Addressing the pain points of high computational costs and significant latency associated with deep learning models on small and medium-sized e-commerce platforms, this study proposes a lightweight sentiment perception and hierarchical response system based on Snow NLP optimization. By refactoring the inference logic to reduce instantiation overhead, the system constructs a multi-level response engine to enable automated interventions. Experimental results indicate that, while maintaining an accuracy of 82.2%, the system's operational efficiency improves by 34.33% compared to the baseline, achieving a response speed 24.5 times faster than BERT. This research demonstrates that lightweight models can expand business depth even under extremely low computing power, offering small and medium-sized enterprises an intelligent customer service solution that balances efficiency with real-time response capabilities. Future work will focus on integrating continuous learning mechanisms to seamlessly adapt to evolving e-commerce terminologies and exploring multi-lingual support. Additionally, expanding the system to handle multi-modal inputs, such as customer emojis and voice snippets, will further enhance interactive experiences while strictly preserving the model's lightweight architecture and low computational footprint.

The past few years have brought a flood of new large language models. Each one arrives with its own design philosophy and strengths, which makes it tough for working professionals to figure out which tool actually fits their daily tasks. Standard test scores do not always point to the right answer. This paper takes a close look at five widely used systems. They are ChatGPT, DeepSeek-V3, Gemini 2.5, Qwen3, and LLaMA. The analysis draws on what the developers themselves have published and what outside researchers have found in controlled experiments. One thing becomes clear right away. These tools have divided up the work in interesting ways. DeepSeek-V3 tackles science computing and coding tasks more effectively than ChatGPT, and it runs at a much lower cost. Gemini 2.5 proves its worth when the job demands handling very long documents or mixing together pictures, sound, and text. Qwen3 pulls ahead in translation work across many languages and in building the parts of software that users see and touch. ChatGPT holds onto its spot as the favorite for spinning stories and cooking up new ideas. LLaMA has grown into a home base for teams that want to craft their own custom tools. The takeaway from the research is straightforward. Choose the tool based on the task sitting in front of people, not on a number from some public leaderboard.

Photonic crystals are artificial microstructures with photonic band gaps, and their band gap characteristics are closely related to structural parameters. Traditional photonic crystal design relies on repeated numerical simulations, which are computationally expensive and inefficient. The core of two-dimensional dielectric rod photonic crystals lies in utilizing periodic dielectric structures to generate photonic band gaps, thereby achieving precise control of light. Compared to three-dimensional structures with complex fabrication processes, the two-dimensional dielectric rod configuration has become a research focus due to its high compatibility with semiconductor micro/nano fabrication techniques. The research trajectory has evolved from band gap engineering and defect engineering to recent topological photonics, aiming to address core issues in integrated photonics such as optical transmission loss, mode confinement, and functional integration. This paper proposes using an artificial neural network (ANN) to establish a mapping relationship between the structural parameters of two-dimensional dielectric rod photonic crystals and their band structures, enabling reverse prediction from band structure to geometric parameters. TM-mode band structures for dielectric rods with different radii are simulated using MPB software to construct a dataset, and a BP neural network is employed for training and testing. The results show that the trained network can predict the dielectric rod radius with reasonable accuracy, achieving a coefficient of determination (R²) above 0.3. This method provides a new approach for the rapid inverse design of photonic crystals.

The rapid development of large language models has made generative artificial intelligence increasingly common in writing, summarization, education, journalism, and knowledge production. However, the quality of AI-generated text does not depend only on the model itself; it is also shaped by the way users formulate prompts. This paper examines how different prompt strategies may influence the quality of generated text, especially in Chinese text generation tasks, such as news summarization and instruction-following writing. Drawing on recent research on prompt engineering, reasoning-oriented prompting, self-refinement, retrieval-augmented generation, and LLM-based evaluation, this study proposes a multidimensional framework for comparing prompt strategies. The main strategies discussed include zero-shot direct prompting, structured instruction prompting, few-shot prompting, plan-then-write prompting, self-refinement prompting, and retrieval-augmented prompting. Text quality is defined through task performance, linguistic fluency, coherence, factuality, instruction compliance, overall preference, robustness, and cost. This paper argues that different prompt strategies are likely to improve different aspects of text quality rather than produce one universally superior outcome. Its contribution is to provide a clear conceptual and methodological framework for evaluating prompt strategies in a way that is understandable for non-technical users and useful for future empirical research.

Micro-robots need to be in the real world and have a very small power supply; generally, the processors the paper currently has cannot meet the high calculation requirements for perception and decision-making in the algorithm. Therefore, in this paper, the paper will introduce the basic acceleration techniques for deep learning perception and reinforcement learning decision-making, as well as related contents from FPGA and ASIC architectures, such as quantization, pipelines, memory hierarchy optimisation, etc. According to the comparison of dedicated architectures and their hardware-software co-design, the power consumption per frame can be reduced to a few milliwatts and the throughput is more than 80 fps. The paper has also set the requirements for the precision of the fixed-point representation and the convergence speed of the algorithm to provide a Design Basis that is both accurate and economical in this paper. As mentioned in the paper, to solve the bottleneck of the "memory wall", in-memory computing and neuromorphic computing have been put forward as new directions to realise high power efficiency. The above technologies have formed a good hardware basis for the next generation of intelligent robots to achieve a high level of self-operation and extend the service life of robots.

In 3D vision, point cloud data functions as a fundamental representation that preserves the geometric structure of objects without loss of spatial information. It plays a key role in tasks like autonomous driving, robotic perception, and 3D reconstruction, and supports understanding and modeling real-world environments. This paper reviews the development of 3D point cloud processing technology. By reviewing the relevant research literature in recent years, it introduces the classification system, key methods, application scenarios, common datasets of point cloud processing, as well as current challenges and future directions. The results demonstrate that current point cloud processing technology has established a complete workflow from low-level data processing to high-level industrial applications, has become the mainstream solution to traditional point cloud challenges, and offers guidance for research, development, and engineering applications in related fields.

Diffusion models have emerged as a powerful tool for high-quality image synthesis and are increasingly favored for restoration tasks such as image inpainting. This work investigates face inpainting within the latent diffusion framework, where denoising is performed by a UNet operating in a VAE-compressed latent space. We introduce PC-Refine, a lightweight residual refinement module attached to the mid-block of the U Net, and employ a parameter-efficient training strategy (pc_only) that freezes the original UNet while optimizing only the newly added parameters. Using DDIM-based sampling on the CelebAMask-HQ dataset, we evaluate performance with mask-only MAE and RMSE metrics focused on unknown regions. A controlled multi-seed evaluation demonstrates that PC-Refine consistently improves upon the baseline, showing that a single mid-block refinement yields practical, stable gains with minimal additional trainable parameters.

Predicting online purchase intention is critical for e-commerce revenue optimization, yet existing approaches often sacrifice interpretability for marginal accuracy gains. This study proposes an interpretable prediction framework combining Random Forest with SHAP post-hoc analysis. Using the UCI Online Shoppers Intention dataset (N=12,330 sessions), Random Forest is benchmarked against Logistic Regression, XGBoost, and LightGBM, with statistical significance validated via McNemar test. Results show that Random Forest achieves the highest classification accuracy and best probability calibration, while gradient boosting models yield marginally higher AUC-ROC and F1-score. SHAP analysis reveals two counter-intuitive findings: (1) PageValues exhibits a saturation effect where marginal contribution to purchase probability plateaus beyond a threshold of approximately 50; (2) high PageValues buffers the negative effect of high ExitRates, suggesting that value-rich content retains users who would otherwise churn. To the best of knowledge in this domain, this is the first study to systematically quantify feature interactions, identify saturation thresholds, and assess user heterogeneity using SHAP on this widely adopted benchmark dataset.

Reading is closely tied to learning, communication, and personal development, but the way people read has changed with the spread of mobile devices. Reading apps now sit on the same phone as short-video platforms, social media, games, and messaging tools. This makes digital reading not only a question of whether people like reading, but also a question of how much of their limited phone time they are willing to allocate to reading. This paper studies reading-app usage using an original questionnaire dataset of 84 respondents. We define the main dependent variable as the ratio of daily reading-app hours to total daily phone-use hours and design a questionnaire that collects demographic background, daily time use, reading platforms, reading habits, motivations, social reading behavior, and content preferences. Methodologically, we compare a full weighted least squares baseline, a weighted ElasticNet feature-selection model followed by post-selection WLS, and a weighted Random Forest. The results show that the unregularized full WLS model severely overfits the high-dimensional survey data. By contrast, the ElasticNet-selected WLS model reduces 110 encoded predictors to 19 selected features and achieves a cross-validated RMSE of 0.156, close to the Random Forest benchmark of 0.154. Across models, WeRead usage, reading routines, social reading behavior, app tenure, and competing app-use patterns are the strongest predictors. These findings suggest that digital reading engagement is shaped more by platform embeddedness and daily behavioral routines than by demographics alone.

Personalized recommendation systems are a key technology to solve information overload, but traditional methods rely heavily on user identity features and interaction, facing cold-start and cross-domain bottlenecks. Large language models(LLMs) provide new paths for recommendation systems with their semantic understanding and generation capabilities. This paper systematically reviews LLM technical routes in personalized recommendation via literature analysis, summarizing three paradigms: semantic representation enhancement(SRE) for data sparsity, generative recommendation(GR) for unified task modeling, and discriminative matching and interaction(DMI) for interactive decision-making. SRE alleviates the cold start problem by generating semantic embeddings; GR reconstructs the recommendation task into text generation to achieve task unity and interpretability; DMI utilizes reasoning capabilities to support complex preference understanding. This paper analyzes typical scenarios such as e-commerce, news, and music videos, and summarizes the challenges in three aspects: computing efficiency, generation controllability, and the evaluation system. Research shows that LLMs are driving the evolution of recommendation systems from collaborative signal statistics to semantic understanding.