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Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject's blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors' knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL). [ABSTRACT FROM AUTHOR]

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade's worth of GBV is compiled and prepared from official sources available due to Spain's open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method's possible use elsewhere in the world. [ABSTRACT FROM AUTHOR]

The primary function of noise barriers is to shield inhabitants of affected areas from excessive noise generated by road traffic. To enhance the performance of noise barriers while simultaneously adhering to height restrictions, the attachment of structures (caps) of different shapes to the tops of conventional screens can be considered. These caps can significantly impact the diffracted sound energy, thereby increasing the desired global acoustic losses. This work presents a comprehensive study of the acoustic performance of noise barriers with single and double attached caps of different shapes through a calculation of their insertion losses (IL). This study comprehensively addresses and compares different types, sizes, combinations, and numbers of noise barrier caps for different scenarios (including sloping and absorbent grounds) and sources ("car" and "ambulance") for an extended frequency band up to 10 kHz. To the best of the authors' knowledge, this is a range that has not previously been analyzed. A variety of different cap shapes were considered including cylinders, rectangles, trapezoids, and Y/T-shaped forms. To calculate the IL, an innovative and fast uniform theory of diffraction (UTD)-based method developed by the authors was applied in all simulations. The results showed that the Y-shaped single and double barrier caps were, in general, the most effective at increasing IL without raising the height of the barrier, thereby successfully managing the aesthetic impact. The results also showed how the consideration of sloping and absorbent floors could also contribute to improved noise abatement. [ABSTRACT FROM AUTHOR]

The metabolic disease Type 1 Diabetes Mellitus (DM1) is caused by a reduction in the production of pancreatic insulin, which causes chronic hyperglycemia. Patients with DM1 are required to perform multiple blood glucose measurements on a daily basis to monitor their blood glucose dynamics through the use of capillary glucometers. In more recent times, technological developments have led to the development of cutting-edge biosensors and Continuous Glucose Monitoring (CGM) systems that can monitor patients' blood glucose levels on a real-time basis. This offers medical providers access to glucose oscillations modeling interventions that can enhance DM1 treatment and management approaches through the use of novel disruptive technologies, such as Cloud Computing (CC), big data, Intelligent Data Analysis (IDA) and the Internet of Things (IoT). This work applies some advanced modeling techniques to a complete data set of glycemia-related biomedical features—obtained through an extensive, passive monitoring campaign undertaken with 25 DM1 patients under real-world conditions—in order to model glucose level dynamics through the proper identification of patterns. Hereby, four methods, which are run through CC due to the high volume of data collected, are applied and compared within an IoT context. The results show that Bayesian Regularized Neural Networks (BRNN) offer the best performance (0.83 R2) with a reduced Root Median Squared Error (RMSE) of 14.03 mg/dL. [ABSTRACT FROM AUTHOR]

Transitioning a Terrestrial Trunked Radio (TETRA) network to a Long-Term Evolution (LTE) network in public protection and disaster relief (PPDR) systems is a path to providing future services requiring high radio interface throughput and allowing broadband PPDR (BB-PPDR) radio communications. Users of TETRA networks are currently considering how to deploy a BB-PPDR network in the coming years. This study offers several radio planning considerations in TETRA to LTE migration for such networks. The conclusions are obtained from a case study in which both measurements and radioelectric coverage simulations were carried out for the real scenario of the Murcia Region, Spain, for both TETRA and LTE systems. The proposed considerations can help PPDR agencies efficiently estimate the cost of converting a TETRA network to an LTE network. Uniquely in this study, the total area is divided into geographical areas of interest that are defined as administrative divisions (region, municipal areas, etc.). The analysis was carried out using a radio planning tool based on a geographic information system and the measurements have been used to tune the propagation models. According to the real scenario considered, the number of sites needed in the LTE network—for a specific quality of service (90% for the whole region and 85% for municipal areas)—is a factor of 2.4 higher than for TETRA network. [ABSTRACT FROM AUTHOR]

Type 1 Diabetes Mellitus (DM1) is a metabolic disease that is characterized by chronic hyperglycemia due to a lack of pancreatic insulin production. This forces patients to perform several blood glucose measurements per day—by means of capillary glucometers—in order to infer a trend and try to predict future values. In this way, a decision about the insulin dosage that has to be exogenously injected to maintain glycemia within the desirable levels is made. Unfortunately, this method usually suffers from relatively high imprecision. However, recent advances in information and communication technologies (ICT), along with novel biosensors that could provide a real-time comprehensive condition of the patient, offer a new perspective in DM1 management. In this sense, new disruptive technologies like Big Data, the Internet of Things (IoT), and Cloud Computing, as well as Machine Learning (ML) can play an important role in managing DM1. In this work, firstly, an analysis of previously published ICT-based methods for the management of diabetes continuous monitoring is carried out. In this way, an assessment of the possible lack of such proposals is presented, along with the challenges to be overcome in forthcoming smart DM1 management systems. Finally, an overview of a holistic ICT-based platform for DM1 management that try to solve the limitations of previous works, while at the same time, taking advantage of the abovementioned disruptive technologies is hereby proposed. [ABSTRACT FROM AUTHOR]