Cannabis Extract Composition Determines Reinforcement in a Vapor Self-Administration Paradigm

The legalization of cannabis and shifting cultural attitudes have driven an increase in cannabis use and the proliferation of vapor delivery devices. The DSM-V recognizes “cannabis use disorder” under the umbrella of substance use disorders, but its neural mechanisms require greater clarity (Peters et al., 2020). Debate in the scientific community and the public sphere alike primarily asks, “is cannabis addictive?” and “are there negative effects from chronic use?” The first issue magnifies the second: if users compulsively seek cannabis or become dependent, then safe regimens become difficult to maintain

Poetical discourse analysis of a Tamil song Ovvoru Puukkalumee

This study is an attempt to analyze the Tamil movie song ‘Ovvoru PuukkaLumee ’ meaning ‘every flower’ from the Tamil movie 'Autograph'. This song is one of the popular songs of P. Vijay, a Tamil lyricist. The texture discourse of this song will be analyzed in terms of grammatical and lexical usages found by making use of discourse analysis

A Recommendation for Online Social Voting using the Evidence based Filtering Method

Marvelous growth within the quality of on-line social networks (OSNs) in recent years. Most of existing on-line social networks like Face book & Twitter area unit designed to bias towards data speech act to an outsized audience and additionally raises variety of privacy and security problems. Though OSNs permits one user to limit access to her/his knowledge, presently they are doing not give any mechanism to enforce privacy considerations over knowledge related to multiple users. During this paper, we tend to propose associate approach to facilitate cooperative privacy management of shared knowledge in OSNs. we tend to extend and formulate a multiparty access management model, named Evidence based aggregation method to capture the essence of voting in OSNs, beside a multiparty policy specification theme and a policy social control mechanism. We tend to additionally demonstrate the relevancy of our approach by implementing a proof-of-concept example hosted in Face book

EFFECTS OF INDIVIDUALIZED TRAINING AND RESPIRATORY MUSCLE TRAINING IN IMPROVING SWIMMING PERFORMANCE AMONG COLLEGIATE SWIMMERS - AN EXPERIMENTAL STUDY

Previous researches have been conducted to determine the types of training to improve swimming performance. Nevertheless, no study has been done on the individualized training approach among swimmers. Hence, this study aimed (i) to examine the effects of respiratory muscle training on swimming performance (ii) to examine the effects of combined respiratory muscle training with individualized training on swimming performance, and (iii) to compare the differences between the isolated respiratory muscle training, combined intervention of respiratory muscle training with individualized training and usual training on swimming performance.  For this, 45 collegiate swimmers with no previous injuries and swims regularly for at least 1 hour per week participated in the study. Participants were randomly assigned into three groups; Group A: Respiratory muscle training and Individualized Training, Group B: Respiratory muscle training alone, and Group C: Usual training session. The difference within the groups after four weeks of the intervention was analyzed using Paired T-test, while the differences between intervention groups were analyzed using repeated measure two-way ANOVA. Both the intervention groups (Group A and B) showed significant improvement after four weeks of intervention, whereas in group comparison, Group A showed tremendous improvement in swimming performance (F (17,238) = 8.385, p<0.05, np2 = 0.375). Thus, the current study has proven that the combination of respiratory muscle training with individualized training could further enhance the swimming performance in terms of heart rate, Vo2 max, stroke volume, perceived exertion, and SWOLF score. Future studies on athletic swimmers with a larger sample size are recommended to further examine the individualized training approach

Isolation of alkaline protease from Bacillus subtilis AKRS3

This research study was mainly focused on phenotypic, biochemical characterization, 16s rRNA sequence based species level identification of isolate and determination of the higher production of alkaline protease through optimization study (carbon, nitrogen, incubation period, temperature, pH and sodium chloride concentration), production by submerged fermentation and analytical studies (protease assay, protein and biomass estimation). The produced crude enzyme was been used for dehairing activity on goat and sheep hides. Primary screening was achieved by skim milk casein hydrolysis method. Microbiological, biochemical characterization and 16S rRNA phylogenetic analysis revealed that isolated bacterium was Bacillus subtilis AKRS3 with an optimum alkaline protease producing temperature, 37°C and pH 9.0. The maximum alkaline protease production was achieved at 24 h of incubation period. Among various nitrogen (organic and inorganic) sources, beef extract was found to be the best inducer for alkaline protease in the concentration of 1.5% as was reported for the maximum alkaline protease production. Effect of carbon sources for example xylose, on protease production proved high protease production than the other tested carbon sources and subsequently 2% concentration registered an optimum to enhance the protease production. The halotolerancy of B. subtilis AKRS3 for alkaline protease production indicated that 3% of sodium chloride was optimum to yield maximum protease activity. During production, agitation rate was 250 rpm at air flow rate of 1 VVM. Maximum protease activity of 42.7556 U/ml was observed at the end of 24 h cell free supernatant of fermentation broth. Crude alkaline protease was most active at 55°C, pH 9 with casein as substrate. According to our knowledge, this study demonstrated the first report on alkaline protease producing B. subtilis AKRS3 isolated from fish waste. The produced enzyme could be effectively used to remove hair from goat and sheep hide indicating its potential application in leather processing industry.Key words: Bacillus subtilis AKRS3, 16S rRNA sequence, alkaline protease, submerged fermentation (SmF), dehairing activity

Smart Health Predicting System Using Data Mining

An overview of the data mining techniques with its applications, medical, and educational aspects of Clinical Predictions. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. Such a large amount of data cannot be processed by humans in a short time to make diagnosis, and treatment schedules. A major objective is to evaluate datamining techniques in clinical and health care applications to develop accurate decisions. It also gives a detailed discussion of medical data mining techniques which can improve various aspects of Clinical Predictions. It is a new powerful technology which is of high interest incomputer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of machine learning and database management to extract new patterns from large datasets and the knowledge associated with these patterns. The actual task is to extract data by automatic orsemi- automatic means. The different parameters included in data mining include clustering, forecasting, path analysis and predictive analysis. It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Health Prediction system is an end user support and online consultation project. Here we propose a system that allows users to get instant guidance on their health issues through an intelligent health care system online. The system is fed with various symptoms and the disease/illness associated with those systems. The system allows user to share their symptoms and issues. It then processes userssymptoms to check for various illness that could be associated with it. Here we use some intelligent data mining techniques to guess the most accurate illness that could be associated with patient’s symptoms. If the system is not able to provide suitable results, it informs the user about the type of disease or disorder it feels user’s symptoms are associated with. If users symptoms do not exactly match any disease in our database

Measuring Semantic Similarity among Text Snippets and Page Counts in Data Mining

Measuring the semantic similarity between words is an important component in various tasks on the web such as relation extraction, community mining, document clustering, and automatic metadata extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words (or entities) remains a challenging task. We propose an empirical method to estimate semantic similarity using page counts and text snippets retrieved from a web search engine for two words. Specifically, we define various word co-occurrence measures using page counts and integrate those with lexical patterns extracted from text snippets. To identify the numerous semantic relations that exist between two given words, we propose a novel pattern extraction algorithm and a pattern clustering algorithm. The optimal combination of page counts-based co-occurrence measures and lexical pattern clusters is learned using support vector machines. The proposed method outperforms various baselines and previously proposed web-based semantic similarity measures on three benchmark data sets showing a high correlation with human ratings. Moreover, the proposed method significantly improves the accuracy in a community mining task

Effectiveness of Social Media Community Using Optimized Clustering Algorithm

Now-a-days social media is used to the introduce new issues and discussion on social media. More number of users participates in the discussion via social media. Different users belong to different kind of groups. Positive and negative comments will be posted by user and they will participate in discussion. Here we proposed system to group different kind of users and system specifies from which category they belong to. For example film industry, politician etc. Once the social media data such as a user messages are parsed and network relationships are identified, data mining techniques can be applied to group of different types of communities. We used K-Means clustering algorithm to cluster data. In this system we detect communities by the clustering messages from large streams of social data. Our proposed algorithm gives better a clustering result and provides a novel use-case of grouping user communities based on their activities. This application is used to the identify group of people who viewed the post and commented on the post. This helps to categorize the users

Design and Implementation an RFID Customer Shopping Behaviour Mining System

Shopping behavior data is of great an importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of the capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers within physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this study, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design Shop Miner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of Shop Miner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that Shop Miner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference