Fitness Technology and Exercise Engagement: How Technology Affordances Facilitate Fitness Goal Attainment

To realize desired health returns, fitness technology providers, users, and corporate wellness program managers need to understand how individuals’ different uses of fitness technologies influence their fitness experience and fitness goal achievements. Thus, this study draws on the theory of affordances and the concept of engagement to develop and empirically test a model of fitness technology use as goal-directed behavior. Doing so highlights the relationship between trying to use fitness technologies and trying to perform fitness activities with fitness goal attainment. Our results show that while actualized self-appraisal affordance amplifies users’ cognitive exercise engagement, cognitive exercise engagement does not significantly influence fitness goal attainment. Furthermore, actualized self-appraisal and social appraisal affordances enhance users’ emotional exercise engagement, positively influencing fitness goal attainment. Thus, facilitating the actualization of self-appraisal and social appraisal affordances that increase individuals’ emotional exercise engagement is essential to the effective use of fitness technologies

Investigation on selection crystal behavior of a Ni3Al-based single crystal superalloy IC6SX

AbstractThe grain selection occurring during the solidification of a Ni3Al-base single crystal superalloy IC6SX prepared by spiral grain selection method was studied systematically. Results showed that the equiaxed grains were transformed into columnar grains within starter block and most of columnar grains then will be eliminated. The crystal were formed after the remained columnar grains were eliminated through preferred growth and coupling of spiral structure in Spiral grain selection. The results can explain the competitive growth mechanism of the spiral grain selection and can be used to optimize process design to lay an important foundation for improving preparation processes of single crystal superalloy

A Review on Consumer Health Information Technology Research in IS

While there is a rapid growth in the application of consumer health information technology (CHIT), its growth as an area of interest in IS research is still relatively slow. While there is great potential for research in this area, knowledge barriers to conducting CHIT research do exist. These include a lack of a clear definition of CHIT and lack of knowledge on the current state of CHIT research in IS. To overcome these barriers, we offer a definition of CHIT and then use that definition, together with the IT artifact perspective, to conduct a thematic analysis of CHIT research in the IS domain. We find that CHIT research spans all five IT views but to different degrees: nominal, proxy, and tool views are the most widely used perspectives. Based on our analysis, we suggest future research directions to enrich understanding of CHIT

Genetic Association Analysis of Complex Diseases Incorporating Intermediate Phenotype Information

Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta- analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the CHRNA3-CHRNA5-CHRNB4 region on chromosome 15q24–25.1, which has been replicated in many other studies. Our results confirm that this CHRNA region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs—rs1800469, rs1982072, and rs2241714—in the promoter region of the TGFB1 gene on chromosome 19 (p = 1.46 X 10-5,1.18 X 10-5, and 6.57 X 10-6, respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis

IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization

Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services. The conventional approaches typically cast the intent mining process as a classification task. Although neural classifiers have proven adept at such classification tasks, the issue of neural network models often impedes their practical deployment in real-world settings. We present a novel graph-based multi-turn dialogue system called , which identifies a user's intent by identifying intent elements and a standard query from a dynamically constructed and extensible intent graph using reinforcement learning. In addition, we provide visualization components to monitor the immediate reasoning path for each turn of a dialogue, which greatly facilitates further improvement of the system.Comment: 4pages, 5 figure

Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization

Recent studies have shown that sequence-to-sequence (seq2seq) models struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost layer is entangled, i.e., the syntactic and semantic representations of sequences are entangled. However, we consider that the previously identified representation entanglement problem is not comprehensive enough. Additionally, we hypothesize that the source keys and values representations passing into different decoder layers are also entangled. Starting from this intuition, we propose \textsc{CompoSition} (\textbf{Compo}se \textbf{S}yntactic and Semant\textbf{i}c Representa\textbf{tion}s), an extension to seq2seq models which learns to compose representations of different encoder layers dynamically for different tasks, since recent studies reveal that the bottom layers of the Transformer encoder contain more syntactic information and the top ones contain more semantic information. Specifically, we introduce a \textit{composed layer} between the encoder and decoder to compose different encoder layers' representations to generate specific keys and values passing into different decoder layers. \textsc{CompoSition} achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of our proposal. Codes are available at~\url{https://github.com/thinkaboutzero/COMPOSITION}.Comment: Accepted by Findings of EMNLP 202

Reactive uptake coefficients for multiphase reactions determined by a dynamic chamber system

Dynamic flow-through chambers are frequently used to measure gas exchange rates between the atmosphere and biosphere on the Earth's surface such as vegetation and soils. Here, we explore the performance of a dynamic chamber system in determining the uptake coefficient γ of exemplary gases (O3 and SO2) on bulk solid-phase samples. After characterization of the dynamic chamber system, the derived γ is compared with that determined from a coated-wall flow tube system. Our results show that the dynamic chamber system and the flow tube method show a good agreement for γin the range of 10−8 to 10−3. The dynamic chamber technique can be used for liquid samples and real atmospheric aerosol samples without complicated coating procedures, which complements the existing techniques in atmospheric kinetic studies.</p