Ports are primarily used for maritime activities such as cruising, maneuvering, and hoteling, which facilitate imports and exports. These activities are crucial for economic growth and development. However, when port activities are scaled up, they become hazardous to the environment and public health due to significant emissions. This exacerbates existing climate change and air quality issues. Despite the economic benefits, it is essential to consider the environmental impacts and strive to mitigate or reduce them to prevent harmful effects. To address this problem, machine learning tools and techniques can be employed to predict emissions and identify the predictors that influence them. The primary goal of this study is to forecast hydrocarbon (HC) emissions resulting from activities like cruising, maneuvering, and hoteling using machine learning models. By implementing these models, we can uncover emission patterns and develop strategies to reduce their impact. Among the models tested, the Random Forest model demonstrated the best performance, followed by Decision Trees. In contrast, our implementation of multiple linear regression models indicated overfitting of the data, making them less suitable for our purposes.
Accurate traffic forecasting has become essential due to increased urban deliveries driven by growing e-commerce and urban expansion. This surge has increased traffic in large cities, resulting in delays and numerous accidents. The convenience of online shopping and home delivery continues to fuel the e-commerce sector. As this sector grows, the complexity of its challenges increases, necessitating quicker solutions. These challenges often extend beyond the control of delivery companies when influenced by external elements such as traffic congestion or adverse weather conditions, particularly in the last-mile delivery. This issue is intensified in regions lacking extensive traffic sensor networks, such as less developed countries. This research contributes by (1) establishing a contextual groundwork for existing traffic prediction frameworks, (2) employing socialmedia and diverse traffic-related data (including weather conditions, significant locations, and event schedules) through social network analysis to refine traffic prediction accuracy, and (3) presenting a method applicable to partially observable traffic situations. The methodology integrates advanced deep learning techniques like Long Short-Term Memory Network. In addition, a future extension aims to develop a robust real-time traffic prediction system, Traffic GPT, utilizing deep learning and transformer methodologies, optimized for diverse urban environments with real-time updates through transfer learning. Key to this approach is the use of sentiment analysis supported by training on datasets like the IMDB sentiment dataset with techniques like AWD-LSTM's attention mechanisms, and Graph Convolutional Networks, along with tools for analyzing social media sentiment.
The port sector is evolving, influencing areas such as operations, infrastructure, governance, technology, partnerships, and commercial strategies. AI can significantly improve the efficiency of intermodal freight transportation by enhancing real-time operational processes and decision-making models. This research evaluates the resilience of machine learning models to various data and architecture threats, focusing on adversarial attacks and data corruption. The study explores a range of attack methods, including white-box attacks, data poisoning, and model evasion, and tests defensive strategies like adversarial training, resilient optimization, and anomaly detection on a developed DNN model. The findings highlight the vulnerabilities of current models and emphasize the need for robust security measures. The paper concludes with recommendations for enhancing model robustness and suggests directions for future research to bolster AI systems against evolving threats, ensuring their reliability and security in unpredictable environments.
The impact of emerging operations and marketing management trends on retail goods returns is significant. While customers are experiencing increasingly lenient return and purchase procedures, retailers face more difficult and expensive processes. The main objective of this paper is to examine the effects of new trends and the COVID-19 pandemic on supply chain expenses, customer behavior, and the pricing and refund strategies of retailers in managing product returns. This study also considers the significant economic consequences of product returns. To achieve this goal, an inclusive analytical model is developed that considers the endowment effect, customer heterogeneity, return leniency, and other widely investigated factors in the literature. The analytical framework and a series of numerical simulations with a wide range of parameter values that reflect practical situations are used to demonstrate the effects of increased supply chain costs on customer purchase and return behavior and the best pricing and refund strategies for retailers. The sensitivity analysis shows that higher supply chain costs may reduce market demand while increasing the optimum price and level of reimbursement. However, the frequency of returns may fluctuate up or down. The results also highlight the significance of the endowment effect in addressing the issue which reduces market demand while raising the optimum price and degree of reimbursement; return frequency, however, may fluctuate up or down. The results further show the importance of the endowment effect in resolving the problem.
