Tomasz Stachurski

Research background: Traditional socio-economic indicators, such as the Gini coefficient, may suffer from significant data lags and fail to capture the long-term dynamics of capital accumulation and wealth-based inequality. Furthermore, real estate forecasting predominantly focuses on predicting price levels rather than price dispersion, typically relying on ex post accuracy assessments that cannot account for unexpected future market disruptions. Purpose of the article: This study introduces a novel methodological framework to predict wealth-based inequality by leveraging real estate price dispersion as a timely proxy for economic welfare. The primary novelty of the approach lies in: (a) the construction of a Regional Inequality and Volatility (RIV) vector, which simultaneously tracks relative stratification and absolute financial barriers; (b) the development of a parametric bootstrap procedure for ex ante prediction assessment; (c) the implementation of stochastic, unexpected shock simulations (green policy, regulatory, macroeconomic, and climate events) to evaluate model robustness beyond historical data. Methods: The framework is applied to a longitudinal dataset of U.S. prefabricated housing transactions (2015–2023) using log-linear Mixed Models and plug-in predictors. This segment is chosen for its critical role in affordable homeownership. Findings & value added: The analysis reveals a significant structural shift toward ‘wealth exclusion’, where surging absolute costs create an economic blockade for lower-income households even when relative inequality appears stable. A critical implication is the identification of 'blind spots' in official metrics. For instance, the study uncovers a 'paradox of prosperity' where economic growth may reduce income-based poverty while intensifying wealth-based exclusion due to rising price volatility. These results suggest that a comprehensive policy framework must monitor both income and asset-based indicators to fully address socioeconomic polarization and the broken ladder of wealth accumulation.

Core‐shell gallium nitride (GaN)‐based nanowires offer noteworthy opportunities for innovation in high‐frequency opto‐ and microelectronics. This work delves deeply into the physical properties of crystalline GaN nanowires with aluminum and hafnium oxide shells. Particular attention is paid to partial coverage of nanowires, resulting with exceptional properties. First, the crystal lattice relaxation is observed by X‐ray diffraction, photoluminescence, and Raman spectroscopy measurements. A high potential of partial coverage for optoelectronic applications is revealed with photo‐ and cathodoluminescence spectra along with an exploration of their temperature dependency. Next, the study focuses on understanding the mechanisms behind the observed enhancement of the luminescence efficiency. It is confirmed that nanowires are effectively protected against photoadsorption using partial coatings. This research advances the frontiers of nanotechnology, investigating the benefits of partial coverage, and shedding light on its complex interaction with cores.

Purpose: We aimed to identify factors that influence student engagement in distance learning.Design/methodology/approach: The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.Findings: The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.Practical implications: The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.Originality/value: The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

Celem artykułu jest opracowanie i zweryfikowanie innowacyjnej metody monitorowania rynku kompetencji stanowiącej składową zarządzania kompetencjami. Sprawne, bieżące uzupełnianie wiedzy o kompetencjach, które powinny być wymagane w organizacjach, jest ważne ze względu na dużą zmienność wymagań kompetencyjnych. W badaniach wykorzystano metodę kart kontrolnych, które zazwyczaj są stosowane w monitorowaniu procesów produkcyjnych w celu zapewnienia jakości. Zastosowana implementacja stanowi innowację w badaniu nad kompetencjami. Wyniki badania odniesiono do wymagań dotyczących wybranych kompetencji miękkich na rynku kompetencji. Wybór tylko jednego regionalnego rynku kompetencji, zawężonego do wybranych wymagań kompetencyjnych stanowi ograniczenie badań. Wprowadzone zawężenie jest jednak zdaniem autorów uprawnione na etapie opracowania i wstępnej weryfikacji metody, która powinna być doskonalona w dalszych badaniach. Przedstawione w opracowaniu rozwiązanie ma praktyczne zastosowanie dla kadry zarządzającej organizacji w poszczególnych jej obszarach funkcjonalnych. Może na przykład stanowić przydatne narzędzie HRM.

In sample surveys there is often a need to estimate not only population characteristics, but subpopulation characteristics as well. We consider the problem of estimating the total value in domains (subpopulations). In this case, the Horvitz‑Thompson estimator could be used. Nevertheless, it does not use any additional information about population units, which are usually known. To increase estimation accuracy we propose to use calibration estimators with auxiliary variables from the current and past periods. In the simulation studies based on real and generated data, we show the influence of using auxiliary information from past periods on the accuracy, and compare properties of two calibration estimators of domain totals in longitudinal surveys.