Abstract
This paper examines the role of digital twins (DTs) in promoting sustainability within seaport operations and logistics. DTs have emerged as promising tools for enhancing seaport performance. Despite the recognized potential of DTs in seaports, there is a paucity of research on their practical implementation and impact on seaport sustainability. Through a systematic literature review, this study seeks to elucidate how DTs contribute to the sustainability of seaports and to identify future research and practical applications. We reviewed and categorized 68 conceptual and practical digital applications into ten core areas that effectively support economic, social, and environmental objectives in seaports. Furthermore, this paper proposes five preliminary potential applications for DTs where practical implementations are currently lacking. The primary findings indicate that DTs can enhance seaport sustainability by facilitating real-time monitoring and decision-making, improving safety and security, optimizing resource utilization, enhancing collaboration and communication, and supporting the development of the seaport ecosystem. Additionally, this study addresses the challenges associated with DT implementation, including high costs, conflicting stakeholder priorities, data quality and availability, and model validation. The paper concludes with a discussion of the implications for seaport managers and policymakers.

Abstract
E-commerce popularity has increased the challenges of parcel deliveries, highlighting the urgency in addressing the sustainability of the last-mile, which is considered the most ineffective part of the supply chain and a source of environmental and social issues. Although different alternative last-mile technologies have already been proposed, they all imply complex trade-offs. This study explores how synergies between delivery technologies could improve last-mile logistics. It evaluates the trade-offs between electric vans, cargo bikes, and parcel lockers, and takes into account factors such as package size, consumer requirement, and urban context. The adoption of a combined delivery strategy suggests environmental and social benefits like the mitigation of congestion and emissions while ensuring advantages for companies due to streamlined operations and offering consumers a broader range of choices.

Abstract
The advent of new technologies has led to significant changes in the field of marketing, demanding a rethinking of existing knowledge and skills. This research proposes a set of transversal curricula in digital marketing. The methodology employed included an exploratory analysis of digital marketing courses offered at universities and major online platforms, focus groups, and interviews, conducted in four countries. The countries included in the study were Finland, Poland, the Netherlands, and Portugal. The findings indicated that an introductory course and specialization blocks would be beneficial. Social media, analytics, digital advertising, search engine optimization (SEO), digital marketing strategies, web content, e-mail marketing, customer experience, landing pages, user experience, leads, conversion rate optimization, and E-commerce were identified as the key subjects of study for the introductory course in digital marketing.

Abstract
Technological evolution is very present in today’s world. The Internet of Everything (IoE) is one of the next steps in this evolution. WordPress is a website-building tool that is used today to its minimum capability. The main problem is how to make WordPress customizable and reliable to support device networking and communication. This can be done by implementing a new architecture that supports WordPress as a powerful tool, allowing for scalability and maintenance. The adoption of a recent trend known as DevOps is an important step in creating and developing a strong website and ensuring its integration with multiple devices. It emphasizes continuous testing, delivery, and integration. This article is based on a literature review to justify using micro-services architecture and the DevOps approach to build a reliable and robust working tool in WordPress that can be part of the IoE. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

Abstract
Leveraging the capabilities of Knowledge Distillation (KD) strategies, we devise a strategy to fight the recent retraction of face recognition datasets. Given a pretrained Teacher model trained on a real dataset, we show that carefully utilising synthetic datasets, or a mix between real and synthetic datasets to distil knowledge from this teacher to smaller students can yield surprising results. In this sense, we trained 33 different models with and without KD, on different datasets, with different architectures and losses. And our findings are consistent, using KD leads to performance gains across all ethnicities and decreased bias. In addition, it helps to mitigate the performance gap between real and synthetic datasets. This approach addresses the limitations of synthetic data training, improving both the accuracy and fairness of face recognition models.

Abstract
The advent of 5th Generation (5G) networks has introduced the strategy of network slicing as a paradigm shift, enabling the provision of services with distinct Quality of Service (QoS) requirements. The 5th Generation New Radio (5G NR) standard complies with the use cases Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), which demand a dynamic adaptation of network slicing to meet the diverse traffic needs. This dynamic adaptation presents both a critical challenge and a significant opportunity to improve 5G network efficiency. This paper proposes a Deep Reinforcement Learning (DRL) agent that performs dynamic resource allocation in 5G wireless network slicing according to traffic requirements of the 5G use cases within two scenarios: eMBB with URLLC and eMBB with mMTC. The DRL agent evaluates the performance of different decoding schemes such as Orthogonal Multiple Access (OMA), Non-Orthogonal Multiple Access (NOMA), and Rate Splitting Multiple Access (RSMA) and applies the best decoding scheme in these scenarios under different network conditions. The DRL agent has been tested to maximize the sum rate in scenario eMBB with URLLC and to maximize the number of successfully decoded devices in scenario eMBB with mMTC, both with different combinations of number of devices, power gains and number of allocated frequencies. The results show that the DRL agent dynamically chooses the best decoding scheme and presents an efficiency in maximizing the sum rate and the decoded devices between 84% and 100% for both scenarios evaluated.