On The Estimation of Parameters of Thick Current Shell Model of Equatorial Electrojet Using Optimisation Method

Equatorial electrojet, an intense current flowing eastward in the low latitude ionosphere within the narrow region flanking the dip equator, is a major phenomenon of interest in geomagnetic field studies. For the first time the five parameters required to fully describe the Onwumechili\'s composite thick current shell model format of equatorial electrojet have been evaluated from a single autonomous set of ground data at solar minimum. The non-linear model was applied to four data points, each with a pair of simultaneously measured horizontal H and vertical Z variation field components. The resultant system of eight non-linear equations with five unknown model parameters were subjected to non-linear least square optimisation method taking advantage of the robust Levenberg-Madquart optimisation subroutine of licensed MATLAB 6.0 version. The thick current shell format model parameters estimated for Indian sector are shown to be within the appropriate limits and in excellent agreement with literature and physical expectation. Keywords: Equatorial electrojet; Numerical models; Optimisation Journal of Science & Technology (Ghana) Vol. 28 (3) 2008: pp. 1-

Prediction Of Clearness Index For Some Nigerian Stations Using Temperature Data

Global solar radiation and mean temperature data for five Nigeria stations have been used to fit the Angstrom model for the clearness index (KT =H/Ho), the mean temperature (Tmean) and maximum temperature (Tmax). The tests of performance of the model for the five stations have been done in terms of the widely used statistical indicators, Mean Bias Error (MBE) and Root Mean Square Error (RMSE). It was found from statistical model performance indicators that the models provided reasonably high degree of precision in the prediction of average monthly global solar radiation on horizontal surfaces. Keywords: Clearness index, Global solar radiation, and Temperature. Journal of Science and Technology (Ghana) Vol. 28 (2) 2008: pp. 94-10

Similarities In Periods Of Meteorological Variables Over Kenya And Solar Activity Periods

Using the fast Fourier transform (FFT) method, we determined the spectral characteristics of some meteorological variables over Kenya and identified the prominent periodicities associated with the variables. The meteorological variables studied are the maximum temperature, mini-mum temperature, average temperature, wind speed, precipitation, relative humidity, solar radia-tion intensity, evaporation and sunshine duration. Data from five terrestrial stations, represent-ing the regional climatic zones in Kenya, were employed in the study. The result reveals periods that are associated with solar activity. It is established that solar forcing is very significant over the Kenyan climate. The Sun-Climate relations were influenced at some locations by local ef-fects such as orography and vegetation.Keywords: periodicity, solar activity, Sun-climate relation

Studying the variability in the diurnal and seasonal variations in GPS total electron content over Nigeria

The study of diurnal and seasonal variations in total electron content (TEC) over Nigeria has been prompted by the recent increase in the number of GPS continuously operating reference stations (CORSs) across Nigeria as well as the reduced costs of microcomputing. The GPS data engaged in this study were recorded in the year 2012 at nine stations in Nigeria located between geomagnetic latitudes – 4.33 and 0.72° N. The GPS data were used to derive GPS TEC, which was analysed for diurnal and seasonal variations. The results obtained were used to produce local GPS TEC maps and bar charts. The derived GPS TEC across all the stations demonstrates consistent minimum diurnal variations during the pre-sunrise hours 04:00 to 06:00 LT, increases with sharp gradient during the sunrise period (∼ 07:00 to 09:00 LT), attains postnoon maximum at about 14:00 LT, and then falls to a minimum just before sunset. Generally, daytime variations are found to be greater than nighttime variations, which range between 0 and 5 TECU. The seasonal variation depicts a semi-annual distribution with higher values (∼ 25–30 TECU) around equinoxes and lower values (∼ 20–25 TECU) around solstices. The December Solstice magnitude is slightly higher than the June Solstice magnitude at all stations, while March Equinox magnitude is also slightly higher than September Equinox magnitude at all stations. Thus, the seasonal variation shows an asymmetry in equinoxes and solstices, with the month of October displaying the highest values of GPS TEC across the latitudes

Enhancing biogas production rate of cattle manure using rumen fluid of ruminants

The effects of rumen fluid from cattle and goats used as inoculums to increase biogas production using cattle manure as a substrate were investigated. Approximately 100 grams of fresh cattle manure (M) was assigned to each biodigester and mixed with rumen fluid (R) and a distilled water (W) according to five different treatment ratios, T1 (1:1:0); T2 (1:0.75:0.25); T3 (1:0.5:0.5); T4 (1:0.25:0.75); and T5 (1:0:1) (correspond to 0; 12.5; 25, 37.5; 50 % rumen fluid, respectively). All treatments were prepared in triplicates and runs at mesophilic condition. No significant different (P>0.05) was observed when comparing the biogas produced between the two type of rumen fluid used in this study. However, significant difference was noted when comparing between hours interval in the cattle manure inoculated with rumen fluid of the cattle and also goats. Data recorded that cattle rumen fluids produced more biogas than the goats. It was established that the increase in the biogas production at certain level was in respond to the amount of rumen fluids added into the mixture. The best performance of biogas production in this study was observed if the rumen fluid used between the ranges of 0.75 to 1 that correspond to 37.5 – 50 % of rumen fluid respectively

Heartbeat murmurs detection in phonocardiogram recordings via transfer learning

Heart murmurs are abnormal heartbeat patterns that could be indicative of a serious heart condition, which can only be detected by trained specialists with the use of a stethoscope. However, it is occasionally the case that those specialists are not available, resulting in the need for a machine-automated system for murmur detection. Many methods might be used to produce such a system, one of which is the utilization of transfer learning. A recent machine learning method that saw popularity due to the little time it needs for training and the boosted accuracy it produces. This paper aims at testing the performance of transfer learning when detecting murmurs of the heart, by evaluating three transfer learning models, namely, VGG16, VGG19, and ResNet50, trained on a database of phonocardiogram (PCG) heartbeat recordings, i.e., PASCAL CHSC database. The data is cleansed, processed, and converted into images using two signal representation methods; Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). The paper compares the results of each model, using metrics of accuracy and loss, where the use of Spectrograms proved to yield the best results with 83.95%, 83.95%, and 87.65%, classification accuracy for VGG16, VGG19, and ResNet50, respectively. Based on the findings of the paper, it is evident that the Spectrogram-ResNet50 transfer learning pipeline could further facilitate the detection of heart murmurs with less time spent on training

Technical and tactical performance indicators discriminating winning and losing team in elite Asian beach soccer tournament

The present study aims to identify the essential technical and tactical performance indicators that could differentiate winning and losing performance in the Asian elite beach soccer competition. A set of 20 technical and tactical performance indicators namely; shot back-third, shot mid-third, shot front-third, pass back-third, pass mid-third, pass front-third, shot in box, shot outbox, chances created, interception, turnover, goals scored 1st period, goals scored 2nd period, goals scored 3rd period, goals scored extra time, tackling, fouls committed, complete save, incomplete save and passing error were observed during the beach soccer Asian Football Confederation tournament 2017 held in Malaysia. A total of 23 matches from 12 teams were notated using StatWatch application in real-time. Discriminant analysis (DA) of standard, backward as well stepwise modes were used to develop a model for the winning (WT) and losing team (LT) whilst Mann-Whitney U test was utilized to ascertain the differences between the WT and LT with respect to the performance indicators evaluated. The standard backward, forward and stepwise discriminates the WT and the LT with an excellent accuracy of 95.65%, 91.30% and 89.13%, respectively. The standard DA model discriminated the teams from seven performance indicators whilst both the backward and forward stepwise identified two performance indicators. The Mann-Whitney U test analysis indicated that the WT is statistically significant from the LT based on the performance indicators determined from the standard mode model of the DA. It was demonstrated that seven performance indicators namely; shot front-third, pass front-third, chances created, goals scores at the 1st period, goals scored at the 2nd period, goals scored at 3rd period were directly linked to a successful performance whilst the incomplete save by the keeper attribute towards the poor performance of the team. The present finding could serve useful to the coaches as well as performance analysts as a measure of profiling successful performance and enables team improvement with respect to the associated performance indicators

Deep learning in Cancer Diagnostics: a feature-based transfer learning evaluation

This book highlights the use of state-of-the-art Deep learning (DL) techniques in cancer diagnosis. It includes the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin. This book also discusses the use of DL methods in combination with imaging techniques to identify cancer correctly

The classification of heartbeat PCG signals via transfer learning

Cardiovascular auscultation is a process of listening to the sound of a heartbeat to pick up on any abnormalities. One of these abnormalities is heart murmurs, which are the result of blood turbulence, in or near the heart. Heart murmurs can be innocent, or they can indicate the existence of very serious diseases. Normally the process is performed with a stethoscope, by a medical professional, where murmurs are identified by the subtle difference in timing and pitch from a normal heartbeat. These professionals, however, are not always available; hence, the need for the automation of this process rises. This paper aims at testing the performance of pre-trained CNN models at the classification of heartbeats. A database of phonocardiogram (PCG) heartbeat recordings, under the name of the PASCAL CHSC database was used to train four pre-trained models: VGG16, VGG19, MobileNet, and inceptionV3. The data was processed, and the features were extracted using Spectrogram signal representation. They were then split into training and testing data, and the results were compared using the metrics of accuracy and loss. The classification accuracies of the VGG16, VGG19, MobileNet, and inceptionV3 models are 80.25%, 85.19%, 72.84% and 54.32%, respectively. The findings of the paper indicate that the use of different transfer learning models can, to a certain extent, enhance the overall accuracy at detecting the murmurs of the heart

A VGG16 feature-based transfer learning evaluation for the diagnosis of Oral Squamous Cell Carcinoma (OSCC)

Oral Squamous Cell Carcinoma (OSCC) is the most prevalent type of oral cancer. Early detection of such cancer could increase a patient’s survival rate by 83%. This chapter shall explore the use of a feature-based transfer learning model, i.e., VGG16 coupled with different types of conventional machine learning models, viz. Support Vector Machine (SVM), Random Forest as well as k-Nearest Neighbour (kNN) as a means to identify OSCC. A total of 990 evenly distributed normal and OSCC histopathological images are split into the 60:20:20 ratio for training, testing and validation, respectively. A testing accuracy of 93% was recorded via the VGG16- RF pipeline from the study. Consequently, the proposed architecture is suitable to be deployed as artificial intelligence-driven computer-aided diagnostics and, in turn, facilitate clinicians for the identification of OSCC