Search Results (57)

Muscle–joint flexibility is defined as the ability of a muscle to stretch in a controlled manner, allowing a wide range of movement at the joints. While numerous methodologies exist for improving flexibility, few studies have investigated the role of athletes’ perceptual processes and awareness related to their own body and movement control during such training. In this pilot study, we explored how two different training protocols—static and dynamic stretching (control group, CON) and multi-joint resistance training (experimental group, EXP)—influence both flexibility and psychophysical awareness, understood as a multidimensional construct involving perceived flexibility improvements, self-assessed control over exercise execution, and cognitive-emotional responses such as engagement, motivation, and satisfaction during physical effort. The study involved 24 male amateur track-and-field athletes (mean age 23 ± 2.5 years), randomized into two equal groups. Over 12 weeks, both groups trained three times per week. Flexibility was assessed using the Sit and Reach Test at three time points (pre-, mid-, and post-intervention). A 2 × 3 mixed ANOVA revealed a significant group × time interaction (F = 20.17, p < 0.001), with the EXP group showing greater improvements than the CON group. In the EXP group, Sit and Reach scores increased from pre = 28.55 cm (SD = 4.91) to mid = 29.39 cm (SD = 4.67) and post = 29.48 cm (SD = 4.91), with a significant difference between pre and post (p = 0.01; d = 0.35). The CON group showed minimal changes, with scores of pre = 28.66 cm (SD = 4.92), mid = 28.76 cm (SD = 5.03), and post = 28.84 cm (SD = 5.10), and no significant difference between pre and post (p = 0.20; d = 0.04). Psychophysical awareness was assessed using a custom questionnaire structured on a 5-point Likert scale, with items addressing perception of flexibility, motor control, and exercise-related bodily sensations. The questionnaire showed excellent internal consistency (Cronbach’s α = 0.92). Within the EXP group, psychophysical awareness increased significantly (from 3.50 to 4.17; p = 0.01; d = 0.38), while no significant change occurred in the CON group (p = 0.16). Post-hoc power analysis confirmed small to moderate effect sizes within the EXP group, although between-group differences lacked sufficient statistical power. These results suggest that resistance training may improve flexibility and concurrently enhance athletes’ psychophysical self-awareness more effectively than traditional stretching. Such findings offer practical implications for coaches seeking to optimize flexibility training by integrating alternative methods that promote both physical and perceptual adaptations. Full article

The transition to hybrid work has become a defining feature of the post-pandemic IT sector, yet organizations lack empirical benchmarks for balancing flexibility with performance and well-being. This study addresses this gap by identifying an optimal hybrid work structure and exposing systematic perception gaps between employees and managers. Grounded in Self-Determination Theory and the Job Demands–Resources model, our research analyses survey data from 1003 employees and 252 managers across 46 countries. The findings identify a hybrid “sweet spot” of 6–10 office days per month. Employees in this window report significantly higher perceived efficiency (Odds Ratio (OR) ≈ 2.12) and marginally lower office-related stress. Critically, the study uncovers a significant perception gap: contrary to the initial hypothesis, managers are nearly twice as likely as employees to rate hybrid work as most efficient (OR ≈ 1.95) and consistently evaluate remote-work resources more favourably (OR ≈ 2.64). This “supervisor-optimism bias” suggests a disconnect between policy design and frontline experience. The study concludes that while a light-to-moderate hybrid model offers clear benefits, organizations must actively address this perceptual divide and remedy resource shortages to realize the potential of hybrid work fully. This research provides data-driven guidelines for creating sustainable, high-performance work environments in the IT sector. Full article

Visual target detection under adverse weather conditions presents a fundamental challenge for autonomous driving, particularly in achieving all-weather operational capabilities. Unlike existing reviews that concentrate on individual technical domains such as image restoration or detection robustness, this review introduces an innovative “restoration–detection” collaborative framework. This paper systematically examines state-of-the-art methods for degraded image recovery and improvement of detection model robustness, encompassing from traditional, physically driven approaches as well as contemporary deep learning paradigms. A comprehensive overview and comparative analysis are provided to elucidate these advancements. Regarding the recovery of degraded images, traditional methods demonstrate advantages in interpretability within specific scenarios, such as those based on dark channel prior. In contrast, deep learning methods have achieved significant breakthroughs in modeling complex degradations and enhancing cross-domain generalization through a data-driven paradigm. In the field of enhancing detection robustness, traditional improvement techniques that utilize anisotropic filtering, alongside deep learning methods such as SSD, R-CNN, and the YOLO series, contribute to perceptual stability through feature optimization and end-to-end learning approaches, respectively. This paper summarizes 11 types of mainstream public datasets, examining their multimodal annotation system and addressing issues related to discrepancies. Furthermore, it provides an extensive evaluation of algorithm performance using PSNR, SSIM, mAP, among others. It has been identified that significant bottlenecks persist in dynamic weather coupling modeling, multimodal heterogeneous data fusion, and the efficiency of edge deployment. Future research should focus on establishing a physically guided hybrid learning architecture, developing techniques for dynamic and adaptive timing calibration, and designing a flexible multimodal fusion framework to overcome the limitations associated with complex environment perception. This paper serves as a systematic reference for both the theoretical development and practical implementation of automatic driving vision detection technology under severe weather conditions. Full article

Grounded in information processing theory, this study explores how ecotourism decisions were formed within the rural district of Akdağmadeni (Türkiye), integrating both heuristic and systematic decision-making processes. The research adopts a two-phase mixed-methods design: First, it employs a survey-based factorial analysis involving 383 participants to examine preferences for nature-based activities such as trekking, cycling, and cultural tourism. Second, it uses in-depth interviews to investigate participants’ strategic evaluations of local landscape and heritage assets. The results reveal that individuals flexibly switch between intuitive and analytical judgments based on contextual factors. Key decision drivers identified include alignment with local development, ecological integrity, and socioeconomic contribution. This dual-process insight is operationalized through a novel “feature-based evaluation model” that synthesizes landscape identity values with cognitive-perceptual cues, providing a new lens for assessing geoheritage-based tourism behavior. It was determined that participants used both intuitive and systematic information processing strategies in their decision-making processes, and factors such as harmony with nature, economic contribution, and local identity were found to affect preferences. The study draws attention to the need to develop sustainable tourism policies, raise public awareness, and support infrastructure investments, and provides a road map for the effective use of the region’s ecotourism potential. Full article

Despite the impressive performance of existing image compression algorithms, they struggle to balance perceptual quality and high image fidelity. To address this issue, we propose a novel invertible neural network-based remote sensing image compression (INN-RSIC) method. Our approach captures the compression distortion from an existing image compression algorithm and encodes it as Gaussian-distributed latent variables using an INN, ensuring that the distortion in the decoded image remains independent of the ground truth. By using the inverse mapping of the INN, we input the decoded image with randomly resampled Gaussian variables, generating enhanced images with improved perceptual quality. We incorporate channel expansion, Haar transformation, and invertible blocks into the INN to accurately represent compression distortion. Additionally, a quantization module (QM) is introduced to mitigate format conversion impact, enhancing generalization and perceptual quality. Extensive experiments show that INN-RSIC achieves superior perceptual quality and fidelity compared to existing algorithms. As a lightweight plug-and-play (PnP) method, the proposed INN-based enhancer can be easily integrated into existing high-fidelity compression algorithms, enabling flexible and simultaneous decoding of images with enhanced perceptual quality. Full article

Background/Objectives: Verbs are considered to be more abstract than nouns, as they represent actions, states, and events, which are less tangible, more flexible in their meaning and thus less univocally specified. It has been suggested that children acquire abstract concepts based on their linguistic contexts of use, making use of semantic and syntactic cues. By contrast, according to theories of embodied cognition, conceptual knowledge is based on physical and perceptual interaction with the world. The present study investigates whether the verbal and the visual modality produce similar or different results in the processes of construction and reactivation of novel verbs, corresponding to new compositional abstract concepts, in children of different ages. In Experiment 1, the acquisition of the concept was determined based on the quality of verbal explanation; in Experiment 2, participants were asked to decide whether a visual representation fitted the concept or not. Thus, response modality could be either explicit or implicit, and either congruent or incongruent with respect to learning modality. Methods: In Experiment 1, 100 children from grade 1 to 5 were asked to explain the meaning of verbs introduced via verbal or visual instances. In Experiment 2, 15 children aged 8 to 10 had to judge pictures as (not) being examples of previously verbally or visually presented novel verbs. Results: The results of Experiment 1 show more accurate explanations after verbal presentation across all grades. In Experiment 2, verbal presentation was no longer associated with more accurate matching responses, but rather with slower decision times. Conclusions: Modality congruence, explicitness and linguistic (semantic and syntactic) factors were all shown to play a role, which is discussed in a developmental perspective. Full article

The proliferation of digital platforms and interactive technologies has transformed the way service providers engage with their customers, particularly in emerging economies, where digital inclusion is an ongoing process. This study explores the relationship between omnichannel strategies and customer satisfaction, conceptualized here as a proxy for Quality of Experience (QoE), within a smart service station located in a digitally underserved region. Grounded in customer journey theory and the expectancy–disconfirmation paradigm, the study investigates how data integration, digital payment systems, and logistical flexibility—key components of intelligent e-service systems—influence user perceptions and satisfaction. Based on a correlational design with a non-probabilistic sample of 108 customers, the findings reveal a moderate association between overall omnichannel integration and satisfaction (ρ = 0.555, p < 0.01). However, a multiple regression analysis indicates that no individual dimension significantly predicts satisfaction (adjusted R² = 0.002). These results suggest that while users value system integration and interaction flexibility, no single technical feature drives satisfaction independently. The study contributes to the growing field of intelligent human-centric service systems by contextualizing QoE and digital inclusion within emerging markets and by emphasizing the importance of perceptual factors in ICT-enabled environments. Full article

As urbanization continues to expand, the design and structure of urban spaces increasingly influence the experiences of individuals, whether intentionally or inadvertently. These effects can result in both positive and negative experiences, with urban facilities generally designed to enhance the comfort and well-being of citizens. However, in certain cases, these spaces can provoke adverse emotional reactions, such as anxiety. Anxiety, a prevalent mental health disorder, is more commonly observed in urban environments than in rural areas. Among various urban settings, rail transport in large cities is often cited as one of the most stressful environments for passengers. In light of the significance of this issue, this study seeks to explore how physical and perceptual components can reduce anxiety and encourage greater use of intra-urban rail transportation. Utilizing a qualitative research approach, the study employed directional content analysis to investigate this topic. Data were collected and analyzed through an exploratory methodology with the assistance of MAXQDA software. The analysis began with guided content coding, drawing on theoretical frameworks pertinent to the research. Through this process, 2387 initial codes were identified, which were then categorized into nine main themes, with the relationships between these codes clarified. The findings were inductively derived from the raw data, leading to the development of a foundational theoretical framework. The study, employing a personalized strategy, identified three key factors that contribute to anxiety: physical, perceptual, and environmental components. Physical factors, such as accessibility, lighting, and signage, were found to have a significant impact on passengers’ psychological well-being. Perceptual factors, including personal perceptions, stress, and fear, played a crucial role in exacerbating anxiety. Additionally, environmental factors, particularly the design of metro networks, rail lines, and flexible transportation lines, such as car-sharing and micromobility, were found to significantly contribute to the overall anxiety experienced by passengers. Moreover, the study suggests that anxiety triggers can be mitigated effectively through the implementation of well-designed policies and management practices. Enhancing the sense of security within transit spaces was found to increase citizens’ willingness to utilize rail transportation. These findings indicate that targeted interventions aimed at improving both the physical and perceptual aspects of the transit environment could enhance the commuter experience and, in turn, foster greater use of rail systems. Full article

Background: This study investigates the complex relationship between accentuation and attention in visual perception, extending classical Gestalt principles by introducing dissimilarity as a complementary mechanism to similarity in perceptual organization. Objectives and Methods: Through a series of phenomenological experiments, we demonstrate how accentuation, driven by dissimilarity, plays a crucial role in shaping visual experience and guiding attention. Results: Our findings reveal that accentuation serves as a pre-attentive mechanism for highlighting salient features, influencing initial perceptual organization, and modulating the apparent shape and orientation of visual elements. We show that while accentuation operates rapidly and automatically, attention acts as a flexible, selective mechanism that can either reinforce or override accentuation-based percepts. This interplay suggests a two-stage process of visual perception, with implications for theories of consciousness and information processing in biological systems. This study also explores the evolutionary significance of accentuation in camouflage and sexual selection, providing insights into how perceptual mechanisms may have evolved to enhance adaptive fitness. Conclusions: Our results have broad implications for understanding visual cognition, design, and clinical applications related to attentional disorders. Full article

The COVID-19 pandemic highlighted the role of urban parks as green spaces in mitigating social isolation and supporting public mental health. Research in this area is limited due to the lack of large-scale datasets. Moreover, timely studies are indeed necessary under pandemic conditions. This study employs quantitative methods to analyze the temporal and spatial changes in social interaction in 160 urban parks before, during, and after the COVID-19 pandemic, and assesses their correlation with the built environment. Social media data from the Dianping platform were collected for this purpose. A two-step analytical approach was employed: first, machine learning-based keyword analysis identified review data related to social interaction, leading to the construction of two indicators: social interaction intensity and social interaction recovery rate. Second, we applied regression models to explore the correlation between the two indicators in urban parks and 18 characteristics of the built environment. The built environment characteristics associated with social interaction intensity varied across different periods, with seven factors, including natural landscapes, perceptual experience, building density, and road intersections, showing significant correlations with the recovery of social interaction capabilities in the post-pandemic era. Based on these findings, it is recommended that urban planners consider integrating more flexible design element, such as adding greenery and enriching the audio-visual experience for visitors. Furthermore, enhancing the quality and accessibility of park amenities can foster social interaction, thereby contributing to public health resilience in future crises. This research recommends that urban park design should not only support communities’ immediate needs but also prepare for unforeseen challenges. Full article

As urban environments become increasingly complex and the costs and challenges of infrastructure upgrades continue to rise, wayfinding signage has become an effective solution to cope with urban dynamics due to its low cost and high flexibility. Although the functionality of wayfinding signage has been extensively studied, the perceptual differences between designers and non-designers have not been adequately explored. Ignoring these differences may lead to the overlooking of users’ real and diverse needs, resulting in suboptimal signage performance in practical applications and ultimately a reduction in the overall functionality and user experience of urban spaces. This study aims to bridge this perceptual gap. For this study, we conducted a questionnaire survey in China to compare the visual preferences of designers and non-designers regarding text, shape, color coding, and patterns. The results indicate that designers prioritize functionality and clarity to ensure the effective use of signage in complex urban environments, whereas non-designers prefer wayfinding signages that reflect local cultural symbols and characteristics. Our conclusions suggest that the public’s expectations for wayfinding signage extend beyond basic navigational functions, with an emphasis on cultural expression and visual appeal. Understanding these perceptual differences is crucial in developing design strategies that balance functionality, esthetics, and sustainability, thereby facilitating the sustainable integration of signage into urban landscapes. Full article

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development. Full article

The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements. The average PSNRs of the cascade model in set5 and set14 are 33.413 dB and 29.658 dB respectively, which are 0.76 dB and 0.621 dB more than the baseline method. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods. Full article

Deformable medical image registration aims to minimize the differences between fixed and moving images to provide comprehensive physiological or structural information for further medical analysis. Traditional learning-based convolutional network approaches usually suffer from the problem of perceptual limitations, and in recent years, the Transformer architecture has gained popularity for its superior long-range relational modeling capabilities, but still faces severe computational challenges in handling high-resolution medical images. Recently, selective state-space models have shown great potential in the vision domain due to their fast inference and efficient modeling. Inspired by this, in this paper, we propose RegMamba, a novel medical image registration architecture that combines convolutional and state-space models (SSMs), designed to efficiently capture complex correspondence in registration while maintaining efficient computational effort. Firstly our model introduces Mamba to efficiently remotely model and process potential dependencies of the data to capture large deformations. At the same time, we use a scaled convolutional layer in Mamba to alleviate the problem of spatial information loss in 3D data flattening processing in Mamba. Then, a deformable convolutional residual module (DCRM) is proposed to adaptively adjust the sampling position and process deformations to capture more flexible spatial features while learning fine-grained features of different anatomical structures to construct local correspondences and improve model perception. We demonstrate the advanced registration performance of our method on the LPBA40 and IXI public datasets. Full article

Flexible pulse-by-pulse regulation of sensorimotor synchronization is crucial for voluntarily showing rhythmic behaviors synchronously with external cueing; however, the underpinning neurophysiological mechanisms remain unclear. We hypothesized that the dorsal anterior cingulate cortex (dACC) plays a key role by coordinating both proactive and reactive motor outcomes based on contextual mental imagery. To test our hypothesis, a missing-oddball task in finger-tapping paradigms was conducted in 33 healthy young volunteers. The dynamic properties of the dACC were evaluated by event-related deep-brain activity (ER-DBA), supported by event-related potential (ERP) analysis and behavioral evaluation based on signal detection theory. We found that ER-DBA activation/deactivation reflected a strategic choice of motor control modality in accordance with mental imagery. Reverse ERP traces, as omission responses, confirmed that the imagery was contextual. We found that mental imagery was updated only by environmental changes via perceptual evidence and response-based abductive reasoning. Moreover, stable on-pulse tapping was achievable by maintaining proactive control while creating an imagery of syncopated rhythms from simple beat trains, whereas accuracy was degraded with frequent erroneous tapping for missing pulses. We conclude that the dACC voluntarily regulates rhythmic sensorimotor synchronization by utilizing contextual mental imagery based on experience and by creating novel rhythms. Full article