This research aims to develop a Synergy Learning Model in the context of science learning.  This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.

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.

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.

This study assesses the implementation of socioformation in Public Higher Education Institutions (HEIs) in Mexico, exploring its impact on the quality of education in the knowledge society. With a sample of 150 educators, gender-balanced (44.7% female, 55.3% male), and an average age of 43.7 years, the research employed a validated socioformative rubric. Significant progress was observed in analytical and creative thinking, while areas related to living conditions and entrepreneurship education showed slower development. The findings highlight the advancements in socioformation but advocate for further research, including classroom observation and student evidence collection. Gender differences, communication, and leadership emerged as critical factors influencing socioformation implementation. Women demonstrated deeper comprehension of the educational model, willingness to adopt innovative strategies, and emphasis on socioformation axes. As educators gain experience, their adaptability to new pedagogical approaches increases. The study underscores the universal relevance of effective communication, leadership, and stakeholder involvement in successful educational model implementation. The research contributes valuable insights, emphasizing the importance of openness to new approaches and collaboration to prepare students for the challenges of the evolving knowledge society.