Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focu...Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .展开更多
This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgama...This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.展开更多
Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the ne...Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.展开更多
A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (I...A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.展开更多
Starting from the basic assumptions and equations of Big Bang theory, we present a simple mathematical proof that this theory implies a varying (decreasing) speed of light, contrary to what is generally accepted. We c...Starting from the basic assumptions and equations of Big Bang theory, we present a simple mathematical proof that this theory implies a varying (decreasing) speed of light, contrary to what is generally accepted. We consider General Relativity, the first Friedmann equation and the Friedmann-Lema?tre- Robertson-Walker (FLRW) metric for a Comoving Observer. It is shown explicitly that the Horizon and Flatness Problems are solved, taking away an important argument for the need of Cosmic Inflation. A decrease of 2.1 cm/s per year of the present-day speed of light is predicted. This is consistent with the observed acceleration of the expansion of the Universe, as determined from high-redshift supernova data. The calculation does not use any quantum processes, and no adjustable parameters or fine tuning are introduced. It is argued that more precise laboratory measurements of the present-day speed of light (and its evolution) should be carried out. Also it is argued that the combination of the FLRW metric and Einstein’s field equations of General Relativity is inconsistent, because the FLRW metric implies a variable speed of light, and Einstein’s field equations use a constant speed of light. If we accept standard Big Bang theory (and thus the combination of General Relativity and the FLRW metric), a variable speed of light must be allowed in the Friedmann equation, and therefore also, more generally, in Einstein’s field equations of General Relativity. The explicit form of this time dependence will then be determined by the specific problem.展开更多
Taking the Big Bang as an established fact, the question inevitably arises about what exactly caused it, in what environment could it have happened and what happened before it. The developed approach allows us to shed...Taking the Big Bang as an established fact, the question inevitably arises about what exactly caused it, in what environment could it have happened and what happened before it. The developed approach allows us to shed light on many raised questions and to establish what universal laws and structures formed what happened before the Big Bang, to understand its cause and the dynamic processes that led to it. This required a radical revision of many views, giving them a new meaning and content. This approach has led to a consistent and conceptually new understanding of these phenomena, which allowed correctly formulate questions to which there are still no clear answers. Based on this formulation of the problem, we came to new ideas about the nature of Dark energy, Dark matter and the region of their birth, formulated and described the mechanism of the formation of worlds and their hierarchy on the other side of the Big Bang and the mechanism of this explosion itself. The Primary Parent Particle was introduced into the concept, which was the basis of everything and is the carrier of the fundamental Primary space introduced by us, which had at least two phase states. This particle consists of Beginnings united in the form of Borromeo rings. This made it possible to calculate the structure and primary spectrum of elementary particles that arose on the other side of the Big Bang, the mechanisms of their formation and the resulting fundamental interactions that lead to the existence of vortices before the Big Bang;the mechanisms of the birth of multiple universes and much more are also considered. The concept of the “cosmic genetic code" is introduced, the characteristics and mechanism of its formation before the Big Bang are presented.展开更多
文摘Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .
文摘This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.
文摘Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.
文摘A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.
文摘Starting from the basic assumptions and equations of Big Bang theory, we present a simple mathematical proof that this theory implies a varying (decreasing) speed of light, contrary to what is generally accepted. We consider General Relativity, the first Friedmann equation and the Friedmann-Lema?tre- Robertson-Walker (FLRW) metric for a Comoving Observer. It is shown explicitly that the Horizon and Flatness Problems are solved, taking away an important argument for the need of Cosmic Inflation. A decrease of 2.1 cm/s per year of the present-day speed of light is predicted. This is consistent with the observed acceleration of the expansion of the Universe, as determined from high-redshift supernova data. The calculation does not use any quantum processes, and no adjustable parameters or fine tuning are introduced. It is argued that more precise laboratory measurements of the present-day speed of light (and its evolution) should be carried out. Also it is argued that the combination of the FLRW metric and Einstein’s field equations of General Relativity is inconsistent, because the FLRW metric implies a variable speed of light, and Einstein’s field equations use a constant speed of light. If we accept standard Big Bang theory (and thus the combination of General Relativity and the FLRW metric), a variable speed of light must be allowed in the Friedmann equation, and therefore also, more generally, in Einstein’s field equations of General Relativity. The explicit form of this time dependence will then be determined by the specific problem.
文摘Taking the Big Bang as an established fact, the question inevitably arises about what exactly caused it, in what environment could it have happened and what happened before it. The developed approach allows us to shed light on many raised questions and to establish what universal laws and structures formed what happened before the Big Bang, to understand its cause and the dynamic processes that led to it. This required a radical revision of many views, giving them a new meaning and content. This approach has led to a consistent and conceptually new understanding of these phenomena, which allowed correctly formulate questions to which there are still no clear answers. Based on this formulation of the problem, we came to new ideas about the nature of Dark energy, Dark matter and the region of their birth, formulated and described the mechanism of the formation of worlds and their hierarchy on the other side of the Big Bang and the mechanism of this explosion itself. The Primary Parent Particle was introduced into the concept, which was the basis of everything and is the carrier of the fundamental Primary space introduced by us, which had at least two phase states. This particle consists of Beginnings united in the form of Borromeo rings. This made it possible to calculate the structure and primary spectrum of elementary particles that arose on the other side of the Big Bang, the mechanisms of their formation and the resulting fundamental interactions that lead to the existence of vortices before the Big Bang;the mechanisms of the birth of multiple universes and much more are also considered. The concept of the “cosmic genetic code" is introduced, the characteristics and mechanism of its formation before the Big Bang are presented.
