Summary
Type 2 diabetes mellitus (T2DM) is associated with a high mortality risk, although the magnitude of this association remains unknown in Latin America (LA). We aimed to assess the strength of ...the association between T2DM and all‐cause and cause‐specific mortality in population‐based cohort studies in LA.
Systematic review and meta‐analysis: inclusion criteria were (1) men and women 18 years old and above with T2DM; (2) study outcomes all‐cause and/or cause‐specific mortality; and (3) using people without T2DM as comparison group. Five databases (Scopus, Medline, Embase, Global Health, and LILACS) were searched. Risk of bias was evaluated with the ROBINS‐I criteria. Initially, there were 979 identified studies, of which 17 were selected for qualitative synthesis; 14 were included in the meta‐analysis (N = 416 821). Self‐reported T2DM showed a pooled relative risk (RR) of 2.49 for all‐causes mortality (I‐squared I2 = 85.7%, p < 0.001; 95% confidence interval CI, 1.96‐3.15). T2DM based on a composite definition was associated with a 2.26‐fold higher all‐cause mortality (I2 = 93.9%, p < 0.001; 95% CI, 1.36‐3.74). The pooled risk estimates were similar between men and women, although higher at younger ages. The pooled RR for cardiovascular mortality was 2.76 (I2 = 59.2%; p < 0.061; 95% CI, 1.99‐3.82) and for renal mortality 15.85 (I2 = 0.00%; p < 0.645; 95% CI, 9.82‐25.57). Using available population‐based cohort studies, this work has identified and estimated the strength of the association between T2DM and mortality in LA. The higher mortality risk compared with high‐income countries deserves close attention from health policies makers and clinicians to improve diabetes care and control hence preventing complications and delaying death.
Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which ...incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.
A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA ...linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities.
The signal conditioning treatment to achieve good relation of power with radio-frequency (RF) conversion in conventional transceiver systems require precise baseband models. A developed framework is ...built to provide a demonstration of the modeling figures of merit with orthogonal frequency division multiplexing (OFDM) support under signal conditioning and transmission restrictions to waveforms with high peak to average power ratio (PAPR) in practical applications. Therefore, peak and average power levels have to be limited to correct high PAPR for a better suited correction power from the amplifier that can lead to compression or clipping in the signal of interest. This work presents an alternative joint crest factor reduction (CFR) algorithm to correct the performance of PAPR. A real-time field-programmable gate array (FPGA) testbed is developed to characterize and measure the behavior of an amplifier using a single-carrier 64-QAM OFDM based on long-term evolution (LTE) downlink at 2.40 GHz as stimulus, across wide modulation bandwidths. The results demonstrate that the CFR accuracy capabilities for the signal conditioning show a reliable clipping reduction to give a smooth version of the clipping signal and provide a factor of correction for the unwanted out-of-band emission validated according to the adjacent channel power ratio (ACPR), PAPR, peak power, complementary cumulative distribution function (CCDF), and error vector magnitude (EVM) figures of merit.
The heat treatment of a metal is a set of heating and cooling cycles that a metal undergoes to change its microstructure and, therefore, its properties. Temperature-time-transformation (TTT) diagrams ...are an essential tool for interpreting the resulting microstructures after heat treatments. The present work describes a novel proposal to predict TTT diagrams of the γ' phase for the Ni-Al alloy using artificial neural networks (ANNs). The proposed methodology is composed of five stages: (1) database creation, (2) experimental design, (3) ANNs training, (4) ANNs validation, and (5) proposed models analysis. Two approaches were addressed, the first to predict only the nose point of the TTT diagrams and the second to predict the complete curve. Finally, the best models for each approach were merged to compose a more accurate hybrid model. The results show that the multilayer perceptron architecture is the most efficient and accurate compared to the simulated TTT diagrams. The prediction of the nose point and the complete curve showed an accuracy of 98.07% and 86.41%, respectively. The proposed final hybrid model achieves an accuracy of 96.59%.
To systematically evaluate short-term efficacy of UCM versus other interventions in preterm infants.
Six engines were searched until February 2020 for randomized controlled trials (RCTs) assessing ...UCM versus immediate cord clamping (ICC), delayed cord clamping (DCC), or no intervention. Primary outcomes were overall mortality, intraventricular hemorrhage (IVH), and patent ductus arteriosus (PDA); secondary outcomes were need for blood transfusion, mean blood pressure (MBP), serum hemoglobin (Hb), and ferritin levels. Random-effects meta-analyses were used.
Fourteen RCTs (n = 1708) were included. In comparison to ICC, UCM did not decrease mortality (RR 0.5, 95% CI 0.2-1.1), IVH (RR 0.7, 95% CI 0.5-1.0), or PDA (RR 1.0, 95% CI 0.7-1.5). However, UCM reduced need of blood transfusion (RR 0.5, 95% CI 0.3-0.9) and increased MBP (MD 2.5 mm Hg, 95% CI 0.5-4.5), Hb (MD 1.2 g/dL, 95% CI 0.8-1.6), and ferritin (MD 151.4 ng/dL, 95% CI 59.5-243.3). In comparison to DCC, UCM did not reduce mortality, IVH, PDA, or need of blood transfusion but increased MBP (MD 3.7, 95% CI 0.6-6.9) and Hb (MD 0.3, 95% CI -0.2-0.8). Only two RCTs had high risk of bias.
UCM did not decrease short-term clinical outcomes in comparison to ICC or DCC in preterm infants. Intermediate outcomes improved significantly with UCM.
In 14 randomized controlled trials (RCTs), umbilical cord milking (UCM) did not reduce mortality, intraventricular hemorrhage, or patent ductus arteriosus compared to immediate (ICC) or delayed cord clamping (DCC). UCM improved mean blood pressure and hemoglobin levels compared to ICC or DCC. In comparison to ICC, UCM reduced the need for blood transfusion. We updated searches until February 2020, stratified by type of control, and performed subgroup analyses. There was low quality of evidence about clinical efficacy of UCM. Most of RCTs had low risk of bias. UCM cannot be recommended as standard of care for preterm infants.
We aimed to study time trends and levels of mean total cholesterol and lipid fractions, and dyslipidaemias prevalence in Latin America and the Caribbean (LAC). Systematic-review and meta-analysis of ...population-based studies in which lipid (total cholesterol TC; 86 studies; 168,553 people, HDL-Cholesterol HDL-C; 84 studies; 121,282 people, LDL-Cholesterol LDL-C; 61 studies; 86,854 people, and triglycerides TG; 84 studies; 121,009 people) levels and prevalences were laboratory-based. We used Scopus, LILACS, Embase, Medline and Global Health; studies were from 1964 to 2016. Pooled means and prevalences were estimated for lipid biomarkers from ≥2005. The pooled means (mg/dl) were 193 for TC, 120 for LDL-C, 47 for HDL-C, and 139 for TG; no strong trends. The pooled prevalence estimates were 21% for high TC, 20% for high LDL-C, 48% for low HDL-C, and 21% for high TG; no strong trends. These results may help strengthen programs for dyslipidaemias prevention/management in LAC.
This research investigates the heat treatment parameters of 6061-aluminum alloy to enhance its mechanical properties. The Taguchi design-of-experiments (DOE) method was employed to systematically ...examine the effects of solutionizing temperature, solutionizing time, aging temperature, and aging time on the tensile strength of the alloy. Mechanical testing suggested a major influence of solutionizing and aging temperatures on the ultimate tensile strength of the alloy. The samples subjected to a solutionizing temperature of 540 °C for 3 h, followed by aging at 170 °C for 18 h, exhibited the highest ultimate tensile strength (293.7 MPa). Conversely, the samples processed at the lowest levels of these parameters displayed the lowest ultimate tensile strength (193.7 MPa). Microstructural analysis confirmed the formation of equiaxed grains, strengthening precipitates, precipitate clusters, and β (Mg2Si) precipitates alongside Fe-Al-Si dispersoids. Energy-dispersive X-ray spectroscopy (EDS) analysis detected the presence of elemental precursors of β phase (Al-Mg-Si) and dispersoid-forming elements (Al-Fe-Si). X-ray diffraction spectroscopy (XRD) analysis revealed the persistence of the β phase in the alloy, indicating its contribution to the improved mechanical properties, which are mainly obtained by aging precipitation phases. Fracture analysis showed a ductile fracture mechanism, and examining fractured samples supported the findings of enhanced tensile properties resulting from the adequate selection of heat treatment parameters. We employed ANOVA (analysis of variance) to analyze the DOE results, using a multiple regression model to express the ultimate tensile strength of the alloy in terms of the variables used in the design. This yielded an adjusted coefficient of determination of 89.75%, indicating a high level of explained variability in the test data for evaluating the model’s predictive capacity.
In this paper, the analysis of electrochemical corrosion performance and mechanical strength of weld joints of aluminum 6061 in two-heat treatment conditions was performed. The joints were produced ...by gas metal arc welding in pulsed mode. The original material exhibited precipitates of β and β” phases in a volume fraction (Vf) of 2.35%. When it was subjected to a solubilization process, these phases were present in a Vf = 2.97%. This increase is due to their change in shape and distribution in clusters within the aluminum matrix. After the welding process, the best sample in the solubilization condition reached 117 MPa, while the original material achieved 104 MPa, but all samples showed a fracture in the fusion zone. This is attributed to the heat input that produces high and low hardness zones along the heat-affected zone and the welding zone, respectively. Moreover, the change in microstructure and phase composition creates a galvanic couple, susceptible to electrochemical corrosion, which is more evident in the heat-affected zone than in the other weld regions, exhibiting uniform and localized corrosion, as was evident by electrochemical impedance spectroscopy. The heat from the welding process negatively affects the corrosion resistance, mainly in the heat-affected zone.
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