Color Information Research Articles

Light consistency correction for the liquid crystal tunable filter hyperspectral imaging system

In hyperspectral images, every pixel encompasses continuous spectral information. Compared with traditional colorimeters, using hyperspectral imaging systems (HIS) for fabric color measurement can result in obtaining richer color information. However, measuring fabric colors with liquid crystal tunable filter HIS can lead to challenges related to light consistency. In this paper, we adopted an innovative approach, integrating gradient boosted decision trees with a sliding window algorithm to develop a uniformity calibration model addressing the illumination uniformity issue. To address the consistency issues across various light sources, we further adopted a deep neural network (DNN) model to correct the reflectance measurements under different light sources. Subsequently, this model was merged with the uniformity calibration model to form a light-consistency correction model. Through calibration, we successfully reduced the color difference of the corrected samples from 3.636 to 0.854, an enhancement of 76.51%. This means that after calibration we can achieve consistency in fabric color measurements under nonuniform lighting and different light sources.

The "Unconventional" Effect of Cysteine on the In Vitro Synthesis of Melanin.

This report details some of our observations regarding the impact of cysteine on the air-mediated oxidation of catecholamines, particularly epinephrine. The intent was to synthesize light-colored, pheomelanin-like materials. Pheomelanin is commonly described as a material generated from a mixture of catecholamines and cysteine. However, we observed that (1) the presence of cysteine resulted in a concentration-dependent delay in the onset of color formation and (2) the presence of cysteine resulted in darker, more eumelanin-like materials. These effects were particularly impactful in the case of epinephrine. More elaborate studies involving other amino acids or scaled-up reactions were conducted with epinephrine as the precursor. These studies show that other amino acids, e.g., methionine or serine, could lead to darker materials, but none were as impactful as cysteine. Although our results are in contrast to typical descriptions regarding the impact of cysteine on the synthesis of melanin, they may reflect crucial differences between the in vitrovsin vivo synthesis of pheomelanin.

Bulk Segregant Analysis Sequencing and RNA-Seq Analyses Reveal Candidate Genes Associated with Sepal Color Phenotype of Eggplant (Solanum melongena L.).

Eggplant is a highly significant vegetable crop and extensively cultivated worldwide. Sepal color is considered one of the major commercial traits of eggplant. Eggplant sepals develop from petals, and sepals have the ability to change color by accumulating anthocyanins, but whether the eggplants in sepal and their biosynthetic pathways are the same as those in petals is not known. To date, little is known about the underlying mechanisms of sepal color formation. In this study, we performed bulked segregant analysis and transcriptome sequencing using eggplant sepals and obtained 1,452,898 SNPs and 182,543 InDel markers, respectively, as well as 123.65 Gb of clean data using transcriptome sequencing. Through marker screening, the genes regulating eggplant sepals were localized to an interval of 2.6 cM on chromosome 10 by bulked segregant analysis sequencing and transcriptome sequencing and co-analysis, combined with screening of molecular markers by capillary electrophoresis. Eight possible candidate genes were then screened to further interpret the regulatory incentives for the eggplant sepal color.

End-to-end optimization of single-shot monocular metasurface camera for RGBD imaging

RGBD cameras that capture color and depth information have a wide range of applications such as robotics, autonomous driving, and cons umer electronics. Traditional RGBD imaging usually requires multi-cameras or extra active illumination which inevitably leads to bulky or complex imaging systems, hindering the growing demand for compact integrated optical devices. Optical metasurface have emerged as a powerful substitute to the traditional diffractive optical element due to the superior dispersion manipulation and extremely compact size. However, it is still a great challenge to realize a single-shot monocular metasurface camera, due to the strong and unignorable wavelength-dependent aberrations. In this work, we demonstrate an end-to-end compact single-shot monocular metasurface camera for RGBD imaging. By utilizing end-to-end joint optimization framework to learn metasurface physical structure in conjunction with deep neural networks reconstruction algorithm, the multidimensional light field information RGBD of a scene in a single shot can be reconstructed within a depth range of 0.5 m. Compared with traditional lens-based RGBD imaging, our proposed metasurface-based imaging achieves an improvement of about 2 dB in chromatic imaging and a 4 times increase in depth estimation accuracy. Our proposed scheme could facilitate the further development of the intelligent computational meta-optics in diverse fields ranging from machine vision to biomedical imaging.

Hexagonal Boron Nitride Quantum Dots Embedded on Layer-by-Layer Films for Peroxidase-Assisted Colorimetric Detection of β-Galactosidase Producing Pathogens.

Pathogen detection has become a major research area all over the world for water quality surveillance and microbial risk assessment. Therefore, designing simple and sensitive detection kits plays a key role in envisaging and evaluating the risk of disease outbreaks and providing quality healthcare settings. Herein, we have designed a facile and low-cost colorimetric sensing strategy for the selective and sensitive determination of β-galactosidase producing pathogens. The hexagonal boron nitride quantum dots (h-BN QDs) were established as a nanozyme that showed prominent peroxidase-like activity, which catalyzes 3,3',5,5'-tetramethylbenzidine (TMB) oxidation by H2O2. The h-BN QDs were embedded on a layer-by-layer assembled agarose biopolymer. The β-galactosidase enzyme partially degrades β-1,4 glycosidic bonds of agarose polymer, resulting in accessibility of h-BN QDs on the solid surface. This assay can be conveniently conducted and analyzed by monitoring the blue color formation due to TMB oxidation within 30 min. The nanocomposite was stable for more than 90 days and was showing TMB oxidation after incubating it with Escherichia coli (E. coli). The limit of detection was calculated to be 1.8 × 106 and 1.5 × 106 CFU/mL for E. coli and Klebsiella pneumonia (K. pneumonia), respectively. Furthermore, this novel sensing approach is an attractive platform that was successfully applied to detect E. coli in spiked water samples and other food products with good accuracy, indicating its practical applicability for the detection of pathogens in real samples.

Genome-wide association study reveals candidate genes critical for skin pigmentation in common carp (Cyprinus carpio) strains including koi

The differentiation and variation of body color in fish is an important component of biological evolution and phylogenetic research, and is influenced by several factors such as heredity, nutrition, physiological hormones and rearing conditions, with heredity being the most critical factor. An improved comprehension of the genetic regulation in different carp populations will facilitate future applications of precision breeding and pigment cell development-related genes editing. In this study, we used restriction site-associated DNA sequencing (2d-RAD) to determine the distribution characteristics of single nucleotide polymorphisms (SNPs) in eight common carp strains including six koi strains, i.e., Kohaku (Hb), Taisho (Dz), Showa (Zh), Golden (Hj), Matsukawabake (Sch) and Five-color (Ws), and two genetically improved breeding strains, i.e., FFRC No. 2 strain (Fr) and red FFRC strain (Hfr), and then SNP markers to resolve genetic information on the population structure and skin color formation of these strains. We found that most individuals of eight strains were clearly divided into eight clades. Genetic diversity within Fr, Sch and Ws is relatively high, while it is low within Dz, Hb and Hfr. A comparison between the populations revealed that Hfr and Ws have the highest degree of genetic differences, while Hb and Dz have the lowest. The GWAS analysis for two strategies identified several SNPs that are significantly related to skin color variation and affect melanin synthesis (ephrinb2, slc22a23, herc2, ephb2), carotenoid metabolism (oca2, bcmo1, bco2) and circadian rhythm(cipc). To summarize, our study provides some information on the genetic structure and diversity of Japanese koi and Chinese FFRC carp. Furthermore, based on the data obtained on relevant genes related to skin color differentiation and pigmentation, it can provide a theoretical basis for the subsequent integration of dominant traits and precision breeding in carps.

A Straightforward Camera Calibration Method Based on a Single Low-cost Cubical Target

Abstract. Calibrating optical sensors with common targets facilitates the efficient and convenient acquisition of the sensor's internal parameters. In this paper, we present a new method of camera calibration utilizing a low-cost foamy cube, in a form of dice, which is based on the fact that arrangement of pip and cubical die surfaces is mutually orthogonal. Initially, each face and pips are identified through the color information on the die’s surfaces. Subsequently, the centers of pips are corrected using a circular projection model, and radial distortion coefficients are estimated based on centers’ one-to-one correspondences. After that, the tangent information between pairs of pips on orthogonal dice faces are utilized to compute vanishing points, leading to estimation of intrinsic parameters. Experimental results demonstrate that our method has similar effects compared to well-known checkerboard calibration method, reaching an average relative error of 2.43%, simplifying the calibration process in practical applications and showcasing good practicality and robustness.

Biomimetic Photonic Elastomer Exhibiting Stress/Moisture Reconfigurable Wrinkle-Lattice for Reversible Deformation Information Storage.

Photonic elastomers, capable of converting imperceptible deformations into visible colors, show significant potential in smart materials. However, instantaneous deformation is arduous to record accurately due to the disappearance of optical information after deformation recovery. Herein, inspired by the folding structures of iridocytes in cephalopods, a stress- and moisture-triggered wrinkling and erasure effect is proposed to be introduced in the construction of a photonic elastomer. Implemented in a dual-network polymer framework with modulatable locking, it allows for reversible deformation storage. The photonic elastomer comprises a surface one-dimensional photonic crystal (1DPC) and a poly(dimethylsiloxane) (PDMS) substrate. The deformed 1DPC lattice transforms into a wrinkled state due to a substrate deformation mismatch, preserving strain-induced structural color information through interchain hydrogen bonding and crystalline shape-locking in dual-network polymers. Reading the color provides multidimensional information about the instantaneous deformation degree and distribution. Moreover, the moisture-induced shape-memory feature of the 1DPC can be triggered with a minute amount of water, like fingertip perspiration or humidity change (35% to 80%), to restore the original color. This stress/moisture-responsive photonic elastomer, with its dynamically reconfigurable wrinkle-lattice, holds great promise for applications in mechanical sensing, inkless writing, and anticounterfeiting, significantly enhancing the versatility of photonic materials.

360-Degree Tri-Modal Scanning: Engineering a Modular Multi-Sensor Platform for Semantic Enrichment of BIM Models

Abstract. Point clouds, image data, and corresponding processing algorithms are intensively investigated to create and enrich Building Information Models (BIM) with as-is information and maintain their value across the building lifecycle. Point clouds can be captured using LiDAR and enriched with color information from images. Complementary to such dual-sensor systems, thermography captures the infrared light spectrum, giving insight into the temperature distribution on an object’s surface and allowing a diagnosis of the as-is energetic health of buildings beyond what humans can see. Although the three sensor modes are commonly used in pair-wise combinations, only a few systems leveraging the power of tri-modal sensor fusion have been proposed. This paper introduces a sensor system comprising LiDAR, RGB, and a radiometric thermal infrared sensor that can capture a 360-degree range through bi-axial rotation. The resulting tri-modal data is fused to a thermo-color point cloud from which temperature values are derived for a standard indoor building setting. Qualitative data analysis shows the potential for unlocking further object semantics in a state-of-the-art Scan-to-BIM pipeline. Furthermore, an outlook is provided on the cross-modal usage of semantic segmentation for automatic, accurate temperature calculations.

The Effect of Apple Vinegar Addition on the Quality and Shelf Life of Cooked Sausage during Chilling Storage

As more and more consumers are becoming conscious of the safety and taste of meat products, the use of natural additives and innovative processing techniques has gained significant attention. Naturally fermented fruit vinegar is rich in organic acids and antioxidant phenolic compounds. In addition, it contains amino acids, minerals, vitamins, and provitamin beta-carotene, and the presence of acetic acid bacteria may have a positive effect on consumer health. The study aimed to assess the impact of different concentrations of apple vinegar addition on the quality of cooked sausage, focusing on physicochemical parameters, including fatty acid profile and oxidative stability, as well as microbiological quality and sensory changes after production and during chilling storage. Four variants of sausage were prepared: C—sausage without apple vinegar; V1—sausage with 1% of apple vinegar; V3—sausage with 3% of apple vinegar; and V5—sausage with 5% of apple vinegar. All of the tests were carried out after production, as well as after 7 and 14 days of refrigeration storage. The addition of apple vinegar decreased the pH value and increased the oxidation-reduction potential and lipid oxidation in the samples V1, V3, and V5. The sausage with the 5% addition of apple vinegar (V5) was characterized by significantly more intensive brightness (parameter L* = 54.67) in comparison to the C sample (parameter L* = 52.78). The sausages that were tested showed good microbiological quality concerning the total number of microorganisms, lactic acid bacteria, and the absence of pathogenic bacteria. The addition of apple vinegar contributed to the reduction in the intensity of the cured meat flavor and the fatty flavor. Therefore, according to the results presented in this work, it can be concluded that 3% of vinegar is the optimal addition, which may be used in the next step of investigation, taking into account color formation abilities as well as microbiological quality and lipid oxidation processes.

Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.

Skin proteomic screening and functional analysis of differential proteins associated with coat color in sheep (Ovis aries).

Coat color is an important characteristic and economic trait in domestic sheep. In this study, we explored the potential mechanisms and the signaling pathways involved in coat color regulation for sheep. Isobaric tags for relative and absolute quantification (iTRAQ) technology was used to catalog global protein expression profiles in skin of sheep with black versus white coat color. Immunofluorescence was used to observe the expression localization of differential protein. Western blot and quantitative real time polymerase chain reaction (qRT-PCR) were used to evaluate their role in the coat color formation of sheep. A total of 136 differential proteins were obtained in different coat colors, including 101 up-regulated and 35 down-regulated. Pigmentation function entries were enriched through gene ontology annotation. Tyrosine metabolism and platelet activation signaling pathway were extracted by Kyoto encyclopedia of genes and genomes analysis. Apolipoprotein A-1 (APOA1) and fibrinogen alpha chain (FGA) were found to be critical differential proteins by the interaction of differential proteins in the direct-interaction network diagram. Strikingly, twenty candidate differential proteins were screened, from which beta-actin (ACTB) protein showed higher expression in white sheep skin, while albumin (ALB), APOA1, MAOA (amine oxidase) and FGA proteins showed higher expression in black sheep skin, which was validated by immunofluorescence, western blot, and qRT-PCR. This study identified several novel proteins that may be involved in the coat color formation of sheep. The white and black sheep skin proteome profiles obtained provide a valuable resource for future research to understand the network of protein expression controlling skin physiology and melanogenesis in sheep.

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Novel insight into the formation and improvement mechanism of physical property in fermented tilapia sausage by cooperative fermentation of newly isolated lactic acid bacteria based on microbial contribution

Single starter can hardly elevate the gel property of fermented freshwater fish sausage. In this work, in order to improve the physical properties of tilapia sausage, two newly isolated strains of lactic acid bacteria (LAB), Latilactobacillus sakei and Pediococcus acidilactici were used for cooperative fermentation of tilapia sausage, followed by the revelation of their formation mechanisms during cooperative fermentation and their improvement mechanisms after comparison with natural fermentation. Both strains, especially L. sakei possessed good growth, acidification ability, and salt tolerance. The gel strength, hardness, springiness, chewiness, whiteness, acidification, and total plate count significantly elevated during cooperative fermentation with starters. Pediococcus, Acinetobacter, and Macrococcus were abundant before fermentation, while Latilactobacillus quickly occupied the dominant position after fermentation for 18–45 h with the relative abundance over 51.5 %. The influence of each genus on the physical properties was calculated through the time-dimension and group-dimension correlation networks. The results suggested that the increase of Latilactobacillus due to the good growth and metabolism of L. sakei contributed the most to the formation and improvement of gel strength, texture properties, color, acidification, and food safety of tilapia sausage after cooperative fermentation. This study provides a novel analysis method to quantitatively evaluate the microbial contribution on the changes of various properties. The cooperative fermentation of LAB can be used for tilapia sausage fermentation to improve its physical properties.

Abstract PO2-28-12: Rapid diagnosis of breast biopsies with open-top light-sheet microscopy

Abstract Introduction: Breast cancer is typically diagnosed through a diagnostic work-up that involves specialized breast imaging, image-guided biopsy, and pathological assessment. Pathology results require 2 to 7 business days before a diagnosis is rendered, creating anxiety in women presenting with a breast abnormality. Decreasing pathology evaluation lead-time has the potential to reduce anxiety, streamline patient care, and reduce heathcare costs. In this pilot study, we evaluated the potential of open-top light-sheet microscopy to deliver a preliminary breast pathology diagnosis within 30 minutes of biopsy. Methods: Fresh 14-gauge breast biopsies (N=40) in normal saline were received directly from the breast imaging clinic and immediately stained using the nuclear marker SYBR Gold (Invitrogen) and pan-protein marker Atto 655 NHS Ester (Sigma) prepared in dimethyl sulfoxide, washed, and cleared for imaging at a refractive index of 1.46 using 2,2’-Thiodiethanol (Sigma). The full process requires approximately 14 minutes for staining and clearing. After staining, we placed the biopsy in an in-house-built specimen holder and imaged a 100 micron cross section along the full length of the biopsy using our custom open-top light-sheet microscope. Images were subsequently converted computationally to a standard H&E color format using Fiji and Aivia (Leica) software and were ready for evaluation by a pathologist the same day they were collected. mages were assessed by two pathologists with subspecialization in breast pathology for a two-category diagnosis: benign or atypical. After imaging, the biopsy was frozen, a frozen section was cut, and an H&E-stained slide was produced. Results: Using the protocol above, we demonstrated the ability to stain, clear, image, and visualize needle core biopsies within 30 minutes of receiving the tissue sample. Processing and converting the data to an H&E color palette required additional time, surpassing the 30-minute turnaround time goal. The images contained readily identifiable stroma, epithelial cells, immune cells, and duct structures to a depth of 100 microns. 29/40 biopsies yielded interpretable results. 11/40 had insufficient staining, clearing, or imaging quality for diagnosis. The number of non-diagnostic images improved in the second half of the study (5%) compared to the first half of the study (50%) with sample prep and imaging optimization. Agreement between the two breast pathologists on classifying the biopsies as benign vs. atypical was 89.7%. Discussion: We describe a novel method to obtain a microscopic image of breast core biopsy specimens to facilitate preliminary diagnosis within 30 minutes of receipt of tissue. The quality of the images produced by the method shows promise for preliminary diagnosis, especially after protocol optimization for the second half of the study. Additional optimization is needed in data processing to meet the 30-minute turnaround time requirement. This optimization can be achieved by parallelization of the data processing on a cluster or cloud, which is currently under investigation by our team. Citation Format: Brandy Olin Pope, Rebeca Alvarez, Charles Childers, Suzanne Dintzis, Sarah Javid, Nicholas Reder, Habib Rahbar. Rapid diagnosis of breast biopsies with open-top light-sheet microscopy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-12.

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water.

Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.

Role of body size and shape in animal camouflage.

Animal camouflage serves a dual purpose in that it enhances both predation efficiency and anti-predation strategies, such as background matching, disruptive coloration, countershading, and masquerade, for predators and prey, respectively. Although body size and shape determine the appearance of animals, potentially affecting their camouflage effectiveness, research over the past two centuries has primarily focused on animal coloration. Over the past two decades, attention has gradually shifted to the impact of body size and shape on camouflage. In this review, we discuss the impact of animal body size and shape on camouflage and identify research issues and challenges. A negative correlation between background matching effectiveness and an animal's body size has been reported, whereas flatter body shapes enhance background matching. The effectiveness of disruptive coloration is also negatively correlated with body size, whereas irregular body shapes physically disrupt the body outline, reducing the visibility of true edges and making it challenging for predators to identify prey. Countershading is most likely in larger mammals with smaller individuals, whereas body size is unrelated to countershading in small-bodied taxa. Body shape influences a body reflectance, affecting the form of countershading coloration exhibited by animals. Animals employing masquerade achieve camouflage by resembling inanimate objects in their habitats in terms of body size and shape. Empirical and theoretical research has found that body size affects camouflage strategies by determining key aspects of an animal's appearance and predation risk and that body shape plays a role in the form and effectiveness of camouflage coloration. However, the mechanisms underlying these adaptations remain elusive, and a relative dearth of research on other camouflage strategies. We underscore the necessity for additional research to investigate the interplay between animal morphology and camouflage strategies and their coevolutionary development, and we recommend directions for future research.

Local Means Binary Networks for Image Super-Resolution.

The success of modern single image super-resolution (SISR) algorithms is inspired by the development of deep convolutional neural networks (CNNs). However, these CNN-based methods require considerable computation and complexity, making it impossible for these methods to perform real-time calculations in edge devices. Thus, lightweight model design has become a development trend in the super-resolution field, including pruning, quantization, and other methods. The 1-bit quantization is an extreme lightweight method which can reduce the calculation amount of the model in an extreme manner and is friendly to hardware such as edge devices. Most existing binary quantization approaches lead to a large information loss during forward propagation, especially in detailed color information (e.g., edge, texture, and contrast). The loss of color information makes modern binary methods unsuitable for SISR tasks. We think the loss occurs because these methods typically utilize a uniform threshold to quantize the weights and activations. Thus, in this article, we thoroughly analyze the difference between normal classification tasks and SISR tasks, and present a binarization scheme based on local means. The proposed method can maintain more detailed information in feature maps using dynamic thresholds during quantization. Specifically, each value in the full precision activations has a corresponding threshold during the quantization process, and those thresholds are determined by the full precision values of the surroundings. In addition, a gradient approximator is introduced to adaptively optimize the gradient for updating binary weights. We then verify the effectiveness of our method for training binary networks on several SISR benchmarks including VDSR and SRResNet. Experimental results show that the proposed method can outperform the state-of-the-art algorithms to obtain binary networks for image super-resolution with better peak signal-to-noise ratio (PSNR) values and visual quality.

Damage detection on cooling tower shell based on model textures

Ensuring the structural integrity of cooling towers is paramount for safety and efficient operation. This paper presents a novel approach for detecting damage on cooling tower shells, utilising textures derived from laser scanning and close-range photogrammetry. The proposed method delves beyond the limitations of solely relying on colour information by harnessing the rich details embedded in various textures, including diffuse, normal, displacement, and occlusion. The study demonstrates the efficacy of this approach for identifying significant concrete damage. A Convolutional Neural Network (CNN) trained on diffuse textures successfully detects high damage instances with minimal misdetection. However, accurately pinpointing low damage, often manifesting as subtle cracks, and mimicking other patterns like air pores, ribbing, and colour variations, presents a formidable challenge. To tackle this challenge, the authors introduce a novel "composed raster layer" that merges information from multiple textures. This pre-processed layer amplifies the visual cues associated with low damage, facilitating its differentiation from similar patterns. While the current implementation employing multi-resolution segmentation and rule-based classification exhibits promising results, further optimization is acknowledged to refine the accuracy of low damage detection. The successful application of textures commonly used in rendering techniques underscores their remarkable potential for enhancing damage detection in civil engineering applications. While acknowledging limitations such as the analysis of a single cooling tower and the reliance on specific software for damage detection, the study proposes future research directions. This research holds significant implications for the field of civil engineering by offering a promising approach for automated and efficient damage detection on cooling tower shells.

Theoretical Study of Cyanidin-Resveratrol Copigmentation by the Functional Density Theory.

Anthocyanins are colored water-soluble plant pigments. Upon consumption, anthocyanins are quickly absorbed and can penetrate the blood-brain barrier (BBB). Research based on population studies suggests that including anthocyanin-rich sources in the diet lowers the risk of neurodegenerative diseases. The copigmentation caused by copigments is considered an effective way to stabilize anthocyanins against adverse environmental conditions. This is attributed to the covalent and noncovalent interactions between colored forms of anthocyanins (flavylium ions and quinoidal bases) and colorless or pale-yellow organic molecules (copigments). The present work carried out a theoretical study of the copigmentation process between cyanidin and resveratrol (CINRES). We used three levels of density functional theory: M06-2x/6-31g+(d,p) (d3bj); ωB97X-D/6-31+(d,p); APFD/6-31+(d,p), implemented in the Gaussian16W package. In a vacuum, the CINRES was found at a copigmentation distance of 3.54 Å between cyanidin and resveratrol. In water, a binding free energy ∆G was calculated, rendering -3.31, -1.68, and -6.91 kcal/mol, at M06-2x/6-31g+(d,p) (d3bj), ωB97X-D/6-31+(d,p), and APFD/6-31+(d,p) levels of theory, respectively. A time-dependent density functional theory (TD-DFT) was used to calculate the UV spectra of the complexes and then compared to its parent molecules, resulting in a lower energy gap at forming complexes. Excited states' properties were analyzed with the ωB97X-D functional. Finally, Shannon aromaticity indices were calculated and isosurfaces of non-covalent interactions were evaluated.