Application of vertical electrical sounding method to decipher the existing subsurface stratification and groundwater occurrence status in a location in Edo North of Nigeria

The interpretation of two resistivity curves over Iyakpi town within geologic terrain often referred to as Ajali formation which bears false-bedded sandstone with associated clay and shale intervals in the bottom section indicates that the area has an abundant groundwater potential. Existence of productive borehole in the study area was field-confirmed. The study area is said to have a standing history of abortive boreholes, resulting from failed drilling attempts. No dug well was sited in the community. The study showed that the main lithologic units penetrated by the sounding curves are laterite, sandstone, sandstone (dry with some clay/shale). This study revealed the possibility of having a maximum drill depth to water table of 260 m (865.80 ft)

Indoor Air Quality and Microclimatic conditions in selected Restaurants and Kitchens at a Tertiary Institution in Benin City, Nigeria

This study investigated the levels of selected indoor air pollutant concentrations and microclimatic conditions in restaurants and kitchens at a tertiary institution in Benin City using standard procedures. Ten (10) restaurants and kitchens were randomly selected within the University environment. Indoor particulates (PM1.0, PM2.5 and PM10), Carbon monoxide (CO), Relative humidity (RH), Temperature (Temp) and Wind speed (WS) were measured using Handheld Portable Air Samplers. The results showed that the indoor meteorological and air quality parameters ranged between 34.8 - 35.8°C and 34.5 - 35.9°C (Temp); 42.8 -70.2% and 39.7 - 66.9 (RH); 1.1 - 2.0 m/s and 1.2 - 1.8 m/s (WS); 0.0 - 25.4 and 0.0 - 28.7 mg/m3 (CO); 28.9 - 42.4 µg/m3 and 24.4 µg/m3 - 30.6 (PM1.0); 47.0 - 75. µg/m3 and 37.4 - 50.3 µg/m3 (PM2.5); 62.3 - 91.0 µg/m3 and 53.6 - 56.8 µg/m3 (PM10) within the restaurants and kitchens respectively. The mean concentrations of the CO and particulates were above the recommended regulatory limits of the WHO in all sampling sites. There were generally weak significant associations between the observed meteorological parameters and the indoor air pollutants (R= -0.352, - 0.419 p<0.001), except for CO and indoor temperature in the kitchens (R=0.649, R2 = 0.429 p<0.001). The Air Quality Index (AQI) status of the sampled sites varied from moderate to unhealthy. This study underscores the need for adequate ventilation in the sampled restaurants and kitchens and the creation of awareness of the health risks associated with indoor air pollutants in the study area

Plantar fascia ultrasound images characterization and classification using support vector machine

The examination of plantar fascia (PF) ultrasound (US) images is subjective and based on the visual perceptions and manual biometric measurements carried out by medical experts. US images feature extraction, characterization and classification have been widely introduced for improving the accuracy of medical assessment, reducing its subjective nature and the time required by medical experts for PF pathology diagnosis. In this paper, we develop an automated supervised classification approach using the Support Vector Machine (Linear and Kernel) to distinguishes between symptomatic and asymptomatic PF cases. Such an approach will facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Six feature sets were extracted from the segmented PF region. Additionally, features normalization, features ranking and selection analysis using an unsupervised infinity selection method were introduced for the characterization and the classification of symptomatic and asymptomatic PF subjects. The performance of the classifiers was assessed using confusion matrix attributes and some derived performance measures including recall, specificity, balanced accuracy, precision, F-score and Matthew’s correlation coefficient. Using the best selected features sets, Linear SVM and Kernel SVM achieved an F-Score of 97.06 and 98.05 respectively

Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis

There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.Lili Niu, Ming Qian, Wei Yang, Long Meng, Yang Xiao, Kelvin K. L. Wong, Derek Abbott, Xin Liu, Hairong Zhen