In response to the global call for strategic information to comprehend prostate cancer, this study evaluated the health communication on behavioral practice of prostate cancer in Kwara state, Nigeria. Existing studies in Nigeria on prostate cancer have mostly focused on health practitioners and their patients, ignoring specific empirical data on semi-urban and urban context. This study looks at health communication channels as predictors of knowledge, attitude, and behavioral practices, with a focus on Ilorin, Nigeria’s Kwara state, which has the highest prostate cancer prevalence rate. A total of 336 respondents from Kwara State, Nigeria, were randomly selected using the multistage sample procedure for the survey. The findings show Knowledge of prostate cancer was highest amongst study participants who used the radio (4.00 ± 1.06) and television (3.64 ± 0.51) while it was low amongst those who relied on the internet (3.48 ± 0.50) and health professionals (3.16 ± 0.66) as their primary source of information. Contrastingly, practice was highest amongst persons who used the internet (3.60 ± 0.20) as their primary information source and lowest amongst those who used the television (2.50 ± 1.52) and Health Professionals (2.44 ± 0.65). Demographically, respondents in the 46-55 age group scored the highest (3.93 ± 0.71) as compared to those in the 26-35 (3.43 ± 0.68) who scored the lowest on the knowledge scale.The study concludes that health communication outlets such as television, the Internet, radio, newspapers, and health workers have a good impact on the people of Ilorin, Kwara State, Nigeria. The study suggests creating a nationwide prostate cancer communication system to improve the knowledge, attitude and practice of people, towards the attainment of Sustainable Development Goal 3.
In the current machine vision technology, accurate detection and classification of the crop dis-eases can protect against spoilage. Different diseases of tomato leaf have similar features or traits, making image disease detection confusing and challenging. Farmers cannot recognize whether a crop is infected or not just by looking at its leaves, because the healthy and infected crops resemble the same at first. Deep learning models can be used to overcome this prob-lem within less computational time. As a result, a new framework is implemented in this work through fine tuning the Deep Convolutional Neural Networks (DCNN) model using hyper parameters like learning rate, batch size, and epochs by applying transfer learning techniques for detecting tomato leaf disease. The data in this work is collected from the Plant Vil-lage database, which includes 20,639 images. The pro-posed model is implemented on three pre trained DCNN models-Alex Net, ResNet50 and VGG16. The proposed framework attains highest classification ac-curacy of 99.26% for fine tuning DCNN. The simula-tion results demonstrates that the fine-tuning Res-Net50 performs better classification of crop diseases when compared to the other DCNN models.
The purpose of this study was to determine the levels and components of essential oils between the rhizome and tuber parts of the white turmeric (Kaempferi rotunda) plant. Sampling of white turmeric was done purposively. The plant parts analyzed were the rhizome and tuber of white turmeric. The study was conducted in August 2021. Sampling of white turmeric was carried out in Hampatung Village, Kapuas Hilir District, Kapuas Regency. Laboratory studies were carried out in 3 places, namely the Laboratory of Chemical Technology for Forest Products, Department of Forestry, University of Palangka Raya, BPOM Laboratory of Palangka Raya City and the Test Laboratory of the Academy of Analytical Chemistry, Bogor Polytechnic. From the results of the analysis of white turmeric essential oil content in the rhizome (0.2969%). The results of GC-MS analysis of essential oils obtained from the rhizome showed 33 components and there were 4 main component compounds, namely Bornyl acetate (64.81%), Champhene (35.07%), Pentadecane (47.53%) and ethyl cinnamate (48.57%).
Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn’t rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT. Here in the presented paper, the classification of BT through DL techniques is put forward for the characterizing BTs using open dataset which categorize them into benign and malignant. The proposed framework achieves a striking precision of 96.65.
Feed supplements of oil and selenium have been studied for their effect on absolute weight growth and a descriptive picture of the nutritional content of protein, fat, cholesterol in tilapia baby fish. Feed experiments using Complete Randomized Design (6x3), R1 (basal/protein ration 28%); R2 addition of a mixture of coconut oil and hazelnut oil without Se and R3 (oil mixture 4%+Se 0.15 mg/kg); R4 (4% coconut oil + Se) and R5 (4% hazelnut oil + Se) and Rs (standard ration of protein 32%). Coconut is dominated by saturated fatty acids (lauric acid 42.67%), while hazelnut is dominated by linoleic unsaturated fatty acids (34.4%) and oleic acid (48.99%). Basal ration with the addition of a mixture of vegetable oils + Se resulted in an absolute growth of 27.33 g and a daily growth rate (DGR) of 0.43 g/day, and matched the Ration with high protein (32%). The addition of vegetable fats and selenium provides fish meat protein content 54.62%-58.54% and meat protein conversion (protein productive value) 27.68-32.03%. The fat content of meat and cholesterol ranges from 7.15%-10.20% and 75.43-103.97 mg/dL, respectively, and Se in tilapia meat ranges from 0.502-0.753 mg/kg).
