What Practice Issues Over 25 Years Most Interest Registered Dietitians? Survey and Interview Results

Dietetics has changed substantially; a mixed-methods project was undertaken to: (i) gauge interest in the profession history since 1993, (ii) identify preferred format(s), (iii) identify possible topics, and (iv) identify possible key informants. An online bilingual survey was conducted in 2018, with follow-up phone interviews among interested respondents. Survey content was organised as 12 major topics. Respondents were invited via a Dietitians of Canada (DC) newsletter, Facebook groups, and at the DC national conference. Survey data, including respondent-generated topics of interest and interview content, were descriptively analyzed. The online survey garnered 360 responses; 332 (92%) completed more than 10% of the survey and were interested in history. Detailed responses were analyzed (296 English; 36 French); 51 were interviewed. An online timeline was the most preferred format (79%). Review of the rise in technology and obesity, aging, supermarket registered dietitians (RDs), the local/organic movement, Practice-based Evidence in Nutrition (PEN), the changes in training models and scope of practice, public awareness of the profession, and advocacy and unique career paths were of most interest (≥ 50% of respondents). These results confirm interest in the recent history of the profession among RDs and provide guidance on preferred format and topics for further work.

Lamb head as a training model for septoplasty and rhinoplasty

Background: Septoplasty and rhinoplasty are difficult operations to learn and teach. Many modalities have been proposed to make the teaching process of these operations easier. In this study, it was investigated if lamb heads were good training models to teach septoplasty and rhinoplasty to trainees or experienced surgeons. Methods: In the first part of the study, 21 lamb heads were dissected according to a dissection protocol and several anatomical distances were measured in order to compare them with human cadavers. In the second project 8 lamb heads were dissected and different preservation rhinoplasty techniques were practiced. Results: The study on 21 lamb heads used showed that the lateral crura were 17.8 x 11.6, average interdomal distance was 8.1 mm, average domal width was 3.7 mm. The average length of the upper lateral cartilages was 31.1 mm laterally and 21.2 medially. The average length of the nasal bones was 63.9 mm and the width was 16 mm. In the second part of the study 8 lamb heads were used to experience where high strip techniques were used in 5 and Cottle technique in 3. Conclusion: This study revealed that lamb head should be considered as an excellent training model for septoplasty and rhinoplasty. Its very low cost, ease of availability, and close similarity to the human cadavers can be counted as the main advantages. This study also proved that it was not only a tool for beginners, but also a very helpful tool for experienced surgeons to try new methods.

DEVELOPMENT OF RESISTANCE-BASED ARCHERY TRAINING MODELS FOR ATHLETES AGED 13-15 YEARS

The purpose of this study was to produce a resistance-based archery training model for athletes aged 13-15 years. The method used in this research is Research and Development. The study was conducted at DSR Archery Club Depok City and the research subjects were athletes aged 13-15 years. This research begins with needs analysis, product planning, development, implementation and evaluation. The instruments used were the 30 M Archery Test in the sport of archery, an analysis of the need for in-depth interviews to coaches at the archery club and an assessment questionnaire to experts to test the validity of variations in the training model. Based on research data consisting of needs analysis, product design, expert validation, small and large group trials, and effectiveness tests. The conclusion is first, resistance-based archery training model for athletes aged 13-15 years can be developed and applied in training archery skills and muscle strength training. Second, the exercise model developed based on research data obtained effective results for athletes aged 13-15 years

The Effect of Shuttle Run and Zig-Zag Run Training On Dribbling Skills

The shuttle run exercise is a form of exercise that can improve agility. The zigzag run exercise is a form of agility training that involves the use of bollards and stakes. UM Palopo futsal players must have good dribbling skills. The conditions experienced by UM Palopo futsal players still often make mistakes in their dribbling skills. The lack of skills possessed by UM Palopo futsal players is due to the lack of agility training models that can support the achievements of futsal players. This study aims to reveal the effect of shuttle run and zig-zag run training on dribbling skill. The method used is a quasi-experimental (quasi-experimental). The population is 20 people from UKM Futsal UM Palopo. The sampling technique is purposive sampling. The data collection technique used the dribbling skill instrument and was analyzed by using the sig level t test. = 0.05. The results of the analysis show that there is a significant effect of shuttle run with a value of tcount value of 20,904 > ttable of 1,812 and pvalue that is 0.000 < 0.05. Zig-zag run with tcount value of 26,826 > ttable of 1,812 and pvalue that is 0.000 < 0.05. The improvement in shuttle run training with an average value of 17.55 and zig-zag run training with an average value of 15.61 and a significant value of 0.000 < 0.05. It can be concluded that the zig-zag run is more effective in improving dribbling skills.

Learning from Disagreement: A Survey

Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition follows from certain premises. While most learning in artificial intelligence (AI) still relies on the assumption that a single (gold) interpretation exists for each item, a growing body of research aims to develop learning methods that do not rely on this assumption. In this survey, we review the evidence for disagreements on NLP and CV tasks, focusing on tasks for which substantial datasets containing this information have been created. We discuss the most popular approaches to training models from datasets containing multiple judgments potentially in disagreement. We systematically compare these different approaches by training them with each of the available datasets, considering several ways to evaluate the resulting models. Finally, we discuss the results in depth, focusing on four key research questions, and assess how the type of evaluation and the characteristics of a dataset determine the answers to these questions. Our results suggest, first of all, that even if we abandon the assumption of a gold standard, it is still essential to reach a consensus on how to evaluate models. This is because the relative performance of the various training methods is critically affected by the chosen form of evaluation. Secondly, we observed a strong dataset effect. With substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from aggregated or even gold labels. This result holds for both hard and soft evaluation. But when the above conditions do not hold, leveraging both gold and soft labels generally achieved the best results in the hard evaluation. All datasets and models employed in this paper are freely available as supplementary materials.

Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud

Empowered by machine learning, edge devices including smartphones, wearable, and IoT devices have become growingly intelligent, raising conflicts with the limited resource. On-device model personalization is particularly hard as training models on edge devices is highly resource-intensive. In this work, we propose a novel training pipeline across the edge and the cloud, by taking advantage of the powerful cloud while keeping data local at the edge. Highlights of the design incorporate the parallel execution enabled by our feature replay, reduced communication cost by our error-feedback feature compression, as well as the context-aware deployment decision engine. Working as an integrated system, the proposed pipeline training framework not only significantly speeds up training, but also incurs little accuracy loss or additional memory/energy overhead. We test our system in a variety of settings including WiFi, 5G, household IoT, and on different training tasks such as image/text classification, image generation, to demonstrate its advantage over the state-of-the-art. Experimental results show that our system not only adapts well to, but also draws on the varying contexts, delivering a practical and efficient solution to edge-cloud model training.

Learning Deep Models: Critical Points and Local Openness

With the increasing popularity of nonconvex deep models, developing a unifying theory for studying the optimization problems that arise from training these models becomes very significant. Toward this end, we present in this paper a unifying landscape analysis framework that can be used when the training objective function is the composite of simple functions. Using the local openness property of the underlying training models, we provide simple sufficient conditions under which any local optimum of the resulting optimization problem is globally optimal. We first completely characterize the local openness of the symmetric and nonsymmetric matrix multiplication mapping. Then we use our characterization to (1) provide a simple proof for the classical result of Burer-Monteiro and extend it to noncontinuous loss functions; (2) show that every local optimum of two-layer linear networks is globally optimal. Unlike many existing results in the literature, our result requires no assumption on the target data matrix [Formula: see text], and input data matrix [Formula: see text]; (3) develop a complete characterization of the local/global optima equivalence of multilayer linear neural networks (we provide various counterexamples to show the necessity of each of our assumptions); and (4) show global/local optima equivalence of overparameterized nonlinear deep models having a certain pyramidal structure. In contrast to existing works, our result requires no assumption on the differentiability of the activation functions and can go beyond “full-rank” cases.

Shorinji kempo basic technique training method based on local wisdom for beginners kenshi

The lack of training models and training creativity in creating traditional game-based training models for kenshi to improve the basic techniques of kempo becomes problems in this study. The purpose of this research is to produce a model of basic martial arts techniques of kempo through traditional games for kenshi beginners. This research is a research and development (R&D) research which was adapted into 8 steps in research: 1). Field news collection 2). Breakdown of the news that has been collected 3). Elaborating initial creation 4). Expert validation and revision 5). Small scale trial 6). Large-scale trial 7). Making the final product 8). Product effectiveness test. The small-scale trial subjects of 20 beginners kenshi were taken from 2 dojos, namely the Panjatan Junior High School dojo and the Amikom dojo. The large-scale test subjects totaling 45 beginners kenshi were taken from 3 dojos, namely Balong, Triharjo, and SD Gunung Kidul. The subjects in this study came from 5 dojos in Perkemi DIY. Data were collected through interviews, distributing questionnaires, field notes, evaluation, and assessment rubrics for beginner kenshi. The results of this study are in the form of training models for basic techniques of kempo martial arts through traditional games for beginner kenshi, and there are five training models: 1. gobak sodor, 2. yeye, 3. pahtil catfish, 4. zogdag, 5. stick dance. Based on the results of this study, it can be concluded that the traditional game-based kempo martial arts training model for kenshi beginners is feasible to use. The author's recommendation to other prospective researchers is to examine traditional games in an approach with other disciplines and use professional research subjects in other sports.

Different Effects of Live Demonstration and Video Demonstration Learning Models on the Performance of the Tennis Service Biomechanics Review from Eye-Hand Coordination

This study aims to determine: (1) The difference in the effect of live demonstration and video demonstration exercise models on the biomechanics performance of court tennis service, (2) Differences in the performance of tennis service biomechanics between players who have high and low eye-hand coordination, (3) Effect of interaction between exercise model and eye-hand coordination on the performance of tennis service biomechanics. This study used an experimental method with a 2 x 2 factorial design. The population in this study were all 12 players of the male KKO SMPN 1 Surakarta. The sampling technique used is saturated sampling, the size of the sample taken is 12 players. The data analysis technique in this study used ANOVA. Before being tested with ANOVA, first using the prerequisite test of data analysis with sample normality test (Lilliefors test with = 0.05 %) and homogeneity test of variance (Bartlett test with = 0.05 %). Based on the results of the study, it can be concluded as follows: (1) there is a difference in the effect of live demonstration and video demonstration exercise models on the performance of tennis service biomechanics. The effect of the live demonstration exercise model is better than the video demonstration exercise model, (2) there are differences in the biomechanics performance of court tennis service between players who have high and low eye-hand coordination. The biomechanics performance of court tennis services on players who have high eye-hand coordination is better than players who have low eye-hand coordination, (3) there is an interaction between training models and eye-hand coordination on the biomechanics performance of court tennis services. Players who have high eye-hand coordination are more suitable if given a live demonstration exercise model. Players who have low eye-hand coordination are more suitable if given a video demonstration exercise model.