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In recent years, the emergence of airborne pathogens has posed significant challenges to public health, especially in high-traffic environments like airports. Early detection of these pathogens is crucial for effective containment and prevention of outbreaks. This literature review examines the current state of airborne pathogen detection systems deployed in airports. Through an analysis of methodologies, technologies, challenges, and advancements, this review aims to contribute to a deeper understanding of the efficacy and limitations of existing systems, as well as to identify areas for further research and development. The observation came about after the world was affected by covid 19 and the cholera outbreak affecting Zambia. The traditional methods, often lab-based and time-consuming, hinder swift responses. Real-time airborne disease detection technologies offer a game-changer, promising rapid on-site results. This review dives into the cutting-edge of real-time detection, exploring diverse technologies and their potential applications. The researcher discusses the challenges and opportunities associated with these advancements, paving the way for future research and development. The system will be monitored in real time taking advantage of emerging technologies such as Artificial Intelligence, Cloud Computing. The results will be noted and analysed giving outputs that will be used to act or ignore. The conclusions, recommendations will be made, and indications of where the system needs improvement for future engineering and technical developments. [ABSTRACT FROM AUTHOR]

When people get older some will have their insulin receptors non-responsive to the hormone leading to excess glucose levels in the blood circulation and the condition is called type 2 diabetes mellitus (DM). Individuals with type 2 DM get treatment that will reduce blood glucose level and in order to see whether or not the treatment is a success individual patient need a continuous glucose monitoring (CGM). This article is a literature survey that focuses on how to develop an intelligent glucose monitoring machine that works for geriatric patients both invasive and noninvasiveness ways. The authors focused on both developed and developing nations. It was found out that developed world geriatric patients use variety which involves an under-the-skin sensor for continuous blood sugar tracking. The sensor sends that glucose level wirelessly to an android device which then uses Wi-Fi 4G to share the glucose levels in database and health practitioners (Rghioui, A et al. 2020), whilst developing nations use blood glucose monitoring machines whereby the diabetic individuals uses a lancet to prick themselves and the blood glucose level is written down to show their health practitioner when they go for review the use of needle prick before and after every meal is painful and puts the patient at a risk of developing an infection at the pricked finger. There is a need therefore to come up with a non-invasiveness method which the authors will produce another paper later after this article. [ABSTRACT FROM AUTHOR]

Orthopedic imaging demands precision for effective diagnosis, surgical planning, and monitoring of musculoskeletal ailments. X-ray imaging, while readily available and efficient, primarily offers 2D views, which can be insufficient for complex orthopedic procedures. Compared to the detailed 3D reconstructions provided by MRI and CT scans, Xrays have limitations. However, considering the cost of MRIs and radiation risks associated with CT scans, researchers are actively developing methods to derive 3D information from conventional X-rays. This paper provides a thorough analysis of diverse methodologies used for X-ray to 3D model generation. It examines the principles, prerequisites, and categories of these reconstruction techniques, alongside their applications, advantages, and disadvantages. We evaluate the accuracy of these methods, offering guidance to researchers working within this dynamic field. [ABSTRACT FROM AUTHOR]

The field of cardiac care is evolving, with innovations in wearable electronics and Health - Internet of Things, (Health - IoT), undoubtedly contributing to effective personalised care of patients with cardiac disease in the home environment. Over recent years, attention to soft electronics has sparked an interest in their application in wearable sensor technology, with the latest contributions in research focusing on multimodal sensing of physiological parameters such as blood pressure, surface body temperature, and bioelectric signals like the ECG. This has led to the increased adoption of flexible and wearable biophysical sensor technology in IoT enabled remote monitoring of cardiac disease, due to its advantages over rigid sensors in achieving intimate skin coupling for bio signal sensing, and improved patient comfort. Moreover, clinical feasibility studies that employ the use of such technology have shown results that compare favourably well with the use of multi-lead Holter monitoring devices. However, widespread implementation of this technology is still lacking in low to middle income countries (LMICs), which have the highest burden of mortality and morbidity attributed to cardiac disease to date. This is largely due to the cost of most commercially available products which limit widespread public use. This article will review cost-effective Internet of Things (IoT) enabled ECG monitoring systems, with a focus on flexible biophysical sensor technology that has wireless connectivity - (Bluetooth enabled or Near Field Communication), energy optimization - (Wireless energy harvesting and low power usage), multimodal sensing, and can be mass produced through roll-to-roll manufacturing processes. [ABSTRACT FROM AUTHOR]

As a growing country, Zimbabwe must adopt these technologies since they offer numerous benefits, including precision and accuracy. Because a significant volume of MRI data must be reviewed, this procedure takes long and is unsuitable for big data. Because automated solutions are more cost-effective, they are essential. Automated medical imaging has become a hot issue in a variety of medical diagnostic checking. Magnetic Resonance Imaging (MRI) tumor diagnosis that is automated is crucial because it provides information about abnormal tissues that are needed for treatment planning. Human inspection is the traditional approach for detecting defects in magnetic resonance brain imaging. This method is impractical when dealing with large amounts of data. As a result, radiologists are developing automated tumor detection technologies to save time. It is necessary to use MATLAB to train an artificial neural network to identify brain cancers. In addition, an algorithm that can distinguish and categorize tumors into carcinogenic and non-cancerous tumors must be developed. The complexity and variety of malignancies make MRI brain tumor diagnosis tough task. Machine learning approaches are utilized to detect malignancies in brain MRI in this study. In this research paper work, three stages are implemented in preprocessing on the brain. To extract texture features, the Gray Level Co-occurrence Matrix (GLCM) is used. They are then classified using a machine learning technique, indicating the feasibility of utilizing machine vision to discriminate between cancerous and non-cancerous brain tumors. As a result, an AI process is used to arrange them correctly, demonstrating the feasibility of using machine vision to distinguish between cancerous and non-malignant cerebrum growths. It has the potential to make patient management easier in the future. Computer vision is one of the most widely utilized technology technologies for skin cancer detection systems in most industrialized countries. [ABSTRACT FROM AUTHOR]

A crucial measurement for the injury evaluation measure is assessing and following injury size. Great area and size assessments can help you choose the right treatment for you. Typically, laboratory wound healing plans include a series of photographs taken at regular intervals that show the wounded area and the healing interaction in a guinea pig, usually a mouse. These images are then examined in person to determine crucial measurements relevant to the research, such as wound size progression. Nonetheless, this task is time-consuming and exhausting; additionally, defining the injury edge can be abstract and move from one person to the next, even among experts. Furthermore, as our understanding of the recuperating interaction grows, we need to track these important factors effectively and precisely for high throughput (for instance, over the enormous scope and long haul tests). Following that, in this study, a calculation was created using Python in an OpenCV-based picture investigation pipeline using image processing techniques such as segmentation and classification, to allow non-uniform injury pictures and concentrating relevant data such as the area of premium, the injury only picture yields, and wound fringe size after some time measurements. To acquire acceptable results, camera positioning and distance from the wounds that need to be photographed are critical. In this case, ultrasonic sensors in the camera system are required to ensure that images are taken at a consistent distance. Because the injury space is properly created, appropriate recuperation procedures are addressed. Image processing using Python provided results in terms of area and estimated chemicals to deliver in the right amounts. In this way, wound healing can be tracked, and appropriate drug doses may be given based on the size of the wound. Furthermore, the wound healing trend can be easily assessed. [ABSTRACT FROM AUTHOR]

Persistent injury research is still poorly understood. Continuous injuries do not follow the usual injury recovery cycle and remain at the flaming stage. Skin wounds and delayed wound healing are a concern in the general well-being domain. The cost of medical services is rising as a result of perplexing and slow methods. There is a growing demand for technology that can track the progress of healing and the physiological status of the skin in real-time for wounds that take a long time to heal. In this work, the researcher developed a system that uses biosensors to monitor wound healing by analyzing the wound's conditions and acting on the wound to promote healthy and rapid healing. This study will look at a variety of active and passive drug administration approaches in wound care. Micro-Electro-Mechanical Systems (MEMS) based technology has been used to continuously monitors wound parameters and assures safe healing. The study's goal is to create a system that examines the temperature, pH, pressure, and moisture levels of a wound. The conveyance stage's strategy and capacity to adjust drug dispersion are also taken into consideration. The framework will remotely deliver the signal to a cell phone. Analysts use the Global System for Mobile Communications (GSM) module to create innovative connections between the device and other fringe devices. MEMS devices have been most suited for accurately applying these drugs since the emergence of Hydrogels. The disposal of this gel necessitates a precise instrument, as this medicine is costly to get and must not be thrown away. MEMS system applications can be utilized to create more modern gadgets that can aid the medical sector of life, such as a blood pressure sensor used in hospitals to monitor blood pressure. Compared to present machinery that requires disinfection and recalibration, this technology hardware can be easily disposed of and replacement hardware may be swiftly created. The research article will assist in identifying when a wound has healed and when it is in an unfavorable state. [ABSTRACT FROM AUTHOR]