Color Defects Research Articles

Evaluation of the Physicochemical Attributes of Beef, Chicken, and Pork Muscles Injected with Microbial Proteases for Designing Senior-Friendly Processed Meat Products.

In developed countries, the growing elderly population has increased the demand for senior-friendly processed meat products. This study investigated the effects of four commercial microbial proteases (Alcalase, Flavourzyme, Neutrase, and Protamex) on the general physicochemical attributes of beef top round, chicken breast, and pork loin, which are lean muscle cuts suitable for developing senior-friendly meat products. Muscle samples were injected with microbial protease solutions (0.7% and 1.2% (w/w)), cooked, and used for analysis. The microbial protease injection significantly reduced the hardness of cooked muscles. Despite the evident degradation of the myosin heavy chain in Alcalase treatment, the lowest hardness values were observed in Protamex-treated samples, suggesting that myosin degradation alone does not fully account for tenderness improvement. Unfortunately, microbial protease treatments increased cooking loss in beef and chicken muscles (p < 0.05). The surface color characteristics, including redness and yellowness, remained unaffected by the enzymatic treatments, supporting the practical use of these proteases for meat tenderization without inducing color defects. While microbial proteases demonstrate potential for improving meat tenderness, future research should focus on mitigating cooking loss and ensuring desirable taste and flavor for the commercial production of senior-friendly processed meat products using the microbial proteases.

Development of a mobile application for rapid detection of meat freshness using deep learning

The freshness or spoilage of meat is critical in terms of meat color and quality criteria. Detecting the condition of the meat is important not only for consumers but also for the processing of the meat itself. Meat quality is influenced by various pre-slaughter factors including housing conditions, diet, age, genetic background, environmental temperature, and stress factors. Additionally, spoilage can occur due to the slaughtering process, though post-slaughter spoilage is more frequent and has a stronger correlation with postslaughter factors. The primary indicator of meat quality is the pH value, which can be high or low. Variations in pH values can lead to adverse effects in the final product such as color defects, microbial issues, short shelf life, reduced quality, and consumer complaints. Many of these characteristics are visible components of quality. This study aimed to develop a mobile application using deep learning-based image processing techniques for the rapid detection of freshness. The attributes of the source and the targeted predictions were found satisfactory, indicating that further advancements could be made in developing future versions of the application.

Nontargeted Metabolomics to Understand the Impact of Modified Atmospheric Packaging on Metabolite Profiles of Cooked Normal-pH and Atypical Dark-Cutting Beef.

Background: Limited knowledge is currently available on the effects of modified atmospheric packaging (MAP) on the metabolite profiles of cooked beef. The objective was to evaluate the impact of packaging on the cooked color and cooked metabolite profile of normal-pH (normal bright-red color) and atypical-dark-cutting beef (inherently slightly dark-colored) longissimus lumborum muscle. Methods: Normal-pH (pH 5.56) and atypical dark-cutting (pH 5.63) loins (n = 6) were procured from a commercial meat processor. Steaks were randomly assigned to one of three different packaging methods: vacuum packaging, carbon monoxide (CO-MAP), and high oxygen (HiOx-MAP). Following 5 d of retail display, steaks were cooked to 71 °C on a clamshell-style grill, and samples were collected for untargeted metabolites using gas-chromatography mass spectrometry. Results: Raw atypical dark-cutting steaks were less red (p < 0.05) than raw normal-pH steaks. However, there were no differences in internal cooked color between normal-pH and atypical dark-cutting steaks. Steaks packaged in HiOx-MAP steaks had a lower (p < 0.05) cooked redness than vacuum and CO-MAP steaks. A total of 129 metabolite features were identified in the study. Serine and tryptophan were over-abundant in cooked atypical dark-cutting beef compared to raw atypical samples. Citric acid levels were greater in HiOx-MAP packaged beef compared with VP both in normal and atypical dark-cutting beef after cooking, while no differentially abundant metabolites were shared between vacuum and CO-MAP steaks after cooking. Discussion: A slight increase in pH did not influence metabolite profiles in different packaging. However, there were packaging effects within normal and atypical dark-cutting beef. Conclusions: This study suggests that packaging conditions change metabolite profiles, which can influence cooked metabolites. Therefore, the metabolomics approach can be used to better understand cooked color defects such as premature browning.

Микробиологические риски как причина пороков внешнего вида сыра

Aerobic Bacillus bacteria, yeasts, and micrococci are pigment-forming microorganisms responsible for color defects in cheese production. The article describes their environment, industry-related characteristics, types of defects they cause, and various color violations in commercial cheeses. The research featured domestic cheeses with yellow and red surface spots that appeared during industrial ripening and storage. Pigment-forming microorganisms developed on cheese surface at a low ripening temperature, high concentration of table salt, and critically low oxygen. Pigment-forming microflora proved to enter cheese from milk, brine, or production environment, e.g., water, air, and processing equipment, dry and succulent feeds being the main source of milk contamination with spore-forming aerobic bacteria, yeast, mold fungi, etc. Contamination of raw milk, milking equipment, and air environment with micrococci was primarily associated with cows’ udder and skin. Cheese production presupposes low-temperature heat treatment of raw milk, which means possible residual microflora in pasteurized milk. As a result, the total count of mesophilic aerobic and optionally anaerobic microorganisms, i.e., the potential pigment formers, was to stay below 104 CFU/cm3. Yeast and spore-forming Bacillus microorganisms demonstrated the acceptable level of ≤102 CFU/cm3 in milk intended for cheesemaking. If their total count exceeded 103 CFU/cm3, it indicated high microbiological risks of cheese appearance defects. The microflora responsible for discoloration of cheese surface was represented by yeast, micrococci, aerobic spores, or their combinations.

Dyschromatopsia due to welding arcs: An underestimated occupational injury

Welders are at risk of developing dyschromatopsia, a color vision deficiency, due to occupational exposure, including welding arcs. This visual impairment can impact occupational safety and performance but has received limited attention in research. : This study aimed to investigate the prevalence and characteristics of defective color vision among welders exposed to welding arcs. A cross-sectional study was conducted with 52 male welders working in various industries. Demographic and occupational data were collected through a questionnaire. Color vision assessment was performed using Ishihara and Farnsworth Munsell test. Welding exposure data were collected through self-reporting and workplace observations. Chi-square test was used for data analysis. A p-value of less than 0.05 was considered statistically significant. The prevalence of defective color vision among welders was 15.4%. Age, gender, years of experience, and types of welding performed did not significantly influence the prevalence. Welders in the age group of 31-40 years showed a higher prevalence. Inadequate use of eye protection was identified as a barrier to preventing color vision impairments. Defective color vision is a prevalent occupational injury among welders exposed to welding arcs. The study underscores the importance of preventive measures, including proper eye protection and education programs, to reduce the risk of visual impairments and improve occupational safety. Further research is needed to understand the long-term consequences of welding arc exposure on color vision and its impact on worker performance.

Structural Parameterizations for Two Bounded Degree Problems Revisited

We revisit two well-studied problems, Bounded Degree Vertex Deletion and Defective Coloring , where the input is a graph G and a target degree Δ, and we are asked either to edit or partition the graph so that the maximum degree becomes bounded by Δ. Both problems are known to be parameterized intractable for the most well-known structural parameters, such as treewidth. We revisit the parameterization by treewidth, as well as several related parameters and present a more fine-grained picture of the complexity of both problems. In particular, we present the following: — Both problems admit straightforward DP algorithms with table sizes \((\Delta +2)^\mathrm{tw}\) and \((\chi _\mathrm{d}(\Delta +1))^{\mathrm{tw}}\) , respectively, where tw is the input graph’s treewidth and \(\chi _\mathrm{d}\) the number of available colors. We show that, under the SETH, both algorithms are essentially optimal, for any non-trivial fixed values of \(\Delta , \chi _\mathrm{d}\) , even if we replace treewidth by pathwidth. Along the way, we obtain an algorithm for Defective Coloring with complexity quasi-linear in the table size, thus settling the complexity of both problems for treewidth and pathwidth. — Given that the standard DP algorithm is optimal for treewidth and pathwidth, we then go on to consider the more restricted parameter tree-depth. Here, previously known lower bounds imply that, under the ETH, Bounded Vertex Degree Deletion and Defective Coloring cannot be solved in time \(n^{o(\sqrt [4]{\mathrm{td}})}\) and \(n^{o(\sqrt {\mathrm{td}})}\) , respectively, leaving some hope that a qualitatively faster algorithm than the one for treewidth may be possible. We close this gap by showing that neither problem can be solved in time \(n^{o(\mathrm{td})}\) , under the ETH, by employing a recursive low tree-depth construction that may be of independent interest. — Finally, we consider a structural parameter that is known to be restrictive enough to render both problems FPT: vertex cover. For both problems the best known algorithm in this setting has a super-exponential dependence of the form \(\mathrm{vc}^{\mathcal {O}(\mathrm{vc})}\) . We show that this is optimal, as an algorithm with dependence of the form \(\mathrm{vc}^{o(\mathrm{vc})}\) would violate the ETH. Our proof relies on a new application of the technique of d -detecting families introduced by Bonamy et al. [ToCT 2019]. Our results, although mostly negative in nature, paint a clear picture regarding the complexity of both problems in the landscape of parameterized complexity, since in all cases we provide essentially matching upper and lower bounds.

Cr-Coot: Chronological Coot Algorithm-Based Deep Learning Model for Video Demosaicing

Usually, digital cameras comprise sensor arrays enclosed by Color Filter Arrays (CFAs), mosaics of minute color filters. Thus, every pixel sensor usually records limited spectral data regarding relevant pixels. Demosaicing is defined as the procedure of deducing the misplaced data for every pixel, which plays a vital role in recreating high-quality full-color images. Denoising and demosaicing are the major processes in the camera imaging chain for both videos and images. Here, reconstruction errors occur in these points and have undesirable effects on the final outcome, when it is not appropriately managed. The demosaicing process provokes color and spatial correlation of noises, and it is improved by means of imagining a pipeline. This organized noise usually destroys the quality of the image as well as fails to prevent accurate interpretation of an image. During the mitigation of this structured noise on processed data, denoising techniques diminish the texture and information. Therefore, an effectual demosaicing technique is essential for recreating the full-color image from the defective color samples. Thus, in this paper, an effectual video demosaicing model is proposed using an optimized deep learning system. The designed video demosaicing system achieved better performance with a Peak Signal-to-Noise Ratio (PSNR) of 59.74 dB, Second Derivative, like Measure of Enhancement (SDME) of 63.51, and Root Mean Squared Error (RMSE) of 0.3660.

Shear Wave Elastography in Breast Cancer: Unveiling Correlations With Histopathological Grades and Subtypes.

Objective This study explores the correlation between shear wave elastography (SWE) features and histopathological grades and subtypes in breast cancer, aiming to enhance diagnostic accuracy andpersonalized treatment strategies. Methods The study retrospectively analyzed 59 consecutive women with breast cancer who underwent breast ultrasound with SWE. SWE parameters and histopathologic information, including histological type and grade, were recorded. Qualitative and quantitative SWE findings were analyzed, and B-mode findings wereevaluated. Sociodemographic and clinical factors and B-mode findings were assessed as predictors of elastography stiffness using logistic regression analysis. Results Of the 59 participants diagnosed with breast cancer,invasive ductal carcinoma of no special type (IDC-NST) was predominantly found in 50 (84.7%) cases, followed by invasive medullary carcinoma in 5 (8.5%) cases. The majority of participants belonged to the 50-59 age group, comprising 19 (32.2%) patients. Histopathological grading revealed grade II tumors in 27 (45.8%) cases and grade III tumors in 24 (40.7%) cases. Notably, grade III tumors exhibited higher tissue stiffness compared to grade II tumors. Out of 36 stiff lesions, 30 (83.3%%) were IDC-NST while 3 (8.3%) wereinvasive medullary carcinoma. A significant association was observed between higher histopathological grade (grade III) and increased tissue stiffness (p < 0.05). Furthermore, among participants with stiff lesions, 21 (58.3%) exhibited color defectswhile 4 (23.5%) cases with soft lesions also displayed color defects Conclusion The correlation between SWE findings and histopathological grades and subtypes underscores the potential of SWE as a valuable tool for predicting tumor aggressiveness and characterizing specific subtypes.SWE enhances diagnostic accuracy and complements traditional imaging modalities, holding promise for personalized treatment strategies.

Bushfire exposure is associated with darker color of beef loin at grading

The effect of bushfires (also known as wildfires) on the Australian livestock industry extends beyond the direct loss of animals and farming assets, with anecdotal reports of increased incidence of dark meat color defects in beef carcasses at grading (AUSMEAT color score >3) from bushfire affected cattle. To understand the association between bushfire exposure and meat color, an investigation using historical MSA (Meat Standards Australia) grading data, pasture data and bushfire data over two bushfire seasons (2018–2019, 2019–2020) was conducted. For bushfire affected beef cattle (n = 451 299), predictive models were developed to estimate the association between bushfire exposure and (1) loin color at grading, and (2) the incidence of ‘dark color’ defects. Both models incorporated bushfire exposure, production, and animal variables, with hierarchical random effects specified as plant/processing date/consignment. For the two predictive models, distance of the animal's originating property from closest bushfire (km), the days the animal was exposed to bushfire, feed type (grass, grain), the use of hormone growth promotants (HGPs; no, yes), the interaction of distance of property from closest bushfire and feed type, and days of bushfire and HGP treatment, were all significant predictors of loin color outcomes (P < 0.05 for all). Model 1 indicated that as distance from closest bushfire decreased, loin from grain-fed animals had darker color (P < 0.001), and that as days of bushfire exposure increased, HGP treatment exacerbated dark color outcomes for grain-fed animals but had little effect on the consistently high color scores of grass-fed HGP treated animals (P = 0.001). For model 2, the highest predicted proportion of dark color defects (11.8 %) was for grass-fed, HGP treated cattle, 10 km from the closest bushfire and exposed to 150 days of bushfire. The lowest predicted proportion of dark colour carcasses (1.3 %) was for grain fed, non-HGP treated cattle, 30 km from the closest bushfire, with no change associated with increasing days of bushfire. Cattle can be categorized in order of increasing susceptibility to dark color defects as a result of being exposed to bushfires; grain-fed cattle with no HGPs were the least susceptible to dark color defects, and grass-fed cattle with HGP treatment were the most susceptible. Remediation strategies are discussed in relation to cattle susceptibility to adverse loin meat color outcomes associated with bushfire exposure.

Formation and stability of point defect color centers in 6H silicon carbide

Point defect color centers acting as single-photon emitters are promising for quantum technology applications and have been extensively studied, e.g., in the 4H polytype of silicon carbide (SiC). However, the physics of such color centers in other SiC polytypes is much less explored. Herein, we study the formation and thermal stability of such color centers in 6H-SiC using photoluminescence spectroscopy. The emissions from typical single-photon emitters, such as silicon vacancies, divacancies, and carbon antisite-vacancy pairs in 6H-SiC, were monitored as a function of the proton irradiation fluence and post-irradiation annealing, and compared to that in 4H-SiC. Overall, at the background of similarities between the emission behavior in 4H- and 6H-SiC polytypes, we observed prominent differences; e.g., for the thermal stability of the carbon antisite-vacancy pair, which exhibited maximized emissions upon 300 and 900 °C anneals in 4H- and 6H-SiC, respectively. Moreover, we observed a range of defect emission signatures not previously reported for 6H-SiC in the literature and discussed their potential origin in the context of the thermal stability. For example, among the PL-lines in 6H-SiC, we detected periodically repeatable emission signatures, resembling the so-called L-lines recently reported in 4H-SiC, even though their exact origin has not yet been settled in the literature. Thus, we use color centers comparison in different polytypes to better understand the nature of the single-photon emitters in SiC.

Implementation of Ant Colony Optimization and Principal Component Analysis for Early Prediction of Tomato Leaf Diseases: A Feature Reduction Approach

This paper details the implementation of a sophisticated algorithmic framework designed for the early prediction of various tomato leaf diseases, crucial for enhancing crop management and yield. The research deploys image processing techniques to extract pivotal features such as Red Mean, Green Mean, Blue Mean, Height, Width, and Defect Color channels from high-resolution images of tomato leaves. These features serve as indicators for diseases including Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, and infestations by Two-spotted Spider Mites. The core of the implementation lies in the integration of Ant Colony Optimization (ACO) with Principal Component Analysis (PCA) for feature reduction, which streamlines the dataset while retaining critical information. This combination not only reduces computational load but also improves the accuracy of the early prediction model. The paper demonstrates the application of this hybrid approach and compares its performance with existing models, emphasizing its efficiency and accuracy in early-stage disease prediction. The findings indicate that the proposed method outperforms traditional techniques, offering a reliable and scalable solution for agricultural disease management.

An online color and shape integrated detection method for flexible packaging surface defects

It is difficult for the spectrophotometer to meet the requirement of real-time color defect detection for flexible packaging prints. The false of shape defect detection is caused by artifact interference and insufficient classification accuracy of defect classification network. A color defect detection method for flexible packaging is proposed, which realizes the adaptive adjustment of the correction parameters of the Commission Internationale de l´Eclairage Delta E 2000 (CIEDE2000) equations with the detection object. It improves the speed and accuracy of the color defect detection for flexible packaging. An quadratic difference strategy is designed for template matching subtraction method to remove artifact interference. A method for enhancing shape defect data set of flexible packaging is proposed. Using discrete images of defects as network input, self-attention mechanism and spectral normalization methods are added to the deep convolutional generative adversarial networks (DCGAN) to enhance the effective dataset for the training of defect classification network. The accuracy of color detection for flexible packaging prints is improved by 38.7% based on optimized CIEDE2000. The average structure similarity index measure (SSIM) value of the improved DCGAN for defect detection is 0.845, and the Fréchet inception distance (FID) is 121.463. It takes 83.63 ms for the color and shape integrated detection method to detect shape defects on flexible packaging surfaces with an accuracy of 98.3%. The online color and shape integrated detection method can be applied to automated flexible packaging workshops to achieve real-time defect detection.

Programming sp3 Quantum Defects along Carbon Nanotubes with Halogenated DNA.

Atomic defect color centers in solid-state systems hold immense potential to advance various quantum technologies. However, the fabrication of high-quality, densely packed defects presents a significant challenge. Herein we introduce a DNA-programmable photochemical approach for creating organic color-center quantum defects on semiconducting single-walled carbon nanotubes (SWCNTs). Key to this precision defect chemistry is the strategic substitution of thymine with halogenated uracil in DNA strands that are orderly wrapped around the nanotube. Photochemical activation of the reactive uracil initiates the formation of sp3 defects along the nanotube as deep exciton traps, with a pronounced photoluminescence shift from the nanotube band gap emission (by 191 meV for (6,5)-SWCNTs). Furthermore, by altering the DNA spacers, we achieve systematic control over the defect placements along the nanotube. This method, bridging advanced molecular chemistry with quantum materials science, marks a crucial step in crafting quantum defects for critical applications in quantum information science, imaging, and sensing.

Minimum collisions assignment in interdependent networked systems via defective colorings

In conjunction with the traffic overload of next-generation wireless communication and computer networks, their resource-constrained nature calls for effective methods to deal with the fundamental resource allocation problem. In this context, the Minimum Collisions Assignment (MCA) problem in an interdependent networked system refers to the assignment of a finite set of resources over the nodes of the network, such that the number of collisions, i.e., the number of interdependent nodes receiving the same resource, is minimized. Given the interdependent networked system's organization in the form of a graph, there already exists a randomized algorithm that converges with high probability to an assignment of resources having zero collisions when the number of resources is larger than the maximum degree of the underlying graph. In this article, differing from the prevailing literature, we investigate the case of a resource-constrained networked system, where the number of resources is less than or equal to the maximum degree of the underlying graph. We introduce two distributed, randomized algorithms that converge in a logarithmic number of rounds to an assignment of resources over the network for which every node has at most a certain number of collisions. The proposed algorithms apply to settings where the available resources at each node are equal to three and two, respectively, while they are executed in a fully-distributed manner without requiring information exchange between the networked nodes.

Room temperature optically detected magnetic resonance of single spins in GaN.

High-contrast optically detected magnetic resonance is a valuable property for reading out the spin of isolated defect colour centres at room temperature. Spin-active single defect centres have been studied in wide bandgap materials including diamond, SiC and hexagonal boron nitride, each with associated advantages for applications. We report the discovery of optically detected magnetic resonance in two distinct species of bright, isolated defect centres hosted in GaN. In one group, we find negative optically detected magnetic resonance of a few percent associated with a metastable electronic state, whereas in the other, we find positive optically detected magnetic resonance of up to 30% associated with the ground and optically excited electronic states. We examine the spin symmetry axis of each defect species and establish coherent control over a single defect's ground-state spin. Given the maturity of the semiconductor host, these results are promising for scalable and integrated quantum sensing applications.

Analisis Pengendalian Kualitas Menggunakan Metode Statistical Process Control (SPC) Pada Part Lever Comp Throttle Di PT.OMI

PT. OMI is a company that operates in the automotive industry in the Karawang area, the goods it produces are very varied and have quite a lot of customers. To increase customer trust, customer satisfaction is the most important thing, with the aim of knowing the process and quality results of a product by using a p-control chart and knowing the various types of defects that occur in the product. The research uses data collection methods in the form of production data as well as product defect data produced and data processing using p control charts and Minitab software to analyze the causes of types of defects and provide suggestions for companies. The results of production quality control in 2022 samples were obtained CL (Middle limit) is 0.0077%, LCL (Lower limit) is 0.0074% and UCL (Upper limit) is 0.0080%. From the analysis, there are 3 types of defects, namely color defects at 48%, chip defects at 40%, and finally burry defects at 12%. Problems occur due to several causes such as human factors, machines, methods, and materials. To solve these problems, a fishbone diagram was created. Product control at PT OMI is under controlled conditions. Keywords: Control Chart, Defect, Quality

Defect Analysis of ZA Fertilizer Products with Fault Tree Analysis (FTA) and Failure Mode Effect Analysis (FMEA) Methods at PT. XYZ

PT XYZ is one of the fertilizer producers in Indonesia. The problem that often occurs in the company is that there are product defects that cause the production process to be ineffective. One of the products that has a high percentage of defects is non-subsidized ZA fertilizer. Research using Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods to find out the causes of rejects or rejects in products and can provide preventive solutions so that these defects do not occur. In this study, 4 types of defects were found, namely failed weighing with a chance of defect occurrence of 0.138%, mixed color defect with a chance of defect occurrence of 0.115%, crystal clumping defect with a chance of defect occurrence of 0.115%, and defect mixed with dirt with a chance of defect occurrence of 0.116%, mixed color, crystal clumping, and mixed with dirt. The root causes of these defects include less careful operators, hasty operators, operators do not check the fertilizer bag machine needs maintenance, machine trouble, operators do not check the level on the machine regularly, poor air circulation, lack of cleaning facilities, operators work not according to SOP, lack of supervision, lazy operators, no barrier with other fertilizers, lack of lighting.

Microabrasion of teeth with fluorosis: A minimally invasive solution to unaesthetic smile

Dental hypomineralization like dental fluorosis leads to a less aesthetic appearance of the teeth visible in the vicinity. Dental fluorosis results from excessive fluoride intake during enamel formation, which may lead to color abnormalities or defects in the tooth surface. Microabrasion is the preferred treatment for aesthetic improvement of stains in mild to moderate cases. In this procedure, trapped stains are removed with the help of the abrasive agent and HCL acid which is directly applied to the stained enamel surface. Casein phosphopeptide - Amorphous calcium phosphate (CPP-ACP) applied topically after microabrasion, improving remineralization and preventing post-operative sensitivity. This case report illustrates the microabrasion technique for the management of mild to moderate dental fluorosis in maxillary incisors to eliminate stains from the enamel surface in a single session, followed by remineralization with CPP - ACP paste and a further one-year follow-up was done to check the long-term effect of the treatment.

Real-time defect detection of saw chains on automatic assembly lines based on residual networks and knowledge coding

The use of machine vision methods has rapidly developed for detecting product defects, but it remains challenging to identify shape and color defects as well as the assembly relationship of parts due to occlusion between components and changes in assembly structures. This paper proposed a method for detecting saw chain assembly defects based on residual networks and knowledge coding to achieve parts segmentation and identification of assembly defects. Firstly, we design an adaptive segmentation algorithm for saw chain parts, which uses the Hough transform to locate rivets and completes the image segmentation based on the inherent assembly features between parts. Secondly, A trained ResNet (Residual Network) based deep learning model was used to identify the individual defects of saw chain parts, in which the transfer learning method was used to improve the detection speed of individual defects. Finally, a parallel inference method based on a knowledge encoding matrix is proposed for detecting part defects and constructing a sparse matrix to locate assembly defects. Evaluating the proposed method in building a saw chain testing platform. Results show that the proposed method can achieve real-time and high-precision detection requirements in different application scenarios. The detection accuracy on individual and assembly defects reaches 94.2% and 89.2%, respectively, which is better than the current state-of-the-art object detection models. Assembly defect detection methods are an important reference for inspecting other types of simple assemblies in the industry.

Defective coloring of hypergraphs

AbstractWe prove that the vertices of every ‐uniform hypergraph with maximum degree may be colored with colors such that each vertex is in at most monochromatic edges. This result, which is best possible up to the value of the constant , generalizes the classical result of Erdős and Lovász who proved the case.