辽宁石油化工大学

Influenza A virus continues to pose a significant global health threat, causing seasonal epidemics and occasional pandemics. Viral transcription and replication rely on the heterotrimeric polymerase complex where the PB2 subunit initiates RNA synthesis through binding to the host mRNA cap structure. In this study, we began with a structure-activity relationship analysis of the pioneering PB2 inhibitor VX-787. Through computer-aided drug design, combined with considerations of molecular docking scores, ADMET property predictions, and a prodrug esterification strategy, we ultimately designed eight novel compounds. Cytopathic effect assays demonstrated that all compounds exhibited superior inhibitory activity against both H1N1 and H3N2 strains compared to oseltamivir acid. In particular, compounds 11 and 15 displayed nanomolar-level activity against H1N1, while compound 18 showed activity against H3N2 superior to that of VX-787. These findings propose a rational design strategy that may offer new avenues for addressing the resistance and metabolic limitations associated with VX-787 and hold potential for advancing the development of next-generation PB2-targeted anti-influenza therapeutics.

To explore the potential contribution of multimodal storytelling to learner outcomes in inclusive educational settings, the present study investigated its effects on engagement, social belonging, and oral communication skills among neurodiverse middle school learners. Drawing on different learning theories, multimodal storytelling was conceptualized as a holistic instructional approach integrating visual, auditory, and textual modes to support diverse cognitive and linguistic profiles. Forty-three Persian EFL students aged 11 to 13 years (22 males, 21 females) participated and were assigned to an experimental group receiving multimodal storytelling instruction and a control group receiving traditional literacy instruction over eight weeks, using validated instruments. Pretest analyses indicated no significant differences between groups, suggesting baseline equivalence. Posttest results, however, revealed significant improvements for the experimental group across engagement, social belonging, and oral communication. Within-group analyses further showed substantial pretest-posttest gains in the experimental group (all p < .001), exceeding those of the control group, which demonstrated moderate but smaller improvements. These results indicate that multimodal storytelling improves behavioral and emotional engagement, strengthens feelings of social belonging, and enhances oral communicative competence among neurodiverse learners. The study also highlights the pedagogical value of multimodal storytelling as an inclusive and cognitively supportive approach aligned with UDL and sociocultural principles, providing empirical evidence that integrating multimodal resources and collaborative storytelling processes can create equitable and engaging learning experiences for students with diverse learning profiles, particularly in EFL contexts where such practices support multiliteracies and reduce communication barriers.

Environmental, Social, and Governance (ESG) ratings serve as a fundamental component of sustainable finance; however, their credibility is substantially compromised by persistent methodological limitations, including the inherent rating performance-interpretability dilemma, lack of procedural transparency, and inconsistencies across rating agencies due to rating subjectivity. To this end, we introduce the Wide and Deep Forest (WDForest), a new objective ESG rating framework developed to enhance both interpretability and objectivity through structural decoupling of the rating process into an intra-layer "wide" enhancement module and a cross-layer "deep" cascade architecture. Depth is achieved via a sequential residual calibration mechanism, wherein each layer iteratively learns and rectifies unexplained residuals from its predecessor, while width is expanded through parallel boosted trees, which collectively capture intricate data patterns to strengthen the model's expressive power. Unlike conventional ESG rating models with fixed structures, WDForest employs a validation-based early stopping mechanism that directly responds to data complexity, WDForest autonomously determines its optimal architectural depth; this dynamic self-adaptation makes it a highly accurate and scalable solution for ESG rating. Comprehensive empirical evaluations across environmental, social, governance, and aggregated ESG domains demonstrate that WDForest consistently surpasses benchmark models such as XGBoost and random forests across key metrics, including mean squared error (MSE), coefficient of determination, and Spearman rank correlation (SRCC), confirming superior rating performance and robustness. Furthermore, the hierarchical architecture of WDForest inherently supports multi-faceted interpretability, enabling global feature importance analysis, partial dependence curves, and interaction visualizations, which collectively mitigate the black-box constraints typical of conventional machine learning approaches. By unifying high predictive performance with transparent and traceable decision logic, WDForest offers a reliable analytical tool for investors, corporate managers, and policymakers involved in sustainable investment decision-making.

This study clarifies the nonlinear relationship between the softening point of coating asphalt and its oxidative cross-linking behavior,as well as the sodium storage performance of the derived hard carbon.Comparative analysis of asphaltes with low(80 ℃),medium(160℃)and high(260 ℃)softening points revealed that both the 80 and 260 ℃ asphaltes incorporated a higher oxygen content(20％-25％)during oxidation,leading to the formation of a deeply cross-linked structure dominated by anhydride and ester groups.This effectively suppressed graphitization during carbonization,yielding hard carbon with large interlayer spacing,high disorder,and abundant closed pores.The derived hard carbon exhibited superior sodium storage performance,the initial charge capacities of EPOC-80 and EPOC-260 reached 314.7 and 306.6 mA·h/g,with first-cycle coulombic efficiencies of 81.3％and 79.3％,respectively,along with excellent cycling stability and rate capability.In contrast,the medium softening point asphalt(160 ℃)showed limited oxygen incorporation(～5％)and insufficient cross-linking after oxidation,resulting in a densely packed hard carbon with smaller interlayer spacing(3.46 Å)and restricted sodium storage sites,which led to a significantly reduced capacity of 173.2 mA·h/g.This work provides new design principles and theoretical support for optimizing hard carbon anode structures through precise control of the precursor softening point.

Multi-factory remanufacturing systems often operate under practical constraints, including heterogeneous taskrequirements and limited availability of skilled labor, wherefactory and workstation operations cannot be assumed to becontinuously active. In such environments, worker availabilityand order demand jointly determine factory activation, workstation utilization, and task allocation, leading to tightly coupleddecisions across multiple resource levels. Effectively coordinatingthese interdependent factors is critical for improving operationalefficiency in distributed remanufacturing networks. This studyinvestigates a multi-factory remanufacturing problem that jointlyoptimizes order allocation, worker assignment, and factory activation decisions under labor constraints. A mixed-integer linearprogramming model is developed to capture the interactionsamong factories, workstations, and workers while consideringdisassembly task structures derived from order requirements.To solve the resulting complex optimization problem, the AlphaEvolution algorithm is employed and compared with severalrepresentative metaheuristic approaches, including the ImprovedBeluga Whale Reproductive Optimization, Coati Population Algorithm, Fruit Fly Optimization Algorithm, Dingo OptimizationAlgorithm, and Modified Dung Beetle Mating Optimization. Experimental results demonstrate that coordinated resource activation can significantly enhance labor utilization and overall systemperformance. The proposed Alpha Evolution algorithm approachachieves competitive or superior performance in solution qualityand stability across multiple test scenarios.