The purpose of this paper is to examine how scavengers salvage Nigerian economy by turning waste to wealth, create job opportunities and help sustain clean environment. The research design used in this study is the descriptive survey method. The study used structured questionnaire as a means of data collection and the collected data were analyzed using simple percentage procedure and the hypotheses were tested with chi-square test. The study reveals that there is a positive relationship between scavengers’ activities, wealth creation and sustained clean environment in Nigeria. The study equally shows that job creation by the scavengers (recycling industries) is low due to inadequate finance and low level of technology employed. The study recommends government involvement and government financial assistance to enable the recycling industries procure modern equipments.

The software tool used for teaching database courses plays an essential role in the learning process and its outcome. It enables students to implement the concepts of database and transfer it into real word applications. This paper examine two of the most famous Database Management Systems: Oracle Database, and Microsoft Access. The examination will aim to identify the most suitable software that should be used to introduce the students into the database concepts. Both Database Management Systems are explored and compared based on analysis of a survey results.

“Sequential Pattern Mining”, is a prominent and significant method to explore the knowledge and innovation from the large database. Common Sequential Pattern Mining algorithms handle static databases. Pragmatically, looking into the functional and actual execution, the database grows exponentially thereby leading to the necessity and requirement of such innovation, research and development culminating into the designing of Mining Algorithm. Once the database is updated, the previous mining result will be incorrect, and we need to restart and trigger the entire mining process for the new updated sequential database. Incremental Mining of Sequential Pattern overcomes limitation of rescanning the entire database. The previous approaches, systems and techniques were Apriori-based framework. In this paper we propose Sequential tree Approach for Incremental Sequential Pattern Mining (STISPM) using sequence tree space structure. STISPM also use depth first approach along with backward tracking and the dynamic look ahead pruning strategy to remove infrequent and irregular patterns. The process and approach from the root node to any leaf node depicts a Sequential Pattern in the database. The structural characteristic of sequence tree makes it convenient and appropriate for Incremental Sequential Pattern mining. Sequence Tree also stores all the sequential patterns with its count and statistics, so whenever the support system is withdrawn or change, proposed STISPM using frequent sequence tree as the storage structure can find and detect all the sequential patterns without mining the database once again.

Environmental and climate change related risks make cotton crop vulnerable, which leads towards an adverse impact on crop productivity as well as farmers’ livelihood and dwindling farm income. For covering the extensive knowledge gap, this article focuses on the awareness and risk management options among 480 cotton farmers from Punjab province of Pakistan. Albeit a limited attention is given to cotton crop in terms of managing and tackling the risks. Factors of risk perception and management strategies among cotton farmers were explored through factor analysis approach to ensure the reliability and validity. Results portrayed that, cotton farmers alleged a diversity of risks, and are adapting their crops to the perceived risks. Factor of change in agricultural policies (3.96) was placed the highest risk source. Likewise, small dams/turbine scheme was ranked at utmost priority in risk management strategy with mean value (4.39). The interaction of multiple regression for factor capital management was significant having an R2 of 0.829, which explains a high level of satisfaction over variation that is covered by the independent variables. Observing the effects of farm and farmers characteristics on risk sources and risk management strategies, it was divulged that these characteristics were significantly prompting the farmers’ opinions about risk sources and management strategies, which must be dealt with.

Keywords search on different kinds of database system has been a very controversial topic in the data processing and managing area. Quantitive methods and algorithms are designed and implemented for the keywords search on massive data. However, few of them have involved in searching keywords on relational database, and less of those have solved the problem effectively and efficiently. The work of this paper is mainly about designing and implementing an efficient model and relevant algorithms to figure out the keywords search problems with given primary and foreign key relationship. As the experiment shows, this new method proved to be more efficient and possess more accuracy.

In this paper, we characterize complex measures $\\mu$ on the unit ball for which the Toeplitz operator $T_\\mu$ is bounded or compact on the space of holomorphic functions in the unit ball $\\mathbb{B}_n$ that have bounded mean oscillation with respect to the Bergman projection and the Bergman metric. Application to the Hankel operators are indicated.

Objective: The aim of the study was to determine whether there is a correlation between changes in visual fields and degenerative changes in the brain of patients with hypertensive (HTG) and normotensive glaucoma (NTG) and whether these findings differ in both diagnostic groups.Patients and methods of examination: The patient cohort comprised a total of three groups. The HTG group consisted of 5 women and 6 men (40-73 years) with the average age of 60.7 years. The second – NTG – group consisted of 11 women and 6 men (45-79 years) with the average age of 63.1 years. The Control group consisted of 9 women and 2 men (56-71) with the average age of 61.7 years. We conducted the visual field examination for all patients, using the Medmont M700 (manufactured by Medmont International Pty Ltd, Australia) fast threshold glaucoma program. We evaluated the pattern defect (PD). MRI examination included T2 TSE axial sequences. We quantified the amount of cerebral white matter T2 hyperintense lesions using the Fazekas scale and determined total cerebral atrophy by measurements of the bicaudate ratio.Conclusion: MR brain imaging revealed progression of the degenerative process as assessed by the bicaudate ratio, associated with disease advancement. Scoring according to the Fazekas scale exposed lesions in the superficial as well as deep layers of white matter in both NTG and HTG.

Background and objectives: To conduct a literature search and analysis of the existing research using natural language processing for improving or helping health literacy, as well as to discuss the importance and potentials of addressing both elds in a joint manner. This review targets researchers who are unfamiliar with natural language processing in the eld of health literacy, and in general, any researcher, regardless of his or her background, interested in multi-disciplinary research involving technology and health care.Methods: We introduce the concepts of health literacy and natural language processing. Then, a thorough search is performed using relevant databases and well-dened criteria. We review the existing literature addressing these topics, both in an independent and joint manner, and provide an overview of the state of the art using natural language processing in health literacy. We additionally discuss how the dierent issues in health literacy that are related to the comprehension of specialised health texts can be improved using natural language processing techniques, and the challenges involved in these processes.Results: The search process yielded 235 potential relevant references, 49 of which fully fullled the established search criteria, and therefore they were later analysed in more detail. These articles were clustered into groups with respect to their purpose, and most of them were focused on the development of specic natural language processing modules, such as question answering, information retrieval, text simplication or natural language generation in order to facilitate the understanding of health information.

This paper studies the effect of seaweed liquid extraction fertilizer (SLF) made from Gracilaria manilaensis on growth and chemical composition of tomato (Lycopersicon esculentum Mill) plant under greenhouse condition using soil drench application of SLF. Soil drench was applied 6 times at three concentrations (10; 30; 50 v/v%) of seaweed extract. The results showed that SLF increased vegetative growth characters (plant height, branches number/plant, leaves number/plant, leaf area, shoots fresh weight and shoots dry weight). Moreover, using SLF increased chemical composition (chlorophyll, carotenoids, carbohydrate, potassium, nitrogen and phosphorus in leaves). In addition SLF increased yield and quality of fruit tomato plants. However, the application of SLF at 10% gave the highest values in all parameters followed by 30% and 50% respectively. Therefore, it could be concluded that the SLF (G. manilaensis) at 10% concentration level can be used to enhance the growth, yield and quality of Lycopersicon esculentum Mill.