This paper introduces a new destriping algorithm for remote sensing data. The method is based on combined Haar Stationary Wavelet transform and Fourier filtering. State-of-the-Art methods based on the discrete wavelet transform (DWT) may not always be effective and may cause different artifacts. Our contribution is three-fold: i) we propose to use the Undecimated Wavelet transform (UWT) to avoid as much as possible shortcomings of the classical DWT; ii) we combine a spectral filtering and UWT using the simplest possible wavelet, the Haar basis, for a computational efficiency; iii) we handle 2D fields with missing data, as commonly observed in ocean remote sensing data due to atmospheric conditions (e.g., cloud contamination). The performances of the proposed filter are tested and validated on the suppression of horizontal strip artifacts in cloudy L2 Sea Surface Temperature (SST) and ocean color snapshots.

Color centers in diamond and nanodiamonds can be utilized as quantum sensors for measuring various physical parameters, particularly magnetic and electric fields, as well as temperature. Due to its small size and possible surface functionalization, fluorescent nanodiamonds are attractive systems for biological and medical applications since they can be used for intracellular experiments. This review focuses on fluorescent nanodiamonds for thermometry with nanoscale spatial resolution for living systems. The current state of the art, possible further development, and potential limitations will be discussed here.

This study aimed to investigate the coloring ingredient potential of liver homogenates that form Zn-protoporphyrin (ZnPP), a natural red pigment, after anaerobic incubation. The liver homogenates were used to develop nitrite-free sterile pork liver pâtés. These liver homogenates can be applied in the formulation of pâtés directly or as a centrifuged insoluble fraction that is highly concentrated in ZnPP. Both the whole homogenate and its insoluble fraction were adjusted to pH 7.5 before their use in the formulation of pâtés with and without antioxidant (0.5% ascorbate plus 0.1% tocopherol) addition. Pâtés formulated with the whole homogenate showed color and texture characteristics similar to those of the positive control with nitrite. However, high levels of the insoluble fraction also led to pâtés with improved color characteristics but with a two-fold softened texture. Therefore, the form and amount of ZnPP added played a role in the final appearance of the product. The ZnPP pigment was more stable than heme was in the sterilization treatment, and antioxidant addition proved to be unnecessary. The ZnPP-rich ingredients allowed for the preparation of nitrite-free cooked liver pâtés with a stable red color and could be potentially applied in other uncured cooked meat products.

The first step in seeing is light absorption by photopigment molecules expressed in the photore-ceptors of the retina. There are two types of photoreceptors in the human retina that are respon-sible for image formation, rods and cones. Except at very low light levels when rods are active, all vision is based on cones. Cones mediate high acuity vision and color vision. Furthermore, they are critically important in the visual feedback mechanism that regulates refractive development of the eye during childhood. The human retina contains a mosaic of three cone types, short-wavelength (S), long-wavelength (L) and middle-wavelength (M); however, the vast major-ity (~94%) are L and M cones. The OPN1LW and OPN1MW genes, located on the X-chromosome at Xq28, encode the protein component of the light-sensitive photopigments. Here we review mechanism by which splicing defects in these genes cause vision disorders.

Over the last decades, the coated glass products, and especially the low-emissivity coatings have become a common building material used in modern architectural projects. More recently, these material systems became common in specialized glazing systems featuring solar energy harvesting. Apart from achieving the stability of optical parameters in multilayer coatings, it is also important to have improved control over the design of visual color properties of the coated glass. We prepare metal-dielectric composite (MDC) based multilayer thin-film structures using the RF-magnetron sputtering deposition and report on their optical and chromaticity properties in comparison with these obtained using pure metal-based Dielectric/Metal/Dielectric (DMD) trilayer structures of similar compositions. Experimentally achieved Hunter L, a, b values of MDC-based multilayer building blocks of coatings provide a new outlook on the engineering of future-generation optical coatings with better color consistency and developing approaches to broaden the range of achievable color coordinates and better environmental stability.

Oenothera flower petals change color during senescence. When in full bloom, the flowers of O. tetraptera are white and those of O. laciniata and O. stricta are yellow; however, the colors change to pink and orange, respectively, when the petals fade. We analyzed the flavonoid components in these petals as a function of senescence using HPLC-DAD and LC-MS. In all three species, cyanidin 3-glucoside (Cy3G) was found in faded petals, and the content of Cy3G increased in senescence. In full bloom (0 h), no Cy3G was detected in any of the petals, but after 12 h, the content of Cy3G in O. tetraptera was 0.97 μmol/gFW and that in O. laciniata was 1.82 μmol/gFW. Together with anthocyanins, major flavonoid components in petals were identified. Quercitrin was detected in the petals of O. tetraptera, and isosalipurposide was found in the petals of O. laciniata and O. stricta. The content of quercitrin did not change during senescence, but that of isosalipurposide in O. laciniata increased from 3.4 μmol/gFW at 0 h to 4.8 μmol/gFW at 12 h. The color change in all the three Oenothera flowers was confirmed to be due to the de novo biosynthesis of Cy3G.

The Total Algae Peak Integration Retrieval TAPIR relates the chlorophyll-a absorption coefficient at 440 nm (a440) to the reflectance peak near 683 nm induced by chlorophyll-a properties. The two-step retrieval provides both the hyperspectral quantification of the phytoplankton fluorescence and scattering and the estimation of a440 from reflectance signals. Integrating the peak, the Total Algae Peak (TAP) accounts for the variance in the peak's magnitude, shape, and central peak wavelength. TAPIR is a solely optical approach estimating a440 and supports the application of retrieval-independent individual regional bio-optical models afterwards to retrieve the chlorophyll-a concentration. Simulations reveal the major sensitivity on the considered model chlorophyll-a absorption spectrum and its single scattering albedo. Additional water and atmosphere constituents have a low impact. An uncertainty assessment reveals uncertainties of less than 30% for TAPIR a440 greater than 0.8 m-1 and less than 38% for lower a440. In optically complex waters, first validation efforts promise the applicability of TAPIR for high chlorophyll-a concentration estimations in the presence of additional water constituents. The technique is generic and considers external conditions (sun zenith angle, number of measurement bands, surface or satellite measurements, and radiometric quantity). TAPIR applies to all kind of waters including optically complex waters, arctic to tropical regions, and inland, coastal, and open ocean waters. Among other hyperspectral satellite sensors, the Environmental Mapping and Analysis Program (EnMAP) provides sufficient sampling bands for the application of TAPIR.

Three cases of Day-Night Band (DNB) observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) are explored for applications to assess the ocean environment and monitor ocean dynamics. An approach to use the DNB radiance ratio was developed in order to better continuously assess the ocean diurnal and short-term environmental changes with VIIRS DNB observations. In the La Plata River Estuary, the sediment fronts showed 20–25 km diurnal inshore-offshore movements on March 13, 2017. In the waters off the Argentina coast in the South Atlantic, VIIRS DNB measurements provided continuous observations and monitoring of the algae bloom development and migration between 24–26 March 2016. This algae bloom generally kept the same spatial patterns, but moved nearly 20 km eastward in the three-day period. In the Yangtze River Estuary and Hangzhou Bay region in China’s east coast, VIIRS DNB observations also revealed the complicated coastal dynamic changes between 12–14 April 2017. Even though there are still some challenges and limitations for monitoring the ocean environment with VIIRS DNB observations, this study shows that satellite DNB observations can provide additional data sources for ocean observations, especially observations during the nighttime.

This investigation introduces an efficient method for assessing the reliability of semi-rigid steel frame connections in existing buildings, explicitly targeting the Top Angle and Seat Pad (TA-SP) semi-rigid connection commonly used in Australian cities' high-rise buildings. The moment capacity of these connections was examined through parameter power model testing, meticulously considering the bending, shear, and prying forces actions to determine the weakest elements in the connection system. A plastic hinge model was applied to ensure computational efficiency and accuracy in assessing the connections' ultimate strength. Comparatively, the finite element (F.E) method M−θ curves were introduced to scrutinise the nonlinear behaviour of the connection, providing validation for the predicted analytical approach. Further emphasis was placed on identifying the reliability of the weakest elements in the connection system, employing the First-Order and Second-Order Reliability Methods models to estimate their reliability index. The correlation between the model's failure probability and a percentile error was also explored, utilising Monte Carlo simulation reference for refined validation. Novel formulas and methodologies were introduced to compute the locked-chain-series system reliability, which accounts for the inherent material and geometric variability. The findings highlight the critical significance of the weakest element in determining the overall reliability of the existing TA-SP connection.

We herein described an investigation of a theory, which describes the energies of neutrinos and the source of neutrino oscillations. A series of experiments were conducted to show evidences of the existence a neutrino mass. We also applied theories to explain the reason for the extremely small energy of a neutrino, mainly by employing a vacuum-derived superconducting energy gap from the Bardeen–Cooper–Schrieffer ground state. Moreover, we succeeded in obtaining the transition probabilities of neutrinos’ flavors (i.e., in terms of neutrino oscillation). We focused on the fact that up- and down-quantized space pairs combine by the Lorentz forces, undertake Bose-Einstein condensation, and then create a superconducting energy gap at the energy level of the vacuum with quantum mechanics fluctuation. Eventually, the superconducting energy gap vanishes to form a real body of the neutrino. Furthermore, assuming that the speed of the neutrino is near the speed of light and exhibits Planck’s blackbody emissions, we derived many-body interactions of neutrinos and applied them in Fermi’s golden rule. As a result, the neutrino energy we calculated agreed well within the realms of the experimental results. The calculated transition probabilities of neutrino’s flavor also explain the experiment results very well.

The sPHENIX experiment is a state-of-the-art jet and heavy flavor physics detector which successfully recorded its first Au + Au collision data at 200 GeV at RHIC. sPHENIX will provide heavy flavor physics measurements covering an unexplored kinematic region and unprecedented precision at RHIC to probe the parton energy loss mechanism, parton transport coefficients in quark-gluon plasma, and the hadronization process under various medium conditions. At the center of sPHENIX, the Monolithic-Active-Pixel-Sensor (MAPS) based VerTeX detector (MVTX) is a high-precision silicon pixel detector. The MVTX provides excellent position resolution and the capability of operating in continuous streaming readout mode, allowing precise vertex determination and recording a large data sample, both of which are particularly crucial for heavy flavor physics measurements. In this work, we will show the general performance of heavy-flavor hadron reconstruction. In addition, we will discuss the commissioning experience with sPHENIX. Finally, we will provide the projection of b-hadron and jet observables and discuss the estimated constraints on theoretical models.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

Natural dyes offer a bio-based opportunity to support attractive coloration to textile fabrics made from natural fibers like cotton, wool, hemp and many other materials. They can be part of a strategy to realize fully bio-based textile materials and clothing. Following this statement, the actual study investigates the use of wood extracts for dyeing of cotton fabrics. Actually extract powders of Logwood (Haematoxylon campechianum L.), Brazilwood (Caesalpinia spp.), and Quebracho wood (Schinopsis lorentzii) are used. The dyeing process is modified using different mordants based on iron and aluminum salts. These mordants are applied in pre-, meta- or post-mordant procedures. Color and fastness properties of prepared textile samples are determined by spectroscopic measurements, color measurements, washing procedures and Xenotest for measuring the light fastness. Finally, a broad range of different colorations can be realized by different combinations of wood extracts and mordanting procedures. A good wash fastness is reached for some color shades. For reaching good wash fastness, pre- and meta-mordanting procedures are more advantageous compared to post-mordanting. However, the light fastness of realized samples is only moderate to low. In conclusion, it can be stated that dyes from wood extracts are excellent materials to dye natural fibers if they are combined with the right mordanting agent in pre- or meta-mordanting procedures. The actual study is therefore a good proof-of-concept for the realization of fully bio-based colored textile materials.

Optimum postharvest storage conditions increase the shelf life and postharvest quality of horticultural crops. The effects of forced-air precooling (FAP) and modified atmosphere packaging (MAP) on shelf life, physicochemical quality, and health-promoting properties of bell pepper (Capsicum annuum L. cv. Nagano) harvested at 90% and 50% coloring stages in May and July respectively, stored at 11℃, 95% relative humidity were assessed. Fruits were treated with FAP + 30 μm polyethylene liner (FOLO), FAP (FOLX), 30 μm polyethylene liner (FXLO), and control (FXLX). The quality attributes, viz. weight loss, firmness, color, SSC, soluble sugars, TPC, TFC, DPPH, and ABTS were evaluated. FOLO maintained sensory quality (weight loss, firmness, and color), physicochemical (soluble solids content and soluble sugars), and health-promoting properties compared to other treatments during storage. The investigated parameters differed significantly (p < 0.05) among treatments except soluble sugars. The 50% coloring fruits had a huge variation between treatments than 90% coloring. The results revealed more TPC and antioxidant capacity in 50% than in 90% coloring fruits. The study highlights the need to consider the ideal fruit coloring stage at harvest under the effect of FAP and MAP treatments in preserving bell pepper's postharvest quality and shelf life.

Traditional diagnosis of patent foramen ovale (PFO) in the heart has involved the use of transcranial doppler (TCD). However, TCD is essentially a blind test that cannot directly visualize the location of blood vessels. Since TCD relies on qualitative assessments by examiners, there is room for errors, such as misalignment of the ultrasound's angle of incidence with the actual blood vessels. This limitation affects the reproducibility and consistency of the examination. In this study, we presented an alternative approach for assessing right-to-left shunt (RLS) associated with PFO using contrast transcranial color-coded doppler (C-TCCD) with bubble contrast. The patient under consideration had been diagnosed with an ischemic stroke through imaging, but the subsequent cardiac work-up failed to determine the cause. Employing C-TCCD for RLS screening revealed a confirmed RLS of Spencer’s three grades. Subsequently, transesophageal echocardiography (TEE) was conducted to evaluate PFO risk factors, confirming an 8 mm PFO size, a 21 mm tunnel length, a hypermobile interatrial septum, and persistent RLS. The calculated high-risk PFO score was 4 points, categorizing it as a very high-risk PFO. This case underscores the importance of C-TCCD screening in detecting RLS associated with PFO, especially in cryptogenic stroke patients when identifying the underlying cause of ischemic stroke becomes challenging.

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired in the visual cortex, are characterized by their hierarchical learning structure, which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable.

Efficient management of basal cell carcinoma (BCC) requires reliable assessments of both tumors and post-treatment scars. We aimed to estimate image similarity metrics that account for BCC's perceptual color and texture deviation from perilesional skin. 176 clinical photographs of BCC were assessed by six physicians using a visual deviation scale. Internal consistency and inter-rater agreement were estimated using Cronbach's α, weighted Gwet's AC2, and quadratic Cohen's Kappa. The mean visual scores were used to validate a range of similarity metrics employing different color spaces, distances, and image embeddings from pre-trained VGG16 neural network. The calculated similarities were transformed into discrete values using ordinal logistic regression models. The Bray-Curtis distance in the YIQ color model and rectified embeddings from the 'fc6' layer minimized the mean squared error and demonstrated strong performance in representing perceptual similarities. Box plot analysis and Wilcoxon rank-sum test were used to visualize and compare the levels of agreement, conducted on a random validation round between the two groups: ‘Human-System’ and ‘Human-Human’. The proposed metrics were comparable in terms of internal consistency and agreement with human raters. The findings suggest that the proposed metrics offer a robust, and cost-effective approach to monitoring BCC treatment outcomes in clinical settings.

Optical motion capture systems are state-of-the-art in motion acquisition, however as any measurement systems they are not error free -- noise is their intrinsic feature. The works so far mostly employ simple noise model, expressing the uncertainty as a simple variance. In the work we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. For the automated readout of the noise coefficients we solve the multidimensional regression problem using sophisticated metaheuristics in exploration-exploitation scheme. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion in our facility. We had also opportunity to observe the influence of camera failure to the overall performance.

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we use chlorophyll-a concentration (Chla), sea surface temperature (SST), absorption due to dissolved and particulate detrital matter (Adg), diffuse attenuation coefficient of downwelling irradiance at 490nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results show that the root mean square error (RMSE) is 8.25μatm, the mean bias error (MAE) is 4.92μatm and the coefficient of determination (R2) can reach 0.946 in the validation set, which mean that the CatBoost model makes an improvement compared to other models in the published studies. In the further analysis of the spatial and temporal distribution of the sea surface pCO2 in the North Atlantic, it can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, and influenced by biological activities in some sub-regions.

We investigated layered titanium nitride (TiN) and aluminum nitride (AlN) for color glasses in Building Integrated Photovoltaic (BIPV) systems. AlN and TiN are among suitable and cost-effective optical materials to be used as thin multilayer films, owing to the significant difference in their refractive index. To fabricate the structure, we used radio frequency magnetron deposition method to achieve the target thickness uniformly. A simple, fast, and cheap fabrication method is achieved by depositing the multilayer films in a single sputtering chamber. It is demonstrated that a multilayer stack that allows light to move from a low refractive index layer to a high refractive index layer or vice-versa can effectively create various distinct color reflections for different film thicknesses and multilayer structures. It is investigated from simulation based on wave optics that, TiN/AlN multilayer offers better color design freedom and cheaper fabrication process as compared to AlN/TiN multilayer films. Blue, green, and yellow color glasses with optical transmittance of more than 80% was achieved by ITO coated glass/TiN/AlN multilayer films. This technology exhibits good potential in commercial BIPV system applications.

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with conveyor belt, robot and industry color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of standard uncertainties of type A and B, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e. measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

Traditional discrimination techniques for Euler deconvolution use only the color spectrums of structural indexes, without considering the spatial distribution characteristics and inherent relationships among the Euler solutions to separate adjacent causative sources. In the present study, a new approach was developed for discriminating uncorrelated Euler solutions from coherent solutions based on the focusing levels indicated by the probability density distributions generated using multivariate kernel density estimations (KDE). A novel multiple coverage technique was proposed by using a series of different sized moving windows over gridded gravity data, which formed tight clusters of Euler solutions for different sized causative sources. The results of the probability density distributions were obtained using a 3-D KDE method for the Euler solution subsets {x, y, z} of synthetical models, and real data from a survey conducted in British Columbia (Canada) which had successfully established more credible and meaningful geological models when compared with three other subsets.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

Synesthesia is a psychological phenomenon where sensory signals become mixed. Input to one sensory modality produces an experience in a second, unstimulated modality. In “grapheme-color synesthesia”, viewed letters and numbers evoke mental imagery of colors. The study of this condition has implications for increasing our understanding of brain architecture and function, language, memory and semantics, and the nature of consciousness. In this work, we propose a novel application deep learning to model perception in grapheme-color synesthesia. Achromatic letter images, taken from database of handwritten characters, are used to induce synesthesia. Results show the model learns to accurately create a colored version of the inducing stimulus, according to a statistical distribution from experiments on a sample population of grapheme-color synesthetes. The spontaneous, creative mental imagery characteristic of the synesthetic perceptual experience is reproduced by the model. A model of synesthesia that generates testable predictions on brain activity and behavior is needed to complement large scale data collection efforts in neuroscience, especially when articulating simple descriptions of cause (stimulus) and effect (behavior). The research and modeling approach reported here begins to address this need.

The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.

In this paper, we use a machine learning method, known as Nonnegative Matrix Factorization, to extract from a given image some new information obtained from a number of color space representations. Such kind of information, termed ``metacolor'', can be considered as a particular color space representation of the image, which can be used to obtain a binary segmentation of the investigated image by distinguishing foreground and background pixels. Some numerical experiments prove that the proposed method shows improved results when compared with common simpler thresholding algorithms.

(1) Background: The research on the potential of regional food products, known for their rich bio-active compound content, could enhance human health in local populations. This is especially pertinent amid the Covid-19 pandemic and global trade disruptions; (2) Methods: The current study evaluated the physico-chemical composition using standard methods, antioxidant activity via DPPH, ABTS, FRAP, and CUPRAC methods, sorption capacity (at 10°C, 25°C, and 40°C with water activity ranging from 0.1 to 0.9) through the static-gravimetric method, and monolayer moisture content (MMC) calculated using the BET model; (3) Results: The approximate physi-co-chemical composition of laboratory-produced quince powder is as follows: proteins - 1.27g, carbohydrates - 75.80g, fats - 0.49g, fibers - 21.50g, ash – 2.31g, and nutritional value – 355.65kcal. Antioxidant activity is confirmed by all methods. The Modified Halsey model is recommended to describe the sorption isotherms with an S-shaped form. The MMC for the adsorption process ranges from 14.41% d.b. to 7.09% d.b., and for the desorption process, it ranges from 13.11% d.b. to 7.80% d.b.; (4) Conclusions: Seasonal and regional foods boast the best nutritional profiles, as they are cultivated and harvested at optimal ripeness in natural sunlight, preserving the essential vitamins and minerals in the food.

Women of color (WoC) in the health professions encounter challenges in advancement to higher positions, disparities in wages, discrimination, lack of expectation to achieve leadership positions, and absence of extensive support networks. Articles in the literature have addressed race and/or gender in the context of professional development. However, applying an intersectionality lens or framework to better understand the contextual issues of professional development for WoC remains to be addressed. Thus, this scoping review aimed to (i) identify health professions literature that addresses disparities affecting WoC, and (ii) describe strategies and approaches to support WoC in the health professions. Methods: Literature searches were conducted in multiple databases. Three independent reviewers reviewed and screened articles for inclusion. Results: 31 articles were included - medicine(17), nursing(1), pharmacy(7), other(4), and multiple health professions(2). Key findings included underrepresentation of women and minority groups, inequities in professional advancement and leadership positions for WoC, and greater dissatisfaction and attrition among minority and women professionals. Conclusion: WoC face unique and distinct challenges and barriers in their professional careers resulting from the intersectionality of not only race and gender, but also lived experiences and opportunities. Strategies to improve diversity and representation should include an intersectionality framework or lens and be critically evaluated.

High-quality ionization injection methods for Wake Field Acceleration driven by lasers or charged beams (LWFA/PWFA) can be optimised so as to generate high-brightness electron beams with tuneable duration in the attosecond range. We present a model of the minimum bunch duration obtainable with low-emittance ionization injection schemes, by spotting the roles of the ionization pulse duration, of the wake field longitudinal shape and of the delay of the ionization pulse position with respect to the node of the accelerating field. The model is tested for the resonant multi-pulse ionization injection (ReMPI) scheme, showing that bunches having length of about 300as can be obtained with a ionization pulse having duration of 30fs FWHM

This research assesses the classification of Arabic coffee into three primary variations (light, medium, and dark) using simulated data based on actual measurements of color information, antioxidant laboratory tests, and chemical composition tests. Two types of simulated data were generated, with the standard deviation of the measures varied. Multiple classifiers were used for classification. The findings demonstrate the use of color information in accurately distinguishing Arabic coffee types. The different CIE color values, as well as the excellent connection between color information and coffee classes, lead to flawless classification results. However, depending primarily on antioxidant information results in lower classification performance, with variability amongst classifiers due to increasing data complexity. Chemical composition information, on the other hand, possesses outstanding discriminatory strength, allowing faultless classification on its own. Certain characteristics, such as crude protein and crude fiber, show strong correlations and are critical in the classification of coffee types. Based on the findings, it is recommended to create a mobile application that uses image recognition to analyze coffee color while providing chemical composition information. End users, such as consumers, will be able to make informed judgments regarding their coffee preferences owing to this application.

Knowledge of the temperature evolution is crucial to understand and control laser beam welding of low-melting materials. Existing temperature determination approaches are restricted to i) one-dimensional temperature information (e.g. ratio-pyrometers), ii) a priori knowledge of the emissivity (e.g. thermography) and iii) high temperature regions (e.g. two-wavelength imaging). In this paper, a ratio-based two-color-thermography approach is developed that allows for two-dimensional temperature determination in low-melting temperature ranges (< 1200 K). For static measurement situations it is demonstrated, that temperature can be determined despite variation in signal intensity and emissivity with high accuracy. The two-color-thermography set-up is further transferred into a commercial laser beam welding machine and experiments are conducted for varying process parameters. The direct application of the developed two-color-thermography system in dynamic process situations is limited as image artifacts presumably caused by internal reflections inside the optical beam path are present.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

Geo-information about the spectral variability of the water-leaving radiance is the key to the validation of satellite and in situ measurements and the development of regional algorithms. In this study, using cluster analysis, five trends were identified that are characteristic of various phenomena in the northwestern part of the Black Sea (summer and winter blooms, turbid waters, river runoff). Typical values of the remote sensing reflectance coefficient are calculated for each case. Additionally, the standard values of the color indices for each cluster are calculated. It is shown that the ratio of the color index CI(412/443 nm) is slightly variable and equals 0.8±0.07.This can be a reference point for recovering incorrect (negative) satellite Rrs(λ) values in the shortwave region. In a similar way, the color index was calculated according to the MODIS and VIRS data, it was shown that on days with a turbid atmosphere (high AOT values), the standard deviation of the color index is 30%. For days with a clear atmosphere, the color index is close to the in situ results.

In today's world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbour Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method.

Previous research has shown that the effectiveness of selecting filter set from a large set of commercial broadband filters by vector analyzing method based on maximum linear independence (MLI). However, the traditional MLI is suboptimal due to predefining the first filter of the selected filter set being the maximum ℓ2 norm among all those of the filters. An exhaustive imaging simulation is conducted to investigate the features of the most competent filter set. In the simulation, every filter in a subset of all the filters is selected serving as the first filter in turn. From the results of the simulation, we found there are remarkable characteristics for the most competent filter set. Besides smaller condition number, the geometric features of the best-performed filter set comprise the distinct peak of the transmittance of the first filter, the generally uniform distributing of the peaks of the transmittance curve of the filters, the substantial overlapping of the transmittance curves with those of the adjacent filer sets. Therefore, the best-performed filter sets can be decided intuitively by simple vector analyzing and just a few experimental verifications. This work would be useful for optimizing spectral sensitivity of broadband multispectral imaging sensors or SFA sensors.

To follow the growth and development of each tree (N = 1200) Pinus sylvestris L., var. Negorelskaya starting from the seed and continuing at the juvenile (or even generative) stage is the goal of an ambitious project to create a plant passport if possible. Five datasets of replenished empirical (tab-ular and file) and systematic data reflect the early results of a unique ongoing experiment conducted with families moved along a climatic gradient from the collection area (1731 degree days, 722 mm) to the experimental area (2326 degree days; 786 mm). Datasets of morphometric, VIS-spectrometric, and germination seed data, combined with the first results of biometric data (including the Dickson quality index) of seedlings produced from these seeds, will allow scientists in the future to conduct correlation and other statistical analysis, or train a neural network to determine the presence of connections between a seed and a plant. These data contribute to the early non-destructive diag-nosis and grading of seeds according to VIS-spectrometric properties. This dataset structure can complement the FLR-Library. In the future, by tracing seedlings in their early growth (for example, over three growing seasons at the site being restored), it will be possible to determine the degree of intensification of FRM production depending on the initial properties of the seed.

The present study sought to determine the effect of incorporating a Chitosan-TiO2 nano-composite on the color stability of pigmented Room Temperature Vulcanization (RTV) maxillofacial silicone subjected to a variety of accelerated aging conditions. The experimental setup consisted of one hundred groups of five disk-shaped specimens (22mm diameter, 2mm thick) each, created using RTV silicon elastomer type A-2186, with the total sample distributed into four groups based on the pigments combined with the silicon—specifically, brilliant red, blue, and yellow dry intrinsic pigments (at 0.2% concentration by weight) and a fourth group that remained non-pigmented. Each of these primary groups was further subdivided into five categories corresponding to the silicon variants treated with different nanoparticles—2% TiO2, 3% Chitosan, a hybrid nano-combination of 1% TiO2/Chitosan, 1% synthesized Chitosan-TiO2 Nanocomposite, and a control group devoid of nanoparticle additions. The synthesized Chitosan-TiO2 Nanocomposite was effectively prepared using the core-shell method. The color stability of each silicon category, irrespective of whether pigmented or non-pigmented was evaluated by calculating the color difference (ΔE) pre- and post-subjection to five distinct accelerated aging conditions: 30 hours in an antibacterial cleaning solution, six months in sebum, six months in sweat, UV-accelerated aging for 720 hours, and outdoor weathering for six months. Color measurements were taken using a Colorimeter within the CIELAB color system. Statistical analyses, including one-way ANOVA at a significance level of p < 0.05, were employed to discern significant differences among the various accelerated aging conditions across the five silicon categories. Further comparisons were made using Dennett's T3 multiple comparison test, Tukey HSD multiple comparisons, and a t-test, with SPSS for Windows version 27.0 as the software platform. Results indicated that all silicon types, whether pigmented or not, exhibited varying degrees of color change under different aging conditions. Red-colored samples, across all silicon categories, demonstrated significant color alterations after 720 hours of UV-accelerated aging, with the most pronounced change in the 3% Chitosan samples and the least in the 2% TiO2 samples. Six months of outdoor weathering led to a marked increase in ΔE values across all categories. The blue-pigmented silicon samples treated with 2% TiO2 showed the highest color modification, while the yellow-pigmented silicon and non-pigmented silicon also underwent significant color shifts, particularly in the control and Chitosan-TiO2 categories, respectively. The study underscores the pervasive influence of outdoor weathering on color stability across all silicon categories. It also demonstrates that while nanoparticle incorporation offers some resilience against accelerated aging, it fails to provide adequate protection against the specific effects of UV radiation encountered during outdoor weathering.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

Color can direct visual attention to specific locations through bottom-up and top-down mechanisms. Using Continuous Flash Suppression (CFS) as way to investigate the factors that gate access to consciousness, the current study investigated whether color also directly affected the timing of conscious perception. Low or high spatial frequency (SF) gratings with different orientations were shown as targets to the non-dominant eye of human participants. CFS patterns were presented at a rate of 10Hz to the dominant eye to delay conscious perception of the targets, and participants had the task to report the target’s orientation as soon as they could see it. With low-SF targets, two types of color-based effects became evident. First, when the targets and the CFS patterns had different colors, the targets entered consciousness faster than in trials where the targets and CFS patterns had the same color. Second, when participants searched for a specific target color, targets that matched these search settings entered consciousness faster compared to conditions where the target color was irrelevant and could vary from trial to trial. Thus, the current study demonstrates that color is a central feature of human perception and leads to faster conscious perception of visual stimuli through bottom-up and top-down attentional mechanisms.

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map, the Quantization Error (SOM-QE). Shape pairs with perfect geometric mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, longer choice RT for ‘yes’ responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

The blue sepal color of hydrangea is due to a metal complex anthocyanin composed of 3-O-glucosyldelphinidin (1) and an aluminum ion with the co-pigments 5-O-caffeoylquinic acid (2) and/or 5-O-p-coumaroylquinic acid (3). The three components, namely, anthocyanin, Al³⁺ and 5-O-acylquinic acids, are essential for blue color development, but the complex is unstable and only exists in aqueous solution. Furthermore, the complex did not give analyzable NMR spectra or crystals. Therefore, many trials to determine the detailed chemical structure of the hydrangea-blue complex have failed to date. Instead, via experiments mixing 1, Al³⁺ and 2 or 3 in a buffered solution at pH 4.0, we obtained the same blue solution derived from the sepals. However, the ratio was not stoichiometric but fluctuated. To determine the composition of the complex, we tried direct observation of the molecular ion of the complex using electrospray-ionization mass spectrometry. In a very low-concentration buffer solution (2.0 mM) at pH 4.0, we reproduced the hydrangea-blue color by mixing 1, 2 and Al³⁺ in ratios of 1:1:1, 1:2:1 and 1:3:1. All solution gave the same molecular ion peak at m/z = 843, indicating that the blue solution has a ratio of 1:1:1 for the complex. By using 3, the observed mass number was m/z = 827 and the ratio of 1, 3 and Al³⁺ was also 1:1:1. A mixture of 1, 3-O-caffeoylquinic acid (4) and Al³⁺ did not give any blue color but instead was purple, and the intensity of the molecular ion peak at m/z = 843 was very low. These results strongly indicate that the hydrangea blue-complex is composed of a ratio of 1:1:1 for 1, Al³⁺ and 2 or 3.

Background: Thyroid nodules are common, affecting nearly 65% of the general population. While they are widespread, it's crucial to recognize that the majority of these nodules are not cancerous and can be effectively managed through careful monitoring. Thyroid ultrasound is commonly used to evaluate thyroid nodules owing to its low cost, wide availability and technical ease of application. In sonography, it's crucial to assess characteristics and vascular flow. Yet, conventional color and power Doppler ultrasound may not always effectively capture small vessels and low velocities due to their limitations. Superior Microvascular Imaging can display the blood flow of small vessels and low-velocity blood flow, which is difficult to visualize with conventional Doppler ultrasound. Meanwhile other thyroid scans, that utilize either [99mTc] pertechnetate or (18)F-fluorodeoxyglucose, can categorize nodules as hyperactive ('hot'), exhibiting normal activity ('warm'), or having reduced/no activity ('cold'). Thus, the main goal of screening and tracking thyroid nodules is to improve the management of potentially worrisome thyroid malignancies. Methodology: The study was conducted within the structural framework of a network meta-analysis, with the index tests being - SMI, Radionuclide Scanning and Color Doppler, and the general link between them being the reference gold standard. The data collection involved searching for all pertinent literature using PubMed, Google Scholar, and the Cochrane Library databases. The essential data was extracted following the application of specific inclusion and exclusion criteria.This meta-analysis included 7 RCTs with 1,152 patients for Radionuclide Scanning, 10 RCTs with 1,847 patients for SMI and 8 RCTs with 4,480 patients for Color Doppler. They were analyzed using the QualSyst tool and the risk of bias was evaluated by using QUADAS-2 analysis.The statistical software packages MetaDiSc 1.4, RevMan (Review Manager, version 5.3), SPSS (Statistical Package for the Social Sciences, version 20) and Excel in Stata 14 were used to perform the statistical analyses. Result: According to the findings, the sensitivity for Radionuclide Scanning is calculated as 0.79 (range: 0.76 to 0.81) and specificity as 0.55 (range: 0.50 to 0.60) with a 95% confidence interval. The overall diagnostic odds ratio (DOR) for Radionuclide Scanning Is 10.54.Additionally, with a 95% confidence interval, SMI exhibits a sensitivity of 0.82 (with a range of 0.79 to 0.84) and a specificity of 0.86 (with a range of 0.84 to 0.88), while the overall DOR is approximately 25.73. Color Doppler demonstrates a calculated sensitivity of 0.71 (ranging from 0.69 to 0.74) and specificity of 0.81 (ranging from 0.79 to 0.83) with a 95% confidence interval, while its DOR is 13.48.Conclusion: As evidenced by this meta-analysis, blood flow dependent imaging techniques are superior in their abilities to be used as diagnostic tests, especially in conjunction with their use in detecting thyroid nodules. This study offers a starting point for delving deeper into the utilization of Radionuclide Imaging, Superior Microvascular Imaging and Color Doppler in diagnosing thyroid nodules in a clinical setting. Key Words: - Thyroid Nodule, Radionuclide Scanning, Superior Microvascular Imaging, Color Doppler Diagnosis, Sensitivity, Specificity, Diagnostic imaging

In this paper, we investigated transducer placement strategies for detecting crack in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the developed approach, finite-element (FE) modelling of a damage tolerant aluminum fuselage has been performed by introducing an artificial crack at a rivet hole into the structural FE model and assessing its influence on the Lamb wave propagation, compared to a baseline measurement simulation. The efficient practical sensor position was determined from the largest change in area that is covered by reflected and missing wave scatter using an additive color model. Blob detection algorithms were employed in order to determine the boundaries of this area and to calculate the blob centroid. To demonstrate that the technique can be generalized, the results from different crack lengths and from tilted crack are also presented.

Computer vision models of salient object detection attempt to mimic the ability of the human visual system to select relevant objects in images. To this end, the development of deep neural networks on high-end computers has recently made it possible to achieve high performance. However, it remains a challenge to develop deep neural network models of the same performance for devices with much more limited resources. In this work, we propose a new approach for a lightweight salient object detection neural network model, inspired by the cone and spatial opponent processes of the primary visual cortex (V1), that inextricably link color and shape in human color perception. Our proposed model, namely CoSOV1net, is trained from scratch, without using backbones from image classification or other tasks. Experiments, on the most widely used and challenging datasets for salient object detection, show that CoSOV1Net achieves competitive performance (i.e. Fβ=0.931 on the ECSSD dataset) with state-of-the-art salient object detection models, while having low number of parameters (1.14M), low FLOPS (1.4G) and high FPS (211.2) on GPU (nvidia Geforce RTX 3090 TI) compared to the state-of-the-art in the salient object detection or lightweight salient object detection task. Thus, CoSOV1net turns out to be a lightweight salient object detection that can be adapted to mobile environments and resource-constrained devices.

In recent times, using image-based deep learning techniques has been very useful for classifying fruits. However, mango is a particular challenge because its shelf life is directly related to the degree of maturity at the time of harvest, which is determined mainly through the visual assessment of the operators when sorting them, doing it incorrectly, coupled with the blows and compressions exposed during storage and transport, trigger the deterioration in their quality, hence the importance of this operation. In this sense, the purpose of this study focuses on designing a computational model based on Deep Learning that uses a convolutional neural network (CNN) parameterized to select mangoes according to their color tone based on their maturity stage. A dataset was established from 201 labeled images, then a CNN architecture was built, obtaining a model on which 3 criteria were evaluated: the number of convolution and grouping layers, the resolution of the input images and the number of training epochs required. As a result, an input image resolution of 32 x 32 pixels was achieved, the process included a convolution layer followed by pooling that fed another layer that processed the results and ended in an output layer with 2 neurons under the One Hot Encoding representation. The model required 20 epochs to perfect its training, achieving 96.04 % accuracy in mango image selection. This study supports the potential of CNNs in quality analysis through mango color selection, even in uncertainty scenarios.

This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. Radish leaf patches of 1.5 cm diameter were photographed under halogen or LED light. The input variables for the SPAD value prediction using random forests were RGB values, HSL values, and HSV values. Additionally, the light color temperature (LCT) and illuminance (ILL) were used as the input variables. Model performance was assessed using Pearson’s correlation coefficient (COR), Nash-Sutcliffe efficiency (NSE), and root mean squared error (RMSE). SPAD value prediction resulted in high accuracy in a stable light environment; CORRGB+ILL+LCT and CORHSL+ILL+LCT were 0.929 and 0.922, respectively. Image-based SPAD value prediction was effective under halogen light with a similar color temperature at dusk; CORRGB+ILL and CORHSL+ILL were 0.895 and 0.876, respectively. The HSL value under LED could be used to predict the SPAD value with high accuracy; COR, NSE, and RMSE were 0.972, 0.944, and 2.07, respectively. The partial dependence plots of the H value indicate a change from blue to green with increasing SPAD values. Further studies are required to examine this method under outdoor conditions in spatiotemporally dynamic light environments.

Video capsule endoscopy (VCE) is increasingly used to decrease the discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared with NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on an average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can be achieved only in post-processing at present. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, namely, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to asses the simulated NBI images. Results show the good performance of the NBI image reproduction method, with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

J/ψ, a charmonium bound state made of a charm and an anti-charm quark, has been discovered in the 1970s and confirmed the quark model. Because the mass of charms quarks is significantly above the Quantum Chromodynamics (QCD) scale ΛQCD, charmonia are considered as excellent probes to test perturbative Quantum Chromodynamics (pQCD) calculations. Over the past decades, they have been studied extensively at different high energy colliders. However, their production mechanisms, which involves multiple scales, are still not very well understood. Recently, in high multiplicity p+p collisions at RHIC and at the LHC, a significant enhancement of J/ψ production yield has been observed, which suggests a strong contribution from the Multi-Parton Interaction (MPI). This is different from the traditional pQCD picture where charm quark pairs are produced from a single hard scattering between partons in p+p collisions. In this work, we will report the J/ψ normalized production yield as a function of normalized charged particle multiplicity over a board range of rapidity and event multiplicity in the J/ψ→μ+μ− channel with PHENIX Run 15 p+p data at \sqrt{s}=200 GeV. The results are compared with PYTHIA 8 simulations with the MPI option on and off. Finally, the outlooks of J/ψ in p+Au and Au+p collisions along with Color Glass Condensate (CGC) predictions and of multiplicity dependent ψ(2S)/J/ψ ratio in p+p data will be briefly discussed.

This study investigated the possible usage of beeswax oleogels instead of milk fat as a fat source in ice cream production and konjac gum as a stabilizer instead of salep. For this aim, 12 different ice cream samples were prepared using various fat and oil sources (milk fat and oleogel), stabilizers (salep and konjac gum), and emulsifiers (monoglyceride (MG), palsgaard (PG), and none emulsifier emulsifier-free (NE)). It was determined that the overrun ratio in ice cream samples containing milk fat was higher than in oleogel and the viscosity of the ice cream mix containing palsgaard and oleogel was greater than that of the other treatments (milk fat, MG, and NE). While the first dripping time of the samples with PG and konjac gum was longer compared to the samples without emulsifier (NE) and Monoglyceride (MG), the complete melting times of the samples were close to each other. Whereas the dissolution rate of the samples with salep was higher than that of konjac gum, the fat destabilization ratios of the samples with oleogel and konjac gum were lower than those of milk fat and salep. The fat destabilization ratio of samples containing PG as an emulsifier was statistically higher (p <0.01) than that of samples containing MG and NE. It was found that the unsaturated fatty acid (C18:1, C18:2, and C18:3) content of the samples containing oleogel was significantly higher (p <0.01) than that of the samples containing milk fat. However, butyric and caproic acids, which are aliphatic fatty acids, were found to be deficient in the samples to which oleogel was added instead of milk fat. As to the microscopic appearance, while water and oil particles were not homogeneously dispersed in the ice cream samples with oleogel, they were fully homogeneously dispersed in the milk fat-added ice cream samples. In addition, it was determined that panelists preferred the C samples (milk fat as fat source, salep and PG as emulsifier).Among the samples with added oleogel as oil source, they liked the J sample more (oleogel as fat source,conjak gum and none emulgator). Therefore, the C and J samples can be recommended for consumption

Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD) patients using neuropsychological testing. The study involved 71 post-COVID patients, with matched control groups: recovered COVID-19 individuals without complications (n=18) and individuals without prior COVID-19 history (n=19). A post-COVID depression group (PCD, n=25) was identified based on psychiatric diagnosis, a comparison group (noPCD, n=46) included participants with neurological COVID-19 complications, excluding clinical depression. The PCD patients showed significantly less scores in the MoCA test, decreased immediate memory recall in the Word Memory Test, decreased processing speed and higher accuracy in the Trail Making Test, and near to significant worse executive control and processing speed in the Stroop task compared to controls and the noPCD patients. The number of post-COVID symptoms negatively correlated with immediate word memory recall and processing speed among all post-COVID patients. In PCD patients, negative correlations between number of post-COVID symptoms and delayed recall, between time after recovery and immediate recall, and positive correlation between the number of acute symptoms and processing speed in the incongruent condition of the Stroop task were found. No differences between groups in Sniffin’s stick olfactory test was found. Overall, our study revealed cognitive impairment in PCD patients similar to those in major depressive disorder.

Background: Ultrasonography (US) represents the gold standard imaging method for the assessment of testicular lesions (TL). The gray-scale (GSUS) and color-Doppler (CDUS) ultrasound examination allow sonographers to investigate size, margins, echotexture and vascular features of TL, with the effort to differentiate benign from malignant lesions. Recently, the use of contrast-enhanced US (CEUS) and sonoelastography (SE) led to further improvements in the differential diagnosis of TL. Although GSUS and CDUS are often sufficient to suggest the benign or malignant nature of TL, CEUS can be decisive in the differential diagnosis of unclear findings, while SE can help to strengthen the diagnosis. The contemporary combination of GSUS, CDUS, CEUS and SE led to a new diagnostic paradigm named multiparametric US (mp-US), able to provide a more detailed characterization of TL than the single techniques alone. This narrative and pictorial review is aimed to describe the mp-US appearance of several TL. Methods: An extensive Medline search was performed identifying studies in the English language focusing on mp-US evaluation of TL. Results: a practical mp-US “identity card” and iconographic characterization of several benign and malignant TL is provided herein. Conclusions: the mp-US characterization of TL reported herein can be useful in daily clinical practice.

Historical maps constitute unique sources of retrospective geographic information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographic information contained in such data archives allows extending geospatial analysis retrospectively beyond the era of digital cartography. However, given the large data volumes of such archives and the low graphical quality of older map sheets, the processes to extract geographic information need to be automated to the highest degree possible. In order to understand the salient characteristics, data quality variation, and potential challenges in large-scale information extraction tasks, preparatory analytical steps are required to efficiently assess spatio-temporal coverage, approximate map content, and spatial accuracy of such georeferenced map archives across different cartographic scales. Such preparatory steps are often neglected or ignored in the map processing literature but represent highly critical phases that lay the foundation for any subsequent computational analysis and recognition. In this contribution we demonstrate how such preparatory analyses can be conducted using classical analytical and cartographic techniques as well as visual-analytical data mining tools originating from machine learning and data science, exemplified for the United States Geological Survey topographic map and Sanborn fire insurance map archives.

In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supply-ing sufficient training data for deep learning applications (DL). A Variational Auto Encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate two use cases. These use cases demonstrated the versatility of our innovative concept in measuring, categorizing, inferring hu-man behavior, and generating gestures for other researchers.

This paper offers a novel geometric and visual approach to the renowned Four Color Map Theorem and K5 non-planarity problem while unveiling their profound connection to the "kissing number" problem in 2D. We represent planar graphs through circles and tangents, simplifying complex structures and shedding light on these classic problems. Our proofs by contradiction, rooted in the kissing number concept, reveal that both the Four Color Map Theorem and K5 non-planarity are fundamentally linked, as they pivot around the concept of coloring. This study bridges the realms of geometry and graph theory, providing fresh insights and emphasizing the significance of the kissing number problem in various fields.