Search Results (119)

This study quantitatively analyses how light sources, polishing methods, and backgrounds affect the color of “Tang yu”. Twenty-four samples were tested with three different light sources (D50, A, D65), two polishing methods, and nine Munsell neutral gray backgrounds. Testing 24 samples revealed that main coloring elements exhibit low concentrations with no linear relationship to color intensity. Light sources selectively alter chromaticity: D65 maintains color balance (recommended for grading), while A enhances red tones. Polishing methods significantly impact color perception, with glassy polishing markedly increasing Lightness (L*↑11.41%) and Chroma (C*↑42.11%) while shifting hues toward red-yellow. Background luminance (γ_b) critically influences color results: Lightness L* and Chroma C* increase via distinct power functions as γ_b rises, though Hue angle (h°) remains stable. Sample color can be predicted through γ_b based equations, with Munsell N9 background proving optimal for grading. Cluster and discriminant analyses effectively classified colors into three distinct groups, establishing a foundation for a reliable grading system. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

Objectives: Diabetes Mellitus involves demanding challenges that interfere with family functioning and routines. In turn, family and social context impacts individual glycemic control. This study aims to identify this recursive interplay, the mutual influences of family systems and diabetes management. Design: Data was collected through a cross-sectional design comparing patients, aged 22–55, with and without metabolic control. Methods: Participants filled out a set of self-report measures of sociodemographic, clinical and family systems assessment. Patients (91) were also invited to describe their perception about disease management interference regarding family functioning. We first examined the extent to which family variables grouped dataset to determine if there were similarities and dissimilarities that fit with our initial diabetic groups’ classification. Results: Cluster analysis results identify a two-cluster solution validating initial classification of two groups of patients: 49 with metabolic control (MC) and 42 without metabolic control (NoMC). Independent sample tests suggested statistically significant differences between groups in family subscales- family difficulties and family communication (p < 0.05). Binary logistic regression shed light on predictors of explained variance to no metabolic control, in four models: Sociodemographic, Clinical data, SCORE-15/Congruence Scale and Eating Behavior. Furthermore, groups differ on family support, level and sources of family conflict caused by diabetes management issues. Considering only patients who co-habit with a partner for more than one year (N = 44), NoMC patients score lower on marital functioning in all categories (p < 0.05). Discussion: Family-Chronic illness interaction plays a significant role in a patient’s adherence to treatment. This study highlights the Standards of Medical Care for Diabetes, considering caregivers and family members on diabetes care. Full article

To address the critical challenges of insufficient monitoring capabilities and vulnerable defense systems against drones in regional airports, this study proposes a multi-source data fusion framework for rapid UAV detection. Building upon the YOLO v11 architecture, we develop an enhanced model incorporating four key innovations: (1) A dual-path RGB-IR fusion architecture that exploits complementary multi-modal data; (2) C3k2-DATB dynamic attention modules for enhanced feature extraction and semantic perception; (3) A bilevel routing attention mechanism with agent queries (BRSA) for precise target localization; (4) A semantic-detail injection (SDI) module coupled with windmill-shaped convolutional detection heads (PCHead) and Wasserstein Distance loss to expand receptive fields and accelerate convergence. Experimental results demonstrate superior performance with 99.3% mAP@50 (17.4% improvement over baseline YOLOv11), while maintaining lightweight characteristics (2.54M parameters, 7.8 GFLOPS). For practical deployment, we further enhance tracking robustness through an improved BoT-SORT algorithm within an interactive multiple model framework, achieving 91.3% MOTA and 93.0% IDF1 under low-light conditions. This integrated solution provides cost-effective, high-precision drone surveillance for resource-constrained airports. Full article

Precision pesticide application is a key focus in orchard management, with targeted spraying serving as a core technology to optimize pesticide delivery and reduce environmental pollution. However, its accurate implementation relies on high-precision environmental sensing technologies to enable the precise identification of target objects and dynamic regulation of spraying strategies. This paper systematically reviews the application of orchard environmental sensing technologies in targeted spraying. It first focuses on key sensors used in environmental sensing, providing an in-depth analysis of their operational mechanisms and advantages in orchard environmental perception. Subsequently, this paper discusses the role of multi-source data fusion and artificial intelligence analysis techniques in improving the accuracy and stability of orchard environmental sensing, supporting crown structure modeling, pest and disease monitoring, and weed recognition. Additionally, this paper reviews the practical paths of environmental sensing-driven targeted spraying technologies, including variable spraying strategies based on canopy structure perception, precise pesticide application methods combined with intelligent pest and disease recognition, and targeted weed control technologies relying on weed and non-target area detection. Finally, this paper summarizes the challenges faced by multi-source sensing and targeted spraying technologies in light of current research progress and industry needs, and explores potential future developments in low-cost sensors, real-time data processing, intelligent decision making, and unmanned agricultural machinery. Full article

Methodologies to measure motion perception are vital for deepening our understanding of the vision system and the factors that influence it. While existing work has primarily focused on the fastest perceivable velocities, less attention has been paid to the lower threshold of motion (LTM; slowest perceivable velocities). In this study, we designed an optical system to measure LTM in a sample of healthy young adults and to assess the influence of retinal location (central vs. peripheral retina) and stimulus composition (broadband vs. mid-wave) on LTM. The system was based on a xenon light source and a fiber-optic cable that created a bright light stimulus that could be moved along a computer-controlled precision translation slide. The stimulus, exposed for one-second intervals at both a central (fovea) and a peripheral (33 deg) location, was moved at varying speeds to determine the slowest detectable speed. In all, 37 healthy young participants (M = 19.32 ± 1.97 years) were tested. We found substantial between-subject variability in LTM and an interaction between stimulus wavelength and retinal location. The measurement of LTM using this novel apparatus and methodology provides insights into the relationship between slow-moving, ecologically valid stimuli and perceptual detection at the slowest speeds. Full article

Legume cultivation is important for a wide array of reasons, including its positive effects on the environment, the economy, and human health. Legumes have different amino acid profiles that complement those of the three globally most important staple foods (rice, corn, and wheat). Therefore, the aim of this work was to assess the functionality of legume flours (as well as hemp as an emerging hemp protein source) as enriching supplements in breadmaking. The research focused on both the nutritional and sensory evaluation of flour with the assistance of novel research techniques such as diffusing wave spectroscopy and static multiple light scattering. The nutritional value of yellow and green peas as well as chickpeas was comparable, with the most noticeable difference being total fiber content, that ranged between 6.8 and 9.7 g/100 g of flour. Hemp flour outclassed all legume flours both in terms of protein content as well as fiber, which was over quadrupled. However, it was associated with the cost of worse technological properties. Addition of all investigated flours increased the dough stability, which was proved by static multiple light scattering and a reduction in the Turbiscan Stability Index. Microrheology of the dough was improved only by the addition of yellow pea flour, which was manifested by an increase in the macroscopic viscosity index and decrease in the fluidity index. This flour had also the most beneficial properties for the bread quality, including texture and sensory perception, including appearance, taste, and overall acceptance. Full article

To address the issue of insufficient reliability of fiber optic sensing networks in complex environments, this study proposes a self-diagnosis and self-healing method based on intelligent algorithms. This method integrates redundant fiber paths and a fault detection mechanism, enabling rapid data transmission recovery through redundant paths during network faults, ensuring the stable operation of the monitoring system. Unlike traditional self-diagnosis techniques that rely on an optical time domain reflectometer, the proposed self-diagnosis algorithm utilizes data structure analysis, significantly reducing dependence on costly equipment and improving self-diagnosis efficiency. On the hardware front, a light switch driving device that does not require an external power source has been developed, expanding the application scenarios of optical switches and enhancing system adaptability and ease of operation. In the experiments, three fiber optic sensing network topologies—redundant ring structure, redundant dual-ring structure, and redundant mesh structure—are constructed for testing. The results show that the average self-diagnosis time is 0.1257 s, and the self-healing time is 0.5364 s, validating the efficiency and practicality of the proposed method. Furthermore, this study also proposes a robustness evaluation model based on sensor perception ability and coverage uniformity indicators, providing a theoretical basis for the self-healing capability of fiber optic sensing networks. This model aids in network topology optimization and fault recovery strategy design, contributing to the improvement of the stability and reliability of fiber optic sensing networks in practical applications. Full article

(1) Background: Perceptual constancies are found in numerous categories of visual perception; color, lightness, and size constancy are notable examples where the perception of a visual scene remains constant, even with changing optical conditions. Constancies such as these are essential for survival, as they reduce the unpredictability of the world. In this study, we tested the resiliency of motion perception under widely differing spectral conditions. (2) Methods: Sixty healthy subjects (age range 18 to 26) were tested. Motion perception performance and thresholds were assessed using a novel, ecologically valid, psychophysical task implementing modern instruments. A broadband xenon bulb was used as a light source to emulate the spectral characteristics of natural daylight; 3 filter conditions were included to emulate different conditions of environmental light (short-wave, 400 nm–500 nm; medium-wave, 500 nm–600 nm; and long-wave, 600 nm–700 nm). (3) Results: In general, our findings showed that varying the spectral content of the broadband source did not change motion perception performance or thresholds for subjects. (4) Conclusions: These findings indicate that motion perception is highly resistant to changes in optical conditions, such as dramatically different spectral illuminants. This evidence is consistent with motion being considered among the perceptual constancies. Full article

With the ever-increasing volume of maritime traffic, the risks of ship navigation are becoming more significant, making the use of advanced multi-source perception strategies and AI technologies indispensable for obtaining information about ship navigation status. In this paper, first, the ship tracking system was optimized using the Bi-YOLO network based on the C2f_BiFormer module and the OC-SORT algorithms. Second, to extract the visual trajectory of the target ship without a reference object, an absolute position estimation method based on binocular stereo vision attitude information was proposed. Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information. Finally, AR technology was integrated to fuse multi-source perceptual information into the real-world navigation view. Experimental results demonstrate that the proposed method achieves a mAP0.5:0.95 of 79.6% under challenging scenarios such as low resolution, noise interference, and low-light conditions. Moreover, in the presence of the nonlinear motion of the own ship, the average relative position error of target ship visual measurements is maintained below 8%, achieving accurate absolute position estimation without reference objects. Compared to existing navigation assistance, the AR-based navigation assistance system, which utilizes ship ST-TF-based perception data fusion mechanism, enhances ship traffic situational awareness and provides reliable decision-making support to further ensure the safety of ship navigation. Full article

Individual differences in attachment orientations may influence how we process emotionally significant stimuli. As one of the most important sources of emotional information are facial expressions, we examined whether there is an association between adult attachment styles (i.e., scores on the ECR questionnaire, which measures the avoidance and anxiety dimensions of attachment), facial emotion perception and face memory in a neurotypical sample. Trait and state anxiety were also measured as covariates. Eye-tracking was used during the emotion decision task (happy vs. sad faces) and the subsequent facial recognition task; the length of fixations to different face regions was measured as the dependent variable. Linear mixed models suggested that differences during emotion perception may result from longer fixations in individuals with insecure (anxious or avoidant) attachment orientations. This effect was also influenced by individual state and trait anxiety measures. Eye movements during the recognition memory task, however, were not related to either of the attachment dimensions; only trait anxiety had a significant effect on the length of fixations in this condition. The results of our research may contribute to a more accurate understanding of facial emotion perception in the light of attachment styles, and their interaction with anxiety characteristics. Full article

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards. Full article

Human point-light displays consist of luminous dots representing human articulations, thus depicting actions without pictorial information. These stimuli are widely used in action recognition experiments. Because humans excel in decoding human motion, point-light displays (PLDs) are often masked with additional moving dots (noise masks), thereby challenging stimulus recognition. These noise masks are typically found within proprietary programming software, entail file format restrictions, and demand extensive programming skills. To address these limitations, we present the first user-friendly step-by-step guide to develop visual noise to mask PLDs using free, open-source software that offers compatibility with various file formats, features a graphical interface, and facilitates the manipulation of both 2D and 3D videos. Further, to validate our approach, we tested two generated masks in a pilot experiment with 12 subjects and demonstrated that they effectively jeopardised human agent recognition and, therefore, action visibility. In sum, the main advantages of the presented methodology are its cost-effectiveness and ease of use, making it appealing to novices in programming. This advancement holds the potential to stimulate young researchers’ use of PLDs, fostering further exploration and understanding of human motion perception. Full article

This study assessed the technological potential and bioactive compounds present in purple yampee (Dioscorea trifida L.f.) lyophilized powder, peeled and whole flour, as well as the tuber peel, starch residual fiber, and wastewater mucilage. Although most values approached neutrality, flour showed a lower pH and high density, while greater acidity was observed in the mucilage. Color differed statistically and perceptibly between all samples, with similar values of °hue to purple flours from other sources, and the maximum chroma was found in lyophilized pulp and lightness in fiber. Average moisture levels around 7.2% and water activity levels of 0.303 (0.194 for whole flour) in fractions suggested favorable storability, while the interaction of the powders with water was similar to other root and tuber powdered derivatives. Yampee periderm had the highest swelling power, oil absorption capacity, water holding capacity, and absorption index and capacity. Mucilage had a higher solubility index and outstanding emulsion activity, greater than 90%. Twelve anthocyanins, with new reports of petunidin derivatives for the species, and more than 30 phytochemicals were identified through advanced liquid chromatography techniques. The greatest amounts of pinitol and myo-inositol were found in the mucilage, and sucrose, glucose, and fructose prevailed in the other powders. Successfully characterized yampee fractions showed high potential as functional food ingredients. Full article

In spacecraft rendezvous and docking, traditional methods that rely on inertial navigation and sensor data face challenges due to sensor inaccuracies, noise, and a lack of multi-approach assurance. Focusing on exploring a new approach as assistance, this study marks the first application of deep learning-based image feature matching in spacecraft docking tasks, introducing the Class-Tuned Invariant Feature Transformer (CtIFT) algorithm. CtIFT incorporates an improved cross-attention mechanism and a custom-designed feature classification module. By using symmetric multi-layer cross-attention, it gradually strengthens inter-feature relationships perception. And, in the feature matcher, it employs feature classification to reduce computational load, thereby achieving high-precision matching. The model is trained on multi-source datasets to enhance its adaptability in complex environments. The method demonstrates outstanding performance across experiments on four spacecraft docking video scenes, with CtIFT being the only feasible solution compared to SIFT and eight state-of-the-art network methods: D2-Net, SuperPoint, SuperGlue, LightGlue, ALIKED, LoFTR, ASpanFormer, and TopicFM+. The number of successfully matched feature points per frame consistently reaches the hundreds, the successful rate remains 100%, and the average processing time is maintained below 0.18 s per frame, an overall performance which far exceeds other methods. The results indicate that this approach achieves strong matching accuracy and robustness in optical docking imaging, supports real-time processing, and provides new technical support for assistance of spacecraft rendezvous and docking tasks. Full article