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Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air–oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli's equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) enabled Intelligent Transportation System (ITS) allows smart vehicles to communicate with other vehicles on road, humans (customers or pedestrians), infrastructure (parking areas, traffic lights etc), Internet, Cloud etc. The vehicles communicate with other entities over wireless open channels directly or indirectly through messages or beacons. Open channel allows various attacks, like replay, man-in-the-middle, impersonation, fabrication etc., during communication. Also, malicious vehicles can be deployed in the network to misuse or have an unauthorized access to the services. To mitigate these issues, we propose a new remote access control scheme that ensures the secure communication among the vehicles. The vehicles are dynamic in nature in an IoV paradigm, that is, they are not under fixed domains. Therefore, whenever a vehicle changes its location it has to register to the nearest trusted authority () in offline or secured channel mode. To make it applicable, we propose remote registration of the vehicles via the. Access control mechanism occurs in two phases: 1) node authentication phase, where vehicles are remotely authenticated by and 2) key agreement phase, where after successful mutual authentication they compute a session key by using cryptographic techniques and pre-loaded information. The computed secret session keys are used for ensuring secure communications in future between two vehicles in a cluster as well. Informal security analysis along with formal security verification using the broadly-used Automated Validation of Internet Security Protocols and Applications (AVISPA) show that our access control scheme is secured against various potential attacks. We also show the competency of our scheme by comparing it with other existing schemes in terms of computation and communication costs. [ABSTRACT FROM AUTHOR]

Cyber threat hunting proactively searches for cyber threats, which are undetected by the traditional defense mechanisms. It scans deep to identify malicious programs (ie, malware) that escape from detection. It is important because sophisticated cyber threats can bypass the cyber security mechanisms. The performance of the cyber threat hunting can be improved through artificial intelligence (AI), especially, explainable AI (XAI), which adds trust component to the cyber threat hunting process. Due to the inclusion of XAI, the security experts get the full explanations of the detected threats as the working of the detection model in XAI is known. Information, like, which one is a threat, how it has been detected, and why it has been detected, can be obtained very easily due to the inclusion of XAI in the cyber threat hunting. Therefore, an XAI‐envisioned mechanism for cyber threat hunting has been proposed (in short, XAISM‐CTH). The network and threat models of XAISM‐CTH are designed and discussed. The conducted security analysis proves the security of XAISM‐CTH against various potential attacks. XAISM‐CTH also performs better than the other existing schemes. At the end, a practical implementation of XAISM‐CTH has been provided to observe its impact on the performance of the system. [ABSTRACT FROM AUTHOR]