Firms, recognizing their Corporate Social Responsibility (CSR), are becoming catalysts for societal change by integrating Environmental, Social and Governance (ESG) criteria into their activities. The fashion industry exemplifies this effort, with an increasing number of companies embracing sustainability and ethical practices. In this context, our purpose is to provide a clear and comprehensive picture of the link between sustainability and business performance in the fashion industry. This work presents a Multivariate Regression Analysis, scrutinizing both external perspectives through stock prices and internal perspectives via profitability indices. Our aim is to discern the intricate relationship between sustainability practices and financial performance within the fashion industry, aligning ESG criteria with long-term economic success. Our regression analysis reveals a significant positive correlation between ESG scores and stock prices, indicating investor recognition of ESG performance as a crucial investment criterion. However, when focusing internally on profitability, the ESG score does not exhibit statistical significance, suggesting a yet-to-be-established connection between ESG policies and corporate profitability. This study underscores the evolving role of companies as sustainability promoters, emphasizing the crucial role of ESG performance in shaping investor perceptions. Nevertheless, it also highlights the need for further exploration into the intricate relationship between sustainable policies and corporate profitability. As businesses increasingly embrace sustainability, in fact, it could become paramount for informed decision-making and fostering ethical societal and environmental progress.

 Beach protection is vital to reduce the damage to shorelines and coastal areas; one of the artificial protections that can be utilized is the tetrapod. However, much damage occurred when using a traditional tetrapod due to the lack of stability coefficient (K_D). Therefore, this research aims to increase the stability coefficient by providing minor modifications to the cape of the tetrapod, such as round-caped or cube-caped. The modification seeks to hold the drag force from the wave and offer a good interlocking in between the tetrapod. This research applied physical model test research using a breakwater model made from the proposed innovative tetrapod with numerous variations in dimensions and layers simulated with several scenarios. The analysis was carried out by graphing the relationship between the parameters of the measurement results and the relationship between dimensionless parameters, such as wave steepness H/gT2, and other essential parameters, such as the K_D stability number and the level of damage in %. The result shows that the modified and innovative tetrapod has a more excellent K_D value than the conventional tetrapod. In addition, the innovative tetrapod with the cube-shaped has a recommended K_D value greater than the round shape. This means that for the modified tetrapod structure and the same level of security, the required weight of the tetrapod with the cube cap will be lighter than the tetrapod with the round cap. These findings have significant practical implications for coastal protection and engineering, potentially leading to more efficient and cost-effective solutions.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

The purpose of this research is to present a bibliometric analysis of the literature on the ways in which the motivations of individual sports consumers impact the creation of sports infrastructure and the creation of sports-related policy. Design/methodology/approach: Based on the PRISMA approach and information gleaned from the Scopus database, 2605 publications were found to be pertinent to the subject. We conducted a literature analysis of trends and patterns using VOSviewer-based knowledge mapping. Findings: Recent years have seen a proliferation of scholarly publications on the topic of individual sports consumption motivation and its influence on policy formulation and infrastructure development. This suggests that interest in this field is expanding. The list of eminent journals, decision-makers, and organizations involved in this issue demonstrates its global influence. The interdisciplinary nature of the subject is reflected in the study’s emphasis on the most widely published authors and key research terminology. Originality/value: This study closes significant knowledge gaps regarding the complex interactions between societal, environmental, and individual factors that affect the motivation to consume sports and how these motivations influence decisions about sports infrastructure and policies. It does this by using bibliometric techniques and the most recent data. The project aims to create a more thorough picture of how public health policy, sports governance, and urban planning are impacted by the motivations behind sports consumption. Policy implications: Policymakers, planners, and sports organizations can use the results to generate more targeted and effective strategies for the development of sports infrastructure and policy formulation. The study highlights how important it is to make well-informed policy decisions and participate in customized involvement in order to improve public welfare and the overall sports consumer experience.

In this paper, all the forests, woodlands and trees in the administrative area of Zhaoling Township in Chuzhou City of Huai'an City were collected and analyzed. The total area of the administrative area is 4852 hectares, the forest coverage rate is 22.07%, and the forest greening rate is 26.13%. This index has exceeded 20% of the forest coverage rate of the well - off society. Tree species is particularly serious. In the forest system (pure forest), the area of pure forest of poplar is accounted for 99.9% of the whole forest area. In the four tree systems, the number of poplar trees accounted for 80% of the total number of trees in the whole tree, and the total amount of poplar trees accounted for 98%. The poplar pure forest age group structure disorders, the unit area is low. The ratio of total area of poplar pure forest in Zhongling and young forests was 92.9%, and the ratio of total area of poplar pure forest and mature forest was 7.1%. The ratio of mature forest and the ratio of mature forest was 0.7%, and the proportion of each group was obviously abnormal.

This paper presents a brief review of risk studies in Geography since the beginning of the 20th century, from approaches focused on physical-natural components or social aspects, to perspectives that incorporate a systemic approach seeking to understand and explain risk issues at a spatial level. The systemic approach considers principles of interaction between multiple variables and a dynamic organization of processes, as part of a new formulation of the scientific vision of the world. From this perspective, the Complex Systems Theory (CST) is presented as the appropriate conceptual-analytical framework for risk studies in Geography. Finally, the analysis and geographic information integration capabilities of Geographic Information Systems (GIS) based on spatial analysis are explained, which position it as a fundamental conceptual and methodological tool in risk analysis from a systemic approach.