Adama University

About: Adama University is a education organization based out in Nazrēt, Ethiopia. It is known for research contribution in the topics: Population & Adsorption. The organization has 840 authors who have published 1010 publications receiving 5547 citations. The organization is also known as: Adama Science and Technology University & ቴክኖሎጂ ዩኒቨርሲቲ, አዳማ ሳይንስና ቴክኖሎጂ ዩኒቨርሲቲ.

Enzyme-loaded nanoparticles for the degradation of wastewater contaminants: a review

Abstract: Preventing water pollution and conserving water are major issues in the context of population growth and worldwide pollution, calling for advanced remediation techniques. Classical remediation techniques of water cleaning such as membrane adsorption are able to separate pollutants from water, yet the separated pollutants require additional treatment or disposal. Therefore, techniques that degrade the pollutant appear promising, provided that pollutants are organic and degradable. Here, we review the enzymatic degradation of organic pollutants with focus on methods to immobilize enzymes and nanoparticles as support materials. We discuss the degradation of pesticides, dyes, phenolic compounds and antibiotics.

Privacy preserving data publishing based on sensitivity in context of Big Data using Hive

Abstract: Privacy preserving data publication is the main concern in present days, because the data being published through internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals with the privacy preservation in context of big data using a data warehousing solution called hive. We implemented nearest similarity based clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity which deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with big data. This framework also supports the execution of existing algorithms without any changes. The model in the article outperforms than existing models.

Isoflavones, anthocyanins, phenolic content, and antioxidant activities of black soybeans (Glycine max (L.) Merrill) as affected by seed weight.

Abstract: Seed weight is regulated by several genes which in turn could affect the metabolite contents, yield, and quality of soybean seeds. Due to these, seed weight is receiving much attention in soybean breeding. In this study, seeds of 24 black soybean varieties and a reference genotype were grown in Korea, and grouped as small ( 24 g) seeds based on their seed weight. The contents of six anthocyanins, twelve isoflavones, and total phenolic, and the antioxidant activities were determined, and the association of each with seed weight was analyzed. The total anthocyanin (TAC) and total isoflavone (TIC) contents were in the ranges of 189.461-2633.454 mg/100 g and 2.110-5.777 mg/g, respectively and were significantly different among the black soybean varieties. By comparison, the average TAC and TIC were the highest in large seeds than in small and medium seeds while the total phenolic content (TPC) was in the order of small seeds > large seeds > medium seeds. Besides, large seeds showed the maximum 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) scavenging activity, whereas small seeds showed the maximum ferric reducing antioxidant power (FRAP) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical (ABTS) scavenging activities. FRAP activity was positively associated with TIC and TAC, the former association being significant. On the other hand, ABTS and DPPH activities were positively correlated to TPC, the later association being significant. Overall, our findings demonstrated the influence of seed weight on anthocyanin, isoflavone, and phenolic contents and antioxidant activities in black soybeans. Besides, the dominant anthocyanins and isoflavones were the principal contributors to the variations observed in the black soybean varieties, and hence, these components could be selectively targeted to discriminate a large population of black soybean genetic resources.

An algorithm for identification of natural disaster affected area

Abstract: An important source of information presently is social media, which reports any major event including natural disasters. Social media also includes conversational data. As a result, the volume of data on social media has an enormous increase. During the time of natural disaster like floods, tsunami, earthquake, landslide, etc., people require information in those situations, so that relief operations like help, medical facilities can save many lives (Bifet et al. in J Mach Learn Res Proc Track 17:5–11, 2011). An attempt is made in this article on Geoparsing which will identify the places of disaster on a Map. Geoparsing is a process of converting free text description of locations into the geographical identifier in an unambiguous manner with the help of longitude and latitude. With the help of geographical coordinates, it can be mapped and entered into geographical information system. A real-time, reliable at robust twitter messages which are the source of the information can handle a large amount of data. After collecting tweets at the real time we can parse them for the disaster situation and its location. This information will help to identify the exact location of the event. For knowing information on the natural disaster, tweets are extracted from twitter to R-Studio environment. First the extracted tweets from twitter are parsed using R about “Natural Disaster”. Later we parsed the tweets and store in CSV format in R database. For all posted data tweets are calculated and stored in a file. Later visual analysis is performed for the data store using R Statistical Software. Further, it is useful to assess the severity of the natural disaster. Sentiment analysis (Rahmath in IJAIEM 3(5):1–3, 2014) of user tweets is useful for decision making (Rao et al. in Int J Comput Sci Inf Technol 6(3):2923–7, 2015).