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Mango is one of the six important fruits crops besides banana, watermelon, apple, orange, and grapes in the world. Of late, insidious fruit rot (IFR) has been of increasing concern among mango growers in Malaysia as this disease does not exhibit visible symptoms but has the potential to reduce the quality of the fruit. Therefore, the objectives of this study were to evaluate and correlate soil fertility status between mango orchards with and without the IFR incidence by comparing content of selected soil nutrients. The soil samples were collected from orchards planted with Harumanis, a mango variety, with and without IFR incidence. The samples were taken at a depth of 0–30 cm, crushed and then sieved using a 2-mm mesh size prior to laboratory analysis. The analyses followed standard practice. The results showed a significant difference between EC, total C, total N, C/N ratio, and exchangeable bases (Ca, Mg, K and Na) between the soil samples from Harumanis orchards with and without IFR incidences. Although soil nutrient content was higher than the optimal range for Harumanis cultivation in both sites, synergistic and antagonistic effects were discovered mostly in soil samples from Harumanis orchard with IFR incidence. Studies show that application of Ca and K fertilizers can suppress IFR incidence. [ABSTRACT FROM AUTHOR]

Nowadays, the quality of air in the environment has been impacted by the industry. It is important to make sure our ambient air especially in an indoor environment is clean from contaminating particles or harmful gases. Therefore, the air quality inside the indoor environment should be monitored regularly. One of the major problems, when a particular environment has been contaminated by harmful gases, is finding the source of the emission. If the indoor environment has been contaminated by a harmful source it should be instantly localized and eliminated to prevent severe casualties. In this paper, we propose the utilization of synthetic data generated by the Computational Fluid Dynamic (CFD) approach to train the Deep Neural Network (DNN) model called CFD-DNN to perform gas source localization in an indoor environment. The model is capable to locate the contaminated source within a small area of an indoor environment. A total of 361 datasets with different locations of contaminated source release have been obtained using the CFD approach. The obtained dataset was divided into training and testing datasets. The training dataset was used for the model training process while the testing dataset is fed into the model to test model reliability to predict the gas source location. The Euclidian distance equation was used to measure the distance error between the actual and predicted location of the source. The result shows that the model is capable to locate the gas source within a minimum and maximum error of 0.03m to 0.46m respectively. [ABSTRACT FROM AUTHOR]

In this study, surface chemistry modification of kenaf adsorbent in adsorptive hydrogen storage system was computationally investigated using Brunauer-Emmett-Teller (BET) surface area, thermogravimetric analysis (TGA), field emission scanning electron microscopy (FESEM), and elemental dispersion X-ray (EDX) analysis, in which the suitability of the adsorbent was determined with detailed physicochemical characterisation analysis. The method used for surface modification was metal deposition method using Ni(NO3)2 with various concentrations at different levels of pressure (up to 6 bars). Based on this research, modified kenaf with 0.4 M Ni(NO3)2 and 2.559 m2 g-1 adsorbed up to 1.013 mmol g-1 of hydrogen compared to the raw kenaf with only 0.396 mmol g-1 of hydrogen adsorbed in 5 h. Two consecutive cycles of adsorption were carried out to show the delivery performance of hydrogen adsorptive storage. Thus, different adsorption ability between the modified kenaf and raw kenaf shows that Ni-metal on kenaf surface assists in hydrogen adsorption process.

The present study investigated the spatial variation of the physico-chemical characteristics of lateritic soil profiles (up to 60 cm in depth) in a sloping mango orchard (1 ha; slope gradient = 6 %) in North Peninsular Malaysia. The study revealed that horizontal variation for the exchangeable Al and Mg, and the particle size fractions in the topmost soil layer (0-15 cm) within the orchard was higher than their vertical variation within the soil profile, and that, in contrast, the opposite trend was found for the total N, cation exchange capacity, and base saturation. Furthermore, preferential accumulation of soil organic matter and nutrients such as total N and available P were found at the lower (LS) and/or middle (MS) slope positions than the upper (US) one, while the lower clay content with the higher clay activity index was observed at LS compared to MS and US. These results suggest that these variations occur by the scattered accumulation of fertilizer-derived nutrients (i.e. N, P, K, and Mg) in the surface soil layers and the translocation of the surface litter, soil and nutrients towards the downslope in addition to enhanced eluviation process with the residual of clays at the downslope in the sloping orchard. [ABSTRACT FROM AUTHOR]

A health screening was done in UNIMAS in August 2008 for 237 undergraduate students. Body mass index (BMI), waist circumference (WC) and blood pressure (BP) were measured for all subjects. Total cholesterol and glucose levels were checked for those who fulfilled the screening criteria. The proportion of participants with cardiovascular (CVD) risk factors was high. The strategies for health promotion should not only be targeted to the older community but also to the younger community.

Selective Laser Melting (SLM) is an Additive Manufacturing (AM) technique that built 3D part in a layer-by-layer method by melting the top surface layer of a powder bed with a high intensity laser according to sliced 3D CAD data. AlSi10Mg alloy is a traditional cast alloy that is broadly used for die-casting process and used in automotive industry due its good mechanical properties. This paper seeks to investigate the requirement SLM in rapid tooling application. The feasibility study is done by examining the surface roughness and dimensional accuracy as compared to the benchmark part produced through the SLM process with constant parameters. The benchmark produced by SLM shows the potential of SLM in a manufacturing application particularly in moulds.

Selective Laser Melting (SLM) is one of the most effective powder bed technique in the additive Manufacturing (AM) which able to fabricate functional metal parts directly from 3D Computer Aided Design (CAD) file data. In this paper, the influence of SLM parameters, such as laser power, scanning speed and hatching distance on the density of AlSi10Mg samples are investigated using one factor at a time (OFAT). Furthermore, the optimum results are used to fabricate samples for hardness, tensile strength, and impact toughness test. It is revealed that AlSi10Mg parts fabricated by SLM achieving the best density of 99.13% at the value of 350 watts laser power, 1650 mm/s scanning speed and hatching distance 0.13mm, whereby resulted comparable and even better mechanical properties to those of conventionally HDPC A360F and HDPC A360T6 alloys although without any comprehensive post processing methods.