PRODUCTION AND CHARACTERIZATION OF LIQUID DETERGENTS FROM SOME AGRICULTURAL WASTE PRODUCTS

The aim of this work is to produce and characterized liquid detergents using potassium hydroxide obtained from waste of agricultural materials. The waste agricultural materials are cocoa pods, maize cobs and plantain peels. This was done by ashing these materials in a furnace at a temperature of 1050oC and dissolving the ash in de-ionized water to obtain the corresponding hydroxides. The metallic ions in their ashes were analyzed at different temperatures in order to study the effect of temperature on the yield of potassium ion by using atomic absorption spectrophotometer. The temperatures range is 450oC to 1050oC. The obtained results show that the metallic ions of the ash of these materials increase with increase in temperature. Analysis was carried out on the ashes of these materials to determine the oxide of elements in the samples ashed at 1050oC using X-ray fluorescence. The characteristics of the detergents formulated were carried out and compared with those of existing detergents purchased in market. The products were characterized based on the following parameters; percentages of active detergent, free oxide, total oxide, lather volume, phosphate, sodium tripolyphosphate, silica and silicate. Also, pH, viscosity and specific gravity. All the characterized properties of the detergents prepared and those purchased in the market fall within the standard for the detergent production. It was discover that the characteristics of the detergents prepared from waste agricultural materials are better than those detergents purchased in the market. http://dx.doi.org/10.4314/njt.v35i1.1

Vehicular CO emission prediction using support vector regression model and GIS

© 2018 by the authors. Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads

Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use

EVALUATION OF TOTAL ANNUAL COSTS OF HEAT EXCHANGER NETWORKS USING MODIFIED PINCH ANALYSIS

This study presents pinch analysis of some heat exchanger networks (HENs) problems using Hint integration (HINT) software. Three examples reported to have been solved using different approaches by various researchers to obtain the least possible total annual cost (TAC) were solved using the Hint software.  In this work, the use of remaining problem analysis (RPA) contained in the heat integration software was used to carry out matching and the general optimization of the networks for minimum TAC for the three problems solved. The results obtained after solving the first problem using RPA based heat integration gave a minimum total annual cost (TAC) of $237, 510 /yr. which is the fourth when compared with the lowest solution that shows the minimum TAC of$235.400 /yr. in that example. However, the TAC obtained in solving problem  2 and 3 were $562, 333 /yr. and$2.881 M/yr. respectively and they are the least total annual cost obtained when compared with what have been obtained using mathematical programming and non-RPA based Hint software. The overall assessment of the various approaches used to solve these problems when compared with the results obtained in this study shows that HINT software is able to obtain TAC that are within the same range as those obtained using mathematically based technique. http://dx.doi.org/10.4314/njt.v35i3.1

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Traffic Emission Modelling Using LiDAR Derived Parameters and Integrated Geospatial Model

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks

Changes in haematology, plasma biochemistry and erythrocyte osmotic fragility of the Nigerian laughing dove (Streptopelia senegalensis) in captivity

The haematology, plasma biochemistry and erythrocyte osmotic fragility of the Nigerian laughing dove (Streptopelia senegalensis) were studied after 4 and 8 weeks in captivity. At 8 weeks, there was a normocytic hypochromic anaemia characterized by reduced values for packed cell volume (PCV), red blood cell count (RBC), haemoglobin (Hb) concentration, mean corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin concentration (MCHC), but the mean corpuscular volume (MCV) was unaltered compared with the corresponding values at 4 weeks. The platelet count, total white blood cell count, heterophil, lymphocyte and monocyte counts were also lower at 8 weeks than those of the birds sampled at 4 weeks in captivity. There was also a stress induced increased heterophil/lymphocyte ratio and the erythrocytes were more fragile in hypotonic solution in birds sampled at 8 weeks. Plasma aspartate transaminase (AST), alanine aminotransferase (ALT) and alkaline phosphate (ALP) increased at 8 weeks, though non-significantly, which might have been due to muscle wasting consequent upon decreased muscular activities associated with prolonged captivity. The results suggest that maintaining wild birds in captivity for a prolonged period could be stressful as shown by the heterophil/lymphocytes ratio and reduced erythrocyte osmotic resistance, and could lead to decreases in erythrocyte parameters and muscle wasting.Keywords: Haematological parameters, erythrocyte osmotic fragility, laughing dove, captivit

Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models

© 2018, Springer Nature Switzerland AG. Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors

Modeling of CO emissions from traffic vehicles using artificial neural networks

© 2019 by the authors. Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model's predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas

Modeling and Simulation of Energy Recovery from a Photovoltaic Solar cell

Photovoltaic (PV) solar cell which converts solar energy directly into electrical energy is one of the feasible alternative sources to fossil fuel. This study aims at predicting the energy recovery from a typical photovoltaic solar cell, BP 3 series 235 W solar panel, by developing a reliable mathematical model of the solar panel which could represent the real systems. The model equation was solved using the ‘solve block’ tool in MathCAD 14 software and validated by physical data obtained from literature. Using the model developed the effects of temperature and solar irradiance on performance of PV solar panel was investigated using nominal conditions of 298 K and 1000 W/m2 as basis. Temperature was varied between 273, 298, 323, 348 and 373 K at constant irradiance of 1000 W/m2. Solar irradiance was also varied using 200, 400, 600, 800 and 1000 W/m2 while maintaining temperature at 298 K. The energy recovery from the PV model was evaluated using the fill factor concept. From the analysis of results, there is some agreement between the simulation results and the reported experimental performance of the PV solar panel. The performance of the PV system increased with increase in temperature and solar irradiance. In purview of values of solar irradiation (200 – 1000 W/m2) and temperature (273 - 373 K) that were studied, the energy recovery was maximum at 79.98% which agrees with values of between 75 and 85% obtained in practical solar cells.Keywords: Photovoltaic, Mathematical model, Energy recovery, Simulatio