Light Changes Research Articles

Genotype by environment interactions for growth and inner shell color disparity in Hyriopsis cumingii under different light conditions

The light plays a pivotal role in influencing the growth and body color for aquaculture animals. The freshwater pearl mussel Hyriopsis cumingii, inhabiting benthic environments naturally, is predominantly cultivated by hanging methods. This aquaculture method altersmutiple environmental factors, with light being one of the maximum variationfactor. In order to comprehend the impact of light change on the growth and inner shell color of H. cumingii, a total of 14 full-sibling families were established and cultivated separately under natural light and dim light conditions for a period of 13 months in this study. The results revealed that under natural light conditions, juvenile mussel exhibited accelerated growth rates, while dim light conditions expedited purple pigmentation in the inner shell. Genetic analysis indicated general heritability range for the entire experimental population falls between 0.07 and 0.28. heritability estimates for all traits under natural light conditions (NL) ranged from 0.33 to 0.72, indicating consistently high heritability. However, heritability estimates for all traits under dim light conditions (DL) ranged from 0.05 to 0.16. The growth and inner shell color traits showed significant G × E interactions under different light conditions. The anal angle radial rib length (AARRL) trait exhibited a significantly higher correlation with body weight (BW) compared to shell height (SH) and total shell height (TSH). Furthermore, the growth and inner shell color traits of different families were compared and evaluated under the two environmental conditions in this study. Families 1 and 6 excelled in various traits under both conditions, making them promising candidates for genetic improvement breeding. In this study, pronounced influences of light exposure on the growth of pearl mussels were identified, emphasizing the need for tailored breeding strategies for both bottom seeding and suspended cultivation methods.

PW-NeRF: Progressive wavelet-mask guided neural radiance fields view synthesis

Neural Radiance Fields (NeRF) can achieve state-of-the-art new view results when given a sufficient number of training views. However, NeRF's rendering is based on minimizing photometric consistency loss, and during the optimization process, it can suffer from overfitting due to factors such as lighting and texture, resulting in poor geometric and color reconstruction. The less data there is in the overlapping areas of the images, the greater the impact on the results, such as insufficient data caused by occlusion relationships. In this paper, we observed through experiments that introducing low-frequency images into NeRF during training can quickly obtain approximate geometric structures, which can guide NeRF to achieve more stable view synthesis results. Therefore, we propose a progressive wavelet mask to assist in the training of neural radiance fields. By first constructing a gaussian pyramid for the training images and then applying wavelet decomposition to them, we can obtain a series of low-frequency region masks to guide the neural radiance field to focus on learning low-frequency pixel regions and gradually introduce high-frequency lighting and texture changes. Our experiments on the LLFF and Blender datasets show that using a progressive wavelet mask in NeRF training can achieve more realistic generation effects with almost no additional computational overhead. At the same time, we also tested our method in sparse scenes, where it still performs well in avoiding overfitting.

Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments.

Smart farm environments, equipped with cutting-edge technology, require proficient techniques for managing poultry. This research investigates automated chicken counting, an essential part of optimizing livestock conditions. By integrating artificial intelligence and computer vision, it introduces a transformer-based chicken-counting model to overcome challenges to precise counting, such as lighting changes, occlusions, cluttered backgrounds, continual chicken growth, and camera distortions. The model includes a pyramid vision transformer backbone and a multi-scale regression head to predict precise density maps of the crowded chicken enclosure. The customized loss function incorporates curriculum loss, allowing the model to learn progressively, and adapts to diverse challenges posed by varying densities, scales, and appearances. The proposed annotated dataset includes data on various lighting conditions, chicken sizes, densities, and placements. Augmentation strategies enhanced the dataset with brightness, contrast, shadow, blur, occlusion, cropping, and scaling variations. Evaluating the model on the proposed dataset indicated its robustness, with a validation mean absolute error of 27.8, a root mean squared error of 40.9, and a test average accuracy of 96.9%. A comparison with the few-shot object counting model SAFECount demonstrated the model's superior accuracy and resilience. The transformer-based approach was 7.7% more accurate than SAFECount. It demonstrated robustness in response to different challenges that may affect counting and offered a comprehensive and effective solution for automated chicken counting in smart farm environments.

Perceptibility and acceptability of lightness difference of a single maxillary central incisor.

The aim of this study was to assess the lightness difference perceptibility and acceptability thresholds, for a single maxillary central incisor, and to investigate possible differences in these thresholds regarding the direction (+ΔL* vs. -ΔL*), the observer group (dentists vs. laypersons), and the gender and age of the observers. A series of images with varying lightness (L*), were created by altering the right maxillary central incisor of a male Caucasian, on a frontal view full-portrait image. Digital modification of one central incisor by 1 ΔL* unit resulted in 15 different images: one control, seven with increased and seven with decreased lightness. The images were presented in random order, on a digitally calibrated monitor, to 158 observers, grouped into 79 dentists, and 79 laypersons, who were asked to evaluate every image and answer whether they perceive a difference and if yes, whether they accept this difference. A multifactorial analysis of covariance was performed to analyze the perception and acceptance of any difference in the central incisors and to estimate the thresholds. The statistical significance level was α = 0.05. Overall, the magnitude of ΔL*, direction of ΔL*, observer group and age were each significant (p < 0.001), as was gender (p = 0.03). The effect of the magnitude of ΔL* interacted with its direction and the observer group (p = 0.045), and the effect of age interacted with the direction of ΔL*, and the gender and the group of the observers. Estimated 50%:50% perceptibility thresholds ranged from 0.1 to 2.7, and 50%:50% acceptability thresholds ranged from 2.3 to 4.5. Overall, dentists perceived lightness differences at lower magnitudes than laypersons; difference in lightness was perceived and accepted at lower magnitudes when the tooth become lighter instead of darker; and female observers perceived and accepted lightness differences at lower magnitudes compared to males. As age increased overall, differences of lightness were perceived and accepted at higher magnitudes. Perceptibility and acceptability of lightness differences of a single maxillary central incisor are affected by the magnitude and the direction of lightness change, as well as the gender, the age, and the type of the observers. Even small changes in lightness are perceivable; however, thresholds of perception and acceptance are dependent on the magnitude and the direction of change and are also dependent on the observer, with women, dentists and younger people perceiving and accepting changes at lower magnitudes of ΔL*.

Redefining Student Learning Identities

This paper presents a focused examination of a practitioner research case study, honing in on twelve college students in a class of thirty-seven, enrolled in a chemistry course within a blended learning framework. Over the course of two years, the research aimed to delve deeply into the impact of networked learning experiences on the learning identities of these students. Through meticulous exploration using in-depth interviews, significant positive transformations in the learning identities of these twelve students were unearthed. The research question guiding this work was to understand how the blended learning approach, which included an online networked learning component supplementing face-to-face learning, brought about a change in student learning. The study investigated the impact of online participation on the learning identities of the students and how, in turn, the class was generally affected by the transformation of student identities. By examining the interplay between online and face-to-face learning modalities, this research sheds light on the complex dynamics shaping the learning experiences and identities of college students in blended learning environments. Distinctly, the investigation identified two main components of the student learning identity. These are the academic and positional sub-identities. The academic identity transformation was particularly noteworthy, revealing an emergent sense of control, agency, empowerment, and competence as a result of the students embracing online collaborative learning. Simultaneously, the research brought to light changes in the positional identity, encompassing persona-related, acted, and relational identities. The interplay of these identities within the class fostered a profound sense of belonging, of being valued, and connectedness among the students, extending its influence into the face-to-face learning contexts.The findings emphasize that networked learning collaborative skills not only enhance traditional educational practices but also play a key role in effecting positive changes in students' learning identities. The integration of online collaborative learning within traditional educational settings emerged as a catalyst for fundamental transformations in how students perceive and engage with their learning processes. The research contributes to knowledge on the impact of digital pedagogies on learning identities and suggests that educators can use online collaborative learning experiences to cultivate a more profound impact on students' learning identities and engagement, thereby enriching both theory and practice in education. It offers valuable insights on fostering dynamic and empowered learning environments to educators.

Solid as a rock: The main drivers of changes in natural, rocky plant communities

AbstractQuestionChanges caused by climate warming and nitrogen pollution are observed in forest, grassland and alpine ecosystems worldwide. However, still little is known about the impact of these globally influencing factors on natural rocky plant communities. Has species composition of natural rocky communities changed over time? What is the role of large‐scale and fine‐scale environmental factors in shaping the compositional, functional and habitat patterns in studied plant communities over time?LocationSudetes Mountains, southwestern Poland.MethodsWe used 214 pairs of replots (collected between 1989 and 2022) of rocky plant communities, with a mean timespan of 14.2 years. The changes in species composition, environmental conditions and functional traits were analysed using ordination techniques and generalised additive models (GAMs) and with reference to large‐scale factors (mean maximum temperatures, actual evapotranspiration, N deposition) and fine‐scale factors (light availability, bedrock type, initial site conditions) for each locality.ResultsSpecies composition of the studied communities has not changed significantly over time. Only for 11 out of 258 species, statistically significant increases (from 2% to 8%) in their proportion were recorded. The changes in environmental conditions were significantly influenced mainly by fine‐scale factors such as changes in light availability and baseline site conditions. Plots that were initially less thermophilic or nitrophilic showed stronger signals of thermophilisation and eutrophication in the resurvey. The influence of large‐scale factors was considerably less pronounced. Similarly, the key role in explaining changes in plant traits for the data set under study falls to local factors, particularly changes in light availability.ConclusionsOur study confirms the validity of considering both large‐ and fine‐scale factors as well as initial site conditions in research on long‐term changes in plant communities. Rocky plant communities respond to global changes in a different way than other types of phytocoenose, showing high stability of species composition and functional traits over time.

Investigating Training Datasets of Real and Synthetic Images for Outdoor Swimmer Localisation with YOLO

In this study, we developed and explored a methodical image augmentation technique for swimmer localisation in northern German outdoor lake environments. When it comes to enhancing swimmer safety, a main issue we have to deal with is the lack of real-world training data of such outdoor environments. Natural lighting changes, dynamic water textures, and barely visible swimming persons are key issues to address. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images, and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets were used to train YOLO architectures for possible future applications in real-time detection. The trained frameworks were then tested and evaluated on outdoor environment imagery acquired using a safety drone to investigate and confirm their usefulness for outdoor swimmer localisation.

A small target detection algorithm based on improved YOLOv5 in aerial image.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network's focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.

DNA Released by Adeno-Associated Virus Strongly Alters Capsid Aggregation Kinetics in a Physiological Solution.

While adeno-associated virus is a leading vector for gene therapy, significant gaps remain in understanding AAV degradation and stability. In this work, we study the degradation of an engineered AAV serotype at physiological pH and ionic strength. Viral particles of varying fractions of encapsulated DNA were incubated between 30 and 60 °C, with changes in molecular weight measured by changes in total light scattering intensity at 90° over time. Mostly full vectors demonstrated a rapid decrease in molecular weight corresponding to the release of capsid DNA, followed by slow aggregation. In contrast, empty vectors demonstrated immediate, rapid colloid-type aggregation. Mixtures of full and empty capsids showed a pronounced decrease in initial aggregation that cannot be explained by a linear superposition of empty and full degradation scattering signatures, indicating interactions between capsids and ejected DNA that influenced aggregation mechanisms. This demonstrates key interactions between AAV capsids and their cargo that influence capsid degradation, aggregation, and DNA release mechanisms in a physiological solution.

Enriching User-Visitor Experiences in Digital Museology: Combining Social and Virtual Interaction within a Metaverse Environment

This study investigates the potential of integrating multilayer animations and sophisticated shader technologies to enhance visitor social interactions within metaverse exhibition spaces. It is part of a broader initiative aimed at developing innovative digital museology strategies that foster social engagement through virtual reality (VR) experiences. The methodology adopted seeks to provide a more immersive and human-centric exploration of 3D digital environments by blending elements of physical spaces with the interactive dynamics common in video games. A virtual exhibition space themed around Mars was created as a testbed to facilitate social interactions among users, who navigate the environment via avatars. This digital space was developed using a specialized Unity template designed by the metaverse platform Spatial.io. Overcoming the programming constraints imposed by Spatial.io, which limits the use of external scripts for security and stability, posed a significant challenge. Nonetheless, by leveraging the ability to modify shader codes used for material creation and employing advanced animation techniques with layered effects, the authors of this work achieved dynamic material responses to lighting changes and initiated complex asset interactions beyond simple linear animations.

Advancing defense capabilities through integration of electro-optical systems and computer vision technologies

This paper presents a comprehensive study on the integration of cutting-edge technologies to enhance defense capabilities, focusing on object detection, tracking, and distance measurement tasks. Also, the integration of IMX219-77 cameras and Nvidia Jetson Nano for object detection, tracking, and distance measurement in electro-optical systems is proposed. The decision to combine these components was based on their individual strengths: the high resolution and wide field of view of the IMX219-77 cameras, and the low-power, high-performance capabilities of the Jetson Nano. Software-wise, OpenCV and GStreamer were chosen for their widespread use and capabilities in computer vision and multimedia processing tasks, respectively. The MOSSE algorithm was selected for object tracking due to its speed, efficiency, and resilience to lighting changes. Additionally, Depth Estimation (Stereo Vision) was employed for distance measurement by analyzing images captured from a pair of cameras. The proposed algorithm involves transmitting images from the cameras to the Jetson Nano, where OpenCV and GStreamer are utilized to process the data. The MOSSE algorithm is employed for object detection and tracking, while Depth Estimation generates depth maps to measure distances between objects and the camera. The study's results demonstrate accurate distance measurements with a margin of error ranging from 0 to 2 mm, falling below the acceptable threshold mentioned in academic literature. This aligns with similar studies in the field, showcasing the efficacy of the proposed integration for object detection, tracking, and distance measurement in electro-optical systems.

A method of treating discoloration in persons associated with exposure to harmful industrial factors

INTRODUCTION. The search for the optimal bleaching method that minimizes the side effects of bleaching and prolongs the result of bleaching is an urgent task of dentistry.AIM. To study the effectiveness of the method of treating discoloritis in people exposed to harmful industrial factors.MATERIALS AND METHODS. 40 somatically healthy patients aged 23 to 45 years old, working at the plant, with harmful production factors, were identified. Of these, 22 were women and 18 were men who had teeth whitening. Tooth shade was determined using the VITA SYSTEM 3D-MASTER® scale before, after, 14 days, 3, 6, 12 months after bleaching.RESULTS. Examination of patients before the procedure of professional teeth whitening revealed the absence of hyperesthesia in all subjects (100 %), immediately after bleaching, the appearance of hypersensitivity in all patients was noted, based on clinical examination data and dynamics of the values of the IIGZ, IRGZ, Schiff and VAS indices, which creates the need for the use of remineralizing in persons working in hazardous production therapy. The assessment of the color shade of teeth after whitening revealed a change in color intensity in 100 % of cases, while the change in color lightness on the VITA SYSTEM 3D-MASTER scale was 1–2 steps. The use of the infiltration method after bleaching led to the restoration of the structure of hard tissues.CONCLUSION. The conducted research indicates the appearance of 100 % dental hyperesthesia during the professional whitening procedure in persons exposed to harmful industrial factors.

Detection of Gannan Navel Orange Ripeness in Natural Environment Based on YOLOv5-NMM

In order to achieve fast and accurate detection of Gannan navel orange fruits with different ripeness levels in a natural environment under all-weather scenarios and then to realise automated harvesting of Gannan navel oranges, this paper proposes a YOLOv5-NMM (YOLOv5 with Navel orange Measure Model) object detection model based on the improvement in the original YOLOv5 model. Based on the changes in the phenotypic characteristics of navel oranges and the Chinese national standard GB/T 21488-2008, the maturity of Gannan navel oranges is tested. And it addresses and improves the problems of occlusion, dense distribution, small target size, rainy days, and light changes in the detection of navel orange fruits. Firstly, a new detection head of 160 × 160 feature maps is constructed in the detection layer to improve the multi-scale target detection layer of YOLOv5 and to increase the detection accuracy of the different maturity levels of Gannan navel oranges of small sizes. Secondly, a convolutional block attention module is incorporated in its backbone layer to capture the correlations between features in different dimensions to improve the perceptual ability of the model. Then, the weighted bidirectional feature pyramid network structure is integrated into the Neck layer to improve the fusion efficiency of the network on the feature maps and reduce the amount of computation. Lastly, in order to reduce the loss of the target of the Gannan Navel Orange due to occlusion and overlapping, the detection frame is used to remove redundancy using the Soft-NMS algorithm to remove redundant candidate frames. The results show that the accuracy rate, recall rate, and average accuracy of the improved YOLOv5-NMM model are 93.2%, 89.6%, and 94.2%, respectively, and the number of parameters is only 7.2 M. Compared with the mainstream network models, such as Faster R-CNN, YOLOv3, the original model of YOLOv5, and YOLOv7-tiny, it is superior in terms of the accuracy rate, recall rate, and average accuracy mean, and also performs well in terms of the detection rate and memory occupation. This study shows that the YOLOv5-NMM model can effectively identify and detect the ripeness of Gannan navel oranges in natural environments, which provides an effective exploration of the automated harvesting of Gannan navel orange fruits.

Reversible near-infrared light-induced spin-state transition on layered Co-MOF

The spin-crossover (SCO) can be triggered by light or pressure changes, making it a promising system for multifunctional switches and memory devices at the molecular level. In this work, we have reported a NIR-responsive spin state switching system via doping layered Co(pyridine)2Ni(CN)4 (Co-MOF) into the stable polyvinylidene fluoride matrix for the first time. The layered Co-MOF has shown a rapid response of color change between gray-white and blue, which could be attributed to the SCO behavior triggered by NIR illumination. The new system ensures the soft network structures of the matrix to facilitate the homogeneous distribution of layered Co-MOF, both of which contribute to outstanding reversibility performance, including fast response, coloration in a short period, and decoloration in the ambient environment. This work provides a viable strategy to construct a new NIR-triggered SCO system for molecular switches and spintronic devices.

A structural description of pedestrian movement behavior in multiple surveillance videos

This study purpose of recording pedestrians' activities in all surveillance videos and then convert these records into structured descriptions to analyze a large number of surveillance videos. This paper proposes a four-stage processing method. The first stage uses convolutional neural network (CNN) to detect objects in the surveillance video and classify them. The second stage finds the correlation of pedestrians in continuous images and describes multiple objects' trajectories in the surveillance video. The third stage calculates the similarity of the pedestrian object sets of the surveillance video at different times as the basis for pedestrian re-identification. In the fourth stage, extract the walking path of the pedestrian in each surveillance video. Each pedestrian's actions in each surveillance video, including whether to stand, walking path, direction, clothing color and walking time. To construct a correlation table of pedestrian motion in multiple surveillance videos.This experiment uses two image datasets for experimentation and analysis to evaluate this method's effectiveness for pedestrian movement in multiple surveillance videos. The image datasets are the Multi-Camera Object Tracking (MCT) Challenge and PETS 2009 Benchmark Data. This experiment includes pedestrian detection, pedestrian identification, and multi-pedestrian tracking of pedestrians in single and multiple surveillance cameras. The results show that in the pedestrian detection experiment, the convolutional neural network can effectively solve the problems caused by static objects, light changes, similar colors, crowded pedestrians, and incomplete objects. In the pedestrian recognition and re-recognition experiments, the accuracy is reduced due to the influence of low-resolution surveillance videos. This detection method of the pedestrian is more accurate than traditional dynamic object detection methods. After the pedestrian tracking and re-identification are completed, the pedestrian relationships in multiple surveillance cameras are integrated, and a pedestrian behavior association table is built for surveillance videos.

Testing the influence of light on nitrite cycling in the eastern tropical North Pacific

Abstract. Light is considered a strong controlling factor of nitrification rates in the surface ocean. Previous work has shown that ammonia oxidation and nitrite oxidation may be inhibited by high light levels, yet active nitrification has been measured in the sunlit surface ocean. While it is known that photosynthetically active radiation (PAR) influences microbial nitrite production and consumption, the level of inhibition of nitrification is variable across datasets. Additionally, phytoplankton have light-dependent mechanisms for nitrite production and consumption that co-occur with nitrification around the depths of the primary nitrite maximum (PNM). In this work, we experimentally determined the direct influence of light level on net nitrite production, including all major nitrite cycling processes (ammonia oxidation, nitrite oxidation, nitrate reduction and nitrite uptake) in microbial communities collected from the base of the euphotic zone. We found that although ammonia oxidation was inhibited at the depth of the PNM and was further inhibited by increasing light at all stations, it remained the dominant nitrite production process at most stations and treatments, even up to 25 % surface PAR. Nitrate addition did not enhance ammonia oxidation in our experiments but may have increased nitrate and nitrite uptake at a coastal station. In contrast to ammonia oxidation, nitrite oxidation was not clearly inhibited by light and sometimes even increased at higher light levels. Thus, accumulation of nitrite at the PNM may be modulated by changes in light, but light perturbations did not exclude nitrification from the surface ocean. Nitrite uptake and nitrate reduction were both enhanced in high-light treatments relative to low light and in some cases showed high rates in the dark. Overall, net nitrite production rates of PNM communities were highest in the dark treatments.

On Control of Growth of Plants by Changing of Light

On Control of Growth of Plants by Changing of Light

Multi-camera joint calibration algorithm for guiding machining robot positioning considering ambient light and error uncertainty

Multi-camera guided robotic machining is widely used in the removal of flash and burrs on the complex automotive castings and forgings. The multi-camera calibration process, however, is easily affected by the ambient light and uncertain error, thereby resulting in the undesired robot positioning accuracy. To overcome the challenging problem, this paper proposes a multi-camera joint calibration (MCJC) algorithm by considering both the ambient light and error uncertainty to optimize and compensate the positioning error of the image-guided machining robot. The high dynamic range imaging is used at first to obtain stable calibration image data for countering the effects of ambient light changes. Then the uncertainty error in camera self-calibration process is removed by the confidence threshold filtering. The hand-eye calibration matrix is corrected finally using the deviation feedback optimization for positioning accuracy enhancement of the image-guided machining robot. Taking the automotive flywheel shell as the experimental object, the results demonstrate that the average absolute positioning error is controlled within 1.3 mm, the optimum can reach 0.43 mm, and the average relative positioning error is smaller than 0.5 mm. Compared with the existing state-of-the-art algorithms, the proposed algorithm shows high positioning accuracy and strong algorithm robustness, and can meet the requirements for the subsequent image-guided robotic machining.

In vitro evaluation of the tooth bleaching efficacy and safety of high-concentration hydrogen peroxide with cold atmospheric plasma

BackgroundUsing hydrogen peroxide (HP) for tooth bleaching may induce various side effects. Cold atmospheric plasma (CAP) is a promising solution. We aimed to evaluate and compare the efficacy and safety of tooth bleaching using high-concentration HP with CAP with conventional in-office bleaching. MethodsFifty-one discolored extracted human teeth were assigned to three groups: Group 1 (light-emitting diode with 35 % HP), Group 2 (CAP with 35 % HP), and Group 3 (only CAP). Bleaching was conducted over three sessions for a total of 20 min. The bleaching effect was evaluated based on the changes in color (ΔE00 and WID) and lightness (ΔL⁎ and ΔG). Safety was assessed by examining the dark areas on the enamel and monitoring the pulp chamber temperature. The study analyzed how different bleaching methods and durations affected ΔE00 and ΔWID using repeated-measures analysis of variance (ANOVA). The Kruskal–Wallis and Mann–Whitney tests were used for ΔL⁎ and dark areas, and one-way ANOVA for ΔG after 20 min of bleaching. ResultsThe ΔE00 and WID demonstrated an interaction based on the bleaching method and time, with Group 1 exhibiting the highest ΔE00 and WID at all treatment times (p < 0.001). ΔL⁎ and ΔG did not differ significantly between Groups 1 and 2 (p = 0.056 and 0.062, respectively) and were lowest in Group 3 (p < 0.001). Group 1 exhibited the darkest areas and highest pulp chamber temperatures (p < 0.001). Group 1 exhibited statistically significant color changes. Group 2 demonstrated similar effects, but with enhanced safety profiles. ConclusionsGroup 2, although displaying a slightly less pronounced color change compared with Group 1, achieved a color alteration readily discernible to the naked eye. This suggests that CAP with 35 % HP could be an interesting area for further investigation as an alternative to traditional in-office bleaching methods.

Robust two-dimensional PCANet with F-norm distance metric

The principal component analysis network (PCANet) is a simplified convolutional neural network (CNN) implemented using a deep subspace learning model. Since its typical PCA filters utilize the squared Frobenius-norm (F-norm) as the distance metric, it exhibits extreme sensitivity to outliers hidden within the data. Furthermore, PCANet faces challenges in extracting structural information in the row and column directions of images as the filters need to be pre-vectorized. To address these issues, we propose a robust bi-directional two-dimensional PCANet (RBDPCANet), wherein the filters utilize the F-norm instead of the squared F-norm to mitigate the impact of outliers. Additionally, the relationship between reconstruction errors and projection distances is simultaneously considered in the objective function. After a concise analytical description of the PCANet concept and related work, we present the methodology for generating three convolutional kernels in the form of RBDPCANet-1, RBDPCANet-2, and RBDPCANet-3 algorithms. These convolution kernels are iteratively obtained and are suitable for numerical implementation. Each of the three algorithms uses another optimization objective function. It has been shown that in image recognition tasks, the developed methods are characterized by better resistance to noise compared to the classic PCANet approach. The rotation invariance and convergence of RBDPCANet are demonstrated according to theoretical principles. The efficacy of the proposed method is validated using images from publicly available datasets. Extensive experimental results on these datasets demonstrate that the proposed RBDPCANet not only significantly boosts the efficiency of image classification but also has strong resistance to occlusions, changes in lighting, changes in orientation, etc. Our implementation code is available at https://github.com/Wesley-li1/RBDPCANet-.