This study investigates the impact of various educational and social factors on the digital skills of vocational education and training (VET) students, emphasizing the significance of continuous skill development in the digital age. Utilizing structural equation modeling (SEM), the paper analyzes data from 382 adult VET students in Greece, examining the effects of Erasmus program participation, daily computer use, educational platforms, and social network engagement on digital competencies. The findings reveal that participation in Erasmus programs and the use of educational platforms significantly enhance students’ digital skills, highlighting the value of international experiences and digital learning tools in VET. Conversely, daily computer use alone does not significantly impact digital skills, suggesting that structured and purposeful digital tool integration is essential for skill development. The study also underscores the positive role of social networks in improving content management skills, advocating for their strategic use in educational settings. These results demonstrate the need for targeted digital literacy initiatives within VET programs to prepare students for modern labor market demands. The research contributes to the theoretical understanding of digital skill acquisition and offers practical insights for educators and policymakers to enhance VET curricula, fostering economic and social progress through improved digital literacy.

A comprehensive proteomic analysis was carried out to evaluate leaf proteome changes of Brassica napus cultivars as an important oilseed crop inoculated with the bacterium Pseudomonas fluorescens FY32 under salt stress. Based on the physiochemical characteristics of canola, Hyola308 was a tolerant and Sarigol was a salt sensitive cultivar. Gel-based proteomics indicated that proteins related to energy/metabolism, cell/membrane maintenance, signalins, stress, and development respond to salt stress and bacterial inoculation in both cultivars. Under salt stress, Hyola308 launches mechanisms similar to Sarigol, but the tolerance was related to consuming less energy consumption than Sarigol for launching the proper pathway/mechanism. Inoculation with plant growth promoting bacteria promotes relative growth rate and net assimilation rate; causes increase in soluble sugar content (12–32% varing to cultivars and salt treatments), as an osmo-protectant, in leaves of Sarigol and Hyola308 in control and salt stress conditions. The groups of proteins that are affected due to inoculation (18 and14 functional groups in Hyola308 and Sarigol, respectively) are varying to stress-influenced groups (10 and 6 functional groups in Hyola308 and Sarigol, respectively) that might be because of regulating tolerance mechanism of plant and/or plant-growth promoting bacteria inoculation. Furthermore, it is recognized that P. fluorescens FY32 has a dual effect on the cultivars including a pathogenic effect and a growth promoting effect on both cultivars under salt stress.

The aim was to examine the relationships between selected demographic and psychographic factors and consumers' willingness to accept content generated by advanced technological innovations (AIGC) in social infrastructure. The sample consisted of 1,308 respondents. Spearman's correlation coefficient was used to examine the relationships between ordinal variables. To assess the differences between groups of respondents, a one-way analysis of variance was used, during which multiple linear regression analysis was used to confirm the predictive power of awareness and experience in relation to AI-generated content in relation to the tendency to accept such content. The study confirmed a statistically significant but weak negative relationship between the age of respondents and their willingness to accept AIGC, with younger age groups showing a slightly higher rate of acceptance. Respondents' attitudes toward the use of personal data through AI and their overall awareness of technological trends had a more significant impact on acceptance. The findings show that respondents who are open to data collection through AI technologies show a significantly higher level of acceptance of automatically generated content. Similarly, respondents who positively evaluate the current quality of AIGC have higher expectations for the future transformation of marketing strategies and media practices. The decisive factors in the social infrastructure for the acceptance of AIGC are not so much the age of the respondents, but rather their awareness, technological literacy, and level of trust in the technology itself. The study therefore recommends increasing transparency and public awareness about the use of AI in marketing and media practices in order to strengthen consumer confidence in automated content.

Fungi can be used to remove or degrade polluting compounds through a mycoremediation process. Sometimes even more efficiently than prokaryotes, they can therefore be used to combat pollution from non-biodegradable polymers. Cellulose acetate is a commonly used material in the manufacture of cigarette butts, so when discarded, it generates pollution. The fungus Pleurotus ostreatus has the ability to degrade cellulose acetate through the enzymes it secretes. The enzyme hydrolyzes the acetyl group of cellulose acetate, while cellulolytic enzymes degrade the cellulose backbone into sugars, polysaccharides, or cellobiose. In addition to cellulose acetate, this fungus is capable of degrading other conventionally non-biodegradable polymers, so it has the potential to be used to reduce pollution. Large-scale cultivation of the fungus has proven to be more economically viable than conventional methods for treating non-biodegradable polymers, which is an additional advantage.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI’s predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.