Beautiful countryside construction in China has moved from visible environmental upgrading toward the coordinated improvement of rural space, ecological governance, local industries and residents’ participation. Taking Jiangsu Province as the empirical context, this study examines how rural environmental attributes are connected with residents’ participation characteristics through field observation, public construction information, local documents and participation-related records. The selected cases cover southern, central and northern Jiangsu. Environmental attributes are classified into spatial environment, ecological environment, infrastructure, industrial space and cultural landscape, while participation is examined through village affairs, public activities, environmental maintenance, rural industries and cultural practices. The results show that environmental attributes acquire practical value when they are used, maintained and interpreted by residents. Public space renewal supports communication and public activity; ecological improvement depends on maintenance participation; industrial spaces create livelihood-oriented participation; and cultural landscapes become meaningful through local narration and collective use. The study provides field-based evidence for understanding beautiful countryside construction as both an environmental and social process.
This study grounded in social cognitive theory and the technology acceptance model, developed a three-dimensional framework of influencing factors encompassing technology, learner, and environment. A structural equation model was employed to investigate and analyze data collected from 240 dancesport students across five universities. The study found that college students’ autonomous learning ability in dancesport was rated at an upper-middle level (M=3.42), with a notable weakness in self-planning skills. The quality of digital teaching resources (β=0.31, p<0.001), students’ digital literacy (β=0.29, p<0.001), and adaptability of tool functions (β=0.21, p<0.01) all had significant positive effects on students’ autonomous learning ability. Additionally, self-efficacy played a crucial intermediary role in this relationship. The direct effect of teachers’ digital teaching ability is not significant, but it has an important indirect impact through the construction of a supportive environment. The research further reveals the three-stage mechanism of resource activation, psychological drive, and behavior generation of digital-enabled autonomous learning ability, and puts forward three-dimensional improvement paths of technology, learners, and environment. This study provides a theoretical basis and practical reference for the digital reform of dancesport education.
The main aim of this research study is to know the extent of using the Public Relations - (PR) in managing the bounced checks’ crisis - (BCC) resulted from the Israeli war on the Gaza in 2023 - (the war), where the researchers selected the Bank of Palestine and Bank of Jordan for that. The Renewal Discourse Theory - (DRT) and Kotter's model were used by the researchers to answer the research study’s questions. Moreover, they used the descriptive analytical approach to analyze the results they got from the research study’s questionnaire and the direct interview. In the questionnaire, the research study’s community sample was 400 beneficiaries of the ‘checks service’ in the above mentioned Banks. As for the direct interview tool’s questions, they were designed to serve the objective of the study, where the intentional sample was merely selected from the PRs’ managers at the Bank of Palestine. Results showed that the PRs’ Department at the Bank has no role in protecting its reputation, responding to the economic crisis, or dealing with the crisis’ effects. None of the banks used PRs’ strategies in dealing with their customers during the crisis. The study also recommended that the banks should: employ qualified PR people, prepare plans, ready to deal with any crisis might face the bank, and remain informed of all crises occurring on the banking sector internationally and locally.
Enterprise Linux systems form the backbone of modern financial, healthcare, and large-scale enterprise infrastructures. These environments are required to meet strict security and compliance standards while supporting continuous operational availability. Traditional security validation approaches for Linux systems rely heavily on periodic vulnerability scans, manual audits, and reactive remediation processes, which often fail to detect configuration drift and emerging security risks in a timely manner. As system scale and complexity increase, these limitations become more pronounced, leading to delayed remediation and increased exposure to compliance violations. This paper presents an AI-driven approach for continuous security validation of enterprise Linux systems using Configuration-as-Code principles. By representing security baselines, hardening standards, and compliance controls as version-controlled configurations, the proposed approach enables consistent enforcement and validation across large Linux environments. Artificial intelligence techniques are applied to analyze configuration deviations, identify recurring misconfigurations, and prioritize remediation efforts based on risk and operational impact. Rather than replacing existing security tools, the approach augments them by providing continuous assessment and intelligent decision support. Through architectural analysis and practical observations from enterprise Linux environments, this study demonstrates how integrating AI-assisted analysis with Configuration-as-Code improves visibility, reduces configuration drift, and strengthens overall security posture. The findings suggest that continuous, automated validation can significantly enhance compliance readiness and operational resilience while reducing manual effort in large-scale Linux infrastructures.
Photovoltaic (PV) energy harvesting systems experience performance deterioration and efficiency drops mostly due to mismatching and partial shading conditions (PSCs). Although different algorithms have been established to mitigate the negative impact due to PSCs, there are still some challenges concerning the algorithms’ robustness, accuracy, and reliability. To alleviate the effects of partial shading and enhance output power, PV array reconfiguration techniques emerged as solutions to this challenge. The purpose of this paper is to introduce the genetic algorithm (GA) based model predictive control (MPC) in order to mitigate the impact of PSCs on the PV array. This algorithm is considered due to its capability of taking multiple inputs and generating multiple output predictive signals. The proposed algorithm is implemented and simulated on MATLAB/Simulink for PV array performance optimization under PSCs. The results show that the GA-based MPC does not only mitigate the effects of PSCs but optimizes the PV array’s performance as well. From the simulation results, the GA-based MPC significantly increased the overall power output from 562.0 W (under PSCs) to 852.6 W which is almost the maximum power (under normal conditions), making an improvement of 290.6 W which is technically 51.17 % of the overall power generated by the system.
