Rainfall-Induced Hydraulic Properties for Unsaturated Soil in Klang Valley

Klang Valley area is one of the most landslide-prone areas in Malaysia, especially at Hulu Kelang, Kuala Lumpur. The area has been frequently hit by landslide since 1990s. Soil instability is agreed by researchers occurred due to high precipitation and long duration of rainfall which cause property damage and leading to injury and fatality. Slope failure is also triggered by the antecedent rainfall leads to infiltration of rainwater into soil. Therefore, study of rainwater infiltration is vital to relates soil – water interaction and soil behaviour for varies of rainfall intensities and duration for unsaturated soil. The objective of this paper is to determine and compare soil water characteristic curve (SWCC) which is one of the soil hydraulic parameters for Klang Valley area. Samples were collected to determine the soil hydraulic properties at Hulu Kelang area, Universiti Kebangsaan Malaysia (UKM) and Universiti Pertahanan Nasional Malaysia (UPNM) campuses. SWCC was obtained by pressure plate extractor apparatus experiment and the analysis was performed using Van Genuchten equation. Result of parameters obtained shows significant differences of soil at Hulu Kelang area compared to soils at UKM and UPNM campuses. This research is relevant to supports national slope master plan 2009-2023

The association of dietary behaviors and practices with overweight and obesity parameters among Saudi university students

Western dietary habits, coupled with a sedentary lifestyle, are potential contributors to the prevalence and rapid increase in the incidence of obesity in Saudi Arabia. This study aimed to investigate the association between students’ weight status and their eating behaviors and practices. Another aim was to assess students’ awareness of the health risks associated with obesity. Methods A cross-sectional survey was conducted among a sample of 416 (53% male and 47% female) undergraduate students, aged 18–26 years old, between January 6 and April 6, 2019, from colleges of Health Sciences at Jazan University in the Kingdom of Saudi Arabia (K.S.A). Students completed a self-administered questionnaire and recorded their measured anthropometric parameters. Results The prevalence of overweight (20.4%) and obesity (14.9%) were relatively high among the participants. There were statistically significant associations between Body Mass Index (BMI) and the different settings of food consumption (i.e., dining on a table (or) in the Islamic way: squatting on the ground) (p<0.001)). BMI was also associated with students’ dietary habits regarding consuming food, snacks, and drinking carbonated beverages while watching television (p<0.001), as well as consuming the same pattern of food/drink while watching television, playing video games on mobile phones or computers (p<0.001). Nearly most of the students were oblivious to the fact that metabolic syndrome, reproductive disorders, respiratory disorders along with liver and gallbladder diseases are some of the health risks associated with obesity. Conclusion The prevalence of obesity and overweight were reasonably high in our study sample and were affected by several factors related to students’ eating behaviors and practices. This warrants the need for rigorous and frequent health education interventions on healthy eating behaviors, dietary practices, with an emphasis on the importance of adopting an active, healthy lifestyle

Significant directed walk framework to increase the accuracy of cancer classification using gene expression data

Early diagnosis methods in cancer diagnosis studies are making great challenge as they require the involvement of different fields. Deoxyribonucleic acid (DNA) microarray analysis is one of the modern cancer diagnosis techniques used by scientists to measure the gene expression level changes in gene expression data. From the perspective of computing, an algorithm can be developed to identify more difficult cases. Numerous cancer studies have combined different machine learning techniques for the cancer diagnosis. This study is conducted to improve the cancer diagnosis technique, directed random walk (DRW) from the direction of framework. Improved directed random walk framework is proposed with the new introduced sub-algorithms, a larger directed graph and a different classifier. It is named as significant directed walk (SDW). In this study, six gene expression datasets are applied to study the effectiveness of the sub-algorithm, directed graph and classifier in SDW in terms of cancer prediction and cancer classification. Sub-algorithms of SDW can be further divided into data pre-processing phase, specific tuning parameter selection, weight as additional variable, and exclusion of unwanted adjacency matrix. Besides that, SDW also incorporated four directed graphs to study the usability of the directed graph. The best directed graph among the four is chosen to be part of the structure in SDW. The experimental results showed that the combination of SDW with walker network and linear regression is the best among all. SDW is achieves accuracy of 95.03% in average which is higher by 8.97% compare to conventional DRW for all cancer datasets. This study provides a foundation for further studies and research on early diagnosis of cancer with machine learning technique. It is found that these findings would improve the early diagnosis methods of cancer classification

Effects of Water Quality and Monogenean Parasite in the Gills of Freshwater Cat Fish, Hemibagrus nemurus Valenciennes 1840

Abstract: The Study on Hemibagrus nemurus (Valenciennes, 1840) gills was designed to investigate the influence of monogenean irritation coupled with water quality condition of the pond (earthen pond) in Perlok, Pahang. Three hundred and eighty fish host were examined, their gills were excised and fixed in 3% glutaraldehyde for scanning electron microscopy and in bouin&apos;s solution for histology. In the infected fish gills, the pathological alterations observed such as proliferative, degenerative and necrotic changes in the epithelium of gill filaments. In the secondary lamellae, telangiectasia, fusion of secondary lamellae and excessive mucous cells proliferation were observed. Correlation was made between water quality parameters and intensity of monogenean infestation in the ponds. It can be concluded that gill alterations as a result of monogeneans irritation and poor water quality in fish ponds may serve as a sensitive biomarker for pollutants

Fusion iris and periocular recognitions in non-cooperative environment

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset

Comparison of feature selection techniques in classifying stroke documents

The amount of digital biomedical literature grows that make most of the researchers facing the difficulties to manage and retrieve the required information from the Internet because this task is very challenging. The application of text classification on biomedical literature is one of the solutions in order to solve problem that have been faced by researchers but managing the high dimensionality of data being a common issue on text classification. Therefore, the aim of this research is to compare the techniques that could be used to select the relevant features for classifying biomedical text abstracts. This research focus on Pearson’s Correlation and Information Gain as feature selection techniques for reducing the high dimensionality of data. Towards this effort, we conduct and evaluate several experiments using 100 abstract of stroke documents that retrieved from PubMed database as datasets. This dataset underwent the text pre-processing that is crucial before proceed to feature selection phase. Features selection phase is involving Information Gain and Pearson Correlation technique. Support Vector Machine classifier is used in order to evaluate and compare the effectiveness of two feature selection techniques. For this dataset, Information Gain has outperformed Pearson’s Correlation by 3.3%. This research tends to extract the meaningful features from a subset of stroke documents that can be used for various application especially in diagnose the stroke disease

Clinical, radiological and therapeutic characteristics of patients with COVID-19 in Saudi Arabia

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a rapidly spreading global pandemic. The clinical characteristics of COVID-19 have been reported; however, there is limited research investigating the clinical characteristics of COVID-19 in the Middle East. This study aims to investigate the clinical, radiological and therapeutic characteristics of patients diagnosed with COVID19 in Saudi Arabia. METHODS: This study is a retrospective single-centre case series study. We extracted data for patients who were admitted to the Al-Noor Specialist Hospital with a PCR confirming SARS-COV-2 between 12th and 31st of March 2020. Descriptive statistics were used to describe patients’ characteristics. Continuous data were reported as mean ± SD. Chi-squared test/Fisher test were used as appropriate to compare proportions for categorical variables. RESULTS: A total of 150 patients were hospitalised for COVID-19 during the study period. The mean age was 46.1 years (SD: 15.3 years). The most common comorbidities were hypertension (28.8%, n = 42) and diabetes mellitus (26.0%, n = 38). Regarding the severity of the hospitalised patients, 105 patients (70.0%) were mild, 29 (19.3%) were moderate, and 16 patients (10.7%) were severe or required ICU care. CONCLUSION: This case series provides clinical, radiological and therapeutic characteristics of hospitalised patients with confirmed COVID-19 in Saudi Arabia