Search Results (1,605)

Although approximately half of global lithium consumption is used in the rechargeable battery industry, lithium is also in demand for other specialized applications, such as high-temperature lubricants, ceramics, glass, and pharmaceuticals. The growing need for efficient lithium recovery and recycling underscores the importance of fast and accurate analytical tools for determining lithium concentrations in non-compliant and waste materials generated by industrial processes. In this paper, we present a machine learning-based procedure utilizing Laser-Induced Breakdown Spectroscopy (LIBS) to accurately quantify lithium concentrations in lithium-rich non-compliant materials derived from the industrial production of enamels used for coating metallic surfaces. This procedure addresses challenges such as strong self-absorption and matrix effects, which limit the effectiveness of conventional univariate calibration methods. By employing a multivariate approach, we developed a single model capable of quantifying lithium content across a wide concentration range. A comparison of the LIBS results with those obtained using conventional laboratory analysis (Inductively Coupled Plasma–Optical Emission Spectrometry, ICP-OES) confirms that LIBS can deliver the speed, precision, and reliability required for potential routine applications in the lithium recovery and recycling industry. Full article

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions. Full article

Climate change is expected to have significant effects on crop yield in the Canadian Prairies. The objective of this study was to investigate these possible effects on spring wheat, barley and canola production using the Decision Support System for Agrotechnology Transfer (DSSAT) modelling platform. We applied 21 climate change scenarios from high-resolution (0.22°) regional simulations to three modules, DSSAT-CERES-Wheat, DSSAT-CERES-Barley and CSM-CROPGRO-Canola, using a historical baseline period (1985–2014) and three future periods: near (2015–2040), middle (2041–2070), and far (2071–2100). These simulations are part of CMIP6 (Coupled Model Intercomparison Project Phase 6) and have been processed using statistical downscaling and bias correction by the NASA Earth Exchange 26 Global Daily Downscaled Projections project, referred to as NEX-GDDP-CMIP6. The calibration and validation results surpassed the thresholds for a high level of accuracy. Simulated yield changes indicate that climate change has a positive effect on spring wheat and barley yields with median model increases of 7% and 11.6% in the near future, and 5.5% and 9.2% in the middle future, respectively. However, in the far future, barley production shows a modest increase of 4.4%, while spring wheat yields decline significantly by 17%. Conversely, simulated canola yields demonstrate a substantial decrease over time, with reductions of 25.9%, 46.3%, and 62.8% from the near to the far future, respectively. Agroclimatic indices, such as Number of Frost-Free Days (NFFD), Heating Degree-Days (HDD), Length of Growing Season (GSL), Crop Heat Units (CHU), and Effective Growing Degree Days (EGDD), exhibit significant correlations with spring wheat. Conversely, precipitation indices, such as very wet days and annual 5- and 10-day maximum precipitation, have a stronger correlation with canola yield changes when compared with temperature indices. The results provide key guidance for policymakers to design adaptation strategies and sustain regional food security and economic resilience, particularly for canola production, which is at significant risk under projected climate change scenarios across the Canadian Prairies. Full article

T-bar penetrometers have been widely used to measure strength parameters of marine clay in laboratory and in situ tests. However, using the deep resistance factor derived from full-flow conditions to evaluate the undrained shear strength of shallow clay layers may lead to significant underestimation. Furthermore, the deep resistance factor is inherently influenced by the strain-softening behavior of clay rather than maintaining the constant value (typically 10.5) as conventionally assumed in practice. To address this issue, large-deformation finite element (LDFE) simulations incorporating an advanced exponential strain-softening constitutive model were performed to replicate the full T-bar penetration process—from shallow embedment to deeper depths below the mudline. A series of parametric studies were conducted to examine the influence of key parameters on the resistance factor and the associated failure mechanisms during penetration. Based on numerical results, empirical formulas were derived to predict critical penetration depths for both trapped cavity formation and full-flow mechanism initiation. For penetration depths shallower than the full-flow depth, an expression for the softening correction factor was developed to calibrate the shallow resistance factor. Finally, combined with global optimization algorithms, a computer-aided back-analysis procedure was established to estimate strain-softening parameters using resistance-penetration curves from initial penetration tests in marine clay. The reliability of the back-analysis procedure was validated through extensive comparisons with a series of numerical simulation results. This procedure can be applied to the interpretation of T-bar in situ test results in soft marine clay, enabling the evaluation of its strain-softening behavior. Full article

Underwater vessel localization using passive magnetic anomaly sensing is a challenging problem due to the variability in vessel magnetic signatures and operational conditions. Data-based approaches may fail to generalize even to slightly different conditions. Thus, we propose an Attentive Neural Process (ANP) approach, in order to take advantage of its few-shot capabilities to generalize, for robust localization of underwater vessels based on magnetic anomaly measurements. Our ANP models the mapping from multi-sensor magnetic readings to position as a stochastic function: it cross-attends to a variable-size set of context points and fuses these with a global latent code that captures trajectory-level factors. The decoder outputs a Gaussian over coordinates, providing both point estimates and well-calibrated predictive variance. We validate our approach using a comprehensive dataset of magnetic disturbance fields, covering 64 distinct vessel configurations (combinations of varying hull sizes, submersion depths (water-column height over a seabed array), and total numbers of available sensors). Six magnetometer sensors in a fixed circular arrangement record the magnetic field perturbations as a vessel traverses sinusoidal trajectories. We compare the ANP against baseline multilayer perceptron (MLP) models: (1) base MLPs trained separately on each vessel configuration, and (2) a domain-randomized search (DRS) MLP trained on the aggregate of all configurations to evaluate generalization across domains. The results demonstrate that the ANP achieves superior generalization to new vessel conditions, matching the accuracy of configuration-specific MLPs while providing well-calibrated uncertainty quantification. This uncertainty-aware prediction capability is crucial for real-world deployments, as it can inform adaptive sensing and decision-making. Across various in-distribution scenarios, the ANP halves the mean absolute error versus a domain-randomized MLP (0.43 m vs. 0.84 m). The model is even able to generalize to out-of-distribution data, which means that our approach has the potential to facilitate transferability from offline training to real-world conditions. Full article

Lead contamination remains a critical global concern due to its persistent toxicity, bioaccumulative nature, and widespread occurrence in water, food, and industrial environments. The accurate, cost-effective, and rapid detection of lead ions (Pb²⁺) is essential for protecting public health and ensuring environmental safety. Among the available techniques, potentiometric sensors, particularly ion-selective electrodes (ISEs), have emerged as practical tools owing to their simplicity, portability, low power requirements, and high selectivity. This review summarizes recent progress in lead-selective potentiometry, with an emphasis on electrode architectures and material innovations that enhance analytical performance. Reported sensors achieve detection limits as low as 10⁻¹⁰ M, broad linear ranges typically spanning 10⁻¹⁰–10⁻² M, and near-Nernstian sensitivities of ~28–31 mV per decade. Many designs also demonstrate reproducible responses in complex matrices. Comparative analysis highlights advances in traditional liquid-contact electrodes and modern solid-contact designs modified with nanomaterials, ionic liquids, and conducting polymers. Current challenges—including long-term stability, calibration frequency, and selectivity against competing metal ions—are discussed, and future directions for more sensitive, selective, and user-friendly Pb²⁺ sensors are outlined. Full article

This study developed an inertial measurement unit (IMU)-enhanced bidirectional reflectance distribution function (BRDF) imaging system to address angular errors in multi-angle polarimetric measurements. The system integrates IMU-based closed-loop feedback, motorized motion, and image calibration, achieving zenith angle error reduction of up to 1.2° and angular control precision of approximately 0.05°. With a modular and lightweight structure, it supports rapid deployment in field scenarios, while the 2000 mm rail span enables detection of large-scale targets and three-dimensional reconstruction beyond the capability of conventional tabletop devices. Experimental evaluations on six representative materials show that compared with mark-based reference angles, IMU feedback consistently improves polarimetric accuracy. Specifically, the degree of linear polarization (DoLP) mean deviations are reduced by about 5–12%, while standard deviation fluctuations are suppressed by 20–40%, enhancing measurement repeatability. For the angle of polarization (AoP), IMU feedback decreases mean errors by 10–45% and lowers standard deviations by 10–37%, ensuring greater spatial phase continuity even under high-reflection conditions. These results confirm that the proposed system not only eliminates systematic angular errors but also achieves robust stability in global measurements, providing a reliable technical foundation for material characterization, machine vision, and volumetric reconstruction. Full article

Reservoir sedimentation, a global challenge causing an annual loss of 0.8–1% of reservoir storage capacity, disrupts fluvial sediment continuity and impacts ecology and societal needs. This study focuses on the Pulangi IV reservoir in the Philippines, a shallow and wide reservoir facing significant sedimentation issues. The research aims to investigate drawdown flushing and dredging of a flushing channel for future sustainable drawdown sluicing. A test flushing event was conducted and monitoring data, including discharge, suspended sediment concentration, bathymetry, and grain size distribution, were collected. Laboratory analyses, such as critical shear stress tests, were performed for model calibration. A 3D reservoir model and a 1D sediment transport model were applied incorporating cohesive sediment behavior. Scenarios were simulated to assess drawdown flushing, dredging and downstream impacts. Results highlight the importance of drawdown level, with cohesive sediment properties playing a critical role. Sedimentation downstream of the dam, resulting from dumped or flushed sediments, was low. However, downstream ecological and morphodynamic monitoring was found to be essential for all modeled strategies. This study demonstrates potential for establishing a flushing channel enabling future sustainable drawdown sluicing during floods by conducting repeated drawdown flushing in combination with dredging in the upper reservoir. Full article

The APSIM (Agricultural Production Systems Simulator)-Wheat model has been widely used to simulate wheat growth, but the sensitivity characteristics of the model parameters at different soil moisture levels in arid regions are unknown. Based on 2023~2025 winter wheat field data from the Changji Experimental Site, Xinjiang, China, this study conducted a global sensitivity analysis of the APSIM-Wheat model using Morris and EFAST methods. Twenty-one selected parameters were perturbed at ±50% of their baseline values to quantify the sensitivity of the aboveground total dry matter (WAGT) and yield to parameter variations. Parameters exhibiting significant effects on yield were identified. The calibrated APSIM model performance was evaluated against field observations. The results indicated that the order of influential parameters varied slightly across different soil moisture levels. However, the WAGT output was notably sensitive to accumulated temperature from seedling to jointing stage (T1), accumulated temperature from the jointing to the flowering period (T2), accumulated temperature from grain filling to maturity (T4), and crop water demand (E1). Meanwhile, yield output showed greater sensitivity to number of grains per stem (G1), accumulated temperature from flowering to grain filling (T3), potential daily grain filling rate during the grain filling period (P1), extinction coefficient (K), T1, T2, T4, and E1. The sensitivity indices of different soil moisture levels under Morris and EFAST methods showed highly significant consistency. After optimization, the coefficient of determination (R²) was 0.877~0.974, the index of agreement (d-index) was 0.941~0.995, the root mean square error (RMSE) was 319.45~642.69 kg·ha^–1, the mean absolute error (MAE) was 314.69~473.21 kg·ha^–1, the residual standard deviation ratio (RSR) was 0.68~0.93, and the Nash–Sutcliffe efficiency (NSE) was 0.26~0.57, thereby enhancing the performance of the model. This study highlights the need for more careful calibration of these influential parameters to reduce the uncertainty associated with the model. Full article

Scientific nitrogen management is essential for maximizing crop growth potential while minimizing resource waste and environmental impacts. Alfalfa (Medicago sativa L.) is the most widely cultivated high-quality leguminous forage crop globally, and is capable of providing nitrogen through nitrogen fixation. However, there remains some disagreement regarding its nitrogen management strategies. This study conducted a three-year field experiment and calibrated the APSIM-Lucerne model. Based on the calibrated model, three typical precipitation year types (dry, normal, and wet years) were selected. Combining field experiments, eight nitrogen application scenarios (0, 80, 120, 140, 160, 180, 200, and 240 kg·ha⁻¹) were set up. With the objectives of increasing alfalfa yield, nitrogen partial productivity, and nitrogen agronomic efficiency, this study investigates the appropriate nitrogen application thresholds for alfalfa under different precipitation year types. The results showed the following: (1) Alfalfa yield increased first and then decreased with the increase in nitrogen application level. The annual yield of the N160 treatment was the highest (13.39 t·ha⁻¹), which was 5.15% to 32.39% higher than that of the other treatments. (2) The APSIM-Lucerne model could well reflect the growth process and yield of alfalfa under different precipitation year types. The R² and NRMSE between the simulated and observed values of the former were 0.85–0.91 and 5.33–7.44%, respectively. The R² and NRMSE between the simulated and measured values of the latter were 0.74–0.96 and 2.73–5.25%, respectively. (3) Under typical dry, normal, and wet years, the optimal nitrogen application rates for alfalfa yield increases were 120 kg·ha⁻¹, 140 kg·ha⁻¹, and 160 kg·ha⁻¹, respectively. This study can provide a basis for precise nitrogen management of alfalfa under different precipitation year types. Full article

This study proposes a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The framework incorporates satellite radar backscatter, meteorological re-analysis, and topographic variables, applied across 15 SM stations and six land use systems at the North Wyke Farm Platform, southwest England, UK. GWQML was implemented using Gaussian and Tricube spatial kernels across a range of kernel bandwidths (500–1500 m). Model performance was evaluated using both in-sample and Leave-One-Land-Use-Out validation schemes, and a global quantile machine learning model (QML) without spatial weighting served as the benchmark. GWQML achieved R² values up to 0.85 and prediction interval coverage probabilities up to 0.9, with intermediate kernel bandwidths (750–1250 m) offering the best balance between accuracy and uncertainty calibration. Spatial autocorrelation analysis using Moran’s I revealed a lower residual clustering under GWQML relative to the benchmark model, which suggests improved handling of local spatial variation. This study represents one of the first applications of geographically weighted kernel functions in a quantile machine learning framework for daily soil moisture prediction. The approach implicitly captures spatially varying relationships while delivering calibrated uncertainty estimates for scalable SM monitoring across heterogenous agricultural landscapes. Full article

Air pollution is causing a global health, climate, and environmental crisis. Air quality (AQ) in hyper-arid regions, such as the Arabian Peninsula, remains under-explored, posing significant concerns for public health and the scientific community. Both long-term and short-term exposure to high pollutant levels, whether from anthropogenic or natural sources, can pose serious health risks. This paper offers a comprehensive review and meta-analysis of urban AQ literature published in the region over the past decade (2013–June 2025). We aim to provide guidance and highlight key directions for future research in the field. This paper examines key pollutants, emission sources, implications of urban sources, and the most studied countries, methodologies, limitations, and recommendations from different case studies. Our analysis reveals a significant research gap highlighting insufficient recent literature. Saudi Arabia was the most studied country with 20 papers, followed by the broader Arabian Peninsula (sixteen), Qatar (twelve), the United Arab Emirates and Iraq (seven each), Kuwait (four), Oman (three), Jordan, and Bahrain (one each). The primary methods employed included measurements and sampling (28%) and remote sensing (24%), with a focus on pollutants such as dust (23.1%), NO_x/NO₂/NO (17.2%), PM_2.5 (17.6%), and PM₁₀ (12%). Industrial emissions (27%) and natural dust (24%) were identified as significant emission sources. Monitoring methods included grab sampling (19%), integrated sampling (34%), and continuous monitoring (47%). Notably, 13.3% of AQ sensors were linked to a station, 27.6% were self-referenced, and 59.1% did not specify calibration methods. The findings highlight the need for further research, regular calibration of air quality monitors, and the integration of advanced modeling approaches. Moreover, we recommend exploring the links between air pollution and urban development to ensure cleaner air and contribute to the global dialogue on sustainable and cross-border AQ solutions. Full article

Forest ecosystems act as major carbon sinks, highlighting the need for the accurate estimation of aboveground biomass (AGB). The Global Ecosystem Dynamic Investigation (GEDI), a full-waveform spaceborne LiDAR system developed by NASA, provides detailed global observations of three-dimensional forest structures, playing a critical role in quantifying biomass and carbon storage. However, its performance has not yet been assessed in the Mediterranean forest ecosystems of Southern Italy. Therefore, the objectives of this study were to (i) evaluate the utility of the GEDI L4A gridded aboveground biomass density (AGBD) product in the Apulia region by comparing it with the Apulia AGBD map, and (ii) develop GEDI-derived AGBD models using multiple GEDI metrics. The results indicated that the GEDI L4A gridded product significantly underestimated AGBD, showing large discrepancies from the reference data (RMSE = 40.756 Mg/ha, bias = −30.075 Mg/ha). In contrast, GEDI-derived AGBD models using random forest (RF), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) demonstrated improved accuracy. Among them, the MGWR model emerged as the optimal choice for AGBD estimation, achieving the lowest RMSE (14.059 Mg/ha), near-zero bias (0.032 Mg/ha), and the highest R² (0.714). Additionally, the MGWR model consistently outperformed other models across four different plant functional types. These findings underscore the importance of local calibration for GEDI data and demonstrate the capability of the MGWR model to capture scale-dependent relationships in heterogeneous landscapes. Overall, this research highlights the potential of the GEDI to estimate AGBD in the Apulia region and its contribution to enhanced forest management strategies. Full article

Global climate change has intensified the frequency and severity of drought events, posing significant threats to agricultural sustainability, particularly for water-sensitive crops such as tea. In northern China, where precipitation is unevenly distributed and evapotranspiration rates are high, tea plantations frequently experience water stress, leading to reduced yields and declining quality. Therefore, accurately simulating soil water content (SWC) is essential for drought forecasting, soil moisture management, and the development of precision irrigation strategies. However, due to the high complexity of soil–vegetation–atmosphere interactions in field conditions, the practical application of the HYDRUS-1D model in northern China remains relatively limited. To address this issue, a three-year continuous monitoring campaign (2021–2023) was conducted in a coastal area of northern China, covering both young tea plantations and adjacent grasslands. Based on the measured meteorological and soil data, the HYDRUS-1D model was used to simulate SWC dynamics across 10 soil layers (0–100 cm). The model was calibrated and validated against observed SWC data to evaluate its accuracy and applicability. The simulation results showed that the model performed reasonably well, achieving an R² of 0.739 for the tea plantation and 0.878 for the grassland, indicating good agreement with the measured values. These findings demonstrate the potential of physics-based modeling for understanding vertical soil water processes under different land cover types and provide a scientific basis for improving irrigation strategies and water use efficiency in tea-growing regions. Full article

Background: Tuberculosis (TB) remains a major global health challenge, including in Thailand, where both asymptomatic and symptomatic cases sustain transmission. The disease burden increases treatment complexity and mortality, requiring integrated care and coordinated policies. Methods: We developed a deterministic compartmental model to examine the transmission dynamics of TB in Thailand, incorporating both latent and active stages of infection, as well as vaccination coverage. The model was calibrated using national TB incidence data, and sensitivity analysis revealed that the TB transmission rate was the most influential parameter affecting the basic reproduction number (R₀). We evaluated the impact of several intervention strategies, including increased treatment coverage for latent and active TB infections and improved vaccination rates. Results: Our analysis indicates that among the single interventions, scaling up effective treatment for latent TB infections produced the greatest reduction in asymptomatic and symptomatic cases, while enhanced treatment for active TB cases was second most effective for reducing both asymptomatic and symptomatic cases. Importantly, our results indicate that combining multiple interventions yields significantly greater reductions in overall TB incidence than any single approach alone. Our findings suggest that a modest investment in integrated TB control can substantially reduce TB transmission and disease burden in Thailand. However, complete eradication of TB would require a comprehensive and sustained investment to achieve near-universal coverage of both preventive and curative strategies. Conclusions: TB remains a significant public health threat in Thailand. Targeted interventions and integrated strategies are key to reducing disease burden and improving treatment outcomes. Full article