration and can inform the development of other decision support systems designed for resource-constrained, medium-scale farms. This work presents an important step towards developing a precision irrigation tool that, if adopted at scale, could increase the adoption of water-efficient irrigation practices on resource-constrained farms that are not served by existing technology, improving sustainable agriculture in MENA. Full article

Ships are highly advanced marine structures that incorporate state-of-the-art technologies. Nevertheless, they still depend on outdated systems in certain critical areas, such as escape lighting. Escape lighting systems are vital components of shipboard safety infrastructure. However, conventional systems rely heavily on decentralized battery-powered luminaires and manual testing, leading to high maintenance costs and environmental burdens. This study addresses these challenges through an engineering-driven redesign of escape lighting systems. A novel system architecture was developed, integrating photovoltaic energy sources with centralized battery storage and Automatic Testing Systems (ATSs) compliant with the IEC 62034 standard. The system interfaces with both main and emergency power networks, reducing reliance on fossil fuels and minimizing battery usage. Engineering simulations and operational data indicate a 20% reduction in fuel oil consumption per escape light and a threefold decrease in maintenance costs over a vessel’s lifecycle. For a standard vessel equipped with 350 luminaires, the system demonstrates significant operational efficiency and environmental benefits, including reduced emissions and hazardous waste. This work exemplifies how ocean engineering innovations can enhance vessel safety while promoting sustainability. The integration of renewable energy and automated diagnostics into critical shipboard systems represents a forward-looking approach to marine engineering, aligning with global goals for greener maritime operations. Moreover, the proposed system supports compliance with evolving maritime regulations and offers a scalable solution adaptable to various vessel types and operational profiles. Full article

The well-known shooting algorithm has produced important results in solving various linear as well as nonlinear BVPs, defined on unbounded intervals, but has become obsolete. The main difficulty lies in the numerical handling of the domain’s infiniteness. This paper presents a three-step strategy that significantly improves the traditional truncation algorithm. It consists of Chebyshev collocation, implemented as Chebfun, in conjunction with rational AAA interpolation and analytic continuation. Furthermore, and more importantly, this approach enables us to provide a thorough analysis of both possible errors in dealing with and the hidden singularities of some BVPs of real interest. A singular second-order eigenvalue problem and a fourth-order nonlinear degenerate parabolic equation, all defined on the real axis, are considered. For the latter, Chebfun provides properties-preserving solutions. Travelling wave solutions are also studied. They are highly nonlinear BVPs. The problem arises from the analysis of thin viscous film flows down an inclined plane under the competing stress due to the surface tension gradients and gravity, a long-standing concern of ours. By extending the solutions to these problems in the complex plane, we observe that the complex poles do not influence their behaviour. On the other hand, the real ones involve singularities and indicate how long solutions can be extended through continuity. Full article

We present a one-parameter family of smooth generalized pinball loss functions to overcome the challenges of non-differentiability, noise sensitivity, and resampling instability inherent in traditional loss functions such as hinge loss. These functions make the objective function in the formulation of the support vector machine (SVM) model twice continuously differentiable and improve model performance by reducing noise sensitivity and preserving the sparsity of the solution. Similarly, a novel twin-bounded support vector machine (TBSVM) model with a smooth generalized pinball loss function is obtained. Furthermore, we compare the performance of the TBSVM with the novel type of smooth loss function against other contemporary approaches, offering a comprehensive assessment of its strengths and limitations by conducting an evaluation with UCI datasets. The experimental results show that the proposed model has the best performance in the TBSVM with RBFSampler. Additionally, we prove that the generalized pinball loss function can be approximated by a novel smooth generalized pinball loss function in the uniform norm with arbitrary precision. We further show that the solutions of the proposed SVM and TBSVM models are unique and that they converge to the solutions of the models with non-smooth generalized pinball loss as the parameter approaches zero. Full article

Recently, different techniques have been proposed for the scheduling and loading problems in cooling plants with chillers in a parallel configuration. Two broad groups can be considered: the online control-based group and the offline optimization-based group. The first group is exemplified by Model Predictive Control, where the selection of control moves provides a solution to both scheduling and loading. The second group includes Optimal Chiller Loading and Optimal Chiller Sequencing algorithms. They usually derive operating plans with some lead time in a batch-like fashion for long horizons. Both groups use forecasts of important factors such as the cooling demand and ambient conditions; hence, they have to deal with inaccuracies in the forecasts. In this paper, a comparison among these two groups is made considering demand uncertainties. The severity of the uncertainty is shown to play a role in the results as well as the controller tuning in the case of the predictive approach. The results are favorable to OCS with respect to overall consumption (up to 15%) but uses more on/off changes in the chiller’s operation (double in some cases). Full article

The detection of defects in Printed Circuit Boards (PCBs) is a critical yet challenging task in industrial quality control, characterized by the prevalence of small targets and complex backgrounds. While deep learning models like YOLOv5 have shown promise, they often lack the ability to quantify predictive uncertainty, leading to overconfident errors in challenging scenarios—a major source of false alarms and reduced reliability in automated manufacturing inspection lines. From a Bayesian perspective, this overconfidence signifies a failure in probabilistic calibration, which is crucial for trustworthy automated inspection. To address this, we propose MFE-YOLO, a Bayesian-enhanced detection framework built upon YOLOv5 that systematically integrates uncertainty-aware mechanisms to improve both accuracy and operational reliability in real-world settings. First, we construct a multi-background PCB defect dataset with diverse substrate colors and shapes, enhancing the model’s ability to generalize beyond the single-background bias of existing data. Second, we integrate the Convolutional Block Attention Module (CBAM), reinterpreted through a Bayesian lens as a feature-wise uncertainty weighting mechanism, to suppress background interference and amplify salient defect features. Third, we propose a novel FIoU loss function, redesigned within a probabilistic framework to improve bounding box regression accuracy and implicitly capture localization uncertainty, particularly for small defects. Extensive experiments demonstrate that MFE-YOLO achieves state-of-the-art performance, with mAP@0.5 and mAP@0.5:0.95 values of 93.9% and 59.6%, respectively, outperforming existing detectors, including YOLOv8 and EfficientDet. More importantly, the proposed framework yields better-calibrated confidence scores, significantly reducing false alarms and enabling more reliable human-in-the-loop verification. This work provides a deployable, uncertainty-aware solution for high-throughput PCB inspection, advancing toward trustworthy and efficient quality control in modern manufacturing environments. Full article