Search Results (534)

Background: Zoonotic influenza viruses pose a significant and evolving public health threat. In response to the recent rise in H5N1 cross-species transmission, the World Health Organization (WHO) R&D Blueprint for Epidemics consultations have prioritized strengthening surveillance, candidate vaccines, diagnostics, and pandemic preparedness. Serological surveillance plays a pivotal role by providing insights into the prevalence and transmission dynamics of influenza viruses. Objective: This scoping review aimed to map the global research landscape on serological surveillance of zoonotic influenza in animals and exposed humans between 2017, the date of the last WHO public health research agenda for influenza review, and 2024, as well as to identify methodological advancements. Methods: Following PRISMA-ScR guidelines, we searched PubMed for English-language peer-reviewed articles published between January 2017 and March 2024. Studies were included if they reported serological surveillance in wild or domestic animals or occupationally exposed human populations, or novel methodologies and their technical limitations and implementation challenges. Results: Out of 7490 screened records, 90 studies from 33 countries, covering 25 animal species, were included. Seroprevalence studies were in domestic poultry and swine. Surveillance in companion animals, wild mammals, and at the human–animal interface was limited. Emerging serological methods included multiplex and nanobody-based assays, though implementation barriers remain. Conclusions: The review is limited by its restriction to one database and English-language articles, lack of quality appraisal, and significant heterogeneity among the included studies. Serological surveillance is a critical but underutilized tool in zoonotic influenza monitoring. Greater integration of serological surveillance into One Health frameworks, especially in high-risk regions and populations, is needed to support early detection and pandemic preparedness. Full article

Japanese scholarship occupies a special place in the study of Old Uyghur philology, Central Asian history and religions on the Silk Road. The German volume edited by J. P. Laut and K. Röhrborn in 1988 provides an overview of Japanese studies of Old Uyghur Buddhism and presents some representative works published up to 1988. For publications subsequent to 1988, an appendix to Laut and K. Röhrborn’s volume by M. Ölmez can be mentioned, though it provides minimal elaboration on Buddhist publications. The present paper offers a comprehensive review of notable contributions to the study of Old Uyghur Buddhism published in Japanese in recent years, which are challenging to access for scholars unacquainted with the Japanese language. It focuses exclusively on publications released between 2000 and 2024 in Japanese, with no English translations available at the time of submission of this paper. Full article

Social media has become a platform for the rapid spread of persuasion techniques that can negatively affect individuals and society. Propaganda detection, a crucial task in natural language processing, aims to identify manipulative content in texts, particularly in news media, by assessing propagandistic intent. Although extensively studied in English, Arabic propaganda detection remains challenging because of the language’s morphological complexity and limited resources. Furthermore, most research has treated propaganda detection as an isolated task, neglecting the influence of sentiments and emotions. The current study addresses this gap by introducing the first multi-task learning (MTL) models for Arabic propaganda detection, integrating sentiment analysis and emotion detection as auxiliary tasks. Three MTL models are introduced: (1) MTL combining all tasks, (2) PSMTL (propaganda and sentiment), and (3) PEMTL (propaganda and emotion) based on transformer architectures. Additionally, seven task-weighting schemes are proposed and evaluated. Experiments demonstrated the superiority of our framework over state-of-the-art methods, achieving a Macro-F1 score of 0.778 and 79% accuracy. The results highlight the importance of integrating sentiment and emotion for enhanced propaganda detection; demonstrate that MTL improves model performance; and provide valuable insights into the interaction among sentiment, emotion, and propaganda. Full article

Purpose: Pellucid Marginal Degeneration (PMD) is a rare ectatic corneal disorder characterized by inferior peripheral thinning and significant irregular astigmatism. Despite its clinical similarities to keratoconus, PMD presents unique diagnostic and therapeutic challenges. This review aims to provide a comprehensive update on the pathophysiology, clinical features, diagnostic approaches, and management strategies for PMD, emphasizing the latest advancements in treatment options. Methods: A systematic literature search was performed in MEDLINE (via PubMed), Google Scholar, and Scopus up to February 2025 using the terms: “pellucid marginal degeneration,” “PMD,” “ectatic corneal disorders,” “keratoplasty in PMD,” “corneal cross-linking in PMD,” “ICRS in PMD,” “toric IOL PMD” and their Boolean combinations (AND/OR). The search was restricted to English-language studies involving human subjects, including case reports, case series, retrospective studies, clinical trials, and systematic reviews. A total of 76 studies met the inclusion criteria addressing treatment outcomes in PMD. Results: PMD is characterized by a crescent-shaped band of inferior corneal thinning, leading to high irregular astigmatism and reduced visual acuity. Diagnosis relies on advanced imaging techniques such as Scheimpflug-based corneal tomography, which reveals the characteristic “crab-claw” pattern. Conservative management includes rigid gas-permeable (RGP) lenses and scleral lenses, which provide effective visual rehabilitation in mild to moderate cases. Surgical options, such as CXL, ICRS, and toric IOLs, are reserved for advanced cases, with varying degrees of success. Newer techniques such as CAIRS, employing donor tissue instead of synthetic rings, show promising outcomes in corneal remodeling with potentially improved biocompatibility. Penetrating keratoplasty (PK) and deep anterior lamellar keratoplasty (DALK) remain definitive treatments for severe PMD, though they are associated with significant risks, including graft rejection and postoperative astigmatism. Conclusions: PMD is a complex and progressive corneal disorder that requires a tailored approach to management. Early diagnosis and intervention are critical to optimizing visual outcomes. While conservative measures are effective in mild cases, surgical interventions offer promising results for advanced disease. Further research is needed to refine treatment protocols and improve long-term outcomes for patients with PMD. Full article

Small Language Models (SLMs) have emerged as a critical area of study within natural language processing, attracting growing attention from both academia and industry. This systematic literature review provides a comprehensive and reproducible analysis of recent developments and advancements in SLMs post-2023. Drawing on 70 English-language studies published between January 2023 and January 2025, identified through Scopus, IEEE Xplore, Web of Science, and ACM Digital Library, and focusing primarily on SLMs (including those with up to 7 billion parameters), this review offers a structured overview of the current state of the art and potential future directions. Designed as a resource for researchers seeking an in-depth global synthesis, the review examines key dimensions such as publication trends, visual data representations, contributing institutions, and the availability of public datasets. It highlights prevailing research challenges and outlines proposed solutions, with a particular focus on widely adopted model architectures, as well as common compression and optimization techniques. This study also evaluates the criteria used to assess the effectiveness of SLMs and discusses emerging de facto standards for industry. The curated data and insights aim to support and inform ongoing and future research in this rapidly evolving field. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

Research on language contact outcomes, such as code-switching, continues to face theoretical and methodological challenges, particularly due to the difficulty of comparing findings across studies that use divergent data collection methods. Accordingly, scholars have emphasized the need for publicly available and comparable bilingual corpora. This paper introduces the El Paso Bilingual Corpus, a new Spanish–English bilingual corpus recorded in El Paso (TX) in 2022, designed to be methodologically comparable to the Bangor Miami Corpus. The paper is structured in three main sections. First, we review the existing Spanish–English corpora and examine the theoretical challenges posed by studies using non-comparable methodologies, thereby underscoring the gap addressed by the El Paso Bilingual Corpus. Second, we outline the corpus creation process, discussing participant recruitment, data collection, and transcription, and provide an overview of these data, including participants’ sociolinguistic profiles. Third, to demonstrate the practical value of methodologically aligned corpora, we report a comparative case study on diminutive expressions in the El Paso and Bangor Miami corpora, illustrating how shared collection protocols can elucidate the role of community-specific social factors on bilinguals’ morphosyntactic choices. Full article

Telemedicine in emergency contexts presents unique challenges, particularly in multilingual and low-resource settings where accurate, clinical understanding and triage decision support are critical. This paper introduces TriagE-NLU, a novel multilingual natural language understanding system designed to perform both semantic parsing and clinical intervention classification from emergency dialogues. The system is built on a federated learning architecture to ensure data privacy and adaptability across regions and is trained using TriageX, a synthetic, clinically grounded dataset covering five languages (English, Spanish, Romanian, Arabic, and Mandarin). TriagE-NLU integrates fine-tuned multilingual transformers with a hybrid rules-and-policy decision engine, enabling it to parse structured medical information (symptoms, risk factors, temporal markers) and recommend appropriate interventions based on recognized patterns. Evaluation against strong multilingual baselines, including mT5, mBART, and XLM-RoBERTa, demonstrates superior performance by TriagE-NLU, achieving F1 scores of 0.91 for semantic parsing and 0.89 for intervention classification, along with 0.92 accuracy and a BLEU score of 0.87. These results validate the system’s robustness in multilingual emergency telehealth and its ability to generalize across diverse input scenarios. This paper establishes a new direction for privacy-preserving, AI-assisted triage systems. Full article

The proliferation of hate speech on social media platforms poses significant threats to digital safety, social cohesion, and freedom of expression. Detecting such content—especially across diverse languages—remains a challenging task due to linguistic complexity, cultural context, and resource limitations. To address these challenges, this study introduces a comprehensive approach for multilingual hate speech detection. To facilitate robust hate speech detection across diverse languages, this study makes several key contributions. First, we created a novel trilingual hate speech dataset consisting of 10,193 manually annotated tweets in English, Spanish, and Urdu. Second, we applied two innovative techniques—joint multilingual and translation-based approaches—for cross-lingual hate speech detection that have not been previously explored for these languages. Third, we developed detailed hate speech annotation guidelines tailored specifically to all three languages to ensure consistent and high-quality labeling. Finally, we conducted 41 experiments employing machine learning models with TF–IDF features, deep learning models utilizing FastText and GloVe embeddings, and transformer-based models leveraging advanced contextual embeddings to comprehensively evaluate our approach. Additionally, we employed a large language model with advanced contextual embeddings to identify the best solution for the hate speech detection task. The experimental results showed that our GPT-3.5-turbo model significantly outperforms strong baselines, achieving up to an 8% improvement over XLM-R in Urdu hate speech detection and an average gain of 4% across all three languages. This research not only contributes a high-quality multilingual dataset but also offers a scalable and inclusive framework for hate speech detection in underrepresented languages. Full article

As streaming platforms adopt artificial intelligence (AI)-powered subtitle systems to satisfy global demand for instant localization, the cognitive impact of these automated translations on viewers remains largely unexplored. This study used a web-based eye-tracking protocol to compare the cognitive load that GPT-4o-generated Arabic subtitles impose with that of professional human translations among 82 native Arabic speakers who viewed a 10 min episode (“Syria”) from the BBC comedy drama series State of the Union. Participants were randomly assigned to view the same episode with either professionally produced Arabic subtitles (Amazon Prime’s human translations) or machine-generated GPT-4o Arabic subtitles. In a between-subjects design, with English proficiency entered as a moderator, we collected fixation count, mean fixation duration, gaze distribution, and attention concentration (K-coefficient) as indices of cognitive processing. GPT-4o subtitles raised cognitive load on every metric; viewers produced 48% more fixations in the subtitle area, recorded 56% longer fixation durations, and spent 81.5% more time reading the automated subtitles than the professional subtitles. The subtitle area K-coefficient tripled (0.10 to 0.30), a shift from ambient scanning to focal processing. Viewers with advanced English proficiency showed the largest disruptions, which indicates that higher linguistic competence increases sensitivity to subtle translation shortcomings. These results challenge claims that large language models (LLMs) lighten viewer burden; despite fluent surface quality, GPT-4o subtitles demand far more cognitive resources than expert human subtitles and therefore reinforce the need for human oversight in audiovisual translation (AVT) and media accessibility. Full article

Non-celiac villous atrophy (NCVA) is a multifaceted and under-recognized clinical entity with an etiology beyond celiac disease. This review critically examines the diverse pathophysiological mechanisms underlying NCVA, including autoimmune enteropathies, immune deficiency-related disorders, infectious processes, drug-induced trauma, and metabolic or environmental influences. A comprehensive synthesis of peer-reviewed literature, clinical studies, and case reports was conducted, adopting a multidisciplinary perspective that integrates immunologic, infectious, metabolic, and pharmacologic insights. The literature search was performed in three phases: identification of relevant studies, critical assessment of selected publications, and synthesis of key findings. Searches were carried out in PubMed, Scopus, Web of Science, and Google Scholar databases. The final search, completed in June 2025, included international, English-language articles, electronic books, and online reports. Studies were included if they addressed NCVA in the context of pathophysiology, clinical manifestations, or management strategies, with priority given to publications from the last ten years (2015–2025). The search strategy used the primary term “non-celiac villous atrophy” combined with supplementary keywords such as autoimmune enteropathy, common variable immunodeficiency, tropical sprue, drug-related enteropathy, pathophysiology, immunological mechanisms, chronic inflammation, genetic factors, environmental influences, and clinical management. Histopathological evaluations reveal that NCVA often manifests with varying degrees of villous blunting, crypt hypertrophy, and intraepithelial lymphocytosis, albeit without the gliadin-specific immune response seen in celiac disease. Various immune pathways are involved, such as autoimmune deregulation and chronic inflammatory responses, while drug-induced and environmental factors further complicate its clinical picture. These findings highlight significant diagnostic challenges and underscore the need to adapt diagnostic algorithms that combine clinical history, serologic evaluations, and histopathologic analysis. In conclusion, an in-depth understanding of the heterogeneous etiology of NCVA is critical to improving diagnostic accuracy and optimizing therapeutic strategies. Future research should prioritize the identification of specific biomarkers and the development of targeted interventions to address the unique mechanisms underlying NCVA, thereby improving patient management and outcomes. Full article

Background: Mycobacterium ulcerans causes Buruli ulcer (BU), a necrotizing skin disease endemic in over 30 countries. Its toxin, mycolactone, drives tissue destruction, and the infection is transmitted via environmental reservoirs or vectors. Disease patterns vary globally, and an improved understanding of their pathogenesis may enhance current antimicrobial and surgical treatments. Methods: A comprehensive literature search from 1901 to 2025 was conducted across major databases to explore antimicrobial and reconstructive surgical strategies for Mycobacterium ulcerans. Search terms included BU, key antibiotics, and surgical interventions. Relevant English-language studies on treatment outcomes were reviewed to summarize evolving management trends and emerging therapeutic approaches. Results and Discussion: This review highlights the importance of early diagnosis and timely antimicrobial therapy in preventing disease progression and limb loss. It reviews WHO-recommended antibiotic regimens and discusses the theoretical risk of drug resistance, although clinical resistance remains rare and unreported in Australia. Surgical interventions in select cases are crucial, with timing being a significant factor in functional outcomes. The review also covers pediatric-specific challenges, including growth preservation and psychosocial support for young patients. Reconstructive options focus on limb salvage and staged reconstructions, with multidisciplinary care essential for optimal outcomes. The paper advocates for RCTs to refine treatment protocols, surgical guidelines, and explore emerging antibiotic therapies such as telacebec. Conclusions: BU remains a global health challenge, requiring early diagnosis, timely antimicrobial therapy, and surgery in selected cases. Future research will refine treatment and reduce long-term impacts. Full article

Cross-lingual summarization (XLS) involves generating a summary in one language from an article written in another language. XLS presents substantial hurdles due to the complex linguistic structures across languages and the challenges in transferring knowledge effectively between them. Although Large Language Models (LLMs) have demonstrated capabilities in cross-lingual tasks, the integration of retrieval-based in-context learning remains largely unexplored, despite its potential to overcome these linguistic barriers by providing relevant examples. In this paper, we introduce Multilingual Retrieval-based Cross-lingual Summarization (MuRXLS), a robust framework that dynamically selects the most relevant summarization examples for each article using multilingual retrieval. Our method leverages multilingual embedding models to identify contextually appropriate demonstrations for various LLMs. Experiments across twelve XLS setups (six language pairs in both directions) reveal a notable directional asymmetry: our approach significantly outperforms baselines in many-to-one (X→English) scenarios, while showing comparable performance in one-to-many (English→X) directions. We also observe a strong correlation between article-example semantic similarity and summarization quality, demonstrating that intelligently selecting contextually relevant examples substantially improves XLS performance by providing LLMs with more informative demonstrations. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

Background: In rapidly aging societies like Japan, socioeconomic status (SES) plays a critical role in shaping older adults’ health behaviors. Disparities in SES influence access to healthcare, engagement in health-promoting activities, and the adoption of digital health technologies. This narrative review synthesizes current evidence on how SES affects health behaviors among older adults and highlights challenges in promoting equitable and sustainable healthcare in aging populations. Methods: A PubMed search was conducted for English-language articles published up to May 2025 using the keywords “socioeconomic status”, “older adults”, and terms related to health behaviors. Studies were included if they focused on individuals aged 65 or older and examined associations between SES and healthcare use, digital health, complementary and alternative medicine (CAM), supplements, or lifestyle behaviors. Results: A total of 24 articles were identified. Higher SES—typically measured by income, education, and occupation—was consistently associated with an increased use of preventive services, digital health tools, CAM, and healthier lifestyle behaviors such as diet, physical activity, and sleep. In contrast, lower SES was linked to healthcare underuse or overuse, digital exclusion, and less healthy behaviors. Structural and regional disparities often reinforce individual-level SES effects. Comorbidity burden and shifting health perceptions with age may also modify these associations. Conclusions: SES is a key determinant of health behavior in older adults. Policies should focus on redistributive support, digital inclusion, and SES-sensitive health system strategies to reduce disparities and promote healthy aging in super-aged societies. Full article