International Journal of Innovative Technology and Interdisciplinary Sciences

A Market Competition Index for the Western Balkans

Ilir Ciko — 2025-11-02

This paper provides a data-driven evaluation of market competition across the six Western Balkan countries by comparing retail prices for identical consumer products sold by the same European operator in its European Union (EU) home market and the local markets in the region. After normalizing for Value Added Tax (VAT) differences, a Market Competition Index (MCI) is constructed to capture the average relative price deviation from EU benchmarks in Albania, Kosovo, Serbia, North Macedonia, Montenegro, and Bosnia and Herzegovina, as a reflection of the degree of market competition in the consumer goods retailing sector of these countries. The 2025 results reveal pronounced variations in competitive intensity across the Western Balkans: Albania tops the ranking with the largest average price deviation from EU benchmark (89 points), followed by Montenegro (60 points) and North Macedonia (46 points); Kosovo occupies a middle position (37 points), while Bosnia and Herzegovina (25 points) and Serbia (23 points) record the smallest deviations, indicating the strongest alignment with EU pricing and, by extension, the most competitive local markets. A complementary Burden Index, which adjusts each country’s Market Competition Index by its Purchasing Power Index (PPI) using 2023 Eurostat data, confirms that low-income markets face the greatest consumer strain. This dual-metric framework offers a precise monitoring tool for policymakers. As the Western Balkans progress toward EU accession, the MCI provides a clear benchmark for measuring reform progress, safeguarding consumer welfare, and supporting interventions to enhance market competition.

Spatiotemporal Assessment of Physicochemical Properties and Anthropogenic Impacts on Seawater Quality in the Gulf of Durrës, Albania

Milidin Bakalli — 2025-11-11

The coastal zone of Durrës, Albania, represents one of the most anthropogenically influenced marine areas along the eastern Adriatic Sea, where intensive tourism, industrial activities, and agricultural runoff exert increasing pressure on coastal ecosystems. This study presents a year-long assessment of seawater quality based on physicochemical parameters measured across seven sampling stations in the Gulf of Durrës from January to December 2024. In situ analyses were conducted using a Horiba “U-50” multiparameter analyzer to evaluate temperature, pH, oxidation–reduction potential (ORP), conductivity, turbidity, dissolved oxygen (DO), total dissolved solids (TDS), salinity, and specific gravity. Overall, the recorded parameters indicated stable water quality within acceptable ecological limits, with limited spatial and temporal variation among most sites. However, localized deviations in ORP and turbidity were observed near the Pista Koka, Fishing Port, and Old Port stations during the summer months, corresponding to periods of intensified human activity and wastewater discharge. These findings emphasize the influence of seasonal anthropogenic inputs on nearshore water quality and underscore the need for continuous monitoring and targeted management strategies to mitigate human-induced degradation and ensure sustainable marine and tourism development in the Durrës coastal region.

The Role of Educators in Promoting Social-Emotional Learning Across Albania’s K-12 Education System

Gezim Bara — 2025-11-13

Social-emotional learning (SEL) is a critical educational framework that stresses the development of skills necessary for emotional intelligence, interpersonal relationships, and decision-making. It fosters a holistic approach to education, addressing both cognitive and emotional aspects of learning. This study aims to explore how educators facilitate SEL in the K-12 framework, emphasizing their understanding and application of SEL competencies in the Albanian educational context. A random sample of 367 educators from various K-12 institutions across Albania were surveyed using structured questionnaires to assess their perceptions and practices regarding SEL. The evaluation of the data was done using inferential as well as descriptive statistics. Educators’ roles in promoting SEL involve creating supportive classroom environments, integrating SEL into the curriculum, and employing active teaching strategies. This includes training sessions, and collaborative learning experiences that empower educators to nurture students’ emotional and social competencies effectively. The analysis incorporates independent sample t-tests and ANOVA to compare results across different educational contexts and levels. Findings are expected to reveal significant correlations between educators’ SEL competencies and students’ emotional well-being, highlighting the significance of continuous professional growth in SEL. Strengthening the role of educators in SEL implementation is vital for fostering resilient in Albania’s K-12 education system.

Predicting Vitamin D Levels Using Ordinal Logistic Regression, Gaussian Process Regression and ARIMA: A Comparative Study

Edlira Lashi — 2025-11-18

Vitamin D deficiency is a common health condition that increases the risk of metabolic, cardiovascular, and musculoskeletal disorders. Many individuals are unaware of their vitamin D deficiency. In this work, we develop and present three complementary machine learning models to explore Vitamin D levels based on regular healthcare data. The dataset consists of anonymized patient records with demographic features, clinical indicators, and laboratory measurements of serum 25(OH)D. It is taken from a healthcare setting and pre-processed to eliminate absent or inconsistent results. Vitamin D level variables were transformed into ordered, clinical categories: severe deficiency, deficiency, insufficiency, and sufficiency. However, for regression and time-series forecasting, the original continuous concentration, measured in ng/mL, was preserved together with monthly averages. A proportional odds Ordinal Logistic Regression model was used to figure out Vitamin D status. The best overall performance was an accuracy of 0.77, a macro recall of 0.76, and an F2-score of 0.78. Most of the mistakes were made between categories that were next to each other. We utilized Gaussian Process Regression to predict continuous Vitamin D concentration. The results were R² = 0.79, MAE = 2.3 ng/mL, and RMSE = 3.4 ng/mL, which means that the model can get close to laboratory values with clinically acceptable error. To capture temporal dynamics, an ARIMA model was fitted to monthly mean Vitamin D levels and showed the best performance with R² = 0.82, MAE = 2.0 ng/mL and RMSE = 3.1 ng/mL, accurately recreating the observed seasonal pattern.

Multimodal Deep Learning for Disease Diagnosis and Risk Stratification: Integrating Genomic, Clinical, and Imaging Data

Tata Balaji — 2025-11-20

Personalized healthcare depends on the smart combination of heterogeneous biomedical information, including genomic sequences, clinical records, and medical imaging, so that it can be predictable with precision and interpretation. To accomplish this, the current study suggests a Hierarchy Attention Fusion based Multimodal Deep Learning (HAF-MDL) framework which improves the diagnostic accuracy and interpretability by intra- and inter-modality attention and Bayesian uncertainty measurement. In contrast to the conventional fusion methods, HAF-MDL learns the modality-relevant dynamically, avoiding uncertainty in heterogeneous patient data. To make the model clinical, it was trained and evaluated using a semi-synthetic dataset of 1,440 patient profiles in statistical agreement with real biomedical repositories TCGA (oncology), MIMIC-IV (clinical), and ADNI (neurology) to make the model clinically realistic. The Kolmogorov Smirnov (Ks) tests (p > 0.05) validation was also performed to ensure that the generated distributions were statistically consistent with real data in the world, which improved the reproducibility. The HAF-MDL framework proposed reached an accuracy of 94.8% and AUC of 0.964, which is higher than the unimodal and conventional fusion models. These results show that the suggested multimodal integration plan has great benefits in terms of the disease diagnosis and risk stratification and provides interpretability and reliability, generating a repeatable pathway to precision medicine.

Modelling Transient Hydraulic Hammer Behavior in Pumped Systems Using WHAMO Software

Gjelosh Vataj — 2025-11-22

This paper presents a mathematical model of the hydraulic hammer phenomenon. The model incorporates the momentum and continuity equations, the construction of the system layout, and the junctions within the pipeline network. Potential scenarios during system operation are analyzed using the Water Hammer and Mass Oscillation (WHAMO) software to simulate transient hydraulic behavior. In closed hydraulic systems, a hydraulic hammer occurs when the system transitions from a stable to an unstable state, causing the kinetic energy of the fluid to be rapidly converted into pressure energy. This results in a powerful pressure surge accompanied by a reverse flow wave. Such pressure fluctuations can lead to extremely low pressures, increasing the risk of contaminant intrusion through cracks or pipe damage. This phenomenon often accompanied by a hammer-like sound poses a significant challenge in drinking water treatment systems. Because the governing equations are nonlinear and hyperbolic, analytical solutions are not feasible; therefore, numerical modeling is required. The main goal of this study is to analyze pump behavior during transient conditions associated with the water hammer phenomenon.