Fire incidences are classed as catastrophic events, which mean that persons may experience mental distress and trauma. The development of a robotic vehicle specifically designed for fire extinguishing purposes has significant implications, as it not only addresses the issue of fire but also aims to safeguard human lives and minimize the extent of damage caused by indoor fire occurrences. The primary goal of the AFRC is to undergo a metamorphosis, allowing it to operate autonomously as a specialized support vehicle designed exclusively for the task of identifying and extinguishing fires. Researchers have undertaken the tasks of constructing an autonomous vehicle with robotic capabilities, devising a universal algorithm to be employed

In this work, a fiber-optic biomedical sensor was manufactured to detect hemoglobin percentages in the blood. SPR-based coreless optical fibers were developed and implemented using single and multiple optical fibers. It was also used to calculate refractive indices and concentrations of hemoglobin in blood samples. An optical fiber, with a thickness of 40 nanometers, was deposited on gold metal for the sensing area to increase the sensitivity of the sensor. The optical fiber used in this work has a diameter of 125μm, no core, and is made up of a pure silica glass rod and an acrylate coating. The length of the fiber was 4cm removed buffer and the splicing process was done. It is found in practice that when the sensitive refractive i

<p>Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and

Rapid and accurate identification of Methicillin Resistant Staphylococcus aureus is essential in limiting the spread of this bacterium. The aim of study is the detection of Methicillin Resistant Staphylococcus aureus (MRSA) and determining their susceptibility to some antimicrobial agent. A total of fifty clinical Staphylococcus aureus, isolated from the nose of health work staff in surgery unit of Kalar general hospital and from ear of patients attended to the same hospital. The susceptibilities of isolates were determined by the disc diffusion method with oxacillin (1 ?g) and cefoxitin (30 ?g), and by the mannitol salt agar supplemented with cefoxitin (MSA-CFOX), susceptibilities of isolates to other antimicrobial agent were determined b

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob