Search Results (1,435)

Incomplete spatio-temporal (ST) data from sensor networks in precision agriculture often limits environmental modeling and decision-making accuracy. To address this, we propose the Spatio-Temporal Conditional Diffusion Framework (ST-CDF), a generative approach for high-fidelity data reconstruction. The framework’s core is a deep denoising network that integrates a Graph Attention Network (GAT) to explicitly model non-Euclidean spatial correlations, a Differential Attention Transformer to capture abrupt temporal dynamics, and an Inverse Discrete Wavelet Transform (IDWT) module to preserve multi-scale signal details. The generative process is constrained by a physics-informed training objective, which injects known physical laws (i.e., the Penman–Monteith equation for reference evapotranspiration, $E{T}_0$) as an inductive bias, ensuring the imputed data maintains physical consistency. For privacy-preserving deployment on resource-constrained IoT devices, we extend the framework with a Federated Cluster-Guided Distillation (Fed-CGD) strategy. We conducted extensive experiments against established methods on two real-world agricultural datasets. ST-CDF demonstrated improved imputation accuracy across evaluated metrics. Its efficacy was most pronounced in the physically-demanding $E{T}_0$ calculation task, where data imputed by ST-CDF at an 80% missing rate achieved a Root Mean Square Error (RMSE) of 0.3485 and a Coefficient of Determination ($R^2$) of 0.7558, outperforming the baseline models. Furthermore, we explore ST-CDF as an explainable (XAI) framework for active agricultural decision support, demonstrating its utility in performing counterfactual simulations of “what-if” interventions, such as irrigation. The findings highlight ST-CDF as an effective, physically-grounded, and interpretable tool for data-driven scientific computation and precision agriculture. Full article

Apical periodontitis is a common inflammatory oral condition and a major cause of endodontic treatment need. The present retrospective clinical study aimed to evaluate the frequency, distribution, and radiographic healing of teeth diagnosed with apical periodontitis following primary endodontic treatment or nonsurgical retreatment within a specific patient cohort. Consecutive patients presenting for endodontic treatment at the study clinic between 2020 and 2021 were screened for inclusion. Eligible cases were those in which patients provided written informed consent, presented with periapical inflammatory pathology, and underwent conservative endodontic treatment. Exclusion criteria were incomplete data, non-functional or non-restorable teeth, third molars, pregnancy, probing depth ≥ 4 mm, radiographic bone loss, pathologic tooth mobility due to attachment loss, periodontal involvement of the lesion, and primary dentition. A total of 277 teeth, all diagnosed with apical periodontitis at baseline, were included. Some patients contributed more than one tooth. All treatments were performed by a single operator according to a standardized clinical protocol, including uniform diagnostic criteria, chemo-mechanical preparation, irrigation regimen, obturation technique, and radiographic follow-up at 12 and 24 months. Periapical healing was assessed radiographically using the Periapical Index (PAI). Within this cohort, elderly patients significantly represented the largest proportion of those treated (p < 0.001). Maxillary teeth also comprised a significantly higher proportion of cases than mandibular teeth (55.2% vs. 44.8%). The mean initial PAI score was 3.37 ± 0.9 points, with a median of 3 points, and the final score was 1.31 ± 0.93 points, with a median of 1 point. Radiographic healing was observed in 56.68% of cases at 12 months and in 84.84% of cases at 24 months. Primary endodontic treatment and nonsurgical retreatment of teeth with apical periodontitis in this selected patient population were associated with substantial radiographic improvement over a 24-month follow-up period. These findings support the value of standardized endodontic management and longitudinal radiographic monitoring. Full article

Background: Small intestine bacterial overgrowth (SIBO) is associated with steatohepatitis (SH) in subjects with metabolic-associated steatotic liver disease (MASLD). The impact of ileocecal valve (ICV) incontinence, a major cause of SIBO in patients with MASLD, remains unknown because of the unmet need for a non-X-ray-dependent diagnosis. Methods: Exploiting water as contrast medium and colonic irrigation via a hydro-colon machine (Clean Colon Srl, Monza, Italy), we developed a new abdominal ultrasound (US) procedure for diagnosing and grading ICV incontinence. In a pilot, observational, feasibility and safety study, we correlated a new ICV incontinence parameter with irritable bowel syndrome (IBS, ROMA IV criteria), serum transaminases (AST, ALT), platelet counts, FIB-4, US liver steatosis and stiffness (LS, measured by Shear Wave and Transient Elastography, SWE and TE). Results: We prospectively studied 32 consecutive subjects with IBS who underwent a pre-colonoscopy colon cleansing after informed consent: 19 males (59%), body mass index (BMI) 26.6 ± 2.6 kg/m², age 57 ± 19 years, 16 (50%) with US liver steatosis. The half-hour (27 min, range 20–35 min) procedure was safe and well tolerated except in two males with prostate hypertrophy. ICV incontinence was graded (after 2500–3000 mL irrigation) according to cecum/right-colon distention with/without (immediate or delayed) reflux into terminal ileum (TI): 0 = cecum distension without TI reflux; 1 = cecum distension with TI reflux; 2 = absence of cecum distension with TI reflux. Cecum/right-colon distention (grade 0 or 1) was perceived by the patients whereas the right colon irrigation with complete ICV incontinence (grade 2) was symptomless. ICV continence associated with LS (p ≤ 0.0001). A histologic diagnosis of non-alcoholic steatohepatitis was confirmed in a 35-year-old obese male with SIBO and LS > 8 kPa (8.7/8.5 kPa by SWE/TE):steatosis (grade S3) with hepatocyte ballooning, lobular inflammation (grade 6/8) without fibrosis (stage 0/4, F0). Conclusions: The new US-based approach provides a feasible, easy-to-perform, mini-invasive tool for the diagnosis and grading of ICV incontinence. Preliminary results prompt prospective studies investigating the impact of ICV incontinence as a possible co-factor of steatohepatitis in patients with MASLD. Full article

In the context of global population growth and intensifying climate change, ensuring food security remains a critical challenge. Orchards are more productive than arable crops, contributing significantly to the nutrition of a growing population. Ecologically, due to the absence of frequent soil tillage, orchards resemble natural forest ecosystems more closely than other agricultural systems. Irrigated orchards are particularly productive and enhance biodiversity in territories where water scarcity is the limiting factor for ecosystems. This review, the result of extensive reflection and a comprehensive analysis of the literature on orchard sustainability, synthesizes evidence on the diverse ecosystem services provided by these perennial systems. Due to their structural complexity, well-managed orchards contribute significantly to climate regulation through carbon sequestration, microclimate cooling, and soil erosion prevention. Furthermore, they support nutrient cycling and provide cultural value. This paper establishes an integrated scientific framework to inform evidence-based policies and reshape societal perceptions. It argues that recognizing orchards as multifunctional landscapes, rather than mere resource consumers, is critical for environmental resilience, supporting their fair valuation as essential components of a sustainable bioeconomy. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Background/Objectives: To compare the incidence of postoperative acute urinary retention (AUR) between traditional open laminectomy and percutaneous endoscopic lumbar surgery (PELS) using a large-scale real-world database, with specific stratification by urologic status, age, and sex. Methods: A retrospective, propensity score-matched analysis was conducted using the TriNetX Global Health Research Network (2015–2024). Adult patients undergoing PELS were compared to those undergoing open laminectomy. To rule out the confounding effect of routine intraoperative catheterization, the primary outcome was defined as de novo AUR occurring between 24 h and 3 months postoperatively. Subgroup analyses were performed for patients with benign prostatic hyperplasia (BPH), females, and age-stratified cohorts (<70 vs. ≥70 years). This study was approved by the Institutional Review Board (IRB/REC: CE25727C) and conducted under a waiver of informed consent. Results: In the matched cohorts of non-BPH males, females, and patients aged < 70 years, PELS was associated with a statistically significant reduction in AUR risk (Hazard Ratios: 0.445, 0.649, and 0.403, respectively) compared to open surgery. However, in males with BPH, the protective benefit of the endoscopic technique was attenuated and did not reach statistical significance (p = 0.0744), suggesting the study was underpowered for this subgroup or that baseline obstruction remains a dominant risk factor. Conclusions: Percutaneous endoscopic lumbar surgery was associated with a significantly lower risk of postoperative AUR compared to open laminectomy, particularly in patients without preexisting urologic obstruction. This benefit is likely attributable to minimized tissue trauma and the anti-inflammatory effects of continuous saline irrigation. However, in patients with BPH, baseline pathology outweighs surgical factors, necessitating medical prophylaxis regardless of the surgical approach. Full article

Efficient irrigation management requires reliable information on soil water content, yet low-cost capacitive sensors often lack proper calibration. This study evaluates the metrological performance of two DF Robot probes, SEN0193 (S1) and SEN0308 (S3), under controlled variations in porous media properties. Glass beads of three size classes (<50 µm, 70–110 µm, and 400–600 µm) were used to simulate fine, medium, and coarse textures. Sensors were tested at four water contents (0, 10, 20, and 30%) and four salinity levels (0, 4, 8, and 16 g NaCl L⁻¹). Results show that the manufacturer-recommended air/water calibration is unsuitable for soils or porous media; calibration should instead be performed under dry and saturated conditions specific to the medium. S1 exhibited stable and homogeneous responses, with intra-unit CV ≤ 2%, but moderate calibration accuracy (R² = 0.68–0.80; RMSE = 8.9–12.9% VWC across textures). S3 showed a wider signal range (80–90% larger than S1), better fit in coarse texture (R² = 0.96; RMSE = 3.5% VWC), but higher unit-to-unit variability (CV = 6–14%) and performance degradation in fine and saline media. Although these sensors cannot provide accurate absolute quantification, their ability to track moisture trends makes them useful for irrigation management, provided calibration accounts for medium texture and salinity. Full article

Climate change intensifies hydrological variability and threatens agricultural water security. This review synthesizes literature on agricultural water system resilience under climate change through a structured critical narrative approach informed by PRISMA/SALSA reporting principles. We examine four linked domains: resilience concepts and indicators, assessment methods under uncertainty, climate impact and vulnerability evidence, and adaptation/governance pathways. The synthesis indicates a broad shift from engineering-centered water-supply approaches toward socio-ecological resilience frameworks that combine infrastructure, ecosystem processes, farmer behavior, and institutions. Methodologically, deterministic optimization is increasingly complemented by stochastic, robust, integrated-assessment, remote-sensing, and machine-learning approaches, although data requirements, uncertainty propagation, and interpretability remain important constraints. Evidence suggests that crop water demand and irrigation requirements may increase substantially under high-emission scenarios, with acute risks in arid and semi-arid regions. Effective adaptation is unlikely to rely on single technologies alone; precision irrigation, nature-based solutions, climate services, and infrastructure investments require complementary demand-side rules, water accounting, equity safeguards, and participatory governance to avoid maladaptation such as the irrigation-efficiency rebound effect. We identify priority research needs in transparent review protocols, uncertainty quantification, cross-scale governance, farmer decision-making, digital inclusion, and monitoring systems. The review provides a moderated conceptual framework and policy-oriented research agenda for strengthening agricultural water resilience. Full article

Accurate soil salinization monitoring in arid oases is crucial for agricultural sustainability and ecological security. However, existing deep learning-based approaches often suffer from insufficient use of multi-scale information and inadequate modeling of feature interactions, limiting their accuracy for retrieving complex salinity patterns. To address these limitations, we propose a multi-scale attention-based optimized hybrid deep learning model that integrates multi-scale 1D convolutional neural networks (1D-CNN), bidirectional gated recurrent units (Bi-GRU), and Transformer mechanisms (termed SMS–1D-CNN–Bi-GRU–Transformer). In this study, “scale” refers to the receptive-field scale formed by different 1D convolutional kernel sizes. The model employs a multi-scale feature extraction module to capture remote sensing signals across different scales, a multi-scale attention mechanism to adaptively weight the most informative features, and a Bi-GRU–Transformer module to explore complex sequential and global feature relationships. The proposed framework is applied to an oasis irrigation zone in Weili County, Xinjiang, using hyperspectral data from the ZY-1E satellite, topographic indices, and spectral-derived variables. The proposed method outperforms conventional 1D-CNN, GRU–Transformer, and other benchmark models on the test set—showing improvements of 2.8% in the coefficient of determination (0.952) and 18.9% in the root mean square error (0.867 g·kg⁻¹), demonstrating practical utility for precision land management and salinity monitoring in vulnerable irrigated ecosystems. Full article

This article presents a new literature-informed benchmark problem for continuous global optimization, inspired by the semantics of weather-aware irrigation scheduling. The problem is formulated as a continuous minimization task in which irrigation decisions are determined under synthetically generated but agronomically motivated weather-dependent water demand, rainfall effects, and nonlinear operational penalties. The proposed benchmark is intentionally synthetic and self-contained requiring no external datasets or field-calibrated parameters while its components are grounded in established concepts from irrigation science such as evapotranspiration-based demand estimation, yield response–water relationships, and weather-dependent scheduling principles. It is designed to be sufficiently challenging for numerical optimization while retaining a clear agronomic interpretation. The article provides the full mathematical formulation of the problem, explains its main components and parameters, and reports an experimental study focused on its optimization behavior using ten established continuous optimizers across five problem dimensions. From this perspective, the proposed problem is positioned within a clear framework for the study of real-world-inspired continuous optimization benchmarks, offering an additional case in which mathematical formulation, practical interpretation, and experimental investigation coexist in a coherent and systematic manner. Full article

Groundwater resources in the Kingdom of Saudi Arabia (KSA) are important for meeting the needs of human communities, agriculture, and industry. In Western KSA, groundwater from coastal aquifers is an essential resource that complements desalinated seawater. Therefore, ensuring the quality and contamination of groundwater has emerged as a critical priority for preserving water security. The aim of this research is to evaluate the groundwater quality and its vulnerability to contamination within the Wadi Marawani Basin. To achieve this aim, water quality indices (WQIs), the DRASTIC model, and machine learning (ML) algorithms were employed alongside a Geographic Information System (GIS). The results of the chemical analysis of 64 water samples were used in these assessments. Furthermore, several input parameters were evaluated using the DRASTIC model to estimate the DRASTIC index (DI) and generate a groundwater vulnerability map. Three ML algorithms—specifically, a Multilayer Perceptron (MLP), a Random Forest (RF), and a Decision Tree (DT)—were utilized to forecast WQIs such as the total dissolved solids (TDS) and sodium adsorption ratio (SAR), in addition to the DRASTIC index (DI). The results revealed that around 36% of the samples were classified as fresh water (<1000 mg/L). The SAR ranged from 1.10 to 32.50, indicating that most samples were suitable for irrigation. Approximately 22% of the basin was classified as demonstrating high vulnerability, whereas about 78% demonstrated low-to-moderate vulnerability. Assessment of the ML models showed high predictive accuracy for the TDS, SAR, and DI. The MLP-Vul. model attained an R² value of 1.00 and RMSE value of 0.01, the RF-Vul. model achieved an R² of 0.94 and RMSE of 3.17, and the DT-Vul. model attained an R² of 0.92 and RMSE of 3.57. Although there was a minor increase in RMSE across all models during the testing phase, their predictive performance remained clear. Full article

United States (U.S.) specialty crop growers face myriad decisions regarding substrate selection as sustainability, market demands, and resource limitations influence soilless production systems. Peat is a common component in container substrates despite public concerns about its limited availability, finite supply, and associations with greenhouse gas emissions and environmental impacts, which have prompted growers to seek alternative materials. However, limited research has focused on the experiences, challenges, and research needs of U.S. specialty crop growers related to substrate transitions. The purpose of this mixed-methods study was to explore growers’ key considerations, research interests, and preferred communication methods associated with substrate transitions. An online survey and three focus groups were used to gain insights into growers’ experiences, concerns, and perceived impacts of peat replacement, research needs, and preferred communication channels. Results indicated that financial costs, peat availability, access to peat alternative substrates, and environmental considerations were the main factors influencing potential changes in substrate use. Growers identified key research priorities to assist decision-making, including (1) irrigation and nutrient management strategies, (2) the economic performance of alternative substrates, and (3) locally validated trials demonstrating system compatibility. They preferred practical communication methods for sharing results, including websites, YouTube videos, in-person meetings, on-farm demonstrations, and short podcasts accessible during routine work hours. The findings implied that research, outreach, and communication efforts tailored to growers’ operational contexts will support informed substrate transitions. Full article

Canal discharge identification is essential for irrigation water metering, water management, and ecological protection. With the rapid advancement of UAV aerial photogrammetry, UAV-based large-scale field flow field observations have emerged as a prominent research focus. Given the capability to accurately retrieve canal surface flow velocities, a critical challenge remains in UAV remote sensing-based canal hydrological monitoring: how to identify key cross-sections, obtain high-resolution surface flow field information, and enable timely canal cross-section discharge estimation during sudden flood events. To address the aforementioned challenges, this manuscript combines deep learning algorithms with Kalman filtering and monocular ranging techniques. Artificial square sheet tracers are released into the canal, based on which a YOLO-DeepSort deep tracking framework is constructed. Based on the established UAV-based canal flow velocity perception platform using deep learning, this manuscript achieves mAP@0.5 of 0.995, with precision and recall both reaching 1.0 for real-time tracer detection and flow velocity identification via UAV low-altitude remote sensing. The average relative error of velocity estimation is within 7%, and discharge inversion errors are 1.7%, 6.4%, and 4.6% for the three canal sections, respectively. The surface flow field and cross-sectional velocity distribution of the observed sections are obtained accurately. This manuscript is expected to provide a systematic scientific basis for UAV low-altitude remote sensing-based canal discharge monitoring. Full article

Vapor Pressure Deficit (VPD) is a critical determinant of atmospheric evaporative demand and plant water stress in tropical agricultural systems. This study applied a Gaussian Mixture Model (GMM) and K-Means clustering to 36,528 hourly meteorological observations collected from Eastern Thailand between August 2021 and September 2025, with the objective of identifying distinct atmospheric moisture regimes relevant to precision irrigation management in durian cultivation. Two input configurations were evaluated: a multivariate feature space comprising air temperature, relative humidity, wind speed, solar radiation, and VPD; and a univariate input consisting of VPD alone. Model selection for GMM was guided by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), while K-Means performance was assessed using the Elbow method, Silhouette Coefficient, Calinski–Harabasz Index, and Davies–Bouldin Index. For the multivariate input, GMM identified K = 7 as the optimal number of clusters, supported by the largest single-step reduction in both AIC and BIC at this transition point. For the univariate VPD input, K = 5 was selected as the most parsimonious and agriculturally interpretable solution. The seven clusters derived from the multivariate GMM were organized into four atmospheric moisture regimes, such as very low, moderate, high, and very high evaporative demand, capturing the full spectrum of diurnal and seasonal VPD variability characteristic of Eastern Thailand. The results demonstrate that GMM-based probabilistic clustering applied to multivariate meteorological inputs provides a more comprehensive characterization of atmospheric moisture dynamics than univariate or geometric clustering approaches, offering a practical framework for tiered irrigation scheduling and drought stress early warning systems in tropical fruit cultivation. Full article

Popping expansion is the main quality trait determining the commercial value of popcorn maize, yet its evaluation requires destructive grain sampling. We investigated whether multitemporal UAV multispectral and textural features could predict grain yield and popping expansion in a native population of Peruvian popcorn and its five purple-kernel corn hybrids grown in 16 drainage lysimeters (80 subplots) under controlled irrigation in Lima, Peru. Eight UAV flights were conducted between 50 and 117 days after sowing, and 8 vegetation indices plus 5 GLCM texture metrics were extracted from canopy-masked imagery. Six regression algorithms were trained using Sequential Forward Selection (SFS; applied to five of six algorithms) and validated by Leave-One-Lysimeter-Out cross-validation (LOGO). Early grain, grain filling, and maturity were the most informative stages for yield prediction. The best model, obtained at maturity, was SVR-rbf using SCCCI and Homogeneity, reaching R² = 0.66 and RMSE = 1.23 t ha⁻¹. SCCCI was the most consistently selected predictor across models. By contrast, popping expansion was poorly predicted (R² = 0.17), indicating that this post-harvest quality trait is only weakly linked to canopy-level spectral information. Multitemporal UAV phenotyping therefore shows promise for non-destructive yield screening, but not for replacing direct popping expansion measurements. Full article