Forecasting serves as a critical cornerstone for strategic planning, operational efficiency, and risk mitigation across modern civilization. By converting historical data into actionable forward-looking insights, it enables organizations and governments to anticipate market shifts, optimize resource distribution, and safeguard against systemic uncertainties. Predictive modeling is a fundamental task in many fields, such as finance, economics, engineering, and artificial intelligence. The aim of this paper is to summarize the methods of statistics and machine learning, outline their inherent challenges, and project future research directions. This paper mainly discusses traditional statistical methods (including Autoregressive Integrated Moving Average [ARIMA] and regression analysis), machine learning approaches (such as Random Forest and Support Vector Machines [SVM]), and deep learning models (such as Long Short-Term Memory [LSTM] networks and hybrid time series-ML models). Nowadays, as these interconnected fields become increasingly complicated, practitioners face severe challenges regarding data quality, computational complexity, and mathematical interpretability. This paper comprehensively reviews these methodologies, establishes a comparative taxonomy, and delineates the evolutionary trajectory of future forecasting applications.

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Software testing accounts for a significant proportion of resource consumption within the software development lifecycle. Traditional automated testing methods rely on predefined scripts and deterministic logic, and have inherent limitations when addressing the dynamic complexities of modern software systems. Artificial intelligence, particularly large language models, offers new technical avenues for test automation.This paper focuses on two key tasks—AI-driven test case generation and defect detection—and provides a systematic review of the technological evolution from traditional automation to intelligent testing. It analyses methods such as prompt engineering, retrieval-augmented generation, model fine-tuning and multi-agent systems,while contrasting traditional deep learning and large language model approaches in defect detection. The paper also examines key challenges, including the hallucination problem, evaluation criteria, interpretability and generalisation capabilities.The fundamental contribution of large language models lies not in replacing human testers, but in driving the transformation of test automation from 'execution automation' to 'decision support'—a distinction that provides important guidance for the design, evaluation and deployment of AI testing tools.

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Low-cost RGB-D sensorsenable practical human motion capture and robot following, key capabilities for service robotics and human-robot interaction. Unlike expensive, high-precision systems, RGB-D sensors offer affordability, easy deployment, and real-time performance, making them ideal for civil robotic applications. This paper reviews RGB-D-based human pose estimation, motion feature extraction, and mobile robot following control, centering on low-cost, deployable solutions. The research sorts out low-cost RGB-D sensor schemes, human pose estimation algorithms, motion feature extraction pipelines, and tracking control strategies for mobile robots. Existing bottlenecks are summarised, including deteriorated pose accuracy under occlusion, insufficient real-time performance of lightweight algorithms, and unstable following behaviours in complex surroundings. Based on a comprehensive survey of current literature, this study concludes that the combination of lightweight visual models and optimised control algorithms serves as a vital development direction to construct low-cost and highly reliable human-following robotic systems. Prospective research trends of integrated RGB-D motion capture and robot following platforms are further discussed, which can offer theoretical and engineering references for relevant follow-up investigations and practical deployments.

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Diffusion models have become a dominant approach to image generation, but their iterative denoising process still creates high inference latency, memory consumption, and deployment cost. Existing surveys summarize efficient diffusion models from the viewpoints of sampling, compression, architecture, or applications, but they often do not make explicit which part of the inference cost is reduced by each method. This short survey reorganizes efficient image diffusion methods through an inference cost-decomposition perspective. The overall inference cost is described by three terms: the number of denoising function evaluations, the cost of each denoising step, and auxiliary overhead from conditioning, decoding, memory movement, and deployment frameworks. Based on this view, representative methods are grouped into sampling-step reduction, few-step student models, per-step computation reduction, and system-level deployment optimization. The discussion highlights that efficient diffusion is not simply a matter of using fewer sampling steps. Practical acceleration requires balancing image quality, latency, memory, training cost, and hardware constraints. This paper provides a compact framework for comparing efficient image diffusion methods and for selecting acceleration strategies under different deployment scenarios.

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