Development of potential dysgraphia handwriting dataset.

This report presents a dataset of offline handwriting samples among Malaysian schoolchildren with potential dysgraphia. The images contained Malay sentences written by primary school students and children under intervention by the Malaysia Dyslexia Association (PDM). Students were expected to copy and write the sentences provided on the paper form that was used to gather data.

PREVALENCE AND RISK FACTORS OF NEEDLE STICK INJURIES AMONG CLEANERS IN A TERTIARY HEALTH FACILITY IN GOMBE, NORTHEAST, NIGERIA.

Introduction: The cleaning staff working in a health facility are at risk of needle stick injuries (NSI) because of the nature of their work which involves the disposal of used needles and sharps. This study aimed to determine the prevalence and risk factors of NSI among cleaning staff at Federal Teaching Hospital Gombe.

Methodology: A cross-sectional study was carried out among 150 cleaning staff, selected using simple random sampling technique.

A Nanocomposite Paste Electrode Sensor for Simultaneous Detection of Uric Acid and Bisphenol A Using Zinc Hydroxide Nitrate-Sodium Dodecylsulfate Bispyribac.

The fabrication of a zinc hydroxide nitrate-sodium dodecylsulfate bispyribac modified with multi-walled carbon nanotube (ZHN-SDS-BP/MWCNT) paste electrode for uric acid and bisphenol A detection was presented in this study. Electrochemical impedance spectroscopy, chronocoulometry, square-wave voltammetry, and cyclic voltammetry were all used to examine the electrocatalytic activities of modified paste electrodes. The modified electrode's sensitivity and selectivity have been considered in terms of the composition of the modifier in percentages, the types of supporting electrolytes used, the pH of the electrolyte, and square-wave voltammetry parameters like frequency, pulse size, and step increment.

Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis.

Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system's ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level.