A. Baset, Qun Liu, B. Liao, A. Waris, I. Ahmad, H. Yanan, Zhang Qingqing
{"title":"Population Dynamics of Saddle Grunt Fish, Pomadasys maculatus (Bloch, 1793) from Pakistani Waters","authors":"A. Baset, Qun Liu, B. Liao, A. Waris, I. Ahmad, H. Yanan, Zhang Qingqing","doi":"10.11648/J.BE.20200401.11","DOIUrl":"https://doi.org/10.11648/J.BE.20200401.11","url":null,"abstract":"Length frequency data of Pomadasys maculatus (Bloch, 1793) was collected during 2012 and 2014 from Pakistan waters. Total 1387 fish individuals (pooled) were collected ranging from 7-22 cm (total length). Weight ranging 5-105 g. The data were analyzed for the estimation of population dynamics. The power coefficient of 2.532 in 2012 and 2.560 in 2014. The von Bertalanffy growth parameters of 23.10 cm (L∞), 0.480year-1 (K) for 2012 and 23.10 cm (L∞), 0.570year-1 (K) for 2014, were calculated by ELEFAN method. The Z was 2.06years-1 for 2012 and 2.07year-1 for 2014. The M) was1.16 year-1 for 2012 and 1.30 year-1 for 2014 calculated with Pauly’s equation. Therefore, the F was 0.9 years-1 in 2012 and 0.77years-1 in 2014. The exploitation ratio was estimated to be 0.43 and 0.32 respectively. The YPRA (yield-per-recruit analysis) indicated that the estimated Fmax was 1 for both years, when tc was 1. Fcurrent was 0.9 and 0.77 (for 2012 and 2014 respectively) which was less than Fmax. With Gulland method, the BRPs for the fishery (Fopt) was estimated at 1.16 years-1 and 1.30 years-1 respectively, higher than current fish mortality. Accordingly, the Pomadasys maculatus fishery is in managing the condition in Pakistani waters.","PeriodicalId":8944,"journal":{"name":"Bioprocess Engineering","volume":"126 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76838448","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}
{"title":"Evaluation of Early Detection Methods for Alzheimer's Disease","authors":"E. Irankhah","doi":"10.11648/J.BE.20200401.13","DOIUrl":"https://doi.org/10.11648/J.BE.20200401.13","url":null,"abstract":"Amnesia, commonly referred to as Alzheimer’s, is a type of brain dysfunction that gradually dissipates the patient’s mental abilities. Memory disorder usually develops gradually and progresses. At first, memory impairment is limited to recent events and lessons, but old memories are gradually damaged. In this disease, the connection between nerve cells by the formation of neurofibrillary nodes disappeared. Currently, treatment for the disease mainly involves symptomatic treatments, treatment of behavioral disorders and medication use. Although there is no cure for Alzheimer's disease yet, medications can slow the progression of the disease and reduce the severity of memory impairment and behavioral problems. Today, whit the spread of definitive treatment for this disease, in this study, new techniques for the treatment of this disease can be explored by examining the early detection methods of the disease through brain signal processing with classifiers and medical imaging such as MRI and CT Scan. Signal processing has included EEG and ERP brain signals and the use of classifiers such as SVM, LDA and Neural network. In medical image processing, a combination of Neural network and Wavelet is used to expedite the time of diagnosis according to the above method. Given the process under consideration, combining brain signals and medical imaging can provide valuable help in early detection of Alzheimer disease.","PeriodicalId":8944,"journal":{"name":"Bioprocess Engineering","volume":"60 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78302600","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}
{"title":"An overview of chemical reaction analysis","authors":"Shijie Liu","doi":"10.1016/B978-0-444-59525-6.00003-2","DOIUrl":"https://doi.org/10.1016/B978-0-444-59525-6.00003-2","url":null,"abstract":"","PeriodicalId":8944,"journal":{"name":"Bioprocess Engineering","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79973925","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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