Estimating Tree Height and Volume of Gmelina arborea and Three other Tree Species in Plantations of South-West, Nigeria

This study explores the estimation of stand structure of Gmelina arborea and three other tree species in two plantations, in Ibadan, South West Nigeria, with the primary objective of estimating plot – level mean tree height, merchantable tree height, and volume of Gmelina arborea, Terminalia montalis, Tectona grandis, and Triplochiton scleroxylon. The number of tree species and the volume of wood in the two selected plantations were determined. Random sampling method was adopted in carrying out the assessment of the stand structure. Each plantation was divided into three plots of dimension 32mx32m. Twenty (20) stands were selected randomly in each plot, hence sixty (60) stands in each plantation. Quantitative data were taken on: Diameter at breast height (DBH), Total tree height (TTH), Basal area (BA) and Total volume (of wood) (TVOL). A total of three species were encountered in the two study area; the family Verbenaceae has the highest tree species (75%) in the two plantations. During the assessment of the tree species in both plantations, the results revealed that majority of the trees’ (68%) diameter were within 10-20cm, and the number of tree species in the upper diameter class (>60cm) (20%) were considerably small. Trees in the Gmelina plantation had on average, lower merchantable heights than those in the College Arboretum, despite having higher total tree height, diameter at breast height, basal area, and area volume. Inventory analysis of these plantations will establish a base-line information about the stand, point out possible improvements to the management plan and provide information on the volume of merchantable logs that can be extracted from the stand

Assessment Of The Prevalence Of Suicide Among Young Adults Using Machine Learning

Due to the high rate of suicide all over the world resulting in about 800,000 people dying by suicide each year. The instances where suicide victims constantly publish suicide messages deliberately to express their feelings on social media, there is need to address suicide issues, and how suicide can be prevented. Therefore, as a solution to this, there is need to create a model that classifies these users" social media posts and identify users with suicidal ideations, so as to prevent future suicide cases by getting the identified users the necessary help needed. The study adopted a binary classification of a suicide-related tweet with respect to age 15 up till 29 years, on a document-level basis. A machine learning approach was employed to solve the problem of tweet classification and predictions. The dataset was generated from a Twitter API. It was observed that suicidal issues are rampant among the young adult, which need urgent attention. The paper recommended that timely intervention should be provided so as to reduce suicidal victims and preserve the future of young adults

Design and Development of A Palm Kernel Nut Cracking Unit

The object of this research is the cracking of the nuts of oil palm (Elaesis guineensis). The oil palm tree is one of the greatest economic assets a nation can have, provided its importance is realized and fully harnessed. After the oil extraction of palm oil from the palm fruits, virtually all methods involved in palm kernel nut cracking both in traditional and small-scale exist in scattered or separate units of operations. Hence, this research focused on designing a palm nut kernel cracking unit incorporating a separator in form of a screen to separate cracked palm kernel nut shell from kernel. The result shows that there were significant difference (p≤0.05) among the moisture content of the palm nuts, shaft speed of the machine and weight (feed rate), having a significant difference between: – moisture content of the palm nut and the shaft speed of the cracker; – moisture content and feed rate; – shaft speed and feed rate. There exist interaction between cracked, uncracked shell, damaged, undamaged kernel, and palm kernel nut breakage ratio. While, there was no significant difference among interaction between moisture content, shaft speed and feed weight. The result also indicated that for the highest speed of 1,800 rpm at a feed rate of 700 kg/h for all moisture contents, the cracking efficiency was between10 to 90 %, which implies that the kernel cracking efficiency increases with an increase in machine speed. However, it was observed that higher cracking efficiency was at the cost of higher kernel damage for all cracking speeds and feed rates, which is a problem. The kernel breakage ratio ranged from 1.040–7.85 for all feed rates and moisture contents. The kernel breakage ratio increased with moisture content and cracking speed but decreases with feed rate weight