Pub Date : 2018-07-09DOI: 10.19080/BBOAJ.2018.07.555719
F. Aslanova
This study was conducted in order to identify the attitudes towards environment and knowledge of students of Agriculture Department of Northern Cyprus Universities about the environment. A questionnaire of four sections was conducted with university students during the research. The survey was organized as; personal information at the first section, environmental knowledge test at the second section, attitudes towards environment at the third section and behavioral scales at the fourth section. The research consisted of students studying in Agriculture Department in Northern Cyprus Universities in spring semester of 2015-2016 academic year, as the sample consisted of 114 female, 186 male, totally 300 students studying in fall semester of the same academic year. The data obtained during the application were analyzed in SPSS 20.00 package program. Whether the attitudes and behaviors’ of students towards environment differed in terms of independent variables of the research were investigated by using t-test for two variable features and Anova, Wilkis Lambda, Manova test for more than two features. According to the findings of the research, the attitudes and behaviors’ of university students towards environment do not differ according to educational level, income level of their parents and whether they received training about environment.
{"title":"Behaviors and Attitudes of Students towards Environmental Issues at Faculty of Agriculture in Northern Cyprus","authors":"F. Aslanova","doi":"10.19080/BBOAJ.2018.07.555719","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.07.555719","url":null,"abstract":"This study was conducted in order to identify the attitudes towards environment and knowledge of students of Agriculture Department of Northern Cyprus Universities about the environment. A questionnaire of four sections was conducted with university students during the research. The survey was organized as; personal information at the first section, environmental knowledge test at the second section, attitudes towards environment at the third section and behavioral scales at the fourth section. The research consisted of students studying in Agriculture Department in Northern Cyprus Universities in spring semester of 2015-2016 academic year, as the sample consisted of 114 female, 186 male, totally 300 students studying in fall semester of the same academic year. The data obtained during the application were analyzed in SPSS 20.00 package program. Whether the attitudes and behaviors’ of students towards environment differed in terms of independent variables of the research were investigated by using t-test for two variable features and Anova, Wilkis Lambda, Manova test for more than two features. According to the findings of the research, the attitudes and behaviors’ of university students towards environment do not differ according to educational level, income level of their parents and whether they received training about environment.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44467720","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 : 2018-07-09DOI: 10.19080/bboaj.2018.07.555720
C. Ampadu
We review the exponentiated generalized (EG) T-X family of distributions and propose some further developments of this class of distributions
我们回顾了指数广义（EG）T-X分布族，并提出了这类分布的一些进一步发展
{"title":"Further Developments on the T-Transmuted X Family of Distributions II","authors":"C. Ampadu","doi":"10.19080/bboaj.2018.07.555720","DOIUrl":"https://doi.org/10.19080/bboaj.2018.07.555720","url":null,"abstract":"We review the exponentiated generalized (EG) T-X family of distributions and propose some further developments of this class of distributions","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47500290","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 : 2018-07-09DOI: 10.19080/bboaj.2018.07.555718
J. Sarkar
The null hypothesis significance testing procedure (NHSTP) was devised to guide scientific researchers decide whether an observed difference between comparative groups is due to chance or if there is a significant effect. However, prolific use of NHSTP by non‐statisticians in many quantitative studies has resulted in widespread misinterpretations, misuses and abuses. We briefly recall a recent ban on p value − and summarize the official statement written by the American Statistical Association (ASA), which explains what p‐value is, what it is not, and how to interpret and use it correctly
{"title":"Will P‐Value Triumph over Abuses and Attacks?","authors":"J. Sarkar","doi":"10.19080/bboaj.2018.07.555718","DOIUrl":"https://doi.org/10.19080/bboaj.2018.07.555718","url":null,"abstract":"The null hypothesis significance testing procedure (NHSTP) was devised to guide scientific researchers decide whether an observed difference between comparative groups is due to chance or if there is a significant effect. However, prolific use of NHSTP by non‐statisticians in many quantitative studies has resulted in widespread misinterpretations, misuses and abuses. We briefly recall a recent ban on p value − and summarize the official statement written by the American Statistical Association (ASA), which explains what p‐value is, what it is not, and how to interpret and use it correctly","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46421998","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 : 2018-07-09DOI: 10.19080/BBOAJ.2018.07.555716
R. Ahmed
A repeated measurement design (RMD) is one in which multiple, or repeated measurements are made on each experimental unit. The experimental unit could be a person or an animal. In repeated measurement design each subject is measured before and one or several times after an intervention. Areas where RMDs are widely used include medicine, pharmacology, animal sciences and psychology. A characteristic feature of RMD is that the effect which a treatment has during its period of application (its direct effect) may persist into the following period(s). If the effect persists only into the immediately following period, the effect is called the first-order residual effect or residual effect. The choice of RMD must be made in a way that the treatments can be efficiently compared after allowing for the residual effects. In this paper circular balanced and strongly balanced repeated measurement designs are constructed to extend the work of Iqbal and Tahir [1] for v ≤ 100 in 3 periods.
{"title":"Catalogue of Balanced Repeated Measurement Designs in Linear Periods of Two Unequal Sizes","authors":"R. Ahmed","doi":"10.19080/BBOAJ.2018.07.555716","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.07.555716","url":null,"abstract":"A repeated measurement design (RMD) is one in which multiple, or repeated measurements are made on each experimental unit. The experimental unit could be a person or an animal. In repeated measurement design each subject is measured before and one or several times after an intervention. Areas where RMDs are widely used include medicine, pharmacology, animal sciences and psychology. A characteristic feature of RMD is that the effect which a treatment has during its period of application (its direct effect) may persist into the following period(s). If the effect persists only into the immediately following period, the effect is called the first-order residual effect or residual effect. The choice of RMD must be made in a way that the treatments can be efficiently compared after allowing for the residual effects. In this paper circular balanced and strongly balanced repeated measurement designs are constructed to extend the work of Iqbal and Tahir [1] for v ≤ 100 in 3 periods.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47760534","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 : 2018-07-09DOI: 10.19080/BBOAJ.2018.07.555715
Ritabrata Dutta
Approximate Bayesian computation (ABC) provides us a rigorous tool to perform parameter inference for models without an easily accessible likelihood function. Here we give a short introduction to ABC, focusing on applications in biological science. Furthermore, we introduce users to a Python suite implementing ABC algorithms, with optimal use of high performance computing facilities.
{"title":"Approximate Bayesian Computation for Biological Science","authors":"Ritabrata Dutta","doi":"10.19080/BBOAJ.2018.07.555715","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.07.555715","url":null,"abstract":"Approximate Bayesian computation (ABC) provides us a rigorous tool to perform parameter inference for models without an easily accessible likelihood function. Here we give a short introduction to ABC, focusing on applications in biological science. Furthermore, we introduce users to a Python suite implementing ABC algorithms, with optimal use of high performance computing facilities.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43412158","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 : 2018-07-06DOI: 10.19080/BBOAJ.2018.07.555714
D. Dasgupta
Commercial sectors increasingly using biometrics to secure their authentication process, and verified transactions. Some example applications include ATM booths, credit cards, facility access, network login, online service and data access. Healthcare and medical science extensively use various forms of biometrics (external and internal) for blood transfusion, organ matching, identification of biological parents, ancestry, etc.
{"title":"Biometrics and its use: Viewpoint","authors":"D. Dasgupta","doi":"10.19080/BBOAJ.2018.07.555714","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.07.555714","url":null,"abstract":"Commercial sectors increasingly using biometrics to secure their authentication process, and verified transactions. Some example applications include ATM booths, credit cards, facility access, network login, online service and data access. Healthcare and medical science extensively use various forms of biometrics (external and internal) for blood transfusion, organ matching, identification of biological parents, ancestry, etc.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48937618","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 : 2018-07-02DOI: 10.19080/BBOAJ.2018.07.555713
Sunday Babuba
{"title":"A Hybrid Solutions Method for Solving One Dimensional Parabolic Partial Differential Equations","authors":"Sunday Babuba","doi":"10.19080/BBOAJ.2018.07.555713","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.07.555713","url":null,"abstract":"","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46478631","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 : 2018-07-02DOI: 10.19080/BBOAJ.2018.07.555712
V. Deo
Finding potent and clinically standardized biomarkers is of utmost importance for early detection of any disease which further helps in developing prevention and cure techniques. Use of statistical techniques becomes inevitable to validate the predictive power of the biomarkers and also to ensure the reproducibility of results. Identification of statistically significant covariates and competing risks is another important aspect of any biostatistical study for understanding disease progression. This paper attempts to review some useful statistical and biostatistical techniques being utilized in the study of dementia, Alzheimer’s disease (AD) in particular, and also proposes some potentially reliable techniques which can be helpful in critically examining the complexities involved in brain activities of AD patients and its association with other pathological and physical changes in the patients. This paper concludes by proposing an objective-wise methodological structure for biostatistical analyses of AD.
{"title":"Prospects of Statistical and Biostatistical Techniques in the Study of Diagnosis, Survival Analysis, and Disease Progression of Alzheimer’s Disease","authors":"V. Deo","doi":"10.19080/BBOAJ.2018.07.555712","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.07.555712","url":null,"abstract":"Finding potent and clinically standardized biomarkers is of utmost importance for early detection of any disease which further helps in developing prevention and cure techniques. Use of statistical techniques becomes inevitable to validate the predictive power of the biomarkers and also to ensure the reproducibility of results. Identification of statistically significant covariates and competing risks is another important aspect of any biostatistical study for understanding disease progression. This paper attempts to review some useful statistical and biostatistical techniques being utilized in the study of dementia, Alzheimer’s disease (AD) in particular, and also proposes some potentially reliable techniques which can be helpful in critically examining the complexities involved in brain activities of AD patients and its association with other pathological and physical changes in the patients. This paper concludes by proposing an objective-wise methodological structure for biostatistical analyses of AD.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68370880","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 : 2018-06-25DOI: 10.19080/BBOAJ.2018.07.555710
Afolabi Ao
With the introduction of streaming multimedia in the past few years, large multimedia files can now be delivered even over modem connections. Streaming multimedia is an Internet data transfer method that facilitates the transfer of audio and video files from computer to computer in a “stream.” Streamed media packets can be played as soon as the data starts arriving at the receiving computer—users do not have to wait until the full file has been downloaded. Streaming audio has been more successful than video, which has generally been limited to small picture sizes or low resolution (grainy) video Paperions, but as the bandwidth increases, higher quality, full-screen video becomes possible. When a sound file is to be prepared for streaming, it is compressed to reduce the overall size of the file. Streaming audio is currently being used as a supplement to classroom-based and online course delivery, usually in the form of pre-recorded lectures, interviews with guests, student Papers, samples of student classroom interaction, or sound bytes of content relevant to the course of study. When implemented wisely, video can alleviate the “page-turning” boredom of many online courses [1]. The multimedia based e-learning content of this site is protected by data encryption, as more users come to understand the internet’s open nature and the dangers of web surfing, Today’s web browsers automatically encrypt text when making a connection to a secure server. This prevents intruders from listening in on private communications. Even if they are able to capture the message, encryption allows them to only view scrambled text or what many call unreadable gibberish. Upon arrival, the data is decrypted, allowing the intended recipient to view. Without this security mechanism, information transferred over the internet can be easily captured and viewed by anyone listening. Data encryption refers to mathematical calculations and algorithmic schemes that transform plaintext into cyphertext, a form that is non-readable to unauthorized parties. The recipient of an encrypted message uses a key which triggers the algorithm mechanism to decrypt the data, transforming it to the original plaintext version. Another security mechanism implemented in this Paper is the Online face recognition which facilitates a user who is equipped with a display device. Our online singleimage face recognition system is implemented using still imagebased algorithms [2]. Principal Component Analysis based facial Abstract
{"title":"Securing E-Learning System with Crypto-Biometric Multimedia","authors":"Afolabi Ao","doi":"10.19080/BBOAJ.2018.07.555710","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.07.555710","url":null,"abstract":"With the introduction of streaming multimedia in the past few years, large multimedia files can now be delivered even over modem connections. Streaming multimedia is an Internet data transfer method that facilitates the transfer of audio and video files from computer to computer in a “stream.” Streamed media packets can be played as soon as the data starts arriving at the receiving computer—users do not have to wait until the full file has been downloaded. Streaming audio has been more successful than video, which has generally been limited to small picture sizes or low resolution (grainy) video Paperions, but as the bandwidth increases, higher quality, full-screen video becomes possible. When a sound file is to be prepared for streaming, it is compressed to reduce the overall size of the file. Streaming audio is currently being used as a supplement to classroom-based and online course delivery, usually in the form of pre-recorded lectures, interviews with guests, student Papers, samples of student classroom interaction, or sound bytes of content relevant to the course of study. When implemented wisely, video can alleviate the “page-turning” boredom of many online courses [1]. The multimedia based e-learning content of this site is protected by data encryption, as more users come to understand the internet’s open nature and the dangers of web surfing, Today’s web browsers automatically encrypt text when making a connection to a secure server. This prevents intruders from listening in on private communications. Even if they are able to capture the message, encryption allows them to only view scrambled text or what many call unreadable gibberish. Upon arrival, the data is decrypted, allowing the intended recipient to view. Without this security mechanism, information transferred over the internet can be easily captured and viewed by anyone listening. Data encryption refers to mathematical calculations and algorithmic schemes that transform plaintext into cyphertext, a form that is non-readable to unauthorized parties. The recipient of an encrypted message uses a key which triggers the algorithm mechanism to decrypt the data, transforming it to the original plaintext version. Another security mechanism implemented in this Paper is the Online face recognition which facilitates a user who is equipped with a display device. Our online singleimage face recognition system is implemented using still imagebased algorithms [2]. Principal Component Analysis based facial Abstract","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47502688","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 : 2018-06-25DOI: 10.19080/bboaj.2018.07.555708
Y. Qi
Since Jing et al. [1] propose the jackknife empirical likelihood method for U-statistics, the method has been developed and applied to inference problems in statistical theory and other areas such as biostatistics, medical statistics and insurance. In this short review paper, we give an introduction on one sample and two-sample jackknife empirical methods and smoothed jackknife empirical likelihood methods and present a brief literature review on applications of these methods.
{"title":"Jackknife Empirical Likelihood Methods","authors":"Y. Qi","doi":"10.19080/bboaj.2018.07.555708","DOIUrl":"https://doi.org/10.19080/bboaj.2018.07.555708","url":null,"abstract":"Since Jing et al. [1] propose the jackknife empirical likelihood method for U-statistics, the method has been developed and applied to inference problems in statistical theory and other areas such as biostatistics, medical statistics and insurance. In this short review paper, we give an introduction on one sample and two-sample jackknife empirical methods and smoothed jackknife empirical likelihood methods and present a brief literature review on applications of these methods.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41559024","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}
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