Search Results (1,144)

This paper presents the first comprehensive review of radon concentration measurements conducted in educational institutions in the Republic of Serbia. The analysis includes data from the national radon survey conducted in 2019, long-term measurements performed within the national radioactivity monitoring program during the period 2011–2024, a local investigation carried out in the city of Šabac, and other available studies conducted throughout Serbia using different measurement methods (CR-39 detectors, activated charcoal canisters, and continuous radon monitors). During the 2011–2024 period, a total of 303 measurements were performed in kindergartens, primary schools, secondary schools, and universities across seven Serbian cities, while 4133 CR-39 detectors were collected during the 2019 national survey. The survey achieved high coverage of primary schools (87%) and kindergartens (76%), whereas the coverage of secondary schools was only 20%. Measured radon concentrations ranged from minimum values of 5–30 Bq/m³, with average concentrations between 60 and 150 Bq/m³ in most buildings, to a maximum of 2970 Bq/m³ recorded in a primary school. A significant proportion of the indoor radon concentration measurements exceeded the WHO reference level of 100 Bq/m³ (approximately 40%), while a smaller proportion also exceeded the threshold of 300 Bq/m³. The observed spatial variability could be associated with differences in geological conditions, building type, construction materials, and ventilation characteristics. On average, the estimated contribution of indoor radon exposure during occupancy of schools and kindergartens was below 1 mSv/y for children and educational staff. The critical analysis highlights insufficient coverage of certain regions and types of educational institutions, methodological differences among individual studies, and the need for a more rational approach to radon measurement and risk management. The findings indicate the need to move from fragmented and repeated measurements toward an integrated, risk-oriented national strategy based on mass screening, active public involvement, and targeted long-term measurements. Short-term measurements could be used for the initial screening of a large number of buildings, while buildings with elevated radon concentrations would be prioritized for confirmatory long-term measurements and mitigation measures, with the potential use of simple and IoT-based devices for large-scale data collection. Such an approach could provide a practical basis for the further development and implementation of the National Radon Action Plan in Serbia, with the aim of reducing individual and collective risks associated with radon exposure. Full article

Sloping farmland erosion in cold-region black soil zones threatens global agriculture, yet regional-scale quantitative evidence on the erosion mitigation performance and spatial suitability of control measures remains lacking. This study integrated 959 field observation datasets from 132 peer-reviewed publications, and used meta-analysis and Boosted Regression Tree (BRT) models to evaluate tillage, biological, engineering, and combined conservation measures in Northeast China. Results showed that all measures significantly reduced erosion, achieving an average runoff reduction of 73.2% and sediment reduction of 83.6%. Specifically, engineering measures showed the highest runoff reduction, while combined measures achieved more than 95% sediment reduction. Machine learning revealed that runoff reduction was primarily regulated by precipitation, whereas sediment reduction was mainly controlled by soil clay content and bulk density. Spatially, tillage, biological, and engineering measures showed the highest suitability in continuous cultivated plains, plain–hill transition zones, and low hilly and gully regions, respectively. This study moves erosion-control assessment beyond comparisons of average runoff and sediment reduction rates toward environmentally matched spatial allocation, providing a basis for targeted black soil conservation and resilient grain production in cold-region agricultural landscapes. Full article

Mutual interactions exist between tropical cyclones and the East Asian mid-latitude trough (EAMT). This study mainly used statistical analysis and numerical experiments to :investigate the influence of TCs on the zonal movement of EAMT. Composite results indicate that TCs can induce an average of 11.82 degrees in the EAMT meridional displacement, and the anomalous remote geopotential height (HGT) triggered by TCs serves as an important factor driving the zonal movement of the EAMT. The EAMT tends to move towards the region of negative HGT difference and away from the region of positive HGT difference. The temperature anomalies induced by TCs are a critical factor leading to the HGT anomalies. For the TC Maria case, it induces a maximum meridional displacement of 0.76 degrees of the EAMT at 450 hPa.‌ TC Maria first triggers anomalous cold advection in the mid-latitude regions of the East Asia–Northwest Pacific area, which then leads to an anomalous decrease in HGT within the EAMT trough region under the constraint of hydrostatic equilibrium. Consequently, the anomalous negative HGT caused by the TC results in the zonal movement of the EAMT line. The results of this study provide evidence that remote disturbances induced by TCs in the tropical WNP can affect weather circulation in the mid-latitudes of East Asia. Full article

Potentially Toxic Elements (PTEs) contamination of the soil is a global environmental problem, which can seriously affect the ecosystem, agricultural development, and human health. To evaluate the degree of potentially toxic metal contamination, 32 soil samples were collected from different sites in the Yanbu and Khulais areas of Saudi Arabia to analyze eight PTEs (Co, Cr, Cu, Fe, Ni, Pb, V, and Zn). The contamination indices used including the Geo-accumulation Index (Igeo), Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI), as well as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) for source identification. Because metals taken up from soil can move along the soil-to-plant pathway into crops, characterizing the degree and likely origin of potentially toxic metal enrichment in agricultural soil is a necessary first step for any subsequent land-management or food-safety assessment, particularly in a country that imports the majority of its food. The results of PCA and HCA indicate that the basement rocks of Arabian Shield-associated minerals are a plausible primary source of these potentially toxic elements. The mean EF values of the PTEs in decreasing order are as follows: Co (0.93) > Ni (0.92) > Cu (0.80) > Zn (0.74) > V (0.65) > Cr (0.59) > Pb (0.27). This implies that the soils in the study area are not enriched or only slightly enriched with PTEs. According to the geoaccumulation index values, nearly every PTE was classified as practically unpolluted (Igeo < 0), the exception being Ni, which reached the unpolluted-to-moderately-polluted class (Igeo ≥ 0) at four Khulais sites. The CF and PLI values indicate that the soil is unpolluted on average. In summary, the combined pollution-index and multivariate framework offers a practical geochemical screening tool for this arid coastal region; monitoring soil quality in this way is a useful contribution to sustainable land management in water- and land-limited environments. Full article

Foundation models for Earth observation are commonly transferred to downstream tasks without explicit attention to the preprocessing mismatch between pretraining and target distributions. This study isolates preprocessing mismatch as a controlled experimental factor in transferring the Hydro multispectral foundation model, a Swin Transformer V2 Base encoder pretrained with SimMIM on Sentinel-2 Level-2A water-body imagery, to the Marine Debris and Oil Spill (MADOS) marine pollution benchmark dataset, processed through ACOLITE Rayleigh reflectance and providing 11 of the 12 spectral bands used during pretraining. The two datasets are therefore produced by different atmospheric correction algorithms under different reflectance conventions, and the resulting per-band statistical discrepancy is quantified as the starting point of the analysis. Three preprocessing dimensions are then systematically varied while all other settings are held constant: input normalisation, spectral band adaptation for the missing B09, and encoder transfer mode. From this, four findings emerge. Normalisation mismatch between training and inference is the single largest source of performance degradation, reducing the mean Intersection over Union (mIoU) by 0.458, more than seven times the largest radiometric perturbation tested. A zero-parameter Frobenius-matched column crop of the patch embedding adapts the 12-band pretrained encoder to the 11-band target, at least as effectively as any learnt linear or nonlinear adapter, at a lower cross-seed variance. Under limited target supervision (1433 training patches against an 87.9 million-parameter encoder), freezing the encoder outperforms both fine-tuning in full and random initialisation training from scratch. The gains of partial unfreezing are attributable to augmented training (very simple copy–paste (VSCP) augmentation, exponential moving average (EMA), and test-time augmentation (TTA)) rather than to encoder adaptation. With matched preprocessing, the frozen encoder reaches 0.600 mIoU and matches the published MariNeXt baseline within seed variability. Mechanistic analysis via band-occlusion attribution and feature-space separability shows that input normalisation determines which spectral bands the encoder relies upon, with the magnitude of the shift correlated to the per-band gap between the source and target distributions. Operationally, preprocessing alignment, rather than architectural modification, carries most of the practical effort in transferring a multispectral foundation model to marine surface segmentation. These results are established for a single encoder–benchmark pair under limited target supervision. The mechanism they identify is more portable than the magnitude reported. A frozen encoder’s representations remain bound to the normalisation statistics of its pretraining dataset, so any transfer that departs from these statistics at inference is predicted to degrade sharply in proportion to the per-band distance between the two distributions. Full article

Real-time event detection from social media has become increasingly important for emergency response, public safety, and situational awareness. However, accurately identifying emerging events from Thai social media remains challenging because Thai is a low-resource language without explicit word boundaries, while social media text is often characterized by informal writing, spelling variations, and noisy user-generated content. This paper proposes an entity-centric real-time event detection framework for Thai social media that employs a Multi-Granularity Thai Character Cluster (TCC)-Aware Named Entity Recognition (NER) model as its core information extraction component. The proposed NER architecture integrates contextual word embeddings, character-level representations, Thai Character Cluster features, and Part-of-Speech embeddings through an attention-based feature fusion mechanism to improve entity recognition under noisy conditions. Recognized entities are subsequently used as semantic anchors for event construction, clustering, temporal trend analysis, event ranking, and alert generation within a unified streaming framework. Event discovery combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Exponential Moving Average (EMA)-based temporal analysis to identify emerging events in real time. Experiments conducted on a large-scale Thai social media corpus demonstrate that the proposed model achieves an F1-score of 94.14% for named entity recognition and 92.2% for downstream event detection, outperforming representative baseline methods. Additional ablation studies, qualitative error analysis, and statistical significance tests confirm the effectiveness of the proposed multi-granularity representation. These results demonstrate that the proposed framework provides an effective solution for real-time event monitoring in low-resource language environments. Full article

This study proposes a nondestructive method for evaluating egg freshness using laser Doppler vibrometry. A vibration measurement platform was constructed to collect vibration signals from intact eggs representing four freshness grades. The vibration signals were processed using multiplicative scatter correction, standard normal variate transformation, moving average filtering, Savitzky–Golay smoothing, and first-order Savitzky–Golay differentiation. Principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were then used for feature extraction and dimensionality reduction. Six classification models were developed and compared, including support vector machine, k-nearest neighbor, random forest, naive Bayes, discriminant analysis, and linear discriminant analysis. The model based on moving average filtering, successive projections algorithm, and discriminant analysis using measurements from three egg locations achieved a classification accuracy of 96.7%. The model based on moving average filtering, competitive adaptive reweighted sampling, and random forest using blunt-end measurements also achieved an accuracy of 96.7%. These results demonstrate that laser Doppler vibrometry combined with machine learning can distinguish eggs with different freshness levels, providing a promising nondestructive approach for egg-quality evaluation. Full article

Accurate estimation of battery state indicators, particularly State of Charge (SoC) and State of Health (SoH), remains a fundamental requirement for advanced BMS, directly affecting safety, reliability, operational efficiency, and battery lifetime. Recent advances in deep learning have demonstrated promising results for SoC estimation and SoH classification individually; however, the impact of SoC estimation errors on subsequent SoH classification has received limited attention. This work investigates whether accurately estimated SoC information can effectively support data-driven SoH classification and quantifies the propagation of uncertainty across a cascaded battery diagnostic framework. A two-stage deep learning architecture combining LSTM-based SoC estimation and ResNet-based SoH classification is proposed and evaluated using three lithium-ion battery datasets. The SoC estimation stage is assessed using two experimental datasets (LG Chem E66 and Panasonic NCR18650PF), while the SoH classification and uncertainty-propagation analysis are conducted using the GS Yuasa LEV50 dataset generated from a validated battery simulation model. To improve temporal feature extraction and noise robustness, signal preprocessing based on Butterworth and Exponential Moving Average filters was incorporated. Experimental results achieved SoC estimation errors as low as 1.46% and an SoH classification accuracy of 94.23% when reference SoC values were used. Under realistic operating conditions, where SoH classification relied on estimated SoC values, the accuracy decreased to 79.65%. These findings provide a quantitative assessment of uncertainty propagation in integrated SoC-SoH pipelines and demonstrate that highly accurate SoC estimation alone does not necessarily guarantee equivalent SoH classification performance. The study highlights the challenges and limitations of cascaded data-driven battery diagnostics and provides insight for the development of more reliable intelligent BMS solutions. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

Toward low-carbon and sustainable power systems, hydro–wind–solar complementarity provides an important pathway for enhancing renewable energy accommodation and operational flexibility, while accurate characterization of uncertainty and cross-energy dependencies is essential for system optimization, operational risk assessment, and sustainable utilization of renewable resources. This study proposes a conditional Wasserstein generative adversarial network with gradient penalty integrating one-dimensional multi-scale channel attention (MSA) and an exponential moving average (EMA) mechanism (MSA-cWGAN-GP) for joint runoff–wind–photovoltaic (PV) scenario generation. The generator employs parallel depthwise 1D convolutions with multiple temporal receptive fields to capture multi-timescale variations, while an EMA shadow generator is used for model validation and scenario generation. Conditional labels are obtained by clustering joint 24 h runoff–wind–PV profiles, enabling generation under typical resource states. Case studies using historical runoff observations from Shuibuya Hydropower Station and wind and PV power series derived from ERA5 reanalysis data show overall absolute errors of the autocorrelation function (ACF) and Kendall coefficient of 0.0113 and 0.0495, respectively. The proposed model achieves the best average performance among the evaluated models in preserving intraday temporal dependence, cross-energy dependencies, and distributional characteristics, providing representative scenarios for uncertainty analysis and subsequent optimization of hydro–wind–solar complementary systems. Full article

Accurate forecasting of electricity prices is essential for efficient operation and decision-making in deregulated power markets, particularly in the Indian electricity market, characterized by high volatility and dynamic pricing. This study presents a comparative analysis of statistical, machine learning, and deep learning methods for electricity price prediction in the Indian market, with a focus on feature dimensionality. The evaluated models include autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average with exogenous variables, categorical boosting, random forest, long short-term memory, bidirectional long short-term memory, and a hybrid convolutional neural network-bidirectional long short-term memory model. Historical data available on the Indian Energy Exchange webpage are deployed in this study. The models are analyzed for varying input vector sizes with features that include date, type of day, day of the week, previous day, month, and year market prices. The results indicate improved predictive performance of all model while increasing the input feature dimensionality from five to seven. The results indicate that while increasing the feature size from five to seven increases the prediction accuracy, the gains become marginal beyond seven, emphasizing the importance of feature relevance over feature quantity. From a theoretical perspective, the study highlights the dominance of short-term temporal dependencies in MCP prediction and provides empirical evidence for the point of diminishing returns in feature expansion. From a practical standpoint, the results endorse the choice of computationally efficient and interpretable models for real-world deployment. Full article

Decarbonizing the transportation sector depends not only on the scale of mitigation programs but also on whether financing systems are capable of generating measurable emission reductions. In Indonesia, climate finance allocation remains substantially below the level required to achieve the transportation-sector target under the Enhanced Nationally Determined Contribution (ENDC). At the same time, mitigation planning rarely establishes a clear relationship between financial expenditure and verified greenhouse gas (GHG) reduction outcomes, making policy effectiveness difficult to assess. This study examines the relationship between climate finance and mitigation outcomes in Indonesia’s transportation sector using verified emission reduction data and realized mitigation expenditures during 2018–2022. A cost-based assessment approach was applied to estimate the financing required to reduce one ton of CO₂ equivalent (tCO₂-e) across direct and indirect mitigation actions. The analysis identified 33 mitigation actions categorized under the Avoid–Shift–Improve (ASI) framework and evaluated their contribution to sectoral emission reduction. The results indicate substantial variation in mitigation costs among intervention types. Direct mitigation actions, particularly mass public transportation expansion, are linked to larger emission reductions at relatively lower costs than enabling or indirect measures. On average, reducing 1 tCO₂-e in Indonesia’s transportation sector requires approximately USD 184–1000 (IDR 3–16.4 million), using a standardized exchange rate of approximately IDR 16,400 per USD. Based on the transportation-sector ENDC target, the estimated financing requirement by 2030 ranges from USD 2.5–13.8 billion (IDR 42–226 trillion). The findings suggest that climate finance policies should move beyond expenditure-oriented approaches toward financing frameworks that explicitly connect investment allocation with verified mitigation performance. Full article

Background: Environmental exposures are biologically plausible contributors to thyroid cancer outcomes, but population-based evidence is heterogeneous and often focuses on incidence rather than stage at diagnosis. Objective: This study aimed to evaluate whether ecological state-year environmental indicators add predictive information for advanced-stage thyroid cancer at diagnosis in a large SEER-based cohort (U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results Program). Methods: We conducted a retrospective registry-linked analysis of 368,726 SEER thyroid cancer cases with valid combined summary stage. Advanced stage was defined as regional or distant disease and occurred in 118,509 cases (32.14%). Environmental variables were linked as state-year indicators using five-year moving averages from the years preceding diagnosis. Regularized logistic regression, gradient boosting, and Extra Trees models evaluated incremental predictive performance. Results: Adding environmental variables to demographic, socioeconomic, histology, and laterality predictors produced small random-split improvements across model families. However, after SEER registry was added before environmental variables, the remaining environmental increment was minimal. Registry-group holdout validation did not support improved geographic generalizability from the environmental feature set. Conclusions: State-year environmental indicators carried limited predictive information for advanced thyroid cancer stage, and much of this information overlapped with registry and geographic structure. These findings clarify both the potential and limitations of ecological environmental linkage in SEER-based prediction studies. Full article

Expectations regarding collective climate action are multidimensional and should be understood in relation to the social contexts in which they are embedded. This study examines whether these expectations are associated with the country-level average belief in the anthropogenic origin of climate change and with individuals’ deviation from that average. Drawing on Round 10 of the European Social Survey, the analysis utilizes 24,014 valid cases from 29 European countries. It estimates Complex Samples General Linear Models (CSGLM) to account for the complex sampling design. The results show that the country-level average belief in the anthropogenic origin of climate change is positively associated with expectations of collective climate action; that is, countries with a higher average belief exhibit higher expectations of collective climate action. Conversely, individuals whose belief in the anthropogenic origin of climate change exceeds the respective country-level average tend to express lower expectations of collective climate action. This negative association becomes stronger at higher levels of climate worry and is attenuated as ideological positioning moves towards the right; in contrast, trust in scientists does not have a significant moderating effect. Overall, the findings support a socially contextualized understanding of expectations of collective climate action, in which both national belief contexts and individuals’ relative positioning within those contexts are relevant. Full article

Environmental noise is a major environmental and public health concern in rapidly urbanizing cities, yet integrated studies combining field measurements, GIS-based spatial analysis, and predictive modelling remain limited in Central Asia. This study assessed the spatial distribution of urban noise pollution in Samarkand (Uzbekistan) and explored its statistical association with selected public health indicators, forecasting future trends. Measurements were conducted at 50 georeferenced sites covering more than 300 streets. The measured data were processed and mapped using ArcGIS 10.5 (Esri, Redlands, CA, USA) to produce the spatial distribution of environmental noise across the study area. Official data on registered vehicles, industrial enterprises, and disease incidence (2014–2024) were analysed using Pearson correlation, Autoregressive Integrated Moving Average (ARIMA), and its extension incorporating exogenous variables (ARIMAX) models. Results revealed pronounced spatial heterogeneity in noise levels, highest along transport corridors and industrial zones. Industrial enterprises showed the strongest correlations with disease incidence; vehicle registrations were excluded from final models owing to collinearity with industrial activity. ARIMA projected continued industrial growth through 2030, while ARIMAX models identified significant associations between industrial activity and diseases of the ear and mastoid process and of the nervous system (MAPE 28.55% and 19.78%). As an ecological, exploratory study using infrastructural proxies rather than measured noise exposure, findings should be interpreted as associations rather than causation. The framework offers a transferable approach for environmental risk assessment and sustainable urban planning. Full article