322 research outputs found

    A Phenomenological Study: Factors Influencing Faculty Attitude Toward Online Teaching During the COVID-19 Pandemic

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    The purpose of this phenomenological study was to discover the factors that influenced the faculty’s lived experiences and perceived preparedness when transitioning to fully online courses in response to the COVID-19 crisis, for full-time faculty members at Greenhill College, North Branch. The central research question for the research was “What factors, such as professional development and other training, related to online learning, influenced faculty attitudes and perceptions of preparedness, as they transitioned to online teaching during the COVID-19 Pandemic 2020?” Ten participants were selected using a random sample drawn from full-time faculty members at Greenhill College, North Branch. Data collection included interviews, focus group interviews and document and artifact examination. The transformative learning theory guided the research into the adult learning process, as the faculty members in the study were adults. This research provides educational studies with a baseline for understanding the factors that impact the development of faculty attitude, especially during a crisis, which in turn can help faculty prepare for such a transition. The results of the study suggested that faculty shared a lack of confidence in online education and their ability to effectively teach students in this environment

    Application of knowledge management principles to support maintenance strategies in healthcare organisations

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    Healthcare is a vital service that touches people's lives on a daily basis by providing treatment and resolving patients' health problems through the staff. Human lives are ultimately dependent on the skilled hands of the staff and those who manage the infrastructure that supports the daily operations of the service, making it a compelling reason for a dedicated research study. However, the UK healthcare sector is undergoing rapid changes, driven by rising costs, technological advancements, changing patient expectations, and increasing pressure to deliver sustainable healthcare. With the global rise in healthcare challenges, the need for sustainable healthcare delivery has become imperative. Sustainable healthcare delivery requires the integration of various practices that enhance the efficiency and effectiveness of healthcare infrastructural assets. One critical area that requires attention is the management of healthcare facilities. Healthcare facilitiesis considered one of the core elements in the delivery of effective healthcare services, as shortcomings in the provision of facilities management (FM) services in hospitals may have much more drastic negative effects than in any other general forms of buildings. An essential element in healthcare FM is linked to the relationship between action and knowledge. With a full sense of understanding of infrastructural assets, it is possible to improve, manage and make buildings suitable to the needs of users and to ensure the functionality of the structure and processes. The premise of FM is that an organisation's effectiveness and efficiency are linked to the physical environment in which it operates and that improving the environment can result in direct benefits in operational performance. The goal of healthcare FM is to support the achievement of organisational mission and goals by designing and managing space and infrastructural assets in the best combination of suitability, efficiency, and cost. In operational terms, performance refers to how well a building contributes to fulfilling its intended functions. Therefore, comprehensive deployment of efficient FM approaches is essential for ensuring quality healthcare provision while positively impacting overall patient experiences. In this regard, incorporating knowledge management (KM) principles into hospitals' FM processes contributes significantly to ensuring sustainable healthcare provision and enhancement of patient experiences. Organisations implementing KM principles are better positioned to navigate the constantly evolving business ecosystem easily. Furthermore, KM is vital in processes and service improvement, strategic decision-making, and organisational adaptation and renewal. In this regard, KM principles can be applied to improve hospital FM, thereby ensuring sustainable healthcare delivery. Knowledge management assumes that organisations that manage their organisational and individual knowledge more effectively will be able to cope more successfully with the challenges of the new business ecosystem. There is also the argument that KM plays a crucial role in improving processes and services, strategic decision-making, and adapting and renewing an organisation. The goal of KM is to aid action – providing "a knowledge pull" rather than the information overload most people experience in healthcare FM. Other motivations for seeking better KM in healthcare FM include patient safety, evidence-based care, and cost efficiency as the dominant drivers. The most evidence exists for the success of such approaches at knowledge bottlenecks, such as infection prevention and control, working safely, compliances, automated systems and reminders, and recall based on best practices. The ability to cultivate, nurture and maximise knowledge at multiple levels and in multiple contexts is one of the most significant challenges for those responsible for KM. However, despite the potential benefits, applying KM principles in hospital facilities is still limited. There is a lack of understanding of how KM can be effectively applied in this context, and few studies have explored the potential challenges and opportunities associated with implementing KM principles in hospitals facilities for sustainable healthcare delivery. This study explores applying KM principles to support maintenance strategies in healthcare organisations. The study also explores the challenges and opportunities, for healthcare organisations and FM practitioners, in operationalising a framework which draws the interconnectedness between healthcare. The study begins by defining healthcare FM and its importance in the healthcare industry. It then discusses the concept of KM and the different types of knowledge that are relevant in the healthcare FM sector. The study also examines the challenges that healthcare FM face in managing knowledge and how the application of KM principles can help to overcome these challenges. The study then explores the different KM strategies that can be applied in healthcare FM. The KM benefits include improved patient outcomes, reduced costs, increased efficiency, and enhanced collaboration among healthcare professionals. Additionally, issues like creating a culture of innovation, technology, and benchmarking are considered. In addition, a framework that integrates the essential concepts of KM in healthcare FM will be presented and discussed. The field of KM is introduced as a complex adaptive system with numerous possibilities and challenges. In this context, and in consideration of healthcare FM, five objectives have been formulated to achieve the research aim. As part of the research, a number of objectives will be evaluated, including appraising the concept of KM and how knowledge is created, stored, transferred, and utilised in healthcare FM, evaluating the impact of organisational structure on job satisfaction as well as exploring how cultural differences impact knowledge sharing and performance in healthcare FM organisations. This study uses a combination of qualitative methods, such as meetings, observations, document analysis (internal and external), and semi-structured interviews, to discover the subjective experiences of healthcare FM employees and to understand the phenomenon within a real-world context and attitudes of healthcare FM as the data collection method, using open questions to allow probing where appropriate and facilitating KM development in the delivery and practice of healthcare FM. The study describes the research methodology using the theoretical concept of the "research onion". The qualitative research was conducted in the NHS acute and non-acute hospitals in Northwest England. Findings from the research study revealed that while the concept of KM has grown significantly in recent years, KM in healthcare FM has received little or no attention. The target population was fifty (five FM directors, five academics, five industry experts, ten managers, ten supervisors, five team leaders and ten operatives). These seven groups were purposively selected as the target population because they play a crucial role in KM enhancement in healthcare FM. Face-to-face interviews were conducted with all participants based on their pre-determined availability. Out of the 50-target population, only 25 were successfully interviewed to the point of saturation. Data collected from the interview were coded and analysed using NVivo to identify themes and patterns related to KM in healthcare FM. The study is divided into eight major sections. First, it discusses literature findings regarding healthcare FM and KM, including underlying trends in FM, KM in general, and KM in healthcare FM. Second, the research establishes the study's methodology, introducing the five research objectives, questions and hypothesis. The chapter introduces the literature on methodology elements, including philosophical views and inquiry strategies. The interview and data analysis look at the feedback from the interviews. Lastly, a conclusion and recommendation summarise the research objectives and suggest further research. Overall, this study highlights the importance of KM in healthcare FM and provides insights for healthcare FM directors, managers, supervisors, academia, researchers and operatives on effectively leveraging knowledge to improve patient care and organisational effectiveness

    A Generic Approach for the Automated Notarization of Cloud Configurations Using Blockchain-Based Trust.

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    Debido a su escalabilidad, las aplicaciones en la nube tienen una importante ventaja de costes para las empresas. En consecuencia, las empresas quieren tanto externalizar sus datos como obtener servicios de la nube. Sin embargo, dado que la mayoría de las empresas tienen políticas internas y requisitos de cumplimiento para operar y utilizar aplicaciones de software, el uso de aplicaciones en la nube crea un nuevo desafío para las empresas. La inclusión de aplicaciones en la nube equivale a la subcontratación de servicios en el sentido de que las empresas deben confiar en que el proveedor de aplicaciones en la nube aplicará los requisitos de cumplimiento interno en las aplicaciones adoptadas. La investigación ha demostrado que la confianza y el riesgo están estrechamente relacionados y son factores clave que influyen en la utilización de aplicaciones en la nube. Esta tesis pretende desarrollar una arquitectura en la nube que aborde este reto, trasladando la confianza en las configuraciones de cumplimiento del proveedor de aplicaciones en la nube a la cadena de bloques. Así, este trabajo pretende reducir el riesgo de adopción de las aplicaciones en la nube debido a los requisitos de cumplimiento. En esta tesis, la investigación de la ciencia del diseño se utiliza para crear la arquitectura para trasladar la confianza mencionada a la cadena de bloques. Un grupo de discusión determinó el alcance del trabajo. La base de conocimientos de este trabajo se construyó utilizando inteligencia artificial y una revisión sistemática de la literatura, y la arquitectura presentada se desarrolló y prototipó utilizando el método de desarrollo rápido de aplicaciones. Se utilizaron entrevistas guiadas semiestructuradas de método mixto para evaluar el enfoque de la arquitectura presentada y valorar las cualidades de reducción del riesgo de adopción. La tesis demostró que la arquitectura de software desarrollada podía trasladar la confianza del proveedor de la nube a la cadena de bloques. La evaluación de la arquitectura de software propuesta demostró además que el riesgo de adopción debido a las configuraciones de la nube basadas en el cumplimiento podía reducirse de "alto" a "bajo" utilizando la tecnología blockchain. Esta tesis presenta una arquitectura que desplaza la confianza para la implementación de configuraciones basadas en el cumplimiento de la normativa desde el proveedor de la nube a la cadena de bloques. Además, muestra que el cambio de confianza puede reducir significativamente el riesgo de adopción de las aplicaciones en la nube.Administración y Dirección de Empresa

    Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding

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    Artificial intelligence’s (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AI’s conceptual and promissory settings tell about the current rebranding of AI? The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AI’s contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AI’s shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise

    The Ethics of Kink

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    This thesis argues that the consent narrative that is used to justify sadomasochistic violence is flawed. Liberals and liberal feminists often use valid consent as a justification for the violence and humiliation inflicted during many sadomasochistic sexual encounters, appealing to ideas of sexual autonomy and sexual freedom to defend those who inflict violence on willing partners. This thesis rejects the consent defence making two arguments: First, that the conditions which need be met for consent to be valid are frequently not met in instances of sadomasochistic violence. Second, that even when consent meets the conditions for validity this does not justify sexually motivated violence as consent does not have the normative power required to justify the infliction of harm. In a final chapter I illustrate the position through a comparison of sadomasochistic violence and domestic violence. Arguing that just as the consent narrative has no place in domestic violence, regardless of how the victim of domestic abuse feels about their circumstances, it should not be regarded as central to the debate about whether sadomasochistic violence is ethical

    What makes an International Financial Centre (IFC) Competitive? An empirical study of the determinants responsible for the competitiveness of an IFC

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    International Financial Centres (IFCs) serve as focal points for implementing international agreements and other transactions between financial sectors located around the world. The competitiveness of an IFC depends on its function to provide easy access to the capital, stability in financial markets and a dynamic business eco-system. The purpose of conducting this study is to identify the most relevant determinants that significantly affect the Global Financial Centres Index (GFCI) ranking of the countries across the world. First published in 2007, the GFCI is considered as the primary source for ranking IFCs globally. GFCI is an index which ranks financial centres based on over 61,499 assessments of financial centres across the world provided by 10,252 respondents to an online questionnaire of GFCI (GFCI33, 2023). The collected date represents 153 key indices provided by sources including the World Bank, the Organisation of Economic Cooperation and Development, and the Economist Intelligence Unit. It utilises qualitative (online questionnaires) and quantitative (economic indices) dataset to publish reports biannually. Through this paper, an attempt has been made to conduct an empirical study of the determinants responsible for the competitiveness of an IFC based on GFCI ranking. To facilitate this study, extensive data has been collected for 196 IFCs (unique financial jurisdictions) along with 238 key factors (determinants) over a period of fourteen years (2007 till 2020). In addition to revisiting some of the existing empirical studies on this subject, this dissertation attempts to further build on the existing empirical research and analyses the impact of unique key factors on the GFCI ranking through the application of a panel regression. From extensive set of variables, the study adopts 17 most relevant determinants (summarised below) by using a Decision Tree approach. The variable of Business Regulations is constructed by using the Ease of doing business index source from the World Bank (GFCI 33). The variable of corporate taxes is constructed by the sum of tax bases and tax rates dataset source from KPMG (GFCI 33). Indexed sourced from Transparency International is used to construct the variable of Corruption Perception Index (GFCI 33). The variable of Credit Market Regulations is constructed by international consortium group by measuring the deposit based financing source from World Bank (GFCI 33). Government size, Property Rights and the Legal System, Reliable Money, Freedom to Trade Internationally Regulation, and Gender equality in legal rights are five broad categories used to construct Economic Freedom Overall Index Variable source from Fraser Institute (GFCI 33). The study adopts the variable of freedom of trade which is sourced from WTO constructed upon non-tariff barrier in exports and imports of a country (GFCI 33). The variable of Global Competitiveness Index is constructed by the macroeconomic and the micro/business aspects of competitiveness into a single index (GFCI 33). The data on volume of high tech exports is modelled and calculated as a function of foreign demand and of price competitiveness in order to construct variable of High Tech Exports source. The variable of inflation is constructed by using Consumer Price Index (CPI). The variable of Internet uses as a percentage of population is derived by dividing the number of Internet users by total population and multiplying by 100. The variable of Labour Market Regulations is constructed through using of the Rigidity of Employment Index. The variable of Legal System Property Rights is constructed by encompassing index of Legal verification and guarantee systems, fair legal rules, and formal compensation mechanism. The variable of quality of roads is constructed through collecting data on the transportation infrastructure and financial spending by using (IRI) International Roughness Index (GFCI 33). Spending, revenue, and employment are all ways to construct the variable of size of a government. An aggregate of money growth (money supply growth minus real GDP growth), standard deviation of inflation (GDP deflator), CPI inflation in most recent year, and freedom to hold foreign currency in bank accounts are used to construct the variable of the sound money index. The index is measured on a scale of 0 (worst) to 10 (best). The variable of percentage of Urban Population is constructed by Individuals living in urban areas as a percentage of total population. A long and solid life, being educated and have a respectable way of life are the three indicators to construct the variable of HDI. The results of the Panel regression show that all the variables positively impact the GFCI ranking except business regulations, labour market regulation, legal system property rights and HDI. This dissertation also establishes to arrange the IFCs in groups (Clusters) based on similar shared characteristics. This has been possible by adopting criteria of developing a centroid for each cluster against each determinant for a number of observations (Years). As a result, each cluster includes all those countries that are experiencing similar characteristics throughout the range of observations (years). By introducing the Elbow method of clustering, the study has identified five optimal groups (clusters). In order to deal with complexities of missing values in the dataset and arranging the IFCs in these five optimal groups based upon a centroid (mean) value, this study has undergone an appropriate clustering methodology using the Majorisation-Minimisation Algorithm named as K-POD means clustering. It is evident that each centroid is seen as representing the average observation within a cluster across all the variables in the analysis. All the observations in a cluster ranging between maxima and minima centrifuge around centroid value. The distances between cluster centroids show how far apart the centroids of the clusters in the final partition are from one another. The study suggests that by minimising the hurdles created by business regulation laws, labour market regulation procedures and legalised process of property rights, the GFCI ranking will improve for the countries. It will help to pave the path of financial stability and creation of wealth. Similarly, by providing better health and education facilities, the Human development Index will help positively to improve the GFCI ranking of countries

    Real Time Crime Prediction Using Social Media

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    There is no doubt that crime is on the increase and has a detrimental influence on a nation's economy despite several attempts of studies on crime prediction to minimise crime rates. Historically, data mining techniques for crime prediction models often rely on historical information and its mostly country specific. In fact, only a few of the earlier studies on crime prediction follow standard data mining procedure. Hence, considering the current worldwide crime trend in which criminals routinely publish their criminal intent on social media and ask others to see and/or engage in different crimes, an alternative, and more dynamic strategy is needed. The goal of this research is to improve the performance of crime prediction models. Thus, this thesis explores the potential of using information on social media (Twitter) for crime prediction in combination with historical crime data. It also figures out, using data mining techniques, the most relevant feature engineering needed for United Kingdom dataset which could improve crime prediction model performance. Additionally, this study presents a function that could be used by every state in the United Kingdom for data cleansing, pre-processing and feature engineering. A shinny App was also use to display the tweets sentiment trends to prevent crime in near-real time.Exploratory analysis is essential for revealing the necessary data pre-processing and feature engineering needed prior to feeding the data into the machine learning model for efficient result. Based on earlier documented studies available, this is the first research to do a full exploratory analysis of historical British crime statistics using stop and search historical dataset. Also, based on the findings from the exploratory study, an algorithm was created to clean the data, and prepare it for further analysis and model creation. This is an enormous success because it provides a perfect dataset for future research, particularly for non-experts to utilise in constructing models to forecast crime or conducting investigations in around 32 police districts of the United Kingdom.Moreover, this study is the first study to present a complete collection of geo-spatial parameters for training a crime prediction model by combining demographic data from the same source in the United Kingdom with hourly sentiment polarity that was not restricted to Twitter keyword search. Six unique base models that were frequently mentioned in the previous literature was selected and used to train stop-and-search historical crime dataset and evaluated on test data and finally validated with dataset from London and Kent crime datasets.Two different datasets were created from twitter and historical data (historical crime data with twitter sentiment score and historical data without twitter sentiment score). Six of the most prevalent machine learning classifiers (Random Forest, Decision Tree, K-nearest model, support vector machine, neural network and naïve bayes) were trained and tested on these datasets. Additionally, hyperparameters of each of the six models developed were tweaked using random grid search. Voting classifiers and logistic regression stacked ensemble of different models were also trained and tested on the same datasets to enhance the individual model performance.In addition, two combinations of stack ensembles of multiple models were constructed to enhance and choose the most suitable models for crime prediction, and based on their performance, the appropriate prediction model for the UK dataset would be selected. In terms of how the research may be interpreted, it differs from most earlier studies that employed Twitter data in that several methodologies were used to show how each attribute contributed to the construction of the model, and the findings were discussed and interpreted in the context of the study. Further, a shiny app visualisation tool was designed to display the tweets’ sentiment score, the text, the users’ screen name, and the tweets’ vicinity which allows the investigation of any criminal actions in near-real time. The evaluation of the models revealed that Random Forest, Decision Tree, and K nearest neighbour outperformed other models. However, decision trees and Random Forests perform better consistently when evaluated on test data

    Book of abstracts and proceedings. 44th international conference of the stress, trauma, anxiety, and resilience society (STAR)

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    Stress and anxiety situations are increasing problems in recent years. In fact, anxiety disorders take first place in the ranking of global psychopathology, followed by depression. Many of these situations are traumatic, and it is essential to develop resilience to make people and communities more resilient to prevent these situations. Several factors have contributed to this, including the high demand in the professional activity, the breakneck pace people have to live and make decisions, the high competitiveness, the lack of social support, the lack of control in many situations, the uncertainty and the insecurity. Recently, it has also highlighted the economic crisis and unemployment, the healthy problems, namely because of the Covid pandemic, and the war in ukraine, as major factors for stress, anxiety and trauma that many people have. It is increasingly important that people know how to handle these situations, through their coping strategies, and resilience skills.info:eu-repo/semantics/publishedVersio

    A Multi-Stakeholder Information Model to Drive Process Connectivity In Smart Buildings

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    Smart buildings utilise IoT technology to provide stakeholders with efficient, comfortable, and secure experiences. However, previous studies have primarily focused on the technical aspects of it and how it can address specific stakeholder requirements. This study adopts socio-technical theory principles to propose a model that addresses stakeholders' needs by considering the interrelationship between social and technical subsystems. A systematic literature review and thematic analysis of 43 IoT conceptual frameworks for smart building studies informed the design of a comprehensive conceptual model and IoT framework for smart buildings. The study's findings suggest that addressing stakeholder requirements is essential for developing an information model in smart buildings. A multi-stakeholder information model integrating multiple stakeholders' perspectives enhances information sharing and improves process connectivity between various systems and subsystems. The socio-technical systems framework emphasises the importance of considering technical and social aspects while integrating smart building systems for seamless operation and effectiveness. The study's findings have significant implications for enhancing stakeholders' experience and improving operational efficiency in commercial buildings. The insights from the study can inform smart building systems design to consider all stakeholder requirements holistically, promoting process connectivity in smart buildings. The literature analysis contributed to developing a comprehensive IoT framework, addressing the need for holistic thinking when proposing IoT frameworks for smart buildings by considering different stakeholders in the building
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