Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much” energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to operate unattended. By adding the information collected by sensors managed with powerful technologies such as big data and analytics, the system is able to efficiently react to environmental factors and respond to consumers’ requirements in real time. According to the weather parameters, the output of PV could be symmetric, supplying an asymmetric electricity demand. Thus, a smart adaptive switching module that includes a forecasting component is proposed to improve the symmetry between the PV output and daily load curve. A scaling approach for smaller off-grid systems that provides an accurate forecast of the PV output based on data collected from sensors is developed. The proposed methodology is based on sensor implementation in RES operation and big data technologies are considered for data processing and analytics. In this respect, we analyze data captured from loggers and forecast the PV output with Support Vector Machine (SVM) and linear regression, finding that Root Mean Square Error (RMSE) for prediction is considerably improved when using more parameters in the machine learning process.

In this study, we explore the challenges and potential solutions to blockchain-based voting. As a first step, we present a comparison of the relevant platforms for implementing smart contracts in decentralized applications (dApps). We analyze the top platforms, highlighting their advantages and disadvantages, their architecture, and which are more reliable for developing smart contracts. The goal is to find a technology that offers various facilities to the developer and multiple functionalities and performance in the development of smart contracts in a field that has seen an incredible pace of innovation. Based on the findings from our research, we propose a framework based on blockchain technology and smart contracts for university-level voting based on blockchains.

This study explores the evolution of wine regulations, trade, and production, with a particular focus on the European Union countries and associated countries. The primary objective is to analyze how historical, regulatory, and economic factors have influenced the wine industry from antiquity to modern times. We assess the administrative role of wine in Roman society, its governance in medieval Europe, and the formation of contemporary regulatory frameworks, such as the EU’s Protected Designation of Origin (PDO) system. The study employs a systematic review approach inspired by the PRISMA 2020 statement, incorporating a diverse array of academic journals, historical texts, and statistical data. We use k-means clustering and Principal Component Analysis (PCA) to analyze 2020 production data and value comparisons among European wine varieties. This quantitative analysis reveals patterns in production and trade profiles across European countries, highlighting Romania’s unique position in the non-PDO segment and its strategic balance of wine production and value. The paper also examines the impact of climate change on viticulture and the industry’s response to sustainability challenges. By integrating historical insights with contemporary data, our research provides a comprehensive view of the wine industry’s evolution, emphasizing the interplay of government oversight, market dynamics, and environmental considerations in shaping the European wine landscape.

Digitalization has significantly reshaped human and social life worldwide, serving as a powerful enabler of a sustainable economy, while being directly aligned with Sustainable Development Goal 9, among others. The literature on digitalization and sustainability boosted since 2017, confirming its importance. Unlike most previous studies, this paper extracted articles from both the Scopus and Web of Science platforms, and the bibliometric analysis was conducted using the new Python library, pyBibX, for the cleaned concatenated dataset, as well as Bibliometrix in R for the parallel analysis on the two platforms. We conducted both a performance analysis to measure scientific impact and citations in the quest to better understand the research field and also a science mapping to visually represent the scientific research and its development. Our findings suggest that Sustainability is the main journal with published articles on digitalization and sustainability, whereas China has the largest number of papers in the field and collaborations between countries. Finally, by applying Natural Language Processing, we identified as best topics: digital, sustainable, development, sustainability, digitalization, study, research, transformation, innovation, and model. Moreover, we dug deeper into policy implications to show how these findings could serve policymakers and stakeholders in academia and industry.

Wine reviews can connect words to flavours; they entwine sensory experiences into vivid stories. This research explores the intersection of artificial intelligence and oenology by using state-of-the-art neural networks to decipher the nuances in wine reviews. For more accurate wine classification and to capture the essence of what matters most to aficionados, we use Hierarchical Attention Networks enhanced with pre-trained embeddings. We also propose an approach to create captivating marketing images using advanced text-to-image generation models, mining a large review corpus for the most important descriptive terms and thus linking textual tasting notes to automatically generated imagery. Compared to more conventional models, our results show that hierarchical attention processes fused with rich linguistic embeddings better reflect the complexities of wine language. In addition to improving the accuracy of wine classification, this method provides consumers with immersive experiences by turning sensory descriptors into striking visual stories. Ultimately, our research helps modernise wine marketing and consumer engagement by merging deep learning with sensory analytics, proving how technology-driven solutions can amplify storytelling and shopping experiences in the digital marketplace.

Digitalisation has become an increasingly prominent topic in research on regional sustainable development. As academic attention to its relationship with Sustainable Development Goals expands, the thematic configuration and structural evolution of this research domain remain fragmented. This study maps the thematic landscape of digitalisation and regional sustainable development through a bibliometric analysis of 451 Web of Science records. Using a complementary dual‐tool approach in Bibliometrix (R) and pyBibX (Python), we examine publication dynamics, co‐occurrence networks, co‐citation structures, collaboration patterns, and thematic evolution within this interdisciplinary field. The results identify dominant research clusters centred on digital economy, innovation, efficiency, energy and regional economic growth, alongside a marked geographical concentration of scientific output. In contrast, sustainability‐related dimensions linked to management, planning, research and artificial intelligence networks appear comparatively peripheral within the mapped thematic landscape. By structuring research trajectories and identifying thematic imbalances, the study provides a comprehensive bibliometric mapping of how the academic community conceptualises the digitalisation–regional sustainability nexus and highlights directions for further scholarly inquiry.