Control of separated flow past a cylinder using tangential wall jet blowing

A theoretical analysis is conducted to study the effect of a tangential wall jet on the control of two dimensional separated flow past a circular cylinder. For a tangential wall jet, the mathematical model derived previously is used and the vortex cloud method is adopted for the calculation of the external flow field. For certain limiting cases, the governing equations are simplified and closed forms of solutions for the wall jet parameters can be obtained. It is observed that the wall jet is very efficient in reducing drag by delaying the separation point in the range of small blowing strength. The suction force induced by the wall jet is negligible to the drag due to the external stream. However this suction increases the drag when the blowing strength is large

Machine learning-based predictive model for prevention of metabolic syndrome

Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model\u27s construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome

UniPrimer: A Web-Based Primer Design Tool for Comparative Analyses of Primate Genomes

Whole genome sequences of various primates have been released due to advanced DNA-sequencing technology. A combination of computational data mining and the polymerase chain reaction (PCR) assay to validate the data is an excellent method for conducting comparative genomics. Thus, designing primers for PCR is an essential procedure for a comparative analysis of primate genomes. Here, we developed and introduced UniPrimer for use in those studies. UniPrimer is a web-based tool that designs PCR- and DNA-sequencing primers. It compares the sequences from six different primates (human, chimpanzee, gorilla, orangutan, gibbon, and rhesus macaque) and designs primers on the conserved region across species. UniPrimer is linked to RepeatMasker, Primer3Plus, and OligoCalc softwares to produce primers with high accuracy and UCSC In-Silico PCR to confirm whether the designed primers work. To test the performance of UniPrimer, we designed primers on sample sequences using UniPrimer and manually designed primers for the same sequences. The comparison of the two processes showed that UniPrimer was more effective than manual work in terms of saving time and reducing errors

Site-specific Forest-assembly of Single-Wall Carbon Nanotubes on Electron-beam Patterned SiOx/Si Substrates

Based on electron-beam direct writing on the SiOx/Si substrates, favorable absorption sites for ferric cations (Fe3+ ions) were created on the surface oxide layer. This allowed Fe3+-assisted self-assembled arrays of single-wall carbon nanotube (SWNT) probes to be produced. Auger investigation indicated that the incident energetic electrons depleted oxygen, creating more dangling bonds around Si atoms at the surface of the SiOx layer. This resulted in a distinct difference in the friction forces from unexposed regions as measured by lateral force microscopy (LFM). Atomic force microscopy (AFM) affirmed that the irradiated domains absorbed considerably more Fe3+ ions upon immersion into pH 2.2 aqueous FeCl3 solution. This rendered a greater yield of FeO(OH)/FeOCl precipitates, primarily FeO(OH), upon subsequent washing with lightly basic dimethylformamide (DMF) solution. Such selective metalfunctionalization established the basis for the subsequent patterned forest-assembly of SWNTs as demonstrated by resonance Raman spectroscopy

Epidemiological and Genome-Wide Association Study of Gastritis or Gastric Ulcer in Korean Populations

Gastritis is a major disease that has the potential to grow as gastric cancer. Gastric cancer is a very common cancer, and it is related to a very high mortality rate in Korea. This disease is known to have various reasons, including infection with Helicobacter pylori, dietary habits, tobacco, and alcohol. The incidence rate of gastritis has reported to differ between age, population, and gender. However, unlike other factors, there has been no analysis based on gender. So, we examined the high risk factors of gastritis in each gender in the Korean population by focusing on sex. We performed an analysis of 120 clinical characteristics and genome-wide association studies (GWAS) using 349,184 single-nucleotide polymorphisms from the results of Anseong and Ansan cohort study in the Korea Association Resource (KARE) project. As the result, we could not prove a strong relation with these factors and gastritis or gastric ulcer in the GWAS. However, we confirmed several already-known risk factors and also found some differences of clinical characteristics in each gender using logistic regression. As a result of the logistic regression, a relation with hyperlipidemia, coronary artery disease, myocardial infarction, hyperlipidemia therapy, hypotensive or antihypotensive drug, diastolic blood pressure, and gastritis was seen in males; the results of this study suggest that vascular disease has a potential association with gastritis in males

Feature Interaction in Terms of Prediction Performance

There has been considerable development in machine learning in recent years with some remarkable successes. Although there are many high-performance methods, the interpretation of learning models remains challenging. Understanding the underlying theory behind the specific prediction of various models is difficult. Various studies have attempted to explain the working principle behind learning models using techniques like feature importance, partial dependency, feature interaction, and the Shapley value. This study introduces a new feature interaction measure. While recent studies have measured feature interaction using partial dependency, this study redefines feature interaction in terms of prediction performance. The proposed measure is easy to interpret, faster than partial dependency-based measures, and useful to explain feature interaction, which affects prediction performance in both regression and classification models