《英语口语》(Success With Engish)是由广州市教育局教学研究室依据国家《英语课程标准》编写的一套英语教材。这套教材的每个单元围绕一个主题展开，内容贴近小学生生活，词汇丰富。教材内容生动活泼，集儿歌、游戏、歌曲、小故事于...《英语口语》(Success With Engish)是由广州市教育局教学研究室依据国家《英语课程标准》编写的一套英语教材。这套教材的每个单元围绕一个主题展开，内容贴近小学生生活，词汇丰富。教材内容生动活泼，集儿歌、游戏、歌曲、小故事于一体，学生乐学，教师易教。而其中的“Story Time”所含的小故事风趣幽默，更是受到学生和教师的喜爱。讲故事不但能使学生所学的知识得到实践和巩固，还能让他们体验到运用新语言带来的喜悦和成就感。在这里，笔者想结合教材谈一谈如何使用“Big Book”讲故事教学生学英语。展开更多
As a simplified, idealized understanding of a physical system the General Relativity model has been highly successful in its gravitational role. However, it fails to address the problem of sufficiently precise measure...As a simplified, idealized understanding of a physical system the General Relativity model has been highly successful in its gravitational role. However, it fails to address the problem of sufficiently precise measurement of “Big G”, the Newtonian Gravitation Constant, and has failed to obtain connection of “Big G” to the rest of physics. Because “Big G” arises naturally from Newton’s treatment of gravitation, this paper elaborates the Modern Newtonian Model of Gravitation and through it resolves the problems of “Big G” at which General Relativity has failed. Specifically: The causes of the problems in measuring “Big G” are resolved, “Big G” is connected to the rest of physics, and a sufficiently precise value of “Big G” is obtained by calculation from other fundamental physical constants. The companion paper The Experimental Data Validation of Modern Newtonian Gravitation over General Relativity Gravitation, which is available in this journal, publishes the results of this paper’s “Part V—Testing the Hypothesis and the Derivation”.展开更多
There is accumulating evidence that the subfamily of large-conductance potassium (“big”, “BK”) channels are involved in diverse, and perhaps coordinated, protective or counteractive responses to local or generaliz...There is accumulating evidence that the subfamily of large-conductance potassium (“big”, “BK”) channels are involved in diverse, and perhaps coordinated, protective or counteractive responses to local or generalized ischemia and hypoxia. Although widely distributed, the physiological differences among BK channels which results from posttranslational modification (alternative splicing) and co-assembly with auxiliary modulatory subunits (<em>β</em><sub>1-4</sub> and <em>γ</em><sub>1-4</sub>), bestows localized differences in subunit composition, distribution, 2<sup>nd</sup>-messenger coupling, and pharmacologic properties. Due to the ubiquitous nature of BK channels and the multiplicity of subtypes, they have many potential therapeutic applications in the maintenance of oxygen homeostasis, cerebro- and cardio-protection, and stimulation of respiration in response to drug-induced respiratory depression. BK channels may also offer other potentially broad and underrecognized promising targets for novel pharmaceutical development.展开更多
Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and...Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and regulatory complexity of healthcare data. This research introduces a tailored partitioning strategy leveraging the MD5 hashing algorithm to enhance data insertion, query performance, and load balancing in healthcare systems. By applying a consistent hash function to patient IDs, our approach achieves uniform distribution of records across partitions, optimizing retrieval paths and reducing access latency while ensuring data integrity and compliance. We evaluated the method through experiments focusing on partitioning efficiency, scalability, and fault tolerance. The partitioning efficiency analysis compared our MD5-based approach with standard round-robin methods, measuring insertion times, query latency, and data distribution balance. Scalability tests assessed system performance across increasing dataset sizes and varying partition counts, while fault tolerance experiments examined data integrity and retrieval performance under simulated partition failures. The experimental results demonstrate that the MD5-based partitioning strategy significantly reduces query retrieval times by optimizing data access patterns, achieving up to X% better performance compared to round-robin methods. It also scales effectively with larger datasets, maintaining low latency and ensuring robust resilience under failure scenarios. This novel approach offers a scalable, efficient, and fault-tolerant solution for healthcare systems, facilitating faster clinical decision-making and improved patient care in complex data environments.展开更多
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
That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through...That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.展开更多
The advent of the big data era has made data visualization a crucial tool for enhancing the efficiency and insights of data analysis. This theoretical research delves into the current applications and potential future...The advent of the big data era has made data visualization a crucial tool for enhancing the efficiency and insights of data analysis. This theoretical research delves into the current applications and potential future trends of data visualization in big data analysis. The article first systematically reviews the theoretical foundations and technological evolution of data visualization, and thoroughly analyzes the challenges faced by visualization in the big data environment, such as massive data processing, real-time visualization requirements, and multi-dimensional data display. Through extensive literature research, it explores innovative application cases and theoretical models of data visualization in multiple fields including business intelligence, scientific research, and public decision-making. The study reveals that interactive visualization, real-time visualization, and immersive visualization technologies may become the main directions for future development and analyzes the potential of these technologies in enhancing user experience and data comprehension. The paper also delves into the theoretical potential of artificial intelligence technology in enhancing data visualization capabilities, such as automated chart generation, intelligent recommendation of visualization schemes, and adaptive visualization interfaces. The research also focuses on the role of data visualization in promoting interdisciplinary collaboration and data democratization. Finally, the paper proposes theoretical suggestions for promoting data visualization technology innovation and application popularization, including strengthening visualization literacy education, developing standardized visualization frameworks, and promoting open-source sharing of visualization tools. This study provides a comprehensive theoretical perspective for understanding the importance of data visualization in the big data era and its future development directions.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
As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and cha...As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.展开更多
In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical D...In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.展开更多
The paper considers the mechanism of the Big Bang energy influence on the creation of space-time fields of four structures of the Universe from the 1st type Ether (the Main Field and three spheres of the Relic). It ex...The paper considers the mechanism of the Big Bang energy influence on the creation of space-time fields of four structures of the Universe from the 1st type Ether (the Main Field and three spheres of the Relic). It explains how the Big Bang energy leads to the processes of “melting” in these structures, generating emergent properties that are different from their properties before the Big Bang. The key role of the Big Bang in completing the process of formation of 70% of DE is emphasized. It is shown that the Big Bang preceded the emergence of the furcation point, which chose several directions for the creation of cosmic matter—it was the combined efforts of these directions that created the visible worlds. The principle of dynamic equilibrium is considered the main criterion of the space-time field, in contrast to other physical fields, which is a necessary prerequisite for the quantization of the gravitational field. A spin particle is introduced, capable of emitting special particles—spitons, the characteristics of which are associated with the topology of the Mobius strip and determine the spinor properties of gravitational fields. The mechanism of interaction of particles of the 2nd type of Ether with the fields of space-time is described, allowing the creation of matter first and then the materiality of visible worlds. At the same time, the role of the “matter-negotiator” in the creation process of visible worlds of the Universe is especially highlighted. Since the new properties of gravitational fields go beyond Einstein’s standard theory of gravity, it is proposed to build a new theory of space-time that generalizes it and has a clear geometric interpretation. The proposed theory is based on the action built on a full set of invariants of the Ricci tensor. Within the framework of the Poincaré theory, the classification of furcation points is considered. The processes at the furcation point are described by the Gauss-Laplace curve, for which the principle of conservation of probability density is introduced when considering the transition at the furcation point to four different directions of development.展开更多
文摘《英语口语》(Success With Engish)是由广州市教育局教学研究室依据国家《英语课程标准》编写的一套英语教材。这套教材的每个单元围绕一个主题展开，内容贴近小学生生活，词汇丰富。教材内容生动活泼，集儿歌、游戏、歌曲、小故事于一体，学生乐学，教师易教。而其中的“Story Time”所含的小故事风趣幽默，更是受到学生和教师的喜爱。讲故事不但能使学生所学的知识得到实践和巩固，还能让他们体验到运用新语言带来的喜悦和成就感。在这里，笔者想结合教材谈一谈如何使用“Big Book”讲故事教学生学英语。
文摘As a simplified, idealized understanding of a physical system the General Relativity model has been highly successful in its gravitational role. However, it fails to address the problem of sufficiently precise measurement of “Big G”, the Newtonian Gravitation Constant, and has failed to obtain connection of “Big G” to the rest of physics. Because “Big G” arises naturally from Newton’s treatment of gravitation, this paper elaborates the Modern Newtonian Model of Gravitation and through it resolves the problems of “Big G” at which General Relativity has failed. Specifically: The causes of the problems in measuring “Big G” are resolved, “Big G” is connected to the rest of physics, and a sufficiently precise value of “Big G” is obtained by calculation from other fundamental physical constants. The companion paper The Experimental Data Validation of Modern Newtonian Gravitation over General Relativity Gravitation, which is available in this journal, publishes the results of this paper’s “Part V—Testing the Hypothesis and the Derivation”.
文摘There is accumulating evidence that the subfamily of large-conductance potassium (“big”, “BK”) channels are involved in diverse, and perhaps coordinated, protective or counteractive responses to local or generalized ischemia and hypoxia. Although widely distributed, the physiological differences among BK channels which results from posttranslational modification (alternative splicing) and co-assembly with auxiliary modulatory subunits (<em>β</em><sub>1-4</sub> and <em>γ</em><sub>1-4</sub>), bestows localized differences in subunit composition, distribution, 2<sup>nd</sup>-messenger coupling, and pharmacologic properties. Due to the ubiquitous nature of BK channels and the multiplicity of subtypes, they have many potential therapeutic applications in the maintenance of oxygen homeostasis, cerebro- and cardio-protection, and stimulation of respiration in response to drug-induced respiratory depression. BK channels may also offer other potentially broad and underrecognized promising targets for novel pharmaceutical development.
文摘Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and regulatory complexity of healthcare data. This research introduces a tailored partitioning strategy leveraging the MD5 hashing algorithm to enhance data insertion, query performance, and load balancing in healthcare systems. By applying a consistent hash function to patient IDs, our approach achieves uniform distribution of records across partitions, optimizing retrieval paths and reducing access latency while ensuring data integrity and compliance. We evaluated the method through experiments focusing on partitioning efficiency, scalability, and fault tolerance. The partitioning efficiency analysis compared our MD5-based approach with standard round-robin methods, measuring insertion times, query latency, and data distribution balance. Scalability tests assessed system performance across increasing dataset sizes and varying partition counts, while fault tolerance experiments examined data integrity and retrieval performance under simulated partition failures. The experimental results demonstrate that the MD5-based partitioning strategy significantly reduces query retrieval times by optimizing data access patterns, achieving up to X% better performance compared to round-robin methods. It also scales effectively with larger datasets, maintaining low latency and ensuring robust resilience under failure scenarios. This novel approach offers a scalable, efficient, and fault-tolerant solution for healthcare systems, facilitating faster clinical decision-making and improved patient care in complex data environments.
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That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.
文摘The advent of the big data era has made data visualization a crucial tool for enhancing the efficiency and insights of data analysis. This theoretical research delves into the current applications and potential future trends of data visualization in big data analysis. The article first systematically reviews the theoretical foundations and technological evolution of data visualization, and thoroughly analyzes the challenges faced by visualization in the big data environment, such as massive data processing, real-time visualization requirements, and multi-dimensional data display. Through extensive literature research, it explores innovative application cases and theoretical models of data visualization in multiple fields including business intelligence, scientific research, and public decision-making. The study reveals that interactive visualization, real-time visualization, and immersive visualization technologies may become the main directions for future development and analyzes the potential of these technologies in enhancing user experience and data comprehension. The paper also delves into the theoretical potential of artificial intelligence technology in enhancing data visualization capabilities, such as automated chart generation, intelligent recommendation of visualization schemes, and adaptive visualization interfaces. The research also focuses on the role of data visualization in promoting interdisciplinary collaboration and data democratization. Finally, the paper proposes theoretical suggestions for promoting data visualization technology innovation and application popularization, including strengthening visualization literacy education, developing standardized visualization frameworks, and promoting open-source sharing of visualization tools. This study provides a comprehensive theoretical perspective for understanding the importance of data visualization in the big data era and its future development directions.
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As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.
文摘In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.
文摘The paper considers the mechanism of the Big Bang energy influence on the creation of space-time fields of four structures of the Universe from the 1st type Ether (the Main Field and three spheres of the Relic). It explains how the Big Bang energy leads to the processes of “melting” in these structures, generating emergent properties that are different from their properties before the Big Bang. The key role of the Big Bang in completing the process of formation of 70% of DE is emphasized. It is shown that the Big Bang preceded the emergence of the furcation point, which chose several directions for the creation of cosmic matter—it was the combined efforts of these directions that created the visible worlds. The principle of dynamic equilibrium is considered the main criterion of the space-time field, in contrast to other physical fields, which is a necessary prerequisite for the quantization of the gravitational field. A spin particle is introduced, capable of emitting special particles—spitons, the characteristics of which are associated with the topology of the Mobius strip and determine the spinor properties of gravitational fields. The mechanism of interaction of particles of the 2nd type of Ether with the fields of space-time is described, allowing the creation of matter first and then the materiality of visible worlds. At the same time, the role of the “matter-negotiator” in the creation process of visible worlds of the Universe is especially highlighted. Since the new properties of gravitational fields go beyond Einstein’s standard theory of gravity, it is proposed to build a new theory of space-time that generalizes it and has a clear geometric interpretation. The proposed theory is based on the action built on a full set of invariants of the Ricci tensor. Within the framework of the Poincaré theory, the classification of furcation points is considered. The processes at the furcation point are described by the Gauss-Laplace curve, for which the principle of conservation of probability density is introduced when considering the transition at the furcation point to four different directions of development.
