Abstract: This study investigates the satisfaction of distance education university students (DEUS) with the use of audio media as a medium of instruction. Studying students’ satisfaction is vital because it shows whether learners are comfortable with a certain instructional strategy or not. Although previous studies have investigated the use of audio media, the satisfaction of students with an instructional strategy that combines radio teaching and podcasts as an independent teaching strategy has not been fully investigated. In this study, all lectures were delivered through the radio and students had no direct contact with their instructors. No modules or any other material in form of text were given to the students. They instead, revised the taught content by listening to podcasts saved on their mobile electronic gadgets. Prior to data collection, DEUS received orientation through workshops on how to use audio media in distance education. To achieve objectives of the study, a survey, naturalistic observations and face-to-face interviews were used to collect data from a sample of 211 undergraduate and graduate students. Findings indicate that there was no statistically significant difference in the levels of satisfaction between male and female students. The results from post hoc analysis show that there is a statistically significant difference in the levels of satisfaction regarding the use of audio media between diploma and graduate students. Diploma students are more satisfied compared to their graduate counterparts. T-test results reveal that there was no statistically significant difference in the general satisfaction with audio media between rural and urban-based students. And ANOVA results indicate that there is no statistically significant difference in the levels of satisfaction with the use of audio media across age groups. Furthermore, results from observations and interviews reveal that DEUS found learning using audio media a pleasurable medium of instruction. This is an indication that audio media can be considered as an instructional strategy on its own merit.
Abstract: This study investigates the removal of silica, alumina and phosphorus as impurities from Sanje iron ore using wet high-intensity magnetic separation (WHIMS). Sanje iron ore contains low-grade hematite ore found in Nampundwe area of Zambia from which iron is to be used as the feed in the steelmaking process. The chemical composition analysis using X-ray Florence spectrometer showed that Sanje low-grade ore contains 48.90 mass% of hematite (Fe2O3) with 34.18 mass% as an iron grade. The ore also contains silica (SiO2) and alumina (Al2O3) of 31.10 mass% and 7.65 mass% respectively. The mineralogical analysis using X-ray diffraction spectrometer showed hematite and silica as the major mineral components of the ore while magnetite and alumina exist as minor mineral components. Mineral particle distribution analysis was done using scanning electron microscope with an X-ray energy dispersion spectrometry (SEM-EDS) and images showed that the average mineral size distribution of alumina-silicate gangue particles is in order of 100 μm and exists as iron-bearing interlocked particles. Magnetic separation was done using series L model 4 Magnetic Separator. The effect of various magnetic separation parameters such as magnetic flux density, particle size, and pulp density of the feed was studied during magnetic separation experiments. The ore with average particle size of 25 µm and pulp density of 2.5% was concentrated using pulp flow of 7 L/min. The results showed that 10 T was optimal magnetic flux density which enhanced the recovery of 93.08% of iron with 53.22 mass% grade. The gangue mineral particles containing 12 mass% silica and 3.94 mass% alumna remained in the concentrate, therefore the concentrate was further treated in the second stage WHIMS using the same parameters from the first stage. The second stage process recovered 83.41% of iron with 67.07 mass% grade. Silica was reduced to 2.14 mass% and alumina to 1.30 mass%. Accordingly, phosphorus was also reduced to 0.02 mass%. Therefore, the two stage magnetic separation process was established using these results.
Abstract: The selection of materials is an essential task in mechanical design processes. This paper sets out to demonstrate the application of analytical decision making during mechanical design and, particularly, in selecting a suitable material for a given application. Equations for the mechanical design of a manual winch rope drum are used to derive quantitative material performance indicators, which are then used in a multiple attribute decision making (MADM) model to rank the candidate materials. Thus, the processing of mechanical design considerations and material properties data into information that is suitable for use in a quantitative materials selection process is demonstrated for the case of a rope drum design. Moreover, Microsoft Excel®, a commonly available computer package, is used in the selection process. The results of the materials selection process are in agreement with current industry practice in rope drum design. The procedure that is demonstrated here should be adaptable to other design situations in which a need arises for the selection of engineering materials, and other engineering entities.
Abstract: The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.
Abstract: Any signal transmitted over a channel is corrupted by noise and interference. A host of channel coding techniques has been proposed to alleviate the effect of such noise and interference. Among these Turbo codes are recommended, because of increased capacity at higher transmission rates and superior performance over convolutional codes. The multimedia elements which are associated with ample amount of data are best protected by Turbo codes. Turbo decoder employs Maximum A-posteriori Probability (MAP) and Soft Output Viterbi Decoding (SOVA) algorithms. Conventional Turbo coded systems employ Equal Error Protection (EEP) in which the protection of all the data in an information message is uniform. Some applications involve Unequal Error Protection (UEP) in which the level of protection is higher for important information bits than that of other bits. In this work, enhancement to the traditional Log MAP decoding algorithm is being done by using optimized scaling factors for both the decoders. The error correcting performance in presence of UEP in Additive White Gaussian Noise channel (AWGN) and Rayleigh fading are analyzed for the transmission of image with Discrete Cosine Transform (DCT) as source coding technique. This paper compares the performance of log MAP, Modified log MAP (MlogMAP) and Enhanced log MAP (ElogMAP) algorithms used for image transmission. The MlogMAP algorithm is found to be best for lower Eb/N0 values but for higher Eb/N0 ElogMAP performs better with optimized scaling factors. The performance comparison of AWGN with fading channel indicates the robustness of the proposed algorithm. According to the performance of three different message classes, class3 would be more protected than other two classes. From the performance analysis, it is observed that ElogMAP algorithm with UEP is best for transmission of an image compared to Log MAP and MlogMAP decoding algorithms.
Abstract: Roadway planning and design is a very complex
process involving five key phases before a project is completed;
planning, project development, final design, right-of-way, and
construction. The planning phase for a new roadway transportation
project is a very critical phase as it greatly affects all latter phases of
the project. A location study is usually performed during the
preliminary planning phase in a new roadway project. The objective
of the location study is to develop alignment alternatives that are cost
efficient considering land acquisition and construction costs. This
paper describes a methodology to develop optimal preliminary
roadway alignments utilizing spatial-data. Four optimization criteria
are taken into consideration; roadway length, land cost, land slope,
and environmental impacts. The basic concept of the methodology is
to convert the proposed project area into a grid, which represents the
search space for an optimal alignment. The aforementioned
optimization criteria are represented in each of the grid’s cells. A
spatial-data optimization technique is utilized to find the optimal
alignment in the search space based on the four optimization criteria.
Two case studies for new roadway projects in Duval County in the
State of Florida are presented to illustrate the methodology. The
optimization output alignments are compared to the proposed Florida
Department of Transportation (FDOT) alignments. The comparison is
based on right-of-way costs for the alignments. For both case studies,
the right-of-way costs for the developed optimal alignments were
found to be significantly lower than the FDOT alignments.
Abstract: This paper describes the problem of building secure
computational services for encrypted information in the Cloud
Computing without decrypting the encrypted data; therefore, it meets
the yearning of computational encryption algorithmic aspiration
model that could enhance the security of big data for privacy,
confidentiality, availability of the users. The cryptographic model
applied for the computational process of the encrypted data is the
Fully Homomorphic Encryption Scheme. We contribute a theoretical
presentations in a high-level computational processes that are based
on number theory and algebra that can easily be integrated and
leveraged in the Cloud computing with detail theoretic mathematical
concepts to the fully homomorphic encryption models. This
contribution enhances the full implementation of big data analytics
based cryptographic security algorithm.
Abstract: Reliable water level forecasts are particularly
important for warning against dangerous flood and inundation. The
current study aims at investigating the suitability of the adaptive
network based fuzzy inference system for continuous water level
modeling. A hybrid learning algorithm, which combines the least
square method and the back propagation algorithm, is used to
identify the parameters of the network. For this study, water levels
data are available for a hydrological year of 2002 with a sampling
interval of 1-hour. The number of antecedent water level that should
be included in the input variables is determined by two statistical
methods, i.e. autocorrelation function and partial autocorrelation
function between the variables. Forecasting was done for 1-hour until
12-hour ahead in order to compare the models generalization at
higher horizons. The results demonstrate that the adaptive networkbased
fuzzy inference system model can be applied successfully and
provide high accuracy and reliability for river water level estimation.
In general, the adaptive network-based fuzzy inference system
provides accurate and reliable water level prediction for 1-hour ahead
where the MAPE=1.15% and correlation=0.98 was achieved. Up to
12-hour ahead prediction, the model still shows relatively good
performance where the error of prediction resulted was less than
9.65%. The information gathered from the preliminary results
provide a useful guidance or reference for flood early warning
system design in which the magnitude and the timing of a potential
extreme flood are indicated.
Abstract: A laboratory study on the influence of compactive
effort on expansive black cotton specimens treated with up to 8%
ordinary Portland cement (OPC) admixed with up to 8% bagasse ash
(BA) by dry weight of soil and compacted using the energies of the
standard Proctor (SP), West African Standard (WAS) or
“intermediate” and modified Proctor (MP) were undertaken. The
expansive black cotton soil was classified as A-7-6 (16) or CL using
the American Association of Highway and Transportation Officials
(AASHTO) and Unified Soil Classification System (USCS),
respectively. The 7day unconfined compressive strength (UCS)
values of the natural soil for SP, WAS and MP compactive efforts are
286, 401 and 515kN/m2 respectively, while peak values of 1019,
1328 and 1420kN/m2 recorded at 8% OPC/ 6% BA, 8% OPC/ 2% BA
and 6% OPC/ 4% BA treatments, respectively were less than the
UCS value of 1710kN/m2 conventionally used as criterion for
adequate cement stabilization. The soaked California bearing ratio
(CBR) values of the OPC/BA stabilized soil increased with higher
energy level from 2, 4 and 10% for the natural soil to Peak values of
55, 18 and 8% were recorded at 8% OPC/4% BA 8% OPC/2% BA
and 8% OPC/4% BA, treatments when SP, WAS and MP compactive
effort were used, respectively. The durability of specimens was
determined by immersion in water. Soils treatment at 8% OPC/ 4%
BA blend gave a value of 50% resistance to loss in strength value
which is acceptable because of the harsh test condition of 7 days
soaking period specimens were subjected instead of the 4 days
soaking period that specified a minimum resistance to loss in strength
of 80%. Finally An optimal blend of is 8% OPC/ 4% BA is
recommended for treatment of expansive black cotton soil for use as
a sub-base material.