Vlad Diaconita

We examine the relationship between student-oriented service responsibility in higher education and students’ degree-value judgments, with perceived skill-development support modeled as the link. Survey data from 1,669 Romanian students (1,655 complete SEM cases) show that enabling resources and standards alignment are both positively related to this support, with a numerically larger unstandardized estimate for enabling resources. Skill-development support is related to higher teaching-quality satisfaction, more favorable EU comparability assessments, and weaker foreign-credential advantage beliefs. All six modeled indirect associations have bootstrap confidence intervals excluding zero, consistent with observable support informing degree-value judgments under delayed verification.

Today, financial institutions’ architecture does not rely on one single technology. Instead, it uses a multi-technology approach in order to cover modern requirements and, at the same time, remain relevant. It integrates technologies such as relational databases, Big Data for analysis, and Cloud environments for distributed capacities within a complex data architecture. At the same time, due to European data governance regulations, governance mechanisms such as encryption, pseudonymization, and incremental versioning must be applied on each architectural layer in order to comply with strict European governance rules. In this study, the impact of data governance is assessed by applying these mechanisms from the data-ingestion level, using diverse data types such as structured, semi-structured, and unstructured data, across relational databases, Big Data analysis, and Cloud distributed systems. In doing so, metrics such as execution time, CPU, and memory usage are assessed in order to properly evaluate the impact of governance mechanisms on financial systems. The results show that governance can be successfully integrated, provided these mechanisms are embedded at the architectural level, ensuring that performance, scalability, and compliance are maintained across the entire processing pipeline.

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.

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a four-layer architecture to address financial data processing issues. The layers of our design are for data sources, data integration, processing, and storage. Data ingestion processes market feeds, transaction records, and customer data. Real-time data are captured by Kafka and transformed by Extract-Transform-Load (ETL) pipelines. The processing layer is composed of Apache Spark for real-time data analysis, Hadoop for batch processing, and an Machine Learning (ML) infrastructure that supports predictive modeling. In order to optimize access patterns, the storage layer includes various data layer components. The test results indicate that the processing of market data in real-time, compliance reporting, risk evaluations, and customer analyses can be conducted in fulfillment of environmental sustainability goals. The metrics from the test deployment support the implementation strategies and technical specifications of the architectural components. We also looked at integration models and data flow improvements, with applications in finance. This study aims to enhance enterprise data architecture in the financial context and includes guidance on modernizing data infrastructure.

Cryptocurrencies have emerged as a popular investment vehicle, prompting numerous efforts to predict market trends and identify metrics that signal periods of volatility. One promising approach involves leveraging on-chain data, which is unique to cryptocurrencies. On-chain data, extracted directly from the blockchain, provides valuable information, such as the hash rate, total transactions, or the total number of addresses that hold a specified amount of cryptocurrency. Some studies have also explored the relationship between social media sentiment and Bitcoin, using data from platforms such as Twitter and Google Trends. However, the quality of Twitter sentiment analysis has been lackluster due to suboptimal extraction techniques. This research proposes a novel approach that combines a superior sentiment analysis technique with various on-chain metrics to improve predictions using a deep learning architecture based on long-short term memory (LSTM). The proposed model predicts outcomes for multiple time horizons, ranging from one day to 14 days, and outperforms the Martingale (random walk) approach by over 9%, as measured by the mean absolute percentage error metric, as well as recent results reported in literature. To the best of our knowledge, this study may be among the first to employ this combination of techniques to improve cryptocurrency market prediction.

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.