The National University of Formosa (Spanish: Universidad Nacional de Formosa, UNF) is an Argentine national university, situated in the city of Formosa, capital of Formosa Province. Its precursor, the University Institute of Formosa, was established in 1971 as a campus of the National University of the Northeast..
In the aviation industry, a prevalent software-related risk is radiation damage to digital signal processor (DSP) code, often caused by Single-Event Upsets (SEUs). There is a persistent demand for effective mitigation strategies, with redundancy considered the most effective method for addressing SEUs. This paper leverages the fact that SEUs do not adversely affect standby programs by implementing a cold-standby strategy to enhance the execution reliability of DSP code. We propose a modified bacterial evolutionary algorithm (BEA) integrated with a cold-standby (CS) strategy, referred to as BEA-CS, to optimize multilevel redundancy allocation problems, particularly within hierarchical coding control and solution search processes. Multilevel systems pose challenges in managing the number of components across various levels. The proposed modified BEA regulates the number of components utilized by units at each level. While the cold-standby strategy has been shown to enhance system reliability, its application in multilevel systems has not been previously explored. This study addresses this gap and extends the findings to practical applications. Consequently, we validate BEA-CS using actual program execution data from the literature, comparing the results obtained with those from state-of-the-art methods. Experimental findings indicate that BEA-CS significantly improves execution reliability by 44 %-82 % based on the maximum possible improvement (MPI). Additionally, an ablation study is conducted to confirm the effectiveness of the critical methodologies employed, specifically simplified swarm optimization and the cold-standby strategy.
Background To enhance the service life of lead-acid batteries (LABs), a lead-carbon cloth composite material (LCF) was developed by combining the advantages of Pb and C to replace Pb plates. This approach was first introduced in the earlier study, Application of Carbon Fibers in Thin-Plate Pure Lead Batteries. This study provides concrete evidence of the Pb-C bond formation process, as documented in JTICE 152 (2023), P105175, using Field Emission Transmission Electron Microscopy (FE-TEM) morphology and highlighting new applications in lead-acid batteries. Methods The cross-sections of LCF plates made from activated carbon cloth and chemically oxidized activated carbon cloth were observed at the Pb-carbon fiber interface using FE-TEM. Homemade LABs, both with and without LCF plates, were compared in high-rate cycling tests with commercial batteries. Significant Findings The insertion of LCF plates into LABs enhances the charging reaction and supports the electrodes, acting like a capacitor during high-rate operation. In the 12 V 6 Ah LAB with LCF plates, the 550 W discharge time remained consistent over 150 cycles — more than twice as many as that of the battery without LCF — while the battery remained available.
Thermal error resulting from thermal deformation is a key factor that significantly affects the accuracy of machine tools. This study aims to develop and evaluate a residual-based hybrid tandem metamodel for thermal error compensation that leverages the complementary strengths of linear and nonlinear modeling techniques. The linear models employed in this study include stepwise regression and time series analysis using the autoregressive integrated moving average (ARIMA) method, while the nonlinear models considered comprise support vector regression (SVR), neural network autoregression (NNAR), and eXtreme Gradient Boosting (XGBoost). Specifically, three combinations were investigated: ARIMA + SVR, Stepwise + NNAR, and ARIMA + XGBoost, with the latter integrating ARIMA to capture linear thermal trends and XGBoost to model nonlinear residual patterns. Datasets collected from actual working machines under different spindle speeds and environmental conditions were used for model training and independent testing. Experimental results demonstrate that the ARIMA + XGBoost tandem metamodel exhibits superior generalization and error control, particularly under heterogeneous axis-specific error patterns, achieving the best performance in 10 out of 12 subdatasets. It achieved average MaxAE, MAE, and MSE values of 0.7522 mu m, 0.2897 mu m, and 0.2997 mu m2, respectively, with corresponding standard deviations (SD) of 0.7013, 0.3841, and 0.5720. Even in the worst case, the MaxAE was controlled to within 1.622 mu m (X-axis), 0.8612 mu m (Y-axis), and 2.2712 mu m (Z-axis). The mean reductions in MaxAE across the three axes were 88.85 %, 97.53 %, and 73.28 %, respectively, yielding an overall mean reduction of 86.56 % (SD = 0.2141). These findings confirm that the proposed ARIMA + XGBoost hybrid metamodel provides robust and effective thermal error compensation, offering a practical pathway for deployment in high-precision manufacturing environments.
Background:Lipoprotein(a) [Lp(a)] is an established independent risk factor for atherosclerotic cardiovascular disease (ASCVD). Although both obesity and elevated Lp(a) are highly prevalent, their interrelationship and combined impact on ischemic heart disease (IHD) remain incompletely understood, particularly in Latin American populations. Objective:To evaluate the distribution of elevated Lp(a) across body mass index (BMI) categories, assess its association with ischemic heart disease (IHD), and determine whether this association differs across BMI categories. Methods:We conducted a multicenter observational study including 2975 adults from specialized centers in Argentina. Patients were stratified into BMI categories: Group 1: <25, Group 2 BMI: 25-29.9, Group 3: 30-34.9 kg/m2, with exploratory subgroups ≥35 and ≥40 kg/m2. Elevated Lp(a) was defined as >50 mg/dL or >125 nmol/L. Results:Median Lp(a) levels decreased across higher BMI categories. The prevalence of elevated Lp(a) declined across increasing BMI categories (34.6% in BMI <25 kg/m2, 29.8% in BMI 25-29.9, 30.1% in BMI ≥30, 26.4% in BMI ≥35 and 24.6% in BMI ≥40). However, after adjustment for demographic and cardiometabolic factors, overweight and obesity were associated with higher odds of elevated Lp(a) compared with normal weight (BMI 25-29.9: OR 1.97, 95% CI 1.38-2.82; BMI ≥30: OR 1.85, 95% CI 1.22-2.81) suggesting that crude prevalence estimates and adjusted associations were influenced by differences in baseline risk profiles across BMI categories. Elevated Lp(a) was independently associated with IHD across all BMI categories, without significant interaction between BMI and Lp(a). Conclusions:In this cross-sectional study, elevated Lp(a) was associated with prevalent ischemic heart disease across BMI categories despite lower crude prevalence among individuals with higher BMI. Because of the observational cross-sectional design, causal inferences cannot be made. Nevertheless, these findings support consideration of Lp(a) assessment across the BMI spectrum.
