Estrus Synchronization and Artificial Insemination in Goats during Low Breeding Season-A Preliminary Study

A pilot project was initiated to introduce artificial insemination (AI) in goats at farmer level with chilled semen. Does (n=18) were synchronized with progesterone impregnated vaginal sponges (60 mg Medroxyprogesterone acetate; MAP) for 11 days. At 48 hrs prior to removal of the sponges, intramuscular injection of 400 IU equine chorionic gonadotropin (eCG) and cloprostenol (0.075 mg) was given. Fixed time vaginal insemination (43-45 hrs after sponge removal) was done twice (at 12 hrs interval) in 17 does with chilled Beetal buck semen (4°C) extended with Tris-citric acid (TCA) or skimmed milk (SM) based extender (75 x 106 sperm/ml). Pregnancy test was performed at 45 days post insemination through ultrasonography. An overall 94.5% (17/18) of does showed heat signs and 78% of them were detected in heat between 12 - 24 hrs after sponge removal. An overall 29.4% (5/17) pregnancy rate was recorded. Higher pregnancy rate (44.4%) was obtained in does inseminated with SM extended semen as compared to 12.5% for TCA extended semen. Results were encouraging in the sense that to the best of our knowledge it was the first report of kidding through AI in heat induced does in Pakistan. Moreover, it indicated the feasibility of using synchronization and fixed time AI during low breeding season to enhance the reproductive efficiency in local goats

Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems

One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure

Oxygen reduction reaction activity in non-precious single-atom (M–N/C ) catalysts-contribution of metal and carbon/nitrogen framework-based sites.

We examine the performance of a number of single-atom M-N/C electrocatalysts with a common structure in order to deconvolute the activity of the framework N/C support from the metal M-N4 sites in M-N/Cs. The formation of the N/C framework with coordinating nitrogen sites is performed using zinc as a templating agent. After the formation of the electrically conducting carbon-nitrogen metal-coordinating network, we (trans)metalate with different metals producing a range of different catalysts (Fe-N/C, Co-N/C, Ni-N/C, Sn-N/C, Sb-N/C, and Bi-N/C) without the formation of any metal particles. In these materials, the structure of the carbon/nitrogen framework remains unchanged-only the coordinated metal is substituted. We assess the performance of the subsequent catalysts in acid, near-neutral, and alkaline environments toward the oxygen reduction reaction (ORR) and ascribe and quantify the performance to a combination of metal site activity and activity of the carbon/nitrogen framework. The ORR activity of the carbon/nitrogen framework is about 1000-fold higher in alkaline than it is in acid, suggesting a change in mechanism. At 0.80 VRHE, only Fe and Co contribute ORR activity significantly beyond that provided by the carbon/nitrogen framework at all pH values studied. In acid and near-neutral pH values (pH 0.3 and 5.2, respectively), Fe shows a 30-fold improvement and Co shows a 5-fold improvement, whereas in alkaline pH (pH 13), both Fe and Co show a 7-fold improvement beyond the baseline framework activity. The site density of the single metal atom sites is estimated using the nitrite adsorption and stripping method. This method allows us to deconvolute the framework sites and metal-based active sites. The framework site density of catalysts is estimated as 7.8 × 1018 sites g-1. The metal M-N4 site densities in Fe-N/C and Co-N/C are 9.4 × 1018 sites-1 and 4.8 × 1018 sites g-1, respectively

Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a group of nodes constituting a network of mobile nodes without predefined and pre-established architecture where mobile nodes can communicate without any dedicated access points or base stations. In MANETs, a node may act as a host as well as a router. Nodes in the network can send and receive packets through intermediate nodes. However, the existence of malicious and selfish nodes in MANETs severely degrades network performance. The identification of such nodes in the network and their isolation from the network is a challenging problem. Therefore, in this paper, a simple reputation-based scheme is proposed which uses the consumption and contribution information for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with good reputation. The simulation results show that our proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, our scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme

Emotion classification and crowd source sensing; a lexicon based approach

In today's world, social media provides a valuable platform for conveying expressions, thoughts, point-of-views, and communication between people, from diverse walks of life. There are currently approximately 2.62 billion active users' social networks, and this is expected to exceed 3 billion users by 2021. Social networks used to share ideas and information, allowing interaction across communities, organizations, and so forth. Recent studies have found that the typical individual uses these platforms between 2 and 3 h a day. This creates a vast and rich source of data that can play a critical role in decision-making for companies, political campaigns, and administrative management and welfare. Twitter is one of the important players in the social network arena. Every scale of companies, celebrities, different types of organizations, and leaders use Twitter as an instrument for communicating and engaging with their followers. In this paper, we build upon the idea that Twitter data can be analyzed for crowd source sensing and decision-making. In this paper, a new framework is presented that uses Twitter data and performs crowd source sensing. For the proposed framework, real-time data are obtained and then analyzed for emotion classification using a lexicon-based approach. Previous work has found that weather, understandably, has an impact on mood, and we consider these effects on crowd mood. For the experiments, weather data are collected through an application-programming-interface in R and the impact of weather on human sentiments is analyzed. Visualizations of the data are presented and their usefulness for policy/decision makers in different applications is discussed