Search Results (950)

This study evaluates temporal variability and algorithm differences in soil moisture estimates over Europe using the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis and the passive Soil Moisture Active Passive (SMAP) soil moisture product. While models and satellite retrievals have improved in capturing the timing of soil moisture dynamics, absolute accuracy and temporal variability magnitudes still diverge. This study compares the representation of short-term and seasonal variability of soil moisture in absolute and normalized terms over two different hydrometeorological growing periods (2021 and 2022). Both datasets exhibit intermediate to high temporal correlations with in situ measurements at selected stations (median Pearson correlation coefficients of all stations range between 0.65 and 0.79), confirming previous studies. However, they overestimate the magnitude of absolute soil moisture variability at most stations (median interquartile range of all stations at 0.085 (0.10) m³m⁻³ for ECMWF and 0.072 (0.079) m³m⁻³ for SMAP opposed to 0.063 (0.072) m³m⁻³ for in situ in 2021 (2022)) due to an overestimation of short-term fluctuations, especially at dry stations in southern France and Eastern Europe. The soil wetness index is underestimated, particularly within SMAP estimates. The performance of both is sensitive to hydrometeorological conditions, with the 2022 European drought causing strong seasonal and weak short-term fluctuations. This is easier to capture than conditions with pronounced short-term and weaker seasonal fluctuations, as in 2021. Overall, SMAP and ECMWF time series show considerable coincident timing, whereas the magnitude of temporal variability and accuracy depend on site-specific characteristics and the pre-processing of the data. Full article

Background/Objectives: Mild Traumatic Brain Injury (mTBI) is a prevalent form of cranial trauma that can elicit a range of acute and chronic neuropsychiatric symptoms, and may increase the risk of neurodegenerative diseases. Its accurate identification remains a significant challenge in the field of forensic medicine. This study aimed to identify differential gut microbiota as potential biomarkers following mTBI and to preliminarily explore the association between alterations in gut microbiota and brain metabolites. Methods: An animal model was used to induce mTBI in male Sprague-Dawley (SD) rats. Dynamic changes in the gut microbiota and brain metabolites were analyzed via 16S rRNA sequencing and untargeted metabolomics. Results: Key discriminative taxa included Staphylococcus, Streptococcus, and Aeromonadaceae. Concurrently, brain metabolites, such as C24:1 Sphingomyelin and Thioetheramide PC, exhibited significant alterations. Multi-omics integration revealed that these changes were strongly correlated; in addition, a pathway analysis implicated disruptions in short-chain fatty acid and glycerophospholipid metabolism, which were linked to the regulation of inflammatory factors. Conclusions: This study demonstrates that mTBI induces distinct, time-dependent alterations in both the gut microbiota and brain metabolome, thereby providing a novel direction for research into the forensic diagnosis and mechanistic investigation of mTBI. Future studies are warranted to validate these potential biomarkers in human cohorts and to further elucidate the causal mechanisms underlying gut–brain axis interactions. Full article

Accurately quantifying the ecological functions of small and micro green spaces in high density urban environments supports urban ecological planning and management. This study assessed 271 pocket parks in the main urban area of Fuzhou, China, using multi-source remote sensing data from the growing seasons of 2019 to 2024. Six indicators were derived, including NDVI, NPP, WET, NDBSI, ISI, and LST. A composite Eco-environmental Index (EEI) was constructed using the entropy weight method. We combined the coefficient of variation, Theil–Sen slope estimation, the Mann–Kendall test, and the Hurst exponent to quantify spatial heterogeneity, interannual stability, and short-term persistence. We also examined climatic associations using correlation analysis. Pocket parks consistently outperformed their surrounding 500 m buffers across all indicators, and park buffer contrasts increased for most indicators. The mean EEI significantly increased from 0.563 in 2019 to 0.650 in 2024, with a pronounced step increase around 2022. At the site level, 261 of 271 parks (96.3%) exhibited an upward trend in EEI, indicating widespread ecological improvement. Specifically, park vegetation greenness (NDVI) rose from 0.413 to 0.578, widening the gap with surrounding areas. Parks consistently maintained a lower land surface temperature (LST) than their buffers, with a cooling magnitude ranging from 3.5 °C to 4.6 °C. Precipitation was positively associated with NDVI and NPP, while LST was positively associated with air temperature and negatively associated with precipitation. These findings support the planning and adaptive management of pocket parks to strengthen urban ecological resilience. Full article

Recent reports have revealed that the number of lower limb amputees worldwide has increased as a result of war, accidents, and vascular diseases and that transfemoral amputation accounts for 39% of cases, highlighting the need to develop an improved functional prosthetic knee joint that improves the amputee’s ability to resume activities of daily living. To enable transfemoral prosthesis users to walk on level ground, accurate prediction of the intended knee joint angle is critical for transfemoral prosthesis control. Therefore, the purpose of this research was to develop a technique for estimating knee joint angle utilizing a long short-term memory (LSTM) network and kinematic data collected from inertial measurement units (IMUs). The proposed LSTM network was trained and tested to estimate the contralateral knee angle using data collected from twenty able-bodied subjects using a lab-developed sensory gadget, which included four IMUs. Accordingly, the present work represents a feasibility investigation conducted on able-bodied individuals rather than a clinical validation for amputee gait. This study contributes to the field of bionics by mimicking the natural biomechanical behavior of the human knee joint during gait cycle to improve the control of artificial prosthetic knees. The proposed LSTM model learns the contralateral knee’s motion patterns in able-bodied gait and demonstrates the potential for future application in prosthesis control, although direct generalization to amputee users is outside the scope of this preliminary study. The contralateral LSTM models exhibited a real-time RMSE range of 2.48–2.78° and a correlation coefficient range of 0.9937–0.9991. This study proves the effectiveness of LSTM networks in estimating contralateral knee joint angles and shows their real-time performance and robustness, supporting its feasibility while acknowledging that further testing with amputee participants is required. Full article

Emerging landslides and severe floods highlight the urgent need to analyse and support predictive models and early warning systems. Soil moisture is a crucial parameter and it can now be determined from space with a resolution of a few tens of meters, potentially leading to the continuous global monitoring of landslide risk. We address this issue by determining the volumetric water content (VWC) of a testbed in Southern Italy (bare soil with significant flood and landslide hazard) through the comparison of two different satellite observations on the same day. In the first observation (Sentinel-1 mission of the European Space Agency, C-band Synthetic Aperture Radar (SAR)), the back-scattered radar signal is used to determine the VWC from the dielectric constant in the microwave range, using a time-series approach to calibrate the algorithm. In the second observation (hyperspectral PRISMA mission of the Italian Space Agency), the short-wave infrared (SWIR) reflectance spectra are used to calculate the VWC from the spectral weight of a vibrational absorption line of liquid water (wavelengths 1800–1950 nm). As the main result, we obtained a Pearson’s correlation coefficient of $0.4$ between the VWC values measured with the two techniques and a separate ground-truth confirmation of absolute VWC values in the range of 0.10–0.30 within $\pm 0.05$. This overlap validates that both SAR and hyperspectral data can be well calibrated and mapped with 30 m ground resolution, given the absence of artifacts or anomalies in this particular testbed (e.g., vegetation canopy or cloud presence). If hyperspectral data in the SWIR range become more broadly available in the future, our systematic procedure to synchronise these two technologies in both space and time can be further adapted to cross-validate the global high-resolution soil moisture dataset. Ultimately, multi-mission data integration could lead to quasi-real-time hydrogeological risk monitoring from space. Full article

Accurate traffic flow forecasting is a fundamental component of Intelligent Transportation Systems and proactive urban mobility management. However, the inherent complexity of urban traffic flow, characterized by non-stationary dynamics and multi-scale temporal dependencies, poses significant modeling challenges. Existing spatio-temporal models often struggle to reconcile the discrepancy between static physical road constraints and highly dynamic, state-dependent spatial correlations, while their reliance on fixed temporal receptive fields limits the capacity to disentangle overlapping periodicities and stochastic fluctuations. To bridge these gaps, this study proposes a novel Multi-scale Graph-Decoupling Spatial–temporal Network (MS-GSTN). MS-GSTN leverages a Hierarchical Moving Average decomposition module to recursively partition raw traffic flow signals into constituent patterns across diverse temporal resolutions, ranging from systemic daily trends to high-frequency transients. Subsequently, a Tri-graph Spatio-temporal Fusion module synergistically models scale-specific dependencies by integrating an adaptive temporal graph, a static spatial graph, and a data-driven dynamic spatial graph within a unified architecture. Extensive experiments on four large-scale real-world benchmark datasets demonstrate that MS-GSTN consistently achieves superior forecasting accuracy compared to representative state-of-the-art models. Quantitatively, the proposed framework yields an overall reduction in Mean Absolute Error of up to 6.2% and maintains enhanced stability across multiple forecasting horizons. Visualization analysis further confirms that MS-GSTN effectively identifies scale-dependent spatial couplings, revealing that long-term traffic flow trends propagate through global network connectivity while short-term variations are governed by localized interactions. Full article

Purpose: This study aimed to translate the Pain Effects Scale (PES) into Arabic, evaluate its cultural adaptation, and assess its psychometric properties (validity and reliability) among patients with Multiple Sclerosis (MS). Method: The translation and cultural adaptation followed published guidelines. A total of 121 patients with MS completed the PES and several other assessments: the Short-Form McGill Pain Questionnaire (SF-MPQ), the Patient Health Questionnaire-9 (PHQ-9), the Modified Fatigue Impact Scale (MFIS), and the Multiple Sclerosis Impact Scale (MSIS-29), to evaluate construct validity. Reliability was assessed after two weeks using the intraclass correlation coefficient (ICC) and internal consistency (Cronbach’s α). Results: The Arabic version of Pain Effects Scale PES-Ar demonstrated good internal consistency (Cronbach’s α = 0.910) and strong test–retest reliability (ICC (2,1) = 0.88; 95% CI: 0.85–0.92). The corrected item–total correlations for all six items ranged from 0.591 to 0.840. No floor or ceiling effects were observed. Content validity indices were high (I-CVI and S-CVI = 1.00). Construct validity was supported by moderate correlations with PHQ-9 (r = 0.677), MFIS (r = 0.66), and SF-MPQ (r = 0.586), and a weak correlation with the MSIS-29. Conclusions: The PES-Ar showed strong validity and reliability for assessing the impact of pain in Arabic-speaking individuals with MS in Saudi Arabia. Full article

Diagnosing faults in Permanent Magnet Synchronous Motors (PMSMs) is critical for ensuring their reliable operation, particularly in detecting internal short-circuit faults in the stator windings. These faults, such as inter-turn and inter-coil short circuits, can significantly affect motor performance and lead to costly downtime if not detected early. However, detecting these faults accurately, especially in the presence of operational noise and varying load conditions, remains a challenging task. To address this, a novel methodology is proposed for diagnosing and classifying fault severity in PMSMs using vibration and current data. The key innovation of the method is the combination of signal processing for both vibration and current data, to enhance fault detection by applying advanced feature extraction techniques such as root mean square (RMS), peak-to peak values, and spectral entropy in both time and frequency domains. Furthermore, a cooperative gain transformation is applied to amplify weak correlations between vibration and current signals, improving detection sensitivity, especially during early fault progression. In this study, the publicly available dataset on Mendeley, which consists of vibration and current measurements from three PMSMs with different power ratings of 1.0 kW, 1.5 kW, and 3.0 kW, was used. The dataset includes eight different levels of stator fault severity, ranging from 0% up to 37.66%, and covers normal operation, inter-coil short circuit, and inter-turn short circuit. The results demonstrate the effectiveness of the proposed methodology, achieving an accuracy of 96.6% in fault classification. The performance values for vibration and current measurements, along with the corresponding fault severities, validate the method’s ability to accurately detect faults across various operating conditions. Full article

Aleppo oak (Quercus infectoria) is among the most industrially and ecologically significant oak species, valued for its medicinal properties and considerable genetic importance. Cytogenetic analysis provides critical insight into evolutionary history, interspecific relationships, and karyotypic differentiation. This study investigated the chromosomal architecture and karyotypic diversity of five natural populations of this species in western Iran (Sardasht, Oramanat, Baneh, Paveh, and Marivan) using actively dividing root meristems and a high-resolution image-based cytogenetic system. All examined cells displayed a basic chromosome number of x = 12 and a diploid condition, and chromosome lengths ranged from 0.90 to 2.12 µm. ANOVA and mean comparisons of five chromosomal parameters (Long Arm, Short Arm and Total Length, Arm Ratio, and Centromeric Index) revealed significant interpopulation differences in chromosome length and arm dimensions. All populations shared the karyotype formula 12 m and were classified into Stebbins’ Category B, indicating a moderately symmetrical, relatively primitive cytogenetic structure. Principal component analysis reduced the dataset to two major axes explaining 99.93% of the total variance, predominantly influenced by SA and TL on PC1 and by LA, AR, and CI on PC2. Hierarchical clustering grouped the populations into three distinct lineages, with Sardasht–Oramanat–Baneh showing the greatest divergence. Biplot vector patterns further clarified trait correlations, highlighting genomic structuring and potential breeding utility. Full article

The radial sand-ridge field off the Jiangsu coast is a distinctive landform in a strongly tide-dominated environment, where sediment supply and geomorphic patterns have been profoundly altered by Yellow River course changes, reduced Yangtze-derived sediment, and large-scale reclamation. Focusing on a typical nearshore sector off Dongtai, this study integrates multi-source data from 1979 to 2025, including historical nautical charts, high-precision engineering bathymetry, full-tide hydro-sediment observations, and surficial sediment samples, to quantify seabed erosion–deposition over 46 years and clarify linkages among tidal currents, suspended-sediment transport, and surface grain-size patterns. Surficial sediments from Maozhusha to Jiangjiasha channel systematically fine from north to south: sand-ridge crests are dominated by sandy silt, whereas tidal channels and transition zones are characterized by silty sand and clayey silt. From 1979 to 2025, Zhugensha and its outer flank underwent multi-meter accretion and a marked accretion belt formed between Gaoni and Tiaozini, while the Jiangjiasha channel and adjacent deep troughs experienced persistent scour (local mean rates up to ~0.25 m/a), forming a striped “ridge accretion–trough erosion” pattern. Residual and potential maximum currents in the main channels enhance scour and offshore export of fines, whereas relatively strong depth-averaged flow and near-bed shear on inner sand-ridge flanks favor frequent mobilization and short-range trapping of coarser particles. Suspended-sediment concentration and median grain size are generally positively correlated, with suspension coarsening in high-energy channels but dominated by fine grains on nearshore flats and in deep troughs. These findings refine understanding of muddy-coast geomorphology under strong tides and may inform offshore wind-farm foundation design, navigation-channel maintenance, and coastal-zone management. Full article

Purpose: This study examined the relationships among fear of cancer recurrence (FCR), unmet healthcare needs, and quality of life (QOL) among breast cancer survivors post-treatment, particularly within the Thai cultural and healthcare context, where limited research has been conducted. Methods: A cross-sectional descriptive correlational design with purposive sampling was used. A total of 122 breast cancer survivors, 1–5 years prior, were recruited from the Breast Clinic and Chemotherapy Unit at the National Cancer Institute. Instruments included a demographic questionnaire, the FCR Inventory Short Form, the Cancer Survivors’ Unmet Needs measure, and the EORTC QOL-C30 with the breast cancer module (QLQ-BR23). Cronbach’s α ranged from 0.82 to 0.92. Data were analyzed using descriptive statistics, Spearman’s rank correlation, and Pearson’s correlation coefficient. Results: Participants reported moderate levels of FCR (M = 13.39, SD = 4.50), low unmet healthcare needs (M = 25.63, SD = 14.82), and moderate overall QOL (M = 54.82, SD = 0.22). FCR was negatively correlated with overall QOL (r = −0.248, p <0.01) and functional QOL (r = −0.242, p < 0.01). Unmet healthcare needs were also negatively correlated with overall QOL (r = −0.261, p < 0.01). Multiple linear regression analysis revealed that both FCR and unmet healthcare needs had a significantly negative relationship with overall QOL (p < 0.05). Conclusions: FCR and unmet healthcare needs independently impair QOL among breast cancer survivors. Early, culturally appropriate survivorship care in Asian contexts is essential to address these needs and improve QOL. Full article

To improve the accuracy of cable temperature anomaly prediction and ensure the reliability of urban distribution networks, this paper proposes a multi-scale spatiotemporal model called MSST-Net (MSST-Net) for medium-voltage power cables in underground utility tunnels. The model addresses the multi-scale temporal dynamics and spatial correlations inherent in cable thermal behavior. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections (medium-voltage power cable segments) of Guangzhou’s underground utility tunnel from 2023 to 2024, using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman’s correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction for power cables in the KN1 and KN2 sections of Guangzhou’s underground utility tunnel, achieving a coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442 °C, and 0.596 °C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425 °C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of medium-voltage power cables in urban underground utility tunnels. Full article

Environmental monitoring systems generate large volumes of multivariate time series data from heterogeneous sensors, including those measuring soil, weather, and air quality parameters. However, sensor malfunctions and transmission failures frequently lead to missing values, compromising the performance of downstream analytical and predictive models. To address this challenge, this study presents a comprehensive and systematic evaluation of previously proposed hybrid architecture that interleaves Long Short-Term Memory (LSTM) layers with a Multi-Head Attention mechanism in a “sandwiched” setting (LSTM–Attention–LSTM) for robust multivariate data imputation in environmental IoT datasets. The first LSTM layer captures short-term temporal dependencies, the attention layer emphasises long-range relationships among correlated features, and the second LSTM layer re-integrates these enriched representations into a coherent temporal sequence. The model is evaluated using multiple environmental datasets of soil temperature, meteorological (precipitation, temperature, wind speed, humidity), and air quality data across missingness levels ranging from 10% to 90%. Performance is compared against baseline methods, including K-Nearest Neighbour (KNN) and Bidirectional Recurrent Imputation for Time Series (BRITS). Across all datasets, the Hybrid model consistently outperforms baseline methods, achieving MAE reductions exceeding 50% and reaching over 80% in several scenarios, along with RMSE reductions of up to approximately 85%, particularly under moderate to high missingness conditions. An ablation study further examines the contribution of each layer to overall model performance. Results demonstrate that the proposed Hybrid model achieves superior accuracy and robustness across datasets, confirming its effectiveness for environmental sensor data imputation under varying missing data conditions. Full article

The in-vial microextraction technique is emerging as an alternative sample treatment, as it integrates sorbent preparation, adsorption, and desorption of analytes in a single device before instrumental analysis. In this work, the applicability of polycaprolactone polymeric film, recently used for the in-vial microextraction of sex hormones from environmental waters, is studied in a low-capacity format for unconjugated sex hormones determination in biological samples by HPLC-MS/MS. Its performance was evaluated in urine and serum, achieving extraction in a short time (10 and 30 min, in turn) and satisfactory elution with ethanol, with recovery in the range of 65–111% in urine, 55–122% in bovine serum albumin (BSA) solution, and 66–121% in fetal bovine serum (FBS). In the case of protein matrices, a dilution to 20 g L⁻¹ protein content and washing step (3 × 1 mL ultrapure water) afore the elution are required to achieve clean extract, as verified by a Bradford assay. Matrix-matched calibration was used for quantification, obtaining correlation coefficients greater than 0.9929; limits of detection and quantification were in the range of 0.01–0.65 and 0.03–1.96 ng mL⁻¹ in urine, 0.02–0.8 and 0.05–2.5 ng mL⁻¹ in BSA, and 0.02–1.0 and 0.06–3.0 g mL⁻¹ in FBS, respectively. The in-vial polycaprolactone film proved to be reusable for several cycles (up to ten), and the greenness assessment revealed a good adhesion to green sample preparation principles. All these achievements further strengthen its feasibility for efficient extraction/clean-up of trace sex hormones in complex biological samples. Full article

Environmental sustainability has emerged as a global imperative in the context of accelerating climate change, rapid industrialization, and increasing ecological stress. Ecological quality is necessary for countries to pursue because of its overall benefits to the entire ecosystem. Therefore, due to the significant role that the United Arab Emirates (UAE) plays in the global environment, this research examines the role of Green Innovation (GI), Financial Globalization (FG), Economic Growth (GDP), and Foreign Direct Investment (FDI) in influencing Environmental Quality (EQ) in the UAE from 1991–2022. The UAE is well known for these economic indices. Furthermore, this study employed the innovative Quantile Augmented Dickey–Fuller (QADF) test, Wavelet Quantile Regression (WQR), Wavelet Quantile Correlation (WQC), and Quantile-on-Quantile Granger Causality (QQGC). WQR is able to identify connections between series over a range of quantiles and periods. WQC evaluates the co-movement between variables at different quantile levels and across several scales. The QQGC captures the causal effect of the regressors on EQ. These methods are quite advanced compared to other traditional econometric methods. Based on the outcome of the WQR and WQC methods, evidence shows that GI contributes to EQ across all quantiles in the short, medium, and long term, while FG, GDP, and FDI reduces EQ across all quantiles in the short, medium, and long term. The QQGC results also affirm causality among the variables, implying that GI, FG, GDP, and FDI can predict EQ across all quantiles. This research recommends that the UAE should improve on its environmental policies both domestically and internationally by making them more stringent, and continue to promote clean energy investments. Full article