The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.

Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers' capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.

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.

Urban public spaces are the interface of any city that could tell about the city’s dynamic and status quo. In addition, Urban public spaces play a pivotal role in shaping societies’ dynamics and can significantly affect conflict and peacebuilding initiatives. In a context marked by Conflict’s profound impact, this article aims to contribute to the knowledge base for informed urban interventions that foster positive interactions and reconciliation in post-conflict cities. The article seeks to explore the intricate relationship between urban spaces and their influence on war or to promote sustainable peacebuilding through investigating the various roles of the urban public spaces during the war and peacetimes via residents’ experiences of the diverse spaces’ functions that shaped the city’s status quo. In addition, considering the interplay of social dynamics, conflict history, and the mental spatial map of cities in public urban spaces can influence lasting peace or upcoming conflicts. This article focuses on Aleppo as a case study, understanding the positive and negative experiences from the residents’ perspective before and during the current war in Syria, and even distinguishes between two periods during the recent war, which are the active violence and after the end of the direct active violence, where it could inform the decision-makers and urban planners on the areas of focus while developing post-war urban public spaces to ensure its positive role in fostering peace and be able to deal with the social dynamic and the mental spatial map that developed along with the conflict history. The paper utilised a mixed-methods approach, encompassing a case study review of Aleppo City from an urban perspective and fieldwork involving focus group discussions and semi-structured interviews with Aleppian from different backgrounds and geographic areas that represent the social dynamic of the city, as well as approached Aleppian who are still in living in the city and those who flee out of it to ensure the coverage of different political direction in addition field work engaged with academia and technical from the city who shared their knowledge and experiences working in the city. Participants were prompted to reflect on their pre-war familiarity with public places and share their experiences. These experiences were categorized by enabling a comprehensive understanding of how conflict context influenced these spaces. The article results offer an understanding of the peace-guiding functions of the urban public spaces based on the city residents’ experiences that could inform architects and urban planners in designing spaces conducive to sustainable peacebuilding. The article’s findings underscore the importance of strategically designed urban public spaces in promoting peace and social cohesion.