Abstract

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
The current era where living demands an accelerated digital transition mainly focused on encouraging a smarter, healthier, and more sustainable mobility, in all its dimensions – a must concern for the young generations. The convergence through several digital services and APP can be an attitudes and perception changer within the group of academic mobility users’, promoting a more sustainable and better mobility choices that impact on the academic user’s mobility routines. Thus, encouraging a global shift to shared and active mobility services and systems bringing significant contributions to environmental sustainability and, also, to users’ health. The Academic Mobility as a Service (AMaaS) provide a digital service with mobility alternatives to support the academic population geographically located in different faculty campuses and Higher Education Institutions (HEI). The AMaaS applied to a restrict group is helpful to test innovative transport solutions and its high cybersecurity vulnerabilities. Despite the shortage of AMaaS case studies and the lack of security reference, it is imperative that a cybersecurity by design is planned and included in AMaaS design. In this paper AMaaS critical cybersecurity challenges, and potential risks are discussed and AMaaS Security by Design framework is described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract

Abstract
Amidst an urgent need for sustainability, novel approaches are required to address environmental challenges. In this context, biomimicry offers a promising logic for catalyzing nature’s wisdom to address this complexity. The purpose of this research is to (1) establish a biomimetic understanding and vocabulary for sustainability and (2) apply biomimicry to upframe service ecosystems as a foundation for sustainability. Our research question is: How can the principles of natural ecosystems inform and enhance the sustainability of service ecosystems? The findings highlight upframed service ecosystems as embodying a set of practices that (1) promote mutualistic interactions, (2) build on local biotic and abiotic components supporting emergence processes, (3) leverage (bio)diversity to build resilience, (4) foster resource sharing for regeneration, and (5) bridge individual roles to optimize the community rather than individual well-being. Our upframed definition of a service ecosystem is a system of resource-integrating biotic actors and abiotic resources functioning according to ecocentric principles for mutualistic and regenerative value creation. The discussion emphasizes the implications of this upframed definition for sustainability practices, advocating for a shift in understanding and interacting with service ecosystems. It emphasizes the potential for immediate mutualistic benefits and long-term regenerative impacts. © Academy of Marketing Science 2025.

Abstract
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random. Currently, the literature offers various algorithms for imputing missing data, each with advantages tailored to specific mechanisms and levels of missingness. This paper introduces a novel approach to missing data imputation using the well-established label propagation algorithm, named Label Propagation for Missing Data Imputation (LPMD). The method combines, weighs, and propagates known feature values to impute missing data. Experiments on benchmark datasets highlight its effectiveness across various missing data scenarios, demonstrating more stable results compared to baseline methods under different missingness mechanisms and levels. The algorithms were evaluated based on processing time, imputation quality (measured by mean absolute error), and impact on classification performance. A variant of the algorithm (LPMD2) generally achieved the fastest processing time compared to other five imputation algorithms from the literature, with speed-ups ranging from 0.7 to 23 times. The results of LPMD were also stable regarding the mean absolute error of the imputed values compared to their original counterparts, for different missing data mechanisms and rates of missing values. In real applications, missingness can behave according to different and unknown mechanisms, so an imputation algorithm that behaves stably for different mechanisms is advantageous. The results regarding ML models produced using the imputed datasets were also comparable to the baselines. © 2013 IEEE.