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Development of Trucking Services In Nigeria: Problems and Perspectives

This study delves into the current state of the Nigerian trucking services, aiming to improve efficiency and identify potential areas for growth. We employ a multi-pronged research approach utilizing both quantitative and qualitative data. First, a comprehensive literature review gathers existing knowledge. Second, secondary data analysis examines the current trucking landscape, including its connections with other transportation modes. Third, stakeholder interviews capture diverse perspectives from truck owners, drivers, manufacturers, and policymakers. Fourth, a public survey gauges public opinion and industry potential. Fifth, in-depth case studies compare and analyze three distinct trucking companies with unique business models with a SWOT analysis to evaluate each company considering their economic value contribution. Finally, the research utilizes a network model to optimize logistics and efficiency. Results are presented visually and discussed in detail. Recommendations for improvement are formulated based on the findings. The study concludes by acknowledging its strengths and limitations.

Author: Olusola Adepoju Ademola

Supervisor: Berdymyrat Ovezmyradov

Degree: Master

Year: 2024

Work Language: English

Improvement of Logistics Sector in Uzbekistan Through Integration of Smart Technologies

The primary aim of this research paper is to comprehensively evaluate the integration of smart technologies in Uzbekistan's logistics sector. For achieving the research aim the author used a mixed method combining both qualitative and quantitative. As for the primary data, the author of the research used a survey among professionals in logistics and potential users to understand their awareness, perceived benefits, and concerns regarding smart technology integration in Uzbekistan. At the end of the research based on the results the author provides conclusions and recommendations on the integration of smart technologies in Uzbekistan's logistics sector

Author: Otabek Usmanov

Supervisor: Berdymyrat Ovezmyradov

Degree: Master

Year: 2024

Work Language: English

INFORMATION SYSTEM FRAMEWORK FOR EU SUSTAINABILITY REPORTING

ABSTRACTMaster's thesis „Information system framework for sustainability reporting”. Author of the paper: Sanita Bringmane. Scientific supervisor: Professor. Dr. Habil.sc.ing. Igors Kabaskins.The purpose of the Master thesis is to provide a framework for a sustainability reporting system that helps entities manage the ESG reporting system development process and help organizations navigate the complexity of ESG reporting. The Master thesis consists of four parts, the first part includes a summary of the requirements of the ESG standards and detailed information on the report and the main three pillars of sustainability: Environment, Social, and Governance reporting information and structure. A brief overview of the reporting information systems follows and reporting system use case, sustainability ecosystem, framework, and reporting system architecture. There are road map for the implementation of a sustainability reporting system included and at the end of the work a brief case study review of sustainability reporting for a company, and a conclusion as a final part of the work.

Author: Sanita Bringmane

Supervisor: Igors Kabaškins

Degree: Master

Year: 2024

Work Language: English

Personal Information Management using Adaptive Information Systems

Nowadays people struggle to handle their personal information in an organized and efficient manner. In average individual consumes around 74Gb per day (Heim, 2017), which contributes to to information overload and limits the amount of information that the human mind can handle.Due to human uniqueness, all people have different information preferences. Adaptive Information Systems (AIS) is a relatively new direction of research on the crossroads of Information Science, Human-Computer Interaction and Artificial Intelligence. It is an alternative to the traditional "one-size-fits-all" approach in the development of Information Systems. Considering that Information overload is a user-personal problem, AIS allow involve an adaption process inside the information system activate reinforcement learning for building knowledge about the user’s behavior in information processing. As a next step collected knowledge AIS is used for performing self-adaptive changes inside the system with the final goal of reducing information overload.This study proposed a new way of handling and control information overload by using AIS, which is supposed to consider and adapt to user's personal experience in personal information processing.

Author: Sergejs Paškovskis

Supervisor: Boriss Mišņevs

Degree: Master

Year: 2024

Work Language: English

Enhancement Strategies for Retrieval-Augmented Generation Systems

This thesis systematically explores the enhancement of Retrieval-Augmented Generation (RAG) systems within Large Language Models, emphasizing optimization of retrieval parameters and generation accuracy. We investigate optimal configurations in RAG systems, including chunk size and overlap percentages, top-k selection, query transformations, different retrieval methods, different LLMs, namely GPT-3.5-Turbo and GPT-4, discovering that a chunk size of 500 tokens generally offers the best performance. Vector search using cosine similarity emerges as the most effective retrieval method, significantly enhancing both context precision and recall across various tasks and knowledge bases. Experimentation within the CRUD-RAG framework demonstrates its applicability in diverse tasks from content creation to knowledge refinement. Our findings indicate that enhancements in retrieval settings can markedly improve the performance of RAG systems, making them more efficient and adaptable for complex information synthesis and retrieval tasks. These results affirm the potential of systematic enhancements to improve AI-driven language models in practical applications, contributing significant insights and practical approaches to the evolving landscape of RAG system research.

Author: Sigita Lapiņa

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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