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Alkenes and alcohols are among the most abundant and commonly used organic feedstock in industrial processes. We report a selective catalytic alkylation reaction of alkenes with alcohols that forms a carbon-carbon bond between vinyl carbon-hydrogen (C-H) and carbon-hydroxy centers with the concomitant loss of water. The cationic ruthenium complex [(C(6)H(6))(PCy(3))(CO)RuH](+)BF(4)(-) (Cy, cyclohexyl) catalyzes the alkylation in solution within 2 to 8 hours at temperatures ranging from 75° to 11

The associations of Internet addiction (IA) and smartphone addiction (SA) with mental health problems have been widely studied. We investigated the effects of IA and SA on depression and anxiety while adjusting for sociodemographic variables. In this study, 4854 participants completed a cross-sectional web-based survey including socio-demographic items, the Korean Scale for Internet Addiction, the Smartphone Addiction Proneness Scale, and the subscales of the Symptom Checklist 90 Items-Revised.

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameter

As the feature size of NAND flash memory decreases, the threshold voltage signal becomes less reliable, and its distribution varies significantly with the number of program-erase (PE) cycles and the data retention time. We have developed parameter estimation algorithms to find the means and variances of the threshold voltage distribution that is modeled as a Gaussian mixture. The proposed methods find the best-fit parameters by minimizing the squared Euclidean distance between the measured thres

Abstract A mesoporous LTA zeolite (MP‐LTA)‐supported palladium catalyst was developed for the highly efficient Suzuki–Miyaura reaction of aryl and heteroaryl chlorides. The couplings of various aryl chlorides with arylboronic acids in aqueous ethanol were efficiently achieved in the presence of 1.0 mol% of the catalyst. Furthermore, the scope of this catalyst was extended to the coupling of heteroaryl chlorides. Regardless of the substituents, all of the coupling reactions were very clean and hi

BACKGROUND: Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be express

Catalyst A was developed for highly efficient Hiyama couplings of less reactive (hetero)aryl chlorides in water. This catalyst also showed excellent reactivity for the one-pot double couplings of aryl dichlorides.

High-density NAND flash memory suffers from the data retention problem because even small charge leakage incurs a large threshold voltage shift as the transistor size shrinks. In this paper, we develop a decision directed estimation (DDE) algorithm to know the effects of charge leakage in NAND flash memory using the error pattern of the accessed data. While the conventional sensing directed estimation (SDE) method demands extra memory sensing to know the signal distribution, the proposed DDE alg

Internet and smartphone addiction have become important social issues. Various studies have demonstrated their association with clinical and psychological factors, including depression, anxiety, aggression, anger expression, and behavioral inhibition, and behavioral activation systems. However, these two addictions are also highly correlated with each other, so the consideration of the relationship between internet and smartphone addiction can enhance the analysis. In this study, we considered t

Bayesian therapeutic drug monitoring (TDM) software uses a reported pharmacokinetic (PK) model as prior information. Since its estimation is based on the Bayesian method, the estimation performance of TDM software can be improved using a PK model with characteristics similar to those of a patient. Therefore, we aimed to develop a classifier using machine learning (ML) to select a more suitable vancomycin PK model for TDM in a patient. In our study, nine vancomycin PK studies were selected, and a

Internet gaming disorder (IGD) has become an important social and psychiatric issue in recent years. To prevent IGD and provide the appropriate intervention, an accurate prediction method for identifying IGD is necessary. In this study, we investigated machine learning methods of multimodal neuroimaging data including Positron Emission Tomography (PET), Electroencephalography (EEG), and clinical features to enhance prediction accuracy. Unlike the conventional methods which usually concatenate al