Search Results (5,585)

Valve stiction remains a persistent nonlinear phenomenon in industrial control loops, often inducing limit-cycle oscillations that degrade control performance, compromise stability, and reduce process efficiency. Reliable detection of stiction is therefore essential for condition-based maintenance and improved operational performance. This study proposes a Hybrid Wavelet–Convolutional Neural Network (HW-CNN) framework for the detection of valve stiction in closed-loop systems. The approach employs the continuous wavelet transform (CWT) to generate time–frequency scalograms that preserve localized energy distributions associated with stick–slip behavior, including transient release events and sustained oscillatory patterns. These representations are subsequently processed using a fine-tuned deep residual neural network to enable automated feature extraction and classification. Unlike conventional signal-based or generic time–frequency learning approaches, the proposed framework is designed to retain control system-specific dynamics within the feature representation, thereby improving the separability of stiction-induced signatures under varying operating conditions. The methodology is evaluated using both simulated control loop data and real industrial datasets obtained from the International Stiction Database (ISDB), ensuring evaluation under controlled and practical conditions. To enhance reliability, performance metrics are reported as averages over repeated experimental runs. The results demonstrate that the proposed HW-CNN framework achieves an accuracy of 96.1% and an F1-score of 96.0% on simulated datasets, and 90.4% accuracy with an F1-score of 90.0% on industrial data. Additional analysis indicates that the model maintains consistent detection capability despite increased variability in real-world conditions. Furthermore, interpretability is supported through Grad-CAM analysis, which shows that the network focuses on physically meaningful regions within the scalograms corresponding to known stiction dynamics. The findings confirm that the integration of wavelet-based feature encoding with deep residual learning provides a robust and interpretable framework for valve stiction detection. Full article

Background: Research indicates that hepatic gluconeogenesis mediates metabolic coupling between the liver and muscles via the Cori cycle and participates in liver–brain axis communication through its metabolic products and regulatory networks, thereby linking it to the pathogenesis of depression. Together, these mechanisms form the molecular basis for the antidepressant effects of exercise-regulated hepatic gluconeogenesis. Regular exercise promotes skeletal muscle contraction, causing the muscles to release more lactate into the circulatory system. Lactate acts as a substrate for gluconeogenesis and activates downstream signaling pathways, thereby enhancing the gluconeogenic response. During exercise, glycogenolysis directly provides energy, while lactate produced by glycolysis enters the liver via the Cori cycle to serve as a substrate for gluconeogenesis. By maintaining blood glucose homeostasis, this process ensures a stable energy supply to the brain, thereby improving cognitive and emotional functions. This study aims to elucidate how key substrates, regulatory factors, and rate-limiting enzymes involved in hepatic gluconeogenesis and exercise influence brain energy supply, cognitive function, and emotional regulation during depression. It seeks to identify the potential targets and mechanisms through which exercise exerts its antidepressant effects via hepatic gluconeogenesis, with the goal of providing a theoretical foundation for research into the mechanisms of depression and for clinical exercise interventions. Methods: This review conducted a comprehensive search of the recent literature on exercise, hepatic gluconeogenesis, and depression in major domestic and international databases. Adopting an interdisciplinary approach that integrates hepatic gluconeogenesis and exercise, it synthesizes existing evidence to explore the metabolic mechanisms by which exercise improves depression through the regulation of hepatic gluconeogenesis pathways. Results: Research has found that exercise may modulate hepatic gluconeogenic substrates and regulate the expression of cAMP-responsive element-binding protein in states of depression, regulatory factors such as liver kinase B1, forkhead box protein 01, hepatocyte nuclear factor 4 alpha, and peroxisome proliferator activated receptor gamma co activator factor 1 alpha are used to affect key rate limiting enzymes of hepatic gluconeogenesis, such as phosphoenolpyruvate carboxykinase and glucose-6-phosphatase, enhance hepatic gluconeogenesis processes, maintain blood glucose homeostasis, ensure brain energy supply, and improve depression. Conclusions: Exercise intervention targeting hepatic gluconeogenesis may be a potential therapeutic strategy for depression. Full article

Predicting garlic prices is difficult because the crop behaves as both an agricultural commodity and a speculative asset. Unlike staple grains, which follow more predictable seasonal supply cycles, garlic can be stored for over a year, its production is geographically concentrated, and its demand remains inelastic. This industry structure makes it susceptible to speculative hoarding, where even minor harvest deficits may trigger sharp price spikes. A typical example is the “Suan Ni Hen” (crazy garlic) phenomenon in the Chinese market: during the 2009–2010 and 2016 periods, speculative capital repeatedly exploited expectations of harvest reduction to engage in large-scale hoarding. According to data released by China’s National Development and Reform Commission (NDRC) at the end of October 2016, national wholesale garlic prices surged by 90% year-on-year, with purchase prices in some major producing areas doubling or multiplying within a short period. Such short-term price bubbles, together with severe volatility and abrupt regime shifts, can make standard forecasting models unreliable in this uncertain environment. Existing methods, ranging from traditional seasonal algorithms to deep learning networks, often overlook the need to decouple the local trend-weekly-seasonal baseline from the dynamic effects of multi-source external signals. This paper proposes GPF-EVMoLE, a compositional multi-step forecasting framework built on an explicit division of labor. The framework first extracts an interpretable local trend and weekly-seasonal baseline through an ETS decomposition module. Two specialized components then process the residual signal: a temporal fusion Transformer-style variable selection network (VSN) uses multi-source external features to identify informative macroeconomic and environmental signals at each forecasting step, while a Mixture of Linear Experts (MoLE) models phase-wise regime shifts within the residual series. Together, these modules adaptively integrate heterogeneous information. This study evaluates the framework on a custom daily evaluation dataset containing 17,685 records across six major producing regions in three provinces. At 7-day and 14-day forecasting horizons, GPF-EVMoLE consistently outperforms eight representative statistical, machine learning, and deep learning baselines across MAE, RMSE, and MAPE metrics. Ablation studies verify the necessity of each component, showing that structural separation of the forecasting tasks helps overcome the limitations of monolithic models and provides an accurate and interpretable solution for complex agricultural markets. Full article

Arbuscular mycorrhizal fungi (AMF) are vital for nutrient cycling, but how lithology across bulk soil and the rock–soil interface influence AMF communities remains poorly understood. We investigated the effects of karst (dolomite, limestone) and non-karst (clastic rock) lithologies across bulk soil and the rock–soil interface on AMF diversity, community composition, and co-occurrence networks in southwest China. AMF diversity did not differ among lithologies or between bulk soil and rock–soil interface, whereas community composition showed significant differences across lithology. The relative abundance of Glomus was lower in karst than in non-karst, whereas Paraglomus showed the opposite pattern. Co-occurrence network analysis revealed that karst soils exhibited higher numbers of nodes and edges but lower network density, transitivity, betweenness centrality, and average path length compared to non-karst soils. Within the same dolomite and limestone, network properties were similar between the rock–soil interface and bulk soil. Soil pH, exchangeable Ca²⁺ and Mg²⁺, total nitrogen, and nitrate nitrogen were negatively correlated with Glomus and network properties (e.g., number of nodes and edges), while ammonium nitrogen showed positive correlations. Our results indicate that lithology exerts a stronger influence than soil compartment on AMF community composition and interspecific interactions, emphasizing the key role of lithological substrates in regulating AMF communities. Full article

Cancer therapy is increasingly shaped by the need for agents that are both mechanistically precise and clinically tolerable. Sulforaphane (SFN), a dietary isothiocyanate enriched in cabbage-family vegetables such as cauliflower and Brussels sprouts, has emerged as a pleiotropic modulator of tumor biology. This review synthesizes current evidence that SFN regulates diverse cancer-relevant processes, including redox homeostasis, cell-cycle progression, apoptosis, autophagy and epigenetic remodeling, largely through coordinated effects on transcriptional (for example, Nrf2, MAPK, NF-κB and AP-1), post-transcriptional (microRNAs and messenger RNAs) and epigenetic (DNA methyltransferases and histone deacetylases) networks. We then examine how functional nucleic acids, including aptamers, small interfering RNAs, microRNAs and tetrahedral DNA nanostructures, can be engineered to guide SFN to tumor cells, amplify pathway-specific effects and overcome resistance. Particular emphasis is placed on nanotechnology-enabled delivery platforms that enhance SFN stability, bioavailability and tumor selectivity. Finally, we outline key challenges, such as context-dependent Nrf2 activity, inter-individual variability in metabolism and incomplete clinical validation, and propose priorities for translating SFN-based functional nucleic acid systems into rational, combination-ready strategies for precision oncology. Full article

Sucrose non-fermenting 1-related kinase (SNRK) is an understudied serine/threonine kinase of the CAMKL family, known for its role in metabolic regulation and cell signaling. Despite its emerging relevance in various biological processes and diseases, the phosphoregulatory landscape of human SNRK (valid substrates or role of its phosphosites) remains unexplored and demands robust, large-scale, data-oriented approaches to predict the potential substrates. A comprehensive analysis of global human phosphoproteomics datasets was performed to systematically identify class I phosphosites on SNRK, along with their predicted upstream kinases, potential downstream substrates, and coregulated phosphoproteins. Our analysis resulted in the identification of 33 dark SNRK phosphosites, of which 19 were differentially regulated across an array of experimental conditions. Among them, S518 and S569, outside their kinase domain, were the most frequently regulated and co-occurred phosphosites under diverse conditions. Notably, S569 is predicted as a candidate autophosphorylation site of SNRK. In these contexts, coregulation analysis of proteins and their phosphorylation sites suggested associations of phospho-SNRK in cell cycle progression, chromatin organization, and DNA replication. Uncovering candidate upstream kinases and potential substrates for prioritized validation, this study provides the first comprehensive phosphoproteomic map of SNRK, serving as a foundation for future investigations into its signaling network associations and therapeutic approaches. Full article

Background: Chromosome 7 gain (chr7 gain) is a highly prevalent early event in glioblastoma (GBM). Because chr7 gain usually involves broad chromosomal amplification, its biological impact is unlikely to be fully explained by canonical loci such as EGFR and MET. The contribution of less-characterized, dosage-sensitive genes on chromosome 7 remains insufficiently defined. This study aimed to identify additional chr7 candidates associated with malignant phenotypes in GBM. Methods: Transcriptomic, copy-number, and clinical data from TCGA-GBM and TCGA-LGG were analyzed to characterize chr7-gain-associated alterations and prioritize candidate genes. Refined GBM and histologic GBM cohorts based on the WHO 2021 framework were used for candidate selection. RPA3-associated pathway features were examined using ssGSEA, PROGENy, WGCNA, and protein–protein interaction analysis, with external validation in the CGGA-693 cohort. Single-cell RNA-seq analysis compared chr7-gain and chr7-normal-copy tumor subclusters. Functional relevance was evaluated by siRNA-mediated knockdown in U87 and U118 cells. Results: Chr7 gain was enriched in high-grade IDH-wildtype gliomas and was associated with cell-cycle- and DNA repair-related programs. RPA3 was prioritized as a dosage-sensitive chromosome 7 candidate based on its upregulation in chr7-gain tumors, association with poor prognosis, and concordance with replication- and repair-related signatures. In vitro, RPA3 knockdown impaired cell growth, proliferation, colony formation, and migration. Single-cell analysis suggested greater transcriptomic and network-level relevance of RPA3 in chr7-gain tumor cells. Conclusions: RPA3 is a dosage-sensitive chromosome 7 candidate associated with aggressive and replication-/repair-related phenotypes in GBM. Increased RPA3 expression may contribute to the selective advantage associated with chr7 gain, which supports further investigation as potential therapeutic target. Full article

Accurate forecasting of water demand enables optimized peak-load management, alleviating pressure during high-demand periods and improving the operational efficiency of urban secondary water supply systems—a critical component in the energy-efficient and sustainable operation of buildings. However, existing water demand prediction methods in some regions suffer from low accuracy and excessively long prediction cycles, posing challenges for real-time water scheduling in building-scale systems. To address these challenges, this study develops a hybrid predictive framework that integrates a BP neural network with the Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms for enhanced parameter optimization. Using hourly water consumption data from a representative residential district, the proposed model is compared against standalone machine learning models—Extreme Learning Machines (ELM), Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Model performance is rigorously evaluated using the coefficient of determination, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). The PSO-GWO-BP hybrid model achieves a predictive accuracy of 97.06%, yielding the lowest MAE, MSE, RMSE, and MAPE, as well as the highest R among all models considered, thereby significantly outperforming the benchmark standalone models. Furthermore, the high-precision short-term prediction outputs enable dynamic regulation of secondary water tank refill thresholds, facilitating refined water allocation and enhanced operational management of building water supply systems. These findings demonstrate the considerable application potential of the proposed hybrid model in enhancing both water resource efficiency and energy utilization performance in the daily operation of green buildings, providing reliable technical support for intelligent and low-carbon building water supply management. Full article

Cervical cancer (CC) is an alarming global health problem, with predominantly higher incidence, lethal progression, and mortality among women of African ancestry (AA) than women of European ancestry (EA). Although persistent high-risk human papillomavirus (HPV) integration and infection are the key etiological factors, currently available evidence implicates epigenetic reprogramming as a prime contributor to ancestry-associated differences in CC pathogenesis. To address these disparities, we performed genome-wide DNA methylation profiling of HPV-positive cervical intraepithelial neoplasia (CIN) lesions from AA (n = 15) and EA (n = 15) women. Differential methylation analysis identified a distinct epigenomic landscape in AA-CIN lesions, with widespread hypermethylation and hypomethylation at promoter-associated and regulatory CpG sites. Pathway enrichment analyses highlighted dysregulation of ECM-receptor interaction, focal adhesion, PI3K-Akt, MAPK, Ras, Rap1, and RUNX-dependent transcriptional networks. Comparative analysis across CIN grades (CIN1–CIN3) revealed progressive epigenetic reprogramming affecting cell cycles, cytoskeletal dynamics, signaling, and metabolic pathways. Among hypermethylated tumor suppressor genes, SH3GL2 and ARHGAP25 showed significantly higher methylation in AA lesions, accompanied by concomitant loss of their protein expression. MBD1, a methylation-binding regulator, was upregulated in AA-CIN lesions, coinciding with global loss of 5-hydroxymethylcytosine (5hmC), suggesting enhanced transcriptional repression. In contrast, EA lesions retained protein expression and 5hmC levels. Collectively, these findings indicate that early, ancestry-specific epigenetic modifications target tumor suppressor pathways and converge on oncogenic signaling, cytoskeletal remodeling, and cell–cell adhesion. Our study provides mechanistic insight into CC health disparities, identifying SH3GL2 and ARHGAP25 hypermethylation as potential biomarkers, and highlighting epigenetic regulation as a contributor to disparate CC progression in AA women. Full article

Driven by increasingly stringent requirements for energy saving and emission reduction in non-road machinery, hybrid wheel loaders have attracted growing attention as a practical pathway toward cleaner construction equipment. However, conventional energy management strategies often show limited adaptability to highly transient operating cycles and struggle to balance fuel economy, real-time applicability, and battery charge sustainability. To address these issues, this study proposes an improved sparrow-search-algorithm-based equivalent consumption minimization strategy (ISSA-ECMS) for a series hybrid wheel loader. A quasi-static powertrain model was established, while ISSA was used to optimize both the hyperparameters of a Convolutional Neural Network-Long Short-Term Memory (CNN–LSTM) stage-recognition model and the stage-dependent ECMS parameters. A hidden Markov model (HMM)-based post-processing framework was further introduced to improve temporal consistency in operating-stage recognition. The results show that the optimized ISSA-CNN–LSTM achieved 93.22% accuracy, 93.08% Macro-F1, and 93.21% Weighted-F1, while HMM refinement further improved recognition accuracy from 94.02% to 97.92%. In energy management simulations, ISSA-ECMS maintained the terminal state of charge (SOC) at 50.0069%, reduced fuel consumption by 2.1% and 1.4% compared with conventional ECMS and A-ECMS, respectively, and increased the proportion of engine operating points in the economical region to 77.549%. Compared with dynamic programming, its fuel-consumption increase was only 0.28%, while retaining online applicability. These results demonstrate that the proposed method provides an effective and practical solution for real-time energy management of series hybrid wheel loaders. Full article

Fetal skeletal muscle development involves coordinated interactions among myogenic, stromal, vascular, and immune compartments, yet the cellular and molecular programs guiding tissue maturation remain incompletely understood. To address this, we generated a high-resolution single-cell atlas of fetal female goat skeletal muscle and performed trajectory analysis, transcription factor activity profiling, and intercellular communication mapping. Unsupervised clustering identified RUNX2 mesenchymal progenitors, fibro-adipogenic progenitors (FAPs), myofibroblasts, endothelial cells, macrophages, differentiating myocytes, and mature skeletal muscle fibers, revealing a heterogeneous ecosystem in which stromal populations support myogenic progression and vascular and immune cells contribute to tissue organization. Pseudotime analysis traced a maturation continuum from differentiation-competent myocytes to contractile fibers, marked by sequential activation of extracellular matrix remodeling, cytoskeletal stabilization, and sarcomere assembly. KEGG and GO enrichment highlighted stage-specific engagement of ErbB, Hedgehog, and Hippo signaling, as well as cell cycle and ubiquitin-mediated proteolysis pathways, linking proliferation, differentiation, and structural maturation. Transcription factor profiling revealed early-stage proliferative and morphogenetically permissive states driven by E2F4/5, HMGA2, and HAND2, transitioning to late-stage differentiation, ECM remodeling, and tissue stabilization orchestrated by CEBPB, CREB3L1, ELK1, and E2F2. Cell–cell communication analysis showed a developmental redistribution of signaling authority, from ECM-driven, progenitor-centered networks to modular, structurally stabilized interactions. These findings define the cellular, transcriptional, and signaling framework orchestrating fetal skeletal muscle maturation. Full article

Submesoscale oceanic eddies play a crucial role in ocean dynamics and climate systems, while Synthetic Aperture Radar (SAR) offers distinct advantages for observing these fine-scale phenomena; the advancement of automated detection algorithms is currently hindered by the lack of publicly available, high-quality benchmark datasets. To address this gap, this paper constructs a universal benchmark dataset for submesoscale eddies and presents an improved anchor-free object detection framework based on Fully Convolutional One-Stage (FCOS). We propose two key innovations: (1) a Deep Fusion Feature Pyramid Network (DF-FPN) that integrates adaptive multi-scale feature fusion directly into the pyramid construction process through deep fusion Adaptive Spatial Feature Fusion (ASFF) modules, enabling bidirectional feature enhancement and global context-aware fusion and (2) a Pixel-level Statistical Description Learning (PSDL) module that enhances feature representation by learning statistical descriptors across multiple scales. The DF-FPN replaces traditional staged optimization with an intrinsic deep fusion paradigm, significantly improving feature quality. Extensive experiments on our constructed dataset demonstrate that our method achieves 66.6% mAP, 91.3% AP₅₀, and 80.5% AP₇₅. These results represent a substantial improvement over the FCOS baseline and outperform other state-of-the-art detectors, providing a robust and efficient solution for operational submesoscale eddy monitoring in SAR imagery. Enhanced detection capacity of this kind offers a critical observational foundation for advancing research on upper-ocean nutrient transport, carbon cycle dynamics, and the dispersion of marine pollutants, thereby supporting broader environmental monitoring and climate-related objectives. Full article

Vegetated field borders are widely promoted as tools to enhance biodiversity and strengthen biological control in agroecosystems. However, their role in pest dynamics remains conceptually fragmented and empirically inconsistent. Here, we develop a unified framework explaining how crop border vegetation influences pest populations through four interlinked ecological mechanisms. First, borders act as host reservoirs and selective filters, providing alternative hosts and overwintering habitat that enhance pest persistence across crop cycles. Second, borders modify pest colonization dynamics by shaping movement, aggregation, and host-location behavior at crop edges. Third, borders restructure multitrophic networks, simultaneously supporting natural enemies, alternative prey, vectors, and pathogens, generating nonlinear effects on pest suppression. Fourth, repeated disturbance and management function as selective filters, determining which plant functional groups dominate borders and, consequently, which pest and natural enemy communities are maintained. To ground this framework, we conduct a structured synthesis of published empirical and conceptual studies on crop-border vegetation, including weed and arthropod surveys, and classify them according to the proposed mechanisms. Our synthesis reveals a strong emphasis on multitrophic effects, whereas colonization processes and disturbance filtering are comparatively underexplored. Across mechanisms, plant identity and dominance structure consistently emerge as stronger predictors of pest outcomes than species richness alone. We argue that borders are not inherently beneficial or harmful but function as selectively structured ecological interfaces shaped by management history and species composition. By integrating temporal persistence, spatial behavior, network interactions, and anthropogenic filtering, our framework provides a predictive basis for IPM-oriented design of field borders, enabling management strategies that reduce pest carryover, disrupt colonization pathways, and enhance biological control while maintaining ecosystem services. This article is part of the theme issue “The Biology, Ecology, and Management of Plant Pests”. Full article

Growing urbanization influences the urban hydrological cycle by increasing stormwater runoff. Consequently, Stormwater Collection Networks may suffer troubling phenomena, such as pressurized flow conditions. One promising strategy to resolve this issue involves the adoption of green roofs. This study investigates the effect of green roof installation on the enhancement of sewer network behaviour. Numerical simulations were conducted using EPA SWMM 5.2. The model was varied by changing the hydraulic roughness and the slope of the drainage network conduits along with the green roof extension. Preliminary results revealed that green roofs can significantly mitigate the pressurization hazard in urban drainage systems. Full article

Microplastics (MPs) are emerging pollutants that affect soil–microbe interactions in paddy ecosystems. Periphytic biofilms (PBs) are complex microbial consortia that ubiquitously distribute at the soil–water interface of paddy ecosystems, playing essential roles in nutrient cycling and pollutant migration. However, whether MPs affect the community composition of PBs remains largely unknown. This microcosm study investigated the effects of three types of MPs (polyacrylonitrile, PAN; polyethylene, PE; and polyethylene terephthalate, PET) on the community characteristics of PBs via high-throughput sequencing (16S/18S rRNA) technology. Results showed that the addition of all MPs significantly increased the biomass and chlorophyll-a content of PBs, with PAN inducing the maximum increase (by 331.9% and 128.6%). However, all MPs had no significant effect on the PB α-diversity of bacterial and eukaryotic communities (p > 0.05). As for PB composition, PAN and PET increased the relative abundance of Cyanobacteria, Proteobacteria and Holozoa, PE increased that of Cyanobacteria, Bacteroidota and Blastocladiomycota, and all MPs decreased the relative abundance of Chloroflexi, Actinobacteriota and Basidiomycota. Furthermore, PET decreased the predicted functional potential of natural polymer degradation (cellulolysis, ligninolysis, xylanolysis, ureolysis), nitrogen fixation and nitrate ammonification, while PE increased predicted potential for plastic degradation, nitrate reduction and denitrification. Co-occurrence network analysis suggested that the PE network showed higher connectivity and lower modularity, while the PAN network showed higher modularity. This study advances our understanding of soil MPs–microbe interactions under high-concentration conditions. It also suggests that PB community characteristics may serve as potential bioindicators for soil MP pollution. Full article