Computer vision (CV) has shown to have tremendous capabilities of providing motor functions assessment in rehabilitation. Nevertheless, the current research is still disjointed, and the particular uses and aims of CV-based rehabilitation evaluation approaches have not been fully revealed. To address this gap, the study will use the method of an exploratory review to give a summary of the current research on the topic of CV based rehabilitation motor function assessment. The findings of the 15 articles used show that CV technology use in the rehabilitation assessment is mostly revealed in estimating the posture and measuring joint kinematics, gait and evaluating spatiotemporal parameters, motion recognition and continuous functional movement, and motion data synthesis and further development. The applications are designed to enhance the scaleability and availability of assessments, attain objective and automated assessments, enable longitudinal follow-ups, as well as feedback and exploration system building. The paper highlights the importance of using more goal-focused and clinically-based CV technologies in the rehabilitation assessment and offers an overall viewpoint to inform further studies and system design.

Unwanted messages in electronic communication, such as spam and phishing attempts, continue to pose significant risks, including information theft, malware distribution, and data loss. Traditional rule-based and keyword-based filtering methods have become less effective due to the evolving tactics used by malicious actors. This study presents a comprehensive machine learning framework for detecting unwanted messages, which combines both textual features—such as term frequency-inverse document frequency (TF-IDF)—and structural features including message length, word count, frequency of special characters, and ratios of uppercase letters. The framework is evaluated using three widely adopted classification algorithms: logistic regression, linear support vector machine, and random forest, applied to a large, publicly available Kaggle dataset of electronic messages. Experimental results demonstrate that the random forest model outperforms the other methods, achieving a precision-recall score of 0.986 and an area under the ROC curve of 0.998. These findings highlight the advantage of integrating diverse engineered features with classical machine learning techniques for effective and interpretable spam detection. Further analysis of feature importance and classification errors provides additional insight into model behavior and error patterns. Future research will focus on incorporating advanced deep learning and explainability methods to further improve detection accuracy and transparency in real-world communication systems.

This comprehensive research synthesizes the foundational principles of Natural Language Processing (NLP) and their realization within ChatGPT, examining the ways deep learning architectures internalize profound linguistic complexity. Structural interplay is investigated. A dual-track empirical framework is systematically employed. By contrasting traditional Long Short-Term Memory (LSTM) networks with the Transformer architecture, the first track effectively demonstrates how parallelized self-attention maintains deep semantic coherence. High-order representational accuracy is achieved. The Qwen2.5-1.5B series is analyzed. By systematically comparing "Base" and "Instruct" models to decouple intelligence origins, the second track reveals that while massive scaling creates an expansive "Cognitive Reservoir" of knowledge, Reinforcement Learning from Human Feedback (RLHF) provides the essential "Functional Bridge" for precise, intent-driven execution. Aligned utility is realized. Ultimately, modern AI is viewed as the synergistic integration of structural efficiency, volumetric growth, and intentional refinement.

Aiming at the drawbacks of slow convergence, proneness to local optima, and insufficient segmentation accuracy of traditional multi-threshold image segmentation algorithms in complex scenarios, an Improved Grey Wolf Optimizer (IGWO) integrated with opposition-based learning strategy and nonlinear dynamic convergence factor is proposed. Specifically, we use improved opposition-based learning for population initialization and iteration optimization. This step helps increase population diversity and speed up convergence. Instead of the traditional linear decreasing convergence factor, we adopt a nonlinear dynamic one. This change achieves an adaptive balance between global exploration and local exploitation of the algorithm. We take the maximum between-class variance (OTSU) as the fitness function and build a multi-threshold segmentation optimization model. We validate IGWO through 6 benchmark test functions and compare it with four advanced swarm intelligence algorithms. The results show that IGWO has obvious advantages in convergence speed, solution accuracy and stability. It also has a strong ability to avoid local optimal solutions. When applied to multi-threshold image segmentation, IGWO produces segmentation regions with clear boundaries and well-preserved details. This algorithm provides a new technical method for efficient and accurate segmentation of complex images. It can be used in fields such as computer vision and communication equipment fault detection.

With the wide application of blockchain in data sharing scenarios such as smart healthcare, the Internet of Things, and federated learning, how to maintain trusted collaboration and motivate nodes to continuously participate under the premise of privacy protection has become a key issue. The blockchain incentive mechanism builds a closed loop of "behavior - reward - continuous participation" through methods such as income rewards, reputation feedback and permission allocation, providing a reliable operation foundation for decentralized systems. This paper systematically reviews the relevant research in recent years based on the "incentive source structure" and "behavioral constraint logic", classifies the existing mechanisms into four categories: revenue-based, reputation-based, revenue-based and reputation-based, and rights-based, and conducts comparative analysis from aspects such as incentive processes, governance structures, and applicable scenarios. Research shows that a single incentive is difficult to balance efficiency, fairness and steady-state participation, while compound incentives have more advantages in enhancing incentive intensity, curbing free-riding and strengthening governance resilience. However, there are still challenges such as insufficient revenue confirmation, unstable reputation quantification and abuse of rights.

In the context of increasingly complex data-driven decision-making, traditional analytical methods face significant challenges in handling heterogeneous data correlations and real-time computational responsiveness. By developing an Integrated Computational Business Analytics Framework (ICBAF), this research proposes a three-layer computational architecture comprising heterogeneous data representation, heuristic computational optimization, and online feedback loops, aiming to achieve a deep coupling of business logic and algorithmic reasoning. The findings indicate that by leveraging the topological representation capabilities of Graph Neural Networks (GNN) and the dynamic optimization mechanisms of real-time stream computing, this framework effectively resolves bottlenecks such as "data silos" and "decision lag," significantly enhancing decision precision in scenarios like intelligent marketing and smart operations. However, as the study primarily focuses on theoretical deduction and scenario-based simulations, it lacks rigorous empirical validation using large-scale, real-world industrial datasets. Consequently, its generalization capabilities under extreme noise and the cost-benefit ratio of computational resources require further verification in future research.

The performance of image classification models depends greatly on the architectural decisions made. Fashion-MNIST, as the mainstream adopted by researchers for model performance analysis, provides another avenue for the systematic comparison of different model architectures. In this paper, we have comparatively studied and analyzed the performances of Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), Random Forests and Residual Networks (ResNet) on this dataset, and found that one of the reasons for the excellent performance of convolutional networks may lie in the ability of extracting spatial features inherently possessed by convolutional layers. Although a deeper ResNet-34 shows an excellent performance (91.15%), its large number of parameters makes it less efficient for general tasks. To improve the efficiency, we find that by increasing the number of channels in the first convolutional layer from 32 to 64, the achieved accuracy (92.44%) is superior to any single task, which verifies the effectiveness of width optimization. In summary, for fashion-mnist such applications, an optimized width convolutional network architecture achieves the best accuracy-to-efficiency balance. We empirically prove that for image classification tasks, model selection and light design are significantly influenced by adopting appropriate architectural optimizations.

The rapid proliferation of social media has fundamentally reshaped the formation and dissemination of public opinion, posing significant challenges to social governance and public decision-making due to its high complexity, abrupt emergence, and amplification effects. Conventional public opinion diffusion studies, including diffusion dynamics and network-based models, are effective in capturing macroscopic propagation patterns, yet they exhibit inherent limitations in semantic understanding, emotion perception, and cross-cultural generalization. Recent advances in Large Language Models (LLMs) have introduced new opportunities for public opinion research by enabling fine-grained semantic analysis and robust emotion recognition in multilingual environments. Existing studies demonstrate that LLMs are effective in opinion and emotion detection, misinformation identification, and complex sentiment analysis, while also revealing limitations such as language bias and insufficient representation of extreme or minority viewpoints. Moreover, LLM-based approaches have been explored for public opinion simulation and deviation analysis, showing the ability to approximate collective behaviors and opinion evolution under realistic communication settings. In addition, LLMs have been applied to public opinion event detection and information activity analysis, including narrative frame classification and real-time policy communication assessment. To further enhance modeling fidelity, emerging hybrid frameworks, such as LLM-integrated diffusion models, combine the reasoning capabilities of LLMs with traditional dynamic propagation mechanisms, yielding improved predictive performance and interpretability. This survey systematically reviews recent progress in LLM-driven public opinion analysis, simulation, and event detection, discusses key challenges and open issues, and outlines future research directions for advancing public opinion governance and decision-support systems.

In recent years, how to intelligently enhance as well as accurately recognize speech and image signals in complex scene environments is a major problem facing the field of Artificial Intelligence Single-modal signals can easily be affected by factors such as noise or occlusion leading to a significant drop in their performance, which becomes a key obstacle to the further development of the field. The multimodal fusion problem involves many disciplines, and its complexity and uncertainty bring great challenges. In this paper, it systematically reviews the research progress of intelligent enhancement and recognition techniques for speech and image multimodal signals oriented to complex environments, summarize the challenges faced by the current techniques, such as data alignment and modal missing, and provide an outlook on the future research direction. Based on the analysis of the basic principles and technical framework of multimodal signal processing, the key issues of related technologies such as intelligent enhancement of multimodal signals and multimodal feature fusion and recognition methods are elaborated.This article sorts out the literature and core challenges of multimodal signal processing in complex environments, provides new ideas for breaking through the complex interference bottlenecks of unimodal signal processing, and offers technical guidance for academic research and industrial implementation in this field.

Large language models are increasingly deployed in interactive systems, yet controlling their behavior over extended multi-turn interactions remains challenging. Most existing approaches rely on prompt-based steering, leaving system behavior sensitive to conversational context and probabilistic drift. This paper presents the Modular Multi-State AI Teaching Protocol (MMA-TP), a protocol-level framework for constraining large language model behavior through structured interaction design rather than model modification. MMA-TP pairs an engineered system prompt, which establishes a persistent runtime persona, with a structured specification that encodes interaction states, transitions, and response constraints. Operating entirely at the interaction level, the framework leverages contextual conditioning and distributional bias to stabilize behavior across extended sessions without altering model parameters or decoding strategies. A mechanistic analysis grounded in transformer attention dynamics explains how persistent structured input biases probabilistic generation toward protocol-consistent behavior. Behavioral evaluation across multiple subject domains demonstrates that MMA-TP reliably enforces declared constraints, preserves phase ordering, and resists structural degradation relative to prompt-only instruction. These results indicate that protocol-level interaction control offers a lightweight and reusable approach for stabilizing large language model behavior in complex interactive settings.