Abstract-It is of great importance to explore the current situation of overseas Vietnamese students and policy improvement, in order to improve the management level of overseas Vietnamese students. Research methods like statistical method, inductive method and policy analysis method are applied to conclude the current situation of overseas Vietnamese students, analyze factors influencing the ambition of them and predicaments in studying abroad, and then propose adjusting study-abroad policy of Vietnamese government. It encourages overseas students to study hard and mobilizes them to return home to work, in order to improve the management level of overseas Vietnamese students and promote the development of study-abroad career in Vietnam.
As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.
Powerlifting is a strength sport that is quite popular in the world. Powerlifters have their power levels varied at different ages and body weights, and their power levels are closely related to their performance. Therefore, studying the impact of age and weight on the performance of powerlifters is an important work. The traditional method relies mainly on artificial experience to judge the performance, and often does not get the desired results. In recent years, machine learning has developed rapidly, and applying machine learning in sports is a very interesting topic. This study is based on a new machine learning algorithm to construct a prediction model for the best performance of powerlifters. We propose a doublelayer extreme learning machine based on affine transformation and two-layer extreme learning machine theory (AF-DELM). Then use a dynamic weight-gravitational search algorithm to improve the AF-DELM networks. The results show that the algorithm can better predict the performance and provide an effective predictive aid for the powerlifting competition.INDEX TERMS Gravitational search algorithm, gravitational-double layer extreme learning machine, powerlifting performance, prediction model.
Forward osmosis (FO) has received considerable interest for water and energy-related applications in recent years. However, FO has not been commercialized yet because of a few reasons. The lack of a high-performance FO membrane is one of the important barriers. To overcome this issue, a novel high-performance thin-film composite (TFC) membrane was successfully fabricated via interfacial polymerization with poly-L-lysine incorporated polysulfone substrate (PSf). Compared to the pristine PSf substrate, the incorporation of lysine (ranging 1 – 15 wt.%) meaningfully alternates the substrates chemical structure, porosity, contact angle, and morphology leading to an enhancement of the lysine -TFC membranes performance. The results showed that the new substrates with higher porosity, more hydrophilic, and smaller in pore size after the introduction of L-lysine. The membrane achieved the highest FO water flux at 15% concentration of lysine and the maximum FO water flux was 35 L/m2.h (LMH) with a comparable specific salt flux (Js/Jw) of 0.002 g/L in the active layer facing the feed side (AL-FS) when 1M NaCl was applied as draw solution. The water flux was increased with increasing concentration of lysine. The addition of poly-L-Lysine in casting solution resulted in a more porous and hydrophilic support layer.
IntroductionPatient involvement in health technology assessment (HTA) has documented advantages, such as improved understanding of disease context, and increased legitimacy and transparency of the HTA process. In the absence of clear metrics, thresholds, or criteria, it is not clear how input regarding patient preferences influences HTA based recommendations of the pan Canadian Oncology Drug Review (pCODR).MethodsThis is a concurrent complementary mixed methods study. A quantitative model (logit) is used to estimate the impact of patient input and other HTA criteria on pCODR recommendations. A qualitative analysis of semi-structured interviews with Canadian HTA committee members is used to describe the mechanisms of action through which patient input influences recommendations.ResultsPatient input was considered important in providing context to the HTA discussion, but committee members were not able to explicate how any specific elements of patient submissions weighted into the committee’s recommendation. There was an element of mistrust in the patient input data. The estimated impact of patient input on funding recommendations is not statistically significant, recommendations remain driven by evidence of clinical benefit.ConclusionsThe commitment to inclusion of patient perspectives in HTA in Canada is strong, and procedurally Canada is among the leaders in this regard. The tangible impact of patient input could be increased with an improved system for collection of most relevant data, and clear guidelines about how patient input should weigh into HTA recommendations.
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