LGB Cancer Survivors are More Likely to Participate in Risky Behaviors than Straight Cancer Survivors, United States, Behavioral Risk Factor Surveillance System, 2014

Cancer registries do not collect sexual orientation in their records, leading to limited information about LGB cancer survivorship. Studies have shown that both the LGB population and the population of cancer survivors participate in risky behaviors (i.e. smoking, drinking, and being overweight/obese; sleep inadequacy among cancer survivors), but information about LGB cancer survivors is limited. 2014 Behavioral Risk Factor Surveillance System (BRFSS) data was used to determine if LGB cancer survivors were more likely to participate in risky behaviors than straight cancer survivors. LGB survivors were more likely to drink at least one alcoholic beverage within the past 30 days (AOR: 1.99, 95% CI: 1.44-2.75), to report being an ever smoker (AOR: 1.59, 95% CI: 1.12-2.25), and to binge drink (AOR: 1.99, 95% CI: 1.21-3.28) than straight cancer survivors. There is a strong association between sexual orientation among cancer survivors and risky behaviors. The findings of this study concludes that risky behaviors may be detrimental to the health and survivorship of LGB cancer survivors and further research is needed to determine the association between LGB cancer survivorship, being an adolescent and young adult (AYA), and risky behavior

Energy forward price prediction with a hybrid adaptive model

This paper presents a forecasting technique for forward electricity/gas prices, one day ahead. This technique combines a Kalman filter (KF) and a generalised autoregressive conditional heteroschedasticity (GARCH) model (often used in financial forecasting). The GARCH model is used to compute next value of a time series. The KF updates parameters of the GARCH model when the new observation is available. This technique is applied to real data from the UK energy markets to evaluate its performance. The results show that the forecasting accuracy is improved significantly by using this hybrid model. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads

Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads

Variational inference for Student-t MLP models

This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to nonlinear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, the evidence procedure, and an optimisation algorithm. The technique was tested on two regression applications. The first one is a synthetic dataset and the second is gas forward contract prices data from the UK energy market. The results showed that forecasting accuracy is significantly improved by using Student-t noise models

Convergence of Augmented Lagrangian Methods for Composite Optimization Problems

Local convergence analysis of the augmented Lagrangian method (ALM) is established for a large class of composite optimization problems with nonunique Lagrange multipliers under a second-order sufficient condition. We present a new second-order variational property, called the semi-stability of second subderivatives, and demonstrate that it is widely satisfied for numerous classes of functions, important for applications in constrained and composite optimization problems. Using the latter condition and a certain second-order sufficient condition, we are able to establish Q-linear convergence of the primal-dual sequence for an inexact version of the ALM for composite programs

The spindle of oocytes observed by polarized light microscope can predict embryo quality

Background: The aim is to evaluate spindle position of metaphase II oocyte and the development of embryos originated from oocytes with spindle and without spindle.Methods: Cross-sectional analysis Research: 250 MII oocytes were analyzed with polarized microscope in Military Institute of Clinical Embryology and Histology, Vietnam Military Medical University.Results: Spindles were detected in 170 (77.98%) of 218 metaphase II oocytes, 115 spindles (67.65%) of MII oocytes is beneath or adjacent to the first polar body, 55 oocytes had the spindle located between 300 and 1800 away from the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle (77.98% and 61.02%) were higher than those in oocytes without a visible spindle (22.02% and 36.84%), the difference was statistically significant with p <0.001 and p <0.05.Conclusions: The spindle position of metaphase II oocytes is not always beneath or adjacent to the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle were higher than those in oocytes without a visible spindle

Phoebe Framework and Experimental Results for Estimating Fetal Age and Weight

Fetal age and weight estimation plays an important role in pregnant treatments. There are many estimation formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community. This research proposes a so-called Phoebe framework that supports physicians and scientists to find out most accurate formulas with regard to the community where scientists do their research. The built-in algorithm of Phoebe framework uses statistical regression technique for fetal age and weight estimation based on fetal ultrasound measures such as bi-parietal diameter, head circumference, abdominal circumference, fetal length, arm volume, and thigh volume. This algorithm is based on heuristic assumptions, which aim to produce good estimation formulas as fast as possible. From experimental results, the framework produces optimal formulas with high adequacy and accuracy. Moreover, the framework gives facilities to physicians and scientists for exploiting useful statistical information under pregnant data. Phoebe framework is a computer software available at http://phoebe.locnguyen.net

How Digital Natives Learn and Thrive in the Digital Age: Evidence from an Emerging Economy

As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digital abilities. Results show that economic status and parents’ level of education are positively correlated with digital literacy. Students from urban schools have only a slightly higher level of digital literacy than their rural counterparts, suggesting that school location may not be a defining explanatory element in the variation of digital literacy and resilience among Vietnamese students. Students’ digital literacy and, especially resilience, also have associations with their gender. Moreover, as students are digitally literate, they are more likely to be digitally resilient. Following SDG4, i.e., Quality Education, it is advisable for schools, and especially parents, to seriously invest in creating a safe, educational environment to enhance digital literacy among students

Designing polymeric adhesives for antimicrobial materials: poly(ethylene imine) polymer, graphene, graphene oxide and molybdenum trioxide – a biomimetic approach

The synthesis of biocompatible polymers for coating applications has gained significant attention in recent years due to the increasing spread of infectious diseases via contaminated surfaces. One strategy to combat this problem is to apply antimicrobial coatings to surfaces prone to microbial contamination. This study presents a series of biomimetic polymers that can be used as adhesives to immobilize known antimicrobial agents on the surfaces as coatings. Several polymers containing dopamine methacrylate as co-polymers were synthesized and investigated as adhesives for the deposition of an antimicrobial polymer (polyethyleneimine) and antimicrobial nanoparticles (graphene, graphene oxide and molybdenum trioxide) onto glass surfaces. The results showed that different antimicrobials required different types of adhesives for effective coating. Overall, the coatings fabricated from these composites were shown to inactivate E. coli and B. subtilis within 1 h. These coatings were also effective to prevent biofilm growth and demonstrated to be non-toxic to the human corneal epithelial cell line (htCEpi). Leaching tests of the coatings proved that the coatings were stable under biological conditions