Search Results (13,820)

Magnetizing living cells with superparamagnetic iron oxide nanoparticles (SPIONs) enables their remote manipulation using external magnetic field. This lays the foundation for magnetically assembling tissue precursors within cell-friendly, proliferation-permissive environments and holds considerable promise for biomedical applications, particularly in the development of complex single- and multicellular tissue constructs for bone and organ reconstruction. However, progress in this field is limited by the lack of robust mathematical tools for accurate control of ensembles of magnetic nano- and micro-objects. In practical printing scenarios, collective behavior and unavoidable statistical heterogeneity—such as variations in SPION size and shape or deviations in cell magnetization—render traditional equation-based modeling inadequate. We developed a hybrid modeling framework integrating conventional physics-based simulations with artificial intelligence-driven image analysis. Dynamic parameters were extracted from video recordings of magnetized cells moving within model microfluidic devices exposed to well-defined magnetic fields and gradients. The AI-based analysis enabled quantitative characterization of ensemble behavior under heterogeneous conditions. The proposed framework successfully captured the collective dynamics of magnetized cell ensembles and enabled accurate control of their spatial organization under external magnetic actuation. The integration of simulation and data-driven analysis provided robust parameter identification despite statistical heterogeneity within the system. This integrated modeling approach provides a practical and effective tool for controlling the three-dimensional magnetic assembly of living cells, with strong potential for applications in tissue engineering. Full article

Bilingual children learn their languages through rich interactions with caregivers within dynamic family contexts. However, little is known about how families align their two languages to support bilingual acquisition and how this varies across bilingual communities. This study examines language choice alignment across two communities: French–English families in Quebec (Canada) and Spanish–English families in New Jersey (United States). Thirty-nine children aged 18–35 months and their families were video-recorded during two 20 min home play sessions—one with a primary caregiver only and one including additional household members. Utterances were coded for speaker identity and language. We found strong turn-by-turn alignment between primary caregivers and children across both communities and sessions, with observed alignment exceeding chance in ⅔ of analyzed sessions at rates 20–22% above baseline. Other family members showed weaker correspondence with children’s language choices. Children’s alignment was modulated by language exposure and age, whereas caregivers’ alignment only decreased when additional members were present. These findings demonstrate that primary caregivers and their bilingual children align language choices consistently across diverse family configurations and communities. This linguistic coupling may support bilingual development across diverse interaction contexts, highlighting primary caregivers’ central role in early bilingual experiences across societies where bilingualism is and is not the norm. Full article

Rationale and objectives: Parkinson’s disease (PD) significantly affects visual function, especially convergence and eye movements, impacting tasks such as reading. The objective was to investigate preliminarily the impact of the use of digital visual training in PD patients with associated convergence insufficiency (CI). Materials and methods: Pre–post pseudo-experimental pilot study to evaluate the impact of a novel digital therapy system (video game for use on a mobile phone or tablet) in 13 patients with PD and CI, with a mean age of 67 years. A comprehensive visual assessment was performed before and after a 6-week home-based visual rehabilitation, including measurement of near point of convergence (NPC), near positive fusional vergence (PFV), oculomotor tests (NSUCO and King-Devick tests), and symptom assessments with two validated questionnaires (CISS and SQVD). Results: Treatment adherence was variable, ranging from 0.8% to 124.7%. Despite this, significant improvements were found after therapy in break (p = 0.022) and recovery points of the NPC (p = 0.007), as well as break (p = 0.003) and recovery points in near PFV (p < 0.001). In the NSUCO test, the total score improved significantly from 23.9 ± 4.2 to 26.2 ± 3.7 after therapy (p = 0.003). Furthermore, a significant reduction in the total King-Devick test time was observed, decreasing from 79.4 ± 28.8 s to 69.0 ± 21.5 s with therapy (p = 0.034). Finally, symptom questionnaire scores also decreased significantly with therapy (CISS p = 0.037, SQVD p < 0.001). Conclusions: The digital vision therapy system evaluated seems to improve oculomotor control and reduce visual symptoms associated with CI in PD patients. Studies with larger sample sizes and a control group are needed to fully validate the therapeutic effectiveness of this tool. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

Learning management systems (LMS) are essential for sustainable teaching and learning procedures due to the growing integration of digital technologies in higher education. Despite the widespread adoption of platforms such as Moodle, limited research has examined the students’ behavioral engagement and their subjective learning experiences. Addressing this gap, this study investigates the relationship between learning analytics indicators and academic performance, and how students’ experiences influence their participation in online learning environments. It adopted a convergent parallel design. Quantitative data were collected from the Moodle-based BirDeHa platform, drawing on learning analytics logs of 137 pre-service teachers enrolled in various programs within a faculty of education. Key indicators included frequency of material downloads, system usage, video engagement, and quiz performance. Qualitative data were collected via focus group interviews with nine participants. The results revealed a clear relationship between students’ interaction patterns within the LMS and their academic performance. Indicators of active engagement, particularly time spent on the platform and frequency of interaction with course materials, emerged as strong predictors of academic success. Qualitative findings further indicate that students perceive the LMS as flexible, inclusive, and supportive of their learning needs. Overall, this study underlines the importance of integrating data-driven insights with student-centered perspectives to achieve a comprehensive understanding of online learning environments and to inform effective design. The findings contribute to the sustainability of digital learning environments by providing behavioral indicators that can inform data-driven instructional design. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in learning complex tasks purely from sequential data. To explore whether such models can internalize strategic world representations, We investigate whether generative transformer models can learn structured world representations from sequential data. Using the domain of Go life-and-death problems as a controlled micro-world, we train a GPT-style generative model to predict moves from serialized board states. Focusing on localized life-and-death (tsumego) scenarios, we train the model to predict valid next moves from serialized board states without providing any explicit Go rules or strategic supervision. Probing the model’s internal activations reveals structured representations aligned with liberties, eyes, and tactical group status. To interpret these representations, we introduce the Multi-Aspect World Probe (MAWP), a modular probing framework that disentangles tactical concepts into orthogonal dimensions. We further apply interventional techniques to manipulate internal representations and causally evaluate their impact on model predictions. Our results show that the proposed model achieves 94.7% accuracy in sequence correctness and 92.1% in outcome validity on life-and-death tasks. This work extends interpretability research into spatially structured domains and offers tools for understanding decision-making in sequence models. Full article

Monitoring both captive animals and wild populations is necessary to ensure adequate animal welfare and wildlife conservation. Existing monitoring tools, e.g., camera traps, enable surveillance, yet analysis can prove time-consuming and labor-intensive if handled manually. The automated nature of machine learning (ML) reduces observer bias and manual workload and improves assessment capacity of behavioral monitoring tools that are often used by staff at zoological institutions. This study investigated the activity and space use of three captive jaguars (Panthera onca) through automated individual recognition, activity tracking, and heatmap visualization using an ML model trained on video footage. In total, 123.8 h of video footage was recorded of the jaguar enclosure in Randers Regnskov, Tropical Zoo. The ML model analyzed all videos containing jaguars from one day. The model achieved satisfactory performance based on its evaluation metrics (mean average precision, recall, precision, and F1-score). The ML model showed repeated movement tracks within specific enclosure areas. The jaguars exhibited significantly more inactive than active behavior and did not seem to exhibit natural bimodal nocturnal or crepuscular hunter activity patterns. It should be stated that, due to the small sample size of only three jaguars and 24 analyzed hours, this study is a proof-of-concept to demonstrate the potential of ML methods as valuable tools for individual recognition, activity tracking, and monitoring of space use to aid in future animal welfare monitoring. Full article

In complex hydrogeological systems, such as multilayered aquifers in densely urbanized coastal areas, multi-parametric, multi-depth networks are required for discriminating between anthropogenic and natural signals. This study presents an evaluation protocol of a pre-existing piezometric network, composed of 66 piezometers, aimed at implementing a near real-time (NRTM) hydrogeochemical monitoring system in the Strait of Messina (Sicily, Italy) area. A rigorous selection process was conducted to determine the suitability of these sites for hosting permanent, above-ground instrumentation. After excluding 55 sites for logistical and administrative reasons, the remaining piezometers were evaluated through a multi-step protocol. Video inspections and vertical logs of temperature and electric conductivity were carried out to identify pipe integrity and screened sections. Water samples were collected, for the execution of geochemical and isotopic analyses, to distinguish between groundwater bodies and stagnant water or local infiltration. Finally, preliminary near real-time monitoring of water level and temperature assessed the response of the sites to hydrological cycles and tidal effects. A scoring system was applied to rank the sites, resulting in a priority list for the installation of the permanent monitoring network. The evaluation protocol was tested in the Strait of Messina, but it is based on a generical approach, independent of the specific setting of a study area, making it suitable for general applications worldwide. Full article

As the population ages and new therapies become available, general practitioners will have a significant role in the early detection, diagnosis, and management of dementia. However, both in Australia and globally, dementia remains under-recognised and under-diagnosed in primary care. The aim of this study is to develop a complex intervention, informed by behaviour change theory, to improve rates of dementia diagnoses in Australian primary care. Co-design participants included GPs, general practice nurses, practice managers and reception staff. A program logic model was used to describe the essential activities and mechanisms of the intervention. Six behaviour changes—education, training, enablement, modelling, persuasion, and environmental restructuring—were identified to address the identified barriers to dementia diagnosis in primary care. The intervention comprises seven activities—peer-led online dementia education and training, geriatrician ‘drop-in’ online support sessions, quality improvement in dementia care sessions, stand-alone videos, auditing and benchmarking, a dementia risk alert tool and a set of dementia diagnosis and management decision-making resources. Using behaviour change theory can assist in the development of complex interventions aimed at changing clinical practice and may assist in their evaluation. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder for which delayed recognition may limit timely clinical management. This study investigates a reproducible computer-aided screening approach based on facial motion analysis from standard RGB video recorded during the diadochokinetic /pataka/ task. Facial landmarks were extracted using a face-mesh model and mapped into spherical coordinates to represent facial motion trajectories. Coordinated facial behavior was characterized through pairwise Pearson correlation matrices computed between landmark trajectories, yielding correlation-based descriptors of inter-region motion patterns. We compared a domain-informed Manual-24 reference configuration with data-driven feature-selection strategies (ElasticNet and mRMR) under a leakage-aware nested cross-validation design using the Toronto NeuroFace dataset. Performance was reported as mean ± standard deviation across outer folds, with sensitivity emphasized because of its relevance for screening-oriented applications. The primary configuration (mRMR, $k=3$, $\varphi$ + kNN) achieved 61.11 ± 19.24% accuracy, 61.11 ± 9.62% sensitivity, and 61.11 ± 34.70% specificity. These results suggest that correlation-derived coordination patterns contain discriminative information for ALS/HC separation, although fold-level variability indicates that performance should be interpreted cautiously. Task-aligned comparisons with prior /pataka/-based studies highlight the influence of sensing modality, evaluation level, and uncertainty reporting on apparent performance. Overall, correlation-based facial motion descriptors combined with leakage-aware feature selection provide a transparent proof-of-concept framework for RGB video-based ALS screening, motivating validation on larger cohorts and independent datasets. Full article

The spread of unverified health claims about drugs, dietary supplements, and alternative remedies on social media poses a growing public health concern. In this study, we present a retrieval-augmented generation (RAG) pipeline that uses large language models (LLMs) grounded in biomedical evidence from PubMed, openFDA adverse event reports, and NIH/NCCIH dietary supplement fact sheets to detect and classify health product misinformation. A total of 3493 health-related posts were collected from Reddit (948 posts across 12 subreddits) and YouTube (2545 video descriptions and comments), from which 8250 structured claims were extracted using Claude Haiku. Each claim was matched to biomedical evidence from three authoritative sources, achieving 79.4% evidence coverage, and classified into one of five veracity categories: supported (7.0%), unsupported (59.9%), exaggerated (22.4%), contradicted (2.0%), or dangerous (8.6%), together with an associated risk tier. Overall, 13.5% of claims were assigned high or critical risk. Cross-platform analysis showed that YouTube contained higher proportions of dangerous (11.3% vs. 2.9%) and exaggerated (27.0% vs. 12.4%) claims than Reddit. Compared with keyword-based and zero-shot transformer baselines, the LLM+RAG pipeline produced a more balanced and fine-grained classification of unsupported, exaggerated, contradicted, and dangerous claims. The most frequently implicated products were ashwagandha, kratom, black seed oil, turmeric, and ivermectin, with disease cure claims showing the highest dangerous classification rate (30.1%). These model-assigned results suggest that evidence-grounded LLM pipelines can support health misinformation surveillance, while also highlighting the need for expert validation and broader cross-platform evaluation. Full article

The way robots represent the environment, make decisions, and express themselves can positively influence human–robot interaction if they clearly communicate their intentions and needs. To improve human–robot communication, biologically inspired models that mimic human communication skills, including task and scenario-specific contextual information, can facilitate mutual understanding and successful task execution. This paper presents a Context-Awareness and Biologically Inspired Behaviour system to generate a more natural human–robot interaction. The architecture combines sensory information processed by a Joint Attention System that prioritises stimuli based on internal processes with task-related motivations to generate context- and goal-adapted verbal and non-verbal interaction. We evaluate the system through a video-based user study that compares two robots with similar appearances but different behaviours, one using the proposed approach and the other not using the internal state and joint attention mechanisms, to make verbal and non-verbal responses. The results show that participants rated the robot endowed with the proposed system as significantly more sociable, agentic, and animated than the robot without it. Additionally, the robot not showing the responses developed in this work was perceived as more disturbing than the robot integrating the proposed system. Full article

Whilst advance care planning (ACP) is important to ensure person-centred end of life care, there is sparse evidence about factors contributing towards engagement for people from Mandarin and Cantonese speaking backgrounds (MCSB) affected by cancer. This study aimed to establish barriers and facilitators for quality ACP among people from MCSB with cancer and carers. A qualitative study utilising semi-structured interviews and focus groups was conducted. Participants included adult community members from MCSB in New South Wales who had accessed cancer care services in Australia as a support person or a patient in the last five years with recruitment done purposefully. Data collected from eligible consenting participants were audio/video recorded, transcribed verbatim and analysed using the Framework Method applying the Theoretical Domains Framework. Eighteen people participated (11 in two focus groups, seven individual interviews). Key barriers to engagement with ACP were unclear understanding of process and conduct, poor quality communication by healthcare staff, resource constraints and cultural misalignment of ACP concepts. The main facilitators were openness of participants to discussions, culturally informed community resources and dedicated ACP services. Co-design provides a useful approach to address varied identified factors. At the system and service level, co-design with these communities and healthcare providers could potentially develop resources to assist these communities in engaging with ACP, including preparing for ACP communication. Understanding and acknowledging cultural factors that impact ACP and integrating this knowledge in ACP communication may enhance engagement. Full article

Introduction: Lung cancer remains highly lethal. Endobronchial ultrasound (EBUS) enables minimally invasive diagnosis and staging. Artificial intelligence (AI) improves image analysis and diagnostic accuracy, though current evidence is limited by retrospective, small, single center studies. Methods: A scoping review following Arksey–O’Malley, Levac, and JBI frameworks, was reported as per PRISMA-ScR. Databases were searched for studies (2015–2026) on AI in EBUS. Two reviewers screened, extracted standardized data, and performed narrative synthesis grouped by algorithm type, application, and performance metrics. Results: A total of 26 studies were included. Of these, 73.1% (19/26) employed deep learning-based models, while 26.9% (7/26) used traditional or hybrid machine learning approaches. The most frequent clinical objective was diagnostic classification of malignancy (14/26; 53.8%), followed by segmentation or cytological analysis (5/26; 19.2%), anatomical navigation or lymph node station classification (3/26; 11.5%), and multimodal predictive or staging support models (4/26; 15.4%). Most studies were based on EBUS-derived images or videos (18/26; 69.2%), including both convex-probe and radial-probe applications. Studies were distributed among Convex Probe-EBUS for mediastinal staging, Radial Probe-EBUS for peripheral lesion assessment, and rapid on-site evaluation-based cytology analysis, reflecting diverse clinical contexts. Most models were developed using static images. Conclusions: AI applications in EBUS are predominantly based on deep learning and mainly focused on diagnostic classification, with growing but still limited exploration of segmentation, navigation, and multimodal approaches. The evidence reflects diverse clinical contexts and data sources, particularly image-based inputs, but remains unevenly distributed across applications. Full article

The knowledge base for many small vertebrate species remains limited, largely because traditional manual data collection methods often overlook less charismatic species, such as reptiles. To address this, our pilot study harnesses open-source deep learning and markerless pose estimation technologies to evaluate the technical feasibility of tracking the spatial use and activity profiles of captive ectotherms. Specifically, we tracked these patterns over two months in a dynamically modified environment for Australian barking geckos (Underwoodisaurus milii). Our findings reveal descriptive changes in spatial occupancy and proximity across varying structural layouts. The system achieved a high raw detection accuracy (96.4%) and spatial categorization accuracy (91.7%) when validated against manual ground-truth data, confirming its robust technical performance and precision. Additionally, we automatically evaluated spatial proxies such as activity time budget, velocity, acceleration, and height usage, standardizing the analysis of extensive video recordings for nocturnal species. This pilot test introduces a simple, cost-effective method for rapid data extraction, offering a reliable, scalable monitoring solution for the management of understudied species. Full article