Search Results (181)

Background: Sleep disturbances are common in dementia and adversely affect both the person with dementia and their caregiver. Non-pharmacological options exist but are seldom dyadic or culturally tailored, limiting their reach and relevance across diverse communities. Objective: We aimed to co-develop DREAM (Dyadic Resilience, Engagement, Awareness & Mind–body intervention)—an 8-week dyadic mind–body programme (mindfulness + gentle Tai Chi) for improving sleep and wellbeing in people with dementia and their caregivers. Methods: The process was informed by Intervention Mapping (Stages 1–4) and underpinned by established behaviour change frameworks, including the Behaviour Change Wheel (BCW), the COM-B model (Capability, Opportunity, Motivation → Behaviour), and the Theoretical Domains Framework (TDF), to systematically identify determinants of engagement. Co-design involved dementia–caregiver dyads, Patient and Public Involvement (PPI) contributors, clinicians, mind–body practitioners, and community stakeholders. Results: The intervention was co-developed and culturally grounded through engagement with White British, Caribbean, Chinese, and South Asian communities. Participants reported high cultural resonance, endorsing DREAM’s concise practices, caregiver-supported home routines, and delivery in trusted community venues. Behavioural insights highlighted the importance of motivational framing (perceived dyadic benefits, cultural meaning), practical enablement (simplified guidance, prompts/cues, environmental restructuring), and caregiver facilitation to support adherence. Conclusions: DREAM demonstrates the practicability of using Intervention Mapping to co-develop a culturally responsive, theory-informed dyadic mind–body intervention for people with dementia and their caregivers. This groundwork supports progression to a feasibility trial focused on implementation processes and preliminary sleep and wellbeing outcomes. Full article

The rapid escalation of Russia’s war in Ukraine prompted many European countries to implement emergency sponsorship schemes for displaced Ukrainians. In the United Kingdom, the Homes for Ukraine scheme emerged as a prominent example enabling non-related hosts to accommodate refugees in private homes or other settings While widely praised for its humanitarian intent, the British Association of Social Workers raised early concerns about safeguarding risks within the scheme’s infrastructure. Key issues included the absence of a centralized matching system, reliance on informal arrangements via social media, and limited expert-led placement assessments. These gaps posed significant risks not only to refugees and hosts but also to frontline professionals tasked with addressing safeguarding concerns. Despite these challenges, research documenting their impact on practitioners is scarce. This article reports on findings from an online survey capturing professionals’ experiences of identifying and managing these safeguarding issues and the implications on their well-being. Results indicate that, although practitioners expressed pride in delivering the scheme, its rapid rollout, uneven local implementation, and lack of clear guidance contributed to safeguarding vulnerabilities and professional strain. These insights highlight the need for robust planning, clearer accountability, and culturally informed practices in future emergency initiatives. Full article

Growing environmental concerns and regulatory pressures are prompting firms to re-examine packaging design to advance sustainability. Focusing on eco-designed retail packaging in the new-style milk tea industry, this study investigates how specific attributes of eco-designed retail packaging influence consumers’ purchase intention. Data were collected from 425 university students in Wuhan. We measured eco-designed retail packaging (ECRP) with a six-dimension scale (functional, aesthetic, eco-materials, eco-information, eco-production, and innovation) and tested the mediating role of green perceived value (GPV) using structural equation modeling (SEM). Results show differentiated effects of ECRP dimensions on GPV and purchase intention. Functional design and clear eco-information increase both GPV and purchase intention, whereas using eco-materials while directly raising purchase intention reduces GPV. Aesthetics and innovation mainly operate through direct enhancement of purchase intention rather than via GPV. GPV mediates part of the effects of functional attributes, eco-materials, and eco-information on purchase intention. The findings imply that optimizing functionality, information clarity, and material choices in eco-designed retail packaging can simultaneously elevate GPV and purchase intention. As green packaging becomes an industry imperative, this study provides theoretical and practical guidance for sustainable packaging innovation and green industry development. Full article

Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand–Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand–protein interactions. The model was trained on 216,482 experimentally validated ligand–protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein–Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic–area under the curve (macro ROC–AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug–Target Affinity Model (DeepDTA), Graph-based Drug–Target Affinity Model (GraphDTA), Molecule–Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug–Target Interaction (CAT–DTI), and Heterogeneous Graph Transformer for Drug–Target Affinity (HGT–DTA) by 3–7%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign. Full article

The rapid proliferation of misinformation on social media underscores the urgent need for scalable digital-literacy instruction. This study presents the design and evaluation of a Generative AI-enhanced serious game system that integrates Large Language Models (LLMs) to drive adaptive non-player characters (NPCs). Unlike traditional scripted interactions, the system employs role-based prompt engineering to align real-time AI dialogue with the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) framework, enabling dynamic scaffolding and authentic misinformation scenarios. A mixed-method experiment with 60 undergraduate students compared this AI-driven approach to traditional instruction using a 40-item digital-literacy pre/post test, the Intrinsic Motivation Inventory (IMI), and open-ended reflections. Results indicated that while both groups improved significantly, the game-based group achieved larger gains in credibility-evaluation performance and reported higher perceived competence, interest, and effort. Qualitative analysis highlighted the HCI trade-off between the high pedagogical value of adaptive AI guidance and technical constraints such as system latency. The findings demonstrate that Generative AI can be effectively operationalized as a dynamic interface layer in serious games to strengthen critical reasoning. This study provides practical guidelines for architecting AI-NPC interactions and advances the theoretical understanding of AI-supported educational informatics. Full article

At present, generative AI has problems of insufficient content generation accuracy, weak personalized response, and low reasoning efficiency in adaptive learning scenarios, which limit its in-depth application in intelligent teaching. To solve this problem, this paper proposed a Transformer fine-tuning method based on low-rank adaptation technology, which realized efficient parameter update of pre-trained models through low-rank matrix insertion, and combined the instruction fine-tuning strategy to perform domain adaptation training on the model for the constructed educational scenario dataset. At the same time, a dynamic prompt construction mechanism was introduced to enhance the model’s context perception ability of individual learners’ behaviors, thereby achieving precise alignment and personalized control of generated content. This paper embeds the “wrong question guidance” and “knowledge graph embedding” mechanisms in the model, provides intelligent feedback based on student errors, and promotes in-depth understanding of subject knowledge through knowledge graphs. Experimental results showed that this method scored higher than 0.9 in BLEU and ROUGE-L. The average response delay was low, which was significantly better than the traditional fine-tuning method. This method showed good adaptability and practicality in the fusion of generative AI and adaptive learning and provided a generalizable optimization path and application solution for intelligent education systems. Full article

Advances in AI are accelerating intelligent ship autonomy, yet robust trajectory tracking remains challenging under nonlinear dynamics and persistent environmental disturbances. Traditional model-based guidance becomes tuning-sensitive and loses robustness under strong disturbances, while data-driven approaches like reinforcement learning often suffer from poor generalization to unseen dynamics and brittleness in out-of-distribution conditions. To address these limitations, we propose a guidance architecture embedding a Large Language Model (LLM) directly within the closed-loop control system. Using in-context prompting with a structured Chain-of-Thought (CoT) template, the LLM generates adaptive k-step heading reference sequences conditioned on recent navigation history, without model parameter updates. A latency-aware temporal inference mechanism synchronizes the asynchronous LLM predictions with a downstream Model Predictive Control (MPC) module, ensuring dynamic feasibility and strict actuation constraints. In MMG-based simulations of the KVLCC2, our framework consistently outperforms conventional model-based baselines. Specifically, it demonstrates superior path-keeping accuracy, higher corridor compliance, and faster disturbance recovery, achieving these performance gains while maintaining comparable or reduced rudder usage. These results validate the feasibility of integrating LLMs as predictive components within physical control loops, establishing a foundation for knowledge-driven, context-aware maritime autonomy. Full article

The proliferation of Large Language Models (LLMs) has transformed natural language processing (NLP) applications across diverse domains. This paper presents a comprehensive comparative analysis of three state-of-the-art language models—GPT-4o, Claude-3, and Julius AI—evaluating their performance across systematic NLP tasks using standardized datasets and evaluation frameworks. We introduce a reusable evaluation methodology incorporating five distinct prompt engineering techniques (Prefix, Cloze, Anticipatory, Heuristic, and Chain of Thought) applied to three categories of linguistic challenges: data extraction, aggregation, and contextual reasoning. Using a bibliometric analysis use case as our evaluation domain, we demonstrate the framework’s application to structured data processing tasks common in academic research, business intelligence, and data analytics applications. Our experimental design utilized a curated Scopus bibliographic dataset containing 3212 academic publications to ensure reproducible and objective comparisons, representing structured data processing tasks. The results demonstrated significant performance variations across models and tasks, with GPT-4o achieving 89.3% average accuracy, Julius AI reaching 85.7%, and Claude-3 demonstrating 72.1%. The results demonstrated significant performance variations across models and tasks, with Claude-3 showing notably high prompt sensitivity (consistency score: 74.3%, compared with GPT-4o: 91.2% and Julius AI: 86.7%). This study revealed critical insights into prompt sensitivity, contextual understanding limitations, and the effectiveness of different prompting strategies for specific task categories. Statistical analysis using repeated measures ANOVA and pairwise t-tests with Bonferroni’s correction confirmed significant differences between models (F(2, 132) = 142.3, p < 0.001), with effect sizes ranging from 0.51 to 1.33. Response time analysis showed task-dependent latency patterns: for data extraction tasks, Claude-3 averaged 1.9 s (fastest), GPT-4o 2.1 s, and Julius AI 2.8 s; however, for contextual reasoning tasks, latency increased as follows for Claude-3: 3.8 s, GPT-4o: 4.5 s, and Julius AI: 5.8 s. Overall averages were as follows for GPT-4o: 3.2 s, Julius AI: 4.1 s, and Claude-3: 2.8 s. While specific performance metrics reflect current model versions (GPT-4o: gpt-4o-2024-05-13; Claude-3 Opus: 20240229; Julius AI: v2.1.4), the evaluation framework provides a reusable methodology for ongoing LLM assessment as new versions emerge. These findings provide practical guidance for researchers and practitioners in selecting appropriate LLMs for domain-specific applications and highlight areas requiring further development in language model capabilities. While demonstrated on bibliometric data, this evaluation framework is generalizable to other structured data processing domains. Full article

Recent advances in large language models (LLMs) have driven substantial progress in knowledge base question answering (KBQA), particularly under few-shot settings. However, symbolic program generation remains challenging due to its strict structural constraints and high sensitivity to generation errors. Existing few-shot methods often rely on multi-turn strategies, such as rule-based step-by-step reasoning or iterative self-correction, which introduce additional latency and exacerbate error propagation. We present CBR², a case-based reasoning framework with dual retrieval guidance for single-pass symbolic program generation. Instead of generating programs interactively, CBR² constructs a unified structure-aware prompt that integrates two complementary types of retrieval: (1) structured knowledge from ontologies and factual triples, and (2) reasoning exemplars retrieved via semantic and function-level similarity. A lightweight similarity model is trained to retrieve structurally aligned programs, enabling effective transfer of abstract reasoning patterns. Experiments on KQA Pro and MetaQA demonstrate that CBR² achieves significant improvements in both accuracy and syntactic robustness. Specifically on KQA Pro, it boosts Hits@1 from 72.70% to 82.13% and reduces syntax errors by 25%, surpassing the previous few-shot state-of-the-art. Full article

Building Information Modeling (BIM) has become a core competency in architectural practice, prompting increasing efforts to integrate BIM into design education. However, existing pedagogical approaches vary widely across institutions, regions, and curricular structures, ranging from software-focused instruction to more holistic, design-centered applications. This study presents a comprehensive review of BIM education in architecture by synthesizing trends, pedagogical models, and implementation strategies reported between 2010 and early 2025. A hybrid review design was employed by combining PRISMA-based systematic procedures with scoping and comparative analysis. Bibliometric mapping of 399 BIM education publications identified major research clusters and global trends, while an in-depth analysis of 31 architecture-focused studies revealed seven thematic categories encompassing curriculum integration, design studio pedagogy, immersive technologies, collaborative models, and algorithmic approaches. The findings show a gradual shift from tool-based training toward integrated studio environments where BIM supports design creativity, interdisciplinary coordination, and process-based learning. Persistent challenges—such as balancing technological proficiency with design thinking, adapting faculty expertise, and aligning curricula with industry expectations—continue to hinder deeper integration. Based on the synthesis, this study proposes an integrated educational framework that connects technological competence, design creativity, and collaborative cognition, offering guidance for the next stage of BIM-enabled architectural education. Full article

Mushroom coffee—blends of coffee with “functional” mushroom powders—has surged in popularity, yet its hemostatic effects are poorly appreciated in perioperative care. We report a postoperative hemorrhage likely potentiated by a commercial mushroom coffee. A 62-year-old man with HIV, hepatitis C, and insulin-treated diabetes underwent colostomy reversal. On postoperative day 9, he developed brisk bleeding at the colonic anastomosis requiring angiography and embolization. Recurrent hemorrhage prompted a detailed supplement history, revealing daily use of mushroom coffee for two months preoperatively. The product’s labeled ingredients include an organic mushroom blend of cordyceps, lion’s mane (Hericium), reishi (Ganoderma), shiitake, turkey tail, and king trumpet, combined with arabica coffee, MCT oil, and coconut milk. Several constituents—reishi, cordyceps, lion’s mane, and chaga (Inonotus obliquus, used in some mushroom blends)—have published antiplatelet or antithrombotic activity in vitro and/or in vivo. After counseling, the patient discontinued mushroom coffee; no further bleeding occurred, and he recovered without additional intervention. This case highlights a clinically important but underrecognized risk: mushroom-based beverages can exert antiplatelet effects comparable to herbal supplements traditionally flagged in preoperative screening. We recommend that preoperative medication reconciliation explicitly query mushroom coffees and “adaptogenic” blends and that such products be held similarly to other agents with antiplatelet properties. Greater awareness among surgeons, anesthesiologists, and internists is needed as functional foods proliferate. Controlled studies are warranted to quantify bleeding risk from multi-mushroom products and to inform evidence-based perioperative guidance Full article

The rapid integration of large language models into higher education creates opportunities for mathematics instruction, but also raises the need for structured interaction strategies that support reflective learning rather than passive answer consumption. This study, conducted within the Erasmus+ MAESTRO-AI project, examines how selected AI prompt patterns can be implemented in concrete university mathematics activities and how students evaluate these AI-supported experiences. Two experimental modules were compared: complex numbers for first-semester Applied Mathematics students in Poland ($n=100$) and conditional probability for second-year Computer Science students in Romania ($n=213$). After completing AI-assisted learning activities with ChatGPT and/or Gemini, students completed a common evaluation questionnaire assessing engagement, perceived usefulness, and reflections on AI as a tutor. Group comparisons and experience-based analyses were performed using the Mann–Whitney test. Results indicate that students who reported regular prior use of AI tools evaluated AI-supported learning significantly more positively than those with occasional or no prior experience. They gave higher ratings across most questionnaire items as well as for the overall score. The findings suggest that prompt-pattern-based designs can support engaging AI-assisted mathematics activities. They also indicate that such designs can provide a structured learning experience, while introductory guidance may be important to ensure comparable benefits for less experienced students. Full article

The causal analysis of historical aviation accidents documented in investigation reports is important for the design, manufacture, operation, and maintenance of aircraft. However, given that most accident data are unstructured or semi-structured, identifying and extracting causal information remain labor intensive and inefficient. This gap is further deepened by tasks, such as system identification from component information, that require extensive domain-specific knowledge. In addition, there is a consequential demand for causation pattern analysis across multiple accidents and the extraction of critical causation chains. To bridge those gaps, this study proposes an aviation accident causation and relation analysis framework that integrates prompt engineering with a retrieval-augmented generation approach. A total of 343 real-world accident reports from the NTSB were analyzed to extract causation factors and their interrelations. An innovative causation classification schema was also developed to cluster the extracted causations. The clustering accuracy for the four main causation categories—Human, Aircraft, Environment, and Organization—reached 0.958, 0.865, 0.979, and 0.903, respectively. Based on the clustering results, a causation knowledge graph for aviation accidents was constructed, and by designing a set of safety evaluation indicators, “pilot—decision error” and “landing gear system malfunction” are identified as high-risk causations. For each high-risk causation, critical combinations of causation chains are identified and “Aircraft operator—policy or procedural deficiency/pilot—procedural violation/Runway contamination → pilot—decision error → pilot procedural violation/32 landing gear/57 wings” was identified as the critical causation combinations for “pilot—decision error”. Finally, safety recommendations for organizations and personnel were proposed based on the analysis results, which offer practical guidance for aviation risk prevention and mitigation. The proposed approach demonstrates the potential of combining AI techniques with domain knowledge to achieve scalable, data-driven causation analysis and strengthen proactive safety decision-making in aviation. Full article

Background: Malnutrition is common among older adults with cognitive impairment and contributes to frailty and poorer health outcomes. Many individuals with dementia require mealtime assistance, yet differences in caregiving practices across hospital and nursing home settings remain underexplored. Aim: The aim of this study was to compare eating encouragement practices, feeding skills, feeding difficulties, and nutritional status between family caregivers in hospitals and professional caregivers in nursing homes. Methods: A cross-sectional observational study was conducted between June 2020 and December 2023 in New South Wales, Australia. The study included 82 older adults (≥65 years) with cognitive impairment: 31 hospital patients supported by family caregivers and 51 nursing home residents supported by assistant nurses. Eating encouragement, feeding skills, and feeding difficulties were assessed using structured observation tools, and nutritional status was evaluated using the Mini Nutritional Assessment–Short Form (MNA-SF). Group differences were analysed using chi-square tests and independent t-tests (p < 0.05). Results: Family caregivers in hospitals demonstrated stronger relational and engagement-based practices, including consistent handwashing (χ² = 31.945, p < 0.001), encouraging self-feeding (χ² = 21.678, p < 0.001), verbal cueing (χ² = 12.083, p = 0.002), touch prompting (χ² = 51.817, p < 0.001), and sitting face to face (χ² = 38.697, p < 0.001). Nursing home caregivers showed more advanced technical skills, such as task simplification (χ² = 54.135, p < 0.001), mirroring (χ² = 78.456, p < 0.001), hand-over-hand guidance (χ² = 73.076, p < 0.001), mouth- and lip-opening techniques (both χ² = 81.000, p < 0.001), and stronger choking management (p < 0.001). Feeding difficulties also differed: refusal behaviours were more common in nursing homes, while distraction and oral–motor issues were more frequent in hospitals. Overall, nursing home residents had significantly poorer nutritional status (t = −12.592, p < 0.001). Conclusions: Family caregivers provide stronger relational support, whereas professional caregivers demonstrate superior technical competence. Integrating these complementary strengths may enhance mealtime care and reduce malnutrition among cognitively impaired older adults. Full article

Background: The management of acute pulmonary embolism (PE) in the Emergency Department (ED) remains challenging, particularly in hemodynamically and respiratory stable patients with minimal symptoms. Diagnostic and therapeutic difficulties are further compounded when the condition is complicated by a mobile right atrial (RA) thrombus, representing an extreme-risk phenotype. Case Presentation: We report the case of a 65-year-old male with a single known venous thromboembolism risk factor-chronic venous insufficiency-who presented to the ED following a transient episode of severe dyspnea at home. On admission, he was hemodynamically and respiratory stable, without the need for oxygen supplementation. Arterial blood gas analysis revealed a metabolically compensated acidosis with elevated lactate, while cardiac biomarkers were moderately increased. Emergency point-of-care transthoracic echocardiography (POCUS-TTE) demonstrated severe right ventricular (RV) dysfunction and a large, mobile intracardiac thrombus prolapsing through the tricuspid valve. Computed Tomography Pulmonary Angiography confirmed pulmonary embolism and revealed a massive and extensive bilateral thrombotic burden (Qanadli score 32 points). Given the extreme risk for fatal embolization, immediate full-dose systemic thrombolysis with Alteplase (100 mg over 2 h) was initiated in the ED. Thrombolysis was completed without hemorrhagic complications. Follow-up POCUS-TTE at 2 h showed complete resolution of the intracardiac thrombus and significant improvement of RV function (RV/RA gradient reduced from 40 mmHg to 28 mmHg). Conclusions: This case highlights the effectiveness and safety of early systemic thrombolysis guided by ED POCUS-TTE in PE with a massive thrombotic burden, complicated by a mobile intracardiac thrombus, even in the absence of shock. Such prompt intervention may reduce mortality risk in intermediate-to-high-risk PE subsets, despite limited guidance in current clinical recommendations. Full article