Ground magnetic survey for the investigation of magnetic minerals at Iboro Village, Abeokuta, south-western Nigeria

The geological map of the old western region of Nigeria indicates the presence of iron ore deposit at Iboro village Ogun state (7.9983o - 7.99933o N, 3.5790o - 3.5890o E). Hence a ground magnetic survey was carried out at a location at Iboro village so as to delineate the subsurface magnetic anomalies and to know whether the anomalies favour accumulation of magnetic minerals. The survey was carried out using high resolution proton precession magnetometer model G-856X. Eight traverses were run at 5m separations and earth magnetic intensity values were measured at 10m intervals along each traverse; the acquired data were corrected for drift. The residual anomalies obtained by removal of regional gradient from observed data using trend analysis were presented as profiles and maps. The treated data were qualitatively and quantitatively interpreted and the results gave values for the total ground magnetic anomalies that varied between a minimum and maximum peak values of about -33.0 and 30.6nT respectively. Depth to the basement rock was estimated using Peter’s half slope method which gave a maximum depth of about 13m. The contour maps and the total relative graphs present the subsurface picture of the geological structure that is assumed to harbour the metallic minerals through the action of the field towards the concentration of anomalies. It was suspected that the overburden was relatively thin in the study area and the minerals were at a shallow depth.

Effect of seasonal and temperature variation on hospitalizations for stroke over a 10-year period in Brazil

Background Seasonal variation in stroke incidence remains controversial. Aims We aimed to describe the pattern of seasonality in hospitalizations for stroke in Brazil. Methods We evaluated age-adjusted hospitalization rates for stroke per month using data from the Brazilian Unified Healthcare System and median monthly temperature data obtained from the National Institute of Meteorology. To detect a seasonality pattern in time series, we used seasonal-trend decomposition using LOESS. We calculated a seasonal strength statistic and used Kruskal–Wallis test to evaluate the presence of seasonality in Brazil and its five regions. We also assessed the association of temperature and stroke hospitalization rates using Spearman’s rho correlation. Results We identified 1,422,496 stroke-related hospitalizations between 2009 and 2018. Mean age was 67 years, 51% were male and 77.5% of stroke diagnoses were not specified as ischemic or hemorrhagic. Median temperature was 23.8℃ (IQR 22.3–24.4). Age-adjusted hospitalizations demonstrated significant seasonal variation during all the years analyzed, with increased rates during the winter. When regional differences were analyzed, seasonal behavior was present in the south, southeast and northeast regions of the country. These were also the regions with lower median temperatures during the winter months and greater amplitude of average temperatures between warmer and colder months. Conclusions In this large national cohort of stroke patients in Brazil, we demonstrated the presence of seasonal variation in the age-adjusted hospitalization rate, with peak rates during the winter months. The regional gradient of incidence of stroke was directly associated with colder winters and greater amplitude of temperature.

Variational Mode Decomposition Based Retinal Area Detection and Merging Of Superpixels in SLO Image

: Scanning Laser Ophthalmoscope (SLO) image can be used to detect retinal diseases. However identifying retinal area is a major task as retinal artefacts such as eyelashes and eyelids are also captured. Major part of retina can be viewed if detection is done with the help of images of SLO. In this paper our novel technique helps in detecting the true retinal area based on image processing techniques. To the SLO image two dimensional Variational Mode Decomposition (VMD) is applied. As a result of this different modes are obtained. Mode-1 is choosed because it has high frequency. Then mode1 is pre-processed using median filtering. After this preprocessed mode1 image is grouped into pixels based on regional size and compactness called superpixels. Superpixels are generated to reduce complexity. Superpixel merging is done next to Superpixel generation. It is done to reduce further difficulty and to enhance the speed. From the merged superpixels feature generation is performed using Regional, Gradient and textural features. It is done to eliminate artefacts and to detect the retinal area. Also feature selection will reduce the processing time and increase the speed. A classifier is constructed using Adaptive Network Fuzzy Inference System (ANFIS)for classification of features and its performance is compared with Artificial Neural Network(ANN). By this novel approach we got an classification accuracy of 98.5%.