Search Results (103)

Hexavalent chromium [Cr(VI)] is the toxic form of chromium often used in industry for its hardness, bright colors, and anticorrosive properties. Cr(VI) is a known human lung carcinogen, making its inhalation an occupational hazard. Growing evidence emphasizes the neurotoxic potential of Cr(VI), though it is not linked to brain cancers. Few studies consider neurotoxicity in chromate workers, reporting impaired olfactory discrimination and an increased risk of death from mental health disorders. A major factor limiting translation of most rodent Cr(VI) studies to human populations has to do with vitamin C, which can reduce the toxic Cr(VI) to non-toxic Cr(III). Rats and mice synthesize vitamin C and are likely more resistant to Cr(VI) than humans. Here, we considered Cr(VI) neurotoxicity in guinea pigs (Cavia porcellus), which do not endogenously synthesize vitamin C. We exposed Hartley guinea pigs (both sexes) to occupationally relevant concentrations of Cr(VI) via oropharyngeal aspiration weekly for 90 days. We observed behavioral effects in the open field assay, elevated plus maze, Y-maze, and novel object recognition test during weeks 9–12 of exposure. After euthanasia, we assessed Cr accumulation and essential metal dyshomeostasis in the hippocampus. We observed significantly increased hippocampal Cr accumulation in females, while males exhibited essential metal dyshomeostasis. Full article

Background: Automatic vehicle recognition has recently become an area of great interest, providing substantial support for multiple use cases, including law enforcement and surveillance applications. In real traffic conditions, where for various reasons license plate recognition is impossible or license plates are forged, alternative solutions are required to support human personnel in identifying vehicles used for illegal activities. In such cases, appearance-based approaches relying on vehicle make and model recognition (VMMR) and vehicle color recognition (VCR) can successfully complement license plate recognition. Methods: This research addresses appearance-based vehicle identification, in which VMMR and VCR rely on inherent visual cues such as body contours, stylistic details, and exterior color. In the first stage, vehicles passing through an intersection are detected, and essential visual characteristics are extracted for the two recognition tasks. The proposed system employs deep learning with semantic segmentation and data augmentation for color recognition, while histogram of oriented gradients (HOG) feature extraction combined with a support vector machine (SVM) classifier is used for make-model recognition. For the VCR task, five different neural network architectures are evaluated to identify the most effective solution. Results: The proposed system achieves an overall accuracy of 94.89% for vehicle make and model recognition. For vehicle color recognition, the best-performing models obtain a Top-1 accuracy of 94.17% and a Top-2 accuracy of 98.41%, demonstrating strong robustness under real-world traffic conditions. Conclusions: The experimental results show that the proposed automatic vehicle recognition system provides an efficient and reliable solution for appearance-based vehicle identification. By combining region-tailored data, segmentation-guided processing, and complementary recognition strategies, the system effectively supports real-world surveillance and law-enforcement scenarios where license plate recognition alone is insufficient. Full article

Waterfront wilderness landscapes in mountainous cities, such as Chongqing, play a vital role in sustaining urban biodiversity and human well-being amid steep topography and hydrological variations that create unique habitats. However, public recognition of their ecological values and potential ecological–aesthetic conflicts remain underexplored. This study investigated biodiversity features and public preferences in Chongqing’s central urban waterfront wilderness through field surveys of 218 quadrats for biodiversity assessment (e.g., Shannon–Wiener and Simpson indices, cluster analysis identifying 12 typical communities) and two questionnaire surveys (N = 260 and 306) evaluating spatial features and plant attributes, with correlation and regression analyses examining relationships between ecological indices and preference scores. Results recorded 116 plant species from 41 families, dominated by herbaceous plants (77.6%), with herbaceous, shrub-herbaceous, and tree-herbaceous communities prevalent. No significant correlations existed between objective diversity indices and preference scores; instead, structure (β = 0.444, p < 0.001) and color (β = 0.447, p < 0.001) drove preferences (explaining 96.7% variance), favoring accessible mid-successional shrub-herbaceous structures over dense, low-diversity evergreen types. These findings reveal ecological–aesthetic conflicts in mountainous settings where aesthetic dominance limits biodiversity recognition. Implications include user-centered zoning: restrict access in low-preference steep areas with buffers for conservation, while enhancing high-preference flat zones via selective pruning and native colorful species introduction, supplemented by educational signage. This research provides a mountainous city archetype, enriching global urban wilderness studies and informing sustainable management in rapidly urbanizing regions. Full article

Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under complex or extreme lighting. We propose SCR-AWB, a novel algorithm that leverages real skin reflectance data to estimate the scene illuminant’s SPD and CCT, enabling accurate skin tone reproduction. The method integrates prior knowledge of human skin reflectance, basis vectors, and camera sensitivity to perform pixel-wise spectral estimation. Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. These findings validate SCR-AWB as an effective and computationally efficient solution for robust skin color correction. Full article

When objects are obscured by shadows or dim surroundings, image quality is improved by fusing near-infrared and visible-light images. At night, when visible and NIR lights are insufficient, long-wave infrared (LWIR) imaging can be utilized, necessitating the attachment of a visible-light sensor to an LWIR camera to simultaneously capture both LWIR and visible-light images. This camera configuration enables the acquisition of infrared images at various wavelengths depending on the time of day. To effectively fuse clear visible regions from the visible-light spectrum with those from the LWIR spectrum, a multi-band fusion method is proposed. The proposed fusion process subsequently combines detailed information from infrared and visible-light images, enhancing object visibility. Additionally, this process compensates for color differences in visible-light images, resulting in a natural and visually consistent output. The fused images are further enhanced using a night-to-day image translation module, which improves overall brightness and reduces noise. This night-to-day translation module is a trained CycleGAN-based module that adjusts object brightness in nighttime images to levels comparable to daytime images. The effectiveness and superiority of the proposed method are validated using image quality metrics. The proposed method significantly contributes to image enhancement, achieving the best average scores compared to other methods, with a BRISQUE of 30.426 and a PIQE of 22.186. This study improves the accuracy of human and object recognition in CCTV systems and provides a potential image-processing tool for autonomous vehicles. Full article

Human action recognition aims to understand human behavior and is crucial in enhancing the intelligence and naturalness of human–computer interaction and bionic robots. This paper proposes a method to improve the complexity and real-time performance of action recognition by combining the Kinect sensor with the OpenPose algorithm, the Levenberg–Marquardt (LM) algorithm, and the Dynamic Time Warping (DTW) algorithm. First, the Kinect V2 depth sensor is used to capture color images, depth images, and 3D skeletal point information from the human body. Next, the color image is processed using OpenPose to extract 2D skeletal point information, which is then mapped to the depth image to obtain 3D skeletal point information. Subsequently, the LM algorithm is employed to fuse the 3D skeletal point sequences with the sequences obtained from Kinect, generating stable 3D skeletal point sequences. Finally, the DTW algorithm is utilized to recognize complex movements. Experimental results across various scenes and actions demonstrate that the method is stable and accurate, achieving an average recognition rate of 95.94%. The method effectively addresses issues, such as jitter and self-occlusion, when Kinect collects skeletal points. The robustness and accuracy of the method make it highly suitable for application in robot interaction systems. Full article

Deltamethrin (Del), a widely utilized pyrethroid pesticide, exhibits significant risks to human health due to its persistent environmental residues. This study aims to develop an efficient sensing detector for rapid Del detection through aptamer-based recognition. A modified Capture-SELEX strategy successfully identified Del-1, a high-affinity DNA aptamer demonstrating specific binding to Del with a dissociation constant (Kd) of 82.90 ± 6.272 nM. Molecular docking analysis revealed strong intermolecular interactions between Del-1 and Del, exhibiting a favorable binding energy of −7.35 kcal·mol⁻¹. Leveraging these findings, we constructed a colorimetric detector using gold nanoparticles (AuNPs) and poly dimethyl diallyl ammonium chloride (PDDA)-mediated aggregation modulation. The sensing detector employed dual detection parameters: (1) a characteristic color transition from red to blue and (2) a quantitative ∆A650/A520 ratio measurement. This optimized system achieved a detection limit of 54.57 ng·mL⁻¹ with exceptional specificity against other competitive pesticides. Practical validation using spiked fruit samples (apples and pears) yielded satisfactory recoveries of 74–118%, demonstrating the sensor’s reliability in real-sample analysis. The developed methodology presents a promising approach for the on-site monitoring of pyrethroid contaminants in agricultural products. Full article

Cetaceans are highly adapted to the underwater environment, which is very different from the terrestrial environment. For cetaceans with neither high visual acuity nor color vision, contrast may be an important cue for visual object recognition, even in the underwater environment. Contrast is defined as the difference in luminance between an object and its background and is known to be perceived as enhanced by the luminance contrast illusion in humans. The aim of this study was to experimentally investigate whether the enhancement of contrast by the luminance contrast illusion could be observed in killer whales. Luminance discrimination tasks were performed on two captive killer whales, which were required to compare the luminance of two targets presented in monitors through an underwater window and to choose the brighter one. After baseline training, in which the target areas were surrounded by black or white inducer areas, the test condition of gray inducer areas was added. Although there were some individual differences, both individuals showed higher correct response rates for gray inducer conditions than for black and white. The results suggest that contrast was perceived as enhanced by the illusion also in killer whales and may help them to extract the contours of objects. Full article

Traditional facial recognition is realized by facial recognition algorithms based on 2D or 3D digital images and has been well developed and has found wide applications in areas related to identification verification. In this work, we propose a novel live face detection (LFD) method by utilizing snapshot spectral imaging technology, which takes advantage of the distinctive reflected spectra from human faces. By employing a computational spectral reconstruction algorithm based on Tikhonov regularization, a rapid and precise spectral reconstruction with a fidelity of over 99% for the color checkers and various types of “face” samples has been achieved. The flat face areas were extracted exactly from the “face” images with Dlib face detection and Euclidean distance selection algorithms. A large quantity of spectra were rapidly reconstructed from the selected areas and compiled into an extensive database. The convolutional neural network model trained on this database demonstrates an excellent capability for predicting different types of “faces” with an accuracy exceeding 98%, and, according to a series of evaluations, the system’s detection time consistently remained under one second, much faster than other spectral imaging LFD methods. Moreover, a pixel-level liveness detection test system is developed and a LFD experiment shows good agreement with theoretical results, which demonstrates the potential of our method to be applied in other recognition fields. The superior performance and compatibility of our method provide an alternative solution for accurate, highly integrated video LFD applications. Full article

We present a lateral flow aptasensor for the visual detection of alpha-fetoprotein (AFP) in human serum. Leveraging the precise molecular recognition capabilities of aptamers and the distinct optical features of gold nanoparticles, a model system utilizing AFP as the target analyte, along with a pair of aptamer probes, is implemented to establish proof-of-concept on standard lateral flow test strips. It is the first report of an antibody-free lateral flow assay using aptamers as recognition probes for the detection of AFP. The analysis circumvents the numerous incubation and washing steps that are typically involved in most current aptamer-based protein assays. Qualitative analysis involves observing color changes in the test area, while quantitative data are obtained by measuring the optical response in the test zone using a portable strip reader. The biosensor exhibits a linear detection range for AFP concentrations between 10 and 100 ng/mL, with a minimum detection limit of 10 ng/mL. Additionally, it has been successfully applied to detect AFP in human serum samples. The use of aptamer-functionalized gold nanoparticle probes in a lateral flow assay offers great promise for point-of-care applications and fast, on-site detection. Full article

Deep Neural Networks (DNNs) have been widely adopted in several advanced artificial intelligence applications due to their competitive accuracy to the human brain. Nevertheless, the superior accuracy of a DNN is achieved at the expense of intensive computations and storage complexity, requiring custom expandable hardware, i.e., graphics processing units (GPUs). Interestingly, leveraging the synergy of parallelism and edge computing can significantly improve CPU-based hardware platforms. Therefore, this manuscript explores levels of parallelism techniques along with edge computation offloading to develop an innovative hardware platform that improves the efficacy of deep learning computing architectures. Furthermore, the multitask learning (MTL) approach is employed to construct a parallel multi-task classification network. These tasks include face detection and recognition, age estimation, gender recognition, smile detection, and hair color and style classification. Additionally, both pipeline and parallel processing techniques are utilized to expedite complicated computations, boosting the overall performance of the presented deep face analysis architecture. A computation offloading approach, on the other hand, is leveraged to distribute computation-intensive tasks to the server edge, whereas lightweight computations are offloaded to edge devices, i.e., Raspberry Pi 4. To train the proposed deep face analysis network architecture, two custom datasets (HDDB and FRAED) were created for head detection and face-age recognition. Extensive experimental results demonstrate the efficacy of the proposed pipeline-parallel architecture in terms of execution time. It requires 8.2 s to provide detailed face detection and analysis for an individual and 23.59 s for an inference containing 10 individuals. Moreover, a speedup of 62.48% is achieved compared to the sequential-based edge computing architecture. Meanwhile, 25.96% speed performance acceleration is realized when implementing the proposed pipeline-parallel architecture only on the server edge compared to the sever sequential implementation. Considering classification efficiency, the proposed classification modules achieve an accuracy of 88.55% for hair color and style classification and a remarkable prediction outcome of 100% for face recognition and age estimation. To summarize, the proposed approach can assist in reducing the required execution time and memory capacity by processing all facial tasks simultaneously on a single deep neural network rather than building a CNN model for each task. Therefore, the presented pipeline-parallel architecture can be a cost-effective framework for real-time computer vision applications implemented on resource-limited devices. Full article

The detection of human activities is an important step in automated systems to understand the context of given situations. It can be useful for applications like healthcare monitoring, smart homes, and energy management systems for buildings. To achieve this, a sufficient data basis is required. The presented dataset contains labeled recordings of 25 different activities of daily living performed individually by 14 participants. The data were captured by five multisensors in supervised sessions in which a participant repeated each activity several times. Flawed recordings were removed, and the different data types were synchronized to provide multi-modal data for each activity instance. Apart from this, the data are presented in raw form, and no further filtering was performed. The dataset comprises ambient audio and vibration, as well as infrared array data, light color and environmental measurements. Overall, 8615 activity instances are included, each captured by the five multisensor devices. These multi-modal and multi-channel data allow various machine learning approaches to the recognition of human activities, for example, federated learning and sensor fusion. Full article

Since ginger has characteristics of both food and medicine, it has a significant market demand worldwide. To effectively store ginger and achieve the drying and color enhancement effects required for better sales, it is often subjected to sulfur fumigation. Although sulfur fumigation methods can effectively prevent ginger from becoming moldy, they cause residual sulfur dioxide, harming human health. Traditional sulfur detection methods face disadvantages such as complex operation, high time consumption, and easy consumption. This paper presents a sulfur-fumigated ginger detection method based on natural image recognition. By directly using images from mobile phones, the proposed method achieves non-destructive testing and effectively reduces operational complexity. First, four mobile phones of different brands are used to collect images of sulfur- and non-sulfur-fumigated ginger samples. Then, the images are preprocessed to remove the blank background in the image and a deep neural network is designed to extract features from ginger images. Next, the recognition model is generated based on the features. Finally, meta-learning parameters are introduced to enable the model to learn and adapt to new tasks, thereby improving the adaptability of the model. Thus, the proposed method can adapt to different devices in its real application. The experimental results indicate that the recall rate, F1 score, and AUC-ROC of the four different mobile phones are more than 0.9, and the discrimination accuracy of these phones is above 0.95. Therefore, this method has good predictive ability and excellent practical value for identifying sulfur-fumigated ginger. Full article

Focusing on the issue of the low recognition rates achieved by traditional deep-information-based action recognition algorithms, an action recognition approach was developed based on skeleton spatial–temporal and dynamic features combined with a two-stream convolutional neural network (TS-CNN). Firstly, the skeleton’s three-dimensional coordinate system was transformed to obtain coordinate information related to relative joint positions. Subsequently, this relevant joint information was encoded as a color texture map to construct the spatial–temporal feature descriptor of the skeleton. Furthermore, physical structure constraints of the human body were considered to enhance class differences. Additionally, the speed information for each joint was estimated and encoded as a color texture map to achieve the skeleton motion feature descriptor. The resulting spatial–temporal and dynamic features were further enhanced using motion saliency and morphology operators to improve their expression ability. Finally, these enhanced skeleton spatial–temporal and dynamic features were deeply fused via TS-CNN for implementing action recognition. Numerous results from experiments conducted on the publicly available datasets NTU RGB-D, Northwestern-UCLA, and UTD-MHAD demonstrate that the recognition rates achieved via the developed approach are 86.25%, 87.37%, and 93.75%, respectively, indicating that the approach can effectively improve the accuracy of action recognition in complex environments compared to state-of-the-art algorithms. Full article

Pitaya fruit is a significant agricultural commodity in southern China. The traditional method of determining the ripeness of pitaya by humans is inefficient, it is therefore of the utmost importance to utilize precision agriculture and smart farming technologies in order to accurately identify the ripeness of pitaya fruit. In order to achieve rapid recognition of pitaya targets in natural environments, we focus on pitaya maturity as the research object. During the growth process, pitaya undergoes changes in its shape and color, with each stage exhibiting significant characteristics. Therefore, we divided the pitaya into four stages according to different maturity levels, namely Bud, Immature, Semi-mature and Mature, and we have designed a lightweight detection and classification network for recognizing the maturity of pitaya fruit based on the YOLOv8n algorithm, namely GSE-YOLO (GhostConv SPPELAN-EMA-YOLO). The specific methods include replacing the convolutional layer of the backbone network in the YOLOv8n model, incorporating attention mechanisms, modifying the loss function, and implementing data augmentation. Our improved YOLOv8n model achieved a detection and recognition accuracy of 85.2%, a recall rate of 87.3%, an F1 score of 86.23, and an mAP50 of 90.9%, addressing the issue of false or missed detection of pitaya ripeness in intricate environments. The experimental results demonstrate that our enhanced YOLOv8n model has attained a commendable level of accuracy in discerning pitaya ripeness, which has a positive impact on the advancement of precision agriculture and smart farming technologies. Full article