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.
The topic of the scope of applicability of criminal law to crimes committed on board ships is a significant issue in criminal law. It focuses on examining the extent to which national and international laws address offenses occurring on ships, particularly in international waters. This study aims to conduct a comparative analysis between international treaties and national legislation in Islamic states to clarify the legal framework governing such crimes. Ships represent a unique legal environment where national and international jurisdictions overlap in a complex manner. With the growth of maritime traffic and international trade, the need for a clear legal framework defining criminal jurisdiction becomes increasingly important to ensure justice and combat crimes. This study seeks to answer the key question: To what extent do national criminal laws apply to crimes committed on board ships, and what role do international treaties play in regulating these jurisdictions? The central focus of this research revolves around: How can national legislation in Islamic states be reconciled with international treaties in defining the scope of criminal law applicability to crimes committed on ships. A descriptive-analytical approach has been adopted in this study, involving the analysis of international and national legal texts, with a particular focus on Islamic states. These texts have been compared to identify their strengths and weaknesses. Additionally, legal precedents and jurisprudential interpretations related to the subject have been examined. The study has reached several important conclusions based on an analysis of the national legislation of various Islamic states. The key findings include:
With the fast progress of deep learning and neural network technology, neural networks are now being used in lots of different subjects. They have been especially popular in predicting energy use in fresh air systems, because of their ability to fit nonlinear data. However, the performance of neural networks largely depends on the selection of their hyperparameters, and how to effectively optimize these hyperparameters has become a key issue to improve the prediction accuracy of the model. This paper discusses the methods of optimizing the hyperparameters of neural networks and compares several optimization methods, and finally finds that the Bayesian optimization algorithm performs optimally in neural network optimization. In the experiment, firstly, the relevant data of the new wind system were collected and data preprocessing was carried out, including missing value interpolation and outlier detection. The key factors affecting the energy consumption of the fresh air system were then used to train a prediction model. Then, PSO and BO were used to improve the neural network's hyperparameters. A Monte Carlo simulation showed the Bayesian algorithm identifies the global optimum more quickly, improving prediction accuracy. Experimental findings show that factors like indoor-outdoor temperature, humidity and air quality influence the energy consumption of fresh air systems.The BP neural network model, optimised by the Bayesian optimisation algorithm, performs better when predicting energy consumption. Research results offer an effective energy consumption prediction method for fresh air systems and important experimental support for related energy prediction tasks.
Increased demands on lightweight and high-performance battery casings of electric vehicles (EVs) and energy storage systems require cutting-edge forming technology to overcome challenges of conventional deep drawing and stamping, where usually thickness inhomogeneity, residual stress, and defects would be caused. The research deals with the designing and optimization of an ultra-thin square aluminum shell power battery forming die utilizing roll forming technology for improving size accuracy and mechanical reliability. A finite element model for simulation to optimize roll forming parameters, such as rolling force and pass geometry, was established and verified experimentally for thickness distribution assessment, defect minimization, and spring back minimization. The comparative study against deep drawing and stamping techniques reveals that roll forming results in 50% thickness variation reduction, 63% dimensional accuracy improvement, and 75% defect rate minimization. Furthermore, spring back effects were decreased by 42–60%, and shape retention and structural stability were improved. The results confirm that roll forming enhances production accuracy, reduces human errors, and improves overall efficiency, making it a good candidate for scalable next-generation production of batteries. From the data, it can be deduced that roll forming is a better alternative when compared to traditional forming as it helps in achieving better sustainability, less material waste, and increased reliability for future energy storage technologies.
Molecular dynamics (MD) simulations were used to investigate the interfacial interactions of hydroxyapatite, β tricalcium phosphate, sil-ma, and filin protein (SF) in the composite. This study analyzed the interface binding energy and radial distribution function between hydroxyapatite (HA), β triccalcium phosphate (β -tcp), sil-ma and bio-polymer SF. The study found that the interface binding energy of Sil-MA hybrid system is higher than that of SF hybrid system, indicating that the Sil-MA hybrid system is more stable, which makes the mechanical properties of this system better.
To establish an evaluation system for the green development of ports, we integrated insights from multiple expert groups using a comprehensive evaluation method. The indicators were screened using the Spearman correlation coefficient and principal component analysis. Subsequently, an ANP model was adopted, and Super Decisions software was utilized to determine the weights of each indicator and construct the model. This model comprehensively evaluates both the economic and green development aspects of ports, providing accurate assessments of their green development status. This model comprehensively evaluates both the economic and green development aspects of ports, providing accurate assessments of their green development status. Additionally, the ideal solution method (TOPSIS) was employed to conduct empirical analysis on ten selected ports. The results indicate that Shanghai Port has largely achieved balanced economic and green development, while other ports exhibit issues in specific areas, aligning The results indicate that Shanghai Port has largely achieved balanced economic and green development, while other ports exhibit issues in specific areas, aligning with real-world observations. These findings validate the effectiveness of the evaluation model and offer recommendations for enhancing port green development. These findings validate the effectiveness of the evaluation model and offer recommendations for enhancing port green development.
This paper presents a novel approach for calculating the magnetic field distribution of magnetic gears(CMGs) with bridge connection in modulator. The modulator and bridge connection regions were divided into several unit modules to derive a convolution matrix of permeability. The magnetic field distribution was then calculated using spatial harmonic modeling method(SHMM). To accurately account for the influence of nonlinear permeability and saturation on the magnetic field distribution, the permeability curve of the material was incorporated, and the permeability distribution function was modified using an iterative method. To verify the method, the magnetic field distribution of two sets of magnetic gears with different bridge parameters was calculated. The proposed method for accurately calculating the magnetic field distribution of CMGs with bridge connections is validated by comparing it with the results obtained from finite element analysis(FEA).
Preventing geological disasters such as highway rock collapses is crucial for ensuring traffic safety and social stability. Traditional research often relies on on-site investigations to identify collapses, which can delay the remediation process. In this study, we focused on a highway rock collapse case in Shuangfeng Town, Longchang City. Using a combination of remote sensing imagery analysis, on-site verification, and surveying, we comprehensively obtained the geological characteristics and surrounding environmental information of the dangerous rock mass. Through detailed analysis of the lithology, structure, weathering degree, and other features of the dangerous rock mass, and considering geological structures, climatic conditions, human activities, and other internal and external factors, we explored the formation mechanism of the rock collapse in depth. Subsequently, we assessed the potential threat level of the dangerous rock mass to provide a scientific basis for the design of the remediation plan. The results indicate that the dangerous rock mass in this area is primarily composed of gray thick-bedded to blocky fine-grained sandstone from the Middle Jurassic Shaximiao Formation, with two sets of steeply dipping "X" joints. Stability analysis reveals that under natural conditions, the dangerous rock mass is relatively stable, but its stability significantly decreases under heavy rainfall conditions, becoming unstable. Based on these findings, we proposed a "manual rock clearance" plan. Testing confirmed that this plan.achieved the intended goals and ensured the safety of the area.
Understanding the micro-level dynamic mechanism of economic growth of the science park is of great significance to the theory of regional economics and the policies to promote innovation. Based on the novel firm-level data from 2005 to 2015 on Beijing Zhongguancun Science Park (Z Park for short) in China, this paper investigates the dynamic impacts of firm entry on Z Park’s growth, measured by productivity and size, respectively, and an extended analysis is conducted from the perspective of energy utilization and innovation. By employing the DOP and EEKT decomposition methods, we first find that new entrants have a lower TFP and output than incumbent firms. However, in the following years, the relative growth rates of them are faster and gradually converge to or even exceed the latter in both the TFP and output level. Second, the new enterprises do not significantly promote the annual TFP growth of Z Park in the year of entry but finally enhance the TFP growth on average during these ten years. Third, different from the impact on the growth of TFP in Z Park, new entrants contribute substantially to the annual output growth of Z Park in the year of entry, and this effect has further increased on average. The analysis combined with energy utilization shows that with the entry and growth of new enterprises, they are gradually showing potential in improving energy efficiency and innovating energy technology. These findings provide valuable insights into the dynamics of economic growth in science parks and suggest potential strategies for promoting innovation for policymakers.
The development of a high-quality economic system in China critically hinges on green finance, particularly green credit as an integral part of the carbon finance framework. This study analyzes panel data from 30 provinces and municipalities across China between 2008 and 2019, employing a dynamic panel model to explore the impact of green credit on carbon emission reduction in six major energy-intensive industries. Results reveal that green credit significantly curtails carbon emissions within these sectors. Notably, the effectiveness varies regionally, with eastern regions demonstrating superior performance compared to their central and western counterparts. This research underscores the necessity for expanding green credit scale, enhancing support mechanisms, and fostering deeper integration and liberalization of green finance nationwide. Special attention should be directed towards advancing green credit initiatives in central and western regions to achieve balanced development. This study innovatively quantifies regional disparities in green credit efficacy, providing actionable insights for policymakers.
Carbon emissions are a critical global issue requiring detailed spatial analyses to support regional carbon peak and neutrality strategies. This study investigates the spatiotemporal evolution and spatial differentiation of county-level land-use carbon emissions (CELU) in the Changchun-Jilin-Tumen (CJT) region from 2012 to 2021 by integrating land use data, nighttime light imagery, and socio-economic statistics with the Optimal Parameter Geodetector (OPGD) model. The analysis identifies key drivers of spatial emission variability, including construction land proportion (q-value: 0.8882), land area per capita (q-value: 0.7609), and urbanization rate (q-value: 0.5875), underscoring the significant role of land-use patterns and urbanization. Results show a 21.2% increase in CELU, from 67,594.46×104 t in 2012 to 81,942.35×104 t in 2021, with emissions concentrated in industrially active and urbanized western and southern counties, while forest-rich central and eastern counties exhibit lower emissions. Using the Grey Model (GM (1,1)), the study forecasts that CELU will rise from 78,484.364×104 t in 2022 to 88,985.198×104 t by 2030, reflecting a 14% increase over the forecast period. This trajectory highlights the misalignment between current trends and the region's goals of creating a "low-carbon industrial zone" and "livable cities," emphasizing the need for transitioning to renewable energy, optimizing industrial structures, and implementing sustainable land-use practices such as brownfield redevelopment and ecological land protection. By combining advanced remote sensing with nonlinear spatial analysis, this study offers a replicable high-resolution framework for understanding carbon emission drivers and spatial patterns, providing actionable insights for refining carbon reduction strategies and achieving sustainable development goals at both national and global scales.
As an important part of the new type of think tank with Chinese characteristics, art think tanks play an important role in enhancing the accuracy of the inheritance of intangible cultural heritage and art, and improving the foresight of the development of intangible cultural heritage and art. As one of the main dissemination paths of intangible cultural heritage today, the accuracy and efficiency of "digital communication" cannot be separated from the active participation of art think tanks. [Method/Process] Starting from the positive role of art think tanks in "digital communication of intangible cultural heritage", this paper explores the necessity of art think tanks participating in the new type of intangible cultural heritage dissemination path through literature research, comparative analysis, induction and summary, as well as the positioning, function and cultural mission of art think tanks in it. [Result/ Conclusion] The positive impact of art think tanks on the dissemination of intangible cultural heritage and other important cultural dissemination in China is mostly in macro-guidance, and they rarely play an important role in the "new dissemination method" of a certain culture. "Digital dissemination" has gradually become the mainstream method of cultural and artistic dissemination with its advantages of "fast speed", "wide range", "large content" and "high public participation". The study believes that exploring the role of art think tanks in the process of "digital dissemination of intangible cultural heritage" will help solve the problems of "uneven quality of dissemination content" and "low participation intention of young groups", and thus disseminate intangible cultural heritage and enhance China's cultural soft power in a more accurate and efficient way.
The Double-Sided Flux Switching Permanent Magnet Linear Motor (DLFSPM) is being increasingly and extensively employed in various fields due to its merits of high efficiency and high power density. Nevertheless, the DLFSPM suffer from significant thrust ripple, which exacerbates electromagnetic vibration. To effectively reduce thrust ripple and electromagnetic vibration, this paper aims to significantly enhance its performance by optimizing the stator structure. Firstly, establish the finite element analysis model of the DLFSPM, and based on theoretical methods, derive the electromagnetic thrust equation and the equation of electromagnetic vibration, clarifying the key indicators. Secondly, effective suppression of thrust ripple is achieved by optimizing the radius of the arc fillet in the stator slot. Subsequently, auxiliary slots are introduced on the stator tooth surface, and their optimal dimensions are determined using the finite element model and response surface methodology, with the objective functions of enhancing electromagnetic thrust and reducing thrust ripple. Finally, a comparative analysis is conducted between the comprehensively optimized DLFSPM and the initial structure motor regarding electromagnetic thrust, thrust ripple, and electromagnetic vibration. The simulation results demonstrate that the optimized DLFSPM not only significantly improves electromagnetic thrust but also effectively suppresses thrust ripple and electromagnetic vibration, thereby validating the effectiveness of the optimization method.
Aeromagnetic exploration is a magnetic field exploration method that detects changes in the spatial magnetic field by carrying a magnetometer on an aircraft. However, during the measurement process, the magnetic field data is often interfered by the aircraft’s own ferromagnetic materials and maneuvers. The role of aeromagnetic compensation is to eliminate this part of the interference, which is crucial to improving the quality of aeromagnetic exploration data. In this study, we introduce a novel method for aeromagnetic compensation, which is employed to eliminate the interference from aircraft platforms. The proposed method utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the magnetic field data into multiple feature components. These decomposed features are subsequently input into a physics- guided neural network (PGNN), which was designed to remove magnetic interference from the data. The core idea behind this method is that CEEMDAN effectively decomposes magnetic field data into features that are more easily learned by the neural network. The method leverages both data-driven and model-driven advantages by embedding the Tolles–Lawson (T-L) model into the neural network, thereby compensating for both linear and nonlinear interference. The results of simulation and real experiments show that the proposed method outperforms traditional model-driven and data-driven techniques, especially when the quantity and quality of data are limited.
Using ArcGIS10.2, a spatial database was constructed for 8.5-2.2 ka historical and cultural sites in Shandong Province. Using a combination of statistical and superposition analysis, the spatial and temporal distribution of ancient settlement sites in Shandong Province since the Neolithic period was studied, and the factors influencing their spatial and temporal distribution were investigated. The study found that as human civilisation has evolved since the Neolithic, the number of ancient settlement culture sites in Shandong province has also been changing, going through four periods: rising, surging, declining and rising. In the HouLi culture period, the settlement sites were mostly concentrated in the plain area in front of the mountains in Luzhong; in the Beixin culture period, Luzhong was the main settlement area of the settlement; in the Dawenkou culture period, there were settlement sites in Luzhong and southeast Luzhong; in the Longshan culture period, there were settlement sites in Luzhong and northwest Luzhong; in the Yueshi culture period, the distribution scale of the settlement sites was obviously reduced and scattered in the plain in front of the mountains. During the Shang culture period, the distribution of settlement sites increased, mostly in the central, southern and northwestern Lu areas; during the Zhou culture period, the distribution of settlement sites increased sharply, mostly in the central, southern and northwestern Lu and Jiaozhou peninsula areas. 8.5-7.5 ka B.P., in general, the natural climate during this time period was warm and humid, with good natural resources, and the Houli from 7.0 to 4.0 ka B.P., climatic conditions remained generally mild, with minor fluctuations but a steady general trend, and the Beixin, Dawenkou and Longshan cultures developed sequentially under stable, excellent climatic conditions during this time period. The Longshan culture gradually declined, and after a more stable period the Yueshi culture emerged. Changes in the overall climatic environment had a great impact on social, cultural and economic development, and the formation and development of Early Neolithic culture is strongly linked to climate, but the influence of many factors, including social productivity, led to spatial changes.
This study selects the Tumen River Basin as the research area. By downloading Global30 land use data for 2000, 2010, and 2020, we utilized ArcGIS for mask extraction, raster reclassification, and data integration to standardize land categories and facilitate comparative analysis. Subsequently, we systematically analyzed the area, change magnitude, and dynamic degree of land use on both sides of the Tumen River Basin over the past two decades. Through spatial operations such as overlay analysis in ArcGIS, we further employed land use transfer matrices and transition probability matrices to examine the characteristics of land use type conversions. Key findings include: 1.Forest reduction: Forest areas on both sides of the basin decreased, primarily due to insufficient environmental awareness and overexploitation of resources in the early 2000s. 2.Water body expansion: China’s water conservancy projects increased total water area by approximately 66.39 km², while North Korea’s water bodies remained stable. 3.Policy-driven shifts: Land use changes were closely linked to national policies and human activities, with recent improvements attributed to sustainable development initiatives
