Coaches and analysts face a significant challenge of inaccurate estimation when analyzing Men's 100 Meter Sprint Performance, particularly when there is limited data available. This necessitates the use of modern technologies to address the problem of inaccurate estimation. Unfortunately, current methods used to estimate Men's 100 Meter Sprint Performance indexes in Iraq are ineffective, highlighting the need to adopt new and advanced technologies that are fast, accurate, and flexible. Therefore, the objective of this study was to utilize an advanced method known as artificial neural networks to estimate four key indexes: Accelerate First of 10 meters, Speed Rate, Time First of 10 meters, and Reaction Time. The application of artificial neural networks in the sports industry in the Republic of Iraq is crucial to ensure successful players. In this study, an artificial neural network model was built to predict Men's 100 meter indexes. Several factors related to the construction of artificial neural networks were studied, including network architecture and internal factors and their impact on the performance of the models. As a result, four easy equations were developed to calculate the four key indexes. The findings of the study indicate that these networks can predict Men's 100 Meter indexes with a high degree of reliability 98.034% and accounting coefficients R = 0.9143.
This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar
... Show MoreInterval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an ef
... Show MoreIncludes search unemployment concept ... types, graduate unemployment a model introduction to the researcher tackled the problem of unemployment being dangerous to the community, it's also growing in size year after year is a waste of a clear human capabilities, also addressed the importance of the research being a touch on the problem of unemployment and its concept and try to find solutions to them , and then came the goals set by the search researcher identifies unemployment and their causes and consequences and to provide a true picture of the situation of unemployed graduates and disclosure about how they treat their graduates for jobs provide him with a decent life problem. And adopted a researcher on the use of a questionnaire add
... Show MoreThis paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar
... Show MoreIn recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc
... Show MoreAbstract
Objective / Purpose: Online social relationships through the emergence of Web 2.0 applications have become a new trend for researchers to study the behavior of consumers to shop online, as well as social networking sites are technologies that opened up opportunities for new business models. Therefore, a new trend has emerged, called social trade technology. In order to understand the behavioral intentions of the beneficiaries to adopt the technology of social trade, the current research aims at developing an electronic readiness framework and UTAUT model to understand the beneficiary's adoption of social trade technology.
Design/ methodology/ Approach: To achieve the obje
... Show More. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a
... Show Moreالخلاصة
اهتم الفكر السياسي في القرنين الاخيرين بدراسة الطبقات على نحو غير مسبوق, واصبح موضوع التحليل الطبقي المعني بالطبقات من حيث تعريفها, وتحديد موقعها في السلم الاجتماعي, فضلاً عن نوعية العلاقة بين شرائحها وفئاتها المختلفة من حيث الصراع والتناغم, المادة الرئيسة والموضوع الاكثر اهمية في دراسات الفكر السياسي والاجتماعي.ومن بين الطبقات, احتلت الطبقة الوسطى مكا
... Show MoreThe research deals with a new type of high-performance concrete with improved physical properties, which was prepared by using metal additives minutes (Metakaolin) and by studing their impact on the properties of mortar and concrete high-performance through destructive and non destructive tests. This type of concrete is used broadly in public buildings and in other structures . The research involved a number of experiments such as finding the activity index of burned at a temperature of 750 º C according to the standard ( ASTM C-311/03), as well as casting models for the cubic mortar mixtures and concrete containers at different rates of metakaolin ranging between (5% - 20%) as an added part to the cement mix to get a high- compressive
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