Pub Date : 2019-04-30DOI: 10.32474/CTBB.2019.01.000115
Davide Frumento
Multiple sclerosis (MS) is an idiopathic chronic inflammatory disease that strikes the Central Nervous System (CNS).
多发性硬化症(MS)是一种侵袭中枢神经系统(CNS)的特发性慢性炎性疾病。
{"title":"E-Muser (Enhanced Multiple Sclerosis Expected Rate): A Technical Improvement","authors":"Davide Frumento","doi":"10.32474/CTBB.2019.01.000115","DOIUrl":"https://doi.org/10.32474/CTBB.2019.01.000115","url":null,"abstract":"Multiple sclerosis (MS) is an idiopathic chronic inflammatory\u0000disease that strikes the Central Nervous System (CNS).","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-27DOI: 10.32474/CTBB.2019.01.000114
A. Babalola, M. Obubu, A. Oluwaseun, Otekunrin
Volume rendering using computer graphics in scientific visualization is a set of techniques used to display a 2D projection of a discreetly sampled 3D data set, typically a 3D data field
{"title":"Visualization of Voxel Volume Emission and Absorption of Light in Medical Biology","authors":"A. Babalola, M. Obubu, A. Oluwaseun, Otekunrin","doi":"10.32474/CTBB.2019.01.000114","DOIUrl":"https://doi.org/10.32474/CTBB.2019.01.000114","url":null,"abstract":"Volume rendering using computer graphics in scientific\u0000visualization is a set of techniques used to display a 2D projection\u0000of a discreetly sampled 3D data set, typically a 3D data field","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116309995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-25DOI: 10.32474/CTBB.2019.01.000113
A. Babalola, M. Obubu, A. Oluwaseun, Otekunrin
Contraception is one of reproductive health’s essential elements. It enables young people to determine the timing and number of their children and empowers them with respect and dignity to manage their live
{"title":"Contraceptive Efficacy a Retrospective Analysis Among Nigeriant","authors":"A. Babalola, M. Obubu, A. Oluwaseun, Otekunrin","doi":"10.32474/CTBB.2019.01.000113","DOIUrl":"https://doi.org/10.32474/CTBB.2019.01.000113","url":null,"abstract":"Contraception is one of reproductive health’s essential\u0000elements. It enables young people to determine the timing and\u0000number of their children and empowers them with respect and\u0000dignity to manage their live","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128915650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-08DOI: 10.32474/CTBB.2019.01.000111
O. Maxwell, O. Oyamakin, E. J. Thomas
Length biased distributions are special case of the more general form known as weighted distribution [1], first introduced by [2] to model ascertainment bias and formalized in a unifying theory by [3]. Lifetime data may be modeled with several existing distributions, although the existing models are not adequate or are less representative of actual data in many situations. Therefore, the development of compound distributions that could better describe certain phenomena and make them more flexible than the baseline distribution is of great importance [4]. Thus, the choice of the model is also an important issue for reliable model parameter estimation. Some exponential distribution generalizations for modeling lifetime data due to some interesting advantages have been recently proposed [5]. In recent years many exponential distribution generalizations have been developed, such as the Marshall Olkin length biased exponential distribution [5], exponentiated exponential [6,7], generalized exponentiated moment exponential [8], extended exponentiated exponential [19], Marshall-Olkin exponential Weibull [10], Marshall-Olkin generalized exponential [5], and exponentiated moment exponential [11] distributions. A random variable X is said to have a length biased exponential distribution with parameter beta if its probability density function (pdf) and cumulative distribution function (cdf) is given by equation (1) and (2) respectively [12]:
{"title":"The Gompertz Length Biased Exponential Distribution and its application to Uncensored Data","authors":"O. Maxwell, O. Oyamakin, E. J. Thomas","doi":"10.32474/CTBB.2019.01.000111","DOIUrl":"https://doi.org/10.32474/CTBB.2019.01.000111","url":null,"abstract":"Length biased distributions are special case of the more general form known as weighted distribution [1], first introduced by [2] to model ascertainment bias and formalized in a unifying theory by [3]. Lifetime data may be modeled with several existing distributions, although the existing models are not adequate or are less representative of actual data in many situations. Therefore, the development of compound distributions that could better describe certain phenomena and make them more flexible than the baseline distribution is of great importance [4]. Thus, the choice of the model is also an important issue for reliable model parameter estimation. Some exponential distribution generalizations for modeling lifetime data due to some interesting advantages have been recently proposed [5]. In recent years many exponential distribution generalizations have been developed, such as the Marshall Olkin length biased exponential distribution [5], exponentiated exponential [6,7], generalized exponentiated moment exponential [8], extended exponentiated exponential [19], Marshall-Olkin exponential Weibull [10], Marshall-Olkin generalized exponential [5], and exponentiated moment exponential [11] distributions. A random variable X is said to have a length biased exponential distribution with parameter beta if its probability density function (pdf) and cumulative distribution function (cdf) is given by equation (1) and (2) respectively [12]:","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121363456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-20DOI: 10.32474/ctbb.2018.01.000110
M. Nichelatti
{"title":"On some Derivatives of Vector-Matrix Products Useful for Statistics","authors":"M. Nichelatti","doi":"10.32474/ctbb.2018.01.000110","DOIUrl":"https://doi.org/10.32474/ctbb.2018.01.000110","url":null,"abstract":"","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127409473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-17DOI: 10.32474/ctbb.2018.01.000109
E. Ahmed
{"title":"Some Simple Mathematical Models in Epilepsy","authors":"E. Ahmed","doi":"10.32474/ctbb.2018.01.000109","DOIUrl":"https://doi.org/10.32474/ctbb.2018.01.000109","url":null,"abstract":"","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127605634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-28DOI: 10.32474/CTBB.2018.01.000107
A. Roy
Data generation is presently is light-years ahead compared to where it was a few years ago. With technological advances and use, huge digital information is now available that is beyond our imagination. It is widely accepted that Big data analytics has revolutionized digital transformation. It enables too quick and indepth analysis, facilitating faster accurate decisions resulting in right insight. In fact, technological advances in data management have helped in timely capture of the informational value of big data. As a result, a wide adoption of analytics has happened that were not economically viable for large-scale applications before the big data era. Importantly, Pet bytes of raw data provide lot of clues for health care services through right use. Data is considered as gold in digital economy era. It is needless to mention that today analytics skills are extremely in high demand. A wide gap has been created in demand and supply of analysts throughout the globe particularly in western countries. According to the experts in the field knowledge of data analytics is essential for this next generation job aspirants. Now we are in the age of data. Everybody talks about big data across all the fields of science and technology. Even Big data analytics is attempted in the non-conventional areas. It is considered as a “the next big thing” will be. Now a day’s data is generated in higher quantities from various field and analyzed at a faster and with higher accuracy that we could not have thought of a few years ago. Researchers adding every day, new tool to extract raw data into valuable insight enabling solutions to the critical problems. The application of big data is enormous in all spheres of scientific investigation. Technologies coupled with and internet of things produces huge data globally. Innovative technologies have added capacity to generate, store, and analyze data from different sources for a various application. Some 2.5 quintillion bytes of data are produced every day, and approximately 90 percent of existing data was produced in the last two years alone [1]. These data are the potential sources for innovative research.
{"title":"Demand for the Emerging AI, Machine, Deep Learning and Big Data Analytics Skill for 21st Century Jobs","authors":"A. Roy","doi":"10.32474/CTBB.2018.01.000107","DOIUrl":"https://doi.org/10.32474/CTBB.2018.01.000107","url":null,"abstract":"Data generation is presently is light-years ahead compared to where it was a few years ago. With technological advances and use, huge digital information is now available that is beyond our imagination. It is widely accepted that Big data analytics has revolutionized digital transformation. It enables too quick and indepth analysis, facilitating faster accurate decisions resulting in right insight. In fact, technological advances in data management have helped in timely capture of the informational value of big data. As a result, a wide adoption of analytics has happened that were not economically viable for large-scale applications before the big data era. Importantly, Pet bytes of raw data provide lot of clues for health care services through right use. Data is considered as gold in digital economy era. It is needless to mention that today analytics skills are extremely in high demand. A wide gap has been created in demand and supply of analysts throughout the globe particularly in western countries. According to the experts in the field knowledge of data analytics is essential for this next generation job aspirants. Now we are in the age of data. Everybody talks about big data across all the fields of science and technology. Even Big data analytics is attempted in the non-conventional areas. It is considered as a “the next big thing” will be. Now a day’s data is generated in higher quantities from various field and analyzed at a faster and with higher accuracy that we could not have thought of a few years ago. Researchers adding every day, new tool to extract raw data into valuable insight enabling solutions to the critical problems. The application of big data is enormous in all spheres of scientific investigation. Technologies coupled with and internet of things produces huge data globally. Innovative technologies have added capacity to generate, store, and analyze data from different sources for a various application. Some 2.5 quintillion bytes of data are produced every day, and approximately 90 percent of existing data was produced in the last two years alone [1]. These data are the potential sources for innovative research.","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123693262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-15DOI: 10.32474/ctbb.2018.01.000106
E. Ahmed
Introduction Hybrid tumor Cells Recently [1,2,3,4] hybrid tumor cells have been discovered. They have the following properties: a) They circulate more than ordinary tumor cells. b) They have greater ability to migrate and invade other tumors. c) They have greater ability to form metastases. Motivated by this the following simple model is presented: Let N1, N2 be the ordinary and hybrid tumor cells respectively. Let N=N1+N2 hence the tumor growth can be represented by
{"title":"A Simple Mathematical Model for a New Type of Cancer Cells","authors":"E. Ahmed","doi":"10.32474/ctbb.2018.01.000106","DOIUrl":"https://doi.org/10.32474/ctbb.2018.01.000106","url":null,"abstract":"Introduction Hybrid tumor Cells Recently [1,2,3,4] hybrid tumor cells have been discovered. They have the following properties: a) They circulate more than ordinary tumor cells. b) They have greater ability to migrate and invade other tumors. c) They have greater ability to form metastases. Motivated by this the following simple model is presented: Let N1, N2 be the ordinary and hybrid tumor cells respectively. Let N=N1+N2 hence the tumor growth can be represented by","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114103812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-04DOI: 10.32474/CTBB.2018.01.000104
Gwaza Ds
{"title":"Phenotypic Correlation Between Egg Weight and Egg Linear Measurements of the French Broiler Guinea Fowl Raised in the Humid Zone of Nigeria","authors":"Gwaza Ds","doi":"10.32474/CTBB.2018.01.000104","DOIUrl":"https://doi.org/10.32474/CTBB.2018.01.000104","url":null,"abstract":"","PeriodicalId":193561,"journal":{"name":"Current Trends on Biostatistics and Biometrics","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114411138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：