The development of sixth generation (6G) communications is aimed at achieving global connectivity, covering oceans and remote areas with limited ground infrastructure. With the integration of sensing, communication and computing (ISCC) technology, the space-air-ground integrated network (SAGIN) is evolving from a simple data transmission channel to an intelligent task processing platform. This paper uses a narrative review method to analyze the evolution path of resource orchestration technology in heterogeneous environments, and focuses on mathematical programming and data-driven intelligent methods to optimize resources. The existing research is classified according to the method system. This paper also examines the architecture paradigm, multi-dimensional resource modeling and algorithm system. Studies have shown that static methods such as Lagrangian dual decomposition and block coordinate descent provide the necessary performance benchmarks, and deep reinforcement learning can achieve fast response in high mobility links. In addition, this paper discusses the potential of large language model (LLM) as a semantic interpreter, which can accelerate the process of parsing task requirements into technical constraints in specific applications. This study provides a methodological reference for achieving task-centered connectivity, and believes that a hybrid framework that combines the accuracy of deterministic methods and the adaptability of learning methods is a feasible development direction for future space infrastructure.

Visible light communication (VLC) gets increasing attention with the continuous advancement of technology. In contrast to conventional wireless networks, VLC possesses numerous innovative advantages. It has an abundant optical spectrum with immunity to radio-frequency interference. More importantly, its compatibility with existing lighting infrastructure demonstrates its broad applicability. However, high-speed VLC systems still need to address numerous challenges during communication such as limited LED bandwidth, inter-symbol interference, device nonlinearity, and receiver noise. Due to the above-mentioned factors, the channel equalization becomes an important physical-layer function. This paper aims to clarify the strengths and limitations of different deep learning-based channel equalization methods with cross-method comparison. Meanwhile, finds suitable application scenarios for different VLC systems. This review explores neural-network architectures including artificial neural networks, convolutional neural networks, long short-term memory networks, and hybrid/model-driven structures. Finally, it also illustrates the importance of lightweight designs. The analysis shows that no single architecture is optimal for all VLC conditions. ANN is effective for nonlinear mapping, CNN is suitable for local feature extraction, while LSTM performs better for channel memory and inter-symbol interference.

World Wide Web (Web) applications have become an important interface for network services. However, the direct connection among user input, business logic, and database operations has also increased security risks. SQL injection vulnerabilities remain an important issue affecting Web application security. To address the low efficiency and repetitive nature of traditional manual detection, this paper designs an automated SQL injection vulnerability detection tool for Web applications. By analyzing the principles of SQL injection and common attack types, a detection process consisting of parameter extraction, payload construction, request transmission, response analysis, and vulnerability confirmation is established. The tool is tested in experimental environments involving MySQL, a Content Management System (CMS), and Damn Vulnerable Web Application (DVWA). Experimental results show that the tool can identify different types of SQL injection vulnerabilities based on characteristics such as page content, error messages, response length, and response time. It can improve vulnerability detection efficiency and the consistency of result interpretation, providing a useful reference for Web application security testing.

Housing is one of the biggest expenses for most households, so changes in how people consume housing can tell us a lot about broader trends in consumption and upgrading. This paper uses a dataset of housing sales in King County, Washington, to explore what factors influence housing prices and what these patterns say about consumer behaviour. The analysis uses descriptive statistics, correlation analysis, and multiple linear regression to identify the key drivers of housing prices. The results show that living area (r = 0.702) and waterfront status are the strongest predictors of price, followed by property grade and view quality. However, the year the house was built does not have a strong effect on price once other factors are accounted for, which suggests that consumers care more about quality and location than about whether a property is brand new. These findings suggest that housing consumption upgrading is less about buying newer houses and more about buying better houses, which has more space, better views, and higher quality construction.

Public assistance is organized by program, while people seeking help often describe overlapping needs without knowing which agency or eligibility category applies. In Her Purse is a deployed web platform that accepts a plain-language description and optional profile information to search approximately 4,489 official U.S. assistance listings. This paper examines its Eligibility Matcher as a design for navigation across housing, income, education, employment, and other support domains. The matcher combines semantic retrieval with language-model analysis, presents criteria that appear satisfied separately from those requiring clarification, and requires neither an account nor a complete profile. A descriptive evaluation used thirty written personas situated in economic abuse, including five Spanish- or Chinese-language inputs. The system returned 297 listing instances representing 131 distinct programs, with a reported mean of 3.9 need domains per persona. The evaluation also exposed organizational funding notices among individual-facing results and lower confidence labels for incomplete and non-English inputs. These results establish output coverage in the tested scenarios, not eligibility accuracy, user benefit, or safety. The paper contributes an implemented navigation approach and an account of the design problems that remain when broad program retrieval must support decisions made with limited information.