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A novel fusion python application of data mining techniques (DMT) was designed and implemented to locate, identify, and delineate the subsurface structural pattern (SSP) of source rocks for the features of interest underlain the study area. The techniques of machine learning tools (MLT) helped to define magnetic anomaly source (MAS) rock and the various depths of these subsurface source rock features. The principal objective is to use straightforward DMT to locate magnetic anomaly features of interest that host mineralization. The required geo-referenced radiometric data, which facilitated the delineation of SSP, were sufficiently covered by combining the application of the Oasis Montaj\c{opyright} 2014 source parameter imaging functions. Relevance basic filtering techniques of data reduction were used to improve the signal-to-noise (S/N) ratio and hence automatically determine depths to the various engrossed features from gridded geo-referenced airborne magnetic datasets before the DMT application was performed. Geological source rock models (GSRM) (i.e., rock contacts, dykes) served as the delineated features based on their structural index (SI) values. The anomalies were perpendicularly oriented, with few inconsequential nonvertical features, and all were generally aligned in NNE-SSW and NE-SW directions. The DMT approach showed that magnetic anomaly patterns (MAP) control the SSP and the ground surface stratigraphy (GSS) on a geological time-scale (GTS) by fusing the subsurface gravitational structural features (SGSF) in the area. The DMT facilitated the determination of depths to these subsurface geological source rock features with a maximum depth of approximately 1.277 km using a 3x3 window size to map the concealed features of interest., Comment: A novel fusion Python application of data mining techniques to evaluate airborne magnetic datasets has 13 pages, 6,661 words, 39,592 characters without spaces, 46,213 characters with spaces, 104 paragraphs, and 558 lines

The fear of the invisible but prevalent Coronavirus (COVID-19), disease cannot be overemphasized since there is the potential possibility of it wiping out the entire world population within a few months if adequate and quick steps are not taken to curb this menace, and the sub-Saharan African (SSAn) region is no exception. It is evident that water, as an essential daily commodity, has long been in a state of emergency in SSAn nations, which is largely attributed to decades of neglect by the successive governments, because it has not been possible to separate the existing bond between water, health, livelihood and the economy. The laudable Millennium Development Goals (MDGs) proposed by the United Nations had yet to achieve the stated objective of improving the standards of living and health conditions of the rural communities in the SSAn region before the COVID-19 pandemic outbreak. This failure has been masked by a sort of delusion in which the people of this region are subjected to the hardship of searching for clean and healthy water in their own ponds, rivers, streams and shallow hand-dug local wells on a continuous basis. Less than 17% of the rural population in all the SSAn communities can access basic hand-washing facilities and sanitation systems. The total water productivity, as measured by the Gross Domestic Product (GDP) per cubic meter of total freshwater withdrawn, for the people was less than 5 GDP., Comment: The manuscript has 15 pages, 10 figures, 1 Table, 5705 Words, 628 Lines, 178 Paragraphs, 31274 Characters without space, 36926 Characters including spaces

In this paper, we analysed real-time measurements of atmospheric CO$_2$ with total electricity production and nation-wide restrictions phases due to the novel coronavirus COVID-19 pandemic, and its effects on atmospheric CO$_2$ concentrations. A decline of 3.7% in the global energy demand at about 150 million tonnes of oil equivalent (Mtoe) in the first quarter (Q1), of 2020 was recorded, as compared to the same first quarter (Q1), of 2019, due to the cutback on global economic activities. Our results showed that: 1) electricity production for the same period in the years 2018, 2019, and 2020, shrunk at an offset of about 9.20%, which resulted in the modest reduction of about (-1.79%), in the atmospheric CO$_2$, to that of 2017-2018 CO$_2$ level; 2) a non-seasonal abrupt; but brief, atmospheric CO$_2$ decrease by about 0.85% in mid-February 2020, could be due to the Phase 1 movement restrictions in China. The results showed that, the reduction in electricity production is significant to the short-term variability of atmospheric CO$_2$. It also highlights the significant contributions from China to the atmospheric CO$_2$, which suggests that, without the national restriction of activities, CO$_2$ concentration are set to exceed 2019 by 1.79%, but it quickly decreased due to the lockdown, and sustained the reduction for two consecutive months. The results underscore the atmospheric CO$_2$ reductions on the monthly time scale that can be achieved, if electricity production from combustible sources were slashed, which could be useful for cost-benefit analyses of the reduction in electricity production from combustible sources, and the impact of these reduction to the atmospheric CO$_2$., Comment: The manuscript has; 13 pages, 3 Figures, 2 Tables, 4706 Words, 26350 characters without space, 30931 characters with spaces,and 466 lines

The MobileNet model was used by applying transfer learning on the 7 skin diseases to create a skin disease classification system on Android application. The proponents gathered a total of 3,406 images and it is considered as imbalanced dataset because of the unequal number of images on its classes. Using different sampling method and preprocessing of input data was explored to further improved the accuracy of the MobileNet. Using under-sampling method and the default preprocessing of input data achieved an 84.28% accuracy. While, using imbalanced dataset and default preprocessing of input data achieved a 93.6% accuracy. Then, researchers explored oversampling the dataset and the model attained a 91.8% accuracy. Lastly, by using oversampling technique and data augmentation on preprocessing the input data provide a 94.4% accuracy and this model was deployed on the developed Android application.

The dead space model of non-local impact ionization has been developed independently by three different research groups in order to include a 'soft' dead space. This paper seeks to compare these models for the first time. The models are fitted to Monte Carlo simulation data and it is found that there are no significant differences between the models except that the Liverpool model cannot be used to fit one data set which has a very soft dead space. Likewise the models give very similar results when used to calculate the excess noise factor for low noise avalanche photodiodes., Comment: 4 pages, 6 figures, accepted for publication in Physica Status Solidi, conference ICOOPMA 2010

The probability distribution function (PDF) for avalanche multiplication and the excess noise factor are calculated using non local ionization coefficients. The dead space effect is shown to reduce the excess noise factor for a given multiplication. At higher electric field values a resonance effect is observed in addition to the dead space effect. The resonance effect leads to a more deterministic behaviour in the PDF for avalanche multiplication and this gives rise to very low excess noise factors for multiplication values of 2, 4, 8 and 16 if the ionization coefficient ratio is small., Comment: 30 pages, 8 figures, originally submitted in January 2002

An alternative definition for the non local impact ionization coefficient is given and its relationship to the ionization pathlength probability distribution function (PDF) is determined. A model for the ionization pathlength PDF is proposed and the resultant non local ionization coefficient is derived. An expression for the avalanche multiplication is derived. Results are calculated using non local ionization coefficients for two electric field values using fits to ionization pathlength PDFs calculated using Monte Carlo techniques. These results show two features: the well known dead space effect and at the higher field value a resonance effect. The resonance effect gives rise to level sections in the multiplication curve for multiplications of 2, 4, 8 and 16 if the ionization coefficient ratio is small., Comment: 35 pages, 9 figures, originally submitted in Nov. 2001

A description of the history and development of the University of Montana's American Indian education option in elementary teacher education is presented. Objectives and goals for the federally funded project are given, and the core personnel involved in the program are introduced. Budgeting procedures for the implementation of the option are briefly discussed. Contributions of the program to the overall field of teacher education are examined, and evaluation methods and results as well as recommendations for subsequent program development are included. (LH)