Search Results (191)

Joint action involves more than coordinated activity; it is cooperation grounded in shared intentionality, whereby partners represent an activity as something “we” are doing together. This “we-mode” stance should shape attention and memory, making partner-relevant information psychologically significant because it supports a collective goal. Using a joint-search paradigm, we tested whether people automatically attend to and remember partner goals. Pairs of participants searched for targets from different item categories, and trials were successful only when both responded correctly. A surprise recognition test followed the joint-search task assessing memory for the items. Across Experiments 1 (animate stimuli) and 2 (inanimate stimuli), participants showed better recognition of partner-goal items compared to distractors. Participants also showed enhanced attention to partner-goal items in Experiment 2. In Experiment 3, participants completed the same task, and returned three days later for a recognition test first followed by a second joint-search task with switched targets. Participants continued to show superior recognition for partner-goal items, and search efficiency improved after targets switched, indicating that partner-goal was retained over time and supported later cooperation. Together, these findings demonstrate that human cognition supports joint actions over time by organizing attention and memory around what “we” are doing together. Full article

A diagnosis of acute leukemia (AL) represents a sudden, life-threatening event that places family caregivers (FCs) at high risk for traumatic stress. While traumatic stress symptoms have been documented among FCs later in the cancer trajectory, little is known about how these responses unfold during the immediate peri-diagnostic period, when acute stress disorder (ASD) may emerge, and early intervention could be most impactful. We conducted a qualitative study using a constructivist grounded theory approach to examine early traumatic stress responses among FCs of adults and children with newly diagnosed AL. Semi-structured interviews were conducted with 18 caregivers within the first six months of diagnosis as part of two clinical trials at major cancer centres in Toronto, Canada, and were analyzed iteratively using constant comparative methods. Caregivers described a coherent trajectory of traumatic stress responses across three phases. The anticipatory phase was characterized by prolonged uncertainty, helplessness, and mounting fear during diagnostic investigations. The acute phase, beginning at diagnosis, involved an abrupt shift toward emotional numbing, deliberate avoidance of catastrophic thoughts, and a narrowed focus on immediate tasks, often described as operating on “autopilot.” In the post-acute phase, as patients stabilized and discharge approached, caregivers reported increased emotional access, including grief, anger, and recognition of their own trauma, alongside emerging concerns about long-term caregiving and life disruption. These findings suggest that FCs of individuals with newly diagnosed AL exhibit a phased pattern of traumatic stress responses, marked by an early, adaptive dissociative coping response followed by delayed emotional processing, underscoring the importance of phase-sensitive psychosocial care in oncology. Full article

Background: Agentic artificial intelligence (AI) systems, characterized by autonomous goal-directed behavior, multi-step reasoning, task decomposition, and tool use, are increasingly proposed for healthcare applications. However, their autonomy raises concerns regarding transparency, accountability, and human oversight. While explainable AI (XAI) has been widely studied in traditional predictive models, less is known about how explainability is implemented within agentic architectures. Objective: To map the emerging literature on explainable agentic AI (XAAI) in healthcare and characterize the types, scope, and forms of explainability used in these systems. Methods: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Embase, IEEE Xplore, and ACM Digital Library were searched through November 2025. Eligible studies described healthcare-related agentic AI systems incorporating explicit explainability mechanisms. Data were extracted on system architecture, explainability type (intrinsic, post hoc, hybrid), explanation scope (local, global), explanation form, and reported clinical outcomes. Results: Nine studies met the inclusion criteria. All systems demonstrated core agentic features, including autonomy, task decomposition, and tool integration, often within multi-agent frameworks. Explainability was predominantly intrinsic and workflow-native, typically delivered through textual reasoning traces and example-based grounding in retrieved clinical evidence. Feature-based and global explanations were comparatively rare and largely confined to hybrid architectures. Across domains including radiology, neurology, psychiatry, and biomedical research, XAAI systems were reported to improve performance and interpretability relative to baseline models in the included studies. However, these findings were derived from heterogeneous, predominantly experimental or retrospective studies, and structured human-in-the-loop oversight was infrequently described. Conclusions: Current XAAI systems appear to emphasize process transparency and evidence grounding rather than mechanistic model-level attribution. The available evidence remains limited and heterogeneous, and findings should be interpreted as early trends rather than established characteristics. Further progress will require standardized evaluation frameworks, clearer reporting of oversight mechanisms, and validation in real-world clinical settings to support safe and trustworthy integration of agentic AI into healthcare practice. Full article

Imitation is crucial for social learning, yet children with autism spectrum disorder (ASD) often show atypical imitation abilities. To probe the neural dynamics that precede overt imitation, electroencephalography (EEG)—with a focus on α (8–12 Hz) and β (13–30 Hz) activity commonly linked to action observation and sensorimotor processing—was used to index pre-imitation processing in preschool-aged children with ASD. Grounded in the social motivation framework, this study combined an EEG experiment and a naturalistic behavioral intervention. In Study 1, 11 preschool children with ASD completed an action-observation (pre-imitation) task under low- versus high-sociality video conditions. Time–frequency and spectral analyses were conducted to compare α- and β-band responses across conditions. In Study 2, four children received a six-week Reciprocal Imitation Training (RIT) program, and imitation and social-communication outcomes were assessed pre-, mid-, and post-intervention. The results showed that low-sociality stimuli elicited stronger frontal and prefrontal power increases in both α and β bands, whereas high-sociality stimuli elicited more temporally dynamic β-band responses but with lower overall power engagement. Although inferential support was limited by sample size, behavioral trends suggested improvements following RIT in imitation and related social functioning, with larger gains in children with mild-to-moderate ASD. Together, these findings suggest that social context modulates pre-imitation neural activity in ASD and that socially grounded imitation training may support broader social development. Full article

International students at English-medium universities in Australia whose first language is not English often struggle with language learning due to challenges sustaining motivation and managing anxiety, while simultaneously needing to strengthen their English skills to succeed academically and fully engage in university life. Although serious games are increasingly used in second-language education, many are not explicitly grounded in established pedagogical strategy frameworks, and grammar-focused serious board games remain underrepresented. In response, this study designed a strategy-embedded serious board game (The Pyramid of Time) that integrates Oxford’s indirect Language Learning Strategies to support grammar-focused practice. Following a Design Thinking process informed by desk-based evidence and refined through two rounds of playtesting, the final prototype was evaluated in a single-session, between-subjects quasi-experiment with 64 international L2 English students studying in Australian English-medium universities, comparing a collaborative board-game condition with an individual textbook self-study condition. Outcomes were assessed using pre- and post-measures of grammar test performance, language learning motivation, and grammar-learning anxiety. The strategy-embedded, collaborative game-based condition showed larger short-term gains in grammar test performance and more favourable changes in motivation and anxiety than the individual textbook self-study condition. An exploratory bootstrapped mediation analysis was consistent with an indirect pattern in which anxiety reduction related to grammar gains primarily via increased motivation, although evidence was modest. Findings provide initial support for theory-informed, strategy-embedded game-based instruction as a promising approach for grammar-focused practice that also improves learners’ short-term motivational and affective experiences. These results should be interpreted in light of differences in instructional format, collaborative structure, and time-on-task across conditions. Full article

The background to this research paper examines why people with disabilities often have additional problems with authentication (i.e., logging in to online services). While the primary focus is on accessible authentication, we also explore its relevance to electronic identification and consider the post-authentication stage of authorization (allowing continued use of a particular service once logged in). While people without disabilities regularly log into websites and applications without too much thought for the process, with an end-goal or task in mind to be achieved with the service that they are accessing, extra barriers exist for people with disabilities. We discover how there is a societal gap in terms of ease-of-use, as previous studies show that people with disabilities can find this step difficult, frustrating, or virtually impossible. For people who have a disability, complications will arise in this process, and we examine the nature of these problems identified by this group. A series of interviews (n = 15) is analyzed using Constructivist Grounded Theory methods to identify patterns in participants’ responses and develop a theory explaining why Accessible Authentication is a problem. While aiming to follow a constructivist methodology, this paper categorizes common traits revealed by participants in interviews. The key findings reveal that most users with disabilities say that the ability to authenticate effectively is reduced by accessibility barriers; in other words, participants felt hindered when logging in because of their disability. This leads us to conclude, with some degree of confidence, that the data implies a lack of accessibility for users of traditional authentication systems. A further area of concern for the participants is that maintaining security alongside ease-of-use was important to them (albeit with no clear winner between usability and security preferences), so future work on improving accessibility should ensure that users with disabilities’ information is not left vulnerable, while maintaining a sufficient level of accessibility for people with disabilities. Further to this, suggestions for achieving an accessible solution are presented in a preliminary Theoretical Framework. Full article

Referring Remote Sensing Image Segmentation (RRSIS) is a representative multimodal understanding task for remote sensing, which segments designated targets from remote images according to free-form natural language descriptions. However, complex remote sensing characteristics, such as cluttered backgrounds, large-scale variations, small scattered targets and repetitive textures, lead to unstable visual grounding and further spatial grounding drift, resulting in inaccurate segmentation results. Existing approaches typically perform implicit visual–linguistic fusion across encoding and decoding stages, entangling spatial grounding with mask refinement. This tightly coupled formulation lacks explicit structural constraints and is prone to cross-modal ambiguity, especially in complex remote sensing layouts. To address these limitations, we propose a Structurally consistent and Grounding-aware Stagewise Reasoning Framework (SGSRF) that follows a grounding-first, segmentation-second paradigm. The framework decomposes inference into three cascaded stages with progressively imposed structural constraints. First, Cross-modal Consistency Refinement (CCR) lays the foundation for stable spatial grounding by enhancing visual–textual structural alignment via CLIP-based features and Structural Consistency Regularization (SCR), producing well-aligned multimodal representations and reliable grounding cues. Second, Grounding-aware Prompt (GPG) Generation bridges grounding and segmentation by converting aligned representations into complementary sparse and dense prompts, which serve as explicit grounding guidance for the segmentation model. Third, Grounding Modulated Segmentation (GMS) leverages the Segment Anything Model (SAM) to generate fine-grained mask prediction under the joint guidance of prompts and grounding cues, improving spatial grounding stability and robustness to background interference and scale variation. Extensive experiments on three remote sensing benchmarks, namely RefSegRS, RRSIS-D, and RISBench, demonstrate that SGSRF achieves state-of-the-art performance. The proposed stagewise paradigm integrates structural alignment, explicit grounding, and prompt-driven segmentation into a unified framework, providing a practical and robust solution for RRSIS in real-world Earth observation applications. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Background: Gait with external load is an inherent element of military tasks, and the mass of equipment carried by soldiers has systematically increased over recent decades. Depending on the nature of the operation, soldiers may carry loads ranging from several to several dozen kilograms, which may affect gait biomechanics and increase the risk of overload injuries. The aim of this study was to evaluate changes in the spatiotemporal gait parameters of Special Forces Operators depending on the mass and type of the carried external load. Methods: The study included 34 active Special Forces Operators (age: 36.47 ± 5.63 years; height: 180.39 ± 5.72 cm; body mass: 85.92 ± 8.54 kg). Gait analysis was performed using an h/p/cosmos gaitway 3D + 1D treadmill equipped with an integrated pressure platform enabling ground reaction force (GRF) measurement. Participants performed gait trials at a speed of 5.5 km/h under four load conditions: 0 kg, 7 kg, 20 kg, and 27 kg. For each condition, 30 s measurement series were recorded, enabling analysis of a stable locomotion pattern and detection of gait phase events. Results: Statistically significant differences were demonstrated for the following parameters: stance phase, load response, single support, pre-swing, swing phase, double stance, foot rotation, step time, stride length, step width, cycle time, and cadence. The greatest changes were observed between unloaded gait and the condition with a helmet and vest. External load mainly caused prolongation of phases related to support and shortening of the swing phase and single support. Conclusions: Military load significantly modifies the temporal structure of gait in Special Forces Operators even at a constant, relatively low speed. The use of an instrumented treadmill with an integrated pressure platform and GRF measurement, as well as the registration of a large number of gait cycles, enabled the detection of subtle differences in spatiotemporal parameters and reliable assessment of stability and dynamic asymmetry under controlled laboratory conditions. Full article

Developing reliable Arabic question answering (QA) systems for Islamic fatwas requires datasets that capture the linguistic complexity and multi-step reasoning inherent in jurisprudential inquiries. However, the existing Arabic religious QA datasets primarily focus on direct retrieval or classification, often failing to address the multi-hop reasoning necessary for complex fatwa questions. To bridge this gap, we introduce MAFQA, a benchmark dataset specifically designed for multi-hop Arabic fatwa question answering. MAFQA was constructed from an extensive corpus of authentic fatwa records sourced from authoritative Islamic institutions. The dataset was developed via a semi-automated pipeline that integrates expert-guided identification of complex inquiries with a structured decomposition framework. This framework employs automated reasoning-pattern classification, semantic feature extraction, and template-guided annotation of subquestions and subanswers, followed by rigorous validation to ensure contextual grounding, logical coherence, and structural consistency. To evaluate the utility of the dataset, we conduct an extensive benchmarking study using Arabic-specialized, multilingual, and instruction-tuned language models across two primary tasks: question decomposition (QD) and generative question answering (QA). Performance is assessed using a comprehensive suite of lexical, semantic, relevance, and faithfulness metrics. Experimental results demonstrate that Arabic-specialized models consistently outperform their multilingual counterparts, with AraT5-base and AraBART achieving the highest performance in terms of lexical similarity, semantic alignment, and answer faithfulness. Full article

High-quality Fourier Transform Infrared (FTIR) imaging usually needs extensive signal averaging to reduce noise and drift, which severely limits clinical speed. Deep learning can accelerate imaging by reconstructing spectra from rapid, single-scan inputs. However, separating noise and baseline drift simultaneously without ground truth is an ill-posed inverse problem. Standard black-box architectures often rely on statistical approximations that introduce spectral hallucinations or fail to generalize to unstable atmospheric conditions. To solve these issues, we propose a physics-informed cascade Unet that separates denoising and baseline correction tasks using a new, deterministic Physics Bridge. This architecture forces the network to separate random noise from chemical signals using an embedded SNIP layer to enforce spectroscopic constraints instead of learning statistical approximations. We benchmarked this approach against a standard single Unet and a traditional Savitzky–Golay smoothing followed by SNIP baseline correction workflow. We used a dataset of human hypopharyngeal carcinoma cells (FaDu). The cascade model outperformed all other methods, achieving a 51.3% reduction in RMSE compared to raw single-scan inputs, surpassing both the single Unet (40.2%) and the traditional workflow (33.7%). Peak-aware metrics show that the cascade architecture eliminates spectral hallucinations found in standard deep learning. It also preserves peak intensity with much higher fidelity than traditional smoothing. These results show that the cascade Unet is a robust solution for diagnostic-grade FTIR imaging. It enables imaging speeds 32 times faster than current methods. Full article

Emotional and workload-related states unfold dynamically during immersive virtual reality (VR) experiences, yet reliable physiological modeling in such environments remains challenging. We investigated whether multi-channel facial electromyography (fEMG), combined with spatio-temporal deep learning, can (i) accurately classify calibrated facial expressions across participants and (ii) transfer to spontaneous, task-elicited behavior in immersive VR. Twelve adults completed a calibration phase involving four intentional expressions (smile, frown, raised eyebrow, neutral), followed by VR scenes designed to elicit emotional, cognitive, physical, and dual task demands. After participant-level physiological normalization, a single shared Convolutional Neural Network–Temporal Convolutional Network (CNN–TCN) model was trained and evaluated using leave-one-participant-out (LOPO) validation. The model achieved strong cross-participant performance (Macro-F1 = 0.88 ± 0.13; ROC-AUC = 0.95 ± 0.06). When applied to unlabeled spontaneous VR task-elicited fEMG recordings, the trained model generated continuous expression classes. Derived static and temporal expression features showed scene-dependent modulation and False Discovery Rate (FDR)-surviving associations, primarily with perceived physical demand (NASA-TLX). The observed muscle activation patterns were physiologically plausible and aligned with Facial Action Coding System (FACS)-based interpretations of underlying muscle activity. These findings demonstrate that end-to-end spatio-temporal modeling of raw fEMG enables facial expression sensing in immersive VR using a single shared model following physiological normalization. The proposed framework bridges calibrated expression learning and spontaneous task-elicited behavior, supporting privacy-preserving, continuous and physiologically grounded monitoring in human-centered VR applications. Full article

Accessing and understanding medication information can be challenging for many people, especially when patient information leaflets (PILs) are long, complex, and printed in small font. This study presents MediChat, an open-source, locally executable chatbot designed to provide reliable, easy-to-read answers to medication-related questions based exclusively on official PILs. MediChat follows a retrieval-augmented generation (RAG) architecture: PILs from the Austrian Medicinal Product Index are received via API, converted to text, split into overlapping chunks, embedded, and stored in a Chroma vector database. From there the top-k relevant chunks are retrieved, and Llama 3.1 generates German responses based on this evidence. The system was evaluated using a hybrid framework. Quantitatively, 200 yes/no questions across ten drugs were answered with 80% accuracy, overall precision 0.977, recall 0.686, F1-score 0.806, and a mean response time of 727 ms. Qualitatively, two personas were used in eight simulated dialogues. Response times were around 1.1–1.3 s, and task completion exceeded 85% with high ratings for relevance and quantity. These results indicate that an open-source RAG chatbot can deliver leaflet-grounded, user-friendly medication information and provide a reproducible template for future healthcare chatbot evaluations. Full article

Background: Accurate vertebral instance segmentation on full-spine radiographs is essential for spinal parameter assessment, but supervised methods require costly instance-level annotations and may be sensitive to domain shift. Methods: We investigated whether promptable segmentation foundation models can generalize zero-shot to raw DICOM spine radiographs without task-specific training. We evaluated SAM-ViT-Huge, SAM2-Hiera-Large, and MedSAM-ViT-Base on 144 full-spine radiographs with 1309 annotated vertebral masks using a standardized pipeline for DICOM decoding, intensity normalization, automatic prompt generation, and instance-level evaluation. For each prompt, models produced three candidate masks. Performance was reported under an oracle protocol selecting the candidate with the highest IoU against ground truth and a model-score protocol selecting the candidate with the highest predicted IoU. Metrics included IoU, Dice, precision, recall, ASSD, and HD95. Results: The best configuration was SAM-ViT-Huge with rectangle prompting, reaching a mean IoU/Dice of 0.782/0.870 under oracle selection and 0.737/0.837 under model-score selection. SAM2-Hiera-Large with rectangle prompting followed (0.744/0.848 oracle; 0.699/0.815 model-score), ahead of MedSAM-ViT-Base (0.599/0.737 oracle; 0.387/0.499 model-score). Point prompting yielded consistently low overlap (IoU 0.224–0.319; Dice 0.276–0.414) despite high recall, indicating systematic over-segmentation and large boundary errors. Conclusions: Zero-shot vertebral instance segmentation on raw DICOM spine radiographs is feasible with promptable foundation models when prompts sufficiently constrain target extent. Rectangle prompting is clearly more effective than point prompting in this setting. Full article

Geothermal exploration and monitoring often require dense temperature observations in terrains where wired networks are impractical and battery replacement for in situ sensors is costly. This paper proposes an infrared-assisted, temperature-aware access scheme for a UAV-enabled backscatter IoT network tailored to geothermal hotspot sensing. A rotary-wing UAV equipped with a thermal infrared camera and an RF transceiver first surveys the area to construct a surface temperature map and identify candidate hotspots, and then hovers above a selected hotspot to perform periodic frames consisting of wireless energy transfer followed by backscatter uplink collection. Ground sensors harvest RF energy, measure their local temperature, and autonomously activate only when both the harvested energy exceeds a threshold and the measured temperature falls within a target interval broadcast by the UAV, thereby concentrating channel access on thermally relevant nodes. We develop a system model that couples a geothermal-like thermal field, RF energy harvesting, and framed slotted backscatter access, and introduce hotspot-oriented performance metrics including effective hotspot throughput, task completion time, and energy per hotspot report. The simulation results show that the proposed temperature–energy-gated access significantly increases the fraction of successfully decoded packets originating from hotspot regions and improves the energy efficiency of geothermal monitoring compared with full activation and purely energy-based activation. Full article