Seeing that the prosperity of people and society is possible with the aid of sustained and inclusive economic growth of all countries and regions, the sustainable development of our world has gained the particular attention of a wide range of decisional factors; civil society, the business sector, and the scientific community. Education has a decisive impact on changes in the way that societies are coping with national, regional, and global challenges and opportunities brought by sustainable development. The paper addressed the lack of capacity of higher education institutions (HEIs) to integrate the principles and practices of sustainable development into all aspects of education and learning, which hampers the capability to act as an entrepreneurial university. Embarking on the path of sustainable development goals (SDGs) requires HEI to design, launch, implement, and customize specific processes architecture to govern the advance of the sustainability approach. The authors applied the process scoping diagram to capture and conceptualize the educational model needed to guide the HEI through the process of change in its daily operations. The SIPOC method (Supplier, Input, Process, Output, Customer) was applied and with the aid of Visio software tool, the processes relationships were articulated and embedded in the educational model of HEI. Finally, the authors shared their views on the scalability of the model, which may be customized and harmonized in accordance with different HEI’s circumstances and priorities.

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. The integration of AI-based technologies for fault diagnosis and decision-making, coupled with proper personnel training, has become essential for optimal ship operation, with AI-assisted predictive maintenance emerging as the key strategy to ensure onboard systems function within desired parameters and enhance equipment availability. This study proposes a machine-learning algorithm for evaluating onboard systems performance by analyzing sensor data and mapping it to fault patterns to estimate functional states, validated through tests on a seawater cooling system from an oil tanker, demonstrating operational efficiency and reliability. The immediate advantage is reduced time for fault diagnosis by rapidly assessing the system using operational data, the algorithm enabling real-time monitoring and fault data management to minimize efforts as part of predictive maintenance assisted by machine learning tools.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

The sustainable development of our world has gain particular attention of a wide range of decisional factors, civil society, business sector, and scientific community, seeing that the prosperity of people and society is possible with the aid of sustained and inclusive economic growth of all countries and regions. Educational environment has a decisive impact on changes in the way that societies are coping with national, regional, and global challenges and opportunities brought by sustainable development. Looking at the implications of HE on the progress of society, the paper addressed the lack of HE institutional capacity to integrate the principles and practices of sustainable development into all aspects of education and learning. The scope of research problem was bounded on the capability of HEI as organization and school to act as entrepreneurial university by combining the scope of its responsibility within the value chain through a practical and effective mechanism needed to align the strategy with sustainable development goals (SDGs). Embarking on the path of SDGs requires HEI to design, launch, implement, and customize specific processes architectures to govern the advance of sustainability approach. The authors applied the process scoping diagram to capture and conceptualize the educational model needed to guide the HEI through the process of change to embrace sustainability into organizational culture and daily operations. It has been used the SIPOC method (Supplier, Input, Process, Output, Customer) with Visio software tool to articulate processes relationships embedded in the educational model of HEI. The benefits relied on the organized view of the work processes needed to be performed to incorporate SDGs into the strategy of any entrepreneurial HEI. Finally, the authors shared their views on the scalability of the model which may be customized and harmonized in accordance with different HE circumstances and priorities. Implementing the proposed educational model requires long-term institutional commitment, transparency, continuous performance improvement, and communicating the strategy for SDGs and its achievements to wider stakeholders.