Search Results (6,591)

In the mining industry, where equipment breakdowns cause expensive unplanned downtime, operational continuity is paramount. Internet of Things (IoT) technologies have the potential to make predictions; however, most solutions lack a holistic view and mapping of complex system interdependencies. This study presents a comprehensive predictive maintenance (PdM) framework specifically designed for continuous-operation mining environments, with a primary focus on Semi-Autogenous Grinding (SAG) mills. By combining exploratory data analysis, advanced feature engineering, classical machine learning (Gradient Boosting Classifier), and deep learning (LSTM with multiple time-window configurations), the system achieves real-time anomaly detection, root-cause explanation, and failure forecasting up to 48 h in advance (average lead time: 17 h). A four-layer digital twin architecture integrated with Streamlit enables actionable alerts classified as emergency, planned, or preventive interventions. Applied to a one-year dataset comprising 99,854 hourly records from an industrial SAG mill, the hybrid model prevented an estimated 219.5 h of unplanned downtime, yielding substantial economic benefits. The proposed solution is deliberately designed for high adaptability across multiple equipment types and industrial sectors beyond mining. Full article

This paper presents the design, modeling and experimental validation of an on-grid photovoltaic system with self-consumption, sized for the sustainable supply of a water pumping station. The system, composed of 68 photovoltaic panels, uses an architecture based on a Boost DC-DC converter controlled by the Perturb and Observe algorithm, raising the operating voltage to a high-voltage DC bus to maximize the conversion efficiency. The study integrates dynamic performance analysis through simulations in the Simulink environment, testing the stability of the DC bus under sudden irradiance shocks, with rigorous experimental validation based on field production data. The simulation results, which indicate a peak DC power of approximately 34 kW, are confirmed by real monitoring data that records a maximum of 35 kW, the error being justified by the high efficiency of the panels and system losses. Long-term validation, carried out over three years of operation (2023–2025), demonstrates the reliability of the technical solution, with the system generating a total of 124.68 MWh. The analysis of energy flows highlights a degree of self-consumption of 60.08%, while the absence of chemical storage is compensated for by injecting the surplus of 49.78 MWh into the national grid, which is used as an energy buffer. The paper demonstrates that using the grid to balance night-time or meteorological deficits, in combination with a stabilized DC bus, represents an optimal technical-economic solution for critical pumping infrastructures, eliminating the maintenance costs of the accumulators and ensuring continuous operation. Full article

The building sector accounts for a large percentage of global greenhouse gas emissions, largely from the embodied carbon in common building materials like concrete and steel. Embodied carbon (EC) refers to the greenhouse gases released during the manufacturing, transportation, installation, maintenance, and disposal of building materials. Although growing in popularity, mass timber is still not nearly as common as other building materials. During the early building design stages, engineers often do not have the time or resources to holistically optimize material selection; consequently, concrete and steel remain the materials of choice. This research focused on the development of a fully automated parametric design tool, APDT, to showcase the viability of evaluating and optimizing mass timber in building construction. The APDT was developed using Autodesk’s Revit 2022 and the visual-based programming tool housed within Revit: Dynamo. The automated designer uses parametric inputs of a building, including size, number of stories, and loading, to create a model of a mass timber building with designed glulam columns and beams and cross-laminated timber floor panels. The designer calculates overall material quantities, which are then used to determine the building’s overall embodied carbon impact. Discussed herein is the development of a building design tool that highlights the benefits of optimized mass timber using existing software and databases. The tool allows the designer to expediently provide an estimate of the amount of material and embodied carbon values, thereby making it easier to consider mass timber when determining the structural system at the infancy stage of the project. The methodology outlined herein provides a replicable methodology for creating an APDT that bridges a critical gap in early-stage design, enabling rapid embodied carbon comparisons and fostering consideration of mass timber as a viable low-carbon alternative. Full article

Biomolecules play pivotal roles in cellular signaling, metabolic regulation and the maintenance of physiological homeostasis in the human body, and their dysregulation is closely associated with the onset and progression of various human diseases. Consequently, the development of highly sensitive, selective, and stable detection platforms for these molecules is of significant value for drug discovery, pharmaceutical quality control, pharmacodynamic studies, and personalized medicine. In recent years, electrochemical biosensors, particularly those integrated with functional nanomaterials and biorecognition elements, have emerged as powerful analytical platforms in pharmaceutics and biomedical analysis, owing to their high sensitivity, exquisite selectivity, rapid response, simple operation, low cost and suitability for real-time or in situ monitoring in complex biological systems. This review summarizes recent progress in the electrochemical detection of representative biomolecules, including dopamine, glucose, uric acid, hydrogen peroxide, lactate, glutathione and cholesterol. By systematically summarizing and analyzing existing sensing strategies and nanomaterial-based sensor designs, this review aims to provide new insights for the interdisciplinary integration of pharmaceutics, nanomedicine, and electrochemical biosensing, and to promote the translational application of these sensing technologies in drug analysis, quality assessment, and clinical diagnostics. Full article

This study compares three detectors—Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Only Look Once v11 (YOLOv11)—for detecting subgrade distress in GPR radargrams. SSD converges fastest but shows weaker detection performance, while Faster R-CNN achieves higher localization accuracy at the cost of slower convergence. YOLOv11 offers the best overall performance. To push YOLOv11 further, we introduce three enhancements: a Multi-Scale Edge Enhancement Module (MEEM), a Multi-Feature Multi-Scale Attention (MFMSA) mechanism, and a hybrid configuration that combines both. On a representative dataset, YOLOv11_MEEM yields a 0.2 percentage-point increase in precision with a 0.2 percentage-point decrease in recall and a 0.3 percentage-point gain in mean Average Precision@0.5:0.95, indicating improved generalization and efficiency. YOLOv11_MFMSA achieves precision comparable to MEEM but suffers a substantial recall drop and slower inference. The hybrid YOLOv11_MEEM+MFMSA underperforms on key metrics due to gradient conflicts. MEEM reduces electromagnetic interference through dynamic edge enhancement, preserving real-time performance and robust generalization. Overall, MEEM-enhanced YOLOv11 is suitable for real-time subgrade distress detection in GPR radargrams. The research findings can offer technical support for the intelligent detection of subgrade engineering while also promoting the resilient development and sustainable operation and maintenance of urban infrastructure. Full article

Background: Follicular lymphoma (FL) is the second most common B-cell lymphoma in Western countries, typically presenting as an indolent disease with prolonged overall survival. Despite favorable initial responses to therapy, most patients experience relapse, and early progression is associated with poor outcomes. Methods: This guideline provides evidence-based recommendations from the Spanish GELTAMO group on the diagnosis, staging, treatment, and follow-up of FL. A systematic literature review was conducted, and recommendations were graded according to the GRADE system. Results: Histopathological diagnosis should be based on excisional biopsy. PET-CT is recommended for staging and response evaluation. For localized disease, involved-site radiotherapy (ISRT) remains the treatment of choice. In asymptomatic patients with advanced-stage disease and low tumor burden, a watch-and-wait approach is appropriate, although rituximab monotherapy is also acceptable. For advanced-stage disease with high tumor burden, immunochemotherapy with anti-CD20 antibodies (rituximab or obinutuzumab) combined with CHOP, CVP, or bendamustine is recommended, followed by maintenance therapy. Management of relapsed disease is tailored based on tumor burden, treatment history, and timing of relapse. Although novel immunotherapies (CAR-T therapy and bispecific antibodies) are emerging as promising options, autologous stem cell therapies may still be a valid option in young patients with early relapse who are sensitive to immunochemotherapy. Conclusions: FL is a heterogeneous disease requiring individualized management strategies. Recent advances in immunotherapy and molecular diagnostics are reshaping the therapeutic landscape. These updated GELTAMO recommendations aim to provide practical guidance for optimal FL management in clinical practice. Full article

The construction industry is undergoing a rapid digital transformation, with Digital Twins (DTs) and Extended Reality (XR) as two emerging technologies with great potential. Despite their potential, there are several challenges regarding DT and XR use in construction projects, including implementation barriers, interoperability issues, system complexity, and a lack of standardized frameworks. This study presents a systematic literature review (SLR) of DT and XR technologies—including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—in the construction industry. The study analyzes 52 peer-reviewed articles identified using the Web of Science database to explore thematic findings. Key findings highlight DT and XR applications for safety training, real-time monitoring, predictive maintenance, lifecycle management, renovation or demolition, scenario risk assessment, and education. The SLR also identifies core enabling technologies such as Building Information Modeling (BIM), Internet of Things (IoT), Big Data, and XR devices, while uncovering persistent challenges including interoperability, high implementation costs, and lack of standardization. The study highlights how integrating DTs and XR can improve construction by making it smarter, safer, and more efficient. It also suggests areas for future research to overcome current challenges and help increase the use of these technologies. The primary contribution of this study lies in deepening the understanding of DT and XR technologies by examining them through the lenses of their benefits as well as drivers for and challenges to their adoption. This enhanced understanding provides a foundation for exploring integrated DT and XR applications to advance innovation and efficiency in the construction sector. Full article

High-rise concrete tower structures located in arid-cold regions with large diurnal temperature variations are subjected to significant solar-induced thermal loads, which can induce considerable thermal stresses and affect long-term durability. However, a comprehensive understanding of the spatiotemporal distribution of the temperature field and its correlation with atmospheric conditions remains insufficient, particularly based on field monitoring studies. This study aims to elucidate these relationships through continuous temperature monitoring of a high-rise concrete tower in Shanshan, Xinjiang, during a period of intense solar radiation. Surface and internal temperatures at different heights were measured alongside atmospheric temperature. The results show that the outer surface temperature closely follows the trend of the atmospheric temperature while generally being higher, indicating a strong correlation. In contrast, the inner surface temperature is lower and exhibits a weaker correlation with the atmosphere. A significant time lag of up to 3 h was observed between the peak temperatures of the outer and inner surfaces, attributable to the thermal inertia of concrete. The study identified notable vertical and through-thickness temperature gradients, with the maximum temperature difference reaching 12 °C. These findings provide crucial empirical data and mechanistic insights into the thermal behavior of high-rise concrete structures under extreme climates, establishing a solid foundation for subsequent thermal stress analysis and durability assessment. This research emphasizes the necessity of considering diurnal thermal cycles in the design and maintenance of such structures. Full article

Water supply and drainage networks are essential components of urban infrastructure, directly influencing both residents’ quality of life and the efficiency of city operations through their safety and stability. Over time, these networks often develop unstructured turbid water conditions (referring to scenarios with irregular pipe geometries due to defects and low visibility caused by suspended matter), which present challenges for traditional maintenance methods. Leveraging the advantages of spatial visualization, three-dimensional environmental reconstruction technology has emerged as a promising solution to address these issues, while also advancing the use of intelligent maintenance technologies within water supply and drainage systems. This paper focuses on the causes of unstructured turbid water in these networks, and evaluates the optimization, effectiveness, and limitations of turbid water imaging, image feature recognition, and 3D environmental reconstruction technologies. Additionally, it reviews the current technical challenges and outlines potential future research directions, aiming to support the development and application of 3D reconstruction technologies for pipeline networks under unstructured turbid water conditions. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

Mining-induced seismicity presents significant challenges to the safety and operational continuity of underground mines, particularly in deep and highly stressed environments. This study proposes a methodological framework for seismic risk evaluation inspired by predictive-maintenance principles and applied to a high-resolution microseismic catalog from a Chilean underground mine. Using a combination of data filtering and correlation analyses, we identify the seismic parameters that control the most variability in the dataset: moment magnitude, frequency corner, and both dynamic and static stresses. Based on this, we perform a Principal Component Analysis (PCA), which clearly demonstrates the physical interconnection between the selected parameters, thereby helping to better characterize the seismic events and the mining environment. Using these results, a PCA-based risk map is constructed, enabling the delineation of zones with different levels of seismic risk. Additionally, a temporal tracking of potentially hazardous seismicity is included. The proposed methodology demonstrates that microseismic behavior can be effectively represented in a reduced-dimension space, offering a promising foundation for predictive and data-driven risk-assessment tools capable of supporting real-time decision-making in underground mining operations. Full article

Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide. Full article

Power transformers are critical assets in power systems, and their reliable operation is essential for grid stability. Conventional fault diagnosis methods suffer from delayed response and limited adaptability, while existing artificial intelligence-based approaches face challenges related to data heterogeneity, limited interpretability, and weak integration of physical mechanisms. To address these issues, this paper proposes a physics-informed enhanced transformer-based framework for power transformer fault diagnosis. A unified temporal representation scheme is developed to integrate heterogeneous monitoring data using Dynamic Time Warping and physics-guided feature projection. Physical priors derived from thermodynamic laws and gas diffusion principles are embedded into the attention mechanism through multi-physics coupling constraints, improving physical consistency and interpretability. In addition, a multi-task diagnostic strategy is adopted to jointly perform fault classification, severity assessment, and fault localization. Experiments on 3000 samples from 76 power transformers demonstrate that the proposed method achieves high diagnostic accuracy and superior robustness under noise and interference, indicating its effectiveness for practical predictive maintenance applications. Full article