Abstract
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Abstract
Councils are a common organisational structure of Portuguese Universities and Polytechnic Institutes. They make the key decisions, in these organisations, by nominal voting at assembly meetings. The COVID pandemic forced the remote work upon most organisations, including universities and polytechnic institutes. Assuming that a remote assembly requires additional efforts in order to guarantee the integrity of the majority decisions taken by votes expressed by its members, opportunity arises for the use of a blockchain-assisted voting system. Benefits of blockchain, such as verifiability, immutability, tamper resistant, and its distributed nature appear to be a good fit. We propose a novel blockchain-assisted system to support the decision making of academic councils that operate by nominal voting in assemblies, gathering remotely and online. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The Internet of Things (IoT) is based on objects or things that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goias as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population.

Abstract

Abstract
Physiological signals offer a vast amount of information about the well-being of the human system. Understanding the behavior and complexity of these signs is important for accurate assessments and diagnoses. This study focuses on fetal heart rate (FHR) analysis and its potential to detect perinatal asphyxia by analyzing how different representations of the FHR series could aid in asphyxia detection. Additionally, different compression schemes were applied to evaluate the potential of compression as a measure of complexity. For this purpose, text files containing data of the last hour of the FHR before birth were converted into different types of images (Time Series, Time Series with fixed axes, Recurrence Plot and Poincaré Plot). We then applied compression schemes for text (BZIP2 and GZIP) and images (Lempel-Ziv-Welch, DEFLATE, and JPG) in 5, 10, and 30-minute windows. Correlation analysis revealed that similar compressed formats, such as BZIP2/GZIP and TIFF LZW/TIFF DEFLATE/JPG LOSSY/JPG LOSSLESS, showed the highest values and the correlation between uncompressed and compressed formats became increasingly more negative for larger time windows. Mann-Whitney test between groups (with and without asphyxia) revealed that compressed patterned images, such as Recurrence Plots, showed the highest potential in detecting asphyxia. Moreover, we confirm that larger time windows allow for better detection, due to the presence of more detailed patterns. These findings confirmed the potential of time series image representation in detecting fetal conditions, as well as show that the compression of images leads to better results than the compression of text files. © 2022 IEEE.

Abstract
Neonatal sepsis is characterized by the system’s extreme response to an infection and persists as one of the biggest life-threatening diseases. The gold standard treatment is administrating an antibiotic, which, unfortunately, is often made too late. The diagnosis should be easier, faster, and achieved through non-invasive methods. Recently, entropy, a non-linear feature, has been applied to different physiological signals to detect diseases having very promising results. In this study, several entropy measures were applied to the breathing cycle duration (TTot) of the respiratory signals for 20 neonates. In total, 18 distinct methods of entropy were initially applied to 30-minute segments. Using Spearman’s correlation, it was detected strong correlation similarities between some of the measures. On the other hand, bubble, attention, phase, and spectral entropies were negatively correlated with all the other measures. To detect the presence of Sepsis, the slope of the multiscale entropy index was analyzed. Also, a changing point in the slope was probed, when possible, and then was applied linear regression to two subsets of data, before and after the changing point. Effectively, the Wilcoxon Sign Rank Test showed that the results for the total slope of the Sample, Corrected Conditional, Distribution, Permutation, Fuzzy, Gridded Distribution, Incremental, and Entropy of Entropy were statistically significant to infer that entropy decreases with time. Nonetheless, further work should confirm these results with a larger dataset that includes healthy and pathological neonates. © 2022 IEEE.