Search Results (11,192)

Reliable long-term runoff prediction for Wudongde and Three Gorges reservoirs, two major reservoirs in the upper Yangtze River basin, is crucial for optimal operation of cascade reservoirs and hydropower generation planning. This study develops a data-driven model that integrates large-scale climate factors with a Gated Recurrent Unit (GRU) neural network to enhance runoff forecasting at lead times of 7–18 months. Key climate predictors were systematically selected using correlation analysis and stepwise regression before being fed into the GRU model. Evaluation results demonstrate that the proposed model can skillfully predict the variability and magnitude of reservoir inflow. For Wudongde Reservoir, the model achieved a mean correlation coefficient (CC) of 0.71 and Kling–Gupta Efficiency (KGE) of 0.57 during the training period, and values of 0.69 and 0.53 respectively during the testing period. For Three Gorges Reservoir, the CC was 0.67 (training) and 0.66 (testing), and the KGE was 0.52 and 0.49 respectively. The model exhibited robust forecasting capabilities across a range of lead times but showed distinct seasonal variations, with superior performance in summer and winter compared to transitional months (April and October). This framework provides a valuable tool for long-term runoff forecasting by effectively linking large-scale climate signals to local hydrological responses. Full article

Cancer is a multifaceted disease characterized by uncontrolled cell division resulting from substantial disruptions of normal biological processes. Central to its development is cellular transformation, which involves a dynamic sequence of events including chromosomal translocations, genetic mutations, abnormal DNA methylation, post-translational protein modifications, and other genetic and epigenetic alterations. These changes compromise physiological regulatory mechanisms and contribute to accelerated tumor growth. A critical factor in this process is the dysregulation of transcription factors (TFs) which regulate gene expression and DNA transcription. Dysregulation of TFs initiates a cascade of biochemical events, such as abnormal DNA replication, that further enhance cell proliferation and increase genomic instability. This microenvironment not only sustains tumor growth but also promotes the accumulation of somatic mutations, thereby fueling tumor evolution and heterogeneity. In this study, we employed an in silico approach to identify TFs regulating 622 key genes whose mutations are implicated in carcinogenesis. Transcriptional regulatory networks were analyzed through bioinformatics methods to elucidate molecular pathways involved in cancer development. A thorough understanding of these processes may help to clarify the function of dysregulated TFs and facilitate the development of novel therapeutic approaches designed to make cancer treatments personalized and efficacious. Full article

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by its aggressive clinical behavior and intricate microenvironment regulation, leading to dismal prognosis. Elucidating the molecular mechanisms underlying PDAC pathogenesis is crucial for developing improved therapeutic approaches. The functional significance and molecular basis of transcobalamin 1 (TCN1) in PDAC remain largely unexplored. Methods and Results: Through integrated analysis of TCGA and GTEx datasets combined with 80 clinical specimens, we identified significant TCN1 overexpression in PDAC, showing a positive association with tumor stage and negative associations with histological differentiation and overall survival. Functional investigations showed that TCN1 enhanced pancreatic cancer cell proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) in both in vitro and in vivo models. Mechanistically, TCN1 physically interacts with signal transducer and activator of transcription 4 (STAT4) to enhance its transcriptional activity. Chromatin immunoprecipitation (ChIP) assays showed that STAT4-mediated transcriptional activation of dual oxidase 2 (DUOX2) occurs through direct promoter binding. As a pivotal reactive oxygen species (ROS)-generating enzyme, DUOX2 overexpression elevates intracellular ROS levels, thereby promoting EMT progression and activating proliferation-related signaling cascades. Antioxidant treatment effectively abrogated TCN1-driven oncogenic phenotypes, establishing ROS as the critical downstream mediator. Conclusions: Collectively, our findings reveal a novel TCN1/STAT4/DUOX2 regulatory axis that exacerbates PDAC progression by remodeling redox homeostasis. This signaling cascade may serve as a prognostic biomarker and a potential therapeutic target for ROS-directed precision therapy in PDAC. Full article

Basic helix-loop-helix (bHLH) transcription factors represent one of the largest transcriptional regulator families in cereal crops such as rice, maize, and wheat. They play crucial and diverse roles in regulating key agronomic traits and essential physiological processes. This review provides a systematic synthesis of the functionally characterized bHLH genes across the three major cereals, offering a comparative perspective on their roles in growth, development, and stress responses. We comprehensively summarize their documented functions, highlighting specific regulators such as TaPGS1 for grain size, rice ILI subfamily for leaf angle, OsbHLH004 for seed dormancy and maize “Ms23-Ms32-bHLH122-bHLH51” cascade for the anther development. Their conserved and species-specific functions in iron homeostasis (e.g., IRO2) and in responses to drought, cold, salinity, and pathogens are also detailed. Additionally, we discuss the underlying molecular mechanisms, including specific binding to E-box/G-box cis-elements, protein dimerization, and integration with hormone signaling pathways. By integrating the current knowledge, this review serves as a consolidated and up-to-date reference that highlights the strategic potential of bHLH transcription factors in molecular breeding programs for improving yield, quality, and stress tolerance in cereals. Full article

Background: The study of psychophysiological responses to disgust-evoking stimuli has long been neglected in favour of other emotional stimuli, especially those evoking fear. While the basic cascade of responses to a frightening stimulus is relatively well-understood, psychophysiological responses to disgust-related threats, such as parasites or rotten food, are scarcely studied. Methods: Here, we aimed to assess skin resistance (SR) change as a measure of electrodermal response to visual cues that signal the presence of disgust-relevant threats. To this aim, we recruited 123 participants and presented them with one of the following varieties of disgust-relevant threats: disgust-evoking animals (e.g., parasites, worms), spoiled food, threat of pandemic, or pollution and toxicity. The latter two represented modern threats to test whether also these modern stimuli can initiate immediate automatic reaction. Results: We found significant differences between the categories: Participants responded with the highest probability to disgust-evoking animals (38%) and sneezing (52%), suggesting that only ancestral cues of pathogen disgust trigger automatic physiological response. Moreover, we found significant inter-sexual differences: women exhibited more SR change responses than men, and the amplitude of these responses was overall larger. Finally, we report a weak effect of subjectively perceived disgust intensity on reactivity to threat stimuli. Conclusions: We discuss heterogeneity of disgust-relevant threats, their adequate behavioural responses, and subsequent heterogeneity of respective SR responses. We conclude that large interindividual variability might eclipse systematic differences between participants with high or low sensitivity to disgust, and that subjectively perceived intensity of disgust is only a weak predictor of electrodermal response to its elicitor. Full article

Efficient resource utilization and low response times are critical challenges in storage area network (SAN) systems, especially as data-intensive applications like those driven by the Internet of Things and Artificial Intelligence place increasing demands on reliable, high-performance data storage solutions. Addressing these challenges, this paper contributes by proposing a proactive, utilization-driven traffic redistribution strategy to achieve balanced load distribution across switches, thereby improving the overall SAN performance and alleviating the risk of overload-incurred cascading failures. The proposed approach incorporates a Jackson Queueing Network-based method to evaluate both utilization and response time of individual switches, as well as the overall system response time. Based on a comprehensive case study of a mesh SAN system, two key parameters—the transition probability adjustment step size and the node selection window size—are analyzed for their impact on the effectiveness of the proposed strategy, revealing several valuable insights into fine-tuning traffic redistribution parameters. Full article

This paper presents an advanced radio frequency (RF)–direct current (DC) power conversion architecture based on a multistage Cockcroft–Walton topology. The proposed design achieves an enhanced voltage conversion ratio while maintaining superior RF-DC conversion efficiency under low input power conditions. To address the inherent limitations of cascading Cockcroft–Walton topologies with class-F load networks, a novel ground plane isolation technique was developed, which utilizes the reverse-side metallization of the circuit board. A 5.8 GHz two-stage Cockcroft–Walton voltage multiplier rectifier was fabricated and characterized. Measurement results demonstrate that the circuit achieves a maximum output voltage of 7.4 V and a peak conversion efficiency of 70.5% with an input power of only 30 mW, while maintaining stable performance across varying load conditions. A comparison with a two-stage Dickson rectifier reveals that the Cockcroft–Walton rectifier exhibits superior output voltage and conversion efficiency. The proposed architecture delivers significant improvements in power conversion efficiency and voltage multiplication capability compared to conventional designs, establishing a new benchmark for low-power wireless energy harvesting applications. Full article

High penetration of Inverter-Based Resources (IBRs) into the power grid could diminish the rotational inertia offered by a traditional power system and thus impact frequency stability. Several techniques are adopted to provide virtual inertial support to the grid for a short duration in the presence of IBRs. This paper uses the combined inertia support of a Dual Active Bridge (DAB) and a Voltage Source Converter (VSC)-fed Electric Vehicle Fast Charging System (EVFCS) is used to provide virtual inertia support to the grid. The Voltage Source Converter is designed to provide DC bus voltage regulation. Coordinated control of DAB converters and VSCs for mitigating frequency oscillations using cascaded droop-integrated Proportional Integral (PI) controllers is proposed. An aggregated low-frequency model of a DAB converter is considered in this work. The inertia of the DC link capacitor of the VSCs and battery is sequentially extracted to offer grid frequency support. In this work, the single droop control, dual droop control, grid-forming and Augmented Frequency Support (AFS) modes are explored to provide virtual inertia support to the grid. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

An integrative approach combining network pharmacology, molecular docking, and cellular assays was used to elucidate the potential mechanisms by which the n-butanol extract of Biebersteinia heterostemon ameliorates type 2 diabetes mellitus (T2DM). Chemical constituents of the n-butanol extract were identified via ultra-high-performance liquid chromatography coupled with Q-Exactive Orbitrap mass spectrometry. Active compounds and T2DM-related targets were retrieved from public databases, and intersecting targets were identified. Protein–protein interaction (PPI) networks were constructed using the STRING database, while Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed via the DAVID database. A comprehensive “drug–compound–target–disease–pathway” network was established, and molecular docking was conducted to evaluate binding affinities of key compounds to core targets. Functional validation was performed in insulin-resistant cell models. Network pharmacology analysis identified 37 active constituents within the extract and 222 overlapping targets associated with T2DM. GO enrichment indicated involvement in protein phosphorylation, MAPK cascade activation, and negative regulation of apoptosis. Key signaling pathways included PI3K/AKT and lipid and atherosclerosis pathways. Molecular docking revealed strong binding affinities (binding energies ≤ −9.3 kcal·mol⁻¹) between core compounds—such as cheilanthifoline, glabridin, acetylcorynoline, skullcapflavone II, liquiritigenin, and dinatin—and pivotal targets including GAPDH, AKT1, TNF, SRC, EGFR, and PPARγ. In vitro experiments demonstrated that the extract significantly enhanced glucose uptake and glycogen synthesis in insulin-resistant cells, while suppressing oxidative stress and the expression of pro-inflammatory mediators such as TNF-α, MMP9, and IL-6. Collectively, B. heterostemon shows potential as an effective intervention for T2DM by targeting key molecular pathways, improving insulin sensitivity, and mitigating oxidative stress and inflammation in insulin-resistant cells. Full article

Background: Multimodal Deep learning has emerged as a crucial method for automated patient-specific quality assurance (PSQA) in radiotherapy research. Integrating image-based dose matrices with tabular plan complexity metrics enables more accurate prediction of quality indicators, including the Gamma Passing Rate (GPR) and dose difference (DD). However, modality imbalance remains a significant challenge, as tabular encoders often dominate training, suppressing image encoders and reducing model robustness. This issue becomes more pronounced under task heterogeneity, with GPR prediction relying more on tabular data, whereas dose difference prediction (DDP) depends heavily on image features. Methods: We propose BMMQA (Balanced Multi-modal Quality Assurance), a novel framework that achieves modality balance by adjusting modality-specific loss factors to control convergence dynamics. The framework introduces four key innovations: (1) task-specific fusion strategies (softmax-weighted attention for GPR regression and spatial cascading for DD prediction); (2) a balancing mechanism supported by Shapley values to quantify modality contributions; (3) a fast network forward mechanism for efficient computation of different modality combinations; and (4) a modality-contribution-based task weighting scheme for multi-task multimodal learning. A large-scale multimodal dataset comprising 1370 IMRT plans was curated in collaboration with Peking Union Medical College Hospital (PUMCH). Results: Experimental results demonstrate that, under the standard 2%/3 mm GPR criterion, BMMQA outperforms existing fusion baselines. Under the stricter 2%/2 mm criterion, it achieves a 15.7% reduction in mean absolute error (MAE). The framework also enhances robustness in critical failure cases (GPR < 90%) and achieves a peak SSIM of 0.964 in dose distribution prediction. Conclusions: Explicit modality balancing improves predictive accuracy and strengthens clinical trustworthiness by mitigating overreliance on a single modality. This work highlights the importance of addressing modality imbalance for building trustworthy and robust AI systems in PSQA and establishes a pioneering framework for multi-task multimodal learning. Full article

Spinal cord injury (SCI) is a neurological condition often resulting in permanent motor and sensory deficits, for which effective treatments remain limited. RNA interference (RNAi) is a post-transcriptional mechanism of the downregulation of gene expression mediated by small interfering RNAs. RNAi has demonstrated therapeutic efficacy in various neurological disorders, positioning it as a promising yet underexplored therapeutic strategy for SCI. Here, we provide a focused overview of the key pathological processes in SCI, including primary mechanical injury and secondary cascades such as inflammation, mitochondrial dysfunction, excitotoxicity, oxidative stress, multiple forms of cell death, and others. The potential of RNAi to selectively silence genes implicated in these pathological processes, thereby enhancing neuroprotection and functional recovery, is highlighted. We point out that not only protein-coding genes, but non-coding RNAs (ncRNAs) are suitable targets for RNAi. Novel RNAi tools such as CRISPR-Cas13 might revolutionize the field and offer new opportunities for SCI therapy. However, despite all these promising findings, relevant translational studies of RNAi remain scarce. Challenges related to delivery methods, long-term efficacy, and cell-specific targeting must be addressed. Importantly, combining RNAi with other strategies such as cell- or biomaterial-based therapies may enhance therapeutic outcomes. Future investigations should prioritize systematic comparisons of RNAi targets and delivery systems, ideally at single-cell resolution and in different SCI models, to identify the most relevant molecular pathways for clinical translation. Overall, RNAi represents a compelling but still underdeveloped approach for SCI therapy, requiring continued refinement to reach clinical application. Full article

Agriculture and its allied activities contribute to the primary sector in India and act as the basis for the country’s economy. Available agricultural landholdings are scattered as multiple plots across the country. Land fragmentation has led to problems achieving economies of scale and economies of scope; lower productivity, efficiency, and modernization; loss of biodiversity; and little scope for mechanization and technology. FPOs are small clusters of farmers who collaborate to enhance their bargaining strength through collective procurement, processing, and marketing efforts. To enhance the performance of FPOs at the grassroots level, the engagement of cluster-based business organizations (CBBOs) is vital. Millet FPOs are similar to voluntary farmer groups that are involved in the cultivation and promotion of millets. IIMR-promoted millet FPOs were selected purposively for the present study as they are involved in millet cultivation and farming. A total of 450 millet farmers from 15 FPOs and 3 states were randomly chosen for this action research study. The present research identified 10 key factors and collected farmers’ opinions toward member participation in millet FPOs using interpretive structural modeling. The ISM approach provided a clear understanding of how the selected factors interconnect hierarchically with each other as foundational drivers and dependent outcomes. The results from the MICMAC analysis demonstrated that foundational interventions, such as post-harvest technology availability (V2) and knowledge transfer by KVKs (V5), directly support higher-level objectives. Intermediate factors like economies of scale (V1) and market and credit linkages (V3) transform these services into operational advantages, while the outcome factors of business planning (V8), FPO branding (V7), and bargaining power (V9) emerge as dependent variables. The model demonstrates that V2 catalyzes improvements across the production, market, and institutional domains, cascading through intermediate enablers (V1, V4, V5, V6) to strengthen outcomes (V3, V7, V8, V9, V10). This hierarchy demonstrates that investing in post-harvest technology and complementary extension services is critical for building resilient millet FPOs and enhancing member participation. Full article

Leveraging our developed Global–Local Integrated Topology inverse design algorithm, we designed an efficient, compact, and symmetrical power splitter on a silicon-on-insulator platform. This device achieves a low insertion loss of 0.18 dB and a power imbalance of <0.0002 dB between its output ports within an ultra-compact footprint of 5.5 µm × 2.5 µm. The splitter, combined with an ultra-compact 0–π phase shifter measuring only 4.5 µm × 0.9 µm on the silicon-on-insulator platform, forms an ultra-compact inverse-designed integrated photonic matrix compute core, thus enabling the function of matrix operations in optical neural networks. Through a networked cascade of power splitters and phase shifters, this silicon-based photonic matrix compute core achieves an integration density of ~26,000 computational units/mm². Moreover, it attained 99.05% accuracy in handwritten digit recognition (0–9) and exhibited strong robustness against fabrication errors, maintaining >80% accuracy with >0.9 probability under simulated random fabrication errors. Full article