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The Impact of the War in Ukraine on Conscription Policies in Western Balkan Countries
No full textThis research paper examines the impact of Russia’s 2022 invasion of Ukraine on the transformation of conscription policies in Western Balkan countries. Several Western Balkan governments have considered reinstating military service in response to escalating tensions in the region. The study analyzes the geopolitical pressures stemming from the conflict and assesses how they have prompted these nations to rethink their defense strategies through the lens of conscription. By exploring the causal relationship between heightened regional insecurity and policy shifts, this paper provides insights into the broader implications of the Ukraine war on military readiness and national security in the Western Balkans
Multicriteria analysis for identification of potential urban green spaces in Gjakova (Kosova)
No full textGreen spaces are essential for maintaining the quality of life. Due to rapid urbanization, they are rapidly disappearing in Gjakova. This study aims to automate the process of identifying the suitable areas for future urban green spaces in Gjakova city, considering multiple criteria, including: topographic, environmental and socio-economic. The study has integrated multi-criteria spatial analysis in Geographic Information System (GIS), in order to evaluate the suitability of the site selection of urban green spaces. It combines a number of factors in both data models (vector and raster), such as land use/land cover, elevation, slope, existing green areas, proximity to road, water bodys and public services. The adopted methodology helps to improve the methodological framework of green space selection in Gjakova. The study emphasizes how crucial strategic planning is to urban growth in order to promote resident’s quality of life. The findings can help guide policy choices and urban decision-making
Applied cryptography with python
No full textCryptography is a key aspect of information security and provides data security. This paper aims to provide a better understanding of cryptography and its application with Python through real life examples. It covers the basics of cryptography, containing information about symmetric and asymmetric encryption methods. Throughout the paper we dive into different encryption methods, from simple to more complex, starting with the Caesar Cipher that has been used by people ever since ancient times, the Reverse Cipher which is one of the simplest encryption methods, all the way to implementing a RSA Algorithm using Python’s cryptography library, always providing knowledge over each of their pros and cons. We’re going to tackle the advanced encryption methods using the cryp-tography library, specifically the “Fernet” class. This paper showcases the real-world applications of cryptography in modern systems like secure communication between devices and hashing techniques for password encryption, high-lighting the importance of cryptography in protecting sensitive information
Emotional Resonance of Space: Exploring the Role of Geometric Order in Design
No full textThis paper aims to present how geometric order influences emotional experience in architectural space. Grounded in a phenomenological framework, the study explores the relationship between human perception and the implicit geometrical logic embedded in spatial compositions. Through analysis of original architectural and interior visualization projects, the research examines how proportion, alignment, and spatial hierarchy can evoke psychological responses such as calm, elevation, or introspection. The methodology combines visual-spatial analysis with experiential reflection, emphasizing the subtle ways geometry mediates meaning beyond formal aesthetics. Special attention is given to how spatial coherence, guided by geometric clarity, contributes to the sense of atmosphere and presence. The expected outcome is a deeper understanding of how intentional geometric structuring enhances spatial resonance, offering valuable insights into design practices that prioritize not only function, but also perceptual depth, emotional impact, and human-centered experience
Design Guidelines for Epoxy-Modified Recycled Asphalt Pavement (RAP) Mixtures: A Performance-Based Approach
No full textRecycling reclaimed asphalt pavement (RAP) is widely practiced, yet the reuse of thermosetting epoxy asphalt has not been adequately explored. This study presents design guidelines for incorporating reclaimed epoxy asphalt (epoxy RAP) and diluted epoxy RAP into hot mix asphalt (HMA) mixtures. The study investigates blending behavior between virgin and aged epoxy binders, evaluates total binder content, and proposes a modified mix design protocol for both undiluted and diluted epoxy RAP mixtures. Results show that appropriate mixing and compaction procedures can yield mixtures with acceptable Marshall stability, air voids, and volumetric properties. This work offers practical insights for sustainable pavement construction using epoxy RAP materials
Modern Trends Towards the Dimensions and Driving Factors of Smart Cities
No full textThe rapid growth in industrial, economic, and construction activities, combined with global population expansion, has led to the development of suburban and underdeveloped regions, underscoring the need for sustainable and innovative urban planning. This study explores modern trends in the dimensions and driving factors of smart cities through a systematic review of recent literature. The study identifies eight core dimensions including economy, inhabitants, lifestyle, environment, governance, transportation, urban services, and construction. A SWOT analysis is employed to highlight strengths, weaknesses, opportunities, and threats influencing smart city development. Findings underscore the importance of innovative data management, collaborative planning among stakeholders, and the integration of smart systems to achieve sustainable urban growth. The study also emphasizes the need for tailored rating systems to evaluate smart city initiatives in different contexts. The results provide practical insights for policymakers to develop inclusive, efficient, and resilient urban environments
Implementation of an AI-Based Legal Assistant
No full textThis paper presents the development of a prototype AI-powered legal assistant aimed at improving the efficiency and quality of legal practice in Kosovo. The system integrates and processes public legal documents, including the Official Gazette, court rulings, and other official justice-related sources. Leveraging large language models (LLMs), RetrievalAugmented Generation (RAG), and complementary techniques, the assistant provides direct support for legal professionals by enabling faster and more accurate access to updated legal information. In addition, the prototype generates personalized legal texts—such as contracts or other legal forms—adapted to individual lawyers’ styles. By allowing practitioners to upload reference documents, the system can produce outputs aligned with their preferences, reducing time spent on drafting and enhancing productivity. The proposed solution represents a significant step toward the digitalization and automation of legal processes in Kosovo, contributing to a more efficient, transparent, and accessible justice system
Sustainable Cloud Computing: AI-Enhanced Models for EnergyEfficient Data Centers
No full textCloud computing has revolutionized digital infrastructure by providing scalable, ondemand computing resources. However, the exponential growth of data centers has introduced significant environmental and energy challenges, making sustainability a central concern in modern computing. Data centers currently consume an estimated 1–2% of global electricity, and this percentage continues to rise. As a result, optimizing energy consumption while maintaining performance has become a key research and industrial priority.This study explores AI-enhanced models for sustainable cloud computing, focusing on how machine learning and predictive analytics can minimize energy usage and carbon emissions in large-scale data centers. The research investigates the integration of artificial intelligence into resource scheduling, workload prediction, and dynamic power management. By leveraging techniques such as reinforcement learning, neural networks, and intelligent auto-scaling, data centers can adapt resource allocation in real-time based on workload patterns and environmental conditions.The proposed framework aims to establish a balance between energy efficiency, computational performance, and cost optimization in cloud infrastructures. Case studies from major cloud providers, such as AWS, Azure, and Google Cloud, are analyzed to evaluate current sustainability practices and identify potential improvements enabled by AI Expected Results:The study is expected to demonstrate that AI-based optimization techniques can significantly reduce energy consumption—by up to 30% according to existing models— without compromising system reliability or throughput. The findings will contribute to a framework for designing energy-aware, self-adaptive, and environmentally sustainable cloud data centers
Plant Classification in Ios: Integration of Machine Learning Models Through Core Ml
No full textThis paper addresses the integration of pre-trained Machine Learning models into iOS applications through the use of Core ML. The primary objective was the development of an application for plant classification by employing an existing model trained on the PlantCLEF 2024 dataset. Initially, an analysis was conducted on available models and the challenges encountered during the conversion of a PyTorch (.pth.tar) model into the .mlpackage format suitable for iOS. The process involved input normalization, model tracing, the addition of a Softmax layer, and the configuration of necessary parameters to preserve the accuracy of the converted model. Once the model was integrated into the application, testing was carried out using real-world images and data from PlantCLEF, with results compared to those obtained from the original PyTorch model. The findings demonstrated satisfactory alignment in classification, although the iOS application exhibited a lower confidence level compared to the source model. This indicates that while conversion does not significantly affect classification accuracy, it may influence the model’s confidence output. Through this project, in-depth insights were gained into the practical challenges of working with ML models and adapting them for mobile deployment
Endodontic Access Cavity – Clinical guidelines
No full textDifferent endodontic access cavity designs have been proposed in the past decade to access the root canal space in a minimally invasive manner, does the endodontic access cavity influence the strength and resistance of the tooth? Can this approach compromise other treatment-related aspects? Which is the proper endodontic access cavity? What are the clinical guidelines of the endodontic access cavity, this will be the topic of discussion in this presentation