Search Results (7,025)

Nitrogen leaching from land and farms is a major global issue that pollutes water, damages ecosystems, and accelerates climate change. This review synthesizes evidence from the literature on how interactions among landscape characteristics, sources of nitrogen input, and temporal dynamics shape leaching vulnerability. It identifies conditions under which nitrogen is most likely to be transported through soil systems into aquatic environments. This review reveals that leaching vulnerability is strongly conditioned by soil hydraulic properties and topographic position. Coarse-textured upland soils exhibit substantially greater nitrate mobilization than finer-textured, hydrologically buffered lowland soils. Fertilizer formulation and application timing further modulate loss potential, with late-season mineral nitrogen inputs disproportionately contributing to subsurface export relative to demand-synchronized applications. Most of the nitrogen leaching occurs outside the active growing period, when vegetative uptake is suppressed and drainage intensity is highest. Farmers can lower nitrate runoff by using targeted fertilization, cover crops, and nitrification inhibitors, while landscape-scale features like controlled drainage and vegetative buffers provide additional downstream filtration. The effectiveness of regulatory approaches is amplified when aligned with economic incentives and regionally calibrated nutrient thresholds. Advances in high-resolution observation platforms and process-based predictive tools offer new capacity for anticipatory management, although widespread deployment is limited by financial and institutional constraints. Collectively, these insights support the development of more targeted and sustainable nitrogen management strategies. Full article

In modern intelligent manufacturing, spindle thermal errors are critical to machining accuracy. To address this, we propose a two-stage prediction framework. First, for feature selection, an enhanced Red-Billed Magpie Optimization algorithm (RBMO-X) optimizes the parameters of a hybrid convolutional neural network (DLTK). Concurrently, PSO-optimized HDBSCAN clustering combined with Pearson correlation selects optimal temperature-sensitive points. The DLTK network integrates LSTM, deformable convolution, Transformer, and Fourier KAN modules for robust spatiotemporal feature extraction. The experimental results demonstrate significant improvements. The proposed feature selection method improves the Silhouette index by 32.39% and increases BWP by 49.16%. Using the selected points reduces prediction RMSE by 31.89% compared to random selection. The final RBMO-X-DLTK model achieves an RMSE of 0.181 μm, an MAE of 0.128 μm, and an R² score of 0.9978, outperforming seven benchmark models (e.g., BP, LSTM, CNN-LSTM). In practical validation, the model enabled an average thermal error reduction of 89%. This integrated approach provides a robust and accurate solution for spindle thermal error prediction, demonstrating strong generalization capability. Full article

Wind energy is a core pillar of global green and sustainable energy transition. However, existing wind power prediction models face three key challenges: traditional long short-term memory (LSTM) models struggle to capture long-term temporal dependencies efficiently and have high training latency, while Transformer-based models exhibit excessive computational complexity and are prone to overfitting for short-term fluctuating data; meanwhile, few models integrate seasonal trend modeling with multi-scale temporal feature extraction, leading to large prediction errors in seasonal transitions. To address these issues, this paper proposes a hybrid prediction framework combining a novel T-LSTM recurrent unit with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The T-LSTM unit fuses a simplified Transformer module and an improved LSTM structure. Thus, the design can synergistically capture both short-term fluctuations and long-term dependencies in wind power data. Complementarily, SARIMA is integrated via weighted fusion to model seasonal trends, addressing the neglect of seasonal characteristics in existing deep learning models. A diverse set of benchmark methods for wind power prediction are selected for comparison, including LSTM, convolutional neural network-gated recurrent unit (CNN-GRU), ns_Transformer, Autoformer, Reformer and least squares support vector machine (LSSVM), with experiments conducted across various prediction horizons. The results show that the proposed T-LSTM model outperformed most benchmark methods in key evaluation metrics across multiple prediction horizons and exhibited no statistically significant difference from Autoformer only in the 90 min horizon, validating its superiority in handling complex wind power time series. Full article

To address the issues of a low single-seed qualification index and a high missed-seeding index in the process of leafy vegetable plug seedling sowing, this study proposes a lightweight seeding performance detection model named VS-YOLO based on YOLO11n. The model is then deployed on the edge device, the NVIDIA Jetson Xavier NX. A concise and intuitive graphical user interface (GUI) was developed and an automated detection system for vegetable seeding performance was constructed. Based on the empty cells identified by the system, a real-time data transmission mechanism between the Jetson device and a PLC-based control unit is established, enabling the intelligent reseeding device to perform precise reseeding at the designated cell location, achieving row-wise and cell-specific intelligent planting. VS-YOLO incorporates several innovative improvements, including the introduction of a Context Anchor Attention (CAA) module to form the C2PSA_CAA module, the adoption of the Wise Intersection over Union version 3 (WIoU v3) loss function, and the addition of an extra-small object detection head. These enhancements significantly improve the classification and recognition capability for small-sized vegetable seeds while notably reducing the number of model parameters. Experimental results show that VS-YOLO achieves a mAP@0.5 of 96.5% and an F1 Score of 93.45% in detecting the seeding performance of three types of vegetable seeds, outperforming YOLO11n’s 91.5% and 85.19% by 5.0% and 8.26%. The parameter count of VS-YOLO is only 1.61 M, which is 37.6% lower than YOLO11n’s 2.58 M, making it lightweight. Operating at a productivity rate of 120 trays per hour, the system achieved an accuracy of 99.03%, 89.83%, and 92.26% for single-seed prediction, multiple-seeding prediction, and missed-seeding prediction. The single-seed qualification index and missed-seeding index were 93.43% and 4.68%. After reseeding, these indices improved to 97.61% and 0.32%, representing an increase of 4.18% in the single-seed qualification index and a decrease of 4.36% in the missed-seeding index. The significant enhancement offers new ideas and technical approaches for the advancement of seeding performance detection and reseeding systems for vegetable plug seedling production. Full article

Modulated twelve-voltage-vector model predictive current control (MPCC), which applies two or three voltage vectors per control period, exhibits superior steady-state performance compared to modulated six-active-voltage-vector MPCC and conventional MPCC. However, implementing modulated twelve-voltage-vector MPCC requires accurate knowledge of the permanent magnet synchronous motor drive’s inductance and permanent magnet (PM) flux linkage parameters for selecting suboptimal and optimal voltage vectors, as well as computing the duty cycles of optimal vectors. Consequently, its control performance is more sensitive to model parameter inaccuracies. To mitigate parameter sensitivity, a robust modulated twelve-voltage-vector MPCC algorithm based on a model reference adaptive system (MRAS) is proposed. The MRAS-based observer estimates the inductance and PM flux linkage parameters in real time, enhancing model accuracy. The observer is designed with a stability analysis framework, where the proportional and integral gains of the MRAS are theoretically derived to ensure precise parameter estimation. The effectiveness of the proposed algorithm is validated through simulation results, demonstrating satisfactory control performance even under parameter mismatches. Specifically, the torque ripple is reduced from 1.1 A to 0.6 A, corresponding to a reduction of 45.5%. Similarly, the stator flux ripple decreases from 1.75 A to 1 A (42.9% reduction), while the total harmonic distortion (THD) is reduced from 8.39% to 5.48%, representing a 34.7% improvement. Full article

The performance degradation of electronic power components during long-term operation can compromise system reliability and safety. Therefore, accurately predicting their remaining useful life (RUL) is critical for the reliability of safety-critical systems that utilize these components. This paper proposes a hybrid model integrating bidirectional long short-term memory networks (BiLSTM) and Gaussian process regression (GPR) for RUL prediction of electronic power components. The BiLSTM module provides high-precision point predictions, while the GPR module leverages the sequence features and trend information extracted by BiLSTM to deliver reliable interval predictions and high-confidence probabilistic outputs. The model’s predictive accuracy was validated using NASA’s publicly available lithium-ion battery dataset. Experimental results demonstrate that, compared to existing models, the proposed model achieves at least a 9.6% improvement in point prediction performance and a 63% improvement in interval prediction performance, fully validating the reliability and accuracy of the BiLSTM-GPR approach. The model was further applied to predict the RUL of DC-DC power modules. The predicted Continuous Ranked Probability Score (CRPS) reached a maximum of 0.050405, while the Probability Integral Transform (PIT) results exhibited a uniform distribution within the (0,1) range, further demonstrating the model’s high reliability and predictive confidence. Full article

Riparian soils co-contaminated with heavy metals and organic pollutants present a formidable environmental challenge; conventional single-target remediation strategies are frequently insufficient due to the synergistic interactions between contaminant classes. This review offers a systematic synthesis of phytoremediation as an integrative and ecologically sustainable paradigm for addressing these complex multi-pollutant scenarios. Through a critical examination of underlying mechanisms—namely phytoextraction, rhizodegradation, phytostabilization, and phytovolatilization—we evaluate the efficacy of selected hyperaccumulator and pollution-tolerant species in simultaneously mitigating inorganic (e.g., Pb, Cd, As) and organic (e.g., PAHs, pesticides) contaminants. Furthermore, the discussion highlights emerging strategic integrations, including genetic engineering for enhanced metal accumulation, the application of engineered nanomaterials to modulate pollutant bioavailability and plant stress tolerance, rhizosphere amendment with low-molecular-weight organic acids, and biochar-mediated immobilization coupled with microbial stimulation. The analysis posits that phytoremediation, particularly when augmented by these advanced synergies, constitutes a viable, multifunctional, and environmentally benign strategy for the holistic restoration of riparian ecosystems. Future inquiries should prioritize the mechanistic elucidation of combined technologies, the development of predictive performance models, and rigorous long-term field validation to guarantee operational efficacy and environmental safety. Full article

Understanding how environmental conditions shape the functional composition of ecological communities is a central goal in community ecology. In this study, we apply this framework to the reptile and amphibian assemblages within Greece’s Mount Chelmos protected area. Based on comprehensive field surveys (2018–2021) across 168 sampling stations, we compiled species trait databases and quantified functional diversity using a corrected Rao’s Q index. We modeled the response of functional diversity to climate, land cover, topography (altitude, slope, aspect), geographic location, and taxonomic diversity, using Generalized Additive Models (GAMs). Additionally, we examined traitspace structure via PCA and evaluated environmental drivers of trait composition with multivariate GAMs. For reptiles, functional diversity was significantly affected by altitude, climate, and aspect, with higher values predicted in water-associated marginal zones surrounding the mountain massif. Traitspace analysis revealed clear ecological structuring along axes related to locomotion, body size, reproductive mode, foraging strategy, and substrate use, shaped by distinct combinations of environmental filters. In amphibians, environmental effects on functional diversity were not statistically significant; however, traitspace showed discernible responses to land cover, climate, and aspect, suggesting weaker—though detectable—filtering processes. Collectively, our findings indicate that Mount Chelmos functions as a system that modulates diversity, with environmental filters operating at fine-to-medium spatial scales to shape the functional composition and diversity of its herpetofauna. Full article

The yak represents a distinct domestic animal species that predominantly inhabits the Qinghai–Tibet Plateau and adjacent areas, possessing considerable value in both scientific and economic contexts. Compared to animals that mainly dwell on plains, such as cattle, the sperm maturation process in yak exhibits a certain degree of species specificity to adapt to their unique reproductive needs in high-altitude environments. Serving as the main storage site for functionally competent sperm, the cauda epididymis plays an integral role in mediating their post-testicular maturation. MiRNAs are vital regulatory molecules in the epididymis, influencing sperm maturation by modulating gene expression after transcription. To investigate the unique regulatory mechanisms of sperm maturation in yak, this study compared the miRNA expression profiles in the cauda epididymis of yak and cattle using high-throughput small RNA (sRNA) sequencing. The comparative analysis identified and characterized sRNA populations in the cauda epididymis of yak and cattle, revealing a similar length distribution that peaked at 22 nt and a predominance of known miRNAs. Notably, eight miRNAs were found to be highly expressed in both species. Furthermore, the first-nucleotide bias differed significantly between known and novel miRNAs within each species. A total of 31 differentially expressed (DE) miRNAs were identified, with 11 upregulated and 20 downregulated in yak compared to cattle. Among these, bta-miR-1298 exhibited the most significant upregulation, while bta-miR-2344 displayed the most pronounced downregulation. Bioinformatic analysis linked the predicted target genes of these miRNAs to numerous critical signaling pathways, including calcium signaling, the mitogen-activated protein kinase (MAPK) signaling pathway, the Ras-associated protein 1 (Rap1) signaling pathway, and the cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) signaling pathway. Furthermore, eight significantly DE miRNAs, including bta-miR-2443, bta-miR-503-3p, bta-miR-6517, bta-miR-2440, bta-miR-2431-3p, bta-miR-2436-3p, bta-miR-6523a, and bta-miR-6775, were predicted to target genes involved in various aspects of sperm structural and functional maturation. These aspects include flagellum formation, sperm motility, chromatin remodeling, acrosome reaction, acrosome structure, sperm capacitation, chemotaxis, and nuclear chromatin condensation. Multiple miRNAs and their corresponding predicted target genes were analyzed by quantitative real-time PCR (qPCR), demonstrating an inverse correlation between miRNA expression and target gene levels. These findings reveal a distinct, species-specific miRNA signature in the yak cauda epididymis, which suggests a potential contribution to regulating the epididymal luminal environment and the process of sperm maturation. This study provides preliminary foundational data for elucidating the differences in sperm maturation mechanisms between yak and cattle, and offers potential novel targets for improving reproductive efficiency in plateau livestock. Full article

Glycogen synthase kinase 3 (GSK3/Shaggy-like) is a highly conserved serine/threonine kinase that orchestrates growth, hormone signaling, and abiotic stress responses in both animals and plants, yet its role in watermelon remains unexplored. In this study, we conducted a whole-genome identification, identifying a total of eight members of the GSK3 gene family (ClGSK3) distributed across seven chromosomes. Phylogenetic and synteny analyses resolved the eight ClGSK3s into four subfamilies that display one-to-one or one-to-many orthology with Arabidopsis and rice GSK3 genes, indicating conserved genomic micro-collinearity across dicots and monocots. Predictions of cis-acting elements and transcriptome data analysis indicate that ClGSK3s may be involved in hormone- and stress-responsive conditions. Protein–protein interaction networks predicted 53 candidate partners for five ClGSK3 proteins; yeast two-hybrid assays subsequently confirmed that ClSK21 associates with three of them—orthologs of the core brassinosteroid (BR)-signaling components BKI1 and BZR1. qRT-PCR revealed that ClSK21, ClSK31, and ClSK41 are rapidly and significantly reprogrammed by BR treatment. Collectively, our data suggest that ClGSK3s modulate fruit development and stress tolerance by integrating hormone-related pathways, especially BR signaling. Future studies are encouraged to integrate genetics and multi-omics approaches to systematically validate the roles of ClGSK3s in hormone signaling and abiotic stress responses. Full article

N6-methyladenosine (m⁶A) has emerged as a pivotal regulator of post-transcriptional gene control, yet its contribution to chemotherapy resistance remains insufficiently defined. Here, we describe a previously unrecognized METTL3-ADAM23 epitranscriptomic regulatory relationship associated with platinum (Pt) resistance in ovarian cancer (OC). We show that cisplatin treatment increases global m⁶A levels and METTL3 expression, linking Pt exposure to activation of the m⁶A machinery. Functional perturbation studies demonstrate that METTL3 overexpression enhances cisplatin resistance, whereas METTL3 knockdown or pharmacologic inhibition with the selective METTL3 inhibitor STM2457 sensitizes OC cells to Pt treatment in vitro and improves Pt response in vivo. Transcriptomic profiling identifies ADAM23, a cell-adhesion-related tumor suppressor, as a METTL3-dependent, m⁶A-associated transcript, with altered mRNA expression observed across multiple experimental systems and several high-confidence predicted m⁶A sites within its transcript. Cisplatin-associated METTL3 upregulation correlates with reduced ADAM23 expression, suggesting a potential regulatory relationship that may contribute to chemoresistance. Together, these findings support a model in which METTL3-associated increases in m⁶A methylation are linked to Pt resistance, in part through modulation of ADAM23 expression, and highlight METTL3 as a potential therapeutic target in OC. Full article

Bisacurone is a sesquiterpenoid constituent of Curcuma longa that has received considerably less attention than curcuminoids despite emerging evidence of its biological activity. In this study, the anti-inflammatory potential of bisacurone isolated from the Ryudai gold variety of Curcuma longa was evaluated using an integrated in vivo and in silico approach. Acute inflammation was assessed in rats using a carrageenan-induced paw edema model, supported by histopathological examination of paw tissues. Bisacurone significantly reduced paw edema during the peak inflammatory phase and markedly attenuated dermal thickening and inflammatory cell infiltration, indicating effective suppression of acute inflammatory responses. The effects of bisacurone were comparable to that of indomethacin. To elucidate the underlying molecular basis, density functional theory calculations, molecular docking, molecular dynamics simulations, and pharmacokinetic and toxicity predictions were performed. In silico analyses revealed favorable electronic properties, drug-likeness, and stable interactions of bisacurone with key inflammatory regulators, particularly IKKβ and COX-1, along with moderate interactions with MAPKs and iNOS. Molecular dynamics simulations confirmed the stability of the protein–ligand complexes. Collectively, these findings demonstrate that bisacurone exerts anti-inflammatory effects through multi-target modulation of inflammatory signaling pathways and highlight its potential as a bioactive functional food component and a lead compound for anti-inflammatory drug development. Full article

This article discusses the fundamental limitations of Light Fidelity (Li-Fi) systems, an emerging visible light communication technology that is constrained by line-of-sight dependency and optical attenuation. Unlike existing adaptive modulation approaches that focus solely on improving signal processing, we present an integrated framework that combines three key contributions: (1) an adaptive modulation optimization algorithm that selects among OOK, PAM, and OFDM schemes based on instantaneous signal-to-noise ratio thresholds, achieving a 30–40% range extension compared to fixed modulation references; (2) a method for spatial optimization of access points (APs) using the L-BFGS-B algorithm to determine the optimal location of APs, taking into account lighting constraints and coverage uniformity; and (3) comprehensive system-level modeling incorporating shot noise, thermal noise, inter-symbol interference, and dynamic shadowing effects for realistic performance evaluation. Through extensive simulations on multiple room geometries (6 m × 5 m to 20 m × 15 m) and AP configurations (one to six APs), we demonstrate that the proposed adaptive system achieves an average throughput 60% higher than that of fixed OOK, while maintaining 98.7% coverage in a 10 m × 8 m environment with two optimally placed APs. The framework provides practical design guidelines for Li-Fi deployment, including an analysis of computational complexity $O(M\times N)$ for coverage assessment, $O(I\times D^3)$ for access point optimization) and a characterization of convergence behavior. A comparative analysis with state-of-the-art techniques (optical smart reflective surfaces, machine learning-based blockage prediction, and Li-Fi/RF hybrid configurations) positions our lightweight algorithmic approach as suitable for resource-constrained deployment scenarios, where system-level integration and practical feasibility take precedence over innovation in individual components. Full article

Mastitis is a prevalent disease in the dairy cattle industry and has adverse effects on dairy cows’ health and milk quality. Importantly, mastitis is associated with the inflammatory response and mitophagy. As a complement-regulatory factor, C4b-binding protein alpha (C4BPA) has been shown to modulate inflammatory factors. This study further investigates its role and mechanisms in regulating mitophagy and inflammatory responses. Following C4BPA knockout, bovine mammary epithelial cells (BMECs) exhibited reduced expression of TLR4 and key pro-inflammatory cytokines, namely the tumour necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). Electron microscopy revealed a marked increase in mitochondrial membrane rupture, as well as cristae disorder and damage and increased reactive oxygen species (ROS) levels. Moreover, Pink1 and Parkin protein levels were increased, as was LC3B lipidation (LC3B-II), whereas p62 protein expression was significantly downregulated. Immunofluorescence indicated substantially increased LC3 colocalization with mitochondria, suggesting that C4BPA gene knockout activated Pink1/Parkin-mediated mitophagy. The fact that C4BPA knockout decreased the levels of p-IκB and p-p65 while increasing those of IκBα and p65 therefore indicates its regulatory role in the NF-κB-mediated inflammatory response. Together, these findings reveal that C4BPA deficiency in BMECs not only activates Pink1/Parkin-mediated mitophagy but also suppresses the NF-κB-mediated inflammatory response. This study provides novel potential molecular targets for predicting mastitis in dairy cattle. Full article

Major depressive disorder (MDD) ranks as a primary contributor to global ill health and disability, with treatments often proving insufficient. Recent study has increasingly found a strong correlation between gut microbiome diversity and mood-related behaviors, including MDD. Depression can alter gut microbiota (GM) composition, while intentional modulation of the GM may conversely influence depressive symptoms. This phenomenon arises from dynamic bidirectional interactions between the gut and brain, although the exact pathways are not yet fully elucidated. Proposed pathways include, but are not limited to, neural circuits, the endocrine system, immune responses, and metabolic regulation. Clinical data have also shown that regulating the GM through probiotics and prebiotics has the potential to alleviate depressive symptoms. This review summarizes contemporary research on the composition and modulatory functions of GM in MDD, and explores the predictive potential of GM for depression as well as the therapeutic prospects of probiotics, aiming to provide insights and directions for future research. Full article