Yams (Dioscorea spp) are herbaceous vines which are cultivated for the consumption of their starchy tubers. And can be consumed either boiled or fried. Drying is a veritable technology for storage beyond immediate consumption. This study thus, investigated the drying rate and the best suitable model of yam slices of different thicknesses (1.0mm, 1.5mm and 2.0mm) in thin layer using a laboratory convective oven dryer. A temperature range of 60-800C in multiples of 100C was selected and applied. Results were fitted to three thin-layer models of Page, Henderson and Lewis, and parameters (R2, RMSE, X2) to select the suitable estimating thin-layer model. R2 values ranged from 0.912090– 0.984462 (pre-treated) and 0.947496 – 0.982675 (untreated) for Lewis model; 0.996625– 0.998228 (pre-treated) and 0.994750– 0.998897 (untreated) for Henderson model; 0.940210 – 0.997329 (pre-treated) and 0.938921–0.995356 (untreated) for page model with rather low RMSE values ranging from0.031620-0.016657(pre-treated) and 0.030334-0.017589 (untreated) for Lewis model; 0.00773 - 0.005247 (pre-treated) and 0.009682-0.004439 (untreated) for Henderson model; 0.032675-0.006907 (pre-treated) and 0.033026-0.009107(untreated) for page model over the range of drying temperatures applied. The respective X2 values ranging from 0.0000204300–0.0011310990 (approximately = 0), therefore, from the statistical analysis the Henderson model showed a reliable prediction of the drying kinetics of the yam slices at the chosen temperatures. Drying rate along with characterizing drying constant also increased exponentially with temperature. From observation, pre-treated yam slices dried faster than untreated yam slices.

Quality assurance and Quantity aftermaths are binary terms, which are interconnected each other closely and its ramification often chaotic in the fast pace world, specifically construction industry. Moreover, it’s being used an inappropriate way as a substitute one for other. Quality over quantity, It’s a simple concept, which could be understand in layman term but taught to construction organization because quality is a process per se, whereas quantity is a form of amount. However it' can be defined in one line that fits like a square peg in a round hole in today's construction/corporate environment. The reason behind that it's so inflexible to emphasize quality over quantity is simple, contemporary businesses are established merely to make money as quickly as possible and its highest possible margins. Crafting high quality components tends to be expensive and time consuming tasks, moreover it required industriousness along with proper management, which seems less attractive margin for the organization and create a question in the mind what about the profit. Lower quality work, produced quickly with a minimal time commitment per task, tends to be far more profitable, with higher margins as well as a lower cost and get more attractive price from consumers. However, corporate shouldn’t entirely overlook the importance of quality over quantity. Here it’s not merely a matter of profit although it’s a matter of lives, which matters more than anything else. Moreover, the elements of a building structure are typically based on the kind of material used in its construction. Each and every division has space in a design takes into account the main theme or concept of the structure. Depending upon the building material(s), each element of the building is related to the other in terms of the concepts of color, dressing and outlook. The materials used for an element also, quite often, contribute to its terminology. Quality take off is widely unaccepted as one of the key factors for companies to be futile in the global market. Quality management has been a kicked issue for many years in various disciplines. The implementation of effective quality management has been witnessed and documented in the Engineering industry, which set up a paradigm for other disciplines such as the design and construction industry in the past few years, things have been changed in the construction sector by welcoming Policies that would improve construction process and lead to successful business strategies. Effective quality management, especially in (QOQ), It has been recognized as an inhibitor for performance improvement in the construction industry. On the flip side Quantity takeoffs are an important aspect of the construction cost estimating process. Quantity takeoffs are used in both large and small projects to determine actual costs ensure that a project remains profitable to the contractor, and its vital component of a final detailed estimate. General contractors, subcontractors, quantity surveyors, and estimators routinely produce quantity takeoffs during the estimation process. Whereas some in the construction industry may not routinely work on quantity takeoffs, they may still wonder what a quantity takeoff in construction is. We’ll also look at the different ways qualities as well as quantity takeoffs are completed, and what are the advantages and drawbacks of these different methods. The study recommends that promoting awareness regarding the values of ethical behavior at different forum and ensuring the effective castigation for unethical activities at different stages of project life cycle might reduce the unethical practices in the construction industry. Nevertheless both terms has to be targeted as per its vitality, as we have used the word of interconnected.

A basalt-based automobile brake pad was developed through optimization of the volume fractions and manufacturing parameters, a mathematical model was developed based on rule of mixture for the optimization of the volume fraction and the solution obtained using excel solver is as follow; basalt 38%, cast iron chips 26%, bronze 12%, glass fibre 12% and phenolic resin 12%. The manufacturing parameters were design using full factorial design of three levels and three factors, twenty-seven runs were obtained from the design, consequently twenty-seven samples were produced by compression moulding. The constituent materials were properly mixed to obtained homogeneous mixture and compressed into a mould with a cavity similar to the shape of the friction pad this was done at different moulding pressures of 30MPa, 27MPa and 24MPa, the curing temperatures were 1800C, 2000C and 2200C and the curing of 5minutes, 10minutes and 15 minutes as obtained from the factorial design. The samples produced were subjected to tests, the results showed that the basalt brake pad has the following properties; compressive strength of 137mPa, coefficient of friction 0.476, hardness 70HRB, density of 2768kg/m3 thermal conductivity of 0.66W/mK, water absorption of 0.377%, oil absorption of 0.25%, void content of 18.93%, wear rate of 9.0X10 -4g/m and the TGA result shows that the basalt-based brake pad is thermally stable up to a temperature of 5000C. The properties of the basalt-based brake pad are within the acceptable range and can satisfactorily substitute basalt.

An appraisal of aeromagnetic data lying between latitude 120N to 130N and longitude 4030”E to5030”E comprising Argungu, Dange Tanbuwal and Gumi was carried out with a view to understanding the structural trends of interest for mineral exploration. A set of aeromagnetic data obtained from the Nigerian geological survey Agency was gridded to produce the total magnetic intensity (TMI) map of the study area, followed with a polynomial fitting to remove the regional anomaly from the total magnetic intensity so as to obtain the residual anomaly. The analysis was preceded by production of Shaded relief map depicting the profile of contact solution, shaded relief map of the profile dyke solution as well as magnetic susceptibility values of structures in each profile was analyzed. The result obtained from the profile contact and profile dyke solution showed an abnormal trend behavior which swept from southwest (SW) towards the center and the Northwest (NW) of the study area. The contacts and dyke solutions can be seen as arranged on the profiles, some portions being highly concentrated than other portions as they were arranged on their respective profiles indicating the possibility of much structural bodies or host for potential minerals. This is evident as the case maybe from the depth values obtained in Werner depth analysis performed with corresponding depth values for both Shallow and deeper bodies indicating the presence of geologic contacts and weak zones highlighting dyke-like bodies which might serve as host to the minerally controlled fluid around the area. Likewise results obtained from the magnetic susceptibility values in all the three profiles of the study area showed a number of structures possessing high and low magnetic susceptibility values indicating different types of minerals at different distance along the area at different depths. Minerals such as schist and limestone are likely to be present.

Vertical electrical sounding was conducted using Schlumberger array at Ahmadu Bello University site II with the aim to determine seasonal changes in subsurface resistivity. A maximum current electrode spacing of 200 m was used for this survey and measurements were taken twice a month for one year. The acquired data was processed using IPI2win software and subsequently used to determine geoelectric sections. Five geoelectric layers were obtained from the interpretations. The first two geoelectric sections form the overburden with thicknesses ranging from 0 – 11 m and resistivity ranging from 122 to 200 Ωm. The third and the fourth geoelectric layers were interpreted as the weathered basement with thicknesses ranging from 11 – 24 m and resistivity ranging from 178 to 363 Ωm while the last layer was interpreted as the fresh basement with resistivity ranging from 422 to 1574 Ωm. Comparing the depth of interpretation of data in both seasons, the wet season data shows more depth probing which is fairly stable; this could be because the earth subsurface was more saturated and hence more conductive. The dry season data interpretation shows more stability compared to that of the wet data depth interpretation. A strong correlation was obtained from the statistical analysis between depth interpretations of the dry and wet season. A linear regression equation was obtained as y=1.0684x-0.2188 and about 99.97% dependence which show that we can use the wet season depth information to determine that of dry. The wet season is more appropriate for more depth probing with the exception of searching for water table since it rises up during the wet season and that may not be the true water table, while the dry season is appropriate for stability in data acquisition when required in a survey.

Several studies has reported the use of neural networks in the dynamic modelling and simulation of heavy duty turbines. However; focus on exhaust gas temperature a key indicator of turbine thermal health is yet to be made. In this paper the modelling of exhaust gas temperature using the non-linear autoregressive network and subsequent multi step prediction with data collected from GT13E2 turbine was embarked upon. Features which were statistically significant for EGT prediction were selected through stepwise regression. One hidden layer was sufficient to approximate the function and The optimal architecture for training was achieved by training the network with a fixed hidden neuron and varying time delay at the inputs and output. It is observed that the optimal performance is realized when the prediction is regressed at tapped input delay of 1 in open loop. 7 hidden neuron and 1 tapped delay is selected for function approximation after series of neurons ranging from 4-15 was tested. The appropriate model was carefully selected by utilizing the method of holdout cross validation, corrected Akaike Informationon Criterion and Schwartz Bayesian information criterion. The final architecture was trained, and converted to close loop NARX network where 100 time steps ahead prediction of EGT was made. Although it was observed that accuracy diminishes as prediction horizon increases, the chosen optimised architecture successfully predicted EGT 100 steps ahead with MAE of 2.9665 and RMSE of 3.9675. Therefore; the dynamic NARX model can be utilized for multistep ahead prediction in incidence of sensor malfunction at the turbine outlet of heavy duty gas turbines.