TULTECH Journals
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Podcasts as Tools for Communication and Lifelong Learning in School Education
No full textAs the adoption of information and communication technology (ICT) tools in education continues to expand, podcasts have emerged as a versatile and cost-effective medium for enhancing communication, teaching, and learning across different educational levels. However, the effective integration of podcasts in early childhood, primary, and secondary education remains under-researched. To address this gap, this paper employs a systematic review methodology to examine the effectiveness of podcasts in relation to communication skills, student engagement, and the development of lifelong learning competencies. A total of 16 peer-reviewed studies were analysed following a comprehensive literature search. The findings indicate that podcast-based learning contributes to improved listening and speaking skills, enhanced teamwork, stronger critical thinking abilities, and increased information literacy. The evidence supports podcasts as a viable alternative to traditional instructional methods, offering flexible and inclusive learning opportunities. This paper provides evidence-based recommendations for policymakers, educational institutions, and educators to guide the effective integration of podcast media into learning environments, with the aim of fostering communication competencies and promoting lifelong learning from an early age
Enhancing Voltage Profile and Power Factor in SLD Distribution Networks via Radial and Cyclic Configurations with Solar Integration
No full textThis study explores the enhancement of voltage stability and power factor in single-line diagram (SLD) networks by integrating solar energy within radial and cyclic systems. As electricity demand rises, effective demand-side management (DSM) becomes essential for optimizing energy consumption and improving grid reliability. The research highlights the challenges posed by voltage fluctuations and power factor imbalances when incorporating solar energy into existing power distribution networks. Utilizing load flow analysis via Electrical Transient Analyzer Program (ETAP), the study evaluates power losses, voltage profiles, and power factor variations across three scenarios: (1) Base Radial Network, (2) Optimized Radial System with photovoltaic (PV) and Capacitors, and (3) Cyclic System with PV and Capacitors, aiming to implement corrective measures for improved power quality. The methodology involves a comprehensive analysis of a 500kW grid-connected solar PV system, including calculations for energy generation, inverter selection, and land area requirements. The findings indicate significant improvements in voltage regulation (from 0.89 p.u. to 0.96 p.u.) and power factor (from 0.85 lagging to 0.98 lagging) through strategic modifications to distribution transformer ratings and the addition of capacitor banks. Crucially, a sensitivity analysis, varying solar irradiance by ±20%, confirms the robustness of the proposed Cyclic System against environmental fluctuations, yielding a system Voltage Deviation Index (VDI) improvement of 48% under the worst-case cloudy scenario. Simulation results demonstrate that the integration of solar energy not only mitigates overloading issues but also enhances overall system performance, reducing power losses and operational costs. The study concludes that the proposed DSM framework, leveraging real-time solar energy forecasting and adaptive techniques, offers a scalable solution for modern electrical networks, ensuring sustainable and reliable power distribution
Tracking Topic Drift in AI and Workforce Narratives on Reddit: A Longitudinal Visualization from 2010–2024
No full textThis article extends previous research on Reddit-based discourse of artificial intelligence (AI) and workforce automation by adding a longitudinal analysis of thematic evolution. With a sample of 4,243 Reddit posts between 2010 and 2024, we examine how attention has evolved over time between top AI-related topics of work, reskilling, regulation, and use cases such as ChatGPT. We apply Latent Dirichlet Allocation (LDA) topic modelling for the research and use advanced visualization tools like word clouds, heatmaps, and time-series plots to estimate topic drift. Our findings indicate that while worries over job loss and ethical governance persist, discussions over certifications and upskilling have augmented steadily. Above all, interest in generative AI started to rise steeply after 2022, indicating a remarkable change in people\u27s opinions. These results represent the development of societal problems and technological awareness over time. This research proved helpful by using the prevalence of topics for policymakers, educators, and industry players who need to understand the changed public dis-course in the role of AI within the labour market. In this respect, it points out the increasing necessity for adaptive skills strategies and evidence-based communication
e-UROZONE: A DSGE-Based Model for a New Financial Architecture
No full textThis study introduces e-UROZONE, a novel decentralized financial architecture for the euro area, modeled within a New-Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework augmented by an AI-based risk layer. In this system, credit intermediation occurs directly between lenders and borrowers, while the European Central Bank (ECB) maintains stability through bounded interest-rate rules and a digital liquidity backstop. The model extends the canonical financial-accelerator DSGE by embedding rule-based policy corridors, liquidity constraints, and an adaptive AI component to capture endogenous risk propagation. Model calibration and validation are performed using Euro-area data, including ECB MFI interest rates, Eurostat GDP, and ECB SAFE series, within a Dynare-based simulation and replication environment. Monte-Carlo experiments (N = 10,000) are conducted under baseline, liquidity, inflation, and financial-volatility shocks, yielding unit-specific statistics, 95% confidence intervals, and impulse-response analyses. Results demonstrate that, relative to a traditional bank-centric baseline, the e-UROZONE architecture enables faster credit reallocation and lower interest-rate volatility while preserving ECB control through parameterized policy bounds. The framework also introduces a CBDC-DSGE benchmark for qualitative comparison. Overall, the paper contributes not merely a new calibration but a market-design paradigm for the euro area, where monetary and financial stability are jointly achieved through a decentralized, rules-based mechanism. This design expands the central bank’s toolkit by introducing previously unavailable policy instruments corridor width, haircut schedule, and backstop intensity—thus offering a scalable foundation for future digital monetary systems
A Latent Dirichlet Allocation Framework to Analyse and Forecast Employability Skills
Full text linkRecently globalization, technological advancement, reorganization of many job positions, have substantially influenced and profoundly transformed the nature and attributes of the labour market in both industrialized and emerging nations. Data pertaining to the online labour market has become a valuable resource for comprehending the dynamics and trends of the job market. Policymakers in Albania must benefit from machine learning algorithms to analyse and forecast skill needs. This paper presents a Latent Dirichlet Allocation (LDA) framework, as a probabilistic topic-modelling technique employed to find latent topics within a dataset of job vacancies. The analysis employs a two-step LDA model. The initial step is the Latent Dirichlet Allocation Model, which detects latent semantic components. The second step of the model maps semantic components calculated by the LDA algorithm to map job vacancies from the topics identified from LDA model into each category of skills. A Shiny app has been developed to forecast the top 10 skills and interests needed, categorized by skill type. The methodology and findings of this study, of this paper are expected to support Albania’s public employment institutions to perform a screening of the current soft skills and interests and predict future skills required by companies operating in Albania.
 
A Fusion-Based Machine Learning Framework for Lung Cancer Survival Prediction Using Clinical and Lifestyle Data
Full text linkLung cancer is one of the deadliest diseases worldwide, highlighting the criticality of precise survival prediction models. This work proposes an exhaustive fusion-based machine learning approach for lung cancer survival prediction using heterogeneous features such as clinical indicators, demographic information, and lifestyle factors. A publicly available dataset of more than 800,000 records was pre-processed, statistically analysed, and dimensionally reduced for computational tractability. Feature-level fusion was used to merge multivariate features, after which decision-level fusion was implemented through soft voting ensembles. Five fusion configurations using Logistic Regression, Random Forest, Support Vector Machine, k-Nearest Neighbours, and Naive Bayes classifiers were evaluated. It was noted that the simpler combinations like Logistic Regression and Random Forest worked better than larger ensembles, with accuracy of 70% and AUC of 0.61 after class balancing. Correlation and statistical analysis also showed weak linear relationships with survival, underscoring the need for non-linear modelling strategies. Every fusion model was assessed with ROC curves and confusion matrices, providing an overall view of prediction strength. The study demonstrates that fusion techniques can significantly improve survival prediction in lung cancer patients and can be the foundation for actual clinical decision support systems
Mapping Ground Displacement Near the Former Dhrovjani Salt Mine Using Differential Radar Interferometry
No full textRemote sensing and Geographic Information Systems (GIS) are essential tools for estimating and monitoring land subsidence. Over the past fifteen years, various applications of deformation analysis using Differential SAR Interferometry (DInSAR) have been developed. This study employs the two-pass interferometric method to assess ground deformation near a closed rock salt mine in southern Albania. By analyzing two radar images captured at different times from the same satellite position, we measure phase differences to create an interferogram. This interferogram illustrates phase shifts that can be converted into a ground deformation map, indicating surface changes in the radar line-of-sight (LOS) direction. The methodology, termed differential interferometry, allows for detecting relative surface deformation with high precision. The interferometric processing workflow, consisting of core registration, interferogram generation, phase unwrapping (executed using SNAPHU), and displacement mapping, showcases the method’s effectiveness in capturing and analyzing surface movement. Accurate interpretation at each phase is vital for reliable results
The Garden City as an Urban Paradigm for a Sustainable Economic Model: The Case of Albania in the Fourth Post-Communist Decade
No full textThis paper examines the role of remittances in shaping regional development in Albania by integrating institutional, financial, and spatial factors within the Digital Garden City (DGC) framework. Using a panel dataset of 12 prefectures for the period 2010–2024, the study applies fixed-effects (FE), spatial Durbin (SDM), and dynamic Generalized Method of Moments (GMM) models to assess whether remittance inflows enhance regional productivity or contribute to economic dependence. The findings indicate that remittances, in isolation, exert a negative and statistically significant effect on regional GDP per capita, reflecting their predominantly consumption-oriented use. However, interaction effects with institutional quality, financial intermediation, and spatial diversification are positive and significant, suggesting that remittances foster growth only in contexts characterized by strong governance and balanced territorial structures. Spatial estimates further reveal positive spillover effects across adjacent regions, underscoring the importance of connectivity and polycentric planning. Overall, the results empirically support the DGC framework as a viable territorial and policy model for transforming migration-driven financial flows into productive, inclusive, and digitally enabled regional development. The study offers recommendations on advancing micro–Digital Garden Cities, strengthening transparent digital governance, and designing financial instruments that link remittances to sustainable regional investment
The Level of AI Application in University STEM Study Programs: A Comprehensive Review
No full textArtificial intelligence (AI) is reshaping engineering, science, and technology education, yet little is known about how AI is embedded in Science Technology Engineering Mathematics (STEM) curricula in late-adopter higher education systems. This paper examines the extent and depth of AI integration in accredited STEM study programs in Albania. Using a two-step approach, we first describe the national landscape of 390 STEM programs and then conduct a detailed curriculum analysis of 35 programs from eight public and private higher education institutions. For each program, we code the presence of AI-related modules, the volume of AI-related to European Credit Transfer and Accumulation System (ECTS), the number of AI courses, and the integration level (introductory, applied, or advanced). We then estimate multilevel regression models to explore how institutional characteristics (public vs. private status, international partnerships, language of instruction) and disciplinary profiles shape the probability and intensity of AI integration. The findings show that around 60% of the analysed programs include AI-related content, with higher intensity and deeper integration in computer science and engineering compared to other STEM fields. Private institutions and English-taught programs display a systematically higher likelihood of AI integration than public institutions. At the same time, explicit AI ethics components remain limited, and AI-related learning outcomes are rarely embedded across non-Information and Communication Technology (ICT) STEM disciplines. The paper discusses these results in relation to European policy frameworks and outlines concrete implications for curriculum reform, quality assurance, and capacity building in Western Balkan higher education
K-Means Clustering for Evolutionary Staging in a Human Evolution Dataset
No full textThis research work applies unsupervised machine learning to explore evolutionary patterns in hominin morphological and temporal data. A dataset comprising 6,000 records of hominin specimens was analysed using three quantitative attributes: geological age (1–8 million years), cranial capacity, and estimated stature. Following data cleaning and z-score normalization, K-means clustering (K = 4) was employed to identify coherent evolutionary groupings without prior taxonomic labelling. The resulting clusters exhibit a clear temporal and morphological progression. The earliest cluster (mean age ≈ 6.66 Ma) is characterized by the smallest cranial capacity (≈156 cm³) and stature (≈106 cm), consistent with early hominin forms. A second cluster (≈3.89 Ma, 367 cm³, 117 cm) corresponds to Australopithecine-like specimens, while a transitional cluster (≈1.96 Ma, 490 cm³, 119 cm) reflects early Homo characteristics. The most recent cluster (≈1.07 Ma) displays substantially larger cranial capacities and statures (≈1063 cm³ and ≈162 cm), aligning with later or near-modern Homo. Visualization through scatter plots, bar charts, and boxplots supports a monotonic increase in cranial capacity and height across evolutionary stages. These findings demonstrate that unsupervised clustering can recover biologically meaningful evolutionary patterns from morphological and temporal data, highlighting its potential as an exploratory tool in paleoanthropological research