Search Results (16,453)

This paper introduces a collocation algorithm for numerically solving the third-order Gilson–Pickering equation (GPE) and the classical Rosenau–Hyman equation (RHE). We employ newly developed shifted Pell polynomials as basis functions. Novel formulas for these polynomials are devised and utilized in constructing the proposed algorithm. Specifically, we establish a new power form and its inversion formula, along with an explicit formula for derivatives of the shifted Pell polynomials, from which the operational matrices of derivatives (OMDs) are derived. These matrices facilitate the conversion of nonlinear dispersive models into systems of algebraic equations, efficiently solved using Newton’s iterative technique. The error analysis of the shifted Pell expansion is discussed in depth. Several numerical examples, including the RHE, its fourth-order variant, and the Fornberg–Whitham equation, are provided to demonstrate the method’s performance and accuracy. Comparative results are also reported. Full article

Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale structural dependencies and the rich semantic information embedded in educational materials. To bridge this gap, we propose EduSheaf—a unified framework that integrates large language models (LLMs) with a sheaflet-based signed graph neural network. Specifically, LLMs are employed to extract fine-grained semantic embeddings from multiple-choice questions (MCQs), thereby enriching graph representations with contextual knowledge. A signed graph is then constructed to encode student–MCQ interactions, where correct and incorrect responses are represented as positive and negative edges. On top of this, a novel sheaflet-based signed graph neural network performs multi-frequency learning through low-pass and high-pass filters, enabling the joint modeling of global consensus and local variations, while sheaf structures enforce edge-level consistency. Extensive experiments on multiple real-world educational datasets demonstrate that EduSheaf consistently outperforms state-of-the-art baselines, including both semantic-enhanced and signed graph models, in terms of prediction accuracy and robustness. Ablation studies further reveal the complementary roles of semantic embeddings and multi-frequency graph filters. Full article

Class imbalance is a formidable and ongoing challenge in image classification tasks. Existing methods address this issue by emphasizing minority classes through class redistribution in the feature space or adjusting decision boundaries. Although such approaches improve the accuracy of minority classes, they often lead to unstable training and performance degradation on majority classes. To alleviate these challenges, we propose a unified redistribution framework termed as ComReg, which explicitly enforces complementary regularization on feature learning and decision boundary optimization in long-tailed image classification. Specifically, ComReg employs a multi-expert learning framework combined with prior-knowledge-guided online distillation to construct distribution-aware decision boundaries. From the feature space learning perspective, we enhance intra-class compactness and inter-class separability through decoupled-balanced contrastive learning. To further align the distributions in both spaces, we introduce a delay-weighted prototype learning strategy, which incorporates the decision boundary constructed by the head-class expert into the decoupled-balanced contrastive learning process. Extensive experiments on widely used long-tailed benchmarks, including CIFAR10-LT and CIFAR100-LT, as well as the real-world long-tailed datasets such as subsets of MedMNIST v2, demonstrate that our method achieves state-of-the-art performance. Full article

Wheat is among the most important staple crops worldwide; however, its yield and quality are severely threatened by stripe rust caused by Puccinia striiformis f. sp. tritici (Pst). CH806 is a Thinopyrum intermedium-derived resistant breeding line developed in our laboratory that is highly resistant to the prevalent Chinese Pst races CYR32, CYR33, and CYR34 in field trials. A genetic population was developed by crossing CH806 with the susceptible cultivar Chuanmai 24. Phenotypic evaluation of the progeny under field conditions revealed segregation for stripe rust resistance in the F₂ generation. On the basis of the resistance phenotypes of the F₂ and F_2:3 populations, homozygous resistant and homozygous susceptible F₂ individuals were selected to construct resistant and susceptible DNA bulks, respectively, for genotyping using the Wheat 120K SNP array. Bulked segregant analysis indicated that the most significant SNPs were predominantly clustered on chromosome 4A. Subsequently, publicly available simple sequence repeat (SSR) markers on chromosome 4A and newly developed SSR markers within the candidate region that were enriched for polymorphic SNPs were used for linkage analysis. The resistance locus, temporarily designated YrCH806, was mapped to an interval flanked by markers Xwmc48/Xwmc89 and SSR4A-60, with genetic distances of 4.4 cM and 2.5 cM, respectively, corresponding to a physical position of 515.8–574.7 Mb on the wheat reference genome. The closest flanking marker, SSR4A-60, was successfully converted into a Kompetitive Allele-Specific PCR (KASP) marker. This high-throughput marker was subsequently utilized to screen a panel of wheat germplasms for the distribution of YrCH806. This study provides a novel resistance source and associated molecular markers for improving stripe rust resistance in wheat breeding programs. Full article

The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition. Full article

Src family tyrosine kinases regulate oocyte maturation and fertilization in many species, yet their physiological roles in Xenopus laevis (X. laevis) remain incompletely defined. Here, we generated three X. laevis Src (xSrc) constructs with defined point mutations allowing for selective immunochemical detection and controlled modulation of kinase activity: wild type (xSrcWT, Arg121His), constitutively active (xSrcKA, Arg121His/Tyr526Phe), and kinase-negative (xSrcKN, Arg121His/Lys294Met). Capped mRNAs were microinjected into immature oocytes, and effects on meiotic maturation and egg activation were analyzed. All constructs produced detectable Src protein within 4–5 h after injection without inducing progesterone-independent maturation. Following progesterone treatment, MAP kinase phosphorylation, CDK1 activation, and germinal vesicle breakdown (GVBD) occurred normally in all groups, although xSrcKA-expressing oocytes showed a modest but reproducible acceleration of MAPK activation and GVBD. Global tyrosine phosphorylation analysis revealed increased phosphorylation of several proteins, including a prominent ~50 kDa substrate, specifically in xSrcKA oocytes. After maturation, oocytes were subjected to artificial activation. xSrcKN-expressing oocytes responded normally to Ca²⁺ ionophore (A23187), indicating that Src activity is not required for direct Ca²⁺-mediated activation. In contrast, xSrcKN oocytes exhibited markedly reduced activation in response to hydrogen peroxide or Cathepsin B, which stimulate membrane-associated signaling pathways. These findings demonstrate that Src kinase activity is required for membrane signal-mediated egg activation but is dispensable for activation driven by direct intracellular Ca²⁺ elevation. Collectively, our results identify Src kinase as a positive regulator of progesterone-induced meiotic maturation and a critical mediator of specific fertilization-like activation pathways in X. laevis. Full article

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 are widely used in the treatment of several cancers and have significantly improved survival outcomes in responsive patients. However, a substantial proportion of patients fail to benefit from these therapies, underscoring the urgent need for accurate prediction of ICI response. We propose a deep learning framework, ICIsc, to accurately predict ICI response by integrating single-cell RNA sequencing (scRNA-seq) data with protein large language models. Specifically, patient representations are constructed using transcriptomic profiles and immune-related gene set scores as latent embedding features, while drug representations are derived from amino acid sequences of ICI encoded by the Evolutionary Scale Modeling 2 (ESM2). For bulk data, ICIsc employs a bilinear attention module to fuse patient and drug embeddings for response prediction. For scRNA-seq data, ICIsc infers cell–cell interactions using a single-sample network (SSN) approach and applies GATv2 to model immune microenvironment heterogeneity at the single-cell level. Benchmark evaluations and independent validation demonstrate that ICIsc consistently outperforms baseline models and exhibits robust generalization performance. SHAP-based interpretability analysis further identifies key genes (e.g., GAPDH) associated with immunotherapy response and patient prognosis. Overall, ICIsc provides an accurate and interpretable framework for predicting immunotherapy outcomes and elucidating underlying mechanisms. Full article

This paper presents a computational framework for constructing and analysing a focal legislative citation network. A depth-limited expansion strategy generates subgraphs of the network that capture the local structural environment of a seed Act while avoiding the global hub dominance present in whole-corpus analyses. Centrality measures and community detection show how the seed Act’s perceived influence changes with network radius. To incorporate semantic information, we develop and apply an Large Language Model (LLM)-assisted topic modelling method in which representative keywords and LLM-generated summaries form a compact text representation that is converted into a Term Frequency-Inverse Document Frequency (TF–IDF) document–term matrix. Although demonstrated on New Zealand’s mental health legislation, the framework generalises to any legislative corpus or jurisdiction. Integrating graph-theoretic structure with LLM-assisted semantic modelling provides a scalable approach for analysing legislative systems, identifying domain-specific clusters, and supporting computational studies of legal evolution and policy impact. Full article

In the context of globalization, balancing economic growth with social equity is a critical challenge for achieving sustainable development. While Global Value Chains (GVCs) have become a defining feature of the contemporary economy, their specific impact on the urban–rural income gap—a key indicator of common prosperity—remains under-explored. This study empirically investigates the impact of GVC embedding on urban–rural common prosperity in China using panel data from 30 provinces spanning the period 2011–2022. Adopting a dual perspective of “efficiency” (income growth) and “equity” (income distribution), this study constructs a mediation model to analyze the transmission mechanisms. Research indicates that embedding in global value chains not only enhances the income-generating capacity of urban and rural residents but also effectively narrows the urban–rural income gap. Furthermore, its positive contribution to urban–rural common prosperity is both long-term and sustainable. This effect of GVC embedding on urban–rural common prosperity remains significant after conducting various robustness tests. Mechanism analyses reveal that GVC embedding achieves these outcomes by promoting agricultural industrial upgrading, fostering agricultural technological innovation, and stimulating rural entrepreneurial vitality. Notably, heterogeneity tests indicate that these positive effects are more pronounced in eastern, coastal, and economically developed regions, whereas the impact is less evident in central, western, and inland areas. This study holds important policy implications for promoting the development of China’s open economy to a higher level in the era of economic globalization, as well as for realizing urban–rural common prosperity and balanced, sustainable development. Full article

This dyadic study distinguishes parents’ general well-being (overall life satisfaction) from parental well-being (satisfaction specific to the parenting role) and examines how each relates to daily beneficial parenting behaviors in mother–father couples. Guided by the Parenting Pentagon Model (PPM), five behavioral constructs—Partnership, Leadership, Expressions of Love, Encouraging Independence, and Adherence to Rules—were assessed in 170 Israeli parents (85 mother–father dyads) of children aged 6 months to 9 years. Parents reported frequent beneficial parenting, with Expressions of Love the most prevalent and Encouraging Independence and Adherence to Rules the least frequent. Mothers reported significantly higher Expressions of Love than fathers (p < 0.01), with no gender differences for the other PPM constructs. Across both parents, higher engagement in beneficial parenting behaviors was consistently associated with higher levels of both general and parental well-being (actor effects), with stronger associations for mothers than fathers. Partner effects showed a clear gender asymmetry: fathers’ parenting behaviors were positively associated with mothers’ general and parental well-being, whereas mothers’ behaviors were not consistently associated with fathers’ well-being. In addition, a larger number of children was negatively associated with mothers’ parental well-being. Overall, the findings highlight the relevance of daily parenting behaviors for parents’ own well-being and underscore the relational nature of parenting, with fathers’ behaviors playing a particularly salient role in mothers’ well-being within families of young children. Full article

Rockbursts are one of the most critical geomechanical hazards during the construction of deep tunnels under high in situ stress conditions, as they can compromise worker safety, damage infrastructure, and disrupt excavation continuity. Despite extensive research on rockburst mechanisms and mitigation, the causal analysis of individual events remains challenging due to the complex interaction between seismicity, geological conditions, stress redistribution, and operational factors. This study proposes a structured and multidisciplinary methodology for the causal analysis of rockbursts in deep high-stress tunnels. The methodology integrates seismicity analysis, geological and geotechnical characterization, operational assessment, field damage inspection, and hypothesis-driven interpretation to systematically reconstruct the sequence of processes leading to rockburst occurrence. The proposed approach is applied to a rockburst that occurred in 2020 in the Conveyor Belt tunnel (TC) of the Andes Norte Project, El Teniente Division, Codelco (Chile). The event reached a local magnitude of Mw = 1.7 and caused significant damage to tunnel support systems. Results indicate that the rockburst was associated with excavation- and blasting-induced stress redistribution, leading to the activation of a sub-horizontal rupture plane and subsequent damage propagation toward the excavated tunnel. The methodology provides a transparent and adaptable analytical framework for integrating multidisciplinary data into a coherent causal interpretation. Although demonstrated using a competent and brittle rock mass, the framework can be adapted to other deep tunneling projects under high-stress conditions by adjusting the governing parameters according to site-specific geological, geomechanical, and operational characteristics. The proposed approach supports improved understanding of rockburst mechanisms and informed decision-making for seismic risk management in deep underground excavations. Full article

Construction management plays a fundamental role in the sustainability of construction projects, as its primary objective is to enhance cost-effectiveness and efficient resource utilization. One of the main challenges encountered at the early stages of a project’s lifecycle, particularly during the planning phase, is the development and agreement of construction schedules among the stakeholders involved. The tools employed for time planning and scheduling during both the planning and construction phases should therefore be capable of modeling complex environments and supporting dynamic updates in response to resource constraints. Petri nets are known for their capability of modeling complex systems, such as resource management. Their use in project management is essential for resource constraint problems. This paper investigates the use of Petri Nets as a tool for the time scheduling of engineering and construction projects. A case study is presented and modeled using Timed Petri nets, enabling dynamic adaptation under time and resource constraints. Through simulation performed with the ROMEO (v3.10.6) software, the study identifies the critical paths and determines the total project duration under various scenarios of sensitivity by adjusting specific project parameters. The results demonstrate the effectiveness of Petri nets in project management and the benefits they offer when used in modeling complex systems, identifying critical activities and calculating resource constraints and time deadlines. Full article

In the sensor array signal reception system, improving the accuracy of weak-signal detection is crucial. Traditional fixed-step algorithms struggle to balance the convergence rate (CR) and low steady-state error (SSE) owing to their inherent trade-off limitations. To address this limitation, we propose a novel variable-step-size least-mean-square (VSS-LMS) algorithm based on a modified versoria function, specifically redesigned to enhance curvature characteristics. This approach establishes dynamic coupling between error statistics and step-size factors through nonlinear mapping. It derives closed-loop equations governing parameters (α, β, and γ) relative to the smoothed instantaneous error correlation function. Consequently, an adaptive feedback system is constructed to achieve real-time adjustment through optimal step-size generation. The optimal parameters (α, β, and γ) are determined through empirical enumeration and analysis of their impact on algorithmic performance. Comparative evaluations against established VSS-LMS algorithms confirm performance: the proposed algorithm accelerates convergence while maintaining a low SSE, and exhibits robust signal recovery capabilities under low-SNR conditions with diverse interference types. Full article

In Cyber Threat Intelligence, anticipating threat events and linking forecasts to standards-based mitigations is essential, yet many approaches rely on non-unified event representations within the analysis window, introducing bias and weakening tactical signal. In this manuscript, an end-to-end workflow is introduced that canonicalizes seven public threat feeds, constructs exogenous covariates, applies Elastic Net under Walk-Forward Cross-Validation (WFCV), and models continuous and intermittent series with SARIMAX and ADIDA estimators. Forecasts are consolidated into a fourteen-dimensional risk vector aligned with the MITRE ATT&CK framework taxonomy and translated into mitigations through a Retrieval-Augmented Generation (RAG) module that also consults the CISA Known Exploited Vulnerabilities catalogue. At a seven-day forecast horizon $h=7$ with weekly seasonality $m=7$, forecasting attains MAPE 12.0%, RMSE 6.8, and MAE 4.9. Mitigation retrieval, evaluated on 73 tactic-specific contextual queries from the test set, achieves 84.5% Exact Match and 91.3% Coverage. Full article

Accurate crop type mapping remains challenging in regions where persistent cloud cover limits the availability of optical imagery. Multi-temporal dual-polarization Sentinel-1 SAR data offer an all-weather alternative, yet existing approaches often underutilize polarization information and rely on single-scale temporal aggregation. This study proposes PTU-Net, a polarization–temporal U-Net designed specifically for pixel-wise crop segmentation from SAR time series. The model introduces a Polarization Channel Attention module to construct physically meaningful VV/VH combinations and adaptively enhance their contributions. It also incorporates a Multi-Scale Temporal Self-Attention mechanism to model pixel-level backscatter trajectories across multiple spatial resolutions. Using a 12-date Sentinel-1 stack over Kings County, California, and high-quality crop-type reference labels, the model was trained and evaluated under a spatially independent split. Results show that PTU-Net outperforms GRU, ConvLSTM, 3D U-Net, and U-Net–ConvLSTM baselines, achieving the highest overall accuracy and mean IoU among all tested models. Ablation studies confirm that both polarization enhancement and multi-scale temporal modeling contribute substantially to performance gains. These findings demonstrate that integrating polarization-aware feature construction with scale-adaptive temporal reasoning can substantially improve the effectiveness of SAR-based crop mapping, offering a promising direction for operational agricultural monitoring. Full article