Search Results (4,844)

Water pollution is a growing concern for aquatic ecosystems worldwide, with threats like plastic waste, nutrient pollution, and oil spills harming biodiversity and impacting human health, fisheries, and local economies. Traditional methods of monitoring water quality, such as ground sampling, are often limited in how frequently and widely they can collect data. Satellite imagery is a potent tool in offering broader and more consistent coverage. This review explores how Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR), including polarimetric SAR (PolSAR), are utilised to monitor harmful algal blooms (HABs) and other types of aquatic pollution. It looks at recent advancements in satellite sensor technologies, highlights the value of combining different data sources (like MSI and SAR), and discusses the growing use of artificial intelligence for analysing satellite data. Real-world examples from places like Lake Erie, Vembanad Lake in India, and Korea’s coastal waters show how satellite tools such as the Geostationary Ocean Colour Imager (GOCI) and Environmental Sample Processor (ESP) are being used to track seasonal changes in water quality and support early warning systems. While satellite monitoring still faces challenges like interference from clouds or water turbidity, continued progress in sensor design, data fusion, and policy support is helping make remote sensing a key part of managing water health. Full article

We compared oxidative markers and their links to inflammation in diabetic nephropathy and hemodialysis to identify independent determinants. We studied 180 adults, 90 patients with type 2 diabetes and diabetic nephropathy and 90 patients on hemodialysis. We measured serum advanced oxidation protein products (AOPP) and thiobarbituric acid reactive substances (TBARS) by enzyme-linked immunosorbent assay (ELISA). Partial correlations were adjusted for age, sex, and albumin with false discovery rate (FDR) control. Principal component analysis (PCA) summarized inflammatory indices and linear models tested predictors of AOPP and TBARS. Oxidative damage was higher in hemodialysis, with AOPP median 25.80 versus 5.06 and TBARS 8.49 versus 1.89, p less than 0.0001. C reactive protein (CRP) and mean corpuscular volume-to-lymphocyte ratio (MCVL) were higher in patients ongoing hemodialysis; systemic immune-inflammation index (SII) was higher in diabetic nephropathy. PCA yielded a dominant inflammation axis in both cohorts, 74.73 percent in hemodialysis and 85.20 percent in diabetic nephropathy. In regression, creatinine (β = 2.47, p = 0.026) predicted AOPP in hemodialysis. Dialysis vintage inversely predicted TBARS, beta minus 0.2305, p = 0.0209. In diabetic nephropathy, the PCA inflammation score predicted AOPP, β = 1.134, p = 0.0003. Protein oxidation tracked systemic inflammation in diabetic nephropathy, but not in hemodialysis. AOPP outperformed TBARS as an inflammatory partner and a practical monitoring candidate in diabetic kidney disease. Prospective studies should test for prognostic value and therapy sensitivity. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor marked by diffuse infiltration into surrounding brain tissue. The peritumoral zone often appears normal on imaging yet harbors microscopic invasion. While perfusion-based studies, such as arterial spin labeling (ASL), have profiled this region, longitudinal radiomic monitoring remains limited. This study investigates delta radiomics using multiparametric MRI (mpMRI) in a GBM mouse model to track subtle peritumoral changes over time. Methods: A G7 GBM xenograft model was established in nine nude mice, imaged at 9- and 12 weeks post-implantation using MRI (T1W, T2W, T2 mapping, DWI-ADC, FA, and ASL) and co-registered histopathology (H&E, HLA staining). Tumor and peritumoral regions were manually segmented, and 107 radiomic features (shape, first-order, texture) were extracted per sequence and histology. The delta features were calculated and compared between timepoints. Results: The robust T2W texture and T2 map first-order features demonstrated the greatest sensitivity and reproducibility in capturing temporal peritumoral brain zone changes, distinguishing between time points used by K-mean. Conclusions: Delta radiomics offers added value over static analysis for early monitoring of peritumoral brain zone changes. The first-order and texture features of radiomics could serve as robust biomarkers of peritumoral invasion. These findings highlight the potential of longitudinal MRI-based radiomics to characterize glioblastoma progression and inform translational research. Full article

Solar panel deployment is vital to generate clean energy and reduce carbon emissions, but sustaining energy output requires regular monitoring and maintenance. This is particularly critical in countries with harsh environmental conditions, such as Qatar, where high dust density reduces solar radiation reaching panels, thereby lowering generating efficiency and increasing maintenance costs. This paper introduces a data-driven model that uses the relationship between generated and consumed energy to track changes in solar panel performance. By applying statistical analysis to real and simulated data, the model identifies when efficiency losses are within the parameters of normal variation (e.g., daily fluctuations) and when they are likely caused by dust accumulation or system ageing. The findings demonstrate that the model provides a reliable and cost-effective way to support timely cleaning and maintenance decisions. It offers decision-makers a practical tool to improve residential solar panel management, reducing unnecessary costs, and ensuring more consistent renewable energy generation. Full article

This paper presents the design and implementation of a vision-based score detection system tailored for smart IoT basketball applications. The proposed architecture leverages a compact, low-cost device comprising a high-resolution overhead camera and a Raspberry Pi 5 microprocessor equipped with a hardware accelerator for real-time object detection. The detection pipeline integrates convolutional neural networks (YOLO-based) and custom preprocessing techniques to localize the basketball hoop and track the ball trajectory. A scoring event is confirmed when the ball enters the defined scoring zone with downward motion over multiple frames, effectively reducing false positives caused by occlusions, multiple balls, or irregular shot directions. The system is part of a scalable IoT analytics platform known as Koško, which provides real-time statistics, leaderboards, and user engagement tools through a web-based interface. Field tests were conducted using data collected from various public and school courts across Niš, Serbia, resulting in a robust and adaptable solution for automated basketball score monitoring in both indoor and outdoor environments. The methodology supports edge computing, multilingual deployment, and integration with smart coaching and analytics systems. Full article

In this work, we introduce a statistical framework for monitoring the performance of a breakwater structure in reducing wave impact. The proposed methodology aims to achieve diligent tracking of the underlying process and the swift detection of any potential malfunctions. The implementation of the new framework requires the construction of appropriate nonparametric Shewhart-type control charts, which rely on order statistics and scan-type decision criteria. The variance of the run length distribution of the proposed scheme is investigated, while the corresponding mean value is determined. For illustration purposes, we consider a real-life application, which aims at evaluating the effectiveness of a breakwater structure based on wave height reduction and wave energy dissipation. Full article

An integrated ESP32-based measurement system called PV-Scope is presented for real-time photovoltaic (PV) module efficiency characterization and small off-grid system testing under field conditions. The system includes pyranometer-calibrated irradiance sensors using a solar simulator, maximum power point tracking, and comprehensive environmental monitoring to enable accurate performance assessment of PV modules across diverse technologies, manufacturers and installation conditions. Unlike standard test condition (STC) measurements at cell temperatures of 25 °C, this system captures the interactions between efficiency and environmental variables that significantly impact real-world efficiency. In particular, measurement of temperature-dependent efficiency under local conditions and validation of temperature-dependent models for extending the results to other environmental conditions are enabled with cell temperature monitoring in addition to ambient temperature, humidity, and wind speed. PV-Scope is designed for integrated sensing versatility, portable outdoor testing, and order-of-magnitude cost savings compared to commercial equipment to meet measurement needs across research, education, and practical PV innovation, including bifacial module testing, assessment of cooling techniques, tandem and multi-junction testing, and agrivoltaics. Full article

Accurate extraction of single-diode photovoltaic (PV) model parameters is essential for reliable performance prediction and diagnostics, yet five-parameter identification from I-V data is ill-posed and computationally expensive. To develop and validate a hybrid analytical–metaheuristic approach that derives the diode ideality factor, saturation current, and photocurrent analytically while optimizing only series and shunt resistances, thereby reducing computational cost without sacrificing accuracy. I-V datasets were collected from a 9.54 kW grid-connected PV installation in Algiers, Algeria (15 operating points; 747–815 W m⁻²; 25.4–28.4 °C). Nine metaheuristics—Stellar Oscillation Optimizer, Enzyme Action Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, Cuckoo Search, Owl Search Algorithm, Improved War Strategy Optimization, Rüppell’s Fox Optimizer, and Artificial Bee Colony—were benchmarked against full five-parameter optimization and a Newton–Raphson baseline, using root-mean-squared error (RMSE) as the objective and wall-time as the efficiency metric. The hybrid scheme reduced the decision space from five to two parameters and lowered computational cost by ≈60–70% relative to full-parameter optimization while closely reproducing measured I-V/P-V curves. Across datasets, algorithms achieved RMSE ≈ 2.49 × 10⁻² − 2.78 × 10⁻². Rüppell’s Fox Optimizer offered the best overall trade-off (lowest average RMSE and fastest runtime), with Whale Optimization Algorithm a strong alternative (typical runtimes ≈ 107–112 s). Partitioning identification between closed-form physics and light-weight optimization yields robust, accurate, and efficient PV parameter estimation suitable for time-sensitive or embedded applications. Dynamic validation using 1498 real-world measurements across clear-sky and cloudy conditions demonstrates excellent performance: current prediction $R^2=0.9882$, power estimation $R^2=0.9730$, and voltage tracking $R^2=0.9613$. Comprehensive environmental analysis across a 39.2 °C temperature range and diverse irradiance conditions ($0-1014W/m^2$) validates the method’s robustness for practical PV monitoring applications. Full article

This paper examines how sustainable leadership and strategic sustainability integration are framed and supported for SMEs in the EU. We apply comparative document analysis (CDA) to 35 policy, industry, and NGO reports published in 2020–2025 for Germany, Sweden, Poland, and Spain. Multi-level materials (EU, national, industry/NGO) were thematically coded, and the synthesis is presented in a multi-level conceptual framework linking policies, leadership, strategy, barriers, and transferable practices. The analysis indicates systematic differences in institutional maturity: Sweden and Germany display denser, more navigable support ecosystems and clearer leadership narratives, whereas Poland and Spain exhibit greater fragmentation and a more compliance-oriented framing. Instrument menus are broadly similar (grants/co-funding, concessional finance, advisory vouchers, training, standards/toolkits, green public procurement), yet accessibility and measurement strength diverge; outcome tracking (e.g., energy savings, CO₂e avoided) is more consistent in Sweden/Germany than in Poland/Spain. Green–digital coupling is pivotal: sequencing “on-ramps” (advisory/vouchers) into innovation finance accelerates adoption; where such on-ramps are thin, uptake concentrates among already prepared firms. Implications follow for policy design and practice: prioritize simple entry points for micro- and small enterprises, strengthen monitoring with meaningful KPIs, and ensure regional parity in access to finance and advisory. For SME leaders, role-modeling, employee development, and experimentation help embed sustainability when formal structures are lean. Beyond mapping patterns, this study provides an auditable operationalization of sustainable leadership for document analysis and a transferable framework to compare policy mixes and ecosystem readiness across countries. Full article

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations, and electromagnetic interferences. To tackle these obstacles, this investigation presents a reinforcement learning (RL)-oriented framework for high-accuracy monitoring in radio telescopes. The suggested system amalgamates a localization control module, a receiver, and an RL tracking agent that functions in scanning and tracking stages. The agent optimizes its policy by maximizing the signal-to-noise ratio (SNR), a critical factor in astronomical measurements. The framework employs a reconditioned 12-m radio telescope at King Mongkut’s Institute of Technology Ladkrabang (KMITL), originally constructed as a satellite earth station antenna for telecommunications and was subsequently refurbished and adapted for radio astronomy research. It incorporates dual-axis servo regulation and high-definition encoders. Real-time SNR data and streaming are supported by a HamGeek ZedBoard with an AD9361 software-defined radio (SDR). The RL agent leverages the Proximal Policy Optimization (PPO) algorithm with a self-attention actor–critic model, while hyperparameters are tuned via Optuna. Experimental results indicate strong performance, successfully maintaining stable tracking of randomly moving, non-patterned targets for over 4 continuous hours without any external tracking assistance, while achieving an SNR improvement of up to 23.5% compared with programmed TLE-based tracking during live satellite experiments with Thaicom-4. The simplicity of the framework, combined with its adaptability and ability to learn directly from environmental feedback, highlights its suitability for next-generation astronomical techniques in radio telescope surveys, radio line observations, and time-domain astronomy. These findings underscore RL’s potential to enhance telescope tracking accuracy and scalability while reducing control system complexity for dynamic astronomical applications. Full article

Background: In Mexico, the measles vaccine was first introduced in 1971. The last case of measles acquired through endemic transmission was recorded in 1995. In 1998, the monovalent measles vaccine was replaced by the combined measles–mumps–rubella (MMR) vaccine. The MMR vaccination schedule consists of two doses: the first is administered at 12 months of age, and the second is administered at either 18 months or 6 years of age. Materials and Methods: A retrospective analysis was conducted using secondary data from 2006 to 2024. Vaccine procurement and administration records from IMSS, ISSSTE, and SSA were reviewed to evaluate the performance of both the MMR and MR programs, focusing particularly on the trends in coverage and data consistency across institutions. Results: The analysis revealed persistent inconsistencies between vaccine procurement and administration for both the MMR and MR vaccines across all institutions. Several years exhibited notable mismatches, including surpluses and deficits in the administered doses relative to their procurement. Between 2006 and 2024, only 69 million of the 91.6 million required MMR doses were administered in Mexico, leaving a deficit of approximately 22.5 million doses (25% of the target population). For MR, a cumulative deficit of approximately 24.6 million procured but unadministered doses was identified. National coverage remained suboptimal, with significant variability across years and institutions. Comparisons with WHO and ENSANUT data indicated marked discrepancies. The seroprevalence findings, along with the 2025 measles outbreak, confirm significant gaps in immunity. Discussion: This study highlights systemic challenges in Mexico’s MMR vaccination program, including inconsistencies in vaccine procurement, administration, and reported coverage across institutions. Overestimated official MMR coverage rates and unclear target definitions for MR contribute to program inefficiencies and missed vaccination opportunities. The resurgence of measles in 2025, along with persistently high incidences of mumps, aligns with the observed immunity gaps, although a direct causal relationship cannot be established from this study. These findings are consistent with previous national studies and seroprevalence data. Conclusions: Despite limitations in the data, this study effectively evaluated the performance of Mexico’s MMR vaccination program, identifying critical gaps in coverage, data reliability, and operational alignment. The findings underscore the need for improved procurement planning, harmonized coverage estimates, and robust monitoring systems. To address the existing gaps in immunity, catch-up campaigns should prioritize the use of the MMR vaccine over MR. Strengthening nominal coverage tracking and implementing evidence-based strategies are essential to restoring public trust and maintaining the goals of measles elimination. 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

To investigate the influence mechanism of hanger damage and arch-beam combined parameters on the failure behavior of tied-arch bridges, this study employs an advanced damage failure model within the LS-DYNA. A comprehensive simulation of the entire failure process was conducted, considering the coupled effects of hanger damage parameters and structural parameters of the arch-beam system, using a tied-arch bridge as the engineering case. The primary innovation of this study lies in overcoming the limitations of previous research, which has largely been confined to single hanger failure or static parameter analysis, by achieving, for the first time, dynamic tracking and quantitative identification of structural failure paths under the coupled influence of multiple parameters. The results demonstrate that both the severity and spatial distribution pattern of hanger damage significantly influence the structural failure mechanism. When damage is either uniformly distributed across the bridge or relatively concentrated—particularly when long hangers experience severe degradation—the structure becomes susceptible to cascading stress redistribution, substantially increasing the risk of global progressive collapse. This finding provides a theoretical foundation for developing risk-informed maintenance and repair strategies for hangers. It is therefore recommended that practical maintenance efforts prioritize monitoring the condition of long hangers and regions with concentrated damage. Furthermore, variations in arch-beam combined parameters are shown to have a significant effect on the structure’s collapse resistance. For the case bridge studied herein, the original design parameters achieve an optimal balance between anti-collapse performance and economic efficiency, underscoring the importance of rational parameter selection in enhancing system robustness. This work offers both theoretical insights and numerical tools for evaluating and optimizing the collapse-resistant performance of under-deck tied-arch bridges, contributing meaningful engineering value toward improving the safety and durability of similar structures. Full article

Amphibian breeding phenology provides key insights into species’ sensitivity to climatic and anthropogenic drivers. We used passive acoustic monitoring (PAM) with automated call detection to examine the calling activity of Dryophytes japonicus and Pelophylax nigromaculatus in South Korean rice paddies across five breeding seasons (2018–2022). Both species exhibited distinct seasonal patterns: D. japonicus showed a synchronous and concentrated calling peak in mid-June (GAM deviance explained = 34%), whereas P. nigromaculatus initiated calling earlier and maintained a longer, less synchronized calling period extending into July (GAM deviance explained = 19%). Zero-inflated negative binomial models demonstrated that temperature was the strongest predictor of calling activity in both species, though responses to humidity and wind differed. D. japonicus maintained high calling rate under warm conditions, with only modest suppression at high humidity, whereas P. nigromaculatus was strongly inhibited by combined warm and humid conditions. These results establish a detailed information on the calling phenology of D. japonicus and P. nigromaculatus in East Asian agroecosystems highlight species-specific sensitivities to local weather variables. Our findings demonstrate that automated acoustic monitoring offers an efficient way to document ecological responses to weather variability and may serve as a long-term tool to track phenological shifts under climate change. Future advances in sound analysis, including the integration of deep-learning algorithms and cross-species detection frameworks, could further improve automated biodiversity monitoring in complex agricultural landscapes. Full article