Background: Recent studies have linked low heart rate variability (HRV) with COVID-19, indicating that this parameter can be a marker of the onset of the disease and its severity and a predictor of mortality in infected people. Given the large number of wearable devices that capture physiological signals of the human body easily and noninvasively, several studies have used this equipment to measure the HRV of individuals and related these measures to COVID-19. Objective: The objective of this study was to assess the utility of HRV measurements obtained from wearable devices as predictive indicators of COVID-19, as well as the onset and worsening of symptoms in affected individuals. Methods: A systematic review was conducted searching the following databases up to the end of January 2023: Embase, PubMed, Web of Science, Scopus, and IEEE Xplore. Studies had to include (1) measures of HRV in patients with COVID-19 and (2) measurements involving the use of wearable devices. We also conducted a meta-analysis of these measures to reduce possible biases and increase the statistical power of the primary research. Results: The main finding was the association between low HRV and the onset and worsening of COVID-19 symptoms. In some cases, it was possible to predict the onset of COVID-19 before a positive clinical test. The meta-analysis of studies reported that a reduction in HRV parameters is associated with COVID-19. Individuals with COVID-19 presented a reduction in the SD of the normal-to-normal interbeat intervals and root mean square of the successive differences compared with healthy individuals. The decrease in the SD of the normal-to-normal interbeat intervals was 3.25 ms (95% CI −5.34 to −1.16 ms), and the decrease in the root mean square of the successive differences was 1.24 ms (95% CI −3.71 to 1.23 ms). Conclusions: Wearable devices that measure changes in HRV, such as smartwatches, rings, and bracelets, provide information that allows for the identification of COVID-19 during the presymptomatic period as well as its worsening through an indirect and noninvasive self-diagnosis.

Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant.

In this work, genetic algorithm (GA) technique was applied to investigate cutting process parameters influence on workpiece price formation using the concepts of contribution margin. The cutting process is one of the most important processes among many others available in manufacturing scenarios. Nevertheless, it is common to find some manufactured item, or at least one part of it, that was made, by cutting process. Because of its importance, many studies related to its optimization were performed, and in the last years, cutting process optimization task has been explored in different approaches from several knowledge areas, such as deterministic methods, operational research and artificial intelligence, and design of experiments. The obtained results from GA application presented in this work are in good agreement with others already reported and could be properly used in cutting process optimization parameters.

In this work we present the spectroscopic properties of ${\text{LiYF}}_4$ (YLF) single crystals activated with thulium and codoped with ytterbium and neodymium ions. The most important processes that lead to the thulium upconversion emissions in the blue region were identified. A time-resolved luminescence spectroscopy technique was employed to measure the luminescence decays and to determine the most important mechanisms involved in the upconversion process that populates ${{}^{1}G}_4\left({\text{Tm}}^{3+}\right)$ excited state. Analysis of the energy transfer processes dynamics using selective pulsed laser excitations in Yb:Tm:Nd, Tm:Nd, and Tm:Yb YLF crystals shows that the energy transfer from ${\text{Nd}}^{3+}$ to ${\text{Yb}}^{3+}$ ions is the mechanism responsible for the enhancement in the blue upconversion efficiency in the Yb:Tm:Nd:YLF when compared with the Yb:Tm system. A study of the energy transfer processes in YLF:Yb:Tm:Nd crystal showed that the ${{}^{1}G}_4$ excited level is mainly populated by a sequence of two nonradiative energy transfers that start well after the ${\text{Nd}}^{3+}$ and ${\text{Tm}}^{3+}$ excitations at 797 nm according to ${\text{Nd}}^{3+}\left({{}^{4}F}_{3/2}\right)\to {\text{Yb}}^{3+}\left({{}^{2}F}_{7/2}\right)$ , followed by ${\text{Yb}}^{3+}\left({{}^{2}F}_{5/2}\right)\to \text{Tm}\left({{}^{3}H}_4\right)\to {\text{Tm}}^{3+}\left({{}^{1}G}_4\right)$ . Results of numerical simulation of the rate equations system showed that a population inversion for 481.4 nm laser emission line is attained for a pumping rate threshold of $26{\text{s}}^{-1}$ , which is equivalent to an intensity of $880\text{W}{\text{cm}}^{-2}$ for a continuous laser pumping at 797 nm. On the other hand, a population inversion was not observed for the case of 960 nm $\left({\text{Yb}}^{3+}\right)$ pumping.

Resumo Este estudo identificou as barreiras e analisou seu grau de influência na Gestão da Cadeia de Suprimentos Verde (GCSV), de acordo com a perspectiva de um fornecedor de primeira camada da indústria automotiva brasileira. Foram encontradas 43 barreiras na literatura especializada, e 13 validadas: apoio e envolvimento (cinco), desempenho operacional (três), desempenho econômico (duas), desempenho ambiental (duas), conhecimento e informação (uma). A validação ocorreu por meio da percepção de especialistas técnicos e acadêmicos com familiaridade nos temas gestão ambiental e cadeia de suprimentos de diversos setores. A hierarquia das prioridades das barreiras foi obtida por meio da aplicação do método de Análise Hierárquica do Processo (Analytic Hierarchy Process [AHP]), tendo como decisores representantes de uma indústria do setor automotivo. A pesquisa mostrou que as implicações de custo representam a barreira mais influente à GCSV, do ponto de vista de um fornecedor de primeira camada do setor estudado.