A KINEMATIC ANALYSIS OF DIFFERENT JUMPING METHODS FOR SPIKING IN VOLLEYBALL

This study was designed to analyze kinematic characteristics of different types of volleyball spikes at take-off. It compared the hop jump and step-close jump with different stride distances (25cm-35cm and 45cm-55cm). Twelve male college volleyball players volunteered for this study. The displacement of squatting, striding, and horizontal hop jumps were shorter than those data of step-close jump. However, when compared with vertical displacement, the performance of hop jump was better than step-cross jump. The angles of lower limbs during the hop jump landing, body mass center in the lowest position, and take-off were significantly narrower than those displayed during step-cross jump. Therefore, the step-close jump demonstrated significantly better kinematics compared to the hop jump

Constructing Cooking Ontology for Live Streams

We build a cooking domain knowledge by using an ontology schema that reflects natural language processing and enhances ontology instances with semantic query. Our research helps audiences to better understand live streaming, especially when they just switch to a show. The practical contribution of our research is to use cooking ontology, so we may map clips of cooking live stream video and instructions of recipes. The architecture of our study presents three sections: ontology construction, ontology enhancement, and mapping cooking video to cooking ontology. Also, our preliminary evaluations consist of three hierarchies—nodes, ordered-pairs, and 3-tuples—that we use to referee (1) ontology enhancement performance for our first experiment evaluation and (2) the accuracy ratio of mapping between video clips and cooking ontology for our second experiment evaluation. Our results indicate that ontology enhancement is effective and heightens accuracy ratios on matching pairs with cooking ontology and video clips

The Research on the Detection of Noteworthy Symptom Descriptions

The advance of mobile devices and communication technologies enable patients to communicate with their doctors in a more convenient way. We have developed an App that allows patients to record their symptoms and submit them to their doctors. Physicians can keep track of patients’ conditions by looking at the self-report messages. Nevertheless, physicians are usually busy and may be overwhelmed by the large amount of incoming messages. As a result, critical messages may not receive immediate attentions, and patient care is compromised. It is imperative to identify the messages that require physicians’ attention, called noteworthy messages. In this research, we propose an approach that applies text-mining technologies to identify medical symptoms conveyed in the messages and their associated sentiment orientation, as well as other factors. Noteworthy messages are subsequently characterized by symptom sentiment and symptom change features. We then construct a prediction model to identify messages that are noteworthy to the physicians. We show from our experiments using data collected from a teaching hospital in Taiwan that the different features have different degrees of impact on the performance of the prediction model, and our proposed approach can effectively identify noteworthy messages

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

Nematic topological superconducting phase in Nb-doped Bi2Se3

A nematic topological superconductor has an order parameter symmetry, which spontaneously breaks the crystalline symmetry in its superconducting state. This state can be observed, for example, by thermodynamic or upper critical field experiments in which a magnetic field is rotated with respect to the crystalline axes. The corresponding physical quantity then directly reflects the symmetry of the order parameter. We present a study on the superconducting upper critical field of the Nb-doped topological insulator NbxBi2Se3 for various magnetic field orientations parallel and perpendicular to the basal plane of the Bi2Se3 layers. The data were obtained by two complementary experimental techniques, magnetoresistance and DC magnetization, on three different single crystalline samples of the same batch. Both methods and all samples show with perfect agreement that the in-plane upper critical fields clearly demonstrate a two-fold symmetry that breaks the three-fold crystal symmetry. The two-fold symmetry is also found in the absolute value of the magnetization of the initial zero-field-cooled branch of the hysteresis loop and in the value of the thermodynamic contribution above the irreversibility field, but also in the irreversible properties such as the value of the characteristic irreversibility field and in the width of the hysteresis loop. This provides strong experimental evidence that Nb-doped Bi2Se3 is a nematic topological superconductor similar to the Cu- and Sr-doped Bi2Se3

Comparison of the surface roughness of gypsum models constructed using various impression materials and gypsum products

AbstractBackground/purposeThis study compared the surface roughness of gypsum models constructed using various impression materials, gypsum products, and storage times before repouring.Materials and methodsThree alginate impression materials, four commercial silicone impression materials, and three types of gypsum product (MG crystal rock, Super hard stone, and MS plaster) were used. Impression materials were mixed and poured into five plastic rings (20 mm in diameter and 2 mm high) for each group, and the surfaces of the set gypsum product models of 63 groups, which were poured immediately, and 1 hour and 24 hours later, were assessed using a surface roughness tester. One-way ANOVA and Bonferroni's comparison tests were used for the statistical analyses.ResultsThe surface roughness: (1) was greater for most specimens constructed from alginate impression material (2.72 ± 0.45–7.42 ± 0.66 μm) than from silicone impression materials (1.86 ± 0.19–2.75 ± 0.44 μm); (2) differed with the type of gypsum product when using alginate impression materials (surface roughness of Super hard stone > MG crystal rock > MS plaster), but differed little for silicone impression materials; and (3) differed very little with the storage time before repouring.ConclusionThe surface roughness of stone models was mainly determined by the type of alginate impression material, and was less affected by the type of silicone rubber impression material or gypsum product, or the storage time before repouring

Geomorphic Gradients in Shallow Seagrass Carbon Stocks

Seagrass meadows are important sinks of organic carbon (Corg), in particular the near-surface Corg pool (≤ 15 cm) compared to deeper sediments. Near-surface carbon is highly susceptible to disturbance and loss to the atmosphere, however, inadequate accounting for variability in this pool of carbon limits their uptake into carbon accounting frameworks. We therefore investigated the spatial variability in seagrass near-surface Corg and biomass Corg across different geomorphic (estuary, lagoonal and reef-associated) and community typologies (pioneer and persistent). Near-surface Corg stock in vegetated areas (25.78 Mg Corg ha−1 ± 26.64) was twice that from unvegetated areas (14.27 Mg Corg ha−1 ± 15.86), reinforcing the paradigm that the presence of seagrass enhances carbon stocks. Lagoonal and reef-associated meadows showed similar Corg stocks (p \u3e 0.05), which were substantially higher (p \u3c 0.05) than estuary meadows. Likewise, persistent seagrass communities (Cymodocea dominance) stored higher (p \u3c 0.05) stocks of Corg than pioneer communities (Halophila and Halodule dominance). Linear regression models showed significant but weak relationships between seagrass cover, shoot density and standing biomass with near-surface Corg stocks, whereas significant and strong relationships were observed for organic matter, dry bulk density and median grain size. The results highlight the need for higher resolution carbon assessments to better understand local and regional variability, in order to better inform carbon accounting and conservation policy