Energy issues are sensitive for the four Visegrad countries as European Union (EU) member states; thus, this area’s convergence might be problematic for these countries. There is a clear research gap concerning the processes of Europeanization of the energy policy in the Visegrad countries. This article aims to identify and evaluate the progress of four Visegrad countries (V4) in implementing the EU energy goals in the context of the Europeanization. The article uses three main methods: Hellwig’s method, Kendall’s rank concordance coefficient, and k-means clustering. These calculations will allow one to study the Europeanization processes, which means checking the gamma convergence. For calculations, we use the available statistical data from Eurostat for the years 2005–2018. Poland and other Central European countries, including Czechia, and Hungary, largely depend on coal for their energy needs. The empirical results have shown that there have been no significant changes in the classification of EU countries in terms of their fulfillment of the EU climate and energy targets in the analyzed period. This is the case in all EU member states, including the Visegrad Group countries, but except for Poland. This means that the level of Europeanization of the energy policy and its effectiveness is similar in all member states except for Poland, which is becoming a kind of the exception. Throughout the investigating period, the Czech Republic, Hungary, and Slovakia were close to meeting the set targets and could be rated high compared to the EU countries. Poland, especially since 2015, has been noticeably and increasingly distanced from the other V4 countries. It can be perceived as a gradual drift away from Europeanization of the EU climate and energy policy in Poland.

Objective: The objective of the article is to offer a thorough exploration and comprehension of the obstacles and potential advantages linked to the application of generative artificial intelligence (GAI) in the business realm, particularly emphasizing ChatGPT. Research Design & Methods: The research utilized a narrative and critical examination of existing literature and constructed a conceptual framework grounded in prior studies. Our theoretical framework was developed through a deductive reasoning approach to ensure the logical and effective organization of the study. Consequently, this work should be considered a conceptual article that sheds light on one hand on the promises and opportunities, and on the other hand on the controversies and risks associated with generative artificial intelligence in the fields of management and economics, using ChatGPT as a specific case study. Findings: In recent years, artificial intelligence has experienced rapid progress, leading to its widespread applications. The chatbot industry, exemplified by ChatGPT, has garnered considerable attention, with experts and researchers asserting that generative artificial intelligence and ChatGPT could transform our work routines and daily existence. Although these technologies have the potential to revolutionize data analysis and report generation, concerns have been raised about their societal impacts, particularly in areas such as ethics, privacy, and security. Implications & Recommendations: The regulation of the GAI market is imperative to ensure fairness, competitive balance, and safeguard intellectual property and privacy while addressing potential geopolitical risks. With the evolving job landscape, individuals must continuously acquire new digital skills through education, particularly in response to the growing prominence of AI system training. Ethical considerations, such as prioritizing user privacy and security, are crucial for GAI developers to mitigate risks related to personal data violation and social surveillance, emphasizing responsible AI practices and adherence to ethical guidelines to prevent social manipulation and maintain goodwill. Contribution & Value Added: The article structures scientific knowledge on the advantages and drawbacks of the generative artificial intelligence in business. The articles attempted to put together the main aspects of this new phenomenon.

Sentiment analysis (SA) is an area of study currently being investigated in text mining. SA is the computational handling of a text’s views, emotions, subjectivity, and subjective nature. The researchers realized that generating generic sentiment from textual material was inadequate, so they developed SA to extract expressions from textual information. The problem of removing emotional aspects through multi-labeling based on data from certain aspects may be resolved. This article proposes the swarm-based hybrid model residual networks with sand cat swarm optimization (ResNet-SCSO), a novel method for increasing the precision and variation of learning the text with the multi-labeling method. Contrary to existing multi-label training approaches, ResNet-SCSO highlights the diversity and accuracy of methodologies based on multi-labeling. Five distinct datasets were analyzed (movies, research articles, medical, birds, and proteins). To achieve accurate and improved data, we initially used preprocessing. Secondly, we used the GloVe and TF-IDF to extract features. Thirdly, a word association is created using the word2vec method. Additionally, the enhanced data are utilized for training and validating the ResNet model (tuned with SCSO). We tested the accuracy of ResNet-SCSO on research article, medical, birds, movie, and protein images using the aspect-based multi-labeling method. The accuracy was 95%, 96%, 97%, 92%, and 96%, respectively. With multi-label datasets of varying dimensions, our proposed model shows that ResNet-SCSO is significantly better than other commonly used techniques. Experimental findings confirm the implemented strategy’s success compared to existing benchmark methods.

Target detection in high-contrast, multi-object images and movies is challenging. This difficulty results from different areas and objects/people having varying pixel distributions, contrast, and intensity properties. This work introduces a new region-focused feature detection (RFD) method to tackle this problem and improve target detection accuracy. The RFD method divides the input image into several smaller ones so that as much of the image as possible is processed. Each of these zones has its own contrast and intensity attributes computed. Deep recurrent learning is then used to iteratively extract these features using a similarity measure from training inputs corresponding to various regions. The target can be located by combining features from many locations that overlap. The recognized target is compared to the inputs used during training, with the help of contrast and intensity attributes, to increase accuracy. The feature distribution across regions is also used for repeated training of the learning paradigm. This method efficiently lowers false rates during region selection and pattern matching with numerous extraction instances. Therefore, the suggested method provides greater accuracy by singling out distinct regions and filtering out misleading rate-generating features. The accuracy, similarity index, false rate, extraction ratio, processing time, and others are used to assess the effectiveness of the proposed approach. The proposed RFD improves the similarity index by 10.69%, extraction ratio by 9.04%, and precision by 13.27%. The false rate and processing time are reduced by 7.78% and 9.19%, respectively.

Accurate electric current measurement is fundamental to the safe and efficient operation of modern power systems. Current transformers (CTs), which serve as a critical interface between high-voltage power networks and measuring or protection devices, are susceptible to various errors and uncertainties that can significantly impact the reliability of measurement results. This article provides a critical and comprehensive review of the current literature concerning the types and causes of errors in CTs, with particular emphasis on current ratio error and phase displacement. Special attention is given to the role of international standards in defining accuracy classes and permissible error limits. Methods for evaluating measurement uncertainty are also discussed, in accordance with the guidelines outlined in the Guide to the Expression of Uncertainty in Measurement (GUM), highlighting their decisive impact on the credibility of measurement results. Modern approaches—such as the application of artificial intelligence in estimating measurement errors—are also considered. This review serves as a comprehensive resource for engineers, metrologists, and researchers seeking to enhance the accuracy of CTs, particularly in measurement and protection applications.