Abstract
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art (SOTA) feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while inference speed improves between 4x and 12x compared to other deep learning approaches on our benchmark datasets. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.

Abstract
Autonomous vehicles and robotic manipulators are two examples of mechanically distinct systems. Whether these areas are indoors or outside, the environment in which such vehicles will be employed will play a crucial role in how their locomotion systems develop. The speed and stability of wheeled traditional mobility on ordinary flooring are superior. Leg traction is an efficient method for navigating uneven floors, but it takes more time and uses more energy. The foundation of the hybrid configuration is the creation of a leg that enables the interchange and fusion of the two previously described locomotion methods. One advantage of the hybrid arrangement is that the robot may now be deployed in a wider variety of environments. The goal of this paper is to showcase the creation of a leg for a hybrid locomotive robot. The leg can be printed and constructed at a reasonably low-cost thanks to the design of the numerous 3D modules, which will be made accessible later. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
This paper analyses the process of generalisation of an innovative government-led public practice in the healthcare sector. The scaling and embedding involved in this generalisation process are assumed to be dependent on the multiple implementation processes (consecutive or simultaneous) that lead to a routine use of the innovation in different adopters. This paper, therefore, proposes the use of a configurational theory approach to conceptualise each implementation of the innovation during the generalisation process and shed light on the generalisation's scaling and embedding efforts. It suggests a set of recommendations and practices for generalisation managers, most notably: i) they should regard generalisations as organic processes where their main role is to create space for experimentation, learning and negotiation, and ii) they should adopt different modes of governance to identify adequate mechanisms and strategies and guide their actions. This configurational perspective allows them to monitor and manage the evolution of implementations, informs the valuable learning processes that take place in a generalisation and has been found to be a useful tool to support the crucial collaboration among the actors involved in a generalisation.

Abstract
Multi-Access Edge Computing (MEC) represents the central concept that enables the creation of new applications and services that bring the benefits of edge computing to networks and users. By implementing applications and services at the edge, close to users and their devices, it becomes possible to take advantage of extremely low latency, substantial bandwidth, and optimized resource usage. However, enabling this approach requires careful integration between the MEC framework and the open 5G core. This work is dedicated to designing a new service that extends the functionality of the Multi-Access Traffic Steering (MTS) API, acting as a strategic bridge between the realms of MEC and the 5G core. To accomplish this objective, we utilize free5GC (open-source project for 5G core) as our 5G core, deployed on the Kubernetes cluster. The proposed service is validated using this framework, involving scenarios of high user density. To conclude whether the discussed solution is valid, KPIs of 5G MEC applications described in the scientific community were sought to use as a comparison parameter. The results indicate that the service effectively addresses the described issues while demonstrating its feasibility in various use cases such as e-Health, Paramedic Support, Smart Home, and Smart Farms.

Abstract
The progressive replacement of thermal power plants by converter-interfaced generation, such as wind and solar power plants, reduces the synchronous component available in the system. Additionally, as converter-interfaced renewable energy sources do not directly provide inertia to the power grid, electric power systems are facing a notorious inertia reduction. When facing disturbances affecting the balance between the generation and demand, reduced inertia systems exhibit higher and faster frequency deviations and dynamics. This can result in the disconnection of generation units as well as load shedding, provoking cascading effects that can compel severe power outages. This work examines the impacts of the progressive integration of converter-interfaced renewable energy sources in the frequency stability, considering critical disturbances involving short-circuits in different locations. To simulate the dynamic behaviour of a network containing high shares of renewable energy generation, the IEEE 39-bus system is used while resorting to the PSS/E simulation package. After obtaining a scenario with reduced synchronous generation, the network's stability is assessed in face of key frequency indicators (frequency nadir and Rate of Change of Frequency, RoCoF). Regarding the critical disturbances applied in a low inertia scenario, different control solutions for the mitigation of frequency stability problems are tested and their performance is assessed comparatively. This involves the investigation of the performance of the active power-frequency control in the renewable energy sources, of synchronous condensers, or fast active power-frequency regulation services from stationary energy storage. Moreover, the influence of the location and apparent power of synchronous condensers (SCs) and Battery Energy Storage Systems (BESS) on the frequency indicators is evaluated.

Abstract
In the last few decades, the area of robotics has evolved immensely, creating new and improved robot mobility solutions for industrial, scientific, medical, and several other purposes. Among these solutions is the car-like robot, using wheels to move. However, there are many different options within this solution, different types of wheels and configurations on the robot that each offer key advantages for a variety of objectives. Choosing a wheel configuration for robot vehicles is extremely important for the robot’s mobility, and its purpose must be considered while studying all the options. Starting on an existing prototype with a differential configuration, other configurations were implemented to study their differences, their strong and weak points, and the trajectories they allow the robot to make. This analysis will make the choice of configuration for each scenario clearer. This paper presents three types of robot configurations and compares them according to requirements using real prototype robots that are shared with the community for many purposes, such as education, among others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.