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Using no-core optical fiber (NCF) and conventional single-mode fiber (SMF), this research needs to increase the sensitivity of temperature and relative humidity measurements for radiology device applications. Operating on the Mach-tender Interferometer (MZI) principle, the sensors make use of surface plasmon resonance. Traditional sensing technologies sometimes confront difficulties in reaching high sensitivity and accuracy, especially in demanding environments like radiology facilities. In order to increase the sensitivity of four relative humidity sensors constructed from SMF, TNCF, and SMF, a 2 cm NCF segment was incorporated. To enhance sensitivity, the sensors have been coated with a variety of substances, such as gold nanoparticles (AuNPs), silica nanoparticles (PVN/PVP), and a layer-by-layer technique. The sensor coated with AuNPS exhibited the greatest sensitivity with respect to temperature, attaining 0.486 db/°C. The fiber optic sensors that have been developed exhibit great potential for utilization in biomedical and environmental domains. Experimental tests are carried out to assess the performance of the proposed sensing system, including sensitivity, accuracy, and reaction time. [ABSTRACT FROM AUTHOR]

High blood sugar levels for an extended time lead to the development of diabetic foot, which is a major medical problem among people with diabetes. Continuous care and management are essential to avoid potential physical complications. Nerve and blood vessel dysfunction caused by diabetic foot disease can lead to skin problems and ulcers, necessitating medical intervention. Diabetes increases the risk of foot-related complications, requiring prompt identification and resolution to prevent serious complications. The goal is to create a system consisting of a wireless sensor network or a system based on the use of thermal imaging for real-time monitoring of individuals with diabetes. This network will collect important physiological data, such as temperature, pressure, and humidity, using machine learning algorithms to predict diabetic foot complications. Automation will enhance forecasting accuracy and efficiency. An in-depth review of prior research on early detection methods for diabetic foot was undertaken to identify the most effective approach. Through rigorous comparative analysis, the proposed machine learning model demonstrates the superiority of the convolutional neural network (CNN) over other deep learning classifiers, achieving an accuracy rate of 98.27%. Different methodologies of ML, including regression (LR) and random forest (RF) classifiers, have also shown predictive solid performance, reaching up to 97% accuracy in forecasting diabetic foot disease. In conclusion, there is an urgent need for an advanced diabetic foot health monitoring system. The review highlights the need for additional research to overcome persistent challenges and unleash the full potential of machine learning models in enhancing diabetic foot disease prediction and treatment. [ABSTRACT FROM AUTHOR]

Diabetic foot is a severe medical problem that occurs as a result of high blood sugar levels. It is a common complication in diabetics. Diabetes can lead to complications, especially in the form of diabetic foot problems. If these problems are not detected and treated promptly, they can worsen, leading to severe consequences. Screening methods for the disease can be conventional and do not predict diabetic foot in the early stages. These prompted researchers to find an alternative solution to detect diabetic foot early and non-surgically. Researchers have sought other non-invasive methods to diagnose and predict diabetic feet using image processing techniques and machine learning algorithms. This study presents a comparative performance between six machine learning algorithms (Neural Network, Random Forest, Adaboost, Naïve Bayes) based on a dataset of images of normal and diabetic feet. The results show that Neural Network has an accuracy of 95.6%, the highest performance among other algorithms used in this study. [ABSTRACT FROM AUTHOR]

In this paper, a new efficient cryptographic system will be designed based on constructing a hybrid cryptosystem which is consisting of stream and block cryptosystems in order to increase the security of the images. Many tests are made to check the efficiency of the proposed cryptosystem, like, General Complexity (Key Space), Key Sensitivity, Mean Squared Error, Correlation Tests, ... etc. were applied. The performance and the security of the new hybrid cryptography system was high which proved by the results of the applied tests. [ABSTRACT FROM AUTHOR]

People are significantly impacted by poor air quality. Millions of fatalities are attributed to health problems and air pollution. To regulate and improve air quality, the World Health Organization and the Environmental Protection Agency have created a number of rules and standards. Recently, several writers proposed low-cost instruments for quickly determining air quality. the plethora of low-cost methods for gathering sensor data and evaluating air quality. In this work, a remote safety monitoring system for temperature, carbon monoxide, and humidity has been designed and implemented as a single tool. The OLED display of the measuring device and the LCD display of a remote monitoring system both display the measurement results. The ESP32 microcontroller and LoRa wireless technology are both used by this device. While the MQ-7 sensor is used to track carbon monoxide levels, the DHT22 sensor measures temperature and humidity. Testing The AS8700A Carbon Monoxide Meter and the 4000 NV Pocket Weather Tracker, both of which are used in the Environmental station, are calibrated using equipment that complies with industry standards. An alert will be sent if irregularities in the recorded parameters are found (buzzer and blinking LED). Patients, staff members, and health care providers can use this device to inspect their surroundings and do remote monitoring. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an interconnected network of physical objects, software, and hardware. IoT- based solutions are becoming increasingly critical to solving these concerns elderly who live alone, limited healthcare resources, and rising medical expenses. The goal of this literature review evaluation was to determine the most common IoT applications in healthcare. Fifty relevant research publications were evaluated and assessed between 2013 and 2021. The most applied fields of IoT in healthcare coverage according to this study were both home health care and outdoor. Most of the studies evaluated have used cloud-based architecture since it provides a lot of flexibility and scalability. In many IoT models, communication technology, for example, Bluetooth, Wi-Fi, ZigBee, and Long-Range Wireless Area Network (LoRaWAN) have been widely deployed. The IoT has enabled the sharing of information, the reduction of hospital stays, and the reduction of healthcare expenses. [ABSTRACT FROM AUTHOR]

This research tries to introduce a new technique concerning the reinforcing of two-way concrete slabs by using steel plate with a suitable shape of openings rather than traditional bar reinforcement. The experimental program consists of casting and testing seven slab models with overall dimensions of 1050*1050*60 mm. One of these models is reinforced by classical deformed bars, while the other six models are reinforced by perforated steel plate. The adopted shapes of openings in used steel plate were rectangular and octagonal. Each shape has three sizes. All models have the same amount of steel and test under a uniform distributed load to provide the same conditions for equitable comparison. The results show that the models reinforced by perforated steel plate display better behavior than the models reinforced by traditional bar reinforcement. The average ultimate load of small, medium and large plate openings was 7.4%, 29.3%, and 6.7% greater than the model reinforced by deformed steel bars, respectively. Moreover, the shape of the openings has no high effect on the behavior, but it can be said that the rectangular shape has slightly better behavior than the octagonal shape. With more investigation, the proposed reinforcing approach may be considered better and more efficient than the conventional method for reinforcing two-way concrete slabs. [ABSTRACT FROM AUTHOR]

Driver drowsiness and fatigue are two of the most common causes of automobile accidents. Yearly, the number of deaths in addition to fatalities rises dramatically owing to a variety of factors. Therefore, a system must be found that alerts the driver to reduce traffic accidents and thus preserve people's lives and public property. Within this research, Driver Drowsiness Detection System (DDDS) created on eyes movement, was designed and implemented to protect the driver from accidents.The proposed DDDS utilizes a method for identifying driver drowsiness that is based on the driver's actions utilizing their visual features using eyes movement (closed or open) by using a high-resolution camera. The features of eyes images were extracted to be used as input to the Deep Cascaded Convolutional Neural Network (DCCNN). The DCCNN was constructed and practically implemented in Raspberry Pi microcontroller (model B3) to distinguish the face district, as a result, the issue of inaccuracy resulting from artificial feature extraction is avoided. The frontal driver Landmarks face in a frame from the Dlib toolkit was used in the current study. As maintained by the eyes Landmarks, an advanced factor, named Eyes Aspect Ratio (EAR) is explored for evaluating the fatigue of the driver within the existing frame of the eyes picture. Then, the output of DCCNN was used to alarm the drive case. In the experiment, a 450×320 eyes resolution image was adopted with 60 frames/second (f/s). The experimental results revealed that the drowsiness detection accuracy was 99%. The results of the current research outperformed some previous studies in terms of drowsiness detection accuracy. [ABSTRACT FROM AUTHOR]