This research was carried out to determine the prevalence of subclinical mastitis in lactating Dairy Cow of Bangladesh Agricultural University dairy farm (BAUDF) and rural areas of Tangail sadar upazila of Bangladesh during the period of July 2009 to April 2010. A total of 200 milk samples (40 from BAUDF and 160 from Tangail sadar upazila) were collected for this study which were subjected to physical examination and subsequently screened for subclinical mastitis using three indirect tests viz. White Side Test (WST), California Mastitis Test (CMT), and Surf Field Mastitis Test (SFMT). Overall prevalence of subclinical mastitis (SCM) in lactating dairy cows found in this study was 29%. Cows were infected with SCM 29.5%, 27.5% and 25.5% detection by CMT, WST and SFMT respectively. Higher prevalence of SCM was detected in milch crossbred cows (36.36%) in comparison to local bred cows (24.61%) maintained under extensive management system in Rural area of Tangail sadar upazila. The prevalence of SCM was recorded in 31.58%, 30.76% and 68.75% in cows of local area of Tangail sadar upazila, and 25.0%, 40.0% and 71.42% in cows of BAU,DF during the early, mid and late stages of lactation respectively. The highest prevalence of SCM was recorded during the early lactation stage in both the local breed cows (30.0%) and cows of BAUDF (45.83%) in comparison to their respective mid and late stages of lactation. The prevalence of SCM was highest in lactating cows having third lactation and high yielding (cows produced >10 liter milk per day) both in local breed and crossbred cows.
A passive local eavesdropper can leverage Website Fingerprinting (WF) to deanonymize the web browsing activity of Tor users. The value of timing information to WF has often been discounted in recent works due to the volatility of low-level timing information. In this paper, we more carefully examine the extent to which packet timing can be used to facilitate WF attacks. We first propose a new set of timing-related features based on burst-level characteristics to further identify more ways that timing patterns could be used by classifiers to identify sites. Then we evaluate the effectiveness of both raw timing and directional timing which is a combination of raw timing and direction in a deep-learning-based WF attack. Our closed-world evaluation shows that directional timing performs best in most of the settings we explored, achieving: (i) 98.4% in undefended Tor traffic; (ii) 93.5% on WTF-PAD traffic, several points higher than when only directional information is used; and (iii) 64.7% against onion sites, 12% higher than using only direction. Further evaluations in the open-world setting show small increases in both precision (+2%) and recall (+6%) with directional-timing on WTF-PAD traffic. To further investigate the value of timing information, we perform an information leakage analysis on our proposed handcrafted features. Our results show that while timing features leak less information than directional features, the information contained in each feature is mutually exclusive to one another and can thus improve the robustness of a classifier.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.