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Malabar blood snapper Lutjanus malabaricus (Bloch & Schneider, 1801) inhabits in coastal marine waters of Nghe An and Ha Tinh provinces, Vietnam. An investigation of the reproductive characteristics of Lutjanus malabaricus in the coastal marine waters of Nghe An and Ha Tinh provinces was undertaken between June 2020 and May 2021. The spawning season of L. malabaricus occurred throughout the year, fish were in the ripe or spawning condition in most months; but spawning peaks from late March/April and August. Females matured at a smaller size than males, with total length (TL) at first maturity being 280–330 mm for males and 230–280 mm for females. The sex ratios of 365 individuals were approximately 1/1, and the total ratio G/W was approximately 0.08−5.65% with a mean of 0.32%. The highest GSI mean value in males was 0.77 (stage IV in July) and the lowest mean value 0.03−0.11 was recorded during stages I and II in November and December. Females had the highest mean value of 1.22−1.41 (stage IV in May) and the lowest mean value of 0.07 during stages I and II in January, and 0.20 at stages I and II in October. These show that fish (L. malabaricus) were ripe or spawning from March and August in the studied waters. The first maturity size was 280−330 mm for males and 230–280 mm for females. Absolute fecundity (Fa) ranged from 24.000 to 769.000 (the mean value 112.000) ova per ovaries for L. malabaricus. In the main spawning period, high gonadal index values were found around the time of the last quarter to the first quarter moon for each month from late March or early April to August. It is suggested that L. malabaricus is a lunar-synchronized spanner in the studied waters.

Phytochemical study on the methanol extract of the stems and leaves of Kadsura induta led to the isolation of six dibenzocyclooctadiene lignans, including three new compounds named kadsindutalignans A–C (1–3), and three known ones, heteroclitalignan B (4), kadsuphilin C (5) and kadsulignan E (6). Their structures were elucidated based on extensive spectroscopic analyses, including HRESIMS, 1D- (1H NMR and 13C NMR), 2D-NMR (HSQC, HMBC, 1H-1H COSY and NOESY), and experimental circular dichroism (CD) spectra. All the isolates inhibited NO production in LPS-activated RAW264.7 cells with IC50 values in the range from 5.67 ± 0.54 µM to 38.19 ± 2.03 µM, compared to that of the positive control of NG-monomethyl-L-arginine acetate (L-NMMA) with an IC50 value of 8.90 ± 0.48 µM. Interestingly, the new compound 2 showed potential inhibition of NO production with an IC50 value of 5.67 ± 0.54 µM, which was higher than that of the positive control.

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

Despite the increasing application of deep learning (DL) models in various socioeconomics such as financial analysis and forecast, intelligent transport, self-driving, disease diagnosis, the effective use of this technology to support agricultural cultivation is still limited. This paper introduces the implementation of the lightest and state-of-the-art YOLOv5 architecture for automatic recognising of important growth stages of Cucumis meloL. from the camera images collected in the greenhouse. This image identification initiative achieved an average accuracy of 96% F1-score in the identification of the five growth stages of Cucumis melo L. using a limited set of training and testing data (total 2,818 images of Cucumis melo L.). These preliminary results lead to the conclusion that the YOLOv5 object detection and classification model is a truly lightweight and promising DL solution after the adoption of the transfer learning technique. Moreover, the YOLOv5 model can execute good performance on edge devices which may open up a new approach in different object detection and classification in real-time directly from a smartphone, Jetson Nano, IP camera...

The present study aims to enrich the specialized literature by proposing and calculating a new flash-flood propagation susceptibility index (FFPSI). Thus, firstly the Flash-Flood Potential Index (F...

This study aims to determine the factors influencing customer satisfaction with the bus service quality of Phan Thiet Automobile Transport Cooperative. Research data are collected from 290 customers who regularly use bus services of Phan Thiet Automobile Transport Cooperative. Applying the exploratory factor analysis and multivariate linear regression, the research results indicate factors positively affecting customer satisfaction with bus service quality are trust, assurance, empathy, traffic culture, perceived value, quality of the station, and tangible facilities. In which, the perceived value puts the most impact on customer satisfaction with the bus service quality of Phan Thiet Automobile Transport Cooperative. Keywords: satisfaction, bus service, customer.