Large cetaceans face several anthropogenic threats. Among these, collisions are a major cause of anthropogenic mortality. Assessing and limiting their impact on populations is essential, as these species play an essential ecological role. All types of vessels, including offshore racing vessels, can collide with cetaceans. When a collision occurs between an offshore racing vessel and a large cetacean, the consequences are severe for both the whale, which is often injured or even killed and the vessel, which can suffer severe damage and be forced to withdraw from the race. Our study aimed to develop an encounter model that takes the characteristics of both cetaceans and racing vessels into account to estimate the number of encounters along vessel routes. The model was applied to three different routes commonly used in offshore racing: the first between Newport, USA and Skagen, Denmark; the second between Dover, England and the Gibraltar Strait; and the third between the Gibraltar Strait and Genoa, Italy. The number of encounters was estimated to be 1.7 for Route 1, 4.1 for Route 2 and 2.6 for Route 3. The model was also used to estimate the impact of routing vessels away from any exclusion zones that may be established in areas of high cetacean abundance. This routing could significantly reduce the number of encounters and offer potential solutions to reduce collisions between cetaceans and all types of vessels. The issue of collisions is becoming increasingly important and requires the development of methods to reduce the number of collisions worldwide.

[Context and Motivation] Global energy consumption has been steadily increasing in recent years, with data centers emerging as major contributors. This growth is largely driven by the widespread migration of applications to the Cloud, alongside a rising number of users consuming digital content. Dynamic adaptation (or self-adaptive) approaches appear as a way to reduce, at runtime and under certain constraints, the energy consumption of software applications. [Question/Problem] Despite efforts to make energy-efficiency a primary goal in the dynamic adaptation of software applications, there is still a gap in understanding how to equip these self-adaptive software systems (SAS), which are dynamically adapted at runtime, with effective energy consumption monitoring tools that enable energy-awareness. Furthermore, the extent to which such an energy consumption monitoring tool impacts the overall energy consumption of the SAS ecosystem has not yet been thoroughly explored. [Methodology] To address this gap, we introduce the EnCoMSAS (Energy Consumption Monitoring for Self-Adaptive Systems) tool that allows to gather the energy consumed by distributed software applications deployed, for instance, in the Cloud. EnCoMSAS enables the evaluation of energy consumption of SAS variants at runtime. It allows to integrate energy-efficiency as a main goal in the analysis and execution of new adaptation plans for the SAS. In order to evaluate the effectiveness of EnCoMSAS and investigate its impact on the overall energy consumption of the SAS ecosystem, we conduct an empirical study by using the Adaptable TeaStore case study. Adaptable TeaStore is a self-adaptive extension of the TeaStore application, a microservice benchmarking application. For this study, we focus on the recommender service of Adaptable TeaStore. Regarding the experiments, we first equip Adaptable TeaStore with EnCoMSAS. Next, we execute Adaptable TeaStore by varying workload conditions that simulate users interactions. Finally, we use EnCoMSAS for gathering and assessing the energy consumption of the recommender algorithms of Adaptable TeaStore. To run these experiments, we use nodes of the Grid5000 testbed. [Results] The results show that EnCoMSAS is effective in collecting energy consumption of software applications for enabling dynamic adaptation at runtime. The observed correlation between CPU usage and energy consumption collected by EnCoMSAS provides evidence supporting the validity of the collected energy measurements. Moreover, we point out, through EnCoMSAS, that energy consumption is influenced not only by the algorithmic complexity but also by the characteristics of the deployment environment. Finally, the results show that the impact of EnCoMSAS on the overall energy consumption of the SAS ecosystem is comparatively modest with respect to the entire set of the TeaStore applications microservices.

BackgroundDigital communication device use is changing rapidly among young people, and current research on this topic is limited or outdated.ObjectiveWe aimed to describe the use of digital communication devices by young people from 4 European countries and investigate their socioeconomic and demographic characteristics.MethodsIn 2023, we administered an online survey to a convenience sample of 4000 young people aged 16 to 25 years in Italy, Poland, Spain, and Switzerland. Participants reported on their regular use of smartphones, tablets, laptops, cordless phones, and smartwatches or activity trackers. Participants answered which activities they regularly engaged in on their devices, the time spent on these devices and activities, and in what position the device was used with respect to their body over the previous 3 months. We also collected information on participant socioeconomic and demographic characteristics, including age, gender, country of birth, employment status, parental educational level, and urbanicity of the place of residence.ResultsReported prevalence of device use was 90.9% (3635/4000) for smartphones, 33.2% (1329/4000) for tablets, 68.7% (2748/4000) for laptops, 11.6% (462/4000) for cordless phones, and 23.3% (931/4000) for smartwatches or activity trackers. Older age groups and women reported higher use across most devices. The activities reported with the highest engagement for smartphones were voice calls (2553/3635, 70.2%); social media (2693/3635, 74.1%); and texting, emailing, and internet use (2530/3635, 69.6%). For tablets and laptops, they were video streaming (849/1329, 63.9% and 1527/2748, 55.6%, respectively); texting, emailing, and internet use (673/1329, 50.6% and 1218/2748, 44.3%, respectively); and social media (659/1329, 49.6% and 1521/2748, 55.3%, respectively). On average, participants used their smartphones 60.9 (SD 83.1) minutes per day for texting, emailing, and internet use; 85.2 (SD 92.7) minutes per day for social media; 46.9 (SD 70.5) minutes per day for video streaming; and 53.7 (SD 80.3) minutes per day for music streaming. Differences across activities and devices were found among socioeconomic and demographic characteristics. For example, the oldest age groups reported lower duration of smartphone use for voice calls, social media, video streaming, and music streaming compared to the youngest age group but reported higher duration of smartphone use for video calls and texting, emailing, and internet use. Moreover, women reported higher duration of use for most activities on smartphones compared to men, except for online gaming, for which men reported higher duration of use.ConclusionsOur findings provide novel information on digital communication device use by young people. We identified differences between socioeconomic and demographic characteristics that warrant further investigation. These results can be used as a point of reference for digital communication devices in public health research, including health communication strategies and epidemiological research.

Digital twin (DT) technology integrates Internet of Things (IoT), communication networks, and sensor systems through high-fidelity modeling and multi-dimensional simulation, enabling dynamic mapping and real-time optimization of physical objects. However, DT development still faces several challenges, including cross-platform interoperability limitations, excessive latency in real-time scenarios, security vulnerabilities in distributed deployments, and the complexity of accurately modeling multi-modal systems. Blockchain (BC) enhances the security and functional scope of DTs across diverse applications. This survey begins by introducing the core principles of BC and DT, and then investigates the rationale and benefits behind their integration. From a data-centric perspective, we explore how Blockchain-empowered Digital Twins (BCDTs) enhance data storage, secure exchange, privacy protection, and system interoperability. The survey further explores the architecture of BCDT systems, covering network topology, functional modules, platform design, and representative prototypes, offering insights into real-world applications. In addition, we survey how BCDT supports the convergence of key Industry 4.0 technologies, including the Internet of Things, vehicle networks, unmanned aerial systems, artificial intelligence, federated learning, 5G mobile networks, and software-defined networking. Industrial-grade quality BCDT-supported applications are highlighted, providing a solid foundation for further research. Finally, we analyze the challenges faced by BCDT and offer some optimistic suggestions for further research in the field of BCDT.

LPWANs are networks characterised by the scarcity of their radio resources and their limited payload size. LoRaWAN offers an open, easy-to-deploy and efficient solution to operate a long-range network. To efficiently communicate using IPv6, the LPWAN working group from the IETF developed a solution called Static Context Header Compression (SCHC). It uses context rules, which are linked to a given End Device, to compress the IPv6 and UDP header. Since there may be a huge variety of End Devices profile, it makes sense to store the rules remotely and use a system to retrieve the profiles dynamically. In this paper we propose a mechanism based on DNS to find the context rules associated with an End Device, allowing it to be downloaded from an HTTP Server. We evaluate the corresponding delay added to the communications using experimental measurements from a real testbed.

A fully automated CAD-to-post toolset for Navier–Stokes and Reynolds Averaged Navier–Stokes (RANS) simulations of high speed vehicles flow is presented. The toolset combines the vertex-centered SoNICS solver with anisotropic mesh adaptation based on the REFINE toolbox, eliminating manual meshing and enabling efficient, parallelized simulations from CAD input to converged solutions. The tool is validated on open cases representative of the complexity of the flow encountered around high-speed vehicles including reentry and airbreathing cruise vehicles. This includes complex three-dimensional forebody laminar simulations and axisymmetric triconic cases. For the internal aerodynamics part, given the lack of proper validation references, a direct numerical simulation of the internal conduit of a generic dual mode ramjet air intake is conducted and compared to legacy RANS simulation and mesh-adapted RANS simulation. A demonstration on a full scramjet-powered cruise vehicle then highlights the toolset’s scalability and adaptability to industrial applications. The automated workflow reduces setup time and human error, making it a valuable asset for hypersonic vehicle design and optimization.