Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between golf performance, anthropometrics, muscular strength and power characteristics in young elite players : original research article

Background : The aim of this study was to determine the relationship between golf performance (handicap, drive ball speed, approach and putting accuracy), anthropometrics and muscular strength and power in young elite golfers. Type of study : Observational, descriptive and transversal study, with accidental sampling (non-randomised). Methods : Forty-four male golfers participated in the study and the following information was obtained: anthropometrics, drive ball speed, approach accuracy, putting accuracy, medicine ball throwing speed, countermovement jump, bench press and parallel full squat. The relationship between these variables was assessed using Pearson's correlation coefficient, while a regression analysis was performed to assess which parameters were the best predictors of handicap. Results : The main results showed that peak and average drive ball speeds were the only golf performance indicators significantly correlated to handicap (r = -0.58 to -0.61, P<0.001). Significant correlations were observed between drive ball speed and body mass (r = 0.4, P<0.01). In addition, handicap and drive ball speed correlated significantly with muscular strength and power, even when adjusted with the anthropometric characteristics (r = 0.50 to 0.70; P<0.01). Finally, the regression analysis showed that 68.7% of the total variance in handicap was explained by height, drive ball speed average and medicine ball throwing performance. Conclusions : The high correlations found between drive ball speed, handicap and upper- and lower-body strength suggest that muscular strength and power are important. These results have important applications as it could help coaches with optimal development of physical and sport-specific conditioning and golf training programmes.
Investigating synchronous and asynchronous class attendance as predictors of academic success in online education

Learning is facilitated by participation and interaction and can be synchronously or asynchronously in online education. This study investigated the relationship between students’ academic success and online interaction and participation and explored their class attendance (synchronous virtual classes and/or watching the recorded virtual classes) in the online study mode of an enabling program at Southern Cross University in Australia. The Preparing for Success at SCU Program equips students with study skills for success at university. The data were retrieved from usage information data provided by the Blackboard Learn learning management system. The results show that it is important for students to attend class, but it does not necessarily make a difference whether students attend synchronous virtual classes or watch the recordings of the virtual classes. A significant relationship was found between academic success and the number of hours students participated in and interacted with the online learning system. Academic success may be increased by providing various options for students to participate and interact online, and to attend classes synchronously or asynchronously. The flexibility of online education can enable students to be successful in their studies. The inclusion of varied activities is therefore recommended to increase academic success in online education.
Determinants of Capital Structure in Listed Insurance Companies in Nigeria

This study investigates the determinants of capital structure in listed insurance companies in Nigeria for the period of thirteen years, from 2006-2018. Ex-post facto research design was adopted for this study. The population of the study is made up of the 28 insurance companies listed on the floor of the Nigerian Stock Exchange (NSE) as at 2018. Since the population is not too large, this study utilized census sampling technique to take all the population. The data used in this study were secondary data derived from annual reports of insurance companies that are listed on the NSE. The study used panel regression with respect to the use of Hausman specification test to determine the use of fixed or random effect model. The random effect regression result revealed that that firm size has insignificant positive effect on capital structure (CST) of listed insurance companies in Nigeria. The study showed a significant positive effect between age and CST of listed insurance companies in Nigeria. Based on the regression result, asset tangibility has insignificant negative effect on CST, the regression result shows that risk has insignificant positive effect on CST, while the study found that insurance growth has significant positive effect on CST of listed insurance companies in Nigeria. The study concludes that size, age, tangibility of asset, insurance risk and growth are determinants of CST of listed insurance companies in Nigeria. The study recommends that insurance companies should have a high consideration for the value of total asset when determining their capital mix. Also, insurance companies that have been incorporated for long should consider external financing likewise, insurance companies should not give fixed asset priority when considering their capital structure mix. Debt providers should seek for high return in order to hold the risk related to the bankruptcy and financial distress. Lastly, debt holders should require such return to hold the risk of agency conflicts with shareholders and management.
Serum Uric Acid As a Predictor Factor of the Response to Deferasirox Therapy for Patients with b-Thalassemia Major

Abstract 5306 Serum uric acid of patients with beta-thalassemia major (b-MA), despite the hyperuricosuria observed in these patients, is usually in the upper normal levels or increased due to excessive catabolism of the red blood cells (ineffective erythropoiesis). Deferasirox a new oral iron chelator with potential nephrotoxicity is recently used as iron overload treatment in patients with b-MA. Aim: Aim of the study is to investigate the effect of deferasirox on uric acid levels of patients with homozygous b-MA. Material-Method: 53 patients were enrolled to the present study with b-MA major, (aged 22.4 ± 14.7 years, range 4–12 years) 36 adults and 17 children, 32 females and 21 male. All the patients were transfusion-dependent with pretransfusional haemoglobin 9gr/dl and treated with iron chelation. The comparison was made between two different time points9 measurements of uric acid and ferritin, at the beginning before Deferasirox, and one year later. The blood was taken from the patients early at mornings before the transfusion. Also uric acid was measured in 24 hour urine of patients under deferasirox, or other iron chelation therapy or after weekly discontinuation of deferasirox. Patients taken allopurinol or thiazide or with abnormal kidney function were excluded. Results: There is statistically significant difference (p Conclusion: The mechanism of uric acid reduction in patients with b-MA major treated with deferasirox is not clear. However, it does not seem to be associated with excess of excretion by urine. The positive correlation between uric acid level and ferritin, allow us to consider uric acid as a predictor factor of the response to deferasirox therapy. Disclosures: Vlachaki: Novartis Hellas S.A. C.I. : Research Funding. Oikonomou: Novartis Hellas S.A. C.I. : Research Funding.
Neumann-Expansion-Based FEM for Uncertainty Quantification of Permittivity Variations

Electromagnetic analysis is subjected to uncertainties due to variations in material properties, excitation functions, boundary conditions, or the geometry itself. In this letter, the stochastic response is obtained using the Neumann expansion method, which is implemented alongside Galerkin’s formulation of vector finite elements for the variations in the properties of the electromagnetic material. Using the proposed approach, a constraint on the maximum allowed variations in permittivity is estimated. The Neumann expansion with a third-order approximation passes the Kolmogorov–Smirnov test resulting in output response matching with that of the Monte Carlo method. Since accurate stochastic simulations are possible within less computational time, we extend the analysis to the full operating frequency range with this method. It has also been shown that the computational complexity of the Neumann expansion method does not scale with number of stochastic regions considered.
The Effect of Duration of Smartphone Use on Head and Shoulders Posture of Young Adults Aged 20-35 Years

Methods: The statistical population of this study was young people aged 20 to 35 with smart phones who were resident of Kish Island in 2018. A sample of 50 people (25 men and 25 women) were selected voluntarily and purposefully. Lowuser and Highuser groups were identified by completing the form of addiction to smart cell phone, in assessing the status of upper limb, the shoulder position was measured by Scapular index and head position was measured by photogrammetric method technique and Kinovea software. Independent t-test was used for comparison of experimental variables between two groups. Data were analyzed with SPSS 22. P<0.05 was considered significant.
Educational Expectations among Immigrant Youth: Links to Segmented Assimilation and School Context

Although schools are important socialization venues for all children, they also serve as sites of acculturation for immigrant youth. According to segmented assimilation theory, first- and second-generation students experience divergent trajectories of incorporation, in part, because they are exposed to school contexts that support or stifle their attainment. I argue that such a process must have social-psychological underpinnings, which I examine by relating children’s educational expectations to their school environment during adolescence. Specifically, I use the National Education Longitudinal Study to assess differences in expectations by school context among immigrant and U.S.-origin youth between eighth and 12th grades. Results indicate that students in comparably disadvantaged school environments report lower expectations, though this relationship is driven by household resources and student characteristics. I also find that most students exhibit increases in their educational expectations, and that such changes are not systemically patterned by school context. This article sheds light on the goals of immigrant youth and the extent to which these plans transform from childhood to adolescence.
Phase I/II, Open-Label, Dose-Escalating Study of MDX-060 Administered Weekly for 4 Weeks in Subjects with Refractory/Relapsed CD30 Positive Lymphoma.

Background: MDX-060 is a fully human anti-CD30 IgG1k monoclonal antibody that induces Fc-receptor mediated killing of CD30 expressing cell lines and inhibits the growth of CD30-expressing tumor cells in a murine xenograft model. We previously reported data from the first 31 patients in a Phase I/II open-label, dose escalation study of MDX-060 in patients with relapsed or refractory CD30+ lymphomas. The purpose of the study is to determine the safety and tolerability profile of the drug, and to preliminarily evaluate efficacy. We now update the findings of this study, including reporting the results of a new higher dose cohort. Methods: In the Phase I portion of the study, MDX-060 was administered intravenously at dose levels of 0.1, 1, 5, or 10 mg/kg weekly for 4 weeks to cohorts of 3–6 patients. In the Phase II portion of the study additional patients were administered MDX-060 intravenously at 10 (n=10) or 15 (n=17) mg/kg weekly for 4 weeks. Responses were assessed at month 2. Results: To date, MDX-060 has been administered to 48 patients; 40 with Hodgkin’s disease (HD), 6 with anaplastic large cell lymphoma (ALCL) and 2 with other CD30 positive lymphomas. MDX-060 has been well tolerated, and a maximum tolerated dose has not been identified. There were 2 drug-related serious adverse events, 1 patient in the 1 mg/kg dosing cohort had Grade 3 elevated liver transaminase, and 1 patient in the 15 mg/kg cohort had Grade 3 pneumonia/Grade 4 ARDS. Objective clinical responses have been observed in 5 patients. Two patients had complete responses; 1 patient with ALCL in the 1 mg/kg cohort lasting 100 days, and 1 patient with HD in the 15 mg/kg cohort lasting 117 days. Retreatment of the ALCL patient with MDX-060 resulted in a second complete response. Three patients had partial responses; 1 patient with HD in the 5 mg/kg cohort lasting 92 days, and 1 patient with HD in the 15 mg/kg cohort, which is continuing at 176 days. In addition, 1 patient with ALCL in the 15 mg/kg cohort had a partial response. Stable disease was observed in 17 patients following MDX-060 administration with a median duration of 132 days (range:15–387 days). Baseline soluble CD30 levels in the plasma were evaluated, but did not appear to correlate with clinical outcome. Conclusions: Preliminary evidence indicates MDX-060 to be well tolerated in heavily pre-treated patients with CD30+ lymphomas, with minimal toxicity, and with clinical activity observed at 1 mg/kg, 5 mg/kg and 15 mg/kg. Additional patients are being accrued at 1 mg/kg and 5 mg/kg to more accurately assess efficacy at these dose levels.
Trust in takaful agents: antecedents and consequences

The purpose of this study is to explain factors contributing towards the development of trust in takaful agents and its consequences. ,A total of 182 completed survey questionnaires have been collected. Descriptive analysis, exploratory factor analysis and structural equation modeling analysis are used to analyse the data. ,The result indicates that clients’ trust in takaful agents is influenced by the agents’ communication and expertise as well as the image of the takaful operator represented by them. Also, trust in takaful agents affects clients’ loyalty and intention to continue service with the existing agent. ,This paper makes contributions by briefly discussing several recommendations to increase clients’ trust in takaful agents. ,This research paper is related to takaful agent–client relationships and can be used to formulate appropriate marketing strategies for building stronger relationships.
A framework to retain the knowledge of departing knowledge workers in the manufacturing industry

The purpose of this study is to develop a conceptual framework on knowledge loss in a manufacturing sector based on three aspects: likelihood of knowledge loss, critical areas of knowledge loss and relevance of each of these knowledge areas in terms of utilization and alignment with organizational goals and strategy. Such a conceptual framework can be helpful to the practicing managers in understanding the types of knowledge that is lost of a given departing employee and thus deciding on a measure to retain the critical employees or capture their knowledge before they leave. ,Using a case study approach, data has been collected from a multinational battery manufacturing company based in Hong Kong. Semi-structured interviews have been conducted and analyzed through CAQDAS ATLAS.ti to generate the themes which were then used to develop the conceptual framework. ,The findings revealed that the likelihood factors of knowledge loss in the manufacturing sector include layoffs, retirement, immigration and job change. The critical areas of knowledge loss comprise the knowledge of relationships and networks, especially with the customers and suppliers, the technical knowledge (battery and process technology) and knowledge of management, among others. The relevance of each of these knowledge areas needs to be determined through proper analysis whether these knowledge areas are needed in future projects, up to date and aligned with organizational goals and strategy along with other factors. ,Using the developed conceptual framework, managers and executives can identify critical employees in the manufacturing sector and accordingly take some appropriate measures to retain their knowledge. Caution should be taken while applying the findings of this study in other industries and context. ,This paper is an attempt to reduce the dearth of empirical studies by exploring knowledge retention in the manufacturing sector, especially in the development of proper conceptual frameworks to assess the potential knowledge loss of employees.
Effect of Surface Modification of Vetiver Fibers on Their Physical and Thermal Properties

ABSTRACT Surface modification of natural fibers is essential in order to improve their compatibility with polymer matrices to develop eco-friendly composite materials. This workdeals with different surface modifications of vetiver fibers to study their effects on mechanical and thermal properties. The efficiency of each treatment has been evaluated by Fourier transform infrared spectroscopy, X-ray diffractometry, and scanning electron microscopy. It has been observed that benzoylation-treated vetiver fiber shows better performance in comparison to other treatments.
Experimental Assessment and Numerical Modelling of Conforming and Non-Conforming RC Frames with and without Infills

ABSTRACT Research efforts are still required for modelling the seismic behaviour of infilled frames, especially for existing buildings, generally characterised by shear failures. This work discusses the experimental seismic behaviour and the related modelling of four conforming (compliant with modern seismic codes) and non-conforming (gravity-loads-designed) Reinforced Concrete (RC) frames with and without infills (with hollow-clay bricks). The experimental results are analysed, showing that non-conforming frames exhibited shear failures involving the infill panel and the RC columns/joints. A proposed modelling approach able to reproduce these shear failures is carried out, providing support towards more reliable numerical simulations of RC buildings.
The Effects of the Flipped Classroom Model on Students' Learning in a College English Class in Shanghai, China

By Xiaoying Wei University of the Pacific 2019 For many decades, college English teaching in China has been teacher-centered, mainly focusing on the enhancement of students’ four basic English language skills of listening, speaking, reading and writing, with little attention paid to the cultivation of students’ higher order thinking skills (Tang, 2016; Wang, Xu, & Zhou, 2016). The teacher-centered teaching approach has led to the problem that after having learned English for many years, students cannot speak English fluently (Dai, 2001). There has been a call for promoting the studentcentered teaching model in China (NACFLT, 2000). One relatively new approach to support student-centered active learning is flipped instruction (Egbert et al., 2015). In a flipped classroom, the transmission of information in a traditional face-to-face class is moved out of class time, and the class time is devoted to engaging students in active learning to foster deeper understanding of course content and problem-solving skills. The purpose of this multiple case study was to explore the effects of the flipped classroom model on the learning of Chinese undergraduate students in a college English class. Using a purposeful sampling strategy, I selected a flipped English class in a private college in Shanghai, China, which can be regarded as a pioneer in promoting the flipped classroom model in China. I identified six second-year college students to be my respondents. During the six
PUConv: Upsampling convolutional network for point cloud semantic segmentation

Due to the issue of disorder, it is difficult to directly utilise a 2D convolutional neural networks to process 3D point clouds. Recently, PointNet can directly use 3D point sets as the input of convolutional neural networks and complete the processing of point clouds with multi-layer perceptron (MLP) and symmetric functions. However, the use of MLP to compute the weight function ignores the problem of non-uniformity sampling caused by the density of point set data. To address the above problem, based on the PointNet++ structure, a kernel density estimation based method is proposed to calculate the density level of the local point sets region under the optimal bandwidth selection principle, and the density re-weighting of the weight function is developed to better fit the structure of local point clouds. In addition, the authors utilise the upsampling convolution operation to avoid duplicate storages and calculations, making the point cloud reconstruction more efficient. The experiments carried out the semantic segmentation on both the synthetic data and the real indoor scenes show that the proposed method is capable of obtaining promising semantic segmentation results.
Properties, Learning Algorithms, and Applications of Chain Graphs and Bayesian Hypergraphs

Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represent possible dependencies among the variables of a multivariate probability distribution. PGMs, such as Bayesian networks and Markov networks, are now widely accepted as a powerful and mature framework for reasoning and decision making under uncertainty in knowledge-based systems. With the increase of their popularity, the range of graphical models being investigated and used has also expanded. Several types of graphs with different conditional independence interpretations also known as Markov properties have been proposed and used in graphical models. The graphical structure of a Bayesian network has the form of a directed acyclic graph (DAG), which has the advantage of supporting an interpretation of the graph in terms of cause-effect relationships. However, a limitation is that only asymmetric relationships, such as cause and effect relationships, can be modeled between variables in a DAG. Chain graphs, which admit both directed and undirected edges, can be used to overcome this limitation. Today there exist three main different interpretations of chain graphs in the literature. These are the Lauritzen-Wermuth-Frydenberg, the Andersson-Madigan-Perlman, and the multivariate regression interpretations. In this thesis, we study these interpretations based on their separation criteria and the intuition behind their edges. Since structure learning is a critical component in constructing an intelligent system based on a chain graph model, we propose new feasible and efficient structure learning algorithms to learn chain graphs from data under the faithfulness assumption. The proliferation of different PGMs that allow factorizations of different kinds leads us to consider a more general graphical structure in this thesis, namely directed acyclic