Antimicrobial Activity and Micro-Flora Quality Evaluation of Commonly Used Toothpastes

To determine the microbiological quality and antimicrobial activity and effectiveness of commonly used toothpaste, thirty products consisting of eight brands of toothpaste were evaluated using standard methods and Staphylococcus aureus and Candida albicans as test organisms. All the toothpastes were sterile, and had some levels of antimicrobial activity at neat and 10-1 dilutions. Colgate and Signal had the highest zones of inhibition 20mm and 12mm against Staphylococcus aureus. Colgate and Macleans herbal neat concentration had the highest inhibition of 11mm and 10mm on Candida albicans. Colgate and Macleans had a minimum inhibitory concentration greater than 10-3 for Staphylococcus aureus. The other toothpastes showed minimum inhibitory concentration of 10-1 and 10-2. Close Up herbal and Colgate had minimum inhibitory concentrations of greater than 10-3 for Candida albicans. At 10-2 dilution, total bacteria count of colonies increased as the time of exposure increased for most of the toothpastes. There was however, a general decline in the number of Candida colonies as the time of exposure increased. The toothpastes reduced and inhibited the test organisms mainly as neat and at 5 and 10 minutes. It is advocated that brushing the teeth for 5 to 10 minutes will allow for enough contact time for toothpaste to act on oral microbes and importantly pathogens for maximum result of good oral hygiene. Further studies on the relationship of brushing mannerism and toothpaste use culture are necessary. Regular survey of personal care products at the consumer level is advised to help keep the consumers informed of quality of products and checkmate producers of fake product and thus help stamp out unwholesome product from our market

Situating requirements engineering methods within design science research

Design Science Research Methodologies (DSRM) are increasingly used to guide research in fields beyond Information Systems, in particular those of Requirements Engineering and Software Engineering (RE/SE). While a number of DSR methodologies have been developed by scholars in the RE/SE fields, there remains a certain level of confusion about the way in which the aim and scope of DSRM and those of methods typically used in RE/SE differ. This issue can be observed in graduate students' work as well as in published literature. In particular, the difference be-tween the research orientation of DSRM and the solution orientation of RE/SE methods can be difficult to navigate. We propose to address this challenge by situating three RE/SE methodologies proposed in published literature within one common DSRM; doing so clarifies the scope of these methodologies and highlights ways in which the knowledge contributions of their results could be further enhanced. This effort is a first step towards providing better guidance to researchers who are new to design science research in order to ensure that recognized DSR principles are promoted and respected

Measuring deal premiums in takeovers

We investigate whether the merger announcement dates provided in the Securities Data Corporation (SDC) database are handled correctly by researchers performing event studies. We find that in 24.1% of deals, the popular choice of using the SDC’s “Date Announced” (DA) field as the event date leads to biased estimates of target firm abnormal returns because of earlier abnormal price movements due to merger-related events such as merger rumors or search-for-buyer types of announcements. We hand collect the merger-related events from news sources and make the complete dataset publicly available at the Financial Management website

A goal-oriented, business intelligence-supported decision-making methodology

In many enterprises and other types of organizations, decision making is both a crucial and a challenging task. Despite their importance, many decisions are still made based on experience and intuition rather than on evidence supported by rigorous approaches. Decisions are often made this way because of lack of data, unknown relationships between data and goals, conflicting goals, and poorly understood risks. This research presents a goal-oriented, business intelligence-supported methodology for decision making. The methodology, which is iterative, allows enterprises to begin with limited data, discover required data to build their models, capture stakeholders goals, and model threats, opportunities, and their impact. It also enables the aggregation of Key Performance Indicators and their integration into goal models. The tool-supported methodology and its models aim to enhance the user's experience with common business intelligence applications. Managers can monitor the impact of decisions on the organization's goals and improve both decision models and business processes. The approach is illustrated and evaluated through a retail business scenario, from which several lessons were learned. One key lesson is that once an organization has a goal model, data can be added iteratively. The example, tool support, and lessons suggest the feasibility of the methodology

A Fuzzy-Ontology Based Information Retrieval System for Relevant Feedback

International audienceObtaining correct and relevant information at the right time to user's query is quite a difficult task. This becomes even complex, if the query terms have many meanings and occur in different varieties of domain. This paper presents a fuzzy-ontology based information retrieval system that determine the semantic equivalence between terms in a query and terms in a document by relating the synonyms of query terms with those of document terms. Hence, documents could be retrieved based on the meaning of query terms. The challenge has been that surface form does not sufficiently retrieve relevant document to user's query. However, the results presented showed that the Fuzzy-Ontology Information Retrieval system successfully retrieve relevant documents to user's query. This is irrespective of different meaning and varieties of domain. The System was tested on words with different meanings and some set of user's query from varied domains

The Role Of Interventional Radiology In Modern Medical Practice

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THE FUTURE OF CRYPTO-CURRENCY IN THE ABSENCE OF REGULATION, SOCIAL AND LEGAL IMPACT

The Internet revolution is fast outpacing the law and creating a newer world with the momentum of gripping the unwary crowd into a boundless world of anarchism. Money is defined as a medium of exchange, store of value, and a unit of account (www.cliffnotes.com Date of use: 28/10/2017). Crypto-currency is a new wake in the digital reality that is performing the above functions of money. There are about 1,541 crypto-currencies traded in 8,894 markets by exchangers (www.coinmarketcap.com Date of use: 28/01/2018). Prominent among these crypto currencies, is the Bitcoin which as a single coin had a monetary value of $0.05 USD in July 2010 and in November 2017, was valued at$18,000 (www.useyourselfmedia.today Date of use: 28/10/2017). The drivers of crypto-currencies are basically the block-chain which is a technology, others are the exchanges, financial services provider, wallet services provider and miners. Under most jurisdictions, the exchanges and service providers are either not regulated or partially regulated. Regulations in the financial sector, are tools used in monitoring the movement of funds, fraud, financial crimes and money laundering, criminal activities, as well as the protection of consumers. In the absence of regulation, this paper seeks to examine the trend and legality of the crypto currency as a virtual currency and its current and future impact on the society. To achieve this, some monetary regulations will be evaluated and an attempt will be made to adapt these regulations to the crypto-currency framework. At the end of the paper, challenges will be identified with a view to recommending a regulated regime in the use of crypto-currencies for a safer society and consumer protection.&nbsp

Evaluation of fall armyworm (Spodoptera frugiperda J. E. Smith) infestation and efficacy of neem extracts in maize (Zea mays L.)

Maize is an important cereal crop in Nigeria. Fall Armyworm (FAW) is one of the most important field insect pests of maize. This study was carried out to evaluate the impact of FAW infestation on maize plants and efficacy of neem extracts in the management of FAW in maize in Federal Capital Territory (FCT), Abuja in 2018. Field experiment was carried out from July to December 2018 at the Teaching and Research Farm of the Faculty of Agriculture, University of Abuja, Abuja, Nigeria, where two maize varieties were assessed for incidence and percentage leaf area damaged. Data collected was analyzed using Generalized Linear Model with multivariate assumptions using SPSS Version 21. Treatment means were separated with Student Newman Keuls Test (SNK) at p≤0.05. Highest number of FAW larvae (1.35±0.09) and incidence (20.30±1.02) were obtained from the control; the lowest was recorded from plants treated with Lambdacyhalothrin (0.05±0.06), followed by Neem oil (0.10± 0.05). Neem extracts reduced foliar damage to maize compared to the untreated control. Non-treated control plants showed extensive leaf injury compared to the synthetic insecticide and neem extract treated plants. There was no significant difference (p≥0.05) between the two maize varieties and interaction effect of variety and treatments in terms of the parameters scored. Findings from this study shows the efficacy of neem extracts in the treatment of Fall Armyworm infesting maize on the field and recommends the use of the neem extracts as an eco-friendly insecticide option for control of Fall Armyworm infestation in Nigeria