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Photoactive catalysts, when illuminated with UV‐light, generate highly reactive radicals that can oxidize the organic contaminants in water. One method to increase the efficiency of the process, and thereby reduce the light energy requirements, is by developing more active catalysts. Several catalysts that were obtained commercially and/or prepared in the laboratory were examined for their photoactivity, and they are: Ti02, Pt‐Ti02 with platinum loading varying from 0.5% to 10% by weight, SrTi03, and 1.5% NiO‐SrTi03. The organic compounds used to identify the best catalyst were trichloroethylene (TCE), toluene, methyl ethyl ketone (MEK), salicylic acid, and 2,4‐dichlorophenol, with initial concentration varying from 0.1 to 10.0 mg/L. This study also examined the impact of catalyst dosage, organic compound and its initial concentration, and electron acceptor concentration on the reaction kinetics. The process efficiency for mineralization of organic compounds is also evaluated. The results demonstrate that the activity of photocatalysts can be improved by approximately 2–4 times over commercially available catalysts.

Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and nonrapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings, which is time-consuming, labor-intensive and often subjective in nature. Therefore, this study develops an automatic sleep staging system, which uses single channel electroencephalogram (EEG) signal, for convenience of wearing and less interference in the sleep, to do automatic identification of various sleep stages. To achieve the automatic sleep staging system, this study proposes a two-layer stacked ensemble model, which combines the advantages of random forest (RF) and LightGBM (LGB), where RF focuses on reducing the variance of the proposed model while LGB focuses on reducing the bias of the proposed model. Particularly, the proposed model introduces a class balance strategy to improve the N1 stage recognition rate. In order to evaluate the performance of the proposed model, experiments are performed on two datasets, including Sleep-EDF database (SEDFDB) and Sleep-EDF Expanded database (SEDFEDB). In the SEDFDB, the overall accuracy (ACC), weight F1-score (WF1), Cohen's Kappa coefficient (Kappa), sensitivity of N1 (SEN-N1) obtained by proposed model are 91.2%, 0.916, 0.864 and 72.52% respectively using subject-non-independent test (SNT). In parallel, the ACC, WF1, Kappa, SEN-N1 obtained by proposed model are 82.4%, 0.751, 0.719 and 27.15% respectively using subject-independent test (SIT). Experimental results show that the performance of the proposed model are competitive with the state-of-the-art methods and results, and the recognition rate of N1 stage is significantly improved. Moreover, in the SEDFEDB, the experimental results indicate the robustness and generality of the proposed model. INDEX TERMS Sleep stage classification, single channel EEG signal, two-layer stacked ensemble model, random forest, LightGBM.

Community Marital status Schizophrenia Social dysfunction a b s t r a c t Objective: Estimate predictive associations of marital status with social dysfunction in schizophrenia patients. Methods: 817 schizophrenia patients lived in the community of Dongguan, Guangdong province, China, were investigated with the Social Disability Screening Schedule (SDSS)during a three-month period (1.2010e3.2010). The demographic data were harvested. The c 2 test, t test, and fisher's exact were used for comparisons between groups, as appropriate.Multinomial logistic regression (MLR) was used to analyze the predictive associations of demographic variables to the grading of social dysfunctions. Results:The study group consisted of male and female patients aged 16e59 years, 407 females, and 410 males with the mean age (40.7 ± 9.5) years. Analysis of the data revealed significant differences in course of disease and marital status among patients (with and without dysfunction). The married patient made a significant difference with divorced/ widowed patient in mildlyemoderately social dysfunction. There was a significant difference in married and never-married patient with mildly and profoundly social dysfunction.Significant differences were noticed in the self care and occupational roles of the married patient with that of the never-married.Conclusion: This study confirmed that bad marital status is associated with higher odds of social dysfunction among patients with schizophrenia living in the community. These effects should be included in considerations of public health investments in preventing and treating mental disorders.