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Search Results (1,236)

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22 pages, 2016 KB  
Review
Human-Centred Design (HCD) in Enhancing Dementia Care Through Assistive Technologies: A Scoping Review
by Fanke Peng, Kate Little and Lin Liu
Digital 2025, 5(4), 51; https://doi.org/10.3390/digital5040051 - 2 Oct 2025
Abstract
Background: Dementia is a progressive neurodegenerative condition that impairs cognitive functions such as memory, language comprehension, and problem-solving. Assistive technologies can provide vital support at various stages of dementia, significantly improving the quality of life by aiding daily activities and care. However, for [...] Read more.
Background: Dementia is a progressive neurodegenerative condition that impairs cognitive functions such as memory, language comprehension, and problem-solving. Assistive technologies can provide vital support at various stages of dementia, significantly improving the quality of life by aiding daily activities and care. However, for these technologies to be effective and widely adopted, a human-centred design (HCD) approach is of consequence for both their development and evaluation. Objectives: This scoping review aims to explore how HCD principles have been applied in the design of assistive technologies for people with dementia and to identify the extent and nature of their involvement in the design process. Eligibility Criteria: Studies published between 2017 and 2025 were included if they applied HCD methods in the design of assistive technologies for individuals at any stage of dementia. Priority was given to studies that directly involved people with dementia in the design or evaluation process. Sources of Evidence: A systematic search was conducted across five databases: Web of Science, JSTOR, Scopus, and ProQuest. Charting Methods: Articles were screened in two stages: title/abstract screening (n = 350) and full-text review (n = 89). Data from eligible studies (n = 49) were extracted and thematically analysed to identify design approaches, types of technologies, and user involvement. Results: The 49 included studies covered a variety of assistive technologies, such as robotic systems, augmented and virtual reality tools, mobile applications, and Internet of Things (IoT) devices. A wide range of HCD approaches were employed, with varying degrees of user involvement. Conclusions: HCD plays a critical role in enhancing the development and effectiveness of assistive technologies for dementia care. The review underscores the importance of involving people with dementia and their carers in the design process to ensure that solutions are practical, meaningful, and capable of improving quality of life. However, several key gaps remain. There is no standardised HCD framework for healthcare, stakeholder involvement is often inconsistent, and evidence on real-world impact is limited. Addressing these gaps is crucial to advancing the field and delivering scalable, sustainable innovations. Full article
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18 pages, 2244 KB  
Article
Unveiling Social Media Content Related to ADHD Treatment: Machine Learning Study Using X’s Posts over 15 Years
by Alba Gómez-Prieto, Alejandra Mercado-Rodriguez, Juan Pablo Chart-Pascual, Cesar I. Fernandez-Lazaro, Francisco J. Lara-Abelenda, María Montero-Torres, Claudia Aymerich, Javier Quintero, Melchor Alvarez-Mon, Ana Gonzalez-Pinto, Cesar A. Soutullo and Miguel Angel Alvarez-Mon
Healthcare 2025, 13(19), 2487; https://doi.org/10.3390/healthcare13192487 - 30 Sep 2025
Abstract
Background: Public discourse on social media plays an increasingly influential role in shaping health-related perceptions and behaviours. Individuals share experiences, concerns, and opinions beyond clinical settings around different issues. X (formerly Twitter) provides a unique lens through which to examine how different treatments [...] Read more.
Background: Public discourse on social media plays an increasingly influential role in shaping health-related perceptions and behaviours. Individuals share experiences, concerns, and opinions beyond clinical settings around different issues. X (formerly Twitter) provides a unique lens through which to examine how different treatments are perceived, used, and debated across diverse communities over time. Objective: The study aims to (a) identify the types of ADHD medications mentioned in posts, depending on language and user type; (b) evaluate the popularity of content related to these medications, considering language and user type; (c) analyse temporal changes in the frequency of mentions between 2006 and 2022; and (d) examine the distribution of tweets across different content categories. By addressing these objectives, this study provides insights into public perceptions of ADHD medications, which may help healthcare professionals better understand online discussions and improve their communication with patients, facilitating more informed treatment decisions. Methods: An observational study was conducted analysing 254,952 tweets in Spanish and English about ADHD medications from January 2006 to December 2022. Content analysis combined inductive and deductive approaches to develop a categorisation codebook. BERTWEET and BETO models were used for machine learning classification of English and Spanish tweets, respectively. Descriptive statistical analysis was performed. Results: Overall, stimulant medications were posted more frequently and received higher engagement than non-stimulant medications. Methylphenidate, dextroamphetamine, and atomoxetine were the most commonly mentioned medications, especially by patients, who emerged as the most active users among the English tweets. Regarding medical content, tweets in English contained more than twice the number of mentions of inappropriate use compared to those in Spanish. There was a high content of online medication requests and offers in both languages. Conclusions: In this study, conducted on X, discussions on ADHD medications highlighted concerns about misuse, adherence, and trivialisation, with clear differences between English and Spanish tweets regarding focus and type of user participation. These findings suggest that monitoring social media can provide early signals about emerging trends, helping clinicians address misconceptions during consultations and informing public health strategies aimed at the safer and more responsible use of ADHD medications. Full article
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20 pages, 776 KB  
Article
Who Speaks to Whom? An LLM-Based Social Network Analysis of Tragic Plays
by Aura Cristina Udrea, Stefan Ruseti, Laurentiu-Marian Neagu, Ovio Olaru, Andrei Terian and Mihai Dascalu
Electronics 2025, 14(19), 3847; https://doi.org/10.3390/electronics14193847 - 28 Sep 2025
Abstract
The study of dramatic plays has long relied on qualitative methods to analyze character interactions, making little assumption about the structural patterns of communication involved. Our approach bridges NLP and literary studies, enabling scalable, data-driven analysis of interaction patterns and power structures in [...] Read more.
The study of dramatic plays has long relied on qualitative methods to analyze character interactions, making little assumption about the structural patterns of communication involved. Our approach bridges NLP and literary studies, enabling scalable, data-driven analysis of interaction patterns and power structures in drama. We propose a novel method to supplement addressee identification in tragedies using Large Language Models (LLMs). Unlike conventional Social Network Analysis (SNA) approaches, which often diminish dialogue dynamics by relying on co-occurrence or adjacency heuristics, our LLM-based method accurately records directed speech acts, joint addresses, and listener interactions. In a preliminary evaluation of an annotated multilingual dataset of 14 scenes from nine plays in four languages, our top-performing LLM (i.e., Llama3.3-70B) achieved an F1-score of 88.75% (P = 94.81%, R = 84.72%), an exact match of 77.31%, and an 86.97% partial match with human annotations, where partial match indicates any overlap between predicted and annotated receiver lists. Through automatic extraction of speaker–addressee relations, our method provides preliminary evidence for the potential scalability of SNA for literary analyses, as well as insights into power relations, influence, and isolation of characters in tragedies, which we further visualize by rendering social network graphs. Full article
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34 pages, 8775 KB  
Review
Towards Fault-Aware Image Captioning: A Review on Integrating Facial Expression Recognition (FER) and Object Detection
by Abdul Saboor Khan, Muhammad Jamshed Abbass and Abdul Haseeb Khan
Sensors 2025, 25(19), 5992; https://doi.org/10.3390/s25195992 - 28 Sep 2025
Abstract
The term “image captioning” refers to the process of converting an image into text through computer vision and natural language processing algorithms. Image captioning is still considered an open-ended topic despite the fact that visual data, most of which pertains to images, is [...] Read more.
The term “image captioning” refers to the process of converting an image into text through computer vision and natural language processing algorithms. Image captioning is still considered an open-ended topic despite the fact that visual data, most of which pertains to images, is readily available in today’s world. This is despite the fact that recent developments in computer vision, such as Vision Transformers (ViT) and language models using BERT and GPT, have opened up new possibilities for the field. The purpose of this review paper is to provide an overview of the present status of the field, with a specific emphasis on the use of facial expression recognition and object detection for the purpose of image captioning, particularly in the context of fault-aware systems and Prognostics and Health Management (PHM) applications within Industry 4.0 environments. However, to the best of our knowledge, no review study has focused on the significance of facial expressions in relation to image captioning, especially in industrial settings where operator facial expressions can provide valuable insights for fault detection and system health monitoring. This is something that has been overlooked in the existing body of research on image captioning, which is the primary reason why this study was conducted. During this paper, we will talk about the most important approaches and procedures that have been utilized for this task, including fault-aware methodologies that leverage visual data for PHM in smart manufacturing contexts, and we will highlight the advantages and disadvantages of each strategy. The purpose of this review is to present a comprehensive assessment of the current state of the field and to recommend topics for future research that will lead to machine-translated captions that are more detailed and accurate, particularly for Industry 4.0 applications where visual monitoring plays a crucial role in system diagnostics and maintenance. Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
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11 pages, 5108 KB  
Proceeding Paper
Chatbot-Enhanced Non-Player Characters Bridging Game AI and Conversational Systems
by Gina Purnama Insany, Maulana Ibrahim, Yayang Rega Abdilah and Rizki Panca Pamungkas
Eng. Proc. 2025, 107(1), 110; https://doi.org/10.3390/engproc2025107110 - 25 Sep 2025
Abstract
Non-player characters (NPCs) play a crucial role in creating engaging and immersive experiences in role playing games (RPGs). Traditional NPC interactions often rely on scripted dialogues, which can limit their ability to adapt dynamically to player input. This study presents a novel framework [...] Read more.
Non-player characters (NPCs) play a crucial role in creating engaging and immersive experiences in role playing games (RPGs). Traditional NPC interactions often rely on scripted dialogues, which can limit their ability to adapt dynamically to player input. This study presents a novel framework that enhances NPC interactions by integrating advanced conversational systems. Utilizing Open AI’s natural language processing capabilities, RPG Maker MZ as the game development platform, and JavaScript for customization, the framework introduces context-aware dialogues that respond intelligently to player queries and actions. By bridging the gap between game AI and conversational systems, this approach enables more lifelike and meaningful NPC behavior. Experimental results indicate that the proposed system significantly improves the narrative depth and overall player experience. These findings demonstrate the potential of combining AI-driven chatbots with game development tools to redefine the role of NPCs in modern gaming. Full article
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26 pages, 12107 KB  
Article
Empowering Older Migrants: Co-Designing Climate Communication with Chinese Seniors in the UK
by Qing Ni, Hua Dong and Antonios Kaniadakis
J. Ageing Longev. 2025, 5(4), 37; https://doi.org/10.3390/jal5040037 - 24 Sep 2025
Viewed by 19
Abstract
This study explores how older Chinese migrants in London engage with climate change discourse using participatory co-design workshops. Although already practising sustainability behaviours such as recycling, this group faces significant barriers—particularly language difficulties and cultural differences—that limit their active participation in broader climate [...] Read more.
This study explores how older Chinese migrants in London engage with climate change discourse using participatory co-design workshops. Although already practising sustainability behaviours such as recycling, this group faces significant barriers—particularly language difficulties and cultural differences—that limit their active participation in broader climate initiatives. The research addresses three key aspects: (1) identifying opportunities for sustainable practices within migrants’ daily routines; (2) understanding their influential roles within families and communities; and (3) examining their trusted sources and preferred channels for climate communication. Results highlight that family and community networks, combined with digital platforms (e.g., WeChat) and visually engaging materials, play essential roles in disseminating climate information. Participants expressed strong motivations rooted in intergenerational responsibility and economic benefits. The findings emphasise the necessity of inclusive and peer-led communication strategies that are attuned to older migrants’ linguistic preferences, media habits, and cultural values—underscoring their significant but often overlooked potential to meaningfully contribute to climate action. Full article
(This article belongs to the Special Issue Aging in Place: Supporting Older People's Well-Being and Independence)
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17 pages, 7481 KB  
Article
A Real-Time Advisory Tool for Supporting the Use of Helmets in Construction Sites
by Ümit Işıkdağ, Handan Aş Çemrek, Seda Sönmez, Yaren Aydın, Gebrail Bekdaş and Zong Woo Geem
Information 2025, 16(10), 824; https://doi.org/10.3390/info16100824 - 24 Sep 2025
Viewed by 62
Abstract
In the construction industry, occupational health and safety plays a critical role in preventing occupational accidents and increasing productivity. In recent years, computer vision and artificial intelligence-based systems have made significant contributions to improving these processes through automatic detection and tracking of objects. [...] Read more.
In the construction industry, occupational health and safety plays a critical role in preventing occupational accidents and increasing productivity. In recent years, computer vision and artificial intelligence-based systems have made significant contributions to improving these processes through automatic detection and tracking of objects. The aim of this study was to fine-tune object detection models and integrate them with Large Language Models for (i). accurate detection of personal protective equipment (PPE) by specifically focusing on helmets and (ii). providing real-time recommendations based on the detections for supporting the use of helmets in construction sites. For achieving the first objective of the study, large YOLOv8/v11/v12 models were trained using a helmet dataset consisting of 16,867 images. The dataset was divided into two classes: “Head (No Helmet)” and “Helmet”. The model, once trained, was able to analyze an image from a construction site and detect and count the people with and without helmets. A tool with the aim of providing advice to workers in real time was developed to fulfil the second objective of the study. The developed tool provides the counts of the people based on video feeds or analyzing a series of images and provides recommendations on occupational safety (based on the detections from the video feed and images) through an OpenAI GPT-3.5-turbo Large Language Model and with a Streamlit-based GUI. The use of YOLO enables quick and accurate detections; in addition, the use of the OpenAI model API serves the exact same purpose. The combination of the YOLO model and OpenAI model API enables near-real-time responses to the user over the web. The paper elaborates on the fine tuning of the detection model with the helmet dataset and the development of the real-time advisory tool. Full article
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16 pages, 700 KB  
Article
Investigation of Intestinal Microbiota and Short-Chain Fatty Acids in Colorectal Cancer and Detection of Biomarkers
by Esra Saylam, Özben Özden, Fatma Hümeyra Yerlikaya, Abdullah Sivrikaya, Serdar Yormaz, Uğur Arslan, Mustafa Topkafa and Salih Maçin
Pathogens 2025, 14(9), 953; https://doi.org/10.3390/pathogens14090953 - 22 Sep 2025
Viewed by 237
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide and a significant global health issue. The human gut microbiota, a complex ecosystem hosting numerous microorganisms such as bacteria, viruses, fungi, and protozoa, plays a crucial role. Increasing evidence indicates that gut [...] Read more.
Colorectal cancer (CRC) is one of the most common cancers worldwide and a significant global health issue. The human gut microbiota, a complex ecosystem hosting numerous microorganisms such as bacteria, viruses, fungi, and protozoa, plays a crucial role. Increasing evidence indicates that gut microbiota is involved in CRC pathogenesis. In this study, the gut microbiota profiles, short-chain fatty acids, zonulin, and lipopolysaccharide-binding protein levels of newly diagnosed CRC patients were analyzed along with healthy controls to elucidate the relationship between CRC and the gut microbiota. The study included 16 newly diagnosed CRC patients and 16 healthy individuals. For microbiota analysis, DNA isolation from stool samples was performed using the Quick-DNA™ Fecal/Soil Microbe Miniprep Kit followed by sequencing using the MinION device. Data processing was conducted using Guppy software (version 6.5.7) and the Python (3.12) programming language. ELISA kits from Elabscience were utilized for analyzing LBP and zonulin serum levels. Fecal short-chain fatty acids were analyzed using GC-MS/MS equipped with a flame ionization detector and DB-FFAP column. Microbial alpha diversity, assessed using Shannon and Simpson indices, was found to be lower in CRC patients compared to healthy controls (p = 0.045, 0.017). Significant differences in microbial beta diversity were observed between the two groups (p = 0.004). At the phylum level, Bacteroidota was found to be decreased in CRC patients (p = 0.027). Potential biomarker candidates identified included Enterococcus faecium, Ruminococcus bicirculans, Enterococcus gilvus, Enterococcus casseliflavus, Segatella oris, and Akkermansia muciniphila. Serum zonulin levels were higher in CRC patients (CRC = 70.1 ± 26.14, Control = 53.93 ± 17.33, p = 0.048). There is a significant relationship between gut microbiota and CRC. A multifactorial evaluation of this relationship could shed light on potential biomarker identification and the development of new treatment options for CRC. Full article
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17 pages, 935 KB  
Review
Utilization of AhR and GPR35 Receptor Ligands as Superfoods in Cancer Prevention for Individuals with IBD
by Olga Poźniak, Robert Sitarz, Monika Zofia Sitarz, Dorota Kowalczuk, Emilia Słoń and Ewa Dudzińska
Int. J. Mol. Sci. 2025, 26(18), 9160; https://doi.org/10.3390/ijms26189160 - 19 Sep 2025
Viewed by 187
Abstract
Carcinogenesis is a complex process characterized by the uncontrolled proliferation of abnormal cells, influenced by environmental, genetic, and epigenetic factors. Chronic inflammation is undoubtedly one of the key contributors to carcinogenesis. Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal [...] Read more.
Carcinogenesis is a complex process characterized by the uncontrolled proliferation of abnormal cells, influenced by environmental, genetic, and epigenetic factors. Chronic inflammation is undoubtedly one of the key contributors to carcinogenesis. Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) due to persistent inflammation resulting from continuous immune system activation and excessive immune cell recruitment. IBD is also linked to certain nutritional deficiencies, primarily due to dietary modifications necessitated by the disease’s pathophysiology. Consequently, individualized nutritional supplementation appears to be a rational approach to addressing these deficiencies. The use of functional foods, including anti-inflammatory nutraceuticals, in individuals with IBD may play a crucial role in modulating cellular pathways that inhibit the release of inflammatory mediators. Thus, the regulation of the aryl hydrocarbon receptor (AhR) and G protein-coupled receptor 35 (GPR35) through dietary ligands appears to be of significant importance not only in the treatment of IBD and maintenance of remission but also in the prevention of tumorigenic transformation, particularly in genetically predisposed individuals. This narrative review was conducted using PubMed, Scopus, and Web of Science databases. The search covered literature published between January 2000 and June 2024. Keywords included ‘inflammatory bowel disease’, ‘colorectal cancer’, ‘AhR’, ‘aryl hydrocarbon receptor’, ‘GPR35’, ‘cytochrome P450’, ‘nutraceuticals’, ‘probiotics’, and ‘superfoods’. Only English-language articles were included. The selection focused on studies investigating mechanistic pathways and the role of dietary ligands in AhR and GPR35 activation in IBD and CRC. The SANRA guidelines for narrative reviews were followed to ensure transparency and minimize bias. Full article
(This article belongs to the Section Molecular Oncology)
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16 pages, 2069 KB  
Article
“Can I Use My Leg Too?” Dancing with Uncertainty: Exploring Probabilistic Thinking Through Embodied Learning in a Jerusalem Art High School Classroom
by Dafna Efron and Alik Palatnik
Educ. Sci. 2025, 15(9), 1248; https://doi.org/10.3390/educsci15091248 - 18 Sep 2025
Viewed by 179
Abstract
Despite increased interest in embodied learning, the role of sensorimotor activity in shaping students’ probabilistic reasoning remains underexplored. This design-based study examines how high school students develop key probabilistic concepts, including sample space, certainty, and event probability, through whole-body movement activities situated in [...] Read more.
Despite increased interest in embodied learning, the role of sensorimotor activity in shaping students’ probabilistic reasoning remains underexplored. This design-based study examines how high school students develop key probabilistic concepts, including sample space, certainty, and event probability, through whole-body movement activities situated in an authentic classroom setting. Grounded in embodied cognition theory, we introduce a two-axis interpretive framework. One axis spans sensorimotor exploration and formal reasoning, drawing from established continuums in the literature. The second axis, derived inductively from our analysis, contrasts engagement with distraction, foregrounding the affective and attentional dimensions of embodied participation. Students engaged in structured yet open-ended movement sequences that elicited intuitive insights. This approach, epitomized by one student’s spontaneous question, “Can I use my leg too?”, captures the agentive and improvisational character of the embodied learning environment. Through five analyzed classroom episodes, we trace how students shifted between bodily exploration and formalization, often through nonlinear trajectories shaped by play, uncertainty, and emotionally driven reflection. While moments of insight emerged organically, they were also fragile, as they were affected by ambiguity and the difficulty in translating physical actions into mathematical language. Our findings underscore the pedagogical potential of embodied design for probabilistic learning while also highlighting the need for responsive teaching that balances structure with improvisation and supports affective integration throughout the learning process. Full article
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12 pages, 532 KB  
Article
Confirmation of Large Language Models in Head and Neck Cancer Staging
by Mehmet Kayaalp, Hatice Bölek and Hatime Arzu Yaşar
Diagnostics 2025, 15(18), 2375; https://doi.org/10.3390/diagnostics15182375 - 18 Sep 2025
Viewed by 267
Abstract
Background/Objectives: Head and neck cancer (HNC) is a heterogeneous group of malignancies in which staging plays a critical role in guiding treatment and prognosis. Large language models (LLMs) such as ChatGPT, DeepSeek, and Grok have emerged as potential tools in oncology, yet [...] Read more.
Background/Objectives: Head and neck cancer (HNC) is a heterogeneous group of malignancies in which staging plays a critical role in guiding treatment and prognosis. Large language models (LLMs) such as ChatGPT, DeepSeek, and Grok have emerged as potential tools in oncology, yet their clinical applicability in staging remains unclear. This study aimed to evaluate the accuracy and concordance of LLMs compared to clinician-assigned staging in patients with HNC. Methods: The medical records of 202 patients with HNC, who presented to our center between 1 January 2010 and 13 February 2025, were retrospectively reviewed. The information obtained from the hospital information system by a junior researcher was re-evaluated by a senior researcher, and standard staging was completed. Except for the stage itself, the data used for staging were provided to a blinded third researcher, who then entered them into the ChatGPT, DeepSeek, and Grok applications with a staging command. After all staging processes were completed, the data were compiled, and clinician-assigned stages were compared with those generated by the LLMs. Results: The majority of the patients had laryngeal (45.5%) and nasopharyngeal cancer (21.3%). Definitive surgery was performed in 39.6% of the patients. Stage 4 was the most common stage among the patients (54%). The overall concordance rates, Cohen’s kappa values, and F1 scores were 85.6%, 0.797, and 0.84 for ChatGPT; 67.3%, 0.522, and 0.65 for DeepSeek; and 75.2%, 0.614, and 0.72 for Grok, respectively, with no statistically significant differences between models. Pathological and surgical staging were found to be similar in terms of concordance. The concordance of assessments utilizing only imaging, only pathology notes, only physical examination notes, and comprehensive information was evaluated, revealing no significant differences. Conclusions: Large language models (LLMs) demonstrate relatively high accuracy in staging HNC. With careful implementation and with the consideration of prospective studies, these models have the potential to become valuable tools in oncology practice. Full article
(This article belongs to the Special Issue Integrative Approaches in Head and Neck Cancer Imaging)
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18 pages, 2229 KB  
Article
Large Language Models for Construction Risk Classification: A Comparative Study
by Abdolmajid Erfani and Hussein Khanjar
Buildings 2025, 15(18), 3379; https://doi.org/10.3390/buildings15183379 - 18 Sep 2025
Viewed by 349
Abstract
Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The [...] Read more.
Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article
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25 pages, 6169 KB  
Article
Processing Written Language in Video Games: An Eye-Tracking Study on Subtitled Instructions
by Haiting Lan, Sixin Liao, Jan-Louis Kruger and Michael J. Richardson
J. Eye Mov. Res. 2025, 18(5), 44; https://doi.org/10.3390/jemr18050044 - 17 Sep 2025
Viewed by 235
Abstract
Written language is a common component among the multimodal representations that help players construct meanings and guide actions in video games. However, how players process texts in video games remains underexplored. To address this, the current exploratory eye-tracking study examines how players processed [...] Read more.
Written language is a common component among the multimodal representations that help players construct meanings and guide actions in video games. However, how players process texts in video games remains underexplored. To address this, the current exploratory eye-tracking study examines how players processed subtitled instructions and resultant game performance. Sixty-four participants were recruited to play a videogame set in a foggy desert, where they were guided by subtitled instructions to locate, corral, and contain robot agents (targets). These instructions were manipulated into three modalities: visual-only (with subtitled instructions only), auditory only (with spoken instructions), and visual–auditory (with both subtitled and spoken instructions). The instructions were addressed to participants (as relevant subtitles) or their AI teammates (as irrelevant subtitles). Subtitle-level results of eye movements showed that participants primarily focused on the relevant subtitles, as evidenced by more fixations and higher dwell time percentages. Moreover, the word-level results indicate that participants showed lower skipping rates, more fixations, and higher dwell time percentages on words loaded with immediate action-related information, especially in the absence of audio. No significant differences were found in player performance across conditions. The findings of this study contribute to a better understanding of subtitle processing in video games and, more broadly, text processing in multimedia contexts. Implications for future research on digital literacy and computer-mediated text processing are discussed. Full article
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19 pages, 1599 KB  
Article
Enhancing Clinical Named Entity Recognition via Fine-Tuned BERT and Dictionary-Infused Retrieval-Augmented Generation
by Soumya Challaru Sreenivas, Saqib Chowdhury and Mohammad Masum
Electronics 2025, 14(18), 3676; https://doi.org/10.3390/electronics14183676 - 17 Sep 2025
Viewed by 391
Abstract
Clinical notes often contain unstructured text filled with abbreviations, non-standard terminology, and inconsistent phrasing, which pose significant challenges for automated medical information extraction. Named Entity Recognition (NER) plays a crucial role in structuring this data by identifying and categorizing key clinical entities such [...] Read more.
Clinical notes often contain unstructured text filled with abbreviations, non-standard terminology, and inconsistent phrasing, which pose significant challenges for automated medical information extraction. Named Entity Recognition (NER) plays a crucial role in structuring this data by identifying and categorizing key clinical entities such as symptoms, medications, and diagnoses. However, traditional and even transformer-based NER models often struggle with ambiguity and fail to produce clinically interpretable outputs. In this study, we present a hybrid two-stage framework that enhances medical NER by integrating a fine-tuned BERT model for initial entity extraction with a Dictionary-Infused Retrieval-Augmented Generation (DiRAG) module for terminology normalization. Our approach addresses two critical limitations in current clinical NER systems: lack of contextual clarity and inconsistent standardization of medical terms. The DiRAG module combines semantic retrieval from a UMLS-based vector database with lexical matching and prompt-based generation using a large language model, ensuring precise and explainable normalization of ambiguous entities. The fine-tuned BERT model achieved an F1 score of 0.708 on the MACCROBAT dataset, outperforming several domain-specific baselines, including BioBERT and ClinicalBERT. The integration of the DiRAG module further improved the interpretability and clinical relevance of the extracted entities. Through qualitative case studies, we demonstrate that our framework not only enhances clarity but also mitigates common issues such as abbreviation ambiguity and terminology inconsistency. Full article
(This article belongs to the Special Issue Advances in Text Mining and Analytics)
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14 pages, 277 KB  
Article
Breaking the Silence: A Narrative of the Survival of Afghan’s Music
by Ângela Teles and Paula Guerra
Soc. Sci. 2025, 14(9), 549; https://doi.org/10.3390/socsci14090549 - 15 Sep 2025
Viewed by 347
Abstract
Humanity currently faces a state of crisis, as it navigates the challenges of a quickly evolving world. The increasing number of conflicts and wars has had serious repercussions on human life, contributing to the displacement of populations and a growing influx of refugees. [...] Read more.
Humanity currently faces a state of crisis, as it navigates the challenges of a quickly evolving world. The increasing number of conflicts and wars has had serious repercussions on human life, contributing to the displacement of populations and a growing influx of refugees. The high number of children and young people among this group requires urgent action to meet their needs for education, health, and a secure upbringing. Music education provides one platform for unique expression and identity for these age groups. In 2022, nearly a hundred young musicians from Afghanistan were welcomed into the cities of Braga and Guimarães in Portugal. They work to defend their culture through orchestral activity which has achieved international reach, thanks to the work of the Afghanistan National Institute of Music (ANIM). This article examines how music connects Afghan refugee youth with host communities. It focuses on the role of musical practice in fostering integration within schools and the broader urban context. Using a qualitative approach, based on ethnographic observation of this orchestra’s rehearsals, this article explores the concept of affordances. Ethnographic observation was conducted throughout school activities, music workshops, and informal interactions during break periods. Field notes focused on participants’ non-verbal expressions, musical engagement, and interactions with both peers and educators. These observations were used to contextualise the interviews and triangulate the data. This theoretical–analytical approach shows that, for these youngsters, music plays a mediating role regarding social actions and experiences, shaping new subjectivities and their externalisations. It is a technology of the self, of (re)adaptation, resistance, and identity re-emergence. The main argument is that ANIM’s music in action is a communication tool that, like migratory processes, reconfigures the identities of its protagonists. Music has been demonstrated to function as a catalyst for connection, predominantly within the context of ensemble and orchestra rehearsals, serving as a shared language. Full article
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