Abstract

Abstract
The development of technology brought an exponential evolution of IoT networks, and with a number of different devices connected to the Internet, meaning, more IP addresses, traffic and devices susceptible to attacks. These types of networks are characterized by their interoperability and low resources, making it a complex ecosystem and making it difficult to implement management and monitoring measures, leading to a lack of security. Considering the impact that IoT networks have on the market, their protection is essential and, is now one of the themes in constant evolution. With the focus on security, this work aims to characterize a CoAP and CoAPS signatures, through the analysis of a flow in a test scenario and recognize the types of messages of the signature and what is their impact on the network and later creating rules that could help an IDS, in order to save resources and time. When analyzing the flows, it was concluded that it is possible to identify some characteristics of a signature, such as identifying Confirmable and Non-Confirmable messages.

Abstract
Several millions of execution flows will be executed in ultrascale computing systems (UCS), and the task for the programmer to understand their coherency and for the runtime to coordinate them is unfathomable. Moreover, related to UCS large scale and their impact on reliability, the current static point of view is not more sufficient. A runtime cannot consider to restart an application because of the failure of a single node as statically several nodes will fail every day. Classical management of these failures by the programmers using checkpoint restart is also too limited due to the overhead at such a scale. The article explores programming models and runtimes required to facilitate the task of scaling and extracting performance on continuously evolving platforms, while providing resilience and fault-tolerant mechanisms to tackle the increasing probability of failures throughout the whole software stack. © The Institution of Engineering and Technology 2019.

Abstract
Background Ulcerative colitis [UC] is a chronic inflammatory disease often accompanied by severe and distressing symptoms that, in some patients, might require a surgical intervention [colectomy]. This study aimed at determining the risk of experiencing progressive disease or requiring colectomy. Material and Methods This was a multicentre study: patients' data [n = 1481] were retrieved from the Portuguese database of inflammatory bowel disease patients. Bayesian networks and logistic regression were used to build risk matrices concerning the outcomes of interest. Results The derivation cohort included a total of 1210 patients, of whom 6% required a colectomy and 37% had progressive disease [over a median follow-up period of 12 syears]. The risk matrices show that previously hospitalised patients with extensive disease, who are not on immunomodulators and who are refractory to corticosteroid treatment, are the ones at the highest risk of undergoing a colectomy [88%]; whereas male patients, with extensive disease and less than 40 years old at diagnosis, are the ones at the highest risk of experiencing progressive disease [72%]. These results were internally and externally validated, and the AUC [area under the curve] of the ROC [receiver operating characteristic] analysis for the derivation cohort yielded a high discriminative power [92% for colectomy and 72% for progressive disease]. Conclusions This study allowed the construction of risk matrices that can be used to accurately predict a UC patient's likelihood of requiring a colectomy or of facing progressive disease, and can be used to individualise therapeutic strategies.

Abstract
The fusion of pre-operative 3D magnetic resonance (MR) images with real-time 3D ultrasound (US) images can be the most beneficial way to guide minimally invasive cardiovascular interventions without radiation. Previously, we addressed this topic through a strategy to segment the left ventricle (LV) on interventional 3D US data using a personalized shape prior obtained from a pre-operative MR scan. Nevertheless, this approach was semi-automatic, requiring a manual alignment between US and MR image coordinate systems. In this paper, we present a novel solution to automate the abovementioned pipeline. In this sense, a method to automatically detect the right ventricular (RV) insertion point on the US data was developed, which is subsequently combined with pre-operative annotations of the RV position in the MR volume, therefore allowing an automatic alignment of their coordinate systems. Moreover, a novel strategy to ensure a correct temporal synchronization of the US and MR models is applied. Finally, a full evaluation of the proposed automatic pipeline is performed. The proposed automatic framework was tested in a clinical database with 24 patients containing both MR and US scans. A similar performance between the proposed and the previous semi-automatic version was found in terms of relevant clinical measurements. Additionally, the automatic strategy to detect the RV insertion point showed its effectiveness, with a good agreement against manually identified landmarks. Overall, the proposed automatic method showed high feasibility and a performance similar to the semi-automatic version, reinforcing its potential for normal clinical routine.

Abstract