Search Results (1,488)

Sunshine duration (SD) is an essential meteorological variable. It represents the sum of time for which direct solar radiation with an intensity above 120 W∙m⁻² reaches the Earth’s surface. In the contemporary observational routine, automatic electronic devices are in use. The pyranometric method based on global solar radiation measurements (K_glob) is also proposed by the WMO to assess SD. The aim of the paper is to study the accuracy of the Slob–Monna method (SD-WMO), recommended by the WMO to calculate sunshine duration. Alternatively, the author’s method, which is based on the Ångström clearness index (SD-ACI), was used to approximate SD. For this purpose, a two-year series of SD and K_glob observations at four locations in Poland (well representing the Central European transitional climate zone) was analyzed. The result shows that, for SD-WMO, sunshine duration values are on average 16% higher than observed ones. For the SD-ACI method, they are only 5% higher. When verifying the accuracy of SD-WMO and SD-ACI approximations, we have found that, both for daily and monthly periods, the calculated SD sums are closer to the observed ones in the case of SD-ACI than for the SD-WMO method. The correlation coefficients are, respectively, 0.98 and 0.82 for daily sums and 0.99 and 0.88 for monthly sums. Full article

This follow-up study investigates the long-term environmental sustainability and remediation outcomes of the UPPER (‘Urban Productive Parks for Sustainable Urban Regeneration’-UIA04-252) project in Latina, Italy, focusing on Nature-Based Solutions (NbS) applied to urban green infrastructure. By integrating proximal and satellite-based remote sensing methodologies, the research evaluates persistent improvements in vegetation health, soil moisture dynamics, and overall environmental quality over multiple years. Building upon the initial monitoring framework, this case study incorporates updated data and refined techniques to quantify temporal changes and assess the ecological performance of NbS interventions. In more detail, ground-based data from meteo-climatic, air quality stations and remote satellite data from the Sentinel-2 mission are adopted. Ground-based measurements such as temperature, humidity, radiation, rainfall intensity, PM10 and PM2.5 are carried out to monitor the overall environmental quality. Updated satellite imagery from Sentinel-2 is analyzed using advanced band ratio indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Moisture Index (NDMI). Comparative temporal analysis revealed consistent enhancements in vegetation health, with NDVI values significantly exceeding baseline levels (NDVI 2022–2024: +0.096, p = 0.024), demonstrating successful vegetation establishment with larger gains in green areas (+27.0%) than parking retrofits (+11.4%, p = 0.041). However, concurrent NDWI decline (−0.066, p = 0.063) indicates increased vegetation water stress despite irrigation infrastructure. NDMI improvements (+0.098, p = 0.016) suggest physiological adaptation through stomatal regulation. Principal Component Analysis (PCA) of meteo-climatic variables reveals temperature as the dominant environmental driver (PC2 loadings > 0.8), with municipality-wide NDVI-temperature correlations of r = −0.87. These multi-scale findings validate sustained NbS effectiveness in enhancing vegetation density and ecosystem services, yet simultaneously expose critical water-limitation trade-offs in Mediterranean semi-arid contexts, necessitating adaptive irrigation management and continued monitoring for long-term urban climate resilience. The integrated monitoring approach underscores the critical role of continuous, multi-scale assessment in ensuring long-term success and adaptive management of NbS-based interventions. Full article

Observed rainfall erosivity risk (ORE) index is defined as the erosivity risk in the event of extreme rainfall events. ORE measures the kinetic energy of raindrops generated during a period of maximum precipitation intensity with the formula $\mathrm{O}\mathrm{R}\mathrm{E}=\left(\mathrm{E}\mathrm{D}·\mathrm{T}\mathrm{E}\mathrm{I}\right)/10$, where ED = erosivity density, TEI = total erosivity index, and ORE is measured in MJ mm ha⁻¹ h⁻¹ yr⁻¹. The goal of this study is to model ORE, estimate its spatiotemporal variability, and predict (PRE) and simulate ORE for the state of Sinaloa (1969–2018). Five indices of rainfall erosivity were calculated: the modified Fournier index, precipitation concentration index, ED, TEI, and rainfall erosivity factor. The nonparametric trend in ORE was calculated. Using multiple nonlinear regressions (MNR), PRE (dependent variable) was calculated as a function of cumulative annual, annual average, seasonal average, and seasonal cumulative rainfall (independent variables). To simulate PRE, cumulative distribution functions, adjusted return periods (ARPs), and the 99th percentile were used. ORE ranged from 51.39 MJ mm ha⁻¹ h⁻¹ yr⁻¹ in 1970 (Culiacán) to 92679.40 MJ mm ha⁻¹ h⁻¹ yr⁻¹ in 1998 (Sta. C. de Alaya). The only year that had very high ORE at all nine stations was 1998. The only significant trend was ORE = 34.64 MJ mm ha⁻¹ h⁻¹ yr⁻¹ (Culiacán). The nine PRE models were significantly predictive (Spearman correlation > 0.280). Guatenipa, Rosario, and Siqueros registered very high PRE, since one to eight extreme erosivity events per century are predicted on average. A new methodology is proposed for calculating ORE and PRE, which can be used to develop alternatives for managing and protecting agricultural land in the state considered “the breadbasket of Mexico”. Full article

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

In the field of underwater laser detection, turbulence causes beam wandering and intensity scintillation, which subsequently alter the angle of incidence and ultimately degrade the quality of the target echo signal. By establishing an experimental platform that simulates oceanic turbulent channels, this study investigates the correlation between turbulence location and the backscattered optical scintillation index. This work lays the foundation for developing reliable assessment techniques for laser backscattering detection channels. Using a thermally driven turbulence simulator and an off-axis blue-green laser, a backscattering model was developed via echo signal analysis. This model captures the relationship between turbulence spatial distribution and the optical scintillation coefficient, revealing distinct nonlinear behavior in this relationship. Experimental results revealed a non-monotonic trend in the optical scintillation coefficient, characterized by an initial decrease followed by an increase, with the distance from the turbulence region. While increased water turbidity preserved this overall trend, it resulted in a dampened response. The proposed model demonstrated high reliability, with R² values of 0.8579 and 0.8844 for the open-sea and coastal environments, respectively. The turbulent laser detection backscattering channel prediction model supports the evaluation of oceanic blue-green laser detection channels. Full article

To address the high incidence of work-related musculoskeletal disorders (WMSDs) at manual edge-banding workstations in furniture factories, and in an effort to tackle the existing research challenges of poor cumulative risk quantification and inconsistent evaluations, this paper proposes a three-stage system for continuous, automated, non-invasive WMSD risk monitoring. First, MediaPipe 0.10.11 is used to extract 33 key joint coordinates, compute seven types of joint angles, and resolve missing joint data, ensuring biomechanical data integrity for subsequent analysis. Second, joint angles are converted into graded parameters via RULA, REBA, and OWAS criteria, enabling automatic calculation of posture risk scores and grades. Third, an Adaptive Pooling Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) dual-branch hybrid model based on the Efficient Channel Attention (ECA) mechanism is built, which takes nine-dimensional features as the input to predict expert-rated fatigue states. For validation, 32 experienced female workers performed manual edge-banding tasks, with smartphones capturing videos of the eight work steps to ensure authentic and representative data. The results show the following findings: (1) system ratings strongly correlate with expert evaluations, verifying its validity for posture risk assessment; (2) the hybrid model successfully captures the complex mapping of expert-derived fatigue patterns, outperforming standalone CNN and LSTM models in fatigue prediction—by integrating CNN-based spatial feature extraction and LSTM-based temporal analysis—and accurately maps fatigue indexes while generating intervention recommendations. This study addresses the limitations of traditional manual evaluations (e.g., subjectivity, poor temporal resolution, and inability to capture cumulative risk), providing an engineered solution for WMSD prevention at these workstations and serving as a technical reference for occupational health management in labor-intensive industries. Full article

This study presents a geospatial assessment and modeling of the water–energy–food (WEF) nexus to enrich the sustainable paddy cultivation of the North Central Province (NCP) of Sri Lanka in the Dry Zone. Increasing climatic variability and limited resources have raised concerns about the need for efficient resource management to restore food security globally. The study analyzed the three components of the WEF nexus for their synergies and trade-offs using GIS and remote sensing applications. The food productivity potential was derived using the Normalized Difference Vegetation Index (NDVI), Soil Organic Carbon (SOC), soil type, and land use, whereas water availability was assessed using the Normalized Difference Water Index (NDWI), Soil Moisture Index (SMI), and rainfall data. Energy potential was mapped using WorldClim 2.1 datasets on solar radiation and wind speed and the proximity to the national grid. Scenario modeling was conducted through raster overlay analysis to identify zones of WEF constraints and synergies such as low food–low water areas and high energy–low productivity areas. To ensure the accuracy of the created model, Pearson correlation analysis was used to internally validate between hotspot layers (representing extracted data) and scenario layers (representing modeled outputs). The results revealed a strong positive correlation (r = 0.737), a moderate positive correlation for energy (r = 0.582), and a positive correlation for food (r = 0.273). Those values were statistically significant at p > 0.001. These results confirm the internal validity and accuracy of the model. This study further calculated the total greenhouse gas (GHG) emissions from paddy cultivation in NCP as 1,070,800 tCO_2eq yr⁻¹, which results in an emission intensity of 5.35 tCO_2eq ha⁻¹ yr⁻¹, with CH₄ contributing around 89% and N₂O 11%. This highlights the importance of sustainable cultivation in mitigating agricultural emissions that contribute to climate change. Overall, this study demonstrates a robust framework for identifying areas of resource stress or potential synergy under the WEF nexus for policy implementation, to promote climate resilience and sustainable paddy cultivation, to enhance the food security of the country. This model can be adapted to implement similar research work in the future as well. Full article

In semi-arid areas like Southeastern Anatolia, where agricultural productivity and water supply are extremely climate-sensitive, drought is a significant environmental and socioeconomic problem. Comprehensive assessment of drought and soil moisture dynamics is fundamental to sustainable agriculture and water security in semi-arid regions. This study analyzes drought patterns across seven provinces in the Southeastern Anatolia (GAP) region of Türkiye (Adıyaman, Diyarbakır, Gaziantep, Kilis, Mardin, Siirt, and Şanlıurfa) from 1963 to 2022, employing four drought indices (SPI, SPEI, CZI, and RDI) at multiple timescales (1-, 3-, and 12-month) to support evidence-based strategies for sustainable water and agricultural resource management. A more thorough evaluation is made possible by this multi-index and multi-scale method, which is rarely used concurrently at the provincial level. Additionally, the drought characterization was validated and enhanced through the analysis of ERA5-Land soil moisture data (1950–2022). According to the findings, the provinces with the lowest median index values and the highest frequency of extreme drought episodes are Diyarbakır and Şanlıurfa. The SPEI-12 (THW) median values showed a neutral long-term drought–wetness balance with seasonal changes, ranging from −0.0714 (Adıyaman) to 0.188 (Şanlıurfa). Particularly after 2009, soil moisture levels decreased to as low as 2–3 mm during the summer, indicating heightened evapotranspiration stress. RDI-12’s reliability in long-term drought evaluation was confirmed by its strongest correlation with other indices (r = 0.87–0.97). According to spatial research, the frequency of moderate droughts in the southwest was as high as 39%, whilst the eastern provinces experienced severe and intense droughts as high as 8%. However, with frequency above 53%, wet occurrences were more common in the east, particularly in Siirt. By clarifying long-term drought and soil moisture patterns, this study provides essential insights for sustainable irrigation planning and agricultural water allocation in the GAP region. Full article

This technical note presents a quantitative image-based framework for evaluating the packing and compressibility of granular soils, specifically applied to building foundation design in civil infrastructure projects. The Sedimaging system replicates hydraulic sedimentation in a controlled column, equipped with a high-resolution camera, to visualize particle orientation after deposition. Grayscale images of the settled bed are analyzed using Haar Wavelet Transform (HWT) decomposition to quantify directional intensity gradients. A new descriptor, termed the sediment index (B), is defined as the ratio of vertical to horizontal wavelet energy at the dominant scale, representing the preferential alignment and anisotropy of particles during sedimentation. Experimental investigations were conducted on fifteen granular materials that include natural sands, tailings, glass beads and rice grains with different shapes. The results demonstrate strong correlations between B and both microscopic shape ratios (d₁/d₂ and d₁/d₃) and macroscopic properties. Linear relationships predict the limiting void ratios (e_max, e_min) with mean absolute differences of 0.04 and 0.03, respectively. A power-law function relates B to the compression index (C_c) with an average deviation of 0.02. These findings confirm that the sediment index effectively captures the morphological influence of particle shape on soil packing and compressibility. Compared with conventional physical testing, the Sedimaging-based approach offers a rapid, non-destructive, and high-throughput solution for estimating soil packing and compressibility of cohesionless, sand-sized granular soils directly from post-settlement imagery, making it particularly valuable for preliminary site assessments, geotechnical screening, and intelligent monitoring of granular materials in building foundation design and other infrastructure applications, such as earth–rock dams. Full article

The maturation of distilled spirits in wooden barrels is a critical process that defines the sensory profile and quality of the final product, primarily through the release of polyphenols and flavonoids. In this study, the early extraction kinetics of these compounds in agave distillates were investigated using laboratory-scale barrels (5 L) with three toasting levels: light (185 °C/60 s), medium (210 °C/90 s), and intense (235 °C/120 s). The barrels were characterized by FTIR and Raman spectroscopy (SEM), as well as Scanning Electron Microscopy to correlate the chemical structure of the wood with the release of phenolic compounds. For this purpose, the Electrochemical Color Index (ECI), representative of polyphenols and flavonoids, was monitored daily for over 60 days. Results showed no statistically significant differences (p > 0.05) among the toasting levels. On the other hand, the observed kinetics exhibited four characteristic phases: (i) a linear increase during the first week due to the extraction of the most exposed compounds, (ii) a partial decrease in the second week associated with the re-adsorption of extracted compounds onto active sites remaining available on the barrel surface, (iii) a pseudo-steady state up to day 60, and finally, (iv) a subsequent linear increase. These findings provide scientific evidence supporting the official standards for the classification of aged distillates, since at least 60 days are required to condition the barrel surface to achieve a balanced extraction of polyphenols and flavonoids. The results highlight ECI as a robust and sensitive tool for monitoring the early maturation of agave distillates. Furthermore, the proposed approach not only offers complementary analytical criteria but also contributes to supporting the regulatory definitions of the reposado category, providing a practical framework for process standardization. Full article

This study examines agricultural labour dynamics and sustainability practices in East Crete, assessing how labour structure, education, and input intensity shape ecological outcomes. Using data from 108 farms in Heraklion and Lassithi, we constructed composite indicators, such as Labour Intensity, Sustainability Engagement, and Training-Adjusted Labour indices. Analysis of 37 farms with data revealed a heterogeneous landscape. Traditional family-based systems persist alongside uneven shifts toward agroecological practices. The Training-Adjusted Labour Index correlated with reduced pesticide use, while subsidy participation alone was not a reliable predictor of sustainable behaviour. Findings highlight limits of compliance-based incentives and the importance of knowledge-driven transitions. This study advocates typology-informed policies and longitudinal research for future policy design. Full article

Background and Objectives: Acute pancreatitis (AP) is one of the most common gastrointestinal emergencies worldwide. Early identification of high-risk patients is essential to improve outcomes. Computed tomography (CT)-derived muscle mass indices, such as the psoas muscle index (PMI) and paravertebral muscle index (PvMI), have recently emerged as potential prognostic markers reflecting both nutritional and inflammatory status. This study aimed to investigate the relationship between CT-derived PMI and PvMI with disease severity, complications, and intensive care unit (ICU) requirement in patients with acute pancreatitis, and to evaluate their prognostic value across age- and sex-specific subgroups. Materials and Methods: This retrospective study included 179 patients hospitalized with AP between January 2023 and February 2025. The psoas muscle area (PMA) and paravertebral muscle area (PvMA) were measured at the L3 vertebral level on CT scans and normalized to height squared to calculate the PMI and PvMI levels. Additionally, patients were classified as having low or normal PMA and PvMA levels based on cutoff values from the existing literature. Clinical, biochemical, and outcome data—including disease severity, complications, and ICU requirement—were analyzed. Subgroup analyses were performed by sex and age (≥65 years). Logistic regression and ROC analyses were used to identify independent predictors and optimal cutoff values. Results: Overall, complications developed in 39.7% of patients, and ICU admission was required in 11.2%. The PMI levels were significantly correlated with albumin, hemoglobin, and inflammatory marker levels. In women, the PMI was independently associated with complicated AP (adjusted OR = 0.655, p = 0.018). In patients ≥65 years, the PvMI level was independently associated with ICU requirement (adjusted OR = 0.780, p = 0.047). The ROC analysis identified PMI ≤ 4.04 cm²/m² as the optimal cutoff for predicting complicated AP (AUC = 0.641, p = 0.049), and PvMI ≤ 18.88 cm²/m² for predicting ICU need (AUC = 0.684, p = 0.020), with moderate discrimination. Conclusions: CT-derived muscle indices might be associated with disease severity and adverse outcomes in AP, particularly among older (≥65 years) and female patients. PMI and PvMI may serve as practical prognostic markers to identify high-risk patients early, enabling timely nutritional and supportive interventions. Validation in larger prospective cohorts is warranted. Full article

Background/Objectives: Dental training is known for its demanding academic pace, early clinical exposure, and constant performance pressure. These stressors may contribute to behavioral and physical manifestations, including bruxism and temporomandibular disorder (TMD). This study aimed to better understand the multidimensional burden experienced in this educational setting by investigating the relationship between education-related stress, bruxism patterns, and temporomandibular symptoms (TMD-related symptoms) in dental students. Methods: A cross-sectional survey was conducted at the Ankara University Faculty of Dentistry in 2025 and completed by 287 undergraduate dental students. The questionnaire collected sociodemographic information, self-reported bruxism status, TMD-related symptoms via the Fonseca Anamnestic Index (FAI), and education-related stressors using the Dental Environment Stress (DES) scale. Descriptive statistics, group comparisons, and Spearman correlation analyses were conducted. Results: Bruxism was reported by 76% of students and was significantly more common among females (p < 0.05). Students with bruxism demonstrated higher DES (3.34 ± 0.84) and FAI (41.81 ± 20.32) scores compared with those without bruxism (p < 0.001). DES and FAI scores showed a significant positive correlation (r = 0.229, p < 0.001). Stressors related to workload, examinations, limited rest time, clinical uncertainty, patient responsibility, and financial concerns were strongly associated with bruxism, while inconsistent academic feedback emerged as a key distinguishing factor. Conclusions: Education-related stress is closely linked to bruxism and TMD-related symptoms among dental students. Beyond overall stress intensity, the nature of experienced stressors plays a critical role. These findings highlight the importance of supportive learning structures, targeted stress-management strategies, and curriculum-level improvements to promote student wellbeing and resilience. Full article

Modern pesticide risk assessment relies on data on bioaccumulation and acute aquatic toxicity, yet generating such data is labour-intensive and animal-demanding. This study evaluated whether phospholipid affinity of pesticides, quantified by the chromatographic hydrophobicity index CHI_IAM obtained from high-throughput gradient biomimetic chromatography, can serve as a surrogate descriptor of these endpoints. Nineteen pesticides representing different chemical and functional classes were analyzed on IAM.PC.DD2 columns, and CHI_IAM values were determined. Bioconcentration factors (BCF) in fish and acute toxicity data (96 h LC₅₀ for fish, 48 h EC₅₀ for Daphnia magna) were retrieved from the Pesticide Properties DataBase. CHI_IAM ranged from −12.1 to 54.8 and correlated strongly with log₁₀BCF (r = 0.84) and log₁₀LC₅₀ in fish (r = −0.84), and moderately with log₁₀EC₅₀ for Daphnia (r = 0.76). Highly lipophilic pesticides with high CHI_IAM showed elevated BCF and low LC₅₀/EC₅₀ values, whereas polar compounds with low CHI_IAM exhibited negligible bioconcentration and low acute toxicity. Deviations from these trends, for compounds with specific modes of action, highlighted the contribution of mechanisms beyond membrane toxicity. Overall, CHI_IAM measured under high-throughput conditions retains prognostic value for ecotoxicological assessment and may serve as a rapid experimental descriptor to support preliminary screening. Full article

(1) Background: Pain assessment still relies primarily on subjective self-report. To address these limitations, we developed Piq, an EEG-based index derived from beta-band brain activity (Piq_β) aimed at providing objective pain identification and quantification. (2) Methods: The study combined cross-sectional and longitudinal designs. Resting-state brain activity was recorded for five minutes, and EEG signals were preprocessed using a dedicated algorithm. Piq_β performance was assessed by identifying an optimal cutoff to discriminate pain from no pain, evaluating its association with VNRS, and estimating agreement using a modified concordance criterion (exact match or ±1 category). A graded scale was also established to classify pain into distinct categories, according to intensity. (3) Results: An optimal cutoff of 10% for Piq_β yielded 97.8% sensitivity and 88.2% specificity. Piq_β correlated with self-reported scores (ρ = 0.60, p < 0.0001) with acceptable agreement (mean bias −1.02), accounting for clinically acceptable discrepancies. Five levels of pain were proposed, and Piq_β demonstrated the ability to track intra-individual fluctuations over time, accounting for clinically acceptable discrepancies. (4) Conclusions: These findings provide strong evidence to support the Piq_β index as a valuable complement to subjective pain ratings. Full article