Abstract The increasing deployment of photovoltaic (PV) systems has created a growing need for reliable, geographically representative datasets for performance assessment, degradation analysis, and power forecasting. However, the spatial organization of large PV monitoring archives is often not... The increasing deployment of photovoltaic (PV) systems has created a growing need for reliable, geographically representative datasets for performance assessment, degradation analysis, and power forecasting. However, the spatial organization of large PV monitoring archives is often not well understood, which can introduce sampling bias and limit the generalizability of predictive models. This study presents a spatial clustering analysis of the National Renewable Energy Laboratory (NREL) Photovoltaic Data Acquisition (PVDAQ) public dataset, examining the geographic structure of 1,862 PV systems distributed across the United States. Using latitude and longitude as clustering variables, three complementary unsupervised methods are applied: K-Means, DBSCAN, and hierarchical Ward clustering. Internal validation using silhouette scores indicates strong and consistent spatial structure. K-Means identifies 16 macro regions with a silhouette score of 0.701, hierarchical Ward clustering identifies 17 similar macro regions with a silhouette score of 0.697, and DBSCAN identifies 34 denser local clusters while classifying 5.16% of systems as spatial outliers. The resulting clusters correspond to recognizable deployment and climatic regions, with pronounced concentration in California and the Northeast corridor. The findings demonstrate that the PVDAQ network is not randomly distributed but exhibits a clear geographic architecture. These spatial strata provide a foundation for stratified sampling, regional performance analysis, outlier detection, and future spatially aware PV forecasting models, including deep learning and physics informed machine learning approaches. Read More Read Less

Abstract This study measures and ranks cross country dispersion and inequality in the allocation of compulsory instruction time across curriculum subjects at two levels of education. Drawing on the OECD Education at a Glance database (Instruction Time per... This study measures and ranks cross country dispersion and inequality in the allocation of compulsory instruction time across curriculum subjects at two levels of education. Drawing on the OECD Education at a Glance database (Instruction Time per Subject by Level of Education), the analysis covers primary education (ISCED 1; N = 347 observations) and lower secondary general education (ISCED 2/24; N = 395 observations) across OECD member and partner countries. Six complementary dispersion and inequality indicators standard deviation, interquartile range, coefficient of variation, Gini coefficient, median absolute deviation, and intercountry range are computed at both the aggregate and subject levels, and synthesised into a composite inequality score via equal weighted percentile ranking. The results reveal three principal findings. First, primary education exhibits substantially greater cross country heterogeneity than lower secondary education on every indicator examined. Second, the dispersion hierarchy is sharply bifurcated: flexible, optional, and vocationally oriented curriculum categories (e.g., Subjects with Flexible Timetable, Options Chosen by Students, Non-compulsory Curriculum) display the highest inequality, whereas core academic subjects Mathematics and Reading, Writing and Literature show the strongest international standardisation. Third, inter-country ranges approaching 100 percentage points for flexible categories reflect fundamentally divergent national philosophies of curriculum design. The findings are interpreted through the ex-ante equality-of-opportunity framework, which treats country of schooling and curriculum category as circumstances beyond individual control, thereby framing instruction time inequality as a structural determinant of educational opportunity. The study contributes a transparent, multi indicator methodology for monitoring curricular convergence and divergence across education systems. Read More Read Less

Abstract Digital transformation has elevated ICT skills to a foundational capability for economic participation and social inclusion, yet existing frameworks rarely examine the internal structure of digital competencies. This study addresses that gap by constructing an empirically grounded... Digital transformation has elevated ICT skills to a foundational capability for economic participation and social inclusion, yet existing frameworks rarely examine the internal structure of digital competencies. This study addresses that gap by constructing an empirically grounded four regime typology of ICT skills polarisation based on the relative prevalence of basic versus above basic communication and collaboration competencies across 49 countries. Drawing on the ITU ICT_SKLS_COM_CB indicator, the analysis integrates descriptive inequality metrics (Gini coefficient, median absolute deviation, interquartile range, and a composite inequality index), principal component analysis, and dual clustering algorithms (K-means and hierarchical Ward). Results reveal a pronounced structural polarisation: above basic skills are, on average, 4.63 times more prevalent than basic skills, with an absolute gap of approximately 53 percentage points. Principal component analysis confirms that this basic-versus-above-basic contrast constitutes the dominant axis of cross country variation, explaining 88.51% of total variance. The resulting four regime typology Low inequality (1 country), Moderate inequality (3 countries), High inequality (16 countries), and Very high inequality (29 countries) is robust across clustering algorithms (Adjusted Rand Index = 0.853) and multiple validation criteria. Critically, 91.8% of countries exhibit large positive gaps, indicating that structural polarisation rather than uniform skill scarcity is the prevailing global pattern. The typology offers policymakers a regime specific diagnostic tool and informs higher education curriculum design by demonstrating the inadequacy of average attainment metrics for capturing digital skills inequality. Read More Read Less