Effect of Individualized, Nonindividualized, and Package Intervention Strategies on Karate Performance

1985 ◽  
Vol 7 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Thomas G. Seabourne ◽  
Robert S. Weinberg ◽  
Allen Jackson ◽  
Richard M. Suinn
Keyword(s):  
Cognitive Strategies ◽  
Intervention Strategies ◽  
Performance Evaluations ◽  
Group Differences ◽  
Placebo Control ◽  
Self Defense ◽  
Different Types ◽  
And Control ◽  
And Training ◽  
Better Than

The purpose of the present investigation was to determine the effectiveness of different types of mental intervention procedures on karate performance. Subjects were 43 male volunteer students enrolled in self-defense classes at a university. They were randomly assigned to one of five conditions: individualized, nonindividualized, package, placebo control, and control. Karate performance evaluations (i.e., skill, combinations, sparring) were administered during the 5th (baseline), 10th, and 15th weeks of classes. All experimental groups received handouts, mini-strategies, manipulation checks, and interviews to aid them in their practice and training of their mental strategies. Thus, over the 10-week period, subjects spent a minimum of 17 hours practicing their cognitive strategies. Data were analyzed by a series of 5 x 2 (treatment X trials) multivariate analyses of variance. Results indicated that the individualized and package groups performed significantly better than all other groups on the karate performance measures of combinations and sparring. No other between-group differences were found. These results are supported by previous research (e.g., Kirschenbaum & Bale, 1980; Silva, 1982) which demonstrates the effectiveness of individualized and packaged intervention strategies in enhancing performance. Additional well controlled intervention studies are imperative before definitive statements can be put forth.

scholarly journals Military Education and Training Supported by Blended Simulation

2017 ◽  
Vol 23 (1) ◽  
pp. 61-65
Author(s):  
Pavel Bučka ◽  
Vladimír Andrassy
Keyword(s):  
Education And Training ◽  
Military Education ◽  
Home Base ◽  
Simulation Techniques ◽  
Wide Range ◽  
Different Types ◽  
Training Simulation ◽  
And Control ◽  
And Training ◽  
Linear Nature

Abstract The existing conflicts, both military and non-military are currently mostly of non-linear nature, where units composed of multiple specializations, both smaller and acting independently are prevailing. Those units are usually separated from home base and proper access to its own central supply lines of communication which strengthens emphasizes on their independent command and control, coordination within deployed forces and other assets within given operation. Therefore, prior collective training and preparation for deployment is one of the crucial operational planning requirements before deployment takes place. Modern training simulation techniques and assets do support preparedness of those units planned for deployment by aligning and synchronizing interoperability of their activities. One of such techniques - Blended simulation - can realistically generate a wide range of situations with exact imitation of activities to practice the ability of different types of units to the declared capabilities across the broad spectrum of tasks. Evaluation of the blended simulation effectiveness does further help to deliver more efficient methodologies and tactical procedures for further use within preparation stag. Such approach is in line with the trend of increased use of modelling and simulation techniques within military education and training.

scholarly journals A Comparative Study of Arabic Part of Speech Taggers Using Literary Text Samples from Saudi Novels

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 523
Author(s):  
Reyadh Alluhaibi ◽  
Tareq Alfraidi ◽  
Mohammad A. R. Abdeen ◽  
Ahmed Yatimi
Keyword(s):  
Comparative Study ◽  
Language Processing ◽  
Corpus Linguistics ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Testing Data ◽  
Modeling Techniques ◽  
Different Types ◽  
And Training ◽  
Better Than

Part of Speech (POS) tagging is one of the most common techniques used in natural language processing (NLP) applications and corpus linguistics. Various POS tagging tools have been developed for Arabic. These taggers differ in several aspects, such as in their modeling techniques, tag sets and training and testing data. In this paper we conduct a comparative study of five Arabic POS taggers, namely: Stanford Arabic, CAMeL Tools, Farasa, MADAMIRA and Arabic Linguistic Pipeline (ALP) which examine their performance using text samples from Saudi novels. The testing data has been extracted from different novels that represent different types of narrations. The main result we have obtained indicates that the ALP tagger performs better than others in this particular case, and that Adjective is the most frequent mistagged POS type as compared to Noun and Verb.

Examining the Role of Language in Play Among Children With and Without Developmental Disabilities

2020 ◽  
Vol 51 (3) ◽  
pp. 795-806 ◽  
Author(s):  
Elizabeth J. Short ◽  
Rachael Cooper Schindler ◽  
Rita Obeid ◽  
Maia M. Noeder ◽  
Laura E. Hlavaty ◽  
...  
Keyword(s):  
Developmental Disabilities ◽  
Autism Spectrum ◽  
Free Play ◽  
Group Differences ◽  
Typically Developing ◽  
Play Skills ◽  
Critical Aspect ◽  
Hyperactivity Disorder ◽  
Better Than

Purpose Play is a critical aspect of children's development, and researchers have long argued that symbolic deficits in play may be diagnostic of developmental disabilities. This study examined whether deficits in play emerge as a function of developmental disabilities and whether our perceptions of play are colored by differences in language and behavioral presentations. Method Ninety-three children participated in this study (typically developing [TD]; n = 23, developmental language disorders [DLD]; n = 24, attention-deficit/hyperactivity disorder [ADHD]; n = 26, and autism spectrum disorder [ASD]; n = 20). Children were videotaped engaging in free-play. Children's symbolic play (imagination, organization, elaboration, and comfort) was scored under conditions of both audible language and no audible language to assess diagnostic group differences in play and whether audible language impacted raters' perception of play. Results Significant differences in play were evident across diagnostic groups. The presence of language did not alter play ratings for the TD group, but differences were found among the other diagnostic groups. When language was audible, children with DLD and ASD (but not ADHD) were scored poorly on play compared to their TD peers. When language was not audible, children with DLD were perceived to play better than when language was audible. Conversely, children with ADHD showed organizational deficits when language was not available to support their play. Finally, children with ASD demonstrated poor play performance regardless of whether language was audible or not. Conclusions Language affects our understanding of play skills in some young children. Parents, researchers, and clinicians must be careful not to underestimate or overestimate play based on language presentation. Differential skills in language have the potential to unduly influence our perceptions of play for children with developmental disabilities.

Knowledge and Awareness of Dengue Fever

2018 ◽  
pp. 219-222
Author(s):  
Y. Arockia Suganthi ◽  
Chitra K. ◽  
J. Magelin Mary
Keyword(s):  
Dengue Fever ◽  
Prevention And Control ◽  
Control Measures ◽  
Infection Transmission ◽  
Standard Guideline ◽  
The World ◽  
Different Types ◽  
Prevention And Control Measures ◽  
Dengue Prevention ◽  
And Control

Dengue fever is a painful mosquito-borne infection caused by different types of virus in various localities of the world. There is no particular medicine or vaccine to treat person suffering from dengue fever. Dengue viruses are transmitted by the bite of female Aedes (Ae) mosquitoes. Dengue fever viruses are mainly transmitted by Aedes which can be active in tropical or subtropical climates. Aedes Aegypti is the key step to avoid infection transmission to save millions of people in all over the world. This paper provides a standard guideline in the planning of dengue prevention and control measures. At the same time gives the priorities including clinical management and hospitalized dengue patients have to address essentially.

SIALIC ACID IN THE PRIMARY AND DIFFERENTIAL DIAGNOSIS OF LUNG CANCER

2017 ◽  
Vol 63 (6) ◽  
pp. 926-932
Author(s):  
Lyudmila Belskaya ◽  
Viktor Kosenok ◽  
Ж. Массард
Keyword(s):  
Lung Cancer ◽  
Enzyme Activity ◽  
Optimization Problems ◽  
Primary Lung Cancer ◽  
Nonparametric Test ◽  
Nonsmall Cell Lung Cancer ◽  
Group Differences ◽  
Diagnosis Of Lung Cancer ◽  
And Control ◽  
Control Study

So far optimization problems for diagnostics and prognostication aids remained relevant for lung cancer as a leader in the structure of cancers. Objective: a search for regularities of changes in the saliva enzyme activity in patients with nonsmall cell lung cancer. In the case-control study, 505 people took part, divided into 2 groups: primary (lung cancer, n=290) and control (conventionally healthy, n=215). All the participants went through a questionnaire survey, saliva biochemical counts, and a histological verification of their diagnosis. The enzyme activity was measured with spectrophotometry. Between-group differences were measured with the nonparametric test. It was shown that in terms of lung cancer, we observe metabolic changes, described with the decreased de Ritis coefficient (p

Design, Development and Comparison of Heuristic Driven Algorithms Based on the Crossed Domain Products’ Reviews for User’s Summarization

2020 ◽  
Vol 13 (5) ◽  
pp. 884-892
Author(s):  
Sartaj Ahmad ◽  
Ashutosh Gupta ◽  
Neeraj Kumar Gupta
Keyword(s):  
Decision Making ◽  
Online Shopping ◽  
Recent Time ◽  
Design Development ◽  
Different Types ◽  
Feature Based ◽  
Use Of Internet ◽  
Day By Day ◽  
Better Than

Background: In recent time, people love online shopping but before any shopping feedbacks or reviews always required. These feedbacks help customers in decision making for buying any product or availing any service. In the country like India this trend of online shopping is increasing very rapidly because awareness and the use of internet which is increasing day by day. As result numbers of customers and their feedbacks are also increasing. It is creating a problem that how to read all reviews manually. So there should be some computerized mechanism that provides customers a summary without spending time in reading feedbacks. Besides big number of reviews another problem is that reviews are not structured. Objective: In this paper, we try to design, implement and compare two algorithms with manual approach for the crossed domain Product’s reviews. Methods: Lexicon based model is used and different types of reviews are tested and analyzed to check the performance of these algorithms. Results: Algorithm based on opinions and feature based opinions are designed, implemented, applied and compared with the manual results and it is found that algorithm # 2 is performing better than algorithm # 1 and near to manual results. Conclusion: Algorithm # 2 is found better on the different product’s reviews and still to be applied on other product’s reviews to enhance its scope. Finally, it will be helpful to automate existing manual process.

scholarly journals Influence of Menstrual Cycle or Hormonal Contraceptive Phase on Energy Intake and Metabolic Hormones—A Pilot Study

Endocrines ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 79-90
Author(s):  
Johanna K. Ihalainen ◽  
Ida Löfberg ◽  
Anna Kotkajuuri ◽  
Heikki Kyröläinen ◽  
Anthony C. Hackney ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Energy Intake ◽  
Hormonal Contraceptive ◽  
Group Differences ◽  
Metabolic Hormones ◽  
Recreational Athletes ◽  
Total Ghrelin ◽  
17Β Estradiol ◽  
The Mean ◽  
And Training

Sex hormones are suggested to influence energy intake (EI) and metabolic hormones. This study investigated the influence of menstrual cycle (MC) and hormonal contraceptive (HC) cycle phases on EI, energy availability (EA), and metabolic hormones in recreational athletes (eumenorrheic, NHC = 15 and monophasic HC-users, CHC = 9). In addition, 72-h dietary and training logs were collected in addition to blood samples, which were analyzed for 17β-estradiol (E2), progesterone (P4), leptin, total ghrelin, insulin, and tri-iodothyronine (T3). Measurements were completed at four time-points (phases): Bleeding, mid-follicular (FP)/active 1, ovulation (OVU)/active 2, mid-luteal (LP)/inactive in NHC/CHC, respectively. As expected, E2 and P4 fluctuated significantly in NHC (p < 0.05) and remained stable in CHC. In NHC, leptin increased significantly between bleeding and ovulation (p = 0.030) as well as between FP and OVU (p = 0.022). No group differences in other measured hormones were observed across the MC and HC cycle. The mean EI and EA were similar between phases, with no significant differences observed in macronutrient intake over either the MC or HC. While the MC phase might have a small, but statistically significant effect on leptin, the findings of the present study suggest that the MC or HC phase does not significantly alter ad libitum EI or EA in recreational athletes.

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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