ALBERT

Description: Energy is considered as a main influence on urban configurations. However, there is a difficulty on translating the city models based on theoretical renewable energy concepts into practical applications. This study considers the possibility of understanding this future model as a transformation of the existing urban centres. With this objective, a methodology to intervene in existing cities based on the study of solar access is developed. Therefore, an analysis of solar potential in relation with urban morphology is carried on through a simulation software in l’Eixample, a neighbourhood of Barcelona. The distribution of the sun factor in the different areas of the building blocks envelope displays possible morphological modifications that would facilitate solar energy collection. Consequently, the analytical method presented could be applied to regulate urban interventions with the aim of obtaining more solar energy based cities.

Description: To move around the environment, human beings depend on sight more than their other senses, because it provides information about the size, shape, color and position of an object. The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in indoor environments a very important and challenging task. In this work, a vision system to detect objects considering usual human environments, able to work on a real mobile robot, is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Different segmentation techniques have been applied to each kind of object. Similarly, two alternatives to extract features of the objects are explored, based on geometric shape descriptors and bag of words. The experimental results have demonstrated the usefulness of the system for the detection and location of the objects in indoor environments. Furthermore, through the comparison of two proposed methods for extracting features, it has been determined which alternative offers better performance. The final results have been obtained taking into account the proposed problem and that the environment has not been changed, that is to say, the environment has not been altered to perform the tests.

Description: Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS) and compares it with the well-known technique called multivariate imputation by chained equations (MICE). The results obtained demonstrate how the proposed method outperforms the MICE algorithm.

Description: This study delves further into the analysis of genomic data by computing a variety of complexity measures. We analyze the effect of window size and evaluate the precision and recall of the prediction of gene zones, aided with a much larger dataset (full chromosomes). A technique based on the separation of two cases (gene-containing and non-gene-containing) has been developed as a basic gene predictor for automated DNA analysis. This predictor was tested on various sequences of human DNA obtained from public databases, in a set of three experiments. The first one covers window size and other parameters; the second one corresponds to an analysis of a full human chromosome (198 million nucleic acids); and the last one tests subject variability (with five different individual subjects). All three experiments have high-quality results, in terms of recall and precision, thus indicating the effectiveness of the predictor.

Description: The use of standardized and optimized protocols has been recently advocated for different arthropod taxa instead of ad-hoc sampling or sampling with protocols defined on a case-by-case basis. We present a comparison of both sampling approaches applied for spiders in a natural area of Portugal. Tests were made to their efficiency, over-collection of common species, singletons proportions, species abundance distributions, average specimen size, average taxonomic distinctness and behavior of richness estimators. The standardized protocol revealed three main advantages: (1) higher efficiency; (2) more reliable estimations of true richness; and (3) meaningful comparisons between undersampled areas.

Description: The use of standardized and optimized protocols has been recently advocated for different arthropod taxa instead of ad-hoc sampling or sampling with protocols defined on a case-by-case basis. We present a comparison of both sampling approaches applied for spiders in a natural area of Portugal. Tests were made to their efficiency, over-collection of common species, singletons proportions, species abundance distributions, average specimen size, average taxonomic distinctness and behavior of richness estimators. The standardized protocol revealed three main advantages: (1) higher efficiency; (2) more reliable estimations of true richness; and (3) meaningful comparisons between undersampled areas.

Description: Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy–pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.

Description: The future of robotics predicts that robots will integrate themselves more every day with human beings and their environments. To achieve this integration, robots need to acquire information about the environment and its objects. There is a big need for algorithms to provide robots with these sort of skills, from the location where objects are needed to accomplish a task up to where these objects are considered as information about the environment. This paper presents a way to provide mobile robots with the ability-skill to detect objets for semantic navigation. This paper aims to use current trends in robotics and at the same time, that can be exported to other platforms. Two methods to detect objects are proposed, contour detection and a descriptor based technique, and both of them are combined to overcome their respective limitations. Finally, the code is tested on a real robot, to prove its accuracy and efficiency.

Description: Sensors, Vol. 18, Pages 2612: Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems Sensors doi: 10.3390/s18082612 Authors: Andrés C. Jiménez Vicente García-Díaz Rubén González-Crespo Sandro Bolaños Planning tasks performed by a robotic agent require previous access to a map of the environment and the position where the agent is located. This creates a problem when the agent is placed in a new environment. To solve it, the RA must execute the task known as Simultaneous Location and Mapping (SLAM) which locates the agent in the new environment while generating the map at the same time, geometrically or topologically. One of the big problems in SLAM is the amount of memory required for the RA to store the details of the environment map. In addition, environment data capture needs a robust processing unit to handle data representation, which in turn is reflected in a bigger RA unit with higher energy use and production costs. This article presents a design for a system capable of a decentralized implementation of SLAM that is based on the use of a system comprised of wireless agents capable of storing and distributing the map as it is being generated by the RA. The proposed system was validated in an environment with a surface area of 25 m 2 , in which it was capable of generating the topological map online, and without relying on external units connected to the system.

Description: IJERPH, Vol. 15, Pages 1957: In the Heart of the Amazon: Noncommunicable Diseases and Apolipoprotein E4 Genotype in the Riverine Population International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15091957 Authors: Gabriela Arrifano Jacqueline Alvarez-Leite José Souza-Monteiro Marcus Augusto-Oliveira Ricardo Paraense Barbarella Macchi André Pinto Reinaldo Oriá José do Nascimento Maria Crespo-Lopez The Amazon River basin is the largest tropical forest in the world. Most of the Amazon belongs to Brazil, a developing country that currently faces huge challenges related to the consolidation of its universal healthcare system. Noncommunicable diseases (NCDs) are the leading cause of death in Brazil, accounting for 74% of all deaths, and NCDs are probably underestimated in Amazonian population because of their geographical isolation and the precariousness of riverine communities. Important risk factors, such as genetic susceptibility, remain undetermined in the riverine population. This study performed fasting blood sugar (FBS) and blood pressure measurements and investigated the presence of the ε4 allele of apolipoprotein E (APOE4) to determine the prevalence of diabetes, hypertension and the genetic risk of NCDs. FBS and APOE4 were measured in blood samples from 763 participants using spectrometry and real-time PCR; 67.5% showed altered measurements, and 57.9% had never been diagnosed or treated. Altered FBS was found in 28.3% of the participants, hypertension in 57.6% and APOE4 in 32.0%. The health profile of the riverine population appears to differ from that of urban population in the Amazon. Additional risk factors for NCDs, such as environmental contamination and nutritional transition, may contribute more than increased genetic susceptibility to the prevalence of altered FBS and hypertension. Our results will help guide the development of preventive strategies and governmental actions for more effective management of NCDs in the Amazon area.