Port management is critical to the maritime transportation industry. Effective port management aims, among other things, to reduce the amount of time required to operate the cargo volume of the vessels. Numerous uncertainty factors, such as weather and mechanical issues, frequently affect maritime transportation, which can hamper port operations planning. The lack of certainty surrounding a number of relevant parameters makes efficient preparation for quayside operations difficult. As a result, when making decisions, it is critical to account for the possibility of uncertainty. The ability to deal with unfavorable and unpredictable events is one of the characteristics of a great practical solution. In this paper, we investigate a scenario in which machine learning techniques are used to predict data for both the berth allocation and the quay crane assignment problems. We model the problem as a mixed integer linear program with multiple objectives that takes berth allocation and quay crane assignment into account. We propose a genetic algorithm scheme to solve the problem. Crossover and mutation procedures are provided. Finally, managerial implications and future research directions are given.

Blockchain technology provides a promising solution for collaborative economy systems by offering a decentralized, transparent, and secure platform. This is mainly accomplished through smart contracts, which are self-executing computer programs that facilitate, verify, and enforce the negotiation or performance of a contract. Digital tokens, on the other hand, are used to represent assets or currencies in these systems. Despite the benefits of Blockchain-based collaborative economy systems, significant security concerns are associated with them. These include the possibility of fraud, risk assessment, bugs in smart contracts, and cyber-attacks. For instance, attackers can exploit vulnerabilities in smart contracts to perform reentrancy and infinite loop attacks, leading to significant financial losses. To address these security challenges, this paper proposes integrating artificial intelligence models to prevent vulnerabilities in smart contracts and detect anomalies. Specifically, Graph Neural Networks models can be utilized to safeguard Blockchain-based collaborative economy platforms from attacks such as reentrancy and infinite loop attacks. According to the findings, this approach can accurately identify both normal and abnormal traffic and classify specific types of attacks. The framework's performance is further evaluated using various metrics to ensure its effectiveness in detecting anomalies, thereby providing an additional layer of security for Blockchain-based collaborative economy systems.

Indian agricultural value chains are marred with a lack of financial inclusion of smallholder farmers, lengthy payout times, poor quality of products, lack of traceability issues, and lack of industrialization in the value chain. Social entrepreneurs have indispensable tools as information and communication technologies are making inroads in rural developing economies. This study studies the impact of technological capabilities on social entrepreneurship for creating sustainable value creation using survey data from 557 respondents associated with farming and allied activities. The results highlight that digital technology capabilities mediate the relationship between social entrepreneurship and sustainable value creation. The findings present essential insights for academia, practitioners, and policymakers to better shape policies and decisions on social entrepreneurship.

Software defined networks (SDNs) represent new centralized network architecture that facilitates the deployment of services, applications and policies from the upper layers, relatively the management and control planes to the lower layers the data plane and the end user layer. SDNs give several advantages in terms of agility and flexibility, especially for mobile operators and for internet service providers. However, the implementation of these types of networks faces several technical challenges and security issues. In this paper we will focus on SDN's security issues and we will propose the implementation of a centralized security layer named AM-SecP. The proposed layer is linked vertically to all SDN layers which ease packets inspections and detecting intrusions. The purpose of this architecture is to stop and to detect malware infections, we do this by denying services and tunneling attacks without encumbering the networks by expensive operations and high calculation cost. The implementation of the proposed framework will be also made to demonstrate his feasibility and robustness.