This paper highlights the growing importance of edge computing and the need for AI techniques to enable intelligent processing at the edge. Edge computing has emerged as a paradigm shift that brings data processing and storage closer to the source, minimizing the need for transmitting large volumes of data to remote locations. The integration of AI capabilities at the edge enables intelligent and real-time decisionmaking on resource-constrained devices. This paper discusses the significance of Edge AI across various domains, including automotive applications, smart homes, industrial IoT, and healthcare. By leveraging AI algorithms on edge devices, efficient implementation and deployment become possible, leading to improved latency, privacy, and security.The various AI techniques used in edge computing are presented, including machine learning, deep learning, reinforcement learning and transfer learning. As AI continues to play a pivotal role in driving edge computing, the integration of hardware accelerators and software platforms is gaining utmost significance to efficiently run inference models. A variety of popular options have emerged to accelerate AI at the edge, and notable among them are NVIDIA Jetson, Intel Movidius Myriad X, and Google Coral Edge TPU. The importance of specialized System-on-a-Chip (SoC) solutions for Edge AI, capable of supporting high-performance video, voice, and vision processing alongside integrated AI accelerators is presented as well. By examining the transformative potential of Edge AI, this paper aims to inspire researchers, practitioners, and industry professionals to explore the vast possibilities of integrating AI at the edge. With Edge AI reshaping the future of edge computing, intelligent decision-making becomes seamlessly integrated into our daily lives, driving advancements across various sectors.

The paper discusses figurative conceptualizations of nations, countries, and institutions as either a container, a person, a sinking ship, a fabric, or hell in media discourse on the European migrant crisis. Applying Steen et al.’s (2010) three-dimensional model of metaphor analysis, we analyze a specific set of metaphorical linguistic expressions, which are inextricably related in the segments of the real discourse on migration, to discuss their rhetorical power and communicative function. The aim of this paper is to describe and identify cases when these are used as perspective-changing devices to influence readers’ opinion on an important issue such as migration.

Although many women perform postural tasks while listening to music, no study has investigated whether preferred music has different effects than non-preferred music. Thus, this study aimed to explore the effects of listening to preferred versus non-preferred music on postural balance among middle-aged women. Twenty-four women aged between 50 and 55 years were recruited for this study. To assess their static balance, a stabilometric platform was used, recording the mean center of pressure velocity (CoPVm), whereas the timed up and go test (TUGT) was used to assess their dynamic balance. The results showed that listening to their preferred music significantly decreased their CoPVm values (in the firm-surface/eyes-open (EO) condition: (p < 0.05; 95% CI [−0.01, 2.17])). In contrast, when the women were listening to non-preferred music, their CoPVm values significantly (p < 0.05) increased compared to the no-music condition in all the postural conditions except for the firm-surface/EO condition. In conclusion, listening to music has unique effects on postural performance, and these effects depend on the genre of music. Listening to preferred music improved both static and dynamic balance in middle-aged women, whereas listening to non-preferred music negatively affected these performances, even in challenged postural conditions.

Aim To compare interleukin-2 levels (IL-2) and IL-2 gene site 1 methylation levels between preterm newborns (PN) and full-term newborns (FN) and investigate their association with the environmental exposure of their mothers during pregnancy. Methods IL-2 and IL-2 gene site 1 methylation levels were assessed in 50 PN and 56 FN. Newborns’ mothers filled in questionnaires about their living and occupational environments, habits, diets, and hobbies. Results The mothers of PN were significantly more frequently agrarian/rural residents than the mothers of FN. PN had significantly higher IL-2 levels, and significantly lower methylation of IL-2 gene site 1 levels than FN. Conclusion IL-2 levels, hypomethylation of the IL-2 gene site 1, and the mother’s rural residence (probably due to pesticide exposure) were predictive biomarkers for preterm birth. For the first time, we present the reference values for the methylation of IL-2 gene site 1 in PN and FN, which can be used in the clinical setting and biomonitoring.

. Screening for lymphedema and accurate quantitative assessment of dermal backflow patterns on ICG represents a major shift in current clinical practice paradigms, putting an emphasis on early detection of lymphedema rather than palliative treatments and symptomatic relief. These findings set the stage for the development of a practical, universal, ICG-based quantification system for the staging of lymphedema, a significant advancement in the field of plastic surgery.

Purpose Lean Management (LM) is a process improvement approach with growing interest from healthcare organizations. Obtaining a culture of continuous improvement is a primary objective of LM, and a culture of continuous improvement indicates a mature LM approach, and here leadership plays a central role. However, a comprehensive overview of leadership activities influencing LM maturity is lacking. This study aims to identify leadership activities associated with continuous improvement and, thus, LM, maturity. Methods Following the PRISMA guidelines, a scoping literature review of peer-reviewed articles was conducted in twenty healthcare management journals. The search provided 466 articles published up until 2023. During the selection process, 23 studies were included in the review. The leadership activities related to continuous improvement maturity were identified using the grounded theory approach and data coding. Results The analysis highlighted a total of 58 leadership activities distributed across nine themes of LM leadership. Next, analysing leadership activities concerning the different maturity levels revealed three maturity stages: beginner, intermediate, and expert. Based on the findings, we propose a framework that guides suitable leadership activities at the various stages of LM maturity. The framework provides leaders in healthcare with a practical overview of actions to facilitate the growth of the LM approach, and the related propositions offer academics a theoretical basis for future studies. Conclusion This review presents the first comprehensive overview of LM leadership activities in relation to continuous improvement and LM maturity. To enhance LM maturity, leaders are encouraged to consider their leadership style, (clinical) stakeholder involvement, alignment with the organizational strategy, and their role in promoting employee autonomy.

The material extrusion fused deposition modeling (FDM) technique has become a widely used technique that enables the production of complex parts for various applications. To overcome limitations of PLA material such as low impact toughness, commercially available materials such as UltiMaker Tough PLA were produced to improve the parent PLA material that can be widely applied in many engineering applications. In this study, 3D-printed parts (test specimens) considering six different printing parameters (i.e., layer height, wall thickness, infill density, build plate temperature, printing speed, and printing temperature) are experimentally investigated to understand their impact on the mechanical properties of Tough PLA material. Three different standardized tests of tensile, flexural, and compressive properties were conducted to determine the maximum force and Young’s modulus. These six properties were used as responses in a design of experiment, definitive screening design (DSD), to build six regression models. Analysis of variance (ANOVA) is performed to evaluate the effects of each of the six printing parameters on Tough PLA mechanical properties. It is shown that all regression models are statistically significant (p<0.05) with high values of adjusted and predicted R2. Conducted confirmation tests resulted in low relative errors between experimental and predicted data, indicating that the developed models are adequately accurate and reliable for the prediction of tensile, flexural, and compressive properties of Tough PLA material.

Attack graphs are a tool for analyzing security vulnerabilities that capture different and prospective attacks on a system. As a threat modeling tool, it shows possible paths that an attacker can exploit to achieve a particular goal. However, due to the large number of vulnerabilities that are published on a daily basis, they have the potential to rapidly expand in size. Consequently, this necessitates a significant amount of resources to generate attack graphs. In addition, generating composited attack models for complex systems such as self-adaptive or AI is very difficult due to their nature to continuously change. In this paper, we present a novel fragment-based attack graph generation approach that utilizes information from publicly available information security databases. Furthermore, we also propose a domain-specific language for attack modeling, which we employ in the proposed attack graph generation approach. Finally, we present a demonstrator example showcasing the attack generator's capability to replicate a verified attack chain, as previously confirmed by security experts.

Magnetic nanoparticles can be electrostatically assembled around sperm cells to form biohybrid micro robots. These biohybrid microrobots possess sufficient magnetic material to potentially allow for pulse-echo localization and wireless actuation. Alternatively, magnetic excitation of these nanoparticles can be used for localization based on Faraday's law of induction using a detection coil. Here, we investigate the influence of the electrostatic attraction between positively charged nanoparticles and negatively charged sperm cells on the activation of the nanoparticles during nonlinear differential magnetometry and wireless magnetic actuation. Activation of clusters of free nanoparticles and nanoparticles bound to the body of sperm cells is achieved by a combination of a high- frequency alternating field and a pulsating static field. The nonlinear response in both cases indicates that constraining the nanoparticles is likely to yield significant decreases in the magnetometry sensitivity. While the attachment of particles to the cells enables wireless actuation (rolling locomotion), the rate of change of the magnetization of the nanoparticles decreases one order of magnitude compared to free nanoparticles.