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The major objective of this research work was to evaluate various physico-chemical characteristics, such as, chemical composition, antioxidant capacity, objective color and texture profile analysis (TPA) of the wheat flour/chickpea flour (CF) blends, so that nutritious baked products could be consumed by the type-2 diabetic persons.

Wholegrain wheat flour (WGF) and white wheat flour (WWF) were substituted with CF at 0 to 40% levels. These wheat flour/CF blends were analyzed for proximate composition, the prepared dough and baked breads were tested for objective color, antioxidant capacity as trolox equivalent antioxidant capacity (TEAC), malondialdehyde (MDA) and total phenolic content (TPC) and TPA.

WGF had the highest TEAC (117.42 mM/100g) value, followed by WWF (73.98 mM/100g) and CF (60.67 mM/100g). TEAC, MDA and TPC values varied significantly among all the three flour samples.

Inclusion of whole chickpea (without dehulling) flour in such type of blends would be another interesting investigation during the future research studies.

These research findings have a great potential for the production of these baked products for human consumption on an industrial scale.

Production of breads using wheat flour and CF blends would benefits the consumers.

Production of Arabic and pan breads using wheat flour and CF blends would, therefore, combine the benefits of both the needed proteins of plant origin and the health-promoting bioactive compounds, in a most sustainable way for the consumers.

The color painting of ancient buildings has high historical and artistic value but is prone to aging due to long-term outdoor exposure. The purpose of this study is to develop a new type of sealing coating to mitigate the impact of ultraviolet (UV) light on color painting.

The new coating was subjected to a 500-h UV-aging test. Compared with the existing acrylic resin Primal AC33, the UV aging behavior of the new coating, such as color difference and gloss, was studied with aging time. The Fourier infrared spectra of the coatings were analyzed after the UV-aging test.

Compared with AC33, the antiaging performance of SF8 was substantially improved. SF8 has a lower color difference value and better light retention and hydrophobicity. The Fourier transform infrared spectroscopy results showed that the C-F bond and Si-O bonds in the resin of the optimized sealing coating protected the main chain C-C structure from degradation during the aging process; thus, the resin maintained good stability. The hindered amine light stabilizer TN292 added to the coating inhibited the antiaging process by trapping active free radicals.

To address the problem of UV aging of oil-decorated colored paintings, a new type of sealing coating with excellent antiaging properties was developed, laying the foundation for its demonstration application on the surface of ancient buildings.

This study aims to evaluate the efficiency of using some polymers at different concentrations in the consolidation of vegetable-tanned leather artifacts.

New vegetable-tanned leather samples were prepared. The consolidants used were polyacrylamide (PAM) and polymethyl methacrylate/hydroxyethyl methacrylate (MMA-HEMA). Accelerated heat aging was applied to the untreated and treated samples. Analytical techniques used were Fourier transform infrared spectroscopy (FTIR), digital microscope, scanning electron microscope (SEM), change of color and mechanical properties.

The characteristic FTIR bands showed the effect of accelerated heat aging on the molecular structure of the studied samples, but treated and aged treated samples used were better than aged untreated samples. Microscopic investigations (digital and SEM), and mechanical properties proved that 2% was the best concentration for polymers used. The change in the total color difference of the treated and aged treated samples was limited.

This study presents the important results obtained from PAM and poly(MMA-HEMA) used for the consolidation of vegetable-tanned leather artifacts. The best results of the studied polymers can be applied directly to protect historical vegetable-tanned leathers.

Titanium(IV) oxide nanoparticles (TiO₂ NP) were deposited to cotton denim fabrics using a self-crosslinking acrylate – a polymer dispersion to extend the lifetime of the products. This study aims to determine the optimum conditions to increase abrasion resistance, to provide self-cleaning properties of denim fabrics and to examine the effects of these applications on other physical properties.

The denim samples were first treated with nonionic surfactant to increase their wettability. Three different amounts of the polymer dispersion and two different pH levels were selected for the experimental design. The finishing process was applied to the fabrics with pad-dry-cure method.

The presence of the coatings and the adhesion of TiO₂ NPs to the surfaces were confirmed by scanning electron microscope and Fourier transform infrared spectroscopy analysis. It was ascertained that the most appropriate self-crosslinking acrylate amount and ambient pH level is 10 mL and “2”, respectively, for providing increased abrasion resistance (2,78%) and enhanced self-cleaning properties (363,4%) in the denim samples. The coating reduced the air permeability and softness of the denim samples. Differential scanning calorimetry and thermogravimetry analysis results showed that the treatments increased the crystallization temperatures and melting enthalpy values of the denim samples. Based on the thermal test results, it is clear that mass loss of the denim samples at 370°C decreased as the amount of self-crosslinking acrylate increased (at pH 3).

This study helped us to find out optimum amount of self-crosslinking acrylate and proper pH level for enhanced self-cleaning and abrasion strength on denim fabrics. With this finishing process, an environmentally friendly and long-life denim fabric was designed.

The purpose of this study is to determine the effect of laser treatment on disperse dye-uptake and fastness values of polyester fabrics. Furthermore, it was aimed to evaluate colors directly over the photos of fabric samples instead of color measuring with spectrophotometer which is thought to be useful in terms of online digital color assessment.

In this study, 100% polyester (150 denier) single jersey knitted fabrics (weight: 145 g/m2, course density: 15 loops/cm, wale density: 24 loops/cm) were used in the trials. The effect of laser treatments before and after dyeing on color was investigated. Laser treatments were applied to fabrics at different resolutions (20, 25 and 30 dpi) and pixel times (60, 80 and 100 µs) before dyeing. The power of the laser beam was 210 W and the wavelength was 10.6 µm. In order to determine the effect of laser treatment on polyester; FTIR analysis, SEM-EDX analysis and bursting strength tests were applied to untreated and treated fabric samples.

It was found that treatments with laser have a significant effect on disperse dye-uptake of polyester fibers, and for this reason laser-treated fabrics were dyed in darker shade. Furthermore, it was determined that the samples treated at 30 dpi started to melt and the fabric was damaged considerably, but the fabrics treated at 20 and 25 dpi were not affected at all. Another result obtained regarding the use of laser technology in polyester fabrics is that if some areas of fabrics are not treated with laser and some other areas are treated with laser at 20 dpi 60 µs and 25 dpi 60 µs, it will be possible to obtain patterns containing three different shades of the same color on the fabric.

When the literature is examined, it is seen that there are various studies on the dyeability and patterning of polyester fabrics with disperse dyes by laser technology. As it is known, today color measurement is done digitally using a spectrophotometer. However, when we look at a photograph on computer screens, the colors we see are defined by RGB (red-green-blue) values, while in the spectrophotometer they are defined by L*a*b* (L*: lightness-darkness, a*: redness-greenness, b*: yellowness-blueness) values. Especially when it is desired to produce various design products by creating patterns with laser technology, it would be more useful to show the color directly to the customer on the computer screen and to be able to speak over the same values on the color. For this reason, in this study, the color measurement of the fabric samples was not made with a spectrophotometer, instead, the RGB values obtained from the photographs of the samples were converted into L*a*b* values with MATLAB and interpreted, that is, a digital color evaluation was made on the photographs. Therefore, it is believed that this study will contribute to the literature.

Fixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.

Through an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.

The comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).

In this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

Color psychology theory reveals that complex images with very varied palettes and many different colors are likely to be considered unattractive by individuals. Notwithstanding, web content containing social signals may be more attractive via the initiation of a social connection. This research investigates a predictive model blending variables from these theoretical perspectives to determine crowdfunding success.

The research is based on data from 176,614 Kickstarter projects. A number of machine learning and artificial intelligence techniques were employed to analyze the listing images for color complexity and the presence of people, while specific language features, including socialness, were measured in the listing text. Logistic regression was applied, controlling for several additional variables and predictive model was developed.

The findings supported the color complexity and socialness effects on crowdfunding success. The model achieves notable predictive value explaining 56.4% of variance. Listing images containing fewer colors and that have more similar colors are more likely to be crowdfunded successfully. Listings that convey greater socialness have a greater likelihood of being funded.

This investigation contributes a unique understanding of the effect of features of both socialness and color complexity on the success of crowdfunding ventures. A second contribution comes from the process and methods employed in the study, which provides a clear blueprint for the processing of large-scale analysis of soft information (images and text) in order to use them as variables in the scientific testing of theory.

Fashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.

To develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.

In comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.

The study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

In this article, we highlight ways in which disability critical race theory (DisCrit) (Annamma et al., 2013), inclusive education and community-based participatory research (CBPR) can be used within professional development schools (PDS) to provide students with disabilities with more access to inclusive classrooms. At a grade 4–6 elementary school, we developed a model of a critical PDS to promote inclusive education and facilitate the transition of students of color with disabilities from self-contained to inclusive classrooms. We conducted semi-structured interviews and used action plan meetings with school administrators, teachers, professionals and students with disabilities and their parents to assess the impact of our critical PDS model. Findings suggest this model had a positive impact on administrators’ and teachers’ critical consciousness, ideological and instructional practices, students of color with disabilities’ social, academic and personal outcomes, as well as a schoolwide culture of inclusion and social justice. This study can inform tailored professional development efforts to improve educators’ inclusive practices.

We conducted semi-structured interviews and used action plan meetings with school administrators, teachers, professionals and students with disabilities and their parents to assess the impact of our critical PDS model.

The findings of this study suggest this model had a positive impact on administrators’ and teachers’ critical consciousness, ideological and instructional practices, students of color with disabilities’ social, academic and personal outcomes, as well as a schoolwide culture of inclusion and social justice.

This study can inform tailored professional development efforts aiming to improve educators’ inclusive practices.

We developed a model of a critical PDS to promote inclusive education and facilitate the transition of students of color with disabilities from self-contained to inclusive classrooms.

This paper aims to investigate associations between firm resources and reliance on entrepreneurial marketing (EM) channels among agrofood ventures. It accounts for agropreneur gender and racial/ethnic status in the context of marketing channel portfolio composition. The authors examine the established assumption that resource limitations drive EM and whether socially disadvantaged status of agropreneurs is associated with marketing strategy beyond standard resourcing measures.

Using 2015 Local Foods Marketing Practices Survey data, the authors apply linear regression to investigate differences in the use of EM channels, accounting for resources, social status and other factors.

Limited-resource ventures rely more on consumer-oriented channels that require EM practices. Socially disadvantaged entrepreneurs favor these channels, even when accounting for resources. Notably, ventures headed by men of color rely more on the most customer-centric local foods marketing channel.

Future research should investigate how social and human capital influences the use of EM.

Entrepreneurial support policy and practice for agropreneurs should be cautious about the “double-burden” folk theorem of intersectional disadvantage and review how to best direct resources on EM to groups most likely to benefit.

This paper uses a unique, restricted, nation-wide, federal data set to examine relationships between resource endowments, social status and the composition of agrofood enterprises’ marketing channel portfolios. To the best of the authors’ knowledge, it is the first to include racial- and ethnic-minority status of agropreneurs and to account for intersectionality with gender.