Search Results (2,172)

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

We present a thermodynamically consistent energetic variational model for active nematics driven by ATP hydrolysis. Extending the classical Toner–Tu framework, we introduce a chemo-mechanical coupling mechanism in which the self-advection and polarization dynamics are modulated by the ATP hydrolysis rate. The model is derived using an energetic variational approach that integrates both chemical free energy and mechanical energy into a unified energy dissipation law. The reaction rate equation explicitly incorporates mechanical feedback, revealing how active transport and alignment interactions influence chemical fluxes and vice versa. This formulation not only preserves consistency with non-equilibrium thermodynamics but also provides a transparent pathway for modeling energy transduction in active systems. We also present numerical simulations demonstrating the positive energy transduction under a specific choice of model parameters. The new modeling framework offers new insights into energy transduction and regulation mechanisms in biologically related active systems. Full article

Stroke often leads to severe motor impairment, especially in the upper limbs, greatly reducing a patient’s ability to perform daily tasks. Effective rehabilitation is essential to restore function and improve quality of life. Traditional therapies, while useful, may lack engagement, leading to low motivation and poor adherence. Gamification—using game-like elements in non-game contexts—offers a promising way to make rehabilitation more engaging. The authors explore a gamified rehabilitation system designed in Unity 3D using a Kinect V2 camera. The game includes key features such as adjustable difficulty, real-time and predominantly positive feedback, user friendliness, and data tracking for progress. The evaluations were conducted with 18 healthy participants, most of whom had prior virtual reality experience. About 77% found the application highly motivating. While the gameplay was well received, the visual design was noted as lacking engagement. Importantly, all users agreed that the game offers a broad range of difficulty levels, making it accessible to various users. The results suggest that the system has strong potential to improve rehabilitation outcomes and encourage long-term use through enhanced motivation and interactivity. Full article

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures. Full article

The evaluation of open-ended questions is typically performed by human instructors using predefined criteria to uphold academic standards. However, manual grading presents challenges, including high costs, rater fatigue, and potential bias, prompting interest in automated essay scoring systems. While automated essay scoring tools can assess content, coherence, and grammar, discrepancies between human and automated scoring have raised concerns about their reliability as standalone evaluators. Large language models like ChatGPT offer new possibilities, but their consistency and fairness in feedback remain underexplored. This study investigates whether ChatGPT can provide stable and fair essay scoring—specifically, whether identical student responses receive consistent evaluations across multiple AI interactions using the same criteria. The study was conducted in two marketing courses at an engineering school in Chile, involving 40 students. Results showed that ChatGPT, when unprompted or using minimal guidance, produced volatile grades and shifting criteria. Incorporating the instructor’s rubric reduced this variability but did not eliminate it. Only after providing an example-rich rubric, a standardized output format, low temperature settings, and a normalization process based on decision tables did ChatGPT-4o demonstrate consistent and fair grading. Based on these findings, we developed a scalable algorithm that automatically generates effective grading rubrics and decision tables with minimal human input. The added value of this work lies in the development of a scalable algorithm capable of automatically generating normalized rubrics and decision tables for new questions, thereby extending the accessibility and reliability of automated assessment. Full article

Currently, exploring digital immersive experiences is a new trend in the innovation and development of cultural tourism. This study addresses the growing demand for digital immersion in cultural tourism by examining the integration of spatial narrative and digitally gamified architectural scenarios. This study synthesizes an optimized framework for narrative design in digitally gamified architectural scenarios, integrating spatial narrative theory and feedback-informed design. The proposed model comprises four key components: (1) developing spatial narrative design methods for such scenarios; (2) constructing a spatial language system for spatial narratives using linguistic principles to organize narrative expression; (3) building a preliminary digitally gamified scenario based on the “Wuhu Jiaoji Temple Renovation Project” after architectural and environmental enhancements; and (4) optimization through thermal feedback experiments—collecting visitor trajectory heatmaps, eye-tracking heatmaps, and oculometric data. The results show that the optimized design, validated in the original game Dreams of Jiaoji, effectively enhanced spatial narrative execution by refining both on-site and in-game architectural scenarios. Post-optimization visitor feedback confirmed the validity of the proposed optimization strategies and principles, providing theoretical and practical references for innovative digital cultural tourism models and architectural design advancements. In the context of site-specific architectural conservation, this approach achieves two key objectives: the generalized interpretation of architectural cultural resources and their visual representation through gamified interactions. This paradigm not only enhances public engagement through enabling a multidimensional understanding of historical building cultures but also accelerates the protective reuse of heritage sites, allowing heritage value to be maximized through contemporary reinterpretation. The interdisciplinary methodology promotes sustainable development in the digital transformation of cultural tourism, fostering user-centered experiences and contributing to rural revitalization. Ultimately, this study highlights the potential use of digitally gamified architectural scenarios as transformative tools for heritage preservation, cultural dissemination, and rural community revitalization. Full article

This paper presents a novel approach to determine dynamic safety and comfort zones in human–robot interactions (HRIs), with a focus on service robots operating in dynamic environments with people. The proposed proxemic model leverages the golden ratio-based comfort zone distribution and ISO safety standards to define adaptive proxemic boundaries for robots around humans. Unlike traditional fixed-threshold approaches, this novel method proposes a gradual and context-sensitive modulation of robot behaviour based on human position, orientation, and relative velocity. The system was implemented on an NVIDIA Jetson Xavier NX platform using a ZED 2i stereo depth camera Stereolabs, New York, USA and tested on two mobile robotic platforms: Go1 Unitree, Hangzhou, China (quadruped) and Scout Mini Agilex, Dongguan, China (wheeled). The initial verification of proposed proxemic model through experimental comfort validation was conducted using two simple interaction scenarios, and subjective feedback was collected from participants using a modified Godspeed Questionnaire Series. The results show that the participants felt comfortable during the experiments with robots. This acceptance of the proposed methodology plays an initial role in supporting further research of the methodology. The proposed solution also facilitates integration into existing navigation frameworks and opens pathways towards socially aware robotic systems. Full article

Curcumin-mediated anti-cancer properties have been correlated with the inhibition of oncogenic molecules such as mutp53 and c-Myc. Their targeting is therapeutically significant, as p53, following point mutations, can acquire oncogenic functions, and c-Myc overexpression, due to translocations, point mutations, protein/protein interactions, or epigenetic modifications, plays a central role in cancer cell proliferation and metabolic reprogramming, particularly in colorectal cancer. In a previous study, we showed that curcumin strongly downregulated mutp53 while activating wtp53 and reduced the expression of methyltransferases such as EZH2, G9a, and MLL-1 in colon cancer cells. Based on this background, here we investigated whether the dysregulation of such methyltransferases could correlate with the effect observed on p53. We also explored whether these epigenetic changes could affect c-Myc expression in these cells. By Western blot analysis and RT-qPCR, we found that the downregulation of EZH2; G9a; and, to a lesser extent, KDM1, which was also reduced by curcumin, correlated with the decrease in mutp53 and that the reduction of EZH2 and KDM1 correlated with the activation of wtp53. Regarding c-Myc, we unveiled the occurrence of a positive feedback loop between it and MLL-1, which was inhibited by curcumin, independently of the p53 status. In conclusion, this study provides new insights into the therapeutic potential of curcumin, which involves its properties to act as an epigenetic modulator and target key molecules in colon cancer cells. Full article

Blind and visually impaired (BVI) people face significant challenges in perception, navigation, and safety during travel. Existing infrastructure (e.g., blind lanes) and traditional aids (e.g., walking sticks, basic audio feedback) provide limited flexibility and interactivity for complex environments. To solve this problem, we propose a real-time travel assistance system based on deep learning. The hardware comprises an NVIDIA Jetson Nano controller, an Intel D435i depth camera for environmental sensing, and SG90 servo motors for feedback. To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. This design ensures high computational efficiency and real-time performance. The system workflow is as follows: (1) the D435i captures real-time environmental data; (2) the processor analyzes this data, converting obstacle distances and path deviations into electrical signals; (3) servo motors deliver vibratory feedback for guidance and alerts. Preliminary tests confirm that the system can effectively detect obstacles and correct path deviations in real time, suggesting its potential to assist BVI users. However, as this is a work in progress, comprehensive field trials with BVI participants are required to fully validate its efficacy. Full article

This study investigates the implementation and impact of the Guided Personalised Learning (GPL) model, a structured pedagogical framework designed to operationalise personalised and student-centred learning in STEM higher education. The GPL model integrates three interconnected components: a three-dimensional knowledge and skill grid, Interactive Learning Progress Assessments (ILPA), and an adaptive learning resource pool. These components were embedded into two undergraduate engineering modules, Network Engineering and Software Engineering, at a UK university. A mixed-method evaluation, centred on student attainment data across two academic years, revealed statistically significant improvements among students who engaged with GPL, particularly those who completed ILPA activities. Participation was associated with higher mean grades, increased proportions of high achievers, and reduced failure rates. These findings demonstrate the GPL model’s effectiveness in supporting learner autonomy, formative assessment, and targeted feedback, while offering a scalable strategy for integrating personalised learning into mainstream STEM curricula. Full article

The rising popularity of ecotourism on the Tibetan Plateau has intensified the tension between wildlife conservation and economic development. Conventional wildlife displays often fail to achieve high-fidelity, non-invasive representations of animal morphology and typically lack immersive, interactive features, limiting public engagement in ecological protection. To address these limitations, this study presents a fur simulation algorithm based on the Texture Procedural Overlay Technique (TPOT), integrated with Augmented Reality (AR) technology, focusing on the endangered white-lipped deer in the Sanjiangyuan region. The proposed TPOT-based algorithm enhances the visual realism of fur through multi-layered procedural texturing and physical property fusion. Combined with an AR-driven interactive framework, it seamlessly integrates high-resolution 3D models into real-world environments, significantly improving user immersion and engagement. Comparative experiments demonstrate that the approach surpasses traditional static display fidelity and animation rendering efficiency methods. User feedback further validates its effectiveness for scientific research and environmental education. This work introduces an innovative technological solution for wildlife conservation on the Tibetan Plateau and provides a practical reference for applying digital technologies in ecotourism. Full article

Virtual reality interaction design and story co-creation design for multiple users is an interdisciplinary research field that merges human–computer interaction, creative design, and virtual reality technologies. Story co-creation design enables multiple users to collectively generate and share narratives, allowing them to contribute to the storyline, modify plot trajectories, and craft characters, thereby facilitating a dynamic storytelling experience. Through advanced virtual reality interaction design, collaboration and social engagement can be further enriched to encourage active participation. This study investigates the facilitation of narrative creation and enhancement of storytelling skills in virtual reality by leveraging existing research on story co-creation design and virtual reality technology. Subsequently, we developed and evaluated the virtual reality card-based collaborative storytelling platform Co-Relay. By analyzing interaction data and user feedback obtained from user testing and experimental trials, we observed substantial enhancements in user engagement, immersion, creativity, and fulfillment of emotional and social needs compared to a conventional web-based storytelling platform. The primary contribution of this study lies in demonstrating how the incorporation of story co-creation can elevate storytelling proficiency, plot development, and social interaction within the virtual reality environment. Our novel methodology offers a fresh outlook on the design of collaborative narrative creation in virtual reality, particularly by integrating participatory multi-user storytelling platforms that blur the traditional boundaries between creators and audiences, as well as between fiction and reality. Full article

Although smart device use among children is increasing, most interventions overlook their cognitive and emotional development or rely too heavily on external control. Such approaches often overlook the developmental needs of children for emotional regulation and autonomy. Therefore, this study aims to propose a child-centred user experience (UX) framework to support digital self-regulation in preschool-aged children. The proposed system integrates multiple psychological theories—including Piaget’s concept of animistic thinking, executive function theory, Self-Determination Theory, and Acceptance and Commitment Therapy—to support cognitive and emotional regulation during screen use. Key features include persistent visual cues to enhance time awareness and behavioural anticipation, narrative-based character interactions to foster empathy and agency, and ritualised closure routines supported by multimodal and tangible interaction elements. Developed as a mobile prototype, the system was iteratively refined through two-stage consultations with child and adolescent psychiatrists and a developmental psychologist, including formative design feedback and follow-up expert review. Their feedback provided preliminary validation of the system’s developmental validity and emotional coherence. These findings suggest that affectively attuned UX design is a viable alternative to conventional control-based screen-time interventions in early childhood. Full article

This paper outlines the development and delivery of a novel, collaborative, co-production approach to incorporating lived experience in the development of policy and practice in the area of domestic abuse. “SEEKERS” (Sharing Experience, Expertise and Knowledge for Effective Responses and Support) is an initiative which brings together victims and advocates, police, practitioners and researchers as equal partners. It creates opportunities for them to share their experiences, expertise, and knowledge, so that others can learn from these and use this learning in addressing domestic abuse-related issues more effectively. Throughout this paper, we discuss some of the challenges encountered in developing and delivering activities and how these were addressed. Notable benefits of the approach will be highlighted, as indicated by feedback from those involved in a range of capacities, including police and law enforcement practitioners, policy makers, councillors, service providers, support services, victim advocates and survivors of domestic abuse. It is hoped that this paper will contribute to ongoing discussions regarding the ways in which different agencies and stakeholders can work together more effectively and how we can create methods and spaces to support meaningful interaction, collaboration, and co-production with victims. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article