Field of view (FOV) algorithms are essential in determining the visible area of a player in 2D games. These algorithms dynamically calculate the visible areas while occluding these hidden areas, and play an important role in games such as roguelikes and stealth games. This survey summarizes three 2D FOV algorithms: ray casting, rectangle-based FOV, and recursive shadowcasting. The ray casting algorithm casts rays to determine which area was hidden from the player, which is a basic FOV algorithm. Rectangle-based FOV optimizes computation for large 2D grids by representing obstacles as rectangles, also using a quadtree to improve the access speed. Recursive shadowcasting efficiently computes the visible area by dividing the grid into 8 octants and recursively splitting the view when obstacles are encountered. This survey also mentioned how to adapt the recursive shadowcasting algorithm to 2.5D and 3D environments.

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The well-known Large Language Model (LLM) compression is essential for enhancing computational efficiency, yet a systematic summary of investigation into structured pruning and low-rank decomposition remains absent in current literature. This work addresses the gap by providing a comprehensive review specifically focused on these two methodologies. Representative approaches are categorized and evaluated, including LLM-Pruner and SlimGPT for structured pruning, and ASVD and SVD-LLM for decomposition. These methods are rigorously analyzed in terms of algorithmic design, accuracy retention, and hardware adaptability. Through unified evaluation and comparative analysis, DISP-LLM and MoDeGPT are identified as the current state-of-the-art within their respective fields. Consequently, a conceptual framework is established to provide practical guidance for future research into efficient, training-free, and scalable LLM compression.

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Cancer remains a major public health challenge in China. This study analyzed a multi-center cohort of 10,000 Chinese cancer patients to evaluate real-world survival outcomes and treatment effectiveness. Kaplan–Meier estimation and Cox proportional hazards regression were employed to assess associations between patient characteristics, treatment types, and overall survival. Survival analysis showed no significant difference in overall survival among six major cancer types (lung, liver, stomach, colorectal, cervical, breast) or among five treatment modalities (chemotherapy, immunotherapy, radiation, targeted therapy, surgery). Cancer stage was the strongest prognostic factor: patients with Stage I–II disease had 100% five-year survival, while Stage III–IV survival fell to about 6%. Metastasis, larger tumor size, and geographic region were independent risk factors for death after adjusting by other covariates, but not modality of treatment. The results highlight the importance of timely diagnosis and availability of healthcare services in different areas are important targets of China's cancer prevention programs.

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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.

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