This study investigates the link between the absence of psychiatric disorders in the elderly and increased longevity through a novel integration of qualitative interviews and quantitative text analysis. By analyzing narratives on life themes such as meaning, purpose, solitude, and spiritual legacy, we aim to identify unique linguistic patterns in elderly individuals without psychiatric disorders, using word embeddings. This technique transforms narrative text into vector space, allowing for the quantitative analysis of semantic relationships between words. We hypothesize that distinctive linguistic patterns reflective of a healthier psychological state may contribute to longevity. Our methodology includes preprocessing of audio and text data, application of pre-trained word embeddings models, and analysis of linguistic patterns to explore correlations with longevity. This research offers insights into the potential impact of mental health on aging and longevity, highlighting the importance of supporting mental well-being in the elderly population.

The automatic identification of bone fractures by medical imaging signifies a notable progress in healthcare, enhancing diagnostic precision, alleviating the burden on medical professionals, and expediting patient care. This paper offers a thorough examination of contemporary techniques employed for the automatic detection of bone fractures, emphasizing image processing methods, conventional machine learning algorithms, and cutting-edge deep learning strategies. A systematic literature review methodology was utilized to examine various studies, contrasting the performance, advantages, and limitations of methodologies such as convolutional neural networks (CNN), region-based convolutional networks (R-CNN), and transformer-based architectures. Challenges like data availability, image quality, computational complexity, and integration into clinical operations are thoroughly examined. The report highlights emerging trends and suggests future research directions, emphasizing opportunities in multimodal data integration, explainable artificial intelligence, and federated learning techniques to enhance fracture diagnosis accuracy and reliability.

This study introduces an innovative approach to indoor air quality monitoring by integrating Internet of Things sensors with interactive student participation in educational settings. Recognizing the critical impact of air quality on health and cognitive function, particularly in university settings, we developed a system where IoT sensors continuously monitor air parameters in academic spaces while students contribute subjective evaluations through a mobile application accessed via QR codes. This dual-methodology enables a comprehensive analysis by correlating objective sensor data with subjective perceptions and symptoms reported by students. Preliminary experiments conducted using Xiaomi sensors in a residential setting showcased the system’s potential, prompting an expansion to involve students across various classrooms. Data collected over a year from high and low traffic environments provides the basis for this study. Through this case study, we aim to assess the accuracy and effectiveness of low-cost sensors in dynamic indoor environments and explore the relationship between measured air quality parameters and their perceived impact on occupants. This approach contributes to the understanding of indoor air quality’s effects as well as engages students directly in environmental science, fostering awareness and participatory learning. The findings are expected to offer valuable insights for improving indoor air quality in educational institutions and enhancing student well-being and performance.

In this paper we will present an approach for interconnecting some of the most important lexical resources for the Romanian language, among which the Corpus for Contemporary Romanian Language (CoRoLa), Romanian WordNet, the Thesaurus Dictionary of Romanian Language in Electronic Format (eDTLR). We will show how they are indexed to each other in order to obtain a standard interconnected structure, and then, we will present a general architecture of a system dedicated to interconnecting lexical resources “on the fly”.

The automatic word sense disambiguation has been a topic of interest since the 1950s. Meaning disambiguation is not an end in itself, it is an intermediate process, necessary at a certain level to use in natural language processing. It is obviously useful for applications that require language interpretation (message communication, human-machine interaction), but it is also used in fields whose main purpose is not to understand natural language. In this paper we propose an approach for word sense disambiguation for Romanian language, based on a supervised learning, a large number of features, and information gain to reduce the dimensions. The evaluation for the Romanian words subtask is produced by the extensive feature set in conjunction with a Maximum Entropy classifier and outcomes that are better than other similar researches.

Research in the area of ​​the interconnection of lexical resources represents a real challenge, because it addresses the difficult problem of semantic understanding and, more precisely, the disambiguation of the meaning of the words - Word Sense Disambiguation (WSD). In the current and prospective context of the information society, the existence of the digital format of the fundamental works of a national culture is strictly necessary. It is a topical issue throughout the world of creating a representative corpus of a language accessible through the Internet, the corpus being a concrete, clear picture of the use of that language. In this study we will describe the development of a Romanian language GOLD corpus, related to the multiple meanings existing for various words. We propose a corpus annotation standard, based on three lexical resources as follows: the Thesaurus Dictionary of the Romanian Language in electronic format (eDTLR), from which we extracted a list of words with multiple meanings; from the Reference Corpus for Contemporary Romanian Language (CoRoLa) we extracted contexts in which these words were founded and from the the Romanian WordNet (RoWN) resource, we took into account the sense meaning of the word from the corpus context.

The Romanian WordNet (RoWN) database is an impressive collection of Romanian nouns, verbs, adjectives, and adverbs, which can be seen as a network of nodes where, words can be found in a certain context to have the same meaning, and in the mesh of that network are found the other words that in turn can become a network node for another word in a specific context. In this study we propose an approach for the problem of aligning the word senses form RoWordNet with those of the Thesaurus Dictionary of the Romanian Language in electronic format (eDTLR), by exploiting the collections of definitions and examples in the two linguistic thesauri, based on a statistical model, where four heuristics are proposed for solving this issue.

Great efforts are being made to increase the utility of linguistic resources in applications related to language processing by interconnecting them. This study is focused on the Romanian language, which is a young language in this domain, for which it has to be made big steps until it is considered well resourced - both qualitatively and quantitatively. The CoRoLa corpus has been developed for 4 years and since 2017 it is visible for research. It cumulates about one billion words, annotated (tokenized, lemmatized, morphologically and syntactically processed). Other two resources used in this study are the eDTLR (the electronic version of the Thesaurus Dictionary of the Romanian Language) and Romanian WordNet. In this study it is described a technology that unifies these resources and brings them to a common standard, which leads the way for an efficient coupling of these resources. With this new obtained standardized resource, a series of test and case studies were performed, with the benefit of a much more diverse exposure on using the Romanian language.