Search Results (735)

In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R² values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R² = 0.98, RMSE = 0.01; test set: R² = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models. Full article

Functional bowel disorders (FBDs) are chronic gastrointestinal conditions characterized by recurrent symptoms associated with gut microbiota dysbiosis. Although accumulating evidence suggests that probiotics can improve symptoms in patients with FBD, the underlying mechanisms remain to be fully elucidated. In this randomized, double-blind, placebo-controlled clinical trial, 38 adults meeting the Rome IV diagnostic criteria of functional constipation (FC) and functional diarrhea (FD) received either a multi-strain probiotic complex or placebo for 8 weeks. Clinical outcomes were evaluated using the Irritable Bowel Syndrome Severity Scoring System (IBS-SSS), bowel habits questionnaire, and IBS Quality of Life (IBS-QoL) instrument. Fecal samples were collected at baseline and at week 8 for gut microbiota profiling via 16S rRNA gene sequencing and metabolomic analysis using gas chromatography–mass spectrometry. Probiotic supplementation significantly reduced the severity of abdominal bloating and its interference with quality of life, and improved the body image domain of the IBS-QoL. Beta diversity analysis showed significant temporal shifts in the probiotic group, while 16S rRNA sequencing revealed an increased relative abundance of Faecalibacterium prausnitzii and Blautia stercoris. Fecal metabolomic analysis further indicated elevated levels of metabolites implicated in the gut–brain axis. Multi-strain probiotic supplementation alleviated gastrointestinal symptoms and improved aspects of psychosocial well-being in adults with FBDs, potentially through modulation of the human gut microbiome. Full article

Stomata play a crucial role in plant immune responses, with their morphological characteristics closely linked to disease resistance. Accurate detection and analysis of stomatal phenotypic parameters are essential for soybean disease resistance research and variety breeding. However, traditional stoma detection methods are challenged by complex backgrounds and leaf vein structures in soybean images. To address these issues, we proposed a Soybean Stoma-YOLO (You Only Look Once) model (SS-YOLO) by incorporating large separable kernel attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8 and Deformable Large Kernel Attention (DLKA) in the Neck part. These architectural modifications enhanced YOLOV8′s ability to extract multi-scale and irregular stomatal features, thus improving detection accuracy. Experimental results showed that SS-YOLO achieved a detection accuracy of 98.7%. SS-YOLO can effectively extract the stomatal features (e.g., length, width, area, and orientation) and calculate related indices (e.g., density, area ratio, variance, and distribution). Across different soybean rust disease stages, the variety Dandou21 (DD21) exhibited less variation in length, width, area, and orientation compared with Fudou9 (FD9) and Huaixian5 (HX5). Furthermore, DD21 demonstrated greater uniformity in stomatal distribution (SEve: 1.02–1.08) and a stable stomatal area ratio (0.06–0.09). The analysis results indicate that DD21 maintained stable stomatal morphology with rust disease resistance. This study demonstrates that SS-YOLO significantly improved stoma detection and provided valuable insights into the relationship between stomatal characteristics and soybean disease resistance, offering a novel approach for breeding and plant disease resistance research. Full article

To meet the dual demands of 6G cellular networks for high spectral efficiency and environmental sensing, this paper proposes a full-duplex (FD) integrated sensing and communication (ISAC) dynamic resource allocation framework. At the heart of the framework lies a dynamic frame structure that can self-adapt the time-domain resource ratio between sensing and communication, designed to flexibly handle complex traffic demands. In FD mode, however, the trade-off between communication and sensing performance, exacerbated by severe self-interference (SI), morphs into a non-convex, NP-hard multi-objective optimization problem (MOP). To tackle this, we propose an Adaptive Hybrid Memetic Multi-Objective Grey Wolf Optimizer (AM-MOGWO). Finally, simulations were conducted on a high-fidelity platform that integrates 3GPP-standardized channels, which was further extended to a challenging multi-cell interference scenario to validate the algorithm’s robustness. AM-MOGWO was systematically benchmarked against standard Grey Wolf Optimizer (GWO), random search (RS), and the genetic algorithm (GA). Simulation results demonstrate that in both the single-cell and the more complex multi-cell environments, the proposed algorithm excels in locating the Pareto-optimal solution set, where its solution set significantly outperforms the baseline methods. Its hypervolume (HV) metric surpasses the second-best approach by more than 93%. This result quantitatively demonstrates the algorithm’s superiority in finding a high-quality set of trade-off solutions, confirming the framework’s high efficiency in complex interference environments. Full article

This research examined how ambient pressure impacts the asymmetrical flow effects of fire induced under natural ventilation. Numerical simulations using Fire Dynamics Simulator (FDS) software were conducted, altering the longitudinal positions of fire sources and ambient pressure. The findings reveal that ambient pressure impacts the movement of smoke and air within the tunnel, with both outgoing smoke and incoming air increasing as ambient pressure rises. Asymmetric flow, influenced by the fire source’s longitudinal position, is observed under different ambient pressures. The intensity of these asymmetric flow effects can be characterized by the parameter of induced longitudinal flow mass rate, $m_i$. A dimensionless ambient pressure, P*, was introduced to assess its impact on longitudinal flow’s induction, leading to the development of a predictive model for calculating the $m_i$. While ambient pressure affects the mass flow values of smoke and airflow in tunnel fires under natural ventilation, it has minimal impact on their fundamental distribution patterns. A predictive model has been proposed for the distribution patterns of smoke overflow and air inflow under various ambient pressures. Full article

Traffic congestion is a primary obstacle to sustainable mobility, leading to increased fuel consumption, harmful emissions, and significant economic losses. Effective and timely congestion detection is therefore a critical enabler for proactive traffic management strategies that can mitigate these negative impacts. This study contributes to this goal by conducting a rigorous comparative analysis of two key detection paradigms: a modern, vehicle-centric approach using a Cooperative Intelligent Transportation Systems (C-ITS) service, and a traditional, infrastructure-based method relying on the fundamental diagram (FD). Using a comprehensive simulation campaign on a bottleneck scenario, we evaluate the performance of both methods under various conditions. The results demonstrate that while the FD-based method can offer faster detection under optimal sensor placement for severe events, the C-ITS approach provides fundamentally greater spatial flexibility and reliability across a wider range of congestion severities. Our techno-economic analysis further reveals that the paradigms rely on distinct investment models, with C-ITS offering superior scalability and a promising path toward network-wide coverage. This highlights the complementary nature of the two approaches and underscores the potential of C-ITS as a key technology to support dynamic, efficient, and sustainable transportation networks. Full article

Tartrazine (TZ, also known as FD&C Yellow No. 5 or E102) is a synthetic, water-soluble yellow food dye widely used in the food and pharmaceutical industries. Some studies have associated TZ with allergic reactions, especially among people with dye sensitivities or pre-existing allergies. Recent research also suggests a possible link between TZ consumption and the worsening of behavioral disorders, especially in children, including symptoms such as hyperactivity, irritability, restlessness, and sleep disturbances. Experimental studies in laboratory animals have highlighted potential risks associated with prolonged or high-dose exposure, including toxic effects on the nervous system and liver function. In addition, increasing evidence indicates that TZ can induce oxidative stress (OS) by increasing the production of reactive oxygen species (ROS), which can contribute to cellular damage and inflammation. Although the evidence remains inconclusive, there are recommendations to limit the intake of synthetic food dyes, especially in children’s diets. The purpose of this review is to examine the potential toxic effects on health of tartrazine by analyzing findings from experimental studies in cell cultures and laboratory animals, as well as correlations observed in humans. We focus on documented adverse reactions, including possible neurotoxic, hepatotoxic, oxidative, and behavioral effects. Through this, we aim to contribute to a more comprehensive understanding of the risks associated with exposure to this synthetic food dye. Full article

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR (R² = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR (R² = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF (R² = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability. Full article

We consider cooperative non-orthogonal multiple access (CNOMA) transmission through a full-duplex (FD) amplify-and-forward (AF) relay. Two CNOMA users are served by an access point (AP) through the FD-AF relay. FD relaying can be spectrally efficient if it sufficiently suppresses the self-interference (SI) from the FD operation. The feedback structure of the FD-AF relaying makes the convergence of the SI a critical problem because catastrophic system failure may occur if the system fails to converge the SI. The two linear beamforming algorithms proposed in this paper address these challenges, where the first is based on the zero-forcing (ZF) of the SI and the other regularizes the SI to improve the first scheme. The ZF-based algorithm completely nulls out the SI and incurs no convergence problems. In contrast, the second method is based on the regularization of SI to improve the first one, and it carefully locates the operating point of the FD-AF relay such that the system avoids the SI divergence. Numerical simulation results are provided to discuss the advantages and disadvantages of the proposed algorithms by comparing their average sum-rate performances. Full article

Agricultural soils are hotspots of nitrous oxide (N₂O) emissions, where carbon substrates act as a critical factor influencing microbial community composition. However, how carbon availability modulates microbial denitrifying pathways and further influences N₂O emissions remains poorly understood. Here, we conducted anaerobic incubations to investigate North China vegetable soil N₂O production and consumption in response to varied glucose concentrations (0, 0.5 (Glu_0.5), 1.0 (Glu_1.0), and 2.0 (Glu_2.0) g C kg⁻¹ d.w. of soil). In this study, the δ¹⁵N^SP/δ¹⁸O mapping approach (δ¹⁵N^SP/δ¹⁸O MAP) and acetylene inhibition technique (AIT) were used to quantify the residual N₂O ratio (r_N2O) and the relative contributions of bacterial (f_BD) and fungal (f_FD) denitrification to N₂O production. The results showed that increasing glucose concentrations significantly increased CO₂ and N₂O emissions, with peak fluxes observed at Glu_2.0 on day 1 (116.22 ± 2.80 mg CO₂-C kg⁻¹ and 1.08 ± 0.02 mg N₂O-N kg⁻¹). Concurrently, δ¹⁵N^SP was also significantly elevated (p < 0.001), indicating enhanced f_FD, which was further corroborated by positive correlations between f_FD and glucose concentration (r = 0.48–0.56, p < 0.001). Nevertheless, bacterial denitrification (BD) still dominated N₂O production throughout the incubation period, except on day 1 in Glu_1.0 and Glu_2.0 of Case 2. Bland–Altman analysis with 95% limits of agreement (LoA) demonstrated strong agreement between the MAP and AIT in r_N2O estimation, particularly under Glu_2.0. All the above revealed glucose-induced denitrifying microbial shifts from BD to fungal denitrification (FD), which consequently modulated N₂O emissions and promoted incomplete denitrification. These findings collectively demonstrate that in vegetable cropping systems, rational carbon management strategies can promote N₂O reduction to N₂, thereby achieving effective N₂O mitigation. Full article

Under varying operating conditions, motor bearings undergo continuous changes, necessitating the development of deep learning models capable of robust fault diagnosis. While meta-learning can enhance generalization in low-data scenarios, it is often susceptible to overfitting. Domain adaptation mitigates this by aligning feature distributions across domains; however, most existing methods primarily focus on global alignment, overlooking intra-class subdomain variations. To address these limitations, we propose a novel Dynamic Balance Domain-Adaptation based Few-shot Diagnosis framework (DBDA-FD), which incorporates both global and subdomain alignment mechanisms along with a dynamic balancing factor that adaptively adjusts their relative contributions during training. Furthermore, the proposed framework implicitly leverages the concept of symmetry in feature distributions. By simultaneously aligning global and subdomain-level representations, DBDA-FD enforces a symmetric structure between source and target domains, which enhances generalization and stability under varying operational conditions. Extensive experiments on the CWRU and PU datasets demonstrate the effectiveness of DBDA-FD, achieving 97.6% and 97.3% accuracy on five-way five-shot and three-way five-shot tasks, respectively. Compared to state-of-the-art baselines such as PMML and ADMTL, our method achieves up to 1.4% improvement in accuracy while also exhibiting enhanced robustness against domain shifts and class imbalance. Full article

Objective: Fabry disease (FD) is a rare, X-linked lysosomal storage disorder resulting from deficient α-galactosidase A activity, which can manifest as left ventricular hypertrophy (LVH). We aimed to assess the prevalence of FD in an unselected cohort of patients with unexplained LVH. Methods and results: We screened 202 unrelated adults with LVH using enzymatic assays for α-galactosidase A in dried blood spots. Patients with low activity underwent GLA gene sequencing. Echocardiographic parameters were evaluated according to ESC guidelines. FD was diagnosed in 4 women (2%), each carrying distinct pathogenic GLA mutations. All affected individuals showed normal or borderline enzyme activity. Cardiac, renal, or neurological symptoms were observed variably among patients. Echocardiographic findings revealed slightly lower wall thickness and preserved systolic function in FD patients compared to those without FD. Cascade genetic screening identified 16 additional family members with the same mutations. One patient (0.5%) was incidentally diagnosed with Gaucher disease based on syndromic features and enzymatic testing. Conclusions: FD was identified in 2% of patients with unexplained LVH, who were females. Enzyme-based screening followed by targeted genetic testing is a cost-effective strategy for FD detection. Early diagnosis is essential for prompt treatment and family counselling, underscoring the importance of routine FD screening in patients with LVH of unclear aetiology. Our findings support the use of targeted screening for Fabry disease in patients with LVH and systemic features, and highlight the potential to identify other lysosomal disorders in selected cases. Full article

The increasing number of intelligent connected vehicles (ICVs) is leading to a growing scarcity of spectrum resources for the Internet of Vehicles (IoV), which has created an urgent need for the use of full-duplex non-orthogonal multiple access (FD-NOMA) techniques in vehicle-to-everything (V2X) communications. Meanwhile, for the flexibility of autonomous aerial vehicles (AAVs), V2X communications assisted by AAVs are regarded as a potential solution to achieve reliable communication between ICVs. However, if the integration of FD-NOMA and AAVs can satisfy the requirements of V2X communications, then quickly and accurately analyzing the total achievable rate becomes a challenge. Motivated by the above, an accurate analytical expression for the total achievable rate over Rician fading channels is proposed to evaluate the transmission performance of NOMA-enhanced AAV-assisted IoV with imperfect channel state information (CSI). Then, we derive an approximate expression with the truncated error, based on which the closed-form expression for the approximate error is theoretically provided. Finally, the simulation results demonstrate the accuracy of the obtained approximate results, where the maximum approximate error does not exceed 0.5%. Moreover, the use of the FD-NOMA technique in AAV-assisted IoV can significantly improve the total achievable rate compared to existing work. Furthermore, the influence of key network parameters (e.g., the speed and Rician factor) on achievable rate is thoroughly discussed. Full article

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent heterogeneous fuels and simulate fire behavior processes with greater detail than conventional models. However, they require accurate information about species composition and 3D distribution of fuel mass and bulk density at the voxel level. Working in a Mediterranean ecosystem study area we developed a methodology based on the use of geometric and spectral features from UAS-based digital aerial photogrammetric point clouds for (i) species segmentation and classification using machine learning algorithms, (ii) generation of biomass prediction models at individual plant level, and (iii) creation of 3D fuel scenarios and modeling wildfire behavior. Field measurements were conducted on 22 circular plots with a radius of 5 m. Data from the field measurements, combined with species-specific allometric equations, were used for the evaluation of classification and prediction models. Fire behavior variables such as rate of spread, heat release rate, and mass loss rate were monitored and assessed as outputs from 20 different scenarios using FDS. The overall species classification accuracy was 80.3%, and the biomass regression R² values obtained by cross-validation were 0.77 for Pinus halepensis and 0.83 for Anthyllis cytisoides. These results are encouraging further improvement based on the integration of sensors onboard UAS, and the characterization of fuels for fire behavior modeling. These high-resolution fuel representations can be coupled with standard risk assessment tools, enabling fire managers to prioritize treatment areas and plan for resource deployment. Full article

This study investigated the impact of nitrogen (N) fertilization on the yield and grain filling (GF) characteristics of two conventional japonica rice varieties with distinct panicle types: Yangchan 3501 (large-panicle: spikelets per panicle $> 150$) and Nangeng 46 (medium-panicle: $100 <$ spikelets per panicle $< 150$). Field experiments were conducted over two growing seasons (2022–2023) with three N application rates (T1: 225 kg ha⁻¹, T2: 270 kg ha⁻¹, T3: 315 kg ha⁻¹). Key measurements included tiller dynamics, panicle composition, GF parameters modeled using the Richards equation, and enzyme activities related to nitrogen metabolism (Fd-GOGAT, NR) and carbohydrate transport (α-amylase, SPS). Results showed that the yield increased with higher N levels for both varieties, with Yangchan 3501 achieving higher yields primarily through increased grains per panicle (15.65% rise under T3 vs. T1), while Nangeng 46 relied on panicle number (8.83% increase under T3 vs. T1). Nitrogen application enhanced Fd-GOGAT and NR activities, prolonging photosynthesis and improving GF rates, particularly in the inferior grains of Yangchan 3501 during middle and late stages. However, a high N reduced seed-setting rates and 1000-grain weight, with larger panicle types exhibiting a greater sensitivity to N-induced changes in branch structure and assimilate allocation. This study highlights that optimizing N management can improve nitrogen-metabolism enzyme activity and GF efficiency, especially in large-panicle rice, while medium-panicle types require higher N inputs to maximize panicle number. These findings provide actionable insights for achieving high yields and efficient nutrient use in conventional rice cultivation. Full article