Search Results (5,977)

Power line sag monitoring is critical for ensuring transmission system reliability and optimizing grid capacity utilization. Traditional sag detection methods rely on hyperbolic cosine models that assume ideal catenary behavior under uniform loading conditions. However, these models impose restrictive assumptions about weight distribution and suspension conditions that limit accuracy under real-world scenarios involving wind loading, ice accumulation, and non-uniform environmental forces. This study introduces a novel Bézier curve-based mathematical framework for transmission line sag detection and monitoring. Unlike traditional hyperbolic cosine approaches, the proposed methodology eliminates idealized assumptions and provides enhanced flexibility for modeling actual conductor behavior under variable environmental conditions. The Bézier curve approach offers enhanced precision and computational efficiency through intuitive control point manipulation, making it well suited for Dynamic Line Rating (DLR) applications. Experimental validation was performed using a controlled laboratory setup with a 1:100 scaled transmission line model. Results demonstrate improvement in sag measurement accuracy, achieving an average error of 1.1% compared to 6.15% with traditional hyperbolic cosine methods—representing an 82% improvement in measurement precision. Statistical analysis over 30 independent experiments confirms measurement consistency with a 95% confidence interval of [0.93%, 1.27%]. The framework also demonstrates a 1.5 to 2 times increase in computational efficiency improvement over conventional template matching approaches. This mathematical framework establishes a robust foundation for advanced transmission line monitoring systems, with demonstrated advantages for power grid applications where traditional catenary models fail due to non-ideal environmental conditions. The enhanced accuracy and efficiency support improved Dynamic Line Rating implementations and grid modernization efforts. Full article

Rapid urban expansion and increasing population density intensify the loss of open spaces, exacerbate flooding frequency and runoff pollution, increase the urban heat island effect, and deteriorate ecological resilience and human well-being. This study presents Gazelle Valley Park (GVP) in Jerusalem (Israel), a unique large-scale ecohydrological infrastructure within a dense Mediterranean city. GVP was established in 2015 following a public-led campaign and comprises a multifunctional nature-based solution designed to collect and circulate stormwater through a series of vegetated ponds, enhancing filtration, aeration, and pollutant removal, while sustaining a wetland ecosystem. Its design follows international ecological standards and embodies the principle “from nuisance to resource”, transforming urban runoff into an asset that supports rich biodiversity while offering recreational, cultural, and educational activities. During the dry summer, reclaimed wastewater is introduced in order to support a perennial aquatic habitat, which introduces various challenges due to increased salinity, oxygen demand, and contaminants. Hydrometric and geochemical monitoring demonstrates strong correlations between rainfall and runoff and point at the role of sedimentation and vegetation in reducing pollutant loads. The park benefits from its holistic operation, where hydrology, ecology, education, and public engagement are integrated, thus making the whole greater than the sum of its parts. Full article

Shallow-buried, closely spaced tunnels under bias loading often encounter stability challenges due to excavation-induced interaction effects. These effects are particularly significant in the middle rock pillar zone. To evaluate the influence of face lag distance on tunnel stability, the Georgia No. 1 Tunnel was selected as a case study. Numerical simulations and field monitoring were combined to analyze the deformation and stress evolution under different face lag distances. The analysis focused on ground surface settlement, vault displacement, and tunnel clearance convergence. The results indicate that ground surface settlement decreases notably as the face lag distance increases. When the face lag distance increased from 0.5 D to 2.0 D, the maximum settlement decreased by about 11.9%, with the absolute maximum measured value of approximately 3.48 mm. Stress concentration occurred mainly within 15 m behind the excavation face, suggesting that a face lag distance exceeding this range can effectively mitigate tunnel interaction effects. The biased tunnel side experienced greater vault settlement and convergence, requiring closer monitoring. An insufficient face lag distance amplifies deformation superposition, whereas an excessive one causes additional horizontal fluctuations. For the geological and structural conditions of the Georgia No. 1 Tunnel, a face lag distance of approximately 2.0 D provides an optimal balance between stability, safety, and construction efficiency. These findings offer practical guidance for the design and safe construction of shallow-buried twin tunnels under bias loading. Full article

This study presents the OGS-YOLOv8 model for coffee bean maturity identification, designed to enhance accuracy in identifying coffee beans at different maturity stages in complicated contexts, utilizing an upgraded version of YOLOv8. Initially, the ODConv (full-dimensional dynamic convolution) substitutes the convolutional layers in the backbone and neck networks to augment the network’s capacity to capture attributes of coffee bean images. Second, we replace the C2f layer in the neck networks with the CSGSPC (Convolutional Split Group-Shuffle Partial Convolution) module to reduce the computational load of the model. Lastly, to improve bounding box regression accuracy by concentrating on challenging samples, we substitute the Inner-FocalerIoU function for the CIoU loss function. According to experimental results, OGS-YOLO v8 outperforms the original model by 7.4%, achieving a detection accuracy of 73.7% for coffee bean maturity. Reaching 76% at mAP@0.5, it represents a 3.2% increase over the initial model. Furthermore, GFLOPs dropped 26.8%, from 8.2 to 6.0. For applications like coffee bean maturity monitoring and intelligent harvesting, OGS-YOLOv8 offers strong technical support and reference by striking a good balance between high detection accuracy and low computational cost. Full article

Big Data Analytics is vital for power grids, as it empowers informed decision-making, anticipates potential operational and maintenance issues, optimizes grid management, supports renewable energy integration, ultimately reduces costs, improves customer service, monitors consumer behavior, and offers new services. This paper describes the AI–Big Data Analytics Architecture based on a data lake architecture that uses a reduced and customized set of Hadoop and Spark as a cost-effective, on-premises alternative for advanced data analytics in power systems. As a case study, a comparative analysis of electricity price forecasting models in the day-ahead market for nodes of the Mexican national electrical system using statistical, machine learning, and deep learning models, is presented. To build and select the best forecasting model, a data science and machine learning methodology is used. The results show that the Gradient Boosting and Support Vector Regression models presented the best performance, with a Mean Absolute Percentage Error (MAPE) between 1% and 4% for five-day-ahead electricity price forecasting. The implementation of the best forecasting model into the Big Data Analytics Platform allows the automation of the calculation of the local electricity price forecast per node (every 24, 72, or 120 h) and its display in a comparative dashboard with actual and forecasted data for decision-making on demand. The proposed architecture is a valuable tool that allows the future implementation of intelligent energy forecasting models in power grids, such as load demand, fuel prices, power generation, and consumption, among others. Full article

Background and Objectives: This study aimed to assess the week-by-week predictive value of Acute:Chronic Workload Ratios (ACWRs) for non-contact injury risk in professional soccer players. Materials and Methods: A cohort of 40 elite players was monitored using GPS over two competitive seasons. Binomial logistic regression and ROC curve analyses were performed on ACWR metrics—including total distance, moderate-to high-speed running, sprinting, acceleration, and deceleration—during the four weeks prior to injury (W4 to W1). p-values were further adjusted for multiple comparisons using the false discovery rate (FDR) correction (q < 0.05). Results: Significant predictive models emerged mainly for ACWR metrics related to moderate-speed running (15–20 km/h), sprinting (>25 km/h), and acceleration/deceleration. The ACWR for 15–20 km/h (DSR15–20) demonstrated the highest predictive accuracy, particularly in Week 3 (AUC = 0.811, p = 0.004). Sprinting (DSR>25) was also significantly associated with injury occurrence across Weeks 1–4 (AUC = 0.709–0.755, p = 0.011–0.024). Acceleration (ACC) and deceleration (DEC) metrics showed significant associations prior to correction—ACC in Weeks 3–4 (AUC = 0.737–0.755, p = 0.020–0.026) and DEC in Weeks 3–4 (AUC = 0.720–0.741, p = 0.029–0.043)—but these associations did not retain significance following FDR adjustment (q = 0.052–0.086). In contrast, total distance (ACWR TD) and high-speed running (20–25 km/h) were weaker predictors, reaching only marginal or nonsignificant levels (e.g., Week 3, AUC = 0.675, p = 0.054). After FDR correction, only DSR15–20 and DSR>25 remained statistically significant (q < 0.05), confirming them as robust predictors of non-contact injury risk. Multivariable models adjusted for age and playing position confirmed these findings, with DSR15–20 and DSR>25 retaining their predictive value independent of confounding factors. Injury risk thresholds were established through Estimated Marginal Means (EMMs), defining the “Sweet Spot” and “Danger Zone” for each metric, whereas the “Low Load” zone was treated as exploratory. Conclusions: This weekly ACWR monitoring approach enables practical injury risk profiling, helping training staff optimize load management and minimize non-contact injury risk in elite soccer. Full article

This work presents the development and experimental validation of a low-cost, piezoelectret-based energy harvesting system integrated into a custom insole, as a promising alternative for future self-powered wearable electronics. The design utilizes eight thermoformed Teflon piezoelectrets, strategically positioned in high-impact regions (heel and forefoot), to convert footstep-induced mechanical motion into electrical energy. The sensors, fabricated using Fluorinated Ethylene Propylene (FEP) and Polytetrafluoroethylene (PTFE) layers via thermal pressing and aluminum sputtering, were connected in parallel to enhance signal consistency and robustness. A solenoid-actuated mechanical test rig was developed to simulate human gait under controlled conditions. The system consistently produced voltage pulses with peaks up to 13 V and durations exceeding ms, even under limited-force loading (10 kgf). Signal analysis confirmed repeatable waveform characteristics, and a Delon voltage multiplier enabled partial conversion into usable DC output. While not yet optimized for maximum efficiency, the proposed setup demonstrates the feasibility of using piezoelectrets for energy harvesting. Its simplicity, scalability, and low cost support its potential for future integration in applications such as fitness tracking, health monitoring, and GPS ultimately contributing to the development of autonomous, self-powered smart footwear systems. It is important to emphasize that the present study is a proof-of-concept validated exclusively under controlled laboratory conditions using a mechanical gait simulator. Future work will address real-time insole application tests with human participants. Full article

In many decarbonization scenarios, heat pumps are seen as a key technology for future heating needs. However, market shares for large-capacity heat pumps are still low despite the potential for significant CO₂ reduction. In particular, boiler replacements face the obstacle of insufficient heat sources due to restrictions imposed by the built environment. In this study, overcoming the restriction of individual heat sources through dual-source integration has been investigated, both by simulation and field monitoring. The results confirm that by downsizing the individual heat sources, limitations relating to noise emissions or drilling space can be overcome. For instance, by combining the ground as a heat source for 50% of the peak load coverage with outdoor air as the base load heat source, the length of the borehole heat exchanger can be reduced by up to 80% compared to when using only the ground as a heat source. Through regeneration of the ground, boreholes can be drilled closer together, and their length can be reduced by more than 50%. Cost-optimal regeneration rates were found to be between 40 and 80%. The related cost savings can make the dual-source system more cost-effective than a single-source system, even without limitations on any individual heat source. Simulation results are verified in a pilot and demonstration (P&D) plant for a boiler replacement in two larger multi-family homes. The first winter measurements confirm the basic simulation results. CO₂ saving potentials are estimated to be around 90%. Ongoing monitoring will further verify results and derive standard configurations and best practices. Full article

In response to the imperative to mitigate methane—one of the most potent greenhouse gases—this study proposes and tests an integrated workflow for designing methane drainage boreholes targeting post-mining areas in an active underground coal mine (Pniówek, Poland). The workflow combines the following: (1) forecasting methane emissions from goafs and active longwalls for 2024–2040; (2) 3D geological characterization (structural and lithofacies models); (3) selection and sealing of goaf zones; and (4) optimization of well placement, drilling, and performance evaluation of drainage boreholes, including an assessment of energy use from the recovered gas. Applying the method delineated priority capture zones and estimated recoverable rates under multiple scenarios. Preliminary field data from a borehole near seam 362/1 indicate stable methane inflow to the drainage system and a concomitant reduction in methane load within the ventilation network. The integrated design improves targeting efficiency and provides a quantitative basis for scheduling, risk management, and sizing of surface-to-underground infrastructure. The results suggest that systematic drainage of post-mining voids can enhance safety, limit fugitive emissions, and create opportunities for on-site power generation. The approach is transferable to other active mines with legacy workings, provided site-specific calibration and monitoring are implemented. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

The increasing demand for electricity and the requirement for transformers to operate under higher loads have driven the search for new insulating materials. On the one hand, papers with enhanced thermal resistance, such as thermally upgraded kraft (TUK) papers, are being introduced; on the other, the use of ester liquids is gaining attention due to their thermal and environmental advantages. This study investigates the thermal ageing behaviour of TUK paper impregnated with five ester liquids—four natural liquids of different origin and one synthetic—subjected to accelerated ageing at 130 °C, 150 °C, and 170 °C, and compared with mineral oil as impregnating fluid as a reference. The degradation of the paper, assessed through its degree of polymerisation, was monitored alongside the evolution of key chemical, physical, and dielectric properties of the liquids. In addition to the experimental analysis, this work also examines the current maintenance standards applied to transformers operating with different insulating fluids. The results showed that while the paper degradation was similar across most of the esters, the ageing behaviour of the fluids differed significantly in terms of acidity, moisture content, interfacial tension, and dielectric dissipation factor. These discrepancies strongly influence the interpretation of fluid condition based on existing transformer maintenance standards, which may lead to inconsistent assessments when applied to ester-filled transformers. The findings highlight both the suitability of natural esters for high-temperature operation and the need to revisit condition assessment criteria in standards that were originally developed for mineral oil systems. Full article

Composite engine fan blades are critical aircraft engine components, and their failure can compromise the safe and reliable operation of the entire aircraft. To enhance aircraft availability and safety within a condition-based maintenance framework, effective methods are needed to identify damage and monitor the blades’ condition throughout manufacturing and operation. This paper presents a unique experimental framework for real-time monitoring of composite engine blades utilizing state-of-the-art structural health monitoring (SHM) technologies, discussing the associated benefits and challenges. A case study is conducted on a representative Foreign Object Damage (FOD) panel, a substructure of a LEAP (Leading Edge Aviation Propulsion) engine fan blade, which is a curved, 3D-woven Carbon Fiber Reinforced Polymer (CFRP) panel with a secondary bonded steel leading edge. The loading scheme involves incrementally increasing, cyclic 4-point bending (loading–unloading) to induce controlled damage growth, simulating in-operation conditions and allowing evaluation of flexural properties before and after degradation. External damage, simulating foreign object impact common during flight, is introduced using a drop tower apparatus either before or during testing. The panel’s condition is monitored in-situ and in real time by two types of SHM sensors: screen-printed piezoelectric sensors for guided ultrasonic wave propagation studies and surface-bonded Fiber Bragg Grating (FBG) strain sensors. Experiments are conducted until panel collapse, and degradation is quantified by the reduction in initial stiffness, derived from the experimental load-displacement curves. This paper aims to demonstrate this unique experimental setup and the resulting SHM data, highlighting both the potential and challenges of this SHM framework for monitoring complex composite structures, while an attempt is made at correlating SHM data with structural degradation. Full article

Yazd province in central Iran is highly prone to dust and sand storms, causing significant environmental, economic, and health impacts. This study investigates the spatiotemporal dynamics of dust storms in Yazd over 2003–2022 using ground-based meteorological station records and satellite-derived aerosol optical depth (AOD) data from MODIS (MYD08_D3 v6.1) at monthly, seasonal, and annual scales. Analysis of ten synoptic stations data revealed an increasing trend of ~0.5 dusty days/year, with the highest frequency in spring and winter, particularly from March to May. MODIS AOD data confirmed these patterns and showed a rising annual aerosol load, peaking in May. Spatial analysis indicated that central and northern regions are most affected, consistent across datasets. The increasing frequency and intensity of dust storms are driven by natural and anthropogenic factors, including regional drought, desertification, drying wetlands, land use changes, and transboundary dust transport (from Iraq, Syria, Saudi Arabia). These findings underscore the value of integrating in situ and remote sensing observations to monitor dust events. To mitigate impacts, policymakers should prioritize long-term environmental monitoring and interventions addressing both natural and human factors influencing dust emissions. This study provides actionable insights for decision-makers to enhance environmental resilience and protect public health in arid regions. Full article

Regular monitoring of mineral dust is essential in order to assess its impact on air quality, human health, and climate, with satellite observations in recent decades playing a crucial role by providing consistent global coverage of various aerosol properties. In this study, the Dust Flag product of the Infrared Atmospheric Sounding Interferometer (IASI), onboard the Meteorological Operational (MetOp) satellites, is evaluated using ground-based measurements from 120 Aerosol Robotic Network (AERONET) sites worldwide. The Dust Flag serves as both an indicator of dust presence and a pseudo-indicator of dust loading. To evaluate this product, a well-established aerosol classification scheme was applied, based on AERONET Aerosol Optical Depth (AOD) and Angstrom Exponent products. Results show that the Dust Flag reliably identifies dust, achieving a 74.1% agreement score with AERONET, although some cases are misclassified. Also, this study concludes that the Dust Flag signal increases with particle load, reaching maximum values during extreme coarse dust events. Cases when IASI does not agree with AERONET are further examined and may stem either from limitations in the AERONET classification methodology or from low atmospheric particle concentrations. Finally, the spatial variability of the agreement score is examined, with the highest scores found within and near the global “dust belt”. Full article

Historic floors, including mosaics, stone slabs, and decorated pavements, are fragile elements that can be easily damaged during restoration works. Risks arise from falling tools, concentrated loads of scaffolding or equipment, and the repeated passage of workers. Traditional protection methods, such as plywood sheets, mats, multilayer systems, or modular plastic panels, have been applied in different sites but often present limitations in adaptability to irregular surfaces, in moisture control, and in long-term reversibility. This paper introduces an innovative approach developed within the 3D-EcoCore project. The proposed solution consists of a bio-inspired modular sandwich system manufactured by 3D printing with biodegradable polymers. Each module contains a Voronoi-inspired cellular core, shaped to match the geometry of the floor obtained from digital surveys, and an upper flat skin that provides a safe and resistant surface. The design ensures mechanical protection, adaptability to uneven pavements, and the possibility to integrate ventilation gaps, cable pathways, and monitoring systems. Beyond heritage interventions, the system also supports routine architectural maintenance by enabling safe, reversible protection during inspections and minor repairs. The solution is strictly temporary and non-substitutive, fully aligned with conservation principles of reversibility, recognizability, and minimal intervention. The Ninfeo Ponari in Cassino is presented as a guiding example, showing how multilevel knowledge and thematic mapping become essential inputs for the tailored design of the modules. The paper highlights both the technical innovation of the system and the methodological contribution of a knowledge-based design process, opening future perspectives for durability assessment, pilot installations, and the integration of artificial intelligence to optimise core configurations. Full article