The economic complexity approach presents a shift from quantitative to qualitative measures of economic performance, while economic complexity refers to the accumulation of know-how. Economic complexity is considered a predictor of economic growth and research evidences a positive relationship between economic complexity and economic growth. In the EU countries, economic convergence is observed. Hence the question of economic complexity convergence arises, too. The paper aims to analyze the convergence of 27 EU countries considering their economic complexity from 1999 to 2021 computing the beta convergence. Using the Barro-type regressions, the econometric estimations focus on four indices of economic complexity—the economic complexity index published by Harvard’s Growth Lab, and economic complexity indices on research, trade, and technology published by the Observatory of Economic Complexity. The absolute beta convergence is observed in the EU except for the economic complexity index referring to trade. When including the dummy referring to the location of EU countries in the West or East of the EU considering their wealth, the conditional beta convergence is observed except for the trade-economic complexity index, again. When altering the condition of location by the GDP per capita and other controls, the conditional beta convergence of economic complexity in the EU is observed when estimating both fixed-effect models and dynamic panel data models based on the system generalized method of moments (GMM) estimator.

In higher eukaryotes, the genes’ architecture has become an essential determinant of the variation in the number of transcripts (expression level) and the specificity of gene expression in plant tissue under stress conditions. The modern rise in genome-wide analysis accounts for summarizing the essential factors through the translocation of gene networks in a regulatory manner. Stress tolerance genes are in two groups: structural genes, which code for proteins and enzymes that directly protect cells from stress (such as genes for transporters, osmo-protectants, detoxifying enzymes, etc.), and the genes expressed in regulation and signal transduction (such as transcriptional factors (TFs) and protein kinases). The genetic regulation and protein activity arising from plants’ interaction with minerals and abiotic and biotic stresses utilize high-efficiency molecular profiling. Collecting gene expression data concerning gene regulation in plants towards focus predicts an acceptable model for efficient genomic tools. Thus, this review brings insights into modifying the expression study, providing a valuable source for assisting the involvement of genes in plant growth and metabolism-generating gene databases. The manuscript significantly contributes to understanding gene expression and regulation in plants, particularly under stress conditions. Its insights into stress tolerance mechanisms have substantial implications for crop improvement, making it highly relevant and valuable to the field.