Application of Network Meta-Analysis in The Field of Physical Activity and Health Promotion: A Case Study

University of Minnesota M.S. thesis. August 2020. Major: Kinesiology. Advisor: Zan Gao. 1 computer file (PDF); vii,Continued advancement in the field of kinesiology and health promotion relies heavily on the synthesis of rigorous quantitative scientific evidence. As such, meta-analyses of randomized controlled trials have led to a better understanding of what intervention strategies are superior (i.e., produce the greatest effects) in physical activity-based health behavior change interventions. Indeed, standard meta-analytic approaches have allowed researchers in the field to synthesize relevant experimental evidence using pairwise procedures which produce reliable estimates of the homogeneity, magnitude, and potential biases in the observed effects. However, pairwise meta-analytic procedures are only capable to discerning differences in effects between a select intervention strategy and a select comparison condition or control condition. In order to maximize the impact of physical activity interventions on health-related outcomes, it is necessary to establish evidence concerning the comparative efficacy of all relevant physical activity intervention strategies. The development of network meta-analysis (NMA)—most commonly used in medical-based clinical trials—has allowed for the quantification of indirect comparisons, even in the absence of direct, head-to-head trials. Thus, it stands to reason that NMA can be applied in the physical activity and health promotion research to identify the best intervention strategies. Given this analysis technique is novel and largely unexplored in the field of kinesiology and health promotion, care must be taken in its application to ensure reliable estimates and discernment of the effect sizes between interventions. Therefore, the purpose of this study is to first comment on the potential application and importance of NMA in the field of kinesiology and health promotion, then describe how to properly and effectively apply this technique using a specific case study evaluating the effects of different lifestyle interventions on children’s body composition, and lastly suggest important considerations for its appropriate application in this field. In this paper, overviews of the foundations of NMA and commonly used approaches for conducting NMA are provided, followed by assumptions of NMA, opportunities and challenges in NMA, and a case study example of the development and conduct of an NMA, as well as the interpretation of the analysis results. The case study collect original data from published randomized controlled studies investigating on some type of intervention on variables including body mass index (BMI), BMI z-score (BMIz), and body fat percentage, divided the used intervention into ten categories in total, from simple single intervention to multiple components mixed intervention (more than three), and used pre-processed data to carry out network meta-analysis. Results of analysis using mean difference (SD) between baseline and immediate post-intervention data showed that PA intervention ranked top two of the most effective approaches among other types of lifestyle interventions in all three variables, suggesting that promoting PA participation is crucial in children’s health status and childhood obesity control. While based on the analysis using combined original pre-and-post data (SE), multiple component interventions were predicted to be the best ranked intervention approach among all ten types of intervention, indicating that taking care of more aspects in children’s lifestyle may also result in an important impact for children to keep healthy and fit

A high-precision facial recognition method for small-tailed Han sheep based on an optimised Vision Transformer

Accurate identification of individual animals plays a pivotal role in enhancing animal welfare and optimising farm production. Although Radio Frequency Identification technology has been widely applied in animal identification, this method still exhibits several limitations that make it difficult to meet current practical application requirements. In this study, we proposed ViT-Sheep, a sheep face recognition model based on the Vision Transformer (ViT) architecture, to facilitate precise animal management and enhance livestock welfare. Compared to Convolutional Neural Network (CNN), ViT is renowned for its competitive performance. The experimental procedure of this study consisted of three main steps. Firstly, we collected face images of 160 experimental sheep to construct the sheep face image dataset. Secondly, we developed two sets of sheep face recognition models based on CNN and ViT, respectively. To enhance the ability to learn sheep face biological features, we proposed targeted improvement strategies for the sheep face recognition model. Specifically, we introduced the LayerScale module into the encoder of the ViT-Base-16 model and employed transfer learning to improve recognition accuracy. Finally, we compared the training results of different recognition models and the ViT-Sheep model. The results demonstrated that our proposed method achieved the highest performance on the sheep face image dataset, with a recognition accuracy of 97.9%. This study demonstrates that ViT can successfully achieve sheep face recognition tasks with good robustness. Furthermore, the findings of this research will promote the practical application of artificial intelligence animal recognition technology in sheep production

Study on Corrosion and Wear Behavior Mechanism of Reactor Material in Metastannic Acid Synthesis

AISI 316L, Ti2, and Zr1 are widely used in the selection of reaction still material, however, there is corrosion wear behavior in the use process. In this paper, the adsorption behavior of oxygen in Fe, Ti, and Zr is studied by the first principles method. Corrosion and wear behaviors of AISI 316L, Ti2 and Zr1 were studied by electrochemical corrosion and wear tests. The results show that AISI 316L can effectively resist the action of friction pair during wear by elastic modulus calculation. Oxygen is easily adsorbed at the top of the Fe(111) crystal plane and the bridge site of the Zr(110) crystal plane to form the most stable adsorption structure. The Ecorr of Zr1 (0.275 V) is greater than that of Ti2 (0.266 V) and AISI 316L (0.094 V), resulting in a ZrO2 passivated film with strong protection in the HNO3 solution. The wear rate of AISI 316L is higher than that of Zr1 and Ti2. In the selection of tin chemical reactor material, it is preferred that Zr1 can withstand corrosion and wear for a long time in a nitric acid system, which provides important guidance for corrosion and wear of reactor materials in the synthesis of tin acid

Adenosine Phosphate Functionalized Magnetic Mesoporous Graphene Oxide Nanocomposite for Highly Selective Enrichment of Phosphopeptides

Developing an efficient strategy to enrich the low abundance phosphopeptides before mass spectrometry detection is a vital preprocessing step in phosphoproteomics. In this work, we synthesized an adenosine phosphate-Ti⁴⁺ functionalized magnetic mesoporous graphene oxide nanocomposite (denoted as MG@mSiO₂-ATP-Ti⁴⁺) to selectively extract phosphorylated peptides from complex biological samples based on the immobilized metal ion affinity chromatography (IMAC). Mesoporous silica was coated on the substrate material of magnetic graphene oxide and then the ATP containing three phosphate groups was grafted on the inwall of mesoporous channels as chelating ligands to immobilize the Ti⁴⁺ cations. With favorable properties, such as large surface area and good hydrophilicity and size-exclusion effect, the MG@mSiO₂-ATP-Ti⁴⁺ exhibited excellent sensitivity and selectivity toward phosphopeptides whether in low concentration of β-casein digest (20 amol μL^–1, 4 fmol) or the digest mixture of β-casein and bovine serum albumin (with molar ratio of 1:1000) as well as good reusability. Furthermore, MG@mSiO₂-ATP-Ti⁴⁺ could also be applied in the selective enrichment of phosphorylated peptides from nonfat milk digest and human saliva and serum

Analysis and Prediction of the Trend Features for Teaching Development Based on Knowledge Discovery

The existing research on teaching development of teachers fails to effectively quantify the teaching development trend. This paper deeply mines the evaluation data on the teaching quality of college teachers, before analyzing and predicting the trend features for teaching development of college teachers based on knowledge discovery. Firstly, the knowledge features of the teaching development trend of college teachers were examined. Next, the fluctuation features of the time series on the teaching quality development of college teachers were described based on chaotic time series. In addition, a prediction model for teaching development of college teachers was established for weighted first-order chaotic time series, and used to simulate the nonlinear features of the time series on the teaching quality development of college teachers. The prediction model was proved effective through experiments

Motivation of Students’ Persistency for Online Learning under Multiple Mediation Effect

This paper probed deep into the motivation of students’ persistency for online learning from the perspective of user experience of online learning platforms, in the purpose of increasing user stickiness and formulating effective operation strategies in a targeted manner. Existing studies on the motivation of students’ persistency for online learning mostly focus on theories, while few of them have talked about the problem with the multiple mediation effect taken into consideration, for this reason, this paper aims to fill in this research gap and explore the mechanism behind the motivation of students to carry out online learning persistently under the multiple mediation effect. At first, this paper built an improved support vector machine (SVM) classifier and used it to predict the duration of students' online learning; then, it adopted a structural equation model to analyze the data of students’ willingness to continue online learning; after that, this paper gave a theoretical analysis on the motivation of students’ persistency for online learning under multiple mediation effect, and constructed a basic regression model for the said matter; at last, this paper employed experimental results to verify the prediction accuracy of the constructed model, and gave the corresponding estimation results

Approach to generating the right active phase in the "Direct" synthesis of trimethoxysilanes using the CuCl-Cu2O catalyst

The "direct" synthesis of trimethoxysilane (M3) by the reaction of Si with methanol over the Cud catalyst often generates a large amount of erosive HCl gas, causing severe environmental pollution. Thus, it calls for the development of greener and more efficient composite catalysts. In this work, to reduce the amount of CuCl, we employed three pretreatment methods to activate the contact mass of Si powder and the composite catalyst of CuCl and Cu2O. It was found that the contact mass activated by argon (Ar), hydrogen argon (H-2/Ar) and methyl chloride (CH3Cl), respectively, exhibited different catalytic performances: the highest Si conversion (34.1%) and M3 yield (24.0%) were obtained on the sample activated by CH3Cl. Notably, the M3 yield was increased 8-fold than that of the un-activated sample. The X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) studies revealed that different activation methods led to the generation of different kinds of CuxSi phases, such as Cu3Si and Cu4Si, both of which gradually transformed into Cu15Si4 during the reaction process. Pretreatment of the contact mass with CH3Cl promoted the formation of CuxSi, which contributed to the generation of more Cu15Si4 active phase, and finally to the superior catalytic performance