Journal of Scientific Research and Reports

The purpose of this mini review is to analyze the controversies surrounding the official death toll of Hurricane Maria, driven by the estimates of excess mortality rates by academics and investigative journalists. This review will be a critique of the aforementioned analyses and articles with the purpose of clarifying their figures, which all present different numbers of victims. In three publications (i.e., Kishore et al., 2018; Santos-Lozada et al., 2018; GWU, 2018), the Commonwealth of Puerto Rico reported different numbers of victims in the aftermath of HM on September 20, 2017. Since the occurrence of HM in PR, the reported number of victims of this disaster has varied. According to the PR government, the official number of deaths is 64 CPI (2017), while Kishore et al.’s (2018) report puts the figure at 4,645 and 2,975 deaths, as reported by George Washington University. This article analyzes why these sources disagree on the number of the dead and the possible reasons why there are discrepancies.

In this paper, we attempt to search for an optimal Autoregressive Moving Average (ARMA) model that best forecast hepatitis B virus infection among blood donors in Lafia-Nigeria. The study uses monthly data in Lafia-Nigeria for the period of 11 years 6 months from January 2007 to June 2018. The data was obtained as secondary data from General Hospital Lafia and Dalhatu Araf Specialist Hospital, Lafia. The time series and stationarity properties of the data are explored using time plots and Dickey-Fuller Generalized Least Squares unit root test. The results indicate that the series is integrated of order zero, I(0). An ARMA (p,q) model in line with Box-Jenkins procedure was employed to model the time series data. The result shows that ARMA (1,1) was the best candidate to model and forecast hepatitis B virus infection among blood donors in Lafia- Nigeria. Critical analysis of the model shows that the HBV infection is chronic among blood donors in the study area. The estimated ARMA (1,1) model was then used to forecast future values of hepatitis B infection among blood donors in Lafia-Nigeria from July 2018 to June 2019. The forecast shows a stable level of infection for the forecasted period. The study provided some policy recommendations.

Background: Breast cancer (BC) is the most frequent cancer of women. The high mortality in developing countries is associated with late detection, and lack of knowledge and adequate screening programmes.

Aims: To determine breast cancer awareness, knowledge and beliefs among Libyan women.

Study Design: A cross-sectional descriptive study.

Place and Duration of Study: Between September and October 2016 among a sample of adult women in western Libya.

Methods: 1091 woman aged between years were asked to fill a validated questionnaire to investigate their knowledge about the risk factors as well as their awareness and screening behaviours of BC.

Results: The majority of women who participated in the study were aware of BC early warning signs and symptoms. Over 90% of the women were able to list at least one symptom of breast cancer correctly. The most frequent warning sign identified was breast lump (91.0%), followed by discharge from the nipples (80.6%). Also, 565 (52.7%) of those surveyed were aware that increasing age was associated with a higher incidence of breast cancer and 747 (68.3%) of the respondents identified positive family history as a risk factor. Moreover, 62% know how to perform self-examination (BSE), and only 59% ever performed BSE. The majority (92%) would seek medical advice if they discovered a mass in the breast whereas, about half of those (59%) would consult a male doctor.

Conclusions: Women who participated in this study were fairly informed about BC risks and warning signs; the results appear to reflect growing awareness of women regarding BC screening methods. Health education message should be presented and delivered in a culturally-sensitive manner and tailored to provide simple and clear information and avoid false beliefs and misconceptions about the disease, its screening methods and management options.

Natural calamity disrupts our daily life activities; thereby bring many sufferings in our life. One of the natural disasters is the flood. Flood is one of the most catastrophic disasters. However, too much rainfall courses environmental hazard. These prompted to flood prediction in order to help communities and Government with the necessary tool to take precaution to safe human life and properties. This work was developed using an (ANFIS) Adaptive Neuro-Fuzzy Inference System to compare some weather parameter (temperature and relative humidity) with rainfall to forecast the amount of rainfall capable of coursing flood in the study area. From the above graph (Fig. 22) it can be seen that the actual and the forecasted rainfall followed the same pattern from 2008 to 2010 with slight decrease in 2011. A high amount of rainfall in 2012 was forecasted to be flooded during that year and tally with the forecasted rainfall on the above graph in 2012. Based on the results on the graph, it shows that from 2014 to 2017 gives a constant flow between the actual and forecasted rainfall. It is predicted that the maximum amount of rainfall forecasted was 124.0 mm which is far below the recommended flood level of 160.0 mm which reveals that, River Benue would not experience flood disaster in the year ahead. The model developed was validated using (MAPE) Mean Absolute Percentage Error as 4.0% with model efficiency of 96.0% which shows very high excellent prediction accuracy.

Bamboo is one of the fastest growing plants on the planet, with many attributes which make it a useful potential resource for humankind. Though having fast growth and good regeneration performance after harvesting is a unique characteristic of the specie. It enhances a high carbon storage potential particularly when the harvested culms are transformed into durable products. China has many bamboo species with distribution and area coverage's, and highly connected in using the production of bamboo resources. Its characteristics make it an ideal solution for the environmental and social consequences of tropical deforestation. This review paper aims to assess the contribution of bamboo in mitigating and adapting impacts of climate change and its importance regarding ecological and socio-economic benefits. The review summarised the role of bamboo forests towards mitigating and adapting its potential to overcome the impacts of climate change currently seen globally and particularly to China. Therefore, advancing bamboo farming systems at different levels, it's advantages to reduce greenhouse gas in the atmosphere and expanding bamboo forests in future under wider use and intensive management is recommended.