Search Results (167)

Cell membranes exhibit specific structural and chirality properties influencing their biological behavior and functionality. Artemia salina endothelial-like cell membranes, structurally simpler, provide insights into fundamental cellular structures, whereas human endothelial cell membranes represent complex, specialized tissues essential for understanding advanced vascular functions. This study aims to compare the structural and chiral properties of Artemia salina endothelial-like cell membranes and human endothelial cell membranes through computational molecular-level modeling, evaluating potential histological and biological implications. Membrane models for Artemia salina and human endothelial cells were developed using Protein Data Bank (PDB) structures. Computational descriptors, including radius of gyration (Rg), solvent-accessible surface area (SASA), geometric asymmetry index (GAI), chiral moment (CM), fractal dimension (FD), and additional chirality indices (SOC, HCI, ACI, CAI, ME, RDF) were calculated to assess membrane complexity, structural asymmetry, and chirality. Significant structural divergences between Artemia salina and human endothelial membranes were identified. Artemia membranes exhibited lower values of Rg, SASA, and chirality metrics, indicating simpler, more symmetrical structures. In contrast, human endothelial membranes displayed elevated structural complexity, pronounced asymmetry, higher chirality indices, and more significant structural heterogeneity, consistent with their specialized physiological functions. Principal Component Analysis (PCA) further highlighted clear structural clustering distinctions between the two models. The comparative analysis underscores fundamental structural and functional divergences between Artemia salina and human endothelial cell membranes. Artemia membranes represent simplified, uniform cellular arrangements optimized for fundamental physiological roles, while human endothelial membranes exhibit complex architectures, structural specialization, and significant chirality essential for dynamic vascular functionalities. These computational descriptors offer potential diagnostic biomarkers for evaluating endothelial functionality and pathological states. Full article

Human–computer interaction (HCI) has evolved from traditional command-based interfaces to adaptive systems powered by artificial intelligence (AI). In industrial environments, particularly manufacturing and logistics, selecting the appropriate interaction modality is crucial for efficiency, safety, and user acceptance. This study presents a conceptual decision support framework that analyzes three modalities—visual, voice, and multimodal—based on a systematic literature review covering the period from 2003 to early 2026. The analysis evaluates differences in usability, cognitive workload, implementation complexity, and operational benefits of HCI and AI-based HCI. To address the selection challenge, a multi-criteria decision analysis (MCDA) model was developed. The proposed MCDA model is based on a structured literature analysis and expert-informed evaluation. The expert-based MCDA ranking is context-dependent and grounded in the reviewed literature. The results indicate that multimodal HCI shows the highest potential in manufacturing scenarios, offering advantages in safety, robustness, flexibility, and potential contributions to sustainability. However, it also indicates more demanding implementation, training requirements, and higher costs. The proposed decision support framework is intended to serve as a methodological tool for the structured evaluation of HCI modality suitability in sustainable manufacturing environments. Full article

This study explores the use of transfer learning in gaze estimation, focusing on the development of personalized models tailored to individual users. Our approach involves collecting gaze data using standard laptop webcams, designed to operate effectively within resource-limited settings, thereby enhancing accessibility and affordability. We conducted a comparative analysis of models using transfer learning against models trained without pre-trained weights, examining their convergence behavior and sensitivity to different dataset sizes. The analysis includes both eye and face images, providing a comprehensive view of model adaptability. Our findings show that while both methods produce comparable results overall, transfer learning offers notable advantages—particularly faster convergence, reduced computational cost, and enhanced stability when data are limited. However, the results also reveal that transfer learning is not universally superior; for face images, models trained from scratch achieved lower mean errors but exhibited higher variability, whereas transfer learning ensured more consistent performance. These insights highlight that the benefits of transfer learning depend on the data characteristics and task complexity. In the most data-constrained setting (100 images), transfer learning reduced the mean error by 20.99 px for the Left Eye model and 35.56 px for the Right Eye model, whereas for face images the models trained from scratch consistently achieved lower mean error across all evaluated dataset sizes. Overall, this study underscores the potential of transfer learning for efficient and scalable gaze estimation, particularly in small-data environments, while providing practical guidance on when and how transfer learning yields the greatest benefit for real-time applications such as human–computer interaction (HCI), assistive technologies, and personalized user experiences. Full article

Real-time strategy (RTS) games provide a cognitively demanding and ecologically valid context for investigating workload dynamics in human–computer interaction (HCI). This multimodal study (HRV, NASA-TLX, behavior, interviews) examined multitasking, visual complexity, and decision pressure in 36 novice RTS players. High multitasking significantly increased subjective workload (total raw-TLX: from 22.50 ± 14.65 to 36.47 ± 20.19, p < 0.001) and prolonged completion time (from 317.17 ± 37.26 s to 354.92 ± 50.70 s, p < 0.001). Decision pressure elevated subjective workload (total raw-TLX: from 20 to 28, p = 0.008) without affecting performance. Although HRV did not consistently differentiate experimental conditions at the group level, it showed stable individual-level associations with perceived workload—both in expected directions (e.g., LF power positively correlated with total raw-TLX across four experiments, r = 0.28–0.53, all p < 0.05) and in inverse relationships that deviate from conventional stress models (e.g., stress index negatively correlated with total raw-TLX, r = −0.34 to −0.40, all p < 0.01). These findings suggest that autonomic responses in complex interactive environments may reflect dynamic engagement processes rather than uniform stress activation, supporting multimodal cognitive load assessment and offering transferable insights for interface design and workload evaluation in demanding HCI contexts. Full article

Sports have progressively incorporated technological advances, yet while the impact on performance and broadcasting is remarkable, the application of Artificial Intelligence (AI) in sports refereeing appears residual. A closer examination of prior research suggests that this limited development reflects deeper conceptual patterns within the field. While existing research on AI in sports officiating has predominantly conceptualized the field under an accuracy-optimization paradigm (focusing on decision precision, visual attention patterns, referee fatigue, and performance enhancement), there is a systematic lack of theoretical and empirical work that frames officiating as a broader socio-technical ecosystem. In particular, the literature does not provide conceptual models addressing (i) AI-assisted risk prevention and athlete safety as a core officiating function, (ii) human–AI task redistribution in cognitively overloaded and hybrid evaluative environments (e.g., disciplines such as artistic gymnastics or bodybuilding, where technical execution and aesthetic judgment are simultaneously assessed), and (iii) the redefinition of the referee’s role when AI operates as an anticipatory or real-time alert system rather than merely as a post hoc verification tool. Thus, the gap is not only one of application but of knowledge production: the dominant paradigm optimizes decision accuracy, yet it leaves the question of how AI can transform refereeing responsibilities, cognitive load distribution, and safety governance within competitive ecosystems under-theorized. This exploratory study adopts a Human–Computer Interaction (HCI) perspective to contrast existing initiatives with the practical expectations of professional referees. The methodology comprises two pillars: a systematic literature review following PRISMA guidelines and qualitative experimentation involving professional referees using focus groups and affinity diagrams techniques. From an initial total of 1251 records retrieved across five academic databases (2019–2025), 1122 articles were analyzed after applying strict inclusion/exclusion criteria. The findings provide preliminary support for our hypothesis of a significant underutilization gap, showing that research is concentrated on accuracy systems, while high-potential areas identified as critical by experts, such as athlete safety, represent only 0.6% of the analyzed literature. The study contributes a conceptual framework based on five categories established by experts, according to the identified use cases, providing guidance for future AI integration and interdisciplinary research in the sports officiating ecosystem. Based on the results, we point to future applications and lines of research aimed at integrating AI as a tool for sports refereeing. Full article

Predefined, user-independent gesture sets do not account for individual differences in movement patterns and physical limitations. This study presents a personalized wrist–forearm static gesture recognition system for human–computer interaction (HCI) using the Vicara Kai^TM wearable controller and a convolutional neural network (CNN). Unlike the system based on fixed, predefined gestures, the proposed approach enables users to define and train their own gesture sets. During gesture recording, users may either select a gesture pattern from a predefined prompt set or create their own natural, unprompted gestures. A dedicated software framework was developed for data acquisition, preprocessing, model training, and real-time recognition. The developed system was evaluated by optimizing the parameters of a lightweight CNN and examining the influence of sequentially applied changes to the input and network pipelines, including resizing the input layer, applying data augmentation, experimenting with different dropout ratios, and varying the number of learning samples. The performance of the resulting network setup was assessed using confusion matrices, accuracy, and precision metrics for both original gestures and gestures smoothed using the cubic Bézier function. The resulting validation accuracy ranged from 0.88 to 0.94, with an average test-set accuracy of 0.92 and macro precision of 0.92. The system’s resilience to rapid or casual gestures was also evaluated using the receiver operating characteristic (ROC) method, achieving an Area Under the Curve (AUC) of 0.97. The results demonstrate that the proposed approach achieves high recognition accuracy, indicating its potential for a range of practical applications. Full article

Reliability assessment of semiconductor devices increasingly requires the consideration of multiple degradation mechanisms acting simultaneously over long stress durations. Conventional lifetime qualification and prediction approaches rely on simplified assumptions that can obscure the interpretation of measured degradation data and lead to large uncertainty when extrapolated over many orders of magnitude in time. A consistent analytical framework is therefore required to relate measured degradation behavior to meaningful reliability metrics. This work presents a general framework for semiconductor device reliability that is consistent with established reliability theory and explicitly accommodates multiple competing degradation mechanisms, consistent with modern JEDEC reliability standards. The framework presented here separates physical degradation processes from analytical representations used to interpret experimental data, allowing the effect of independent mechanisms to be combined without imposing an implied physical model. Degradation behaviors exhibiting sublinear time dependence, which are commonly observed across device technologies, are discussed within this context. We show that common data interpretation practices can introduce systematic errors when ssublinearkinetics are present, particularly regarding lifetime extrapolation. A reformulated analytical representation is introduced that improves clarity and robustness in lifetime extraction while remaining fully compatible with standard reliability theory. This framework supports more consistent reliability assessment and more credible lifetime prediction across materials, devices, and operating conditions. Full article

This paper addresses the growing sustainability fatigue in advanced economies. By analyzing Amazon’s artificial intelligence (AI) agent strategy as a model for “Rational Sustainability”, the study identifies a self-propagating growth trajectory that reconciles economic rationality with value creation. It provides a theoretical and empirical framework to overcome technological saturation and strategic homogenization in the generative AI era. To ensure methodological transparency, the analysis was conducted through two distinct stages: (i) Techno-econometric analysis (macro-level): Using an empirical dataset of 160 countries (40 advanced, 70 emerging, and 50 developing) from 2014 to 2024, the study utilized regression models to quantify the correlations and elasticities between three key proxies: GDP per capita (Y); the Human Capital Index (HCI), representing Institutional Capacity Building (ICB); and the E-Government Development Index (EGI), representing Endogenous Institutional Evolution (EIE). (ii) Hybrid AI analysis (case study): Utilizing process-tracing research, the paper examines Amazon’s R&D structure and AI agent strategy. This qualitative and structural analysis identifies how Amazon co-evolves EIE and ICB to conceptualize tacit knowledge and operationalize it into a competitive advantage. The findings reveal a marked disruption of the co-evolutionary mechanism in advanced economies, where the elasticity of EGI to GDP has declined since 2019, leading to a withdrawal state. In contrast, Amazon’s model demonstrates that the co-evolution of EIE and ICB creates a self-propagating growth engine. This research concludes that “Rational Sustainability”—grounded in evidence, economic rationality, and clear trade-offs—offers a viable pathway for revitalizing sustainability strategies in mature digital economies. Full article

Most pre-trained language representation models are designed to encode contextualized semantic information for general language processing tasks. However, they are insufficient for math word problem (MWP) solving, which requires not only linguistic syntax and semantic understanding but also numerical reasoning. In this work, we introduce SSN4Solver, a deep neural solver that improves MWP-solving performance by symmetrically fusing syntax, semantics, and numeracy representations within its contextual encoder. Our approach jointly captures syntactic structures from dependency trees, semantic features from part-of-speech tags, and the attributes and relations of numerical entities. By treating these heterogeneous information sources in a balanced and aligned manner, SSN4Solver constructs a rich, multi-faceted representation for MWP solving without introducing substantial computational overhead, empowering human–computer interaction (HCI) applications such as adaptive educational interfaces and intelligent tutoring systems. Extensive experiments demonstrate that SSN4Solver outperforms existing baseline models. In addition, a visualization scheme is designed to elucidate how the three types of representations contribute to the solving process. SSN4Solver thus offers a scalable solution, contributing to the development of HCI systems that are both intelligent and mathematically effective. Full article

The integration of Internet of Things (IoT) and mobile computing in education offers new avenues to address complex health behaviors that affect cognitive performance. While traditional health education relies on passive information delivery, emerging research suggests that interactive systems can bridge the gap between intent and action. This study addresses the “double burden of malnutrition” in Ecuadorian schoolchildren (N = 120) as a Human-Computer Interaction (HCI) challenge. By utilizing a quantitative profiling approach rooted in the Social Dimensions of Health framework, we modeled the user requirements for a proposed intervention system. The findings identified a critical “Action Gap”: while 78.3% of users possess the motivation to improve habits for academic gain, 53.3% remain entrenched in high-sugar consumption patterns due to environmental latency. Statistical profiling reveals a significant dissonance ($p<0.05$) between cognitive intent and behavioral execution. Consequently, this paper presents the “Digital Bridge Architecture,” a computational framework that leverages these motivation metrics to design an Alternate Reality Game (ARG) logic. We conclude that conventional static applications may be limited in their capacity to support sustained behavioral change in this context. The proposed framework suggests that context-aware, gamified feedback mechanisms can offer a promising direction for aligning academic motivation with healthier behavioral outcomes. Full article

The lack of measurement constructs for both users’ literacy and artifacts difficulty in HCI hinders the quality of assessment tests. This paper proposes DRIVE-T (Discriminating and Representative Items for Validating Expressive Tests), a methodology designed to construct and evaluate items for measuring progressive levels of users’skills and artifacts usability. Given an artifact (a text, a data visualization, an interface prototype), DRIVE-T supports the identification of items discriminability and representativeness for measuring distinct traits of a quality property to be measured and its levels of progression in users and artifacts. DRIVE-T consists of three steps: (1) tagging task-based items associated with an artifact; (2) rating them by independent raters for their difficulty; (3) analyzing raters’ raw scores through a Many-Facet Rasch Measurement model. The emergence of difficulty levels of the measurement construct can be derived from the discriminability and representativeness of items for each artifact, ordered into Many-Facets construct levels. The DRIVE-T methodology operationalizes an inductive, practice-based measurement construct design. Based on the expertise of the authors in the data visualization domain, visualization literacy is the quality property of users that is exploited as a case scenario for applying DRIVE-T. Results from a pilot test show the validity of the approach. Full article

Affective computing has emerged as a pivotal field in human–computer interaction. Recognizing human emotions through electroencephalogram (EEG) signals can advance our understanding of cognition and support healthcare. This study introduces a novel subject-independent emotion recognition framework by integrating multiple EEG emotion databases (DEAP, MAHNOB HCI-Tagging, DREAMER, AMIGOS and REFED) into a unified dataset. EEG segments were transformed into feature vectors capturing statistical, spectral, and entropy-based measures. Standardized pre-processing, analysis of variance (ANOVA) F-test feature selection, and six machine learning models were applied to the extracted features. Classification models such as Decision Tree, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Naive Bayes, and Artificial Neural Networks (ANN) were considered. Experimental results demonstrate that SVM achieved the best performance for arousal classification (70.43%), while ANN achieved the highest accuracy for valence classification (68.07%), with both models exhibiting strong generalization across subjects. The results highlight the feasibility of developing biomimetic brain–computer interface (BCI) systems for objective assessment of emotional intelligence and its cognitive underpinnings, enabling scalable applications in affective computing and adaptive human–machine interaction. Full article

Understanding how nanocomposite gels regulate pore–throat structures and flow behavior is essential for improving profile control and flow diversion in deep-water tight reservoirs. In this study, a dual-structure-regulated nanocomposite gel (DSRC-NCG) was designed, and its structure–flow coupling behavior during gel injection, curing, and degradation was systematically investigated using multiscale flow configurations, including microfluidic models, artificial cores, and sandpack systems. Microstructural evolution and pore–throat connectivity were characterized using μCT imaging, mercury intrusion porosimetry, nitrogen adsorption, and image-based flow simulations, while macroscopic flow responses were evaluated through permeability variation, dominant-channel evolution, injectivity behavior, and quantitative indices including the structure regulation index (SRI) and pore–flow matching index (HCI). The results show that increasing SiO₂ content induces a progressive optimization of pore–flow matching by refining critical throats and suppressing preferential flow channels, whereas excessive nanoparticle loading leads to aggregation and attenuation of these effects. This study proposes a multiscale structure–flow coupling framework that quantitatively connects pore–throat regulation with macroscopic flow responses during nanocomposite gel injection and degradation. These findings offer mechanistic insights and practical guidance for the design of nanocomposite gels with improved flow-regulation efficiency and reversibility in deep-water tight reservoir applications. 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

This paper presents a novel hybrid multimodal deep learning model for robust and real-time eye gaze estimation. Accurate gaze tracking is essential for advancing human–computer interaction (HCI) and assistive technologies, but existing methods often struggle with environmental variations, require extensive calibration, and are computationally intensive. Our proposed model, GazeNet-HM, addresses these limitations by synergistically fusing features from RGB, depth, and infrared (IR) imaging modalities. This multimodal approach allows the model to leverage complementary information: RGB provides rich texture, depth offers invariance to lighting and aids pose estimation, and IR ensures robust pupil detection. Furthermore, we introduce a personalized adaptation module that dynamically fine-tunes the model to individual users with minimal calibration data. To ensure practical deployment, we employ advanced model compression techniques, enabling real-time inference on resource-constrained embedded systems. Extensive evaluations on public datasets (MPIIGaze, EYEDIAP, Gaze360) and our collected M-Gaze dataset demonstrate that GazeNet-HM achieves state-of-the-art performance, reducing the mean angular error by up to 27.1% compared to leading unimodal methods. After model compression, the system achieves a real-time inference speed of 32 FPS on an embedded Jetson Xavier NX platform. Ablation studies confirm the contribution of each modality and component, highlighting the effectiveness of our holistic design. Full article