Search Results (2,021)

Neurogenic bladder is a debilitating condition caused by neurological dysfunction that impairs urinary control, often requiring timed intermittent catheterisation. Although effective, intermittent catheterisation is invasive, uncomfortable, and associated with infection risks, reducing patients’ quality of life. SafeBladder is a low-cost wearable device developed to enable real-time, non-invasive bladder volume monitoring using near-infrared spectroscopy (NIRS) and machine learning algorithms. The prototype employs LEDs and photodetectors to measure light attenuation through abdominal tissues. Bladder filling was simulated through experimental tests using stepwise water additions to containers and tissue-mimicking phantoms, including silicone and porcine tissue. Machine learning models, including Linear Regression, Support Vector Regression, and Random Forest, were trained to predict volume from sensor data. The results showed the device is sensitive to volume changes, though ambient light interference affected accuracy, suggesting optimal use under clothing or in low-light conditions. The Random Forest model outperformed others, with a Mean Absolute Error (MAE) of 25 ± 4 mL and $R^2$ of 0.90 in phantom tests. These findings support SafeBladder as a promising, non-invasive solution for bladder monitoring, with clinical potential pending further calibration and validation in real-world settings. Full article

The accurate monitoring of short-term bathymetric changes in shallow waters is essential for effective coastal management and planning. Machine Learning (ML) applied to Unmanned Aerial Vehicle (UAV)-based multispectral imagery offers a rapid and cost-effective solution for bathymetric surveys. However, models based solely on multispectral imagery are inherently limited by confounding factors such as shadow effects, poor water quality, and complex seafloor textures, which obscure the spectral–depth relationship, particularly in heterogeneous coastal environments. To address these issues, we developed a hybrid bathymetric inversion model that integrates digital surface model (DSM) data—providing high-resolution topographic information—with ML applied to UAV-based multispectral imagery. The model training was supported by multibeam sonar measurements collected from an Unmanned Surface Vehicle (USV), ensuring high accuracy and adaptability to diverse underwater terrains. The study area, located around Lazarus Island, Singapore, encompasses a sandy beach slope transitioning into seagrass meadows, coral reef communities, and a fine-sediment seabed. Incorporating DSM-derived topographic information substantially improved prediction accuracy and correlation, particularly in complex environments. Compared with linear and bio-optical models, the proposed approach achieved accuracy improvements exceeding 20% in shallow-water regions, with performance reaching an R² > 0.93. The results highlighted the effectiveness of DSM integration in disentangling spectral ambiguities caused by environmental variability and improving bathymetric prediction accuracy. By combining UAV-based remote sensing with the ML model, this study presents a scalable and high-precision approach for bathymetric mapping in complex shallow-water environments, thereby enhancing the reliability of UAV-based surveys and supporting the broader application of ML in coastal monitoring and management. Full article

Climate change and rapid urbanization exert significant impacts on ecosystem services (ESs). The rational assessment and prediction of ESs are crucial for urban sustainable development. This study analyzes the spatiotemporal changes in land use in Shanghai from 2000 to 2020 and evaluates the key ESs, including water yield, soil retention, carbon storage, and habitat quality. Furthermore, integrated “climate change-land use” scenarios were constructed to systematically simulate the response characteristics of ESs under different climate change and development pathways. The results indicate that Shanghai’s land use from 2000 to 2020 was characterized by continuous expansion of built-up land and a significant reduction in cropland. Ecological land exhibited a low and fragmented coverage. By 2040, the ecological protection (EP) scenario could effectively curb the disorderly expansion of built-up land and maintain the stability of cropland and woodland, whereas the natural development (ND) scenario would exacerbate urban sprawl towards the east and further fragment ecological land. From 2000 to 2020, water yield in Shanghai showed an increasing trend, soil retention initially decreased followed by a gradual recovery, carbon sequestration experienced minor fluctuations, and habitat quality exhibited a continuous decline. By 2040, the EP scenarios will effectively maintain water yield and soil retention functions, steadily enhance carbon sequestration and habitat quality, and mitigate the negative impacts of climate change. In contrast, the ND scenarios show an unstable trend of initial increase followed by decrease. Spatially, the western and northern regions consistently remain high-value ESs zones under both scenarios. In 2040, Shanghai’s ESs will exhibit distinct administrative district disparities, characterized by “peripheral sensitivity and central stability”. This pattern underscores the necessity for implementing zone-specific regulation strategies in future urban planning. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

This study presents the implementation and evaluation of a high-resolution operational forecasting system for the Tagus estuary (Portugal), focusing on inundation and water quality predictions to support estuarine management. Developed using the relocatable Water Information Forecast Framework (WIFF), the system integrates two implementations of SCHISM: a 2D barotropic model including wave–current interactions for flood-prone areas, and a 3D baroclinic model simulating salinity, temperature, and biogeochemical variables. Forecasts were assessed over six months using in situ and satellite near real-time observations. Results show that the operational models represent well water levels, waves, salinity, temperature, and water quality dynamics. Compared to a regional model, the local forecast system generally offers improved accuracy within the estuary due to higher spatial resolution and better representation of local dynamics. Several challenges remain, including uncertainties in oceanic and riverine boundary conditions and limited high-resolution near real-time observations to continuously assess and improve operational models. Furthermore, the absence of operational two-way coupling between regional and local models limits cross-scale integration of physical and biogeochemical processes. The forecasting system for the Tagus estuary demonstrates the potential of local high-resolution operational models as reliable, user-oriented tools for managing transitional water systems, and as core elements for coastal management. Full article

Efficient drying is crucial for the preservation and high-value utilization of tricholoma matsutake (TM). Traditional hot-air drying is inefficient, energy-intensive, and prone to quality degradation. This study investigates the application of microwave drying for TM, systematically analyzing its dielectric properties and moisture states, and elucidating the dielectric response mechanisms during drying. Response surface methodology (RSM) was employed to optimize key process parameters, including microwave power, drying time, and sample mass, and to validate the feasibility of the optimized process for industrial applications. Results revealed that the dehydration process of TM comprises three distinct stages, with free water evaporation contributing 69.8% of the total weight loss. Dielectric properties correlated strongly with apparent density and temperature, with the loss tangent ($\mathit{tan}\delta$) increasing by 213.0% at higher temperatures, confirming dipole loss as the primary heating mechanism. Under optimized drying conditions (power: 620.00 W, time: 2.70 min, mass: 13.2 g), a dehydration rate (DR) of 85.41% was achieved, with a 1.50% deviation from the model-predicted values. The optimized process effectively maintained the relative integrity of the microstructure of TM, with the C/O ratio increasing from 1.03 to 1.31. Steam pressure-driven moisture migration was identified as the primary mechanism facilitating microwave-enhanced dehydration. Pilot-scale experiments scaled up the processing capacity to 15 kg/h and confirmed that the new process reduced total costs by 38% compared to traditional hot-air drying. The study developed an efficient and reliable microwave drying model, supporting industrial-scale TM processing. Full article

In the arid and semi-arid climate of Southern Kazakhstan, groundwater is the primary and most resilient source of water for pasture irrigation. This study provides an integrated assessment of the predicted, natural, and operational groundwater resources across five administrative regions—Almaty, Zhetysu, Zhambyl, Kyzylorda, and Turkestan—considering water quality (total dissolved solids, TDS), potential well yield, and aquifer depth. Hydrogeological maps at 1:200,000 and 1:1,000,000 scales, a regional well inventory, and GIS-based spatial analysis were combined to classify resource availability and identify surplus and deficit zones. Results show that 92.5% of predicted exploitable resources (totaling 1155.2 m³/s) have TDS ≤ 3 g/L, making them suitable for domestic and livestock use. Regional disparities are pronounced: Zhetysu, Almaty, and Zhambyl exhibit resource surpluses, Kyzylorda approaches balance, while Turkestan faces a marked deficit. The developed groundwater availability map integrates mineralization, well productivity, and recommended drilling depth, enabling the design of water intake systems without costly field exploration. This decision-support tool has practical value for optimizing water allocation, reducing operational costs, and improving the sustainability of pasture management under the constraints of limited surface water resources. Full article

This paper aims to assess the water quality of the Ismailia Canal, Egypt, in accordance with Article 49 of Law 92/2013. QUAL2K and Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) are utilized to simulate the water quality parameters of dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), nitrate nitrogen (NO₃-N), and ammonium (NH₃-N) in winter and summer 2023. The parameters of the QUAL2K and CNN-LSTM models were calibrated and validated in both winter and summer through trial and error, until the simulated results agreed well with the observed data. Additionally, the model’s performance was measured using different statistical criteria such as mean absolute error (MAE), root mean square (RMS), and relative error (RE). The results showed that the simulated values were in good agreement with the observed values. The results show that all parameter concentrations follow and did not exceed the limit of Article 49 of Law 92/2013 in winter and summer, except for dissolved oxygen concentration (8.73–4.53 mg/L) in winter and summer, respectively, which exceeds the limit of 6 mg/L, and in June, biological oxygen demand exceeds the limit of 6 mg/L due to increased organic matter. It is imperative to compare QUAL2K and CNN-LSTM models because QUAL2K provides a physics-based simulation of water quality processes, whereas CNN-LSTM employs deep learning in modeling complex temporal patterns. The two models enhance prediction accuracy and credibility towards enabling enhanced decision-making for Ismailia Canal water management. This research can be part of a decision support system regarding maximizing the benefits of the Ismailia Canal. Full article

With the rapid development of socioeconomics and the continuous advancement of urbanization, water environment issues in plain river networks have become increasingly prominent. Accurate and reliable water quality (WQ) predictions are a prerequisite for water pollution warning and management. Data-driven modeling offers a promising approach for WQ prediction in plain river networks. However, existing data-driven models suffer from inadequate capture of spatiotemporal (ST) dependencies and misalignment between direct prediction strategy assumptions with actual data characteristics, limiting prediction accuracy. To address these limitations, this study proposes a spatiotemporal graph neural network (ST-GNN) that integrates four core modules. Experiments were performed within the Chengdu Plain river network, with performance comparisons against five baseline models. Results suggest that ST-GNN achieves rapid and accurate WQ prediction for both short-term and long-term, reducing prediction errors (MAE, RMSE, MAPE) by up to 46.62%, 37.68%, and 45.67%, respectively. Findings from the ablation experiments and autocorrelation analysis further confirm the positive contribution of the core modules in capturing ST dependencies and eliminating data autocorrelation. This study establishes a novel data-driven model for WQ prediction in plain river networks, supporting early warning and pollution control while providing insights for water environment research. Full article

The Loess Plateau (LP), Earth’s largest loess deposit, has experienced significant vegetation recovery since 2000 despite water scarcity. Using 2001–2022 satellite-derived normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) data, we analyze vegetation structural (greenness) and functional (photosynthesis) responses, addressing critical knowledge gaps in cover expansion—functional enhancement relationships during ecological restoration. Sustained warming and increased moisture have consistently enhanced both the NDVI and SIF across the LP, with water availability remaining the key limiting factor for vegetation structure and function. Notably, the relative trend of SIF (RT_SIF: 3.92% yr⁻¹) significantly exceeded that of the NDVI (RT_NDVI: 1.63% yr⁻¹), producing a mean divergence (ΔRT_SIF-NDVI) of 2.38% yr⁻¹ (p < 0.01) across the LP. This divergence indicates faster functional enhancement relative to structural expansion during vegetation recovery, with grasslands exhibiting the most pronounced difference in ΔRT_SIF-NDVI compared to forests and shrublands. Hydrothermal conditions regulated vegetation structural–functional divergence, with regions experiencing stronger water stress exhibiting significantly greater ΔRT_SIF-NDVI values. These findings demonstrate substantial hydrological constraint alleviation since 2001. Increased precipitation enhanced light use efficiency, accelerating photosynthetic function—especially in grasslands due to their rapid precipitation response. In contrast, forests maintained higher structure–function synchrony (lower values of ΔRT_SIF-NDVI) through conservative strategies. Our findings indicate that grasslands may evolve as carbon sink hotspots via photosynthetic overcompensation, whereas forests remain reliant on sustaining current vegetation and are constrained by deep soil water deficits. This contrast highlights the value of ΔRT_SIF-NDVI as a physiologically based indicator for quantifying restoration quality and predicting carbon sequestration potential across the LP. Full article

Water quality is crucial to the economy and ecology because a healthy aquatic eco-system supports human survival and biodiversity. We have developed the Neuro-Adapt Fuzzy Strategist (NAFS) to improve water quality index (WQI) forecasting accuracy. The objective of the developed model is to achieve a balance by improving prediction accuracy while preserving high interpretability and computational efficiency. Neural networks and fuzzy logic improve the NAFS model’s flexibility and prediction accuracy, while its optimized backward pass improves training convergence speed and parameter update effectiveness, contributing to better learning performance. The normalized and partial derivative computations are refined to improve the model. NAFS is compared with ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS), and current machine learning (ML) models such as LSTM, GRU, and Transformer based on performance evaluation metrics. NAFS outperforms ANFIS and ANN, with MSE of 1.678. NAFS predicts water quality better than ANFIS and ANN, with RMSE of 1.295. NAFS captures complicated water quality parameter interdependencies better than ANN and ANFIS using principal component analysis (PCA) and Pearson correlation. The performance comparison shows that NAFS outperforms all baseline models with the lowest MAE, MSE, RMSE and MAPE, and the highest R², confirming its superior accuracy. PCA is employed to reduce data dimensionality and identify the most influential water quality parameters. It reveals that two principal components account for 72% of the total variance, highlighting key contributors to WQI and supporting feature prioritization in the NAFS model. The Breusch–Pagan test reveals heteroscedasticity in residuals, justifying the use of non-linear models over linear methods. The Shapiro–Wilk test indicates non-normality in residuals. This shows that the NAFS model can handle complex, non-linear environmental variables better than previous water quality prediction research. NAFS not only can predict water quality index values but also enhance WQI estimation. Full article

This study presents a novel approach to the parametrization of 3D PETRO FACIES and SEISMO FACIES using supervised and unsupervised learning, supported by a coherent structural and stratigraphic framework, to enhance understanding of the presence of hydrocarbons in the Dzików–Uszkowce region. The prediction relies on selected seismic attributes and well logging data, which are essential in hydrocarbon exploration. Three-dimensional seismic data, a crucial source of information, reflect the propagation velocity of elastic waves influenced by lithological formations and reservoir fluids. However, seismic response similarities complicate accurate seismic image interpretation. Three-dimensional seismic data were also used to build a structural–stratigraphic model that partitions the study area into coeval strata, enabling spatial analysis of the machine learning results. In the 3D seismic model, PETRO FACIES classification achieved an overall accuracy of 80% (SD = 0.01), effectively distinguishing sandstone- and mudstone-dominated facies (RT1–RT4) with F1 scores between 0.65 and 0.85. RESERVOIR FACIES prediction, covering seven hydrocarbon system classes, reached an accuracy of 70% (SD = 0.01). However, class-level performance varied substantially. Non-productive zones such as HNF (No Flow) were identified with high precision (0.82) and recall (0.84, F1 = 0.83), while mixed-saturation facies (HWGS, BSWGS) showed moderate performance (F1 = 0.74–0.81). In contrast, gas-saturated classes (BSGS and HGS) suffered from extremely low F1 scores (0.08 and 0.12, respectively), with recalls as low as 5–7%, highlighting the model’s difficulty in discriminating these units from water-saturated or mixed facies due to overlapping seismic responses and limited training data for gas-rich intervals. To enhance reservoir characterization, SEISMO FACIES analysis identified 12 distinct seismic facies using key attributes. An additional facies (facies 13) was defined to characterize gas-saturated sandstones with high reservoir quality and accumulation potential. Refinements were performed using borehole data on hydrocarbon-bearing zones and clay volume (VCL), applying a 0.3 VCL cutoff and filtering specific facies to isolate zones with confirmed gas presence. The same approach was applied to PETRO FACIES and a new RT facie was extracted. This integrated approach improved mapping of lithological variability and hydrocarbon saturation in complex geological settings. The results were validated against two blind wells that were excluded from the machine learning process. Knowledge of the presence of gas in well N-1 and its absence in well D-24 guided verification of the models within the structural–stratigraphic framework. Full article

Although significant progress has been made in the conservation of Père David’s deer (Elaphurus davidianus) populations, rapid population growth in coastal wetlands has caused severe habitat degradation. This highlights the urgent challenge of balancing ungulate population dynamics with wetland restoration efforts, particularly considering the limited data available on post-disturbance ecosystem recovery in these environments. In this study, we evaluated soil quality and bacterial community dynamics at an abandoned feeding site and a nearby control site within the Dafeng Milu National Nature Reserve during 2020–2021. The goal was to provide a theoretical basis for the ecological restoration of Père David’s deer habitat in coastal wetlands. The main findings are as follows: among the measured indicators, bulk density (BD), soil water content (SWC), sodium (Na⁺), total carbon (TC), total nitrogen (TN), total phosphorus (TP), available potassium (AK), microbial biomass nitrogen (MBN), and the Chao index were selected to form the minimum data set (MDS) for calculating the soil quality index (SQI), effectively reflecting the actual condition of soil quality. Overall, the SQI at the feeding site was lower than that of the control site. Based on the composition of bacterial communities and the functional prediction analysis of bacterial communities in the FAPROTAX database, it is shown that feeding sites are experiencing sustained soil carbon loss, which is clearly caused by the gathering of Père David’s deer. Co-occurring network analyses demonstrated the structure of the bacterial community at the feeding site was decomplexed, and with a lower intensity than the control. In RDA, Na⁺ is the main soil property that affects bacterial communities. These findings suggest that the control of soil salinity is a primary consideration in the development of Père David’s deer habitat restoration programmes, followed by addressing nitrogen supplementation and carbon sequestration. Full article

Improved irrigation guidelines are needed to maximize crop water use efficiency. Combining field data with simulation models can provide information for better irrigation management. The objective of the present study was to evaluate the effects of two flood irrigation treatments on fiber yield (FY) and quality during the 2023 and 2024 growing seasons in Maricopa, Arizona, USA. Two irrigation treatments, denoted as F100% and F80%, were arranged in a randomized complete block design with three replicates. Then, AquaCrop was used to simulate cotton yield (Y_Tot), water use (ET_obs), and total soil water content (WC_Tot) for the two irrigation treatments. Six statistical metrics, including the coefficient of determination (R²), the normalized root-mean-square error (NRMSE), the mean absolute error (MAE), simulation error (S_e), the index of agreement (D_index), and the Nash–Sutcliffe efficiency coefficient (NSE), were employed to assess model performance. The results of the field trial demonstrated that reducing the irrigation rate to 80% of ET_c negatively impacted cotton FY and ET water productivity (ETWP); the FY declined by 45.2% (ETWP = 0.097 kg·ha⁻¹) in 2023 and by 38.1% (ETWP = 0.133 kg·ha⁻¹) in 2024. Conversely, F100% produced a more uniform and stronger fiber than F80%, with the uniformity index (UI) and fiber strength (STR) measuring 81.7% and 29.5 g tex⁻¹ in 2023 and 82.2% and 30.0 g tex⁻¹ in 2024, indicating that UI and STR were well correlated with soil water during both growing seasons. AquaCrop showed an excellent performance in simulating cotton CC during the two growing seasons. The R², NRMSE, D_index, and NSE were between 0.97 and 0.99, 8.45% and 14.36%, 0.98 and 0.99, and 0.96 and 0.98, respectively. Moreover, the AquaCrop model accurately simulated Y_Tot during these seasons, with R², NRMSE, D_index, and NSE for pooled yield data of 0.93, 8.05%, 0.95, and 0.78, respectively. The model consistently overestimated Y_Tot, ET_obs, and WC_Tot, but within an acceptable S_e (S_e < 15%) during both growing seasons, except for WC_Tot under the 80% treatment in 2023 (S_e = 26.4%). Consequently, AquaCrop can be considered an effective tool for irrigation management and yield prediction in arid climates such as Arizona. Full article

Global climate change, combined with the construction of impermeable urban elements, tends to increase runoff, which might cause flooding and reduce groundwater recharge. Moreover, the first flash of these areas might accumulate pollutants that might deteriorate groundwater quality. A digital elevation model (DEM) describes urban landscapes by representing the watershed relief at any given location. While, in concept, finer DEMs and land use classification (LUC) are yielding better hydrological models, it is suggested that over-accuracy overestimates minor tributaries that might be redundant. Optimal DEM resolution with integrated spectral and feature-based LUC was found to reflect the hydrological network’s significant tributaries. To cope with the karstic urban watershed complexity, ModClark Transform and SCS Curve Number methods were integrated over a GIS-HEC-HMS platform to a nominal urban watershed sub-basin analysis procedure, allowing for detailed urban runoff modeling. This precise urban karstic terrain modeling procedure can predict runoff volume and discharge in urban, mountainous karstic watersheds, and may be used for water-sensitive design or in such cities to control runoff and prevent its negative impacts. Full article