Search Results (61,980)

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

High spatio-temporal resolution satellite imagery has become increasingly accessible thanks to advancements in the aerospace industry which, combined with a growing computational power, has enabled the spring of novel techniques regarding recognition in remote sensing (RS) images. However, there is still a lack of studies around 3D convolutions for spatio-temporal data applied to classification problems in RS. Hence, this study investigates the feasibility of 3D convolutional neural networks (3DCNNs) within a spatio-temporal perspective for scene classification and semantic segmentation in RS images, focusing on the identification of mining sites. We firstly developed a dataset covering several parts of Brazil based on MapBiomas products and Planet imagery, then we evaluated the effectiveness of 3DCNNs in capturing temporal information from a sequence of monthly captured images. Moreover, not only for scene classification but also for semantic segmentation, we compared 3D and 2D approaches. As for scene classification, a 3DCNN was better than the corresponding 2D model, while a 2D U-Net was better than a U-Net3D for semantic segmentation. The main explanation for this lies in the fact that a less costly annotation and training time strategy was adopted, but this may have harmed spatio-temporal approaches for semantic segmentation but not for scene classification. However, U-Net3D presented the highest Precision of all models, meaning that it is highly accurate when it predicts a positive. Moreover, 3DCNN (U-Net3D) presented significantly better performance with respect to semantic segmentation compared to other spatio-temporal approaches like ConvLSTM+U-Net and TempCNN. Sensitivity analysis revealed that the near-infrared (NIR) band played a decisive role in distinguishing mining areas, emphasizing its importance in highlighting subtle spectral variations associated with land-cover disturbances. Full article

Mitochondrial dysfunction drives persistent inflammation in severe asthma, yet its upstream metabolic regulation remains unclear. Induced sputum from patients with severe asthma was analyzed and integrated with transcriptomic datasets from independent cohorts. Two mouse models (C57BL/6J) were used for in vivo validation with multi-omics profiling, and mechanistic studies were performed in air–liquid interface-cultured primary human airway epithelial cells. Glutathione reduced form (GSHr) was markedly depleted in sputum and associated with poor disease control and mixed granulocytic inflammation in patients with severe asthma. Multi-omics analyses revealed coordinated disruption of glutathione (GSH) metabolism, including oxidized GSH accumulation, reduced synthesis and glutathione-S-transferase activity, and impaired mitochondrial GSH transport. GSH supplementation alleviated airway inflammation, oxidative stress, and mitochondrial dysfunction, whereas pharmacological inhibition of GST exacerbated these effects. Mitochondrial analyses identified suppressed SLC25A39 expression as a key mediator of defective GSH transport and redox imbalance. Transcriptomic profiling of airway biopsies showed upregulation of Neuropilin-1 (Nrp1), closely associated with altered glutathione pathways. Targeting the Nrp1 b1 domain restored mitochondrial GSH metabolism and attenuated airway inflammation. These findings identify an Nrp-centered metabolic pathway that disrupts mitochondrial homeostasis and drives inflammatory amplification, highlighting mitochondria-targeted therapeutic strategies for severe asthma. Full article

The safe and efficient operation of deep-sea towing systems is heavily governed by the highly nonlinear dynamic interaction between the flexible towing cable and complex seabed topographies. While existing studies accurately predict cable dynamics in mid-water or over flat seabeds, the transient responses—such as local stress concentrations and extreme tension fluctuations—induced by discontinuous topographies (e.g., stepped or 3D irregular seabeds) remain inadequately quantified. In this study, we develop an advanced 3D dynamic numerical model combining the lumped-mass finite element formulation with a modified non-linear penalty-based seabed-contact mechanics algorithm. This framework systematically evaluates the tension distribution, bending curvature, and spatial configuration shifts in the cable during the touchdown and detachment phases across inclined, stepped, and 3D seabeds. Quantitative validation against established benchmarks demonstrates robust accuracy. Results indicate that steeper seabed inclinations linearly reduce detachment time but exponentially amplify initial contact tension. Over-stepped terrains, “point-to-line” transient collisions trigger sudden tension spikes exceeding steady-state values by up to 45%. Furthermore, 3D irregular seabeds induce severe multi-directional spatial deformations, precipitating destructive whiplash effects at high towing speeds (e.g., m/s). These findings provide critical physical insights and a quantitative reference for optimizing tugboat maneuvering strategies and designing fatigue-resistant cables in complex sub-sea environments. Full article

Demand for camel milk products is growing in Morocco and worldwide, creating opportunities to strengthen the livelihoods of populations living in arid regions through the development of camel-based dairy value chains. In addition to their economic potential, such value chains may contribute to sustainability by supporting food systems adapted to arid environments, promoting the use of locally resilient livestock species, and enhancing the socio-economic viability of vulnerable rural communities. This exploratory qualitative study investigates urban consumer behavior related to dairy consumption with a specific focus on the potential integration of camel milk products into local dietary habits. To capture nuanced consumer perspectives, gender-segregated focus-group discussions were conducted in three Moroccan cities using a semi-structured questionnaire on dairy consumption habits. Key factors examined included milk types, product preferences, purchasing locations, consumption frequency and willingness to include camel products in the household diet. The results indicate that camel milk is rarely consumed outside areas where camels are raised. Nevertheless, participants expressed interest in several camel milk-based products, particularly fermented milk and spreadable cheeses. This interest was primarily driven by perceptions of camel milk as a healthy product and by its association with traditional food practices. These findings suggest that expanding camel milk consumption in urban markets could support more sustainable and territorially rooted dairy systems by linking consumer demand with production models suited to dryland conditions. This study indicates promising market opportunities for the development of camel milk products in urban areas, particularly if challenges related to pricing strategies, distribution network, and region-specific supply chains are strategically managed. Full article

This study reconstructs annual streamflow variability in the Yenice Stream Basin (northwestern Türkiye) for the period 1809–2020 using tree-ring data, substantially extending the short instrumental record (1979–2020). Three moisture-sensitive conifer chronologies were integrated using principal component analysis (PCA), and the first two principal components were employed as predictors in a multiple linear regression model calibrated against observed streamflow. The model explains a significant proportion of interannual variability (R² = 0.39; adjusted R² = 0.36; p < 0.001). Temporal stability was assessed using a 30-year moving-window correlation analysis, which reveals consistently positive and statistically significant relationships across all subperiods, indicating a stable and persistent calibration relationship through time. Years exceeding ± 1 standard deviation account for approximately 24% of the record, while extreme events (±2 standard deviations) represent about 5%. The reconstruction identified several extreme events, including severe drought years (e.g., 1840, 1887, and 1907) and extremely wet years (e.g., 1896 and 1936). Among these, 1887 stands out as one of the most severe drought years, while the period 1927–1928 represents a persistent low-flow episode. The reconstruction provides a long-term perspective on streamflow variability and contributes baseline information for regional water resource planning and hydroclimatic risk assessment. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

In this study, we consider an extended form of the Kadomtsev–Petviashvili–Boussinesq equation motivated by wave propagation phenomena in dissipative media. The primary aim of this work is to construct exact analytical solutions and clarify the types of nonlinear wave structure admitted by the considered model. For this purpose, the Riccati equation expansion method is applied for the first time within this framework. This method allows us to obtain several distinct families of solitary wave solutions whose qualitative behaviors and physical characteristics are illustrated through graphical representations. In addition, modulation instability analysis is carried out to assess the stability of continuous wave solutions and further elucidate the underlying nonlinear dynamics of the system. Full article

Background: Post-stroke dysphagia is a frequent complication associated with aspiration, malnutrition, and prolonged dependence on enteral feeding. Systemic inflammation and impaired nutritional status may adversely affect neuromuscular recovery; however, their relative and combined associations with swallowing recovery and transition from enteral to oral feeding remain insufficiently characterized. Objective: This study aimed to examine the independent associations of inflammatory and nutritional indices with swallowing function recovery and to evaluate their relationship with enteral-to-oral feeding transition in patients with post-stroke dysphagia. Methods: In this retrospective observational study, patients with dysphagia following ischemic stroke were evaluated before (T0) and after (T1) routine dysphagia rehabilitation. Inflammatory indices including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune–inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune–inflammation value (PIV), as well as the prognostic nutritional index (PNI), were calculated at both time points. Changes in indices (Δ = T1 − T0) were analyzed in relation to changes in swallowing function assessed by the Functional Oral Intake Scale (FOIS) and the Penetration–Aspiration Scale (PAS). Results: Changes in PNI were independently associated with greater improvement in functional oral intake (ΔFOIS) and reductions in aspiration severity for both liquid and soft consistencies (ΔPAS; all p < 0.01). In contrast, changes in inflammatory indices (ΔSIRI, ΔSII, ΔPLR, and ΔPIV) were consistently associated with less favorable swallowing outcomes. In multivariable logistic regression analysis, baseline stroke severity (NIHSS) was the only independent determinant of transition from enteral to oral feeding (OR = 0.72, p = 0.002). The model demonstrated good discrimination (AUC = 0.81). Conclusions: Changes in nutritional status, as reflected by ΔPNI over time, were the biomarker most consistently associated with functional swallowing recovery and reduced aspiration severity in patients with post-stroke dysphagia. While inflammatory burden was associated with less favorable swallowing physiology, transition from enteral to oral feeding appeared to be primarily driven by neurological severity rather than inflammatory or nutritional indices alone. These findings may support the clinical value of monitoring nutritional reserve alongside inflammatory burden during dysphagia rehabilitation. Full article

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

This study presents a unified mechanics-based framework for evaluating bonded composite patch repairs. Discrete fracture, fatigue, and adhesive responses are transformed into continuous master equations over the design space. Low-order polynomial surfaces model stress intensity and concentration responses, enabling continuous prediction of repair performance without repeated finite-element analyses. A fracture-based repair efficiency index is derived from the analytical master surface. This index quantifies the average reduction in crack-driving force across the domain. Combined with adhesive stiffness and strength, it defines an adhesive-based repair efficiency index (A-REI), providing a direct link between structural response and material properties. The results show that repair effectiveness is strongly influenced by both geometric severity and adhesive properties. Fatigue performance decreases significantly with increasing notch ratio in single-sided repairs. Double-sided configurations maintain consistently higher efficiency. Symmetric reinforcement more effectively reduces stress concentration, with improvements exceeding 40% at intermediate notch ratios. Adhesive selection is governed by stiffness and strength. Structural adhesives achieve significantly higher A-REI values, whereas compliant adhesives contribute negligibly. Overall, repair symmetry controls the magnitude of improvement, while adhesive properties determine performance ranking. This framework provides a clear, practical basis for design and material selection. Full article

Hyperspectral remote sensing provides essential biochemical and structural information for crop disease monitoring, yet its application to cotton Verticillium wilt has largely focused on single-period evaluations or multi-temporal classifications. Such approaches overlook the progressive nature of this vascular disease, whose pigment, water, and mesophyll responses evolve over time, making temporal hyperspectral information critical for reliable severity estimation but still insufficiently utilized. To overcome this limitation, we conducted daily time-series observations on cotton leaves and collected 2895 hyperspectral reflectance measurements and 770 high-resolution RGB images together with disease severity records, generating a temporally dense spectral-severity dataset spanning symptom-free to severe stages. Five categories of disease-related vegetation indices were derived and organized into 5-day spectral–temporal slices. Based on these features, we introduce a dual-branch Transformer-TCN model that integrates global temporal dependencies captured by self-attention with local temporal variations resolved by dilated causal convolutions for severity inversion. The model delivers the strongest performance with an R² of 0.8813, exceeding multiple single and hybrid time-series alternatives by 0.0446–0.1407 in R², equivalent to a relative improvement of 5.33–19.00%. Temporal spectral features also outperform their non-temporal counterparts, highlighting that disease progression dynamics captured by time-series spectra are critical for reliable severity retrieval. Feature contribution analysis indicates that the blue red index BRI provides the highest contribution, consistent with the single-index time-series modelling results. Photosynthesis- and water-related indices provide secondary but complementary support. Collectively, our results demonstrate that the dual-branch Transformer-TCN model can capture complex spectral–temporal relationships between cotton Verticillium wilt and disease severity, providing methodological support for crop disease monitoring and evaluation. Full article

During the installation of prefabricated vertical drains (PVDs) in soft soil foundations, the smear effect induced by mandrel shoe penetration can severely damage the soil structure and reduce permeability, thereby becoming a key factor restricting foundation consolidation efficiency. Previous studies have generally neglected the smear disturbance caused by the geometry of the mandrel shoe. Although existing studies have conducted numerical and theoretical analyses on the smear effect induced by PVD installation, most of them are still based on equivalent circular simplifications and are therefore unable to characterize the anisotropic disturbance induced by a triangular mandrel shoe. To address this limitation, a three-dimensional CEL penetration model considering the real triangular geometry was established, and the traditional cavity expansion theory was directionally modified. The effects of penetration rate, geometric angular structure, and soil type of the triangular mandrel shoe on the smear zone were systematically investigated. The results show that, with increasing penetration rate, the near-field peak stress and far-field displacement increase simultaneously; from slow penetration to fast penetration, the near-field peak stress increases by approximately 42%. By quantitatively defining the critical threshold corresponding to a sharp 50% attenuation in radial displacement as the boundary of the strong smear zone, it was found that increasing the size of the mandrel shoe significantly amplifies the geometric corner effect, and the near-field disturbance range increases by about 21% compared with that of the small-sized case. The larger the size, the more pronounced the anisotropic disturbance characteristics become: the stress concentration effect and displacement splitting in the vertex direction are further enhanced, causing the disturbance range in that direction to far exceed that in the side direction. Soil properties are the key medium parameters controlling the smear zone. Owing to its relatively high stiffness index and skeleton strength, Clayey Silt shows the largest displacement range, whereas Common Clay exhibits the smallest smear zone because of its stronger structural constraint. The modified theoretical model agrees well with the CEL numerical simulation results, verifying its effectiveness under conditions that consider the geometric characteristics of the mandrel shoe. This study provides a theoretical basis and numerical support for the structural design of mandrel shoes in soft-ground PVD construction. Full article

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next $\tau =0.2$ s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor ($w=d=0.1$ s, $k=2$) is warnable on only $18.3\%$ of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget $\alpha$ with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at $\tau =0.2$ s, the scorer achieves AUPRC $\approx 0.251$ against a base rate of $0.638\%$, AUROC $\approx 0.986$, and ECE $\approx 0.034$. After calibration at $\alpha =5\%$, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FAR${}_{\mathrm{run}}$, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported $\mu$-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions. Full article

Neurodegenerative diseases involve progressive neuronal loss associated with oxidative stress (OS) and inflammation. Given the limited efficacy of current therapies, natural compounds with multitarget neuroprotective potential are of growing interest. In this study, we investigated the neuroprotective effects of a standardized Magnolia officinalis (L.) bark extract (MOE) in rat brain cortical slices exposed to hydrogen peroxide-induced OS. MOE significantly recovered tissue viability and reduced ROS and malondialdehyde levels caused by OS while attenuating caspase-3, -8, and -9 activation, suggesting modulation of intrinsic and extrinsic apoptotic pathways. Shotgun proteomics using LC-HRMS/MS identified OS-induced protein expression changes reversed by MOE, with fourteen of thirty-three altered proteins rescued by MOE co-treatment. These proteins participate in several processes, including neuronal survival, OS response, and proteostasis. Bioinformatic analysis demonstrated that genes responsible for protein synthesis regulated by MOE are subjected to transcriptional regulation by factors associated with OS, including FOXO4, NRF2, and SP1. The present findings support the hypothesis that MOE exerts multitarget neuroprotective effects by modulating key proteins involved in OS responses and neuronal survival in an acute ex vivo oxidative injury model, suggesting potential relevance for mechanisms associated with NDs. Full article