Search Results (3,941)

Background: Chronic pain affects nearly one in five adults worldwide and is increasingly recognized not only as a disease but as a potential risk factor for neurocognitive decline and dementia. While some evidence supports this association, existing systematic reviews are static and rapidly outdated, and none have leveraged advanced methods for continuous updating and robust uncertainty modeling. Objective: This protocol describes a living systematic review with dose–response Bayesian meta-analysis, enhanced by artificial intelligence (AI) tools, to synthesize and maintain up-to-date evidence on the prospective association between any type of chronic pain and subsequent cognitive decline. Methods: We will systematically search PubMed, Embase, Web of Science, and preprint servers for prospective cohort studies evaluating chronic pain as an exposure and cognitive decline as an outcome. Screening will be semi-automated using natural language processing models (ASReview), with human oversight for quality control. Bayesian hierarchical meta-analysis will estimate pooled effect sizes and accommodate between-study heterogeneity. Meta-regression will explore study-level moderators such as pain type, severity, and cognitive domain assessed. If data permit, a dose–response meta-analysis will be conducted. Living updates will occur biannually using AI-enhanced workflows, with results transparently disseminated through preprints and peer-reviewed updates. Results: This is a protocol; results will be disseminated in future reports. Conclusions: This living Bayesian systematic review aims to provide continuously updated, methodologically rigorous evidence on the link between chronic pain and cognitive decline. The approach integrates innovative AI tools and advanced meta-analytic methods, offering a template for future living evidence syntheses in neurology and pain research. Full article

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis. Full article

The deployment of large language models (LLMs) in academic paper evaluation is increasingly widespread, yet their trustworthiness remains debated; to expose fundamental flaws often masked under conventional testing, this study employed extreme-scenario testing to systematically probe the lower performance boundaries of LLMs in assessing the scientific validity and logical coherence of papers from the humanities and social sciences (HSS). Through a highly credible quasi-experiment, 40 high-quality Chinese papers from philosophy, sociology, education, and psychology were selected, for which domain experts created versions with implanted “scientific flaws” and “logical flaws”. Three representative LLMs (GPT-4, DeepSeek, and Doubao) were evaluated against a baseline of 24 doctoral candidates, following a protocol progressing from ‘broad’ to ‘targeted’ prompts. Key findings reveal poor evaluation consistency, with significantly low intra-rater and inter-rater reliability for the LLMs, and limited flaw detection capability, as all models failed to distinguish between original and flawed papers under broad prompts, unlike human evaluators; although targeted prompts improved detection, LLM performance remained substantially inferior, particularly in tasks requiring deep empirical insight and logical reasoning. The study proposes that LLMs operate on a fundamentally different “task decomposition-semantic understanding” mechanism, relying on limited text extraction and shallow semantic comparison rather than the human process of “worldscape reconstruction → meaning construction and critique”, resulting in a critical inability to assess argumentative plausibility and logical coherence. It concludes that current LLMs possess fundamental limitations in evaluations requiring depth and critical thinking, are not reliable independent evaluators, and that over-trusting them carries substantial risks, necessitating rational human-AI collaborative frameworks, enhanced model adaptation through downstream alignment techniques like prompt engineering and fine-tuning, and improvements in general capabilities such as logical reasoning. Full article

Background: Stroke is a leading cause of seizures and epilepsy, both of which are linked to increased mortality, disability, and hospital readmissions. Early recognition and management of seizures in acute stroke are crucial for improving outcomes. Electroencephalogram (EEG) is not routinely used for post-stroke seizure monitoring and is typically initiated only after clinical suspicion arises, making bedside recognition essential. This scoping review aimed to map the existing literature on clinical methods used for identifying and observing early post-stroke seizures (EPSSs) at the bedside. Methods: We included literature involving adults with acute ischaemic stroke or primary intracerebral haemorrhage who were diagnosed or suspected of having inpatient EPSS. Searches were conducted in Medline, CINAHL, Embase, and the Cochrane Library for English-language publications up to April 2023. Eligible sources included primary research, case reports, systematic reviews, clinical guidelines, consensus statements, and expert opinion. Reference lists of included articles were also reviewed. Data were charted and synthesised to assess the scope, type, and gaps in the evidence. Results: Thirty papers met inclusion criteria: 17 research studies, six expert opinions, four case reports, and three clinical guidelines. Empirical evidence on clinical methods for seizure recognition and monitoring in acute stroke was limited. No studies evaluated the effectiveness of different approaches, and existing recommendations lacked detail and consensus. Conclusions: Accurate EPSS diagnosis is vital due to its impact on outcomes. This review highlights inconsistency in monitoring methods and a clear need for targeted research into effective clinical identification strategies in acute stroke care. Full article

The conceptual design phase in architecture establishes the foundation for subsequent design decisions and influences up to 80% of a building’s lifecycle environmental impact. While Building Information Modeling (BIM) demonstrates transformative potential for sustainable design, its application during conceptual design remains constrained by perceived technical complexity and limited support for abstract thinking. This research examines how BIM tools can facilitate conceptual design through diagrammatic reasoning, thereby bridging technical capabilities with creative exploration. A mixed-methods approach was employed to develop and validate a Diagrammatic BIM (D-BIM) framework. It integrates diagrammatic reasoning, parametric modeling, and performance evaluation within BIM environments. The framework defines three core relationships—dissection, articulation, and actualization—which enable transitions from abstract concepts to detailed architectural forms in Revit’s modeling environments. Using Richard Meier’s architectural language as a structured test case, a 14-week quasi-experimental study with 19 third-year architecture students assessed the framework’s effectiveness through pre- and post-surveys, observations, and artifact analysis. Statistical analysis revealed significant improvements (p < 0.05) with moderate to large effect sizes across all measures, including systematic design thinking, diagram utilization, and academic self-efficacy. Students demonstrated enhanced design iteration, abstraction-to-realization transitions, and performance-informed decision-making through quantitative and qualitative assessments during early design stages. However, the study’s limitations include a small, single-institution sample, the absence of a control group, a focus on a single architectural language, and the exploratory integration of environmental analysis tools. Findings indicate that the framework repositions BIM as a cognitive design environment that supports creative ideation while integrating structured design logic and performance analysis. The study advances Education for Sustainable Development (ESD) by embedding critical, systems-based, and problem-solving competencies, demonstrating BIM’s role in sustainability-focused early design. This research provides preliminary evidence that conceptual design and BIM are compatible when supported with diagrammatic reasoning, offering a foundation for integrating competency-based digital pedagogy that bridges creative and technical dimensions of architectural design. Full article

This pilot study examined how sociodemographic factors (age, education, internet usage) influence patients’ comprehension of written healthcare communications, and their understanding of patient rights as articulated in appointment letters. A cross-sectional study was conducted among in-clinic patients at three Danish hospitals. Participants completed a self-administered questionnaire, assessing health literacy through four domains: assessing, understanding, appraising, and applying information from appointment letters. The questionnaire included sociodemographic data, Internet usage, IT competencies, and self-assessed health. Overall, 364 patients participated, with the majority being female and aged between 35 and 74 years. The mean scores for the domains of understanding and applying information were higher compared to assessing and appraising. Multiple linear regression analysis revealed that higher education levels positively correlated with the ability to appraise legal information, while frequent internet usage also enhanced appraisal skills. Findings highlight a concerning gap in patients’ ability to understand and appraise their patient rights within written healthcare communications. While patients demonstrate reasonable skills in understanding basic information, critical legal aspects remain challenging. Enhancing education and digital literacy may improve comprehension, emphasizing the need for simplified language and alternative formats in appointment letters. Further research is warranted to optimize communication strategies for patient rights. Full article

Augmented Reality (AR) continues to generate interest as a pedagogical tool in contexts where English is a Foreign Language (EFL). However, its role in developing higher-order cognitive skills, such as inferencing, remains underexplored. This exploratory, mixed-methods study investigates whether AR can scaffold inference-making in advanced EFL learners. Forty-seven university students in XX were assigned to either a control group (CG) or an experimental group (EG). Both groups read Edgar Allan Poe’s “The Tell-Tale Heart” in digital format. The CG received a conventional inference-based comprehension lesson, while the EG engaged with two interactive AR scenes developed using the Onirix Studio platform. Pre- and post-tests assessed inferential comprehension, and qualitative data were gathered through open-ended responses. While the CG demonstrated modest post-test gains and the EG showed a slight decline, neither change reached statistical significance. Notably, qualitative findings revealed that a salient AR element—a ticking clock—likely prompted misinterpretation in the EG, disrupting symbolic reasoning and contributing to schema misalignment. However, some learners exhibited metacognitive insight suggestive of productive struggle. These results suggest that AR may hold untapped potential for developing metacognitive reflection and critical literacy. Directions for future research are outlined. Full article

This study evaluated emergency preparedness, covering knowledge, challenges, and culturally and linguistically appropriate resources among five resettled refugee communities in Central Ohio: Afghan, Bhutanese, Congolese, Ethiopian/Eritrean, and Somali. It also explored youth perceptions of community engagement and the effectiveness of current crisis support initiatives. A mixed-methods approach was used, consisting of a cross-sectional quantitative survey of 266 adults and a qualitative 60-min focus group discussion with 10 youth from the resettled communities. Quantitative data was analyzed with descriptive statistics and Chi-square tests assessing differences in preparedness across communities. Quantitative findings showed that the Bhutanese group reported the highest familiarity with preparedness concepts (33.3%), while Afghan and Congolese communities showed significant gaps. Of the 266 overall respondents, only 39.5% had an emergency plan, and 15.8% felt extremely confident in handling emergencies. Language barriers were major challenges, along with those related to race and ethnic identity, religious practices, and cultural norms. Effective platforms for engagement included community meetings, religious sites, and social media, with text messages and phone calls preferred for emergency communication. Youth discussion highlighted key public health concerns, particularly gun violence, substance abuse, and mental health crises, with stigma and limited resources further hindering preparedness efforts. Participants emphasized the need for culturally relevant interventions and stronger community engagement. Full article

Background/Objectives: Spanish-speaking patients face persistent barriers in accessing equitable audiological care, particularly when standardized language-appropriate tools are lacking. Two Spanish-language sentence recognition tests, the Spanish AzBio Sentence (SAzB) and the Latin American Hearing in Noise Test (LAH), are commonly used to evaluate speech perception in adults with hearing loss. However, performance differences between these measures may influence referral decisions for hearing intervention, such as cochlear implantation. This study compared test performance under varying noise and spatial conditions to guide appropriate test selection and reduce the risk of misclassification that may contribute to healthcare disparities. Methods: Twenty-one bilingual Spanish/English speaking adults with normal bilateral hearing completed speech perception testing using both the SAzB and LAH. Testing was conducted under two spatial configurations: (1) speech and noise presented from the front (0° azimuth) and (2) speech to the simulated poorer ear and noise to the better ear (90°/270° azimuth). Conditions included quiet and three signal-to-noise ratios (+10, +5, and 0 dB). Analyses included paired t-tests and one-way ANOVAs. Results: Participants scored significantly higher on the LAH than on the SAzB across all SNR conditions and configurations, with ceiling effects observed for the LAH. SAzB scores varied by language dominance, while LAH scores did not. No other differences were observed based on any further demographic information. Conclusions: The SAzB provides a more challenging and informative assessment of speech perception in noise. Relying on easier tests like the LAH may obscure real-world difficulties and delay appropriate referrals for hearing loss intervention, including cochlear implant evaluation. Selecting the most appropriate test is critical to avoiding under-referral and ensuring Spanish-speaking patients receive equitable and accurate care. Full article

Background/Objectives. Patient-Reported Outcome Measures (PROMs) capture patients’ perspectives about their health status, quality of life, and medical care outcomes. LIMB-Q Kids is a validated PROM designed to assess health-related quality of life (HRQL) in children with lower limb differences. It evaluates physical, social, and psychological function; symptoms related to the leg, hip, knee, foot and ankle; leg-related distress, appearance, and school-related concerns. It has now been translated and culturally adapted from English to other languages. The aim of this study was to translate and culturally adapt LIMB-Q Kids to Hebrew. Methods. Following international guidelines, two independent forward translations from English to Hebrew were produced and reconciled into a single version. A backward translation was then compared with the original to identify discrepancies. This draft underwent cognitive debriefing interviews (CDIs) with 11 children (ages 8–15) having lower limb differences to assess comprehension and cultural relevance. Results. CDIs revealed general issues including lengthy or unclear text, high-level language, cultural unfamiliarity and duplication of descriptors. Specific to Hebrew, issues included gender inflections and the need for vowel diacritics to support younger, less proficient readers. Revisions to 14 items were made. Conclusions. A translation and cultural adaption (TCA) process led to a linguistically validated and culturally adapted Hebrew version of LIMB-Q Kids. It can now be used for the clinical follow-up of children with lower limb differences including pre- and post-operatively, and as an aid to decision-making for surgery. Full article

Flexible laryngoscopy (FL) is the standard diagnostic tool for vocal cord paralysis (VCP), but it involves patient discomfort, and its interpretation is subjective and operator-dependent. Dynamic digital radiography (DDR) is a novel imaging technique that acquires high-resolution sequential radiographs at a low radiation dose. While DDR has been widely applied in chest and diaphragmatic imaging, its use for laryngeal motion analysis has been poorly investigated. We present the case of a 50-year-old male referred for Computed Tomography (CT) of the neck and chest for suspected vocal cord paralysis. The referring physician did not specify the side of the suspected paralysis. Due to a language barrier and the absence of prior documentation, a detailed history could not be obtained. To assess vocal cord motion, we performed, for the first time in our Institution, a DDR study of the neck. During phonation maneuvers, DDR demonstrated fixation of the left vocal cord in an adducted paramedian position. CT confirmed this finding and did not highlight any further anomaly. This case demonstrates the feasibility of DDR as a low-cost, low-dose, non-invasive technique for functional evaluation of the larynx and may represent a valuable complementary imaging tool in laryngeal functional assessment. Full article

Urban green spaces are essential for mitigating the heat island effect, supporting ecosystem services, and maintaining biodiversity. The distribution, fragmentation, and connection of the green spaces significantly impact the behavior of species in cities, serving as key indicators of environmental resilience and ecological benefits. However, current studies, as well as planning standards, often prioritize green spaces independently through their coverage or density, overlooking the importance of continuity and its impact on thermal regulation and accessibility. In this research, urban “hidden green corridors” refer to the unrecognized but functionally significant pathways that link fragmented green spaces through ecological behaviors, which enhance both biological and human habitats. This research focuses on developing an agent-based simulation (ABS) model based on the Physarealm plugin in Rhino, which can assess the effectiveness of these hidden corridors in different urban settings by integrating geographic information systems (GIS) and space syntax. Based on three case studies in Italy (Lambrate District, Bolognina, and Ispra), the simulation results are further interpreted through the AI agentic workflow “SOFIA”, developed by IMM Design Lab, Politecnico di Milano, and compared using manual analysis as well as mainstream large language models (ChatGPT 4.0 Web). The findings indicate that the “hidden green corridors” are essential for urban heat reduction, enhancement of urban biodiversity, and strengthening ecological flows. Full article

 COVID-19 infection is often accompanied by psychological symptoms, which may persist long after the end of the infection (long COVID). The symptoms include fatigue, cognitive impairment, and anxiety. The reason for these long-term effects is currently unclear. Therapeutic approaches have included cognitive rehabilitation therapy, physical activity, and serotonin reuptake inhibitors (SSRIs) if depression co-exists. The neuropsychological evaluation of subjects with suspected cognitive issues is essential for the correct diagnosis. Most of the COVID-19 studies used the Montreal Cognitive Assessment (MoCA) or the Mini Mental State Examination (MMSE). However, MoCA scores can be confusing if not interpreted correctly. For this reason, we have developed an original technique to map cognitive domains and motor performance on various brain areas in COVID-19 patients aiming at improving the follow-up of long-COVID-19 symptoms. To this end, we retrospectively reanalyzed data from a cohort of 40 patients hospitalized for COVID-19 without requiring intubation or hemodialysis. Cognitive function was tested during hospitalization and six months after. Global cognitive function and cognitive domains were retrieved using MoCA tests. Laboratory data were retrieved regarding kidney function, electrolytes, acid–base, blood pressure, TC score, and P/F ratio. The dimensionality of cognitive functions was represented over cortical brain structures using a transformation matrix derived from fMRI data from the literature and the Cerebroviz mapping tool. Memory function was linearly dependent on the P/F ratio. We also used the UMAP method to reduce the dimensionality of the data and represent them in low-dimensional space. Six months after hospitalization, no cases of severe cognitive deficit persisted, and the number of moderate cognitive deficits reduced from 14% to 4%. Most cognitive domains (visuospatial abilities, executive functions, attention, working memory, spatial–temporal orientation) improved over time, except for long-term memory and language skills, which remained reduced or slightly decreased. The Cerebroviz algorithm helps to visualize which brain regions might be involved in the process. Many patients with COVID-19 continue to suffer from a subclinical cognitive deficit, particularly in the memory and language domains. Cerebroviz’s representation of the results provides a new tool for visually representing the data.  Full article

Urban planning in post-industrial cities is often limited by stakeholders’ understanding of the potential for intensifying mixed industrial and cultural uses, and the benefits to each of their co-location. In Brussels, differences in language and governance pose additional challenges to actors trying to bring together diverse stakeholders to cooperatively plan for urban regeneration. As part of a wider action research project on regeneration of the city, five co-design workshops were held across Brussels in 2017–2019 and interviews with participants were conducted. Later, in 2023, the researchers returned to the city to assess the practical outcomes of the project. This paper explores the role of the workshops in harnessing the untapped potential for integrating and expanding mixed industrial and cultural uses, and highlights the factors required to inspire and motivate diverse stakeholders to drive sustainable innovation and growth. Full article