Search Results (160)

Landscapes are vital systems where ecological, cultural, perceptual, and socio-economic values meet, making their quality assessment essential for sustainable development. Landscape Quality (LQ), shaped by the interaction of natural processes and human activities, remains methodologically challenging due to its interdisciplinarity and the need to integrate multiple dimensions. This challenge is particularly perceived in peri-urban areas, predominantly understudied in landscape research. This article addresses this gap in LQ assessment at peri-urban landscapes, through the case of Houch Al Oumaraa, Zahleh, a peri-urban area of patrimonial significance and agricultural landscape value. To evaluate the four spatial dimensions of LQ (structural, ecological, cultural and visual), we adopted a mixed methodology, where a pre-developed set of landscape indicators (LIs) applied within GIS and spatial technics, were supplemented by expert analysis through visual studies. Two questions framed this research: (i) is remote sensing sufficient to assess peri-urban LQ, and (ii) what are the limits of applying pre-developed LIs to diverse landscape contexts? Results show moderate fragmentation (CONTAG 61.6%), low diversity (MSDI 0.27), high density of cultural monuments (PROTAP 4.19) and average visual disharmony (FCDHI 0.49). Findings reveal that spatial dimensions alone are insufficient for assessing LQ of peri-urban landscapes, where socio-economic dimensions must also be integrated. Structural indicators (PLAND, MPA, ED, CONTAG) and MSDI proved transferable, while ECOLBAR was less applicable, cultural indicators (PROTAP, HLE) were limited to tangible heritage, and visual indicators (FCDHI, SDHI) highly context dependent. Establishing a differentiated yet standardized framework would not only enhance methodological precision but also ensure that LQ assessment remain relevant across diverse contexts, providing policymakers with actionable insights to align planning with sustainability goals. Full article

Autonomous social robots aim to reduce human supervision by performing various tasks. To achieve this, they are equipped with multiple perceptual channels to interpret and respond to environmental cues in real time. However, multimodal perception often leads to sensory overload, as robots may receive numerous simultaneous stimuli with varying durations or persistent activations across different sensory modalities. Sensor overstimulation and false positives can compromise a robot’s ability to prioritise relevant inputs, sometimes resulting in repeated or inaccurate behavioural responses that reduce the quality and coherence of the interaction. This paper presents a Bioinspired Attentional System that uses Reinforcement Learning to manage stimulus prioritisation in real time. The system draws inspiration from the following two neurocognitive mechanisms: Inhibition of Return, which progressively reduces the importance of previously attended stimuli that remain active over time, and Attentional Fatigue, which penalises stimuli of the same perception modality when they appear repeatedly or simultaneously. These mechanisms define the algorithm’s reward function to dynamically adjust the weights assigned to each stimulus, enabling the system to select the most relevant one at each moment. The system has been integrated into a social robot and tested in three representative case studies that show how it modulates sensory signals, reduces the impact of redundant inputs, and improves stimulus selection in overstimulating scenarios. Additionally, we compare the proposed method with a baseline where the robot executes expressions as soon as it receives them using a queue. The results show the system’s significant improvement in expression management, reducing the number of expressions in the queue and the delay in performing them. Full article

Background: Sweet taste adaptation, the decline in perceived sweetness with repeated exposure, may influence dietary behavior and differs across sweeteners. Low-calorie sweeteners (LCSs) such as sucralose strongly activate the T1R2+T1R3 receptor and are generally associated with greater adaptation than sugars, although this effect can be reduced with sweetener blends. Aim: We investigated whether habitual LCS consumption affects sweet taste perception and whether blending sucralose with small amounts of sugars attenuates adaptation using sensory tests in humans and in vivo calcium imaging in a rodent model. Methods: In study 1, habitual (HC; n = 39) and non-habitual (NHC; n = 42) LCS consumers rate sweetness of sucralose (0.6 mM), glucose (800 mM), fructose (475 mM), and blends with low glucose (111 mM) or fructose (45 mM) across repeated trials (1–10) using a generalized labeled magnitude scale. In study 2, a microfluidic-based intravital tongue imaging system was used to assess in vivo responses to sweet adaptation in genetically modified C57BL/6 mice (n = 8) expressing a calcium indicator in type II/III cells of taste buds. Results: Habitual LCS use was not associated with differences in sweetness perception or adaptation (all p-values > 0.6). Sucralose alone produced stronger adaptation than when blended with sugars in both humans (p-values < 0.002) and mice (p < 0.001). Glucose and fructose alone showed adaptation (relative decrease reached on final trial compared to the first trial: −27% ± 4% for glucose, −38% ± 5% for fructose, both p-values < 0.002) but to a lower degree compared with sucralose (−66% ± 5%). Conclusions: Sweetener composition, rather than habitual LCS use, drives sweet taste adaptation. Blending sucralose with small amounts of sugars reduces adaptation at both perceptual and cellular levels, providing mechanistic insights relevant to the formulation of LCS products. Full article

This study introduces a machine learning–driven extended reality (XR) interaction framework that leverages electroencephalography (EEG) for decoding consumer intentions in immersive decision-making tasks, demonstrated through functional food purchasing within a simulated autonomous vehicle setting. Recognizing inherent limitations in traditional “Preference vs. Non-Preference” EEG paradigms for immersive product evaluation, we propose a novel and robust “Rest vs. Intention” classification approach that significantly enhances cognitive signal contrast and improves interpretability. Eight healthy adults participated in immersive XR product evaluations within a simulated autonomous driving environment using the Microsoft HoloLens 2 headset (Microsoft Corp., Redmond, WA, USA). Participants assessed 3D-rendered multivitamin supplements systematically varied in intrinsic (ingredient, origin) and extrinsic (color, formulation) attributes. Event-related potentials (ERPs) were extracted from 64-channel EEG recordings, specifically targeting five neurocognitive components: N1 (perceptual attention), P2 (stimulus salience), N2 (conflict monitoring), P3 (decision evaluation), and LPP (motivational relevance). Four ensemble classifiers (Extra Trees, LightGBM, Random Forest, XGBoost) were trained to discriminate cognitive states under both paradigms. The ‘Rest vs. Intention’ approach achieved high cross-validated classification accuracy (up to 97.3% in this sample), and area under the curve (AUC > 0.97) SHAP-based interpretability identified dominant contributions from the N1, P2, and N2 components, aligning with neurophysiological processes of attentional allocation and cognitive control. These findings provide preliminary evidence of the viability of ERP-based intention decoding within a simulated autonomous-vehicle setting. Our framework serves as an exploratory proof-of-concept foundation for future development of real-time, BCI-enabled in-transit commerce systems, while underscoring the need for larger-scale validation in authentic AV environments and raising important considerations for ethics and privacy in neuromarketing applications. Full article

Background: Time perception (TP) is increasingly recognized as a key cognitive domain in children and adolescents with cerebral palsy (CP), yet existing studies are scarce, heterogeneous, and methodologically limited. Objective: To synthesize empirical evidence on TP in pediatric CP, distinguishing perceptual timing deficits from motor-based impairments and outlining putative cognitive mechanisms. Methods: Following PRISMA where appropriate, we systematically searched Scopus, Embase, and PubMed Central for studies on TP in individuals with CP under 18 years. Four studies met inclusion criteria. Risk of bias was appraised with STROBE, AXIS, and RoB 2. Results: Available evidence suggests that TP difficulties in CP are not solely due to motor dysfunction but also reflect broader cognitive–perceptual challenges. Studies using low-motor-demand tasks sometimes report intact TP, whereas tasks requiring overt movement often confound perceptual timing with execution demands. Intervention findings are mixed: time-related supports show promising but inconsistent effects on everyday time processing, while motor-focused timing training demonstrates limited impact on TP itself. However, conclusions are constrained by the small number of studies and variability in samples, tasks, and outcomes. Conclusions: TP should be considered a distinct, clinically relevant construct in pediatric CP. Future work should employ motor-minimal paradigms, report standardized CP classifications, and adopt longitudinal designs to isolate TP deficits and guide targeted interventions. Clarifying TP profiles may improve cognitive characterization and rehabilitation planning in CP. Full article

Communications in intelligent transportation systems (ITS) face explosive data growth from applications such as autonomous driving, remote diagnostics, and real-time monitoring, imposing severe challenges on limited spectrum, bandwidth, and latency. Reliable semantic image reconstruction under noisy channel conditions is critical for ITS perception tasks, since noise directly impacts the recognition of both static infrastructure and dynamic obstacles. Unlike traditional approaches that aim to transmit all image data with equal fidelity, effective ITS communication requires prioritizing task-relevant dynamic elements such as vehicles and pedestrians while filtering out largely static background features such as buildings, road signs, and vegetation. To address this, we propose an Offline Knowledge Base and Attention-Driven Semantic Communication (OKBASC) framework for image-based applications in ITS scenarios. The proposed framework performs offline semantic segmentation to build a compact knowledge base of semantic masks, focusing on dynamic task-relevant regions such as vehicles, pedestrians, and traffic signals. At runtime, precomputed masks are adaptively fused with input images via sparse attention to generate semantic-aware representations that selectively preserve essential information while suppressing redundant background. Moreover, we introduce a further Bi-Level Routing Attention (BRA) module that hierarchically refines semantic features through global channel selection and local spatial attention, resulting in improved discriminability and compression efficiency. Experiments on the VOC2012 and nuPlan datasets under varying SNR levels show that OKBASC achieves higher semantic reconstruction quality than baseline methods, both quantitatively via the Structural Similarity Index Metric (SSIM) and qualitatively via visual comparisons. These results highlight the value of OKBASC as a communication-layer enabler that provides reliable perceptual inputs for downstream ITS applications, including cooperative perception, real-time traffic safety, and incident detection. Full article

Monte Carlo Path Tracing (MCPT) provides highly realistic visualization of biomedical volumes, but its computational cost limits real-time interaction. The Advanced Realistic Rendering Technique (AR²T) adapts MCPT to enable interactive exploration through coarse images generated at low sample counts. This study explores the application of deep learning models for denoising in the early iterations of the AR²T to enable higher-quality interaction with biomedical data. We evaluate five deep learning architectures, both pre-trained and trained from scratch, in terms of denoising performance. A comprehensive evaluation framework, combining metrics such as PSNR and SSIM for image fidelity and tPSNR and LDR-FLIP for temporal and perceptual consistency, highlights that models trained from scratch on domain-specific data outperform pre-trained models. Our findings challenge the conventional reliance on large, diverse datasets and emphasize the importance of domain-specific training for biomedical imaging. Furthermore, subjective clinical assessments through expert evaluations underscore the significance of aligning objective metrics with clinical relevance, highlighting the potential of the proposed approach for improving interactive visualization for analysis of bones, joints, and vessels in clinical and research environments. Full article

This study contributes to the growing interest in evaluating environmental qualities and characteristics for the enhancement of social and individual well-being by introducing and validating the Landscape Assessment Scale (LAS), a standardized tool designed to assess key environmental qualities across both natural and urban landscapes within metropolitan settings. The scale comprises 30 items related to 10 key environmental components: coherence, complexity, ephemera, imageability, naturalness, safety, visual scale, stewardship, disturbance, and historicity of places. In study 1, the LAS was first tested on 327 participants, who evaluated either a natural (N = 176) or urban (N = 151) environment. Exploratory Factor Analysis (EFA) revealed three correlated factors: Landscape Disharmony, Landscape Organized Complexity, and Landscape Naturalistic Impact. In study 2, participants (N = 185) were asked to select and to assess two environments (natural and urban) using the shortened LAS and the Perceived Restorativeness Scale (PRS). A Confirmatory Factor Analysis (CFA) was used to investigate the invariance of the LAS factor structure in both natural and urban environments, and the correlational analysis was used to investigate LAS convergent validity. The CFA supported the three-factor structure and showed significant correlations between LAS and PRS components, supporting convergent validity. By capturing key perceptual dimensions that are relevant across landscape types, the LAS offers a practical and scientifically robust tool for informing evidence-based urban planning and landscape design. Full article

Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individual’s quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. Full article

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A360° panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated in the study, generating over 2.6 million gaze samples (IRB No. 23-095-B). The collected eye-tracking data were analyzed using statistical summarization, temporal alignment techniques (Earth Mover’s Distance and Needleman-Wunsch algorithms), and machine learning models, including K-means clustering, random forest regression, and support vector machines (SVMs). Clustering achieved a classification accuracy of 78.26%, revealing distinct visual behavior patterns across participant groups. Proficiency prediction models reached optimal performance with a random forest regression ($R^2$ = 0.7034), highlighting scan-path variability and fixation regularity as key predictive features. These findings demonstrate that eye-tracking metrics—particularly sequence-alignment-based features—can effectively capture differences linked to both experiential training and cultural context. Beyond its immediate forensic relevance, the study contributes a structured methodology for encoding visual attention strategies into analyzable formats, offering valuable insights for cognitive modeling, training systems, and human-centered design in future perceptual intelligence applications. Furthermore, our work advances the development of autonomous vehicles by modeling how humans visually interpret complex and potentially hazardous environments. By examining expert and novice gaze patterns during simulated forensic investigations, we provide insights that can inform the design of autonomous systems required to make rapid, safety-critical decisions in similarly unstructured settings. The extraction of human-like visual attention strategies not only enhances scene understanding, anomaly detection, and risk assessment in autonomous driving scenarios, but also supports accelerated learning of response patterns for rare, dangerous, or otherwise exceptional conditions—enabling autonomous driving systems to better anticipate and manage unexpected real-world challenges. Full article

Sign language avatar systems have emerged as a promising solution to bridge communication gaps where human sign language interpreters are unavailable. However, the design of these avatars often fails to account for the diversity in how users acquire and perceive sign language. This study presents a rapid review of 17 empirical studies (2020–2025) to synthesize how linguistic and cognitive variability affects sign language perception and how these findings can guide avatar development. We extracted and synthesized key constructs, participant profiles, and capture techniques relevant to avatar fidelity. This review finds that delayed exposure to sign language is consistently linked to persistent challenges in syntactic processing, classifier use, and avatar comprehension. In contrast, early-exposed signers demonstrate more robust parsing and greater tolerance of perceptual irregularities. Key perceptual features, such as smooth transitions between signs, expressive facial cues for grammatical clarity, and consistent spatial placement of referents, emerge as critical for intelligibility, particularly for late learners. These findings highlight the importance of participatory design and user-centered validation in advancing accessible, culturally responsive human–computer interaction through next-generation avatar systems. Full article

As ad-supported subscription models proliferate across over-the-top (OTT) media platforms, understanding the psychological mechanisms and perceptual factors that underlie consumers’ transition decisions becomes increasingly consequential. This study integrates the Uses and Gratifications framework with a contemporary motivation-based perspective to examine how users’ media consumption motivations and advertising attitudes predict intentions to adopt ad-supported OTT plans. Data were collected via a nationally representative online survey in South Korea (N = 813). The sample included both premium subscribers (n = 708) and non-subscribers (n = 105). The findings reveal distinct segmentation in decision-making patterns. Among premium subscribers, switching intentions were predominantly driven by intrinsic motivations—particularly identity alignment with content—and by the perceived informational value of advertisements. These individuals are more likely to consider ad-supported plans when ad content is personally relevant and cognitively enriching. Conversely, non-subscribers exhibited greater sensitivity to extrinsic cues such as the entertainment value of ads and the presence of tangible incentives (e.g., discounts), suggesting a hedonic-reward orientation. By advancing a dual-pathway explanatory model, this study contributes to the theoretical discourse on digital subscription behavior and offers actionable insights for OTT service providers. The results underscore the necessity of segment-specific advertising strategies: premium subscribers may be engaged through informative and identity-consistent advertising, while non-subscribers respond more favorably to enjoyable and benefit-laden ad experiences. These insights inform platform monetization efforts amid the evolving dynamics of consumer attention and subscription fatigue. Full article

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model’s foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

Self-motion perception is a complex multisensory process that relies on the integration of various sensory signals, particularly visual and vestibular inputs, to construct stable and unified perceptions. It is essential for spatial navigation and effective interaction with the environment. This review systematically explores the mechanisms and computational principles underlying visual–vestibular integration in self-motion perception. We first outline the individual contributions of visual and vestibular cues and then introduce Bayesian inference as a normative framework for the quantitative modeling of multisensory integration. We also discuss multisensory recalibration as a critical mechanism in resolving conflicts between sensory inputs and maintaining perceptual stability. Using heading perception as a model system, we further describe the relevant visual and vestibular pathways involved in this process, as well as the brain regions involved. Finally, we discuss the neural mechanisms mediating visual–vestibular interactions through models of the Bayesian optimal integration and divisive normalization. Full article