In urban environments, the scarcity of available land often necessitates the construction of closely spaced, high-rise buildings, which rely heavily on pile foundations to support substantial loads. However, the pile-driving process, essential for such foundations, generates vibrations that can propagate through the ground and affect surrounding structures, potentially leading to adverse consequences. These vibrations can disrupt the comfort of residents and cause structural damage to adjacent buildings, including residential properties, hotels, and hospitals, where both the comfort and safety of occupants are of paramount importance. Furthermore, pile-driving-induced vibrations can result in the development of cracks in the architecture, settlement of foundations, or even severe structural failure in sensitive installations. To assess the effects of pile-driving on nearby buildings, a series of 77 finite element models were developed using PLAXIS 3D, which simulated varying pile-to-building distances and driving depths. The analyses focused on both the comfort of residents and the structural integrity of adjacent buildings, with comparisons drawn against international standards for vibration levels. The results revealed that the optimal driving depth could effectively minimize peak vibration levels, thereby reducing the risk of disruption to nearby structures. Additionally, the influence of parameters such as pile-driving load, pile penetration depth, and soil characteristics on vibration propagation was systematically explored. The findings provide critical insights into the mitigation of pile-driving-induced vibrations in urban settings and offer guidance for optimizing pile-driving operations to safeguard both resident comfort and structural safety.
Cutoff walls are an essential method for seepage prevention in dams. During the construction and operation of reservoirs, factors such as construction techniques, variations in groundwater conditions within the dam body, geological movements, and climatic factors may lead to potential seepage risks, necessitating inspection. Traditional methods like borehole coring and water pressure tests have limited monitoring ranges, while non-destructive methods like high-density electrical surveys and shallow seismic exploration have low deep-resolution capabilities, making them unsuitable for detecting deep-seated seepage in concrete walls. In recent years, Cross-borehole Tomography (CT) geophysical techniques, based on boreholes on both sides, have been widely applied in various engineering geophysical projects. Seepage in cutoff walls can lead to an increase in local moisture content, resulting in low-resistivity anomalies, providing a physical basis for the exploration using cross-borehole resistivity CT. This study investigates the resistivity response characteristics of cross-borehole resistivity CT through numerical simulation based on the resistivity characteristics of seepage in cutoff walls. The numerical simulation results indicate that this method effectively identifies seepage conditions in cutoff walls, and the resolution of cross-borehole resistivity CT is significantly related to the cross-hole spacing and the distance to the seepage points. This study provides a preliminary verification of the feasibility of applying cross-borehole resistivity CT for detecting seepage in cutoff walls and offers insights for seepage detection strategies.
In recent years, representing computer vision data in tensor form has become an important method of data representation. However, due to the limitations of signal acquisition devices, the actual data obtained may be damaged, such as image loss, noise interference, or a combination of both. Using Low-Rank Tensor Completion (LRTC) techniques to recover missing or corrupted tensor data has become a hot research topic. In this paper, we adopt a tensor coupled total variation (t-CTV) norm based on t-SVD as the minimization criterion to capture the combined effects of low-rank and local piecewise smooth priors, thus eliminating the need for balance parameters in the process. At the same time, we utilize the Non-Local Means (NLM) denoiser to smooth the image and reduce noise by leveraging the nonlocal self-similarity of the image. Furthermore, an Alternating Direction Method of Multipliers (ADMM) algorithm is designed for the proposed optimization model, NLM-TCTV. Extensive numerical experiments on real tensor data (including color, medical, and satellite remote sensing images) show that the proposed method has good robustness, performs well in noisy images, and surpasses many existing methods in both quality and visual effects.
In the context of rapidly advancing digital technology, where touchscreen interactions dominate, the tactile sensory development of children is increasingly compromised. This shift towards digital media can hinder the ability of children to effectively engage with and observe their surroundings. One promising solution to this issue is the integration of art appreciation into educational practices. However, in regions such as Indonesia, there is a noticeable scarcity of comprehensive learning kits aimed at teaching art appreciation. This study addresses this gap by designing and developing an art appreciation learning kit intended for children aged 7 to 11, aiming to teach art appreciation through artful thinking (AT). The kit employs the “see, think, wonder” (STW) thinking routine, a structured three-step process that encourages children to observe art, analyze their observations, and engage with the art through inquiry. The analysis, design, development, implementation, and evaluation (ADDIE) framework was employed as the instructional design model. Additionally, a qualitative research through design (RtD) methodology was adopted to guide the design process and ensure the creation of an innovative educational tool. The developed learning kit integrates physical components, multimedia resources, and hands-on arts and crafts activities that complement the STW routine, thereby fostering deeper engagement and critical thinking skills among young learners. The study emphasizes the value of employing the ADDIE model to assess the learning needs and challenges faced by children, particularly during the STW activity. Collaboration with educators during the design and development phases was identified as crucial for refining the learning kit. Key recommendations include the integration of graphic visualizations, clear demonstrations, and interactive activities to enhance children’s engagement and enthusiasm for art appreciation. The findings offer empirical evidence supporting the effective use of the ADDIE model in educational kit design, providing a valuable reference for future product designers in the educational technology field.
This study investigates the application of the horizontal stratified mining method to the extraction of steeply inclined unstable coal seams at the Puxi Mine. The stress environment in the mining area, the relationship between the supports and surrounding rock, the control of the rock layers in the caving zone, and the mechanical analysis of the roof collapse following the extraction of the steeply inclined coal seam were examined. The stress conditions in the mining area under the horizontal stratified mining method were explored, and a numerical analysis model was established using FLAC3D software, based on the rock mechanics parameters of the Puxi Mine’s rock layers and strata. The results indicate that, in the stress environment of the horizontal stratified mining method, the mining area is subject to not only the self-weight stress from the surrounding strata, large horizontal ground stresses, and gas pressures, but also concentrated stresses in both the dip and strike directions. When using this mining method, the stability of the two sides of the tunnel is generally good due to the surrounding rock being of a relatively stable nature. However, the roof collapse in the upper layers during the extraction of the lower layers is one of the factors affecting the safety of the support structures in the lower layers, necessitating enhanced support management. Deformation is expected in the mining face of the lower layers during extraction, and measures must be taken to prevent any instances of roof spalling. Therefore, the horizontal stratified mining method is considered feasible for the extraction of steeply inclined unstable coal seams at the Puxi Mine.
This study assesses the long-term impacts of anthropogenic activities on Morocco's Ecological Footprint (EF), employing a dataset from 1980 to 2022 within the framework of the STIRPAT model and utilizing a model (VAR/VECM) approach. Results indicate that Demographic Growth (DG) and Economic Growth (EG) have contributed to an increase of EF by 13.76% and 119.13% per unit output, respectively. Conversely, Higher Educational (HE) attainment scores is shown to alleviate EF, reducing its output by 50.59%. This analysis underscores the urgent need for policy pathways in Morocco that prioritize ecosystem preservation, foster green growth, and promote Human Capital (HC). Recommendations include enhancing the valorization and expansion of the natural ecosystem, aligning economic and demographic trajectories with the region's Bio-Capacity (BC) regeneration limits, and optimizing EF management through sustainable consumption and production practices.
This study investigates the long-term relationship between human development and environmental sustainability in Azerbaijan, with a particular focus on carbon dioxide (CO₂) emissions as a key indicator of environmental impact. Using data spanning from 1997 to 2022, sourced primarily from World Bank and United Nations databases, the analysis applies the Autoregressive Distributed Lag (ARDL) model to examine how human development—measured by the Human Development Index (HDI), which integrates Gross National Income (GNI), life expectancy, and educational attainment—affects CO₂ emissions. Developing economies, such as Azerbaijan, often face the challenge of balancing economic growth and industrialization with environmental sustainability, as the former can exacerbate environmental pressures, particularly the increase in CO₂ emissions. A long-run equilibrium relationship between HDI and CO₂ emissions is identified, with a one-unit increase in HDI associated with a 2.793-unit reduction in CO₂ emissions. This negative relationship suggests that improvements in human development, reflected in better educational outcomes, higher income levels, and improved healthcare, can foster more sustainable environmental practices. Enhanced energy efficiency, greater adoption of green technologies, and increased environmental awareness are among the mechanisms through which human development may contribute to reducing CO₂ emissions. The findings underscore the need for a synergistic approach to human development and environmental sustainability, advocating for policies that integrate socio-economic growth with environmental stewardship. By aligning human development strategies with sustainability goals, countries like Azerbaijan can mitigate ecological degradation while fostering long-term economic and social well-being. These insights provide important implications for policymakers seeking to achieve sustainable development in Azerbaijan and beyond, contributing to global efforts to reconcile growth with environmental preservation.
Research on Taiwan's science parks has frequently concentrated on isolated aspects, often neglecting the interplay between diverse indicators and the multifaceted dynamics influencing the development of these parks. Additionally, existing applications of environmental, social, and governance (ESG) frameworks in science parks have been found to inadequately capture the complexity of their performance metrics. This study aims to establish a comprehensive ESG evaluation framework tailored to the unique characteristics of Taiwan's science parks. Through the integration of the Fuzzy Delphi Method (FDM) and cluster analysis, a classification system was developed, demonstrating operational feasibility. The proposed evaluation framework is structured around two primary dimensions-Environmental Resource Management and Socioeconomic Resilience-encompassing ten critical indicators. Findings indicate that indicators under the Environmental Resource Management dimension, including water resource utilization, air quality management, greenhouse gas (GHG) emissions, renewable energy adoption, and waste management, exert the most significant impact on the sustainable development of science parks. In contrast, indicators under the Socioeconomic Resilience dimension, such as transportation planning, labour rights protection, public facility services, and financial sustainability, are deemed moderately influential yet essential to fostering balanced development. Indicators related to high-tech talent cultivation and gender equality in decision-making were determined to have limited relevance to the immediate operational needs of science parks. Consequently, it is suggested that these indicators be excluded from resource allocation priorities in resource-constrained settings. Emphasis is placed on prioritizing investments in the Environmental Resource Management dimension to ensure sustainability and compliance with global environmental standards. Additional resources, if available, should be allocated based on the specific contextual needs of individual science parks. The proposed framework not only provides actionable insights into resource allocation strategies but also establishes a robust, comparable basis for evaluating the ESG performance of science parks in Taiwan and beyond. By addressing the interdependencies among critical indicators, the framework enhances the capacity of science parks to contribute to sustainable industrial development.
This study investigates the relationship between capital structure and financial performance at Robin Corporation Ltd, a leading beverage manufacturer in Zimbabwe. A quantitative research methodology was employed, with data collected from 31 employees through structured questionnaires. The study focuses on external and internal financing sources—debt, equity, retained earnings, and reserves—and their impact on the company’s financial outcomes. The analysis reveals a positive correlation between capital structure and financial performance, suggesting that both debt and equity financing play significant roles in shaping financial results. However, it was also observed that factors such as managerial efficiency, inflation, and broader economic conditions exert substantial influence on performance. While capital structure is a critical determinant, the results indicate that effective management of these other variables is equally essential for optimizing financial outcomes. The findings underscore the importance of strategic capital management in the Zimbabwean beverage sector, emphasizing that an appropriate balance between external and internal financing is pivotal for enhancing financial performance. The study contributes to the broader understanding of capital structure in emerging markets and provides valuable insights for companies seeking to navigate the complexities of financial decision-making in volatile economic environments.
The complex challenges of addressing climate change at the local level necessitate a nuanced understanding of the policy networks that shape climate governance. This study investigates the policy network surrounding the Climate Village Program (CVP) in Pekanbaru City, Riau, examining the roles of various stakeholders and the collective dynamics that underpin local climate resilience efforts. A mixed-methods approach was employed, integrating both qualitative and quantitative data, and utilising Social Network Analysis (SNA) with UCINET 6 software to map and analyse the relationships between key actors in the network. The results reveal that the Department of Environment and Hygiene (DLHK) of Pekanbaru City occupies the most central and influential position in the policy network, acting as the primary leader. The Pekanbaru City Government plays a pivotal intermediary role, coordinating interactions between stakeholders. Despite the use of a multistakeholder approach in policy development, the process is predominantly driven by government institutions, with limited participation from businesses and non-governmental organisations (NGOs). This study highlights the potential for expanding the role of the private sector and NGOs in local climate governance, while also advocating for the increased involvement of universities in the development and implementation of climate policy. The findings offer a model for enhancing multistakeholder collaboration in local climate policy networks, with implications for broader application in other regions. The insights gained could contribute to more inclusive, participatory, and successful climate action initiatives, potentially transferable and scalable across various contexts to improve local climate governance globally.
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