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The rise of the digital era presents mounting difficulties for ideological and political instruction, as the swift dissemination of internet-based public sentiment and the expanding reach of social media frequently outpace conventional pedagogical approaches. Present methodologies suffer from poor precision in detecting risks and lack explicit metrics for assessing the fundamental competencies of instructors. To surmount these challenges, this paper presents a dual-core system framework. Chiefly, the Multi-modal Decision Support System (MADF-SE) merges classroom behavioral identification, semantic evaluation, and blockchain-enabled traceability to enhance both the precision and punctuality of hazard detection. Secondary to this, the Dynamic Capability Evolution Model (DCEM) quantitatively monitors and upgrades the cognitive, practical, and inventive skills of teachers over time. Furthermore, a hierarchical federated learning architecture is deployed to strike a balance between individual privacy safeguarding and inter-collegiate cooperation. Empirical testing and case analyses across more than 30 higher education institutions indicate that the introduced framework cuts the public opinion management timeframe by 50%, elevates instructor engagement in strategic choices from 20% to 60%, and boosts their collective core competencies by greater than 25% on an annual basis. These outcomes highlight a viable and scalable methodology for driving the digital evolution of ideological and political schooling.

Merging Artificial Intelligence (AI) with Business Process Management Systems (BPMSs) has given rise to AI-Augmented Business Process Management Systems (ABPMSs). These modern setups deliver flexible adaptability, instantaneous process refinement, and superior knowledge management workflows. Yet, notable hurdles persist, especially regarding contextual clarity, user buy-in, and the incorporation of behavioral dynamics. This research introduces a novel conceptual model that unifies the Socialization, Externalization, Combination, and Internalization (SECI) knowledge paradigm with Agile workflows (specifically Scrum) and advanced AI capabilities like explainable AI (XAI), process mining, and Robotic Process Automation (RPA). This setup streamlines the formalization, validation, and dissemination of insights through structured, intuitive software and collaborative frameworks, particularly Communities of Practice (CoPs). The system highlights context-sensitive transparency, step-by-step deployment, and iterative refinement to foster productive human–AI partnerships. It delivers both conceptual and practical blueprints for linking AI strengths with corporate knowledge assets. The introduced model eases the upgrade path from legacy BPMSs to advanced ABPMSs by utilizing systematic practices and technologies. This strategy accelerates knowledge distribution and operational refinement, mapped out via exhaustive BPMN 2.0 diagrams.

Offline reinforcement learning (RL) focuses on training policies exclusively from passively gathered datasets, establishing it as a data-driven decision-making framework. A major obstacle in offline RL is the phenomenon of distribution shift, which leads the algorithm to encounter out-of-distribution (OOD) samples. While constraining the divergence between the target policy and the behavior policy can alleviate distribution shift, this strategy can unduly restrict the target policy and degrade algorithmic performance, because it fails to explicitly differentiate between in-distribution and OOD samples. Furthermore, accurately learning and representing a multi-modal behavior policy is highly challenging when datasets are compiled from multiple distinct behavior policies. To resolve these limitations, the authors tackle the distribution shift problem through implicit policy constraints utilizing energy-based models (EBMs), bypassing the explicit modeling of the behavior policy. EBMs offer a powerful framework for characterizing complex multi-modal distributions while effectively distinguishing between in-distribution and OOD samples. Experimental outcomes demonstrate that this approach substantially surpasses explicit policy constraint methods and other baseline techniques. Furthermore, the trained energy model serves as an effective indicator of OOD encounters, providing alerts for potential failures.

Laminectomy represents one of the most frequently performed posterior spinal procedures. Because the lamina is situated in close proximity to critical neural structures, any operative damage can induce profound complications, potentially culminating in paralysis. To mitigate these risks and associated adverse events, surgical robotics and ultrasonic bone scalpels have been integrated into spinal laminectomy procedures, prompting extensive academic investigation into methods for detecting bone tissue states. At present, virtually all state-recognition methodologies rely on sensor signals acquired via high-precision instrumentation integrated into the end-effectors of surgical robots. Nevertheless, previous approaches have struggled to achieve highly accurate identification of spinal bone tissue states. In an innovative departure, this study frames the identification of bone tissue states as a time-series classification problem. The LSTM-FCN classification algorithm was deployed to analyze multi-sensor fusion signals combining force and cutting depth data, thereby enabling precise categorization of the laminal bone tissue state. Furthermore, experimental validation demonstrated that this approach achieves an accuracy of 98.85% in identifying bone tissue states during porcine spinal laminectomy. Crucially, the maximum penetration depth can be restricted to within 0.6 mm, confirming its safety and viability for practical clinical application.