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The Mind of the Moratorium: A psychosocial exploration of the concept of self in Irish male adolescents
No full textThe reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man(Shaw, 1903). Erikson (1968, p. 143) discusses George Bernard Shaw granting himself a psychosocial moratorium, a time, he describes between childhood and adulthood for identity and role experimentation. A time to find a defined section of society uniquely made for him. Erikson refers to self as evolving. The self evolves as an individual negotiates and resolves psychosocial crises and integrates life experiences and relationships into a sense of who they are. In his theory of psychosocial development, Erikson discusses the fifth stage as Identity v Role Confusion where he links the concept of self and identity during adolescence. He believed the social and psychological outcome of adolescence is critical for forming a coherent identity based on an evolving sense of self. Shaw suggests that the courage to resist conformity, to question and be unreasonable creates change. For Erikson, the unreasonable individual who actively explores and experiments with roles and beliefs, not just discovers identity but creates it through resistance, reflection and action. During his exploration, Shaw was drawn to the Socialist revival of the 1880s and eventually settled down to study and write extraordinary work. This study aims to explore the concept of self in Irish male adolescents today. The study is rooted in theories of development from Erikson and the discussion is supported by contemporary research and writings. The researcher employed a qualitative approach within the context of the psychotherapeutic relationship and interviewed seven psychotherapists working with male adolescents in Ireland. The qualitative data gathered was analysed using Thematic Analysis. From the perspective of psychotherapeutic theory, three major themes were identified and discussed: 1. Role exploration; 2. The therapeutic relationship, and 3. Messages to leaders. The study concluded with recommendations for further exploration
A study of the Albanian Car Rental Industry in the Impact of Digital Marketing Strategies to Generate Customer Purchase
No full textThe subject of this thesis is to look at how digital marketing strategies are changing the way customer acquisition is approached within the Albanian car rental industry, which is directly related to the growing tourism market in Albania. A qualitative approach has been applied with an interpretivist philosophical base, alongside semi-structured interviews of owners and managers of small/medium car-rental businesses. The analysis of data was conducted having in mind the six step framework of Braun and Clarke (2006). One core theme with four sub-themes was identified: dependence on online travel agents (OTAs), lack of digital presence and marketing expertise, consumer trust, methods of payment, as well as resource and skills constraints. The results show that transparency regarding visibility as well as limited profitability and brand building capabilities are characteristic of OTAs and that both low-trust levels and digital knowledge gaps are holding back any direct online channel adoption. Based on the Technology Acceptance Model (TAM) and the AIDA, the study aims to promote the prototyping of a professional, search engine optimized, multilingual web sites that can help overcome the dependency on OTA, develop consumer trust and increase the competitiveness of SMEs
The science of cohabitation: A study on roommate compatibility using machine learning
No full textThis thesis investigates the combination of psychological analysis and advanced data techniques to improve predictions of roommate compatibility. It emphasizes the importance of skilled data management and a deep understanding of psychology in creating effective predictive models. By integrating the Big Five personality traits—Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism—with machine learning methods such as Cosine Similarity and K-Means clustering, this research introduces a novel approach for evaluating compatibility in shared living spaces.
The study employs a rigorous methodology that blends quantitative analysis with psychological insights, supported by a comprehensive dataset from Bustudymate’s student community and supplemented with secondary data from Openpsychometrics.org. This approach strengthens the foundation for the models used in predicting roommate compatibility.
Findings reveal that the use of multi-dimensional data analysis and advanced algorithms significantly improves the accuracy of compatibility predictions, surpassing traditional matching methods. This thesis not only advances the application of machine learning in assessing social compatibility but also highlights its potential to make roommate matching a more objective and data-driven process.
The contributions of this research extend across the fields of machine learning and social sciences, underscoring the critical role of quality data and accurate psychological assessment in developing effective predictive tools. The practical applications of this work are broad, providing improved resources for housing authorities and online platforms to ensure more harmonious communal living situations
Optimizing project efficiency: impact of cloud computing solution in modern construction project management
No full textThe study is intended to determine the effect of introducing cloud computing platforms on the efficiency of current construction project management. Working with construction engineers and using SPSS statistics, this study gets the desired results of better interactions, collaboration in work, a scalability, flexibility, workflow optimization, and payroll cuts. The fact that cloud technology allows for faster sharing of data, improved resource allocation, and streamlined implementation of projects helps in the project execution and with reduced costs. The data we collected support the Thought Acceptance Model (TAM), the core of which lies in both perceived usefulness and ease of usage. Even though the limitations of the small sample size and the regional perspective were identified, this study recommends the future of research needs to go wider encompassing the cloud computing family of technologies and compare it with emerging technologies as well. The study ends by providing suggestions to industrial players so that they integrate cloud solutions to get better from the projects they conduct
The impact of remote working environments and virtual teamwork in the manufacturing industry
No full textThe dissertation aims to investigate the challenges associated with virtual cooperation in the industrial sector, such as maintaining effective interactions, ensuring data security, and overcoming any barriers to synchronization and teamwork. The research adopts a quantitative research design where primary data has been collected through a survey.
The survey includes around 17 questions that are answered by industry professionals who are part of remote teams. The collected data has been analysed using IBM SPSS and the findings are presented graphically.
The findings of the study indicate that there are various challenges faced by remote team members of the manufacturing industry. The communication barrier is found to be the major problem for the members of the remote team. Moreover, they also face challenges related to maintaining team cohesion and time zone differences.
There are also issues related to the lack of access to necessary resources for the team members. Moreover, technical issues also can lead to problems for the team members. Virtual team meetings are conducted mostly weekly as per the findings of the survey.
To assess the performance of the remote team members both qualitative and quantitative methods are adopted. Various measures are being taken by companies to ensure data security in the remote teams
Web-Based GUI for SSH Server Management (VTERM-X)
No full textThis project presents a web-based application designed to streamline the management of virtual machines (VMs) and SSH configurations through an intuitive user interface. Leveraging modern web technologies, including JavaScript and Microsoft.AspNetCore.Mvc, the application offers seamless CRUD (Create, Read, Update, Delete) operations, enabling users to efficiently add, view, edit, and remove VM connections without the need for page reloads. The frontend utilizes dynamic content loading and real-time updates to enhance user experience, while the backend ensures robust data management and security. This integration of client-side interactivity with server-side reliability exemplifies the project's innovative approach to simplifying VM and SSH configuration management
in a web environment
Risk Assessment of Cardiovascular Diseases Utilizing Individual Lifestyle Factors with Machine Learning Techniques
No full textThe role of project risk management data in accurately budgeting Irish healthcare projects
No full textThe aim of this dissertation is to investigate the Role of Project Risk Management Data in Accurately Budgeting Irish Healthcare Projects examines how project risk management data impacts budgeting accuracy within the Irish healthcare sector. This study objectives to identify and prioritize key risk factors affecting Irish healthcare project budgets, propose strategies to mitigate these risks and enhance budget accuracy, ensuring effective budget management in healthcare projects. The methodology used is qualitative approach, involved a pre-survey of 11 participants and in-depth interviews with 5 selected individuals, the interviews analyzed using NVivo software focusing on thematic analysis. Relevant results reveal that 90.9% of participants consider risk management data crucial for effective budgeting, supporting the hypotheses that scope changes and strong governance significantly affect budget performance. The study concludes that incorporating advanced analytics and comprehensive risk management practices is essential for optimizing budget accuracy and mitigating cost overruns in Irish healthcare projects
From Ancient Rome to Modern Security: Evolving the Caesar Cipher for Today's Threat Landscape
No full textIn today’s world our privacy is constantly under threat. We are faced with threats from bad actors both of whom could steal confidential information or modify or delete it. This means that cryptography is more important than ever .Cryptography encodes messages which make them undecipherable to anyone but the reader.(Salmi and Siagian, 2022,p99.
Aim The aim of this dissertation is to explore the evolution of the Caesar cipher, analyse its vulnerabilities in the context of modern cybersecurity threats, and propose enhancements or adaptations that make it more resilient in today's threat landscape. The method involved comparing and contrasting different algorithms to the original Caesar Cipher which involved the setting up of a suitable artefact for their implementation in pycharm. The Robustness of each algorithm was also tested in pycharm with brute forcing and known plaintext attacks as well as Cryptanalysis tools such as Crank and Cryptool2. The results showed that out of the four algorithms proposed to solve the weakness of the Caesar Cipher the Combined Caesar was the strongest while the Caesar Cipher expanded with number and the Caesar Cipher with Euler totient proved to be weak and lacking in security
Empowering Communication: The Evolution and Potential of Speech Recognition System
No full textThis project explores the classification of emotions in speech data using neural network models. The project aims to build robust models to correctly identify emotions such as happiness, sadness, anger, fear, and disgust from audio clips. The process involves data collection, cleaning up, and feature extraction using Mel-frequency Cepstral Coefficients (MFCCs) data, creating, and assessing three unique neural network structures. The first model, a sequence structure with dense layers, is used to compare the performances of the subsequent models. The second model, which uses an upgraded dataset, performed better. The third model had mixed results when adding an LSTM layer, compared to models only using dense layers. The results underline the importance of varied data in improving emotion identification accuracy from speech data. This research brings essential knowledge to the field of neural network-based emotion classification. It sets the foundation for valuable applications in monitoring areas, including mental health and interactions between humans and computers