Search Results (573)

This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, an indicator of student viewing behavior, serves as a practical proxy for student engagement. The increasing volume of recorded material renders manual editing and annotation impractical, making the automatic identification of high-SAF segments crucial for improving accessibility and supporting targeted content review. The approach focuses on lecture archives from a real-world blended learning context, characterized by resource constraints such as no specialized hardware and limited student numbers. The model integrates multimodal features from instructor’s actions (via OpenPose and optical flow), audio spectrograms, and slide page progression—a selection of features that makes the approach applicable regardless of lecture language. The model was evaluated on 665 labeled one-minute segments from one such course. Experiments show that the best-performing model achieves a Pearson correlation of 0.5143 in 7-fold cross-validation and 61.05% average accuracy in a downstream three-class classification task. These results demonstrate the system’s capacity to enhance lecture archives by automatically identifying key segments, which aids students in efficient, targeted review and provides instructors with valuable data for pedagogical feedback. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

Football performance results from the dynamic interaction between physical, tactical, technical, and psychological dimensions—each of which also influences player well-being, recovery, and readiness. However, integrated monitoring approaches remain scarce, particularly in youth and sub-elite contexts. This systematic review screened 341 records from PubMed, Scopus, and Web of Science, with 46 studies meeting the inclusion criteria (n = 1763 players; age range: 13.2–28.7 years). Physical external load was reported in 44 studies using GPS-derived metrics such as total distance and high-speed running, while internal load was examined in 36 studies through session-RPE (rate of perceived exertion × duration), heart rate zones, training impulse (TRIMP), and Player Load (PL). A total of 22 studies included well-being indicators capturing fatigue, sleep quality, stress levels, and muscle soreness, through tools such as the Hooper Index (HI), the Total Quality Recovery (TQR) scale, and various Likert-type or composite wellness scores. Tactical behaviours (n = 15) were derived from positional tracking systems, while technical performance (n = 7) was assessed using metrics like pass accuracy and expected goals, typically obtained from Wyscout^® or TRACAB^® (a multi-camera optical tracking system). Only five studies employed multivariate models to examine interactions between performance domains or to predict well-being outcomes. Most remained observational, relying on descriptive analyses and examining each domain in isolation. These findings reveal a fragmented approach to player monitoring and a lack of conceptual integration between physical, psychological, tactical, and technical indicators. Future research should prioritise multidimensional, standardised monitoring frameworks that combine contextual, psychophysiological, and performance data to improve applied decision-making and support player health, particularly in sub-elite and youth populations. Full article

Conventional detectors based on ionization chambers, semiconductors, or thermoluminescent materials generally cannot be used to verify the in vivo dose delivered during brachytherapy treatments with γ-ray sources. However, certain adaptations and alternative methods, such as the use of miniaturized detectors or other specialized techniques, have been explored to address this limitation. One approach to solving this problem involves the use of dosimetric materials based on efficient scintillation crystals, which can be placed in the patient’s body using a long optical fiber inserted intra-cavernously, either in front of or next to the tumor. Scintillation crystals with a density close to that of tissue can be used in any location, including the respiratory tract, as they do not interfere with dose distribution. However, in many cases of radiation therapy, the detector may need to be positioned behind the target. In such cases, the use of heavy, high-density, and high-Z_eff scintillators is strongly preferred. The delivered radiation dose was registered using the radioluminescence response of the crystal scintillator and recorded with a compact luminescence spectrometer connected to the scintillator via a long optical fiber (so-called fiber-optic dosimeter). This proposed measurement method is completely non-invasive, safe, and can be performed in real time. To complete the abovementioned task, scintillation detectors based on YAG:Ce (ρ = 4.5 g/cm³; Z_eff = 35), LuAG:Ce (ρ = 6.75 g/cm³; Z_eff = 63), and GAGG:Ce (ρ = 6.63 g/cm³; Z_eff = 54.4) garnet crystals, with different densities ρ and effective atomic numbers Z_eff, were used in this work. The results obtained are very promising. We observed a strong linear correlation between the dose and the scintillation signal recorded by the detector system based on these garnet crystals. The measurements were performed on a specially prepared phantom in the brachytherapy treatment room at the Oncology Center in Bydgoszcz, where in situ measurements of the applied dose in the 0.5–8 Gy range were performed, generated by the ¹⁹²Ir (394 keV) γ-ray source from the standard Fexitron Elektra treatment system. Finally, we found that GAGG:Ce crystal detectors demonstrated the best figure-of-merit performance among all the garnet scintillators studied. Full article

The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light transmission to solar panels. As part of our research, the present investigation involves monitoring concentrations of PM using a high-performance optical instrument, the in situ calibration protocol of which is described in detail. For the city of Rabat, observations revealed significant variations in concentrations between day and night, with peaks observed around 8 p.m. correlating with high relative humidity and low wind speeds, and the highest levels recorded in February with a monthly average value reaching 75 µm/m³. In addition, an experimental protocol was set up for an analysis of the elemental composition of particles in the same city using SEM/EDS, providing a better understanding of their morphology. To assess the impact of meteorological variables on PM concentrations in two distinct climatic environments, a database from the city of Marrakech for the year 2024 was utilized. Overall, the distribution of PM values during this period did not fluctuate significantly, with a monthly average value not exceeding 45 µm/m³. The random forest method identified the most influential variables on these concentrations, highlighting the strong influence of the type of environment. The findings provide crucial information for the modeling of solar installations’ soiling and for improving understanding of local air quality. Full article

Recent achievements in ultrafast spin physics have enabled the use of heterostructures composed of ferromagnetic (FM)/non-magnetic (NM) thin layers for terahertz (THz) generation. The mechanism of THz emission from FM/NM multilayers has been typically ascribed to the inverse spin Hall effect (ISHE). In this work, we probe the mechanism of the ISHE by inserting a second ferromagnetic layer in the form of an alloy between the FM/NM system. In particular, by utilizing the co-sputtering technique, we fabricate Fe/L1₀-FePt/Pt ultra-thin heterostructures. We successfully grow the tetragonal phase of FePt (L1₀-phase) as revealed by X-ray diffraction and reflection techniques. We show the strong magnetic coupling between Fe and L1₀-FePt using magneto-optical and Superconducting Quantum Interference Device (SQUID) magnetometry. Subsequently, by utilizing THz time domain spectroscopy technique, we record the THz emission and thus we the reveal the efficiency of spin-to-charge conversion in Fe/L1₀-FePt/Pt. We establish that Fe/L1₀-FePt/Pt configuration is significantly superior to the Fe/Pt bilayer structure, regarding THz emission amplitude. The unique trilayer structure opens new perspectives in terms of material choices for the future spintronic THz sources. Full article

The balance between cortical excitation and inhibition (E/I balance) in the cerebral cortex is critical for cognitive processing and neuroplasticity. Modulation of this balance has been linked to a wide range of neuropsychiatric and neurodegenerative disorders. The human visual system has well-differentiated magnocellular (M) and parvocellular (P) pathways, which provide a useful model to study cortical excitability using non-invasive visual flicker stimulation. We present an Arduino-driven non-image forming system to deliver controlled flickering light stimuli at different frequencies and wavelengths. By triggering the critical flicker fusion (CFF) frequency, we attempt to modulate the M-pathway activity and attenuate P-pathway responses, in parallel with induced optical scattering. EEG recordings were used to monitor cortical excitability and oscillatory dynamics during visual stimulation. Visual stimulation in the CFF, combined with induced optical scattering, selectively enhanced magnocellular activity and suppressed parvocellular input. EEG analysis showed a modulation of cortical oscillations, especially in the high frequency beta and gamma range. Our results support the hypothesis that visual flicker in the CFF, in addition to spatial degradation, initiates detectable neuroplasticity and regulates cortical excitation and inhibition. These findings suggest new avenues for therapeutic manipulation through visual pathways in diseases such as Alzheimer’s disease, epilepsy, severe depression, and schizophrenia. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

Accurate range of motion (ROM) assessment is essential for evaluating musculoskeletal function and guiding rehabilitation, particularly in pediatric populations. Traditional methods, such as optical motion capture and handheld goniometry, are often limited by cost, accessibility, and inter-rater variability. This study evaluated the feasibility and accuracy of the Microsoft Azure Kinect-powered Monitored Augmented Rehabilitation System (MARS) compared to Kinovea. Sixty-five pediatric participants (ages 5–18) performed standardized shoulder and elbow movements in the frontal and sagittal planes. ROM data were recorded using MARS and compared to Kinovea. Measurement reliability was evaluated using intraclass correlation coefficients (ICC3k), and accuracy was evaluated using root mean squared error (RMSE) analysis. MARS demonstrated excellent reliability with an average ICC3k of 0.993 and met the predefined accuracy threshold (RMSE ≤ 8°) for most movements, with the exception of sagittal elbow flexion. These findings suggest that MARS is a reliable, accurate, and cost-effective alternative for clinical ROM assessment, offering a markerless solution that enhances measurement precision and accessibility in pediatric rehabilitation. Future studies should enhance accuracy in sagittal plane movements and further validate MARS against gold-standard systems. Full article

Access to significant amounts of data is typically required to develop structural health monitoring (SHM) systems. In this study, a novel SHM approach was evaluated, with all training data collected solely from a validated finite element analysis (FEA) of a reinforced concrete (RC) beam and the structural health based on the tension side of a rebar under flexural loading. The developed SHM system was verified by four-point bending experiments on three RC beams cast in the dimensions of 4000 mm × 200 mm × 400 mm. Distributed optical fibre sensors (DOFS) were mounted on the concrete surface and on the bottom rebar to maximise sample points and investigate the reliability of the strain data. The FEA model was validated using a single beam and subsequently used to generate labelled SHM strain data by altering the dilation angle and rebar sizes. The generated strain data were then used to train an artificial neural network (ANN) classifier using deep learning (DL). Training and validation accuracy greater than 98.75% were recorded, and the model was trained to predict the tension state up to 90% of the steel yield limit. The developed model predicts the health condition with the input of strain data acquired from the concrete surface of reinforced concrete beams under various loading regimes. The model predictions were accurate for the experimental DOFS data acquired from the tested beams. Full article

This paper presents MZAP, a mobile application designed to digitalize basketball match tracking and generate secure, searchable endgame reports. Used by the Basketball League of Serbia, MZAP creates tamper-proof digitally signed records stored as password-protected PDFs with unique UUIDs, digital signatures, and QR codes. Each report is accompanied by a JSON file containing match data, enabling efficient validation through hashed checksums and facilitating data extraction and searchability. The system supports both online and offline modes, bilingual interfaces, mobile and tablet use, and includes features such as WiFi-based monitoring, physical printing, and various sharing options. The solution aims to reduce officials’ working time and increase data accuracy by minimizing human error through structural and UI-level validation methods and real-time monitoring by multiple observers during games. As part of the MZAP software suite, MZAP Converter is under development to support the digitization of legacy paper-based reports using custom CRNN neural networks to optically recognize and digitize historical paper-based reports, bringing them to the same standard as newly created digital ones. The paper also reflects on the broader impact of digital transformation within the Basketball League of Serbia. Full article

Scattered light makes up a significant amount of recorded intensities during tomographic imaging, thereby leading to severe misinterpretation and artifacts in the reconstructed volume images. Correcting artificial intensities that stem from scattered light, therefore, is of primary interest and demands quantitative measurements. While numerous methods have been developed to reduce X-ray scattering artifacts, fewer methods deal with optical scattering. In this study, a measurement method for determining optical scattering in scintillators is presented with the aim of further developing correction algorithms. A theoretical model based on internal multiple reflections was developed for this purpose. This model assumes an additive exponential kernel with a certain scattering length to the system’s point spread function. This assumption was confirmed, and the scatter length was estimated from three new different kinds of experiments (hgap, rect, and LSF) on the BM18 beamline of the European synchrotron. The experiments further revealed significant differences in scattering proportion and length when different coatings are applied to the front and back faces of crystalline LuAG scintillators. Anti-reflective coatings on the backside show an effect of reducing the scattering magnitude while reflective coatings on the front side increase the proportion of the unscattered signal and, thus, show proportionally less scattering than black coating or no front coating. In particular, roughened black coating is found to worsen optical scattering. In summary, our results indicate that a combination of reflective (front) and anti-reflective (back) coatings yields the least optical scattering and, hence, the best image quality. Full article

Silicon Photomultipliers (SiPMs) are optical sensors widely used in space applications due to their high photon detection efficiency, low power consumption, and robustness. However, in Low Earth Orbit (LEO), their performance degrades over time due to prolonged exposure to ionizing radiation, primarily from trapped protons and electrons. The dominant radiation-induced effect in SiPMs is an increase in dark current, which can compromise detector sensitivity. This study investigates the potential of thermal annealing as a mitigation strategy for radiation damage in SiPMs. We designed and tested PCB-integrated heaters to selectively heat irradiated SiPMs and induce recovery processes. A PID-controlled system was developed to stabilize the temperature at 100 °C, and a remotely controlled experimental setup was implemented to operate under irradiation conditions. Two SiPMs were simultaneously irradiated with 9 MeV protons at the EDRA facility, reaching a 1 MeV neutron equivalent cumulative fluence of (9.5 ± 0.2) × 10⁸ cm⁻². One sensor underwent thermal annealing between irradiation cycles, while the other served as a control. Throughout the experiment, dark current was continuously monitored using a source measure unit, and I–V curves were recorded before and after irradiation. A recovery of more than 39% was achieved after only 5 min of thermal cycling at 100 °C, supporting this recovery approach as a low-complexity strategy to mitigate radiation-induced damage in space-based SiPM applications and increase device lifetime in harsh environments. Full article

Danionella, a transparent freshwater species belonging to the Cyprinidae family, has emerged as a valuable model organism in biological and medical research due to its optical transparency. The cardiovascular system of Danionella larvae provides a unique opportunity for non-invasive heart rate monitoring in aquatic animals. Traditional approaches for evaluating larval heart rate often require manual or semi-automated definition of the cardiac region in video recordings. In this study, we developed a simplified heart rate monitoring system that estimates heartbeat activity by analyzing interframe luminance differences in video sequences of Danionella larvae. Our system successfully measured heart rates in the range of 150–155 beats per minute (bpm), consistent with previous findings reporting rates between 140 and 200 bpm. The non-invasive nature of this method offers significant advantages for high-throughput screening and long-term physiological monitoring. Furthermore, this system has potential applications in evaluating environmental stressors, supporting survival and health assessments, and guiding habitat management strategies to ensure stable populations of adult fish in both natural and laboratory settings. Full article

The October 2024 cut-off low event triggered one of the most catastrophic floods recorded in the Valencia Metropolitan Area, exposing significant vulnerabilities in urban planning, infrastructure resilience, and emergency preparedness. This study presents a novel comprehensive assessment of the event, using a multi-sensor satellite approach combined with socio-economic and infrastructure data at the metropolitan scale. It provides a comprehensive spatial assessment of the flood’s impacts by integrating of radar Sentinel-1 and optical Sentinel-2 and Landsat 8 imagery with datasets including population density, land use, and critical infrastructure layers. Approximately 199 km² were inundated, directly affecting over 90,000 residents and compromising vital infrastructure such as hospitals, schools, transportation corridors, and agricultural lands. Results highlight the exposure of peri-urban zones and agricultural areas, reflecting the socio-economic risks associated with the rapid urban expansion into flood-prone plains. The applied methodology demonstrates the essential role of multi-sensor remote sensing in accurately delineating flood extents and assessing socio-economic impacts. This approach constitutes a transferable framework for enhancing disaster risk management strategies in other Mediterranean urban regions. As extreme hydrometeorological events become more frequent under changing climatic conditions, the findings underscore the urgent need for integrating remote sensing technologies, early warning systems, and nature-based solutions into regional governance to strengthen resilience, reduce vulnerabilities, and mitigate future flood risks. Full article