Advancing Chronic Respiratory Disease Care with Real-Time Vital Sign Prediction

Cardiovascular and chronic respiratory diseases, being pervasive in nature, pose formidable challenges to the overall well-being of the global populace. With an alarming annual mortality rate of approximately 19 million individuals across the globe, these diseases have emerged as significant public health concerns warranting immediate attention and comprehensive understanding. The mitigation of this elevated mortality rate can be achieved through the application of cutting-edge technological innovations within the realm of medical science, which possess the capacity to enable the perpetual surveillance of various physiological indicators, including but not limited to blood pressure, cholesterol levels, and blood glucose concentrations. The forward-thinking implications of these pivotal physiological or vital sign parameters not only facilitate prompt intervention from medical professionals and carers, but also empower patients to effectively navigate their health status through the receipt of pertinent periodic notifications and guidance from healthcare practitioners. In this research endeavour, we present a novel framework that leverages the power of machine learning algorithms to forecast and categorise forthcoming values of pertinent physiological indicators in the context of cardiovascular and chronic respiratory ailments. Drawing upon prognostications of prospective values, the envisaged framework possesses the capacity to effectively categorise the health condition of individuals, thereby alerting both caretakers and medical professionals. In the present study, a machine-learning-driven prediction and classification framework has been employed, wherein a genuine dataset comprising vital signs has been utilised. In order to anticipate the forthcoming 1-3 minutes of vital sign values, a series of regression techniques, namely linear regression and polynomial regression of degrees 2, 3, and 4, have been subjected to rigorous examination and evaluation. In the realm of caregiving, a concise 60-second prognostication is employed to enable the expeditious provision of emergency medical aid. Additionally, a more comprehensive 3-minute prognostication of vital signs is utilised for the same purpose. The patient's overall health is evaluated based on the anticipated vital signs values through the utilisation of three machine learning classifiers, namely Support Vector Machine (SVM), Decision Tree and Random Forest. The findings of our study indicate that the implementation of a Decision Tree algorithm exhibits a high level of accuracy in accurately categorising a patient's health status by leveraging anomalous values of vital signs. This approach demonstrates its potential in facilitating prompt and effective medical interventions, thereby enhancing the overall quality of care provided to patients

ERP and system inegration na Qimonda Portugal S. A.

Estágio realizado na Qimonda Portugal, S. A. e orientado pelo Eng.º Nuno FelinoTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Promoting Energy-Conservation Behavior in a Smart Home App: Kano Analysis of User Satisfaction with Feedback Nudges

Smart home technologies and apps are on a rise. This allows to implement digital nudging elements to foster energy-conservation behavior and, thus, contribute to mitigating climate change. Digital nudging via feedback can be effective in improving energy-conservation behavior, as substantial prior research has shown. However, the investigation of users’ preferences concerning feedback nudges is missing. This lack of knowledge is crucial, as user satisfaction influences their continuous app usage, a precondition for achieving positive effects. To close this gap, we perform a structured literature review, categorize the feedback nudge features from extant research, and conduct an online survey. Based on survey data and the Kano model, we analyze the effect of feedback nudge features on user satisfaction. Our study complements the traditional focus on the effectiveness of these nudges with a perspective on user satisfaction. The combination of both perspectives suggests which feedback nudge features should be considered for implementation

Maturity model of incident management

Over the last 25 years the concern of most organizations with Information Technology (IT) has been clearly exponential. In order to plan, organize, select, support and deliver IT services, it was necessary to implement IT frameworks. These frameworks are a set of the best practices to implement on IT service management. These frameworks are included in several processes of different areas of IT. This research work is focused very concretely on the Incident Management process. Once the organizations technical consulting services are available 24/7, an implementation of this process is crucial. Many of the IT frameworks contain similar processes. Many organizations, when trying to apply more than one framework, end up doing redundant work. Therefore, eliminating overlaps of activities becomes very useful for any process of IT frameworks. In this way, the process becomes simpler and less expensive for the organization. Given the need of the organizations evaluate the maturity of their incident management process and different organizations and different IT frameworks, was born the need for this research. This thesis proposes a Maturity Model for the incident management process that covers the main and most used IT frameworks.Nos últimos 25 anos a preocupação da generalidade das empresas com as Tecnologias da Informação (TI) é claramente exponencial. De tal forma que, para conseguirem organizar, planear, selecionar, suportar e entregar os serviços de TI, foi necessário implementar frameworks de TI. Estas frameworks são um conjunto de boas práticas a implementar para gestão de serviços de TI. Nestas frameworks estão incluídos vários processos das diferentes áreas das TI. Este trabalho de investigação focou-se muito concretamente no processo de Gestão de Incidências. Sendo que a operacionalidade da generalidade dos serviços requerer disponibilidade de quase 24/7, a implementação deste processo é fulcral. Muitas das frameworks de TI contem processos similares. Muitas organizações, quando tentam aplicar mais que uma framework acabam por fazer trabalho redundante. Assim sendo, a eliminação de sobreposições das atividades torna-se bastante útil para qualquer processo das frameworks de IT. Desta forma o processo torna-se mais simples e menos dispendioso para a organização. Dada a necessidade das organizações de avaliarem a maturidade do seu processo de gestão de incidências e que diferentes organizações têm diferentes frameworks de TI, nasceu a necessidade deste trabalho de investigação. Esta tese propõe um Modelo de Maturidade para o processo de gestão de incidências que abranja as principais e mais utilizadas frameworks de TI

EFL pedagogy students' self-directed learning: use of edmodo and e-portfolio

Tesis (Pedagogía en Inglés)This study aims at exploring how Edmodo and E-Portfolio affect EFL Pedagogy Students’ Self-Directed Learning (SDL). The theory is framed within the concepts of Self-Directed Learning and online platforms. This mixed type of research (qualitative and quantitative) considered 3 different instruments to collect data: questionnaires (PRO-SDLS), interviews, and a focus group. The participants were 26 freshmen in an English Pedagogy program. From the data obtained and analyzed, the study showed that there is a statistically significant difference in the participants’ SDL between the pre and posttest. At the same time, students perceived that E-Portfolio is more useful than Edmodo in their learning process.Este estudio tiene como objetivo explorar cómo Edmodo e E-Portfolio afectan en el aprendizaje autodirigido en estudiantes que aspiran a ser profesores de EFL. La teoría se enmarca en los conceptos de aprendizaje autodirigido y plataformas en línea. Este tipo de investigación mixta (cualitativa y cuantitativa) consideró tres instrumentos diferentes para recopilar datos: cuestionarios (PRO-SDLS), entrevistas y un grupo focal. Los participantes fueron 26 estudiantes de primer año en un programa de Pedagogía en Inglés. A partir de los datos obtenidos y analizados, el estudio mostró que existe una diferencia estadísticamente significativa en el SDL (siglas en Inglés del Aprendizaje Autodirigido) de los participantes entre el primer cuestionario y el segundo. Al mismo tiempo, los estudiantes percibieron que E-Portfolio es más útil que Edmodo en su proceso de aprendizaje

Development and application of computer software techniques to human factors task data handling problems Final report, 21 Jun. 1965 - 21 Jun. 1966

Computer software techniques applied to human factors task data handling problem

Finding Forensic Evidence In the Operating System\u27s Graphical User Interface

A branch of cyber security known as memory forensics focuses on extracting meaningful evidence from system memory. This analysis is often referred to as volatile memory analysis, and is generally performed on memory captures acquired from target systems. Inside of a memory capture is the complete state of a system under investigation, including the contents of currently running as well as previously executed applications. Analysis of this data can reveal a significant amount of activity that occurred on a system since the last reboot. For this research, the Windows operating system is targeted. In particular, the graphical user interface component that includes the taskbar, start menu and notification system will be examined for possible forensic artifacts. The techniques presented in this research are valuable to a forensic investigator trying to find evidence. They are also useful for penetration testers trying to determine if a tool has left any evidence behind for investigators to find. The research described in this thesis led to development of a scanning technique that served as the basis for a Volatility plugin that automates finding GUI related artifacts. To support this research, a lab consisting of three virtual machines (VM) was created using VMware. Two Windows 10 virtual machines were created for generating artifacts and one Linux was created for scanning the Windows machines. These machines were connected to a live router briefly for gathering network information. This these explores the strengths and limitations of this searching discovered during research. Lastly, future applications of this research are covered