This paper investigates the implementation of ijarah muntahiyah bittamlik (IMBT) as an infrastructure project financing scheme within the Public-Private Partnership (PPP) models from a collaborative governance perspective. This paper follows a case study methodology. It focuses on two Indonesian non-toll road infrastructure projects, i.e., the preservation of the East Sumatra Highway projects, each in South Sumatra province and Riau province. The findings revealed that Indonesia’s infrastructure development priorities and its vision to become a global leader in Islamic finance characterized the system context that shaped the implementation of IMBT as an infrastructure project financing scheme within the PPP-AP model. Key drivers include leadership from the government, stakeholder interdependence, and financial incentives for the partnering business entity to adopt off-balance sheet solutions. Principled engagement, shared motivation, and the capacity for joint action characterized the collaboration dynamics, leading to detailed collaborative actions crucial for implementing IMBT as a financing scheme.

This research intends to find out the compliance acts based on the manufacturing industry of Bangladesh and lead to the development of the integrated theory of compliance model. There are several compliance regulations, that are separately dealt with in any manufacturing organization. These compliance regulations are handled at various ends of the organization making the process quite scattered, time-consuming, and tedious. To fix this problem, the integration of organizational compliance regulations is brought under one platform. Researchers have applied the qualitative approach with multiple case studies methodology scrutinizing the in-depth interviews and transcripts. Furthermore, the NVIVO tool has been used to analyze, where the necessary themes of the Organizational Compliance Regulations are found. Therefore, we have proposed a conceptual framework to inaugurate a standalone combined framework, which is an innovative and novel measure.

The Modern Cities Program is the largest-scale urban development effort in the history of the country, with which the Government of Hungary aims to promote the simultaneous development of municipalities at the same hierarchical level. Its projects focus on the preservation of intangible and tangible cultural heritage, the transformation of urban public spaces and green areas into community spaces, the creation of institutions for sports and recreational activities, research and development, digitalization, projects for innovative and creative professionals, and public educational and cultural institutions. The study aims to analyze the funding granted for developing the cultural and creative sector of cities with county rights through the Modern Cities Program in the period 2016–2025, by comparing the size of their population, their strategic importance in regional economic policy and the relationship between the value of the cultural heritage with the amount of funding received. The paper unveils the distribution of grants over time and space, the modalities and proportion of grants, and the way the cities that has received grants align with the national strategy. This will also reveal a shift in the regional importance of the cities and their relationship. Until February 2024, the Government of Hungary has contributed more than HUF 322.6 billion (809.5 million EUR) to the implementation of 98 cultural and creative projects in 22 cities with county rights through its urban development support program that has been established for the development and regeneration of cities with county rights and to counter the dominance of the capital.

The article examines the appearance of various unfortunate situations and tragic events in modern Kazakh novels that arise due to human and natural ecology problems. The research’s primary goal is to analyze human and natural ecology issues based on contemporary Kazakh novels. We have chosen A. Nurpeyisov’s novel “The Last Duty” as our research material, which focuses on issues of human and natural ecology, and we will discuss the large-scale issues concerning the fate of human, nature, and society as a collective. The research topic’s practical significancelies in examining Kazakh novels that address crucial issues like safeguarding the ecological environment and preserving the green earth, which directly impact the destiny and future of humanity. It also aims to highlight their role in advancing societal development, elevating human values, and safeguarding our spiritual heritage. The research method involves mentioning the names of Kazakh novels that specifically and indirectly focus on the topic of human and natural ecology and summarizing their common features. The article also employed research methods such as analysis, comparison, and discussion. The novelty of the research result: Here are some relevant points. First, in the article, the core topic of the problem of human and natural ecology, which is common to all humanity in modern Kazakh novels, was highlighted. Second, analyzing the three characters, Zhadiger, Pakizat and Azim, which reveal the actual idea of the novel “The Last Duty,” the writer’s stylistic features and skillful aspects were also mentioned during the analysis of the character image through deep psychological analysis, landscape description, clear image, and artistic language, and theoretical conclusions and analyses were presented.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.