An integrated access control and lighting configuration system for smart buildings

This article presents an integrated access control and lighting configuration system for smart buildings. The system uses two-factor authentication, one based on face recognition and other on RFID TAG, and identifies the user inside a room and performs an automatic lighting configuration based on user’s behavior. The communication among the devices is performed by Radio Frequency using the low to medium frequency spectrum (LMRF), without providing direct Internet access, and, hence, avoiding known Internet security issues. This system can be easily deployed on meeting rooms or offices in business or government buildings. Through the evaluations we observe an acceptable processing execution time, an acceptable communication time and the robustness of the system

A detailed characterization of complex networks using Information Theory

Understanding the structure and the dynamics of networks is of paramount importance for manyscientific fields that rely on network science. Complex network theory provides a variety of features thathelp in the evaluation of network behavior. However, such analysis can be confusing and misleading asthere are many intrinsic properties for each network metric. Alternatively, Information Theory methodshave gained the spotlight because of their ability to create a quantitative and robust characterizationof such networks. In this work, we use two Information Theory quantifiers, namely Network Entropyand Network Fisher Information Measure, to analyzing those networks. Our approach detects nontrivialcharacteristics of complex networks such as the transition present in the Watts-Strogatz modelfrom k-ring to random graphs; the phase transition from a disconnected to an almost surely connectednetwork when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-freenetworks when considering a non-linear preferential attachment, fitness, and aging features alongsidethe configuration model with a pure power-law degree distribution. Finally, we analyze the numericalresults for real networks, contrasting our findings with traditional complex network methods. Inconclusion, we present an efficient method that ignites the debate on network characterization.Fil: Freitas, Cristopher G. S.. Universidade Federal de Alagoas; BrasilFil: Aquino, Andre L. L.. Universidade Federal de Alagoas; BrasilFil: Ramos, Heitor S.. Universidade Federal de Minas Gerais; BrasilFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Aníbal. Instituto Universitario del Hospital Italiano de Buenos Aires; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Statistical Properties of the Entropy from Ordinal Patterns

The ultimate purpose of the statistical analysis of ordinal patterns is to characterize the distribution of the features they induce. In particular, knowing the joint distribution of the pair Entropy-Statistical Complexity for a large class of time series models would allow statistical tests that are unavailable to date. Working in this direction, we characterize the asymptotic distribution of the empirical Shannon's Entropy for any model under which the true normalized Entropy is neither zero nor one. We obtain the asymptotic distribution from the Central Limit Theorem (assuming large time series), the Multivariate Delta Method, and a third-order correction of its mean value. We discuss the applicability of other results (exact, first-, and second-order corrections) regarding their accuracy and numerical stability. Within a general framework for building test statistics about Shannon's Entropy, we present a bilateral test that verifies if there is enough evidence to reject the hypothesis that two signals produce ordinal patterns with the same Shannon's Entropy. We applied this bilateral test to the daily maximum temperature time series from three cities (Dublin, Edinburgh, and Miami) and obtained sensible results

A Non Intrusive Low Cost Kit for Electric Power Measuring and Energy Disaggregation

This article presents a kit to collect data of electric loads of single and three phases main power supply of a house and perform the energy disaggregation. To collect the data, we use sensors based on open magnetic core to measure the electromagnetic field induced by the current in the electric conducting wire in a non intrusive way. In particular, each sensor from the three-phase device wraps/encloses each phase without alignment. In order to calibrate the three-phase device, we present a method to calculate the neutral RMS without complex numbers using (Analysis of Variance) ANOVA and post hoc Tukey’s multiple comparison test to assert the differences of measures among phases. We managed to validate the method using a measure reference. Additionally, to perform the energy disaggregation, we use the NILMTK tool. This toll was used, initially, to compare disaggregation algorithms on many public datasets. We use in our system two disaggregation algorithms Combinatorial Optimization and Factorial Hidden Markov Model algorithms. The results show that is possible to collect and perform energy disaggregation through our embedded system

A Non Intrusive Low Cost Kit for Electric Power Measuring and Energy Disaggregation

This article presents a kit to collect data of electric loads of single and three phases main power supply of a house and perform the energy disaggregation. To collect the data, we use sensors based on open magnetic core to measure the electromagnetic field induced by the current in the electric conducting wire in a non intrusive way. In particular, each sensor from the three-phase device wraps/encloses each phase without alignment. In order to calibrate the three-phase device, we present a method to calculate the neutral RMS without complex numbers using (Analysis of Variance) ANOVA and post hoc Tukey’s multiple comparison test to assert the differences of measures among phases. We managed to validate the method using a measure reference. Additionally, to perform the energy disaggregation, we use the NILMTK tool. This toll was used, initially, to compare disaggregation algorithms on many public datasets. We use in our system two disaggregation algorithms Combinatorial Optimization and Factorial Hidden Markov Model algorithms. The results show that is possible to collect and perform energy disaggregation through our embedded system

Data Driven Performance Evaluation of Wireless Sensor Networks

Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries