The issue of valuation variation in real estate has received increasing attention from researchers in recent years. Valuation variation generally refers to differences in property valuations made by different real estate appraisers, influenced by the appraisers’ individual beliefs. These differences arise from each appraiser’s subjective values and the different strategies they use in their valuation process. From a behavioral finance perspective, this study examines the effects of mental accounting, heuristics (availability and anchoring), and client stress on real estate appraisers’ valuation variation. Because mental accounting, heuristics, and client stress are treated as latent variables, we used structural equation modeling to analyze appraisers’ decision-making in their valuations. The empirical results show that mental accounting has a positive and significant effect on valuation variation, while client stress has a negative and significant effect. Additionally, the availability heuristic, mental accounting, and client stress have a positive and significant effect on anchoring; however, anchoring does not have a significant effect on valuation variation.

Many institutions are no longer able to provide sufficient housing for their student on-campus. The housing deficit has caused the students to migrate to off-campus student housing. This study examined the factors influencing the financial performance of student housing in Nigeria, with a specific focus on studentified neighbourhoods in Calabar and Ile-Ife. The study adopted a quantitative research design, utilising a closed-ended questionnaire administered to property managers across 132 student housing units. Data collected were analysed using Principal Component Analysis (PCA) to identify key factor groupings influencing financial performance. The results reveal four major components that drive financial performance in student housing: Institutional and Property-Specific Factors; Locational and Socio-Economic Factors; Regulatory and Financial Factors; and Macroeconomic and Infrastructure Factors. The primary factor influencing the financial performance of student housing is institutional and property-specific factors, with a total variance of (54.986%). The study concludes that both micro-level property features and macro-level environmental conditions influence the financial viability of student housing investments in Nigeria.

Housing plays a vital role in urban settings facing shortages, particularly in emerging economies where challenges are more pronounced and efficient housing policies are essential to maintain a balance between housing values and consumer incomes. Among the factors influencing housing value, the impact of accessibility and proximity to transport infrastructure has long been established in the literature, considering various contributors such as physical and socio-economic characteristics. However, research focusing on emerging economies like Iran remains limited. This study employs a Hedonic Price Model to examine the effects of metro accessibility, alongside other factors, over time and across different socio-spatial areas within the Mashhad metropolis, Iran. Surprisingly, the results reveal that metro presence does not significantly influence housing prices, which are instead shaped by a combination of physical property attributes, buyer behavior, market conditions, property types, and broader socio-economic factors. These findings have important implications for real estate professionals, policymakers, and urban planners, offering insights to better allocate resources, adapt to evolving conditions, and promote long-term sustainable and equitable urban development.

This study investigates the influence of socio-economic variables on the physical characteristics of housing in District 2 of Tehran, an area marked by pronounced spatial and socio-economic polarization. Employing a mixed-methods quantitative approach, including factor analysis and regression modeling, the research develops a composite index titled “Housing Physical Condition” (HPC) to represent the physical status of residential buildings. Social and economic indicators—such as literacy rate, income-to-housing expenditure ratio, and unit density—were analyzed for their correlation with the HPC index. The results reveal that physical housing quality is strongly shaped by both social structures and economic capacities, with indicators like household literacy and housing cost burdens showing significant explanatory power. The study underscores that physical upgrades alone are insufficient for housing improvement; rather, a multidimensional planning approach that integrates socio-economic interventions is critical to addressing urban disparities. The proposed framework offers valuable insights for inclusive urban housing policies in similarly fragmented metropolitan contexts.

Aiming at the problem of high false detection rate of PCB defect detection, an improved YOLOv8-C2f_DBB-SPFF_LSKA (YOLOv8-CDSL) is used to detect defects of the PCB. The diverse branch block (DBB) is used to improve the faster version of CSP bottle-neck with two convolutions (C2f) module in the YOLOv8 backbone network. The large separable kernel attention (LSKA) mechanism is also added to the SPPF module of the YOLOv8 network. The C2F_DBB module can significantly improve the model’s ability to identify small-scale PCB defects, and greatly enhance the model’s performance in comprehensive feature extraction. Thus significantly improving the overall accuracy of the model. The SPPF_LSKA module can reduce the computing power consumption of model training. Consequently, it significantly enhances the detection capability of the improved YOLOv8-CDSL network. The effectiveness of the improved scheme was verified by ablation experiments. At the same time, it is verified by comparative experiments that the improved YOLOv8-CDSL network has the highest detection accuracy of 99.13% for six common surface defects of PCB.

In modern Wireless Sensor Networks (WSNs) and cyber-physical systems, multi-source, heterogeneous, and high-dimensional spatiotemporal data pose major challenges for accurate and robust multi-target prediction. Traditional models often fail to capture nonlinear dependencies and long-term temporal patterns, while deep learning methods may lack generalization, stability, and interpretability—especially under the resource constraints of WSNs.This paper proposes STGNet (Spatiotemporal Gradient Network), a fusion framework tailored for complex sequence prediction in WSN environments. By integrating LSTM’s temporal memory with Transformer’s global dependency modeling, STGNet captures both localized node dynamics and cross-node interactions, effectively modeling spatial correlations and routing variability inherent to WSNs. To improve robustness and adaptability, STGNet leverages TPE-based Bayesian optimization for efficient, automated hyperparameter tuning, and incorporates a SHAP-based interpretability module to quantify the contribution of each sensor or feature dimension—enhancing transparency and trust in model outputs. Extensive experiments on real-world WSN datasets show that STGNet consistently outperforms LSTM, Transformer, and ensemble baselines in prediction accuracy, temporal consistency, and feature sensitivity. These results validate STGNet as a scalable and interpretable solution for environmental monitoring, resource scheduling, and adaptive control in intelligent wireless sensing systems.

The rapid development of the digital economy and the deployment of large-scale electricity big-data platforms have highlighted both the opportunities and risks associated with energy data circulation. Conventional identity management frameworks in the energy sector suffer from weak authentication, fragmented governance, and high compliance costs, limiting the secure and efficient realization of data value. This paper proposes a decentralized digital identity (DID) management framework tailored for the energy data space. By integrating blockchain-based traceability, verifiable credentials, and smart-contract-driven privacy protection, the framework establishes a sovereign, interoperable, and privacy-preserving identity infrastructure. Through simulation experiments using Monte Carlo modeling, we evaluate the performance of the proposed system under different blockchain infrastructures (Fabric vs. EVM) and disclosure mechanisms (plain vs. zero-knowledge proofs). The results demonstrate that Fabric achieves lower latency and higher throughput compared to EVM, while zero-knowledge proofs introduce moderate but acceptable overhead, enabling stronger privacy guarantees. The proposed framework effectively tackles the key challenges of secure identity verification, fine-grained and dynamic authorization, and tamper-resistant auditing, thereby establishing a scalable and reliable foundation for trusted circulation of energy data.

Spectral variability remains a major challenge in hyperspectral unmixing, as the spectral signatures of the same material often fluctuate under different illumination, atmospheric, or scale conditions, which invalidates the fixed endmember assumption. To address this issue, we propose Meta-Spectral Net, a meta-learning-based framework for adaptive spectral variability modeling in hyperspectral unmixing. The proposed framework leverages a task-driven meta-learning strategy, where each acquisition scenario is defined as a task, to enable the endmember generator to rapidly adapt to unseen spectral conditions with only a few samples. Furthermore, a spectral variability adaptation module is introduced to explicitly account for environmental factors, thus improving the robustness of endmember representation. Comprehensive experiments on both synthetic and real hyperspectral datasets demonstrate that Meta-Spectral Net significantly outperforms state-of-the-art unmixing methods in terms of endmember reconstruction accuracy and abundance estimation, while offering superior generalization to novel scenarios. These results suggest that meta-learning provides an effective paradigm for tackling spectral variability, paving the way toward more adaptive and reliable hyperspectral unmixing in real-world applications.

The rapid economic development, coupled with accelerated industrialization and urbanization, has brought significant challenges to sustainable development due to environmental pollution and climate change. Promoting the adoption of eco-friendly home products is essential for mitigating household pollution and advancing green, low-carbon, and sustainable consumption. This study investigates the key factors influencing consumers’ purchasing behavior of eco-friendly home products and proposes targeted strategies. Grounded in the Theory of Planned Behavior (TPB) and the “Attitude- Intention-Behavior” model, the research constructs a theoretical framework for understanding green consumption behavioral intentions. Data were collected through an online survey of 397 consumers of home products in the Sichuan-Chongqing region, and Structural Equation Modeling (SEM) was employed to empirically examine the determinants of consumers’ eco-friendly purchasing behavior. The results demonstrate that the proposed model effectively explains and predicts consumers’ purchasing decisions regarding eco-friendly home products. Among the identified factors, attitude toward green products is the most significant determinant, while environmental concern and environmental awareness are the strongest antecedents influencing purchase intentions. Perceived behavioral control also exhibits a positive and significant effect on purchase intentions, whereas perceived environmental knowledge shows no significant impact. These findings offer valuable theoretical insights for manufacturers and market practitioners aiming to better align with and influence consumer decision-making in the ecofriendly home products sector. Additionally, the study provides practical recommendations for policymakers focused on fostering green consumption and promoting the sustainable development of eco-friendly home products.

In the context of contemporary higher education, campus space design serves as a vital tool for teacher student interaction and the development of a sense of community in addition to serving the needs of studying and living. However, social contact and psychological belonging are frequently overlooked in traditional campus design, creating a gap between physical arrangement and real demands. This study offers a number of creative approaches for managing and designing campus spaces that are based on the student community interaction paradigm. Teaching, housing, and community services are organically integrated to improve the interaction between teachers and students through the introduction of the “molecular unit” concept. Resources are shared and more opportunities for social practice are realized through the establishment of a comprehensive mechanism between the campus and the urban society. At the same time, the psychological identity and sense of belonging of the teachers and students are enhanced through the construction of the event space and the spirit of the place. Furthermore, the idea of hybrid living that this study promotes can aid in removing barriers between students and instructors and further improve campus life. The results encourage spatial innovation and cultural growth in higher education settings and offer theoretical support and useful guidance for future campus design.

The traditional English teaching mode ignores the differences between individual students, resulting in some students’ low motivation to learn, while personalized English learning based on the support of Internet of Things can effectively improve students’ learning efficiency. In this paper, we construct the evaluation index system of student engagement from four dimensions: learning attitude, learning process, learning effectiveness and emotional experience, and quantify the degree of student engagement in IoT-supported personalized English learning through a fuzzy mathematical model. In order to further improve student engagement, a student engagement improvement mechanism is designed based on four evaluation results: excellent, good, moderate and poor. The study takes the freshman (1) class of English majors in School A, which uses IoT technology for personalized English learning, as the research object and uses a fuzzy mathematical model to quantify student engagement. The evaluation grade of student engagement in this class is “medium”, and the evaluation score is 71.37. Accordingly, a student engagement improvement mechanism was introduced. After 8 weeks of rectification, the final grade of student engagement in English learning is “excellent”, with an evaluation score of 88.49, which is a total improvement of 17.12 points, and the student engagement enhancement mechanism designed in this paper has a significant effect.

In recent years, music has been developing rapidly in China and has become one of the hot items of current school teaching, and more and more schools have carried out college music education. However, there are still many problems that need to be solved in college music education, among which the problem of unreasonable teaching methods is particularly prominent. In this study, we use federal reinforcement learning technology to explore in depth the theory and method of the situational classroom teaching model and implement it into music education in China’s general colleges. Through empirical testing, we further analyze the optimization effect and empirical variability of the model to provide reference for the implementation and promotion of the situational classroom teaching model in China’s general college music education. The experimental results proved that the optimized federal average algorithm achieved better results than the traditional teaching method, using the situational classroom teaching method in college music education, which could improve the quality of set completion and significantly improve the teaching quality, and the accuracy of the optimized model of federal learning increased by 8%.