Kinetic Modeling of Mango Fruit Ripening

In this work, three stages of mango ripening (mango ripening, ethylene inducement, rotting) are modelled kinetically.  Data for mango ripening are obtained from internet, and are used to perform regression analysis of the kinetic models developed.  It is seen that the results show linear relationship between concentrations and conversion for all the chemical components in all the models (fig. 1a, 2a, 3a).  Also the results of the concentration-time relationship are highly non-linear (fig. 1b, 2b and 3b).  The reciprocal of the reaction rates varies non-linearly with conversion: profile of ethylene inducement and rotting rise exponentially while that of mango ripening falls non-linearly.  The result of this study will help those dealing with fruits in orchard during harvesting and post harvest handling. Keywords: mango fruit ripening, ethylene, kinetic modelling, maturing, rotting, stoichiometr

MODELING AND OPTIMIZATION OF WATER-JET TRANSPORT PHENOMENON IN FIRE SERVICE

A model is developed that will allow the fireman to stand as far back as possible from a collapsing wall of a storey building while directing a water jet into a window of the burning building. The variables in the model are therefore, the initial angle (α) and the distance of the fireman from the wall (x). Data collected from Imo State Fire Service, Nigeria, were used in validating the model. The model gives 60o as an optimum initial water jet angle to the horizontal. With 60o as the initial angle, the optimum distance is dependent on the initial velocity of the gun. This can be applied in a burning storey building that is in the risk of collapsing anywhere in the world. The optimum distance from the wall must satisfy equation (10). The work enables the fireman to know a particular point to stand near the building with respect to initial velocity of water fountain and its initial tilt to the horizontal

Kinetic Modeling of Mango Fruit Ripening

In this work, three stages of mango ripening (mango ripening, ethylene inducement, rotting) are modelled kinetically. Data for mango ripening are obtained from internet, and are used to perform regression analysis of the kinetic models developed. It is seen that the results show linear relationship between concentrations and conversion for all the chemical components in all the models (fig. 1a, 2a, 3a). Also the results of the concentration-time relationship are highly non-linear (fig. 1b, 2b and 3b). The reciprocal of the reaction rates varies non-linearly with conversion: profile of ethylene inducement and rotting rise exponentially while that of mango ripening falls non-linearly. The result of this study will help those dealing with fruits in orchard during harvesting and post harvest handling

Modeling The Mechanism Of Carbon Capture And Sequestration (Ccs) In A System

Rate of carbon sequestration or annual uptake was modeled. Data from Mississippi Delta, ponderosa pine and black walnut, all in USA were used to validate the models. The co-relations of these models for these three sources of data were very high, suggesting that carbon sequestration is modelable and predictable provided that there is a perfect experimental method to capture and sequester the carbon compound with time. This work is a stepping stone to solving carbon capture and sequestration problem of our planet earth. Through a global engineering and technology it is feasible

Chemical Composition and Modeling of the Functions of Termitarium

The work was carried out in Fugro Consultants Nigeria Limited, Port Harcourt. It was the identification, extraction and characterization of chemical compounds responsible for making termination. The results showed that termitarium contains 11.6 (g/kg) of TOM; 15.9 (mg/kg) of Magnesium, 11690 (mg/kg) of iron and 23.3 (mg/kg) of extractable chloride which are responsible for structural stability of termitarium. The result also revealed that increase of clay content enhances the structural stability of termitarium. % water absorbed Vs time(s) for control and termitarium, mean number of seedings Vs time (days) for control and termitarium, gave their R-square (correlation coefficient) as 0.9975, 0.9951, 0.9959, 09807 and 0.09995 respectivel

THE EXPLAINABILITY OF GRADIENT-BOOSTED DECISION TREES FOR DIGITAL ELEVATION MODEL (DEM) ERROR PREDICTION

Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science. However, the explainability of GBDT predictions especially with earth observation data is still an open issue requiring more focus by researchers. In this study, we investigate the explainability of Bayesian-optimised GBDT algorithms for modelling and prediction of the vertical error in Copernicus GLO-30 digital elevation model (DEM). Three GBDT algorithms are investigated (extreme gradient boosting - XGBoost, light boosting machine – LightGBM, and categorical boosting – CatBoost), and SHapley Additive exPlanations (SHAP) are adopted for the explainability analysis. The assessment sites are selected from urban/industrial and mountainous landscapes in Cape Town, South Africa. Training datasets are comprised of eleven predictor variables which are known influencers of elevation error: elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover, bare ground cover, and urban footprints. The target variable (elevation error) was calculated with respect to accurate airborne LiDAR. After model training and testing, the GBDTs were applied for predicting the elevation error at model implementation sites. The SHAP plots showed varying levels of emphasis on the parameters depending on the land cover and terrain. For example, in the urban area, the influence of vector ruggedness measure surpassed that of first-order derivatives such as slope and aspect. Thus, it is recommended that machine learning modelling procedures and workflows incorporate model explainability to ensure robust interpretation and understanding of model predictions by both technical and non-technical users

Positional Accuracy Assessment of Historical Google Earth Imagery

Google Earth is the most popular virtual globe in use today. Given its popularity and usefulness, most users do not pay close attention to the positional accuracy of the imagery, and there is limited information on the subject. This study evaluates the horizontal accuracy of historical GE imagery at four epochs between year 2000 and 2018, and the vertical accuracy of its elevation data within Lagos State in Nigeria, West Africa. The horizontal accuracies of the images were evaluated by comparison with a very high resolution (VHR) digital orthophoto while the vertical accuracy was assessed by comparison with a network of 558 ground control points. The GE elevations were also compared to elevation data from two readily available 30m digital elevation models (DEMs), the Shuttle Radar Topography Mission (SRTM) v3.0 and the Advanced Land Observing Satellite World 3D (AW3D) DEM v2.1. The most recent GE imagery (year 2018) was the most accurate while year 2000 was the least accurate. This shows a continuous enhancement in the accuracy and reliability of satellite imagery data sources which form the source of Google Earth data. In terms of the vertical accuracy, GE elevation data had the highest RMSE of 6.213m followed by AW3D with an RMSE of 4.388m and SRTM with an RMSE of 3.682m. Although the vertical accuracy of SRTM and AW3D are superior, Google Earth still presents clear advantages in terms of its ease of use and contextual awareness.Comment: 36 page

Corrosion impact of AA6061/clay composite for industrial application

The search for novel products with enhanced function is increasing daily due to technological innovation. The development of products is crucial to minimize the exorbitant price of material acquisition and better performance of developed material. The research was carried out on samples of clay kaolinite pulverized to obtain 75 µm. 75 µm of clay was blended with AA6061 in.different composition to produce 4 samples as follows: sample A 2 % clay with 98 % aluminium alloy, B, 4 % clay 96 % aluminium alloy, C, 6 % clay 94 % aluminium alloy, D, 8 % clay 92 % aluminium alloy. Each sample was analysed for mechanical properties. Polarization test carried out in 0.75 M of H2SO4 on the composite shows enhanced corrosion susceptibility. Corrosion analysis of clay inserted in AA6061 revealed improved corrosion performance. Also, the changes in microstructure through SEM show that the integration of cla