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Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90% of vulnerabilities emerge from medical error and human error. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices, and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate medical errors while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring criteria to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native microservices for authentication, encryption, logging, and authorizations. Second, a bond trust scoring system is created to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model that generates the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent in the creation of the scoring system. The advantage of this method is that it applies to any language and adapts to all attributes, as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F 1 score of 93.5%, which proves that it is valid for detecting medical errors. [ABSTRACT FROM AUTHOR]

This paper presents a systematic student-level approach to assess the impact of problem-based learning on enhancing students' performance in an undergraduate Digital Logic Design course. The problem was assigned as a mini-project and chosen to strengthen the student's understanding of an advanced part of the course, namely the sequential logic design. The study included data from semesters where students were assigned projects and other semesters without projects. The proposed approach relies on dividing students into groups based on their academic performance level to study how the project impacts these groups' performance. A baseline performance metric was created at the beginning of the semester to classify the students into three groups. At the end of the semester, the students' groups were reassessed to capture performance changes. The results consistently showed improvement across all students' levels in the semesters where the project was conducted. While it is natural for students' performance to change between the beginning and the end of the semester, the results show that the percentage of students who improved their performance level has increased in semesters with a project compared to the semesters with no projects. Also, the percentage of students whose performance degraded by the end of the semester has decreased in the semesters with a project. These results were also supported by an independent student survey that confirmed the positive project impact on the students' grasp of sequential circuit design. [ABSTRACT FROM AUTHOR]