Volume 15, Issue 2, May-June 2024

Lessons Learned from Continuous Quality Improvement Training for 3rd Year Medical Students

Adam Mastrocola, Helen Wu, Biju Wang, Robin H. Pugh Yi, Bruce Gould

Resident Physician, Connecticut Children’s Hospital
Department of Psychiatry, Connecticut Convergence Institute for Translation in Regenerative Engineering at University of Connecticut Health Center
President of Akeso Consulting, LLC
Primary Care and Director of the Connecticut Area Health Education Program, University of Connecticut School of Medicine

Abstract- Purpose: The University of Connecticut School of Medicine developed a continuous quality improvement (CQI) curriculum for which third year medical students conducted independent CQI projects. The current study analyzes outcomes of and student’s reflections on projects conducted between 2005 and 2018. Results are intended to inform future design of medical student education about CQI. Materials and Methods: An analyst abstracted data from printed slides of students’ CQI Symposium poster presentations and conducted content analysis. Results: A total of 979 third year medical students conducted the CQI projects included in this study. Projects addressed key issues in clinical care quality and demonstrated understanding of using plan-do-study-act research. Conclusion: The current study provides an example of how a medical school responded to the call to restructure clinical education to prepare the workforce to measure care quality and work to improve quality continuously. Results show that medical students have the ability to conduct CQI projects with practical value in clinical settings in underserved communities. Students learned the importance of CQI and key skills for implementing CQI studies in clinical practice. Future training efforts should address these issues by ensuring preceptor skills, allotting more time for CQI training, and formally integrating CQI into medical education curricula.

Keywords- Continuous Quality Improvement, Plan-Do-Study-Act and Medical Students

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Comparative Analysis of Augmented Reality, Mixed Reality, and Virtual Reality for Controlling Appliances Based on Parameters

Areej Mazhar, Maria Arooj, Rimsha Arshad

Department of Computer Science, University of Engineering and Technology Lahore

Abstract- This paper offers a comparative assessment of home assistant, mixed reality, and augmented reality technologies for managing household appliances via a single control panel interface. The introduction of cutting-edge technology has become crucial for improving user comfort and experience with the rise of smart home systems. By superimposing digital content over the actual world, augmented reality allows users to interact with virtual aspects. Conversely, mixed reality allows for more immersive experiences by blending the virtual and actual worlds flawlessly. Home assistants that provide voice-activated control over different smart devices in a home include Google Assistant, Apple HomeKit, Amazon Alexa, and Apple HomeKit. In this study, we examine these technologies' usability, functionality, and user satisfaction with regard to home appliance control through a centralized control panel. We examine elements including responsiveness, accuracy, ease of setup, and compatibility with various appliance kinds. We also evaluate each technology's overall user experience, perceived ease, and user preferences. We seek to determine the benefits and drawbacks of AR, MR, and home assistant-based control panels through user research and tests. The comparison investigation yielded insights that can help manufacturers and developers create smart home control systems that are easier to use and more effective. In the end, our research opens the door for more smooth integration of technology into day-to-day living and advances human-computer interaction in the context of smart home environments. The study also looks at how each technology can affect user security and privacy, taking into account things like data collecting, sharing, and security precautions. Gaining an understanding of these elements is essential to guaranteeing user confidence and trust when implementing smart home technologies. In order to give useful information for consumers and industry stakeholders, we hope to address these concerns and offer a thorough assessment of the viability of AR, MR, and home assistant technologies for operating home appliances via a centralized control panel. In addition to user experience and interface design, data management, security, and privacy issues are all covered in detail in this study. Compatibility problems between various platforms and devices give rise to interoperability challenges, which can impede control panel systems' smooth operation and integration. Data protection, illegal access, and possible breaches are all covered under security and privacy concerns, which emphasizes the need for strong security measures to secure user data and uphold privacy rights.

Keywords- Augmented Reality, Mixed Reality, Virtual Reality and Home Assistant

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Intrusion Detection in IoT Under Various Machine Learning Models

Arfa Farooqi, M. Junaid Arshad

UET Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan

Abstract- Internet of Things refers to the network of physical objects embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the internet. These objects can range from simple household items like refrigerators and thermostats to complex industrial machinery. IoT allows for greater automation, control, and data analysis in various domains, including home automation, healthcare, transportation, agriculture, and manufacturing, bringing in addition to many benefits, challenges related to security issues. Intrusion Detection Systems (IDS) have been an important tool for the protection of networks and information systems. Many machine learning models have been used to enhance its performance and accuracy. In this paper, we present a survey of IDS research efforts under machine learning models for IoT. Our objective is to identify issues in previous models and review leading trends. We classified the IDSs proposed in the literature according to the following attributes: machine learning models, datasets and accuracy.

Keywords- Internet of Things, Intrusion Detection System, Machine Learning, Datasets, Transformers and Accuracy

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Exploring Emerging Applications and Future Trends in IoT Ecosystems

Minahil Tayyab, Umna Khalid

Department of Computer Science, University of Engineering and Technolog, Lahore-Pakistan

Abstract- The Internet of Things has reformed the business with an interconnected environment comprised of brilliant gadgets and sensors. Though generally known utilization of IoT has gained attention, the statement will analyze the unexplored areas, a novel approach, exceptional method for the present and arising trends in the situation of IoT. The paper briefly investigates rare execution of IoT across multiple areas from agriculture to fashion, tourism to art, catastrophe regulation, and more. Using case research, it illustrates the commercial organism regulation for these extraordinary applications of IoT, where they have been succeeded, troubled areas, and possible. Furthermore, the paper views the peculiarities for the perspective of the startling in the IoT environment, like edge computing, AI revolution, blockchain integration, environmentalism, and human-machine interface. By discovery more about such unknown parts of IoT, the paper explores human development. It gives the way for next- gen innovation, changing markets, and boosting public health.

Keywords- Internet of Things, Blockchain, Edge Computing, RFID and Human-Machine Interaction

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