GTD System & Software Engineering (Spain)

Even if hypothermia is described as one of the main causes of cardiac arrest, in medical literature, it is not often mentioned as a main cause of shock. Nevertheless, it is known to contribute to shock development, maintenance, and worsening of its prognosis by lowering cardiac performance and blood pressure; this is particularly relevant in sepsis. Diagnosing hypothermia can be challenging, especially in patients not suffering an obvious environmental exposure. Digital medical thermometers cannot accurately estimate hypothermia, so when suspected, the placement of an esophageal thermometer is indicated. Below 32°C, the electrocardiogram (ECG) often shows a characteristic elevation of the J point called the Osborne wave (OW). We present a case of a patient with mixed shock in which this electrocardiographic disturbance allowed for an early suspicion of hypothermia, enabling prompt initiation of adequate treatment.

Freiberg's disease is a rare disease of the bone affecting the head of the second metacarpal in young females in the second decade resulting from osteonecrosis due to a multifactorial aetiology [1]. In this study, we present a case of Freiberg's disease affecting the head of the third metacarpal bone in a 43-year-old female with characteristic X ray findings, to increase its awareness as early diagnosis and treatment can limit disability.

R. Gomes, D. Vieira, Y. Ghamri-Doudane and M. F. de Castro, "Network Slicing for Massive Machine Type Communication in IoT-5G Scenario," 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, 2021, pp. 1-7, doi: 10.1109/VTC2021-Spring51267.2021.9448904.

Edge Computing emerges as a solution that overcomes some obstacles of traditional central data centers to support the performance-critical Internet of Things applications. However, a challenge therein is the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. In this paper, we investigate how to place replicas of applications, and distribute requests among these replicas to optimize multiple objectives. We propose a genetic algorithm based on Pareto fronts as a problem-solving meta-heuristic to prioritize latency-sensitive applications and optimize conflicted objectives. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other important and sometimes conflicting objectives, such as cost and availability.

Machine-to-machine communication (M2M) is a core element for the Internet of Things (IoT). The Long-Term Evolution-Advanced (LTE-A) is expected to become a potential access network for M2M devices. However, M2M communication poses some challenges to LTE-A, with highlight to the congestion and overload problems in the Radio Access Network (RAN) during the Random Access Channel (RACH) procedure. In this paper, we propose two solutions to mitigate the impact of M2M communication in the context of LTE-A network. More specifically, we model how the random access resources should be allocated to the different types of devices as a bankruptcy problem. In addition, we propose solutions for the bankruptcy problem using a game theory and an axiomatic strategy. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over H2H accesses and define priority among the different classes of devices.