The intensification of urbanization worldwide, particularly in China, has led to significant challenges in maintaining sustainable urban environments, primarily due to the Urban Heat Island (UHI) effect. This effect exacerbates urban thermal stress, leading to increased energy consumption, poor air quality, and heightened health risks. In response, urban green spaces are recognized for their role in ameliorating urban heat and enhancing environmental resilience. This paper has studied the microclimate regulation effects of three representative classical gardens in Suzhou—the Humble Administrator’s Garden, the Lingering Garden and the Canglang Pavilion. It aims to explore the specific impacts of water bodies, vegetation and architectural features on the air temperature and relative humidity within the gardens. With the help of Geographic Information System (GIS) technology and the Inverse Distance Weighted (IDW) spatial interpolation method, this study has analyzed the microclimate regulation mechanisms in the designs of these traditional gardens. The results show that water bodies and lush vegetation have significant effects on reducing temperature and increasing humidity, while the architectural structures and rocks have affected the distribution and retention of heat to some extent. These findings not only enrich our understanding of the role of the design principles of classical gardens in climate adaptability but also provide important theoretical basis and practical guidance for the design of modern urban parks and the planning of sustainable urban environments. In addition, the study highlights GIS-based spatial interpolation as a valuable tool for visualizing and optimizing thermal comfort in urban landscapes, providing insights for developing resilient urban green spaces.

Every production day in Nigeria, and in other oil producing countries, millions of barrels of produced water is generated. Being very toxic, remediation of the produced water before discharge into environment or re-use is very essential. An eco-friendly and cost effective approach is hereby reported for remediative pre-treatment of produced water (PW) obtained from Nigerian oilfield. In this approach, Telfairia occidentalis stem extract-silver nanoparticles (TOSE-AgNPs) were synthesized, characterized and applied as bio-based adsorbent for treating the PW in situ. The nanoparticles were of average size 42.8 nm ± 5.3 nm, spherical to round shaped and mainly composed of nitrogen and oxygen as major atoms on the surface. Owing to the effect of addition of TOSE-AgNPs, the initially high levels (mg/L) of Total Dissolved Solids (TDS), Biological Oxygen Demand (BOD) and TSS of 607, 3.78 and 48.4 in the PW were reduced to 381, 1.22 and 19.6, respectively, whereas DO and COD improved from 161 and 48.4 to 276 and 19.6 respectively, most of which fell within WHO and US-EPA safe limits. Particularly, the added TOSE-AgNPs efficiently removed Pb (II) ions from the PW at temperatures between 25 ℃ to 50 ℃. Removal of TOSE-AgNPs occurred through the adsorption mechanism and was dependent contact time, temperature and dose of TOSE-AgNPs added. Optimal remediation was achieved with 0.5 g/L TOSE-AgNPs at 30 ℃ after 5 h contact time. Adsorption of Pb (Ⅱ) ions on TOSE-AgNPs was spontaneous and physical in nature with remediation efficiency of over 82% of the Pb (Ⅱ) ions in solution. Instead of discarding the stem of Telfairia occidentalis, it can be extracted and prepared into a new material and applied in the oilfield as reported here for the first time.

The research aimed to: 1) analyze components and indicators of digital transformation leadership among school administrators, 2) assess their leadership needs, and 3) develop mechanism models to promote this leadership. A mixed-method approach was applied, involving three sample groups: 8 experts, 406 administrators, and 7 experts. Data collection tools included semi-structured interviews, leadership scales, needs assessments, and focus group discussions, with analysis performed through construct validity testing, needs assessment, and content analysis. The findings revealed: 1) The components and indicators of digital transformation leadership showed structural validity, as confirmed by the model’s alignment with empirical data (Chi-Square = 82.3, df = 65, p = 0.072, CFI = 0.998, TLI = 0.997, RMR = 0.00965, RMSEA = 0.0256). 2) Among the leadership components, “innovative knowledge” ranked highest in need (PNImodified = 0.075), followed by “ideological influence” (0.066), “consideration of individuality” (0.055), “intellectual stimulation” (0.052), and “inspiration” (0.053). 3) Mechanism models for promoting leadership emphasized enhancing these five components to strengthen administrators’ skills in applying technology, managing teaching and development plans, and fostering innovation. Administrators were encouraged to tailor strategies to individual needs, inspire personnel, and create a commitment to organizational change and development. These mechanisms aim to equip administrators to effectively lead transformations, motivate staff, and drive educational institutions to adapt and thrive in evolving environments.

This project analyzes the evolution of the manufacturing sector in Portugal from 2009 to 2021, focusing on the variations in the number of active companies across various subcategories, such as food, textiles, and metal product industries. The goal of this analysis is to understand the dynamics of growth and contraction within each sector, providing insights for companies to adjust their market and operational strategies. Key objectives include analyzing the overall evolution in the number of companies, identifying subcategories with notable changes, and providing a comprehensive analysis of observed trends and patterns. The study is based on data from PORDATA 2024, and the research employs temporal trend analysis, linear and quadratic regression, and the Pareto representation to identify patterns of growth and decline. By comparing annual data, the project uncovers periods of growth and decline, allowing for a deeper understanding of the sector’s dynamics. The findings also highlight variations in periods of economic crises and during the Covid-19 pandemic, and recommendations for action are presented to support businesses resilience and continuity. These results are valuable for companies within the manufacturing sectors analyzed and policy makers, guiding strategic decisions to navigate the complexities of the market dynamics and to ensuring long-term organizational sustainable success.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.