Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients.

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In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80 % .

Objective The efficacy and safety of lurasidone in schizophrenia has been demonstrated in multiple controlled trials, primarily in US and European populations. The aim of the current study was To evaluate lurasidone for the treatment of schizophrenia among patients in Japan, Korea, and Taiwan. Methods Hospitalized patients (N = 460) with schizophrenia were randomized to 6 weeks of fixed‐dose lurasidone 40 mg/d, lurasidone 80 mg/d, risperidone 4 mg/d, or placebo. Efficacy was assessed using the Positive and Negative Syndrome Scale (PANSS) and Clinical Global Impression‐Severity (CGI‐S). Results No significant endpoint differences in PANSS total score were found for lurasidone or risperidone vs placebo. Lurasidone was safe and well tolerated, with minimal effects on weight and metabolic parameters. Discussion The current study was inconclusive regarding the efficacy of lurasidone in schizophrenia but further confirmed its safety and tolerability.