Fourier Transforms - High-tech Application and Current Trends

The main purpose of this book is to provide a modern review about recent advances in Fourier transforms as the most powerful analytical tool for high-tech application in electrical, electronic, and computer engineering, as well as Fourier transform spectral techniques with a wide range of biological, biomedical, biotechnological, pharmaceutical, and nanotechnological applications. The confluence of Fourier transform methods with high tech opens new opportunities for detection and handling of atoms and molecules using nanodevices, with potential for a large variety of scientific and technological applications

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded

The Optimal Population Size for Uniform Crossover and Truncation Selection

In this paper the optimal population size N is empirically computed for the ONEMAX function and truncation selection. N depends on the size of the problem n, the probability p 0 of the advantageous allele in the initial population and the selection intensity I . The dependency of N on I is very complex. By numerically fitting the data the following formula could be obtained: N = 1 + f(I) \Delta p n \Delta ln n \Delta (1= p p 0 \Gamma 1). Furthermore it is shown that the minimal number of function evaluations FE needed to find the optimum is fairly constant in the range 1:0 I 1:4. 1 Introduction In [MSV93] the breeder genetic algorithm was introduced. For a simplified model a predictive theory could be developed ([MSV94]). This model assumes additive gene effects and uniform crossover. The most simple example of additive gene effects is the fitness function ONEMAX of size n. ONEMAX gives just the number of 1's in the string. The model needs five parameters to descr..

Article hepatoprotective effect of mixture of dipropyl polysulfides in concanavalin a-induced hepatitis

The main biologically active components of plants belonging to the genus Allium, responsible for their biological activities, including anti-inflammatory, antioxidant and immunomodulatory, are organosulfur compounds. The aim of this study was to synthetize the mixture of dipropyl polysul-fides (DPPS) and to test their biological activity in acute hepatitis. C57BL/6 mice were administered orally with DPPS 6 h before intravenous injection of Concanavalin A (ConA). Liver inflammation, necrosis and hepatocytes apoptosis were determined by histological analyses. Cytokines in liver tissue were determined by ELISA, expression of adhesive molecules and enzymes by RT PCR, while liver mononuclear cells were analyzed by flow cytometry. DPPS pretreatment significantly attenuated liver inflammation and injury, as evidenced by biochemical and histopathological observations. In DPPS-pretreated mice, messenger RNA levels of adhesion molecules and NADPH oxidase complex were significantly reduced, while the expression of SOD enzymes was enhanced. DPPS pretreatment decreased protein level of inflammatory cytokines and increased percentage of T regulatory cells in the livers of ConA mice. DPPS showed hepatoprotective effects in ConA-induced hepatitis, charac-terized by attenuation of inflammation and affection of Th17/Treg balance in favor of T regulatory cells and implicating potential therapeutic usage of DPPS mixture in inflammatory liver diseases

Depression, anxiety, and quality of life as predictors of rehospitalization in patients with chronic heart failure

Abstract Background Chronic heart failure (CHF) is a severe condition, often co-occurring with depression and anxiety, that strongly affects the quality of life (QoL) in some patients. Conversely, depressive and anxiety symptoms are associated with a 2–3 fold increase in mortality risk and were shown to act independently of typical risk factors in CHF progression. The aim of this study was to examine the impact of depression, anxiety, and QoL on the occurrence of rehospitalization within one year after discharge in CHF patients. Methods 148 CHF patients were enrolled in a 10-center, prospective, observational study. All patients completed two questionnaires, the Hospital Anxiety and Depression Scale (HADS) and the Questionnaire Short Form Health Survey 36 (SF-36) at discharge timepoint. Results It was found that demographic and clinical characteristics are not associated with rehospitalization. Still, the levels of depression correlated with gender (p ≤ 0.027) and marital status (p ≤ 0.001), while the anxiety values ​​were dependent on the occurrence of chronic obstructive pulmonary disease (COPD). However, levels of depression (HADS-Depression) and anxiety (HADS-Anxiety) did not correlate with the risk of rehospitalization. Univariate logistic regression analysis results showed that rehospitalized patients had significantly lower levels of Bodily pain (BP, p = 0.014), Vitality (VT, p = 0.005), Social Functioning (SF, p = 0.007), and General Health (GH, p = 0.002). In the multivariate model, poor GH (OR 0.966, p = 0.005) remained a significant risk factor for rehospitalization, and poor General Health is singled out as the most reliable prognostic parameter for rehospitalization (AUC = 0.665, P = 0.002). Conclusion Taken together, our results suggest that QoL assessment complements clinical prognostic markers to identify CHF patients at high risk for adverse events. Clinical Trial Registration: The study is registered under http://clinicaltrials.gov (NCT01501981, first posted on 30/12/2011), sponsored by Charité – Universitätsmedizin Berlin