Assessment of the Impacts of Tropical Cyclone Fantala to Tanzania Coastal Line: Case Study of Zanzibar

The study investigated the impacts of tropical cyclone (TC) Fantala (11th to 27th April, 2016) to the coastal areas of Tanzania, Zanzibar in particular. Daily reanalysis data consisting of wind speed, sea level pressure (SLP), sea surface temperatures (SSTs) anomaly, and relative humidity from the National Centres for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) were used to analyze the variation in strength of Fantala as it was approaching the Tanzania coastal line. In addition observed rainfall from Tanzania Meteorological Authority (TMA) at Zanzibar office, Global Forecasting System (GFS) rainfall estimates and satellite images were used to visualize the impacts of tropical cyclone Fantala to Zanzibar. The results revealed that, TC Fantala was associated with deepening/decreasing in SLP (from 1012 - 1010 mb) around the north-western Madagascar and coastal Tanzania, whereas the mean SSTs was greater than 28°C and an SSTs anomaly ranged from 0 to 2.3°C. The vertical wind shear which ridged at Mozambican Channel and over north-eastern Madagascar was high enough (12 - 15 ms-1) to support the intensifying of Fantala. The thermodynamic and dynamic conditions of Fantala influenced heavy rainfall of greater than 170 mm over most stations in Zanzibar. Moreover, Fantala disrupted the temporal variability of 2016 March to May (MAM) seasonal rainfall. Besides, more than 420 people were homeless, at least 3330 houses were destroyed, and about 2 people died. As for mainland Tanzania Fantala resulted in a death of 12 people in Kilimanjaro and Arusha, more than 315 houses were washed away by flooding leading to 13,933 people being homeless. Conclusively the study calls for an extensive research work based on examining and forecasting the TCs rainfall impacts and their contribution during the two rainfall seasons of OND and MAM in Tanzania.

Smart CardioWatch System for Patients with Cardiovascular Diseases Who Live Alone

The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis.In this study,we propose a framework referred to as smart forecasting CardioWatch(SCW)to measure the heart-rate variation(HRV)for patients with myocardial infarction(MI)who live alone or are outside their homes.In this study,HRV is used as a vital alarming sign for patients with MI.The performance of the proposed framework is measured using machine learning and deep learning techniques,namely,support vector machine,logistic regression,and decision-tree classification techniques.The results indicated that the analysis of heart rate can help health services that are located remotely from the patient to render timely emergency health care.Further,taking more cardiac parameters into account can lead to more accurate results.On the basis of our findings,we recommend the development of health-related software to aid researchers to develop frameworks,such as SCW,for effective provision of emergency health.

Bayesian bootstrap quantile regression for probabilistic photovoltaic power forecasting

Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.