Dynamics of Electroweak Phase Transition in the 3-3-1-1 Model

The bubble nucleation in the framework of 3-3-1-1 model is studied. Previous studies show that first order electroweak phase transition occurs in two periods. In this paper we evaluate the bubble nucleation temperature throughout the parameter space. Using the stringent condition for bubble nucleation formation we find that in the first period, symmetry breaking from $SU(3)\rightarrow SU(2)$, the bubble is formed at the nucleation temperature $T=150$ GeV and the lower bound of the scalar mass is 600 GeV. In the second period, symmetry breaking from $(SU(2)\rightarrow U(1)$, only subcritical bubbles are formed. This constraint eliminates the electroweak baryon genesis in the second period of the model

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure

QUALITY OF TRANSMISSION AWARE ROUTING IN AD HOC NETWORKS BASED ON CROSS-LAYER MODEL COMBINED WITH THE STATIC AGENT

The physical effects happening on the transmission routes in ad hoc networks influence the network performance seriously. These impacts decrease the quality of transmission, especially ad hoc networks with the wide area and high node density. This paper focused on investigating the routing techniques in ad hoc networks taking into account the quality of transmission. Thence, we proposed an improved routing algorithm of DSR based on the cross-layer model in combined with the static agent. The objective of the proposed algorithm is to improve the quality of the transmission signal, reduce the blocking probability of the data packets due to the unguaranteed quality of transmission

SLBQT-DSR: SOURCE-BASED LOAD BALANCING ROUTING ALGORITHM UNDER CONSTRAINTS OF QUALITY OF TRANSMISION FOR MANET

The routing technique under the constraints of the quality of transmision (QoT) in mobile ad hoc networks (MANET) has been studied widely recently. For this routing technique, QoT of the data transmission routes is improved. However, for the network models with mesh topologies such as MANET, The routing technique under the constraints of QoT can increase the bottlenecks due to the unbalanced traffic load. In this paper, we improve the DSR protocol by using the source-based load balancing in combined with the QoT constraint. The simulation results have shown that, the proposed algorithm outperforms the original algorithms in terms of the signal to noise ratio, bit error rate, blocking probability of the data packets and throughput.The routing technique under the constraints of the quality of transmision (QoT) inmobile ad hoc networks (MANET) has been studied widely recently. For this routing technique, QoTof the data transmission routes is improved. However, for the network models with mesh topologiessuch as MANET, The routing technique under the constraints of QoT can increase the bottlenecksdue to the unbalanced trac load. In this paper, we improve the DSR protocol by using the source-based load balancing in combined with the QoT constraint. The simulation results have shown that,the proposed algorithm outperforms the original algorithms in terms of the signal to noise ratio, biterror rate, blocking probability of the data packets and throughput

Characterization of pig farms in Hung Yen, Hai Duong and Bac Ninh provinces

peer reviewedIn order to characterization of pig farms in the Red River Delta, a study was conducted on 90 pig farms in Hung Yen, Hai Duong and Bac Ninh provinces from June to December 2006. Results show that most of the pig farms had been built for five years with a small size (0.5 hectare per farm). The invested capital was about 300-400 millions VND per farm. Four main sow groups used in the farms included crossbred exotic sows (51.1%), crossbred sow between local and exotic breeds (14.4%), purebred Landrace and Yorkshire breeds (15.6 and 18.9%, respectively). The boars were various (Duroc 30%, Yorkshire 21%, Landrace 13%, PiÐtrain × Duroc 36% and others). The pigs farms were faced with several difficulties such as limited land, lack of invested capital, uncontrolled quality of breeding pigs, high costs of feed, poor hygiene condition and diseases

Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), least squares support vector machine (LSSVM), and multivariate adaptive regression spline (MARS) models, were employed to reconstruct the missing daily-averaged discharge in a mega-delta from 1980 to 2015 using upstream-downstream multi-station data. The performance and accuracy of each ML model were assessed and compared with the stage-discharge rating curves (RCs) using four statistical indicators, Taylor diagrams, violin plots, scatter plots, time-series plots, and heatmaps. Model input selection was performed using mutual information and correlation coefficient methods after three data pre-processing steps: normalization, Fourier series fitting, and first-order differencing. The results showed that the ML models are superior to their RC counterparts, and MARS and RF are the most reliable algorithms, although MARS achieves marginally better performance than RF. Compared to RC, MARS and RF reduced the root mean square error (RMSE) by 135% and 141% and the mean absolute error by 194% and 179%, respectively, using year-round data. However, the performance of MARS and RF developed for the climbing (wet season) and recession (dry season) limbs separately worsened slightly compared to that developed using the year-round data. Specifically, the RMSE of MARS and RF in the falling limb was 856 and 1, 040 m3/s, respectively, while that obtained using the year-round data was 768 and 789 m3/s, respectively. In this study, the DT model is not recommended, while the GPR and SVR models provide acceptable results

Corporate Financial Distress of Industry Level Listings in an Emerging Market

Any critical analysis of the corporate financial distress of listed firms in international exchange would be incomplete without a serious dissection at the industry level because of the different levels of risks concerned. This paper considers the financial distress of listed firms at the industry level in Vietnam over the last decade. Two periods are considered, namely during the Global Financial Crisis (GFC) (2007 - 2009) and post-GFC (2010 - 2017). The logit regression technique is used to estimate alternative models based on accounting and market factors. The paper also extends the analysis to include selected macroeconomic factors that are expected to affect the corporate financial distress of listed firms at the industry level in Vietnam. The empirical findings confirm that the corporate financial distress prediction model, which includes accounting factors with macroeconomic indicators, performs much better than alternative models. In addition, the evidence confirms that the GFC had a damaging impact on each sector, with the Health & Education sector demonstrating the most impressive recovery post-GFC, and the Utilities sector recording a dramatic increase in bankruptcies post-GF

Mental health and its determinants among adolescents living in families with separated or divorced parents in an urban area of Vietnam

Objectives We assessed the prevalence of stress, anxiety, and depression among adolescents living in families with separated or divorced parents in Hue City, Vietnam and identified factors associated with these conditions. Methods This cross-sectional study enrolled 309 adolescents, aged 12 to 17 years, living in families with separated or divorced parents in Hue City, Vietnam. The depression anxiety stress scale-21 (DASS-21) was used to measure stress, anxiety, and depression. Predictors of overall and individual mental health problems were identified using ordered and binary logistic regression, respectively. Results The DASS-21 scale revealed a 49.2% prevalence of stress, while anxiety and depression had s prevalence rates of 61.5%. Among participants, 42.4% experienced all 3 mental health issues. Several factors were identified as significant predictors of mental health problems, including poor to average economic status (adjusted odds ratio [aOR], 2.00; 95% confidence interval [CI], 1.21–3.31; p=0.007); being in high school (aOR, 5.02; 95% CI, 2.93–8.60; p<0.001); maternal occupation of teacher, healthcare professional, or official (aOR, 2.39; 95% CI, 1.13–5.03; p=0.022); longer duration of family separation or divorce (aOR, 1.24; 95% CI, 1.05–1.45; p=0.009); living with one’s mother (aOR, 1.69; 95% CI, 1.03–2.76; p=0.04); alcohol consumption (aOR, 1.70; 95% CI, 0.99–2.92; p=0.050); and being bullied (aOR, 5.33; 95% CI, 1.10–25.69; p=0.037). Most of these factors were associated with stress, anxiety, and depression. Additionally, smoking was associated with stress. Conclusion Adolescents with separated or divorced parents were at increased risk of stress, anxiety, and depression. The findings of this study provide important implications for prevention programs

Preparation of self-assembly silica redox nanoparticles to improve drug encapsulation and suppress the adverse effect of doxorubicin

Background and Purpose: The utilization of doxorubicin (DOX) in clinal trials is also challenging owing to its adverse effects, including low oral bioavailability, generation of reactive oxygen species (ROS), cardiotoxicity, and epithelial barrier damage. Recently, scavenging of ROS reduced the cytotoxicity of DOX, suggesting a new approach for using DOX as an anticancer treatment. Thus, in this study, non-silica and silica redox nanoparticles (denoted as RNPN and siRNP, respectively) with ROS scavenging features have been designed to encapsulate DOX and reduce its cytotoxicity. Experimental Approach: DOX-loaded RNPN (DOX@RNPN) and DOX-loaded siRNP (DOX@siRNP) were prepared by co-dissolving DOX with RNPN and siRNP, respectively. The size and stability of nanoparticles were characterized by the dynamic light scattering system. Additionally, encapsulation efficiency, loading capacity, and release profile of DOX@RNPN and DOX@siRNP were identified by measuring the absorbance of DOX. Finally, the cytotoxicity of DOX@RNPN and DOX@siRNP against normal murine fibroblast cells (L929), human hepatocellular carcinoma cells (HepG2), and human breast cancer cells (MCF-7) were also investigated. Key results: The obtained result showed that RNPN exhibited a pH-sensitive character while silanol moieties improved the stability of siRNP in physiological conditions. DOX@RNPN and DOX@siRNP were formed at several tens of nanometers in diameter with narrow distribution. Moreover, DOX@siRNP stabilized under different pH buffers, especially gastric pH, and improved encapsulation of DOX owing to the addition of silanol groups. DOX@RNPN and DOX@siRNP maintained anticancer activity of DOX against HepG2, and MCF-7 cells, while their cytotoxicity on L929 cells was significantly reduced compared to free DOX treatment. Conclusion: DOX@RNPN and DOX@siRNP could effectively suppress the adverse effect of DOX, suggesting the potential to become promising nanomedicines for cancer treatments