In the context of a globalized economic environment, businesses are facing an increasing number of environmental challenges, prompting them not only to pursue economic benefits but also to focus on environmental protection and social responsibility. Green supply chain management (GSCM) and green innovation have become key strategies for enterprises aiming for sustainable development. This study explores the impact of green supply chain practices on green innovation performance, with a focus on how knowledge management and organizational integration serve as mediating variables in this relationship. Grounded in the resource-based view (RBV) and knowledge-based view (KBV) theories, this research employs surveys and in-depth interviews with companies across various industries, combined with the analysis of structural equation modeling, to reveal the complex relationship between GSCM practices, knowledge management capabilities, levels of organizational integration, and green innovation performance. The results show that GSCM practices significantly enhance corporate green innovation performance through effective knowledge management and organizational integration. These findings enrich the theories of GSCM and green innovation, providing practical guidance for enterprises on how to enhance green innovation performance through strengthening knowledge management and organizational integration. Finally, this study discusses its limitations and suggests possible directions for future research, such as exploring the differences in findings across different industry backgrounds and examining other potential mediating or moderating variables.

The Guangdong-Macao Intensive Cooperation Zone in Hengqin (Intensive Cooperation Zone) has emerged as a pivotal economic hub, attracting Macao residents and enterprises. However, disparities in contract-related rules between the zone and Macao have led to legal challenges. This article delves into a comparative study of contract laws between the People’s Republic of China (PRC) and Macao. Analyzing key facets such as pacta sunt servanda, freedom of contract, principle of equity, contract form, principles of interpretation, and termination of contract, the study identifies nuanced differences. Recognizing the imperative of aligning contract laws for the Intensive Cooperation Zone’s development, the article advocates for a unified legal environment. To achieve this, the author proposes a model contract law that prioritises the United Nations Convention on Contracts for the International Sale of Goods (CISG) as the basis. Notably, Macao’s contract-related rules should govern aspects not covered by the CISG given the policy trend in the Intensive Cooperation Zone. The proposed model law serves as a foundation for legislative reform, aiming to address the existing disparities and promote the Intensive Cooperation Zone’s economic growth.

The recent development of characteristic towns has encountered a multitude of challenges and chaos. Nevertheless, there have been many instances of information asymmetry due to the absence of an effective management model and an intuitive digital management system. Consequently, this has caused the erosion of public interests and inadequate supervision by public agencies. As society is progressing at a rapid pace, there is a growing apprehension regarding poor management synergy, outdated management practices, and limited use of technology in traditional construction projects. In today's technologically sophisticated society characterized by the “Internet+” and intelligent management, there is an urgent requirement to identify a more efficient collaborative management model, thereby reducing errors caused by information asymmetry. This paper focuses on the integration of building information modeling (BIM) and integrated project delivery (IPD) for collaborative management within characteristic towns in the PPP mode. By analyzing the available literature on the application status, this study investigates the implementation methods and framework construction of collaborative management while exploring the advantages and disadvantages. On this basis, this study highlights the problems that arise and provides recommendations for improvement. Considering this, the application of the BIM-based IPD model to characteristic towns in PPP mode will enhance the effectiveness of collaborative management among all parties involved, thereby fostering an environment that facilitates decision-making and operational management in the promotion of characteristic industries.

In this study, we consider the extended Brinkman's-Darcy model for a triple diffusive convection system which consists of some parameters such as Taylor number (Ta), Solutal Rayleigh numbers (RC1 , RC2 ), and Prandtl number (Pr). To investigate the range of these parameters, a dynamical system of the Ginzburg-Landau equation is developed. The parametric analysis and comparative study of the model for the three Rayleigh numbers which leads to the clear fluid layer, sparsely packed porous layer, and densely packed porous layer is done with the help of bifurcation maps and the Lyapunov exponents. It is found that for a certain range of parameters, the system exhibits a chaotic behaviour.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.