Similar Texture Research Articles

The effect of green tea extract and oleogels on the physico-chemical properties and oxidative stability of short-dough biscuits during storage

The goal of this research was to analyze the possibility of replacing palm fat (PF) by a novel and “healthier” alternative – high-oleic rapeseed oil oleogels in short-dough biscuits in terms of the oxidative stability of lipids. Oleogelators ethylcellulose (EC), candelilla wax (CLX) and monoacyloglycerol (MAG) were used in the research. In order to decrease the oxidation of the oleogels green tea extract (GTE) was added. Biscuits with oleogels and GTE that had similar oxidative stability were used in the control with PF. The anisidine value of the lipid fractions obtained from biscuits with GTE was lower. The biggest changes during the storage of the fatty acids profile were found for the lipid fraction of biscuits with PF. However, according to the accelerated oxidative test the best stability index was that of biscuits with PF, this test also confirmed the positive effect of GTE on oxidative stability. The recipe composition influenced the color of the products. Biscuits with GTE had lower values of total color differences after 15 weeks of storage. Products with MAG oleogel had the highest values of breaking strength. Finally, biscuits with CLX had the most similar texture to the control sample with PF.

Effect of Zr on growth kinetics, microstructure and microtexture of Nb3Sn by bronze technique

We have experimentally studied a systematic quantitative effect of Zr on the factors influencing the critical current density of Nb3Sn, such as growth kinetics, microstructure and microtexture using a diffusion couple method. The Nb3Sn phase develops in the interdiffusion zone of the Cu(Sn)/Nb diffusion couple. For the Zr-alloyed diffusion couples, a two-phase mixture of Zr-containing Nb3Sn and sub-micron β-(Nb,Zr) solid solution second-phase develops. The growth kinetics of Zr-containing Nb3Sn is higher than the Zr-free Nb3Sn phase. Enhancement of growth kinetics is attributed to an increase in the diffusion flux of the Sn atoms along the grain boundaries of Nb3Sn owing to grain refinement. The Zr-free Nb3Sn phase has a weak crystallographic texture. On the other hand, the Zr-containing Nb3Sn phase develops not only with a strong crystallographic texture, but also has similar texture component as that of the adjacent Nb-rich parent phase.

Reversible data hiding for color images based on pixel value order of overall process channel

Color image Reversible Data Hiding (RDH) is getting more and more important since the number of its applications is steadily growing. This paper proposes an efficient color image RDH scheme based on pixel value ordering (PVO), in which the channel correlation is fully utilized to improve the embedding performance. In the proposed method, the channel correlation is used in the overall process of data embedding including prediction stage, block selection and capacity allocation. In the prediction stage, since the pixel values in the co-located blocks in different channels are monotonically consistent, the large pixel values are collected preferentially by pre-sorting the intra-block pixels. This can effectively improve the embedding capacity of RDH based on PVO. In the block selection stage, the description accuracy of block complexity value is improved by exploiting the texture similarity between the channels. The smoothing the block is then preferentially used to reduce invalid shifts. To achieve low complexity and high accuracy in capacity allocation, the proportion of the expanded prediction error to the total expanded prediction error in each channel is calculated during the capacity allocation process. The experimental results show that the proposed scheme achieves significant superiority in fidelity over a series of state-of-the-art schemes. For example, the PSNR of the Lena image reaches 62.43 dB, which is a 0.16 dB gain compared to the best results in the literature with a 20,000bits embedding capacity.

Evaluation of citrus fiber as a natural alternative to sodium tripolyphosphate in marinated boneless broiler chicken breast and inside beef skirt (transversus abdominis).

This research was conducted to evaluate the effects of citrus fiber (CF) as a natural alternative to sodium tripolyphosphate (STPP) in marinated broiler boneless chicken breast and inside beef skirt on overall retention rate, shear force, and consumer sensory attributes. Five different marinade formulations were targeted to include 0.9% salt, either 0.25% or 0.50% STPP or CF and water on a finished product basis. Water and salt only were considered the negative control (CON). Chicken breasts (n = 14) and inside beef skirt (n = 14) were randomly assigned to a treatment, raw weights recorded and then placed in a vacuum tumbler. Marinated weights were recorded, individually packed, and randomly assigned to either retail display for 10-day retention rate, shear force analysis, cook loss, or consumer sensory panel. Pickup percentage, and overall retention was similar among treatments for chicken breast and inside beef skirt. Citrus fiber treatments resulted in higher cooking loss compared to the CON in chicken breast; though, CF050 resulted in similar cooking loss compared to STPP025 in inside beef skirt. No differences were found in sensory attributes for chicken breast, however, WBSF data showed CF025 was tougher than CF050, STPP050, and CON. Inside beef skirt with CF050 were least liked overall by the consumer panel. Citrus fiber included in marinades at a lower percentage rate can produce similar texture characteristics, and sensory properties compared with those marinated with STPP.

Camouflage Object Segmentation Using an Optimized Deep-Learning Approach

Camouflage objects hide information physically based on the feature matching of the texture or boundary line within the background. Texture matching and similarities between the camouflage objects and surrounding maps make differentiation difficult with generic and salient objects, thus making camouflage object detection (COD) more challenging. The existing techniques perform well. However, the challenging nature of camouflage objects demands more accuracy in detection and segmentation. To overcome this challenge, an optimized modular framework for COD tasks, named Optimize Global Refinement (OGR), is presented. This framework comprises a parallelism approach in feature extraction for the enhancement of learned parameters and globally refined feature maps for the abstraction of all intuitive feature sets at each extraction block’s outcome. Additionally, an optimized local best feature node-based rule is proposed to reduce the complexity of the proposed model. In light of the baseline experiments, OGR was applied and evaluated on a benchmark. The publicly available datasets were outperformed by achieving state-of-the-art structural similarity of 94%, 93%, and 96% for the Kvasir-SEG, COD10K, and Camouflaged Object (CAMO) datasets, respectively. The OGR is generalized and can be integrated into real-time applications for future development.

CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.

To quantitatively compare DLIR and ASiR-V with realistic anatomical images. CT scans of an anthropomorphic phantom were acquired using three routine protocols (brain, chest, and abdomen) at four dose levels, with images reconstructed at five levels of ASiR-V and three levels of DLIR. Noise power spectrum (NPS) was estimated using a difference image by subtracting two matching images from repeated scans. Using the max-dose FBP reconstruction as the ground truth, the structure similarity index (SSIM) and gradient magnitude (GM) of difference images were evaluated. Image noise magnitude (σ), frequency location of the NPS peak (fpeak), mean SSIM (MSSIM), and mean GM (MGM) were used as quantitative metrics to compare image quality, for each anatomical region, protocol, algorithm, dose level, and slice thickness. Image noise had a strong (R2 > 0.99) power law relationship with dose for all algorithms. For the abdomen and chest, fpeak shifted from 0.3 (FBP) down to 0.15 mm-1 (ASiR-V 100%) with increasing ASiR-V strength but remained 0.3 mm-1 for all DLIR levels. fpeak shifted down for the brain protocol with increasing DLIR levels. Three levels of DLIR produced similar image noise levels as ASiR-V 40%, 80%, and 100%, respectively. DLIR had lower MSSIM but higher MGM than ASiR-V while matching imaging noise. Compared to ASiR-V, DLIR presents trade-offs between functionality and fidelity: it has a noise texture closer to FBP and more edge enhancement, but reduced structure similarity. These trade-offs and unique protocol-dependent behaviors of DLIR should be considered during clinical implementation and deployment. • DLIR reconstructed images demonstrate closer noise texture and lower structure similarity to FBP while producing equivalent noise levels comparable to ASiR-V. • DLIR has additional edge enhancement as compared to ASiR-V. • DLIR has unique protocol-dependent behaviors that should be considered for clinical implementation.

Foaming properties and sensory acceptance of plant-based beverages as alternatives in the preparation of cappuccino style beverages

The physicochemical composition and foaming properties of five commercial beverages used by baristas for the production of cappuccino style beverages were investigated at two temperatures (4 and 65 °C). The samples included were two cow milks, a soy beverage, a coconut/soy beverage, and an oat beverage. In addition, the samples were used for the sensory analysis of their foams in freddo cappuccino and cappuccino style beverages. At 4 °C, the cow milk samples exhibited better foaming properties compared to the plant-based ones, due to their higher protein content and performance of proteins at the air/water interface and were also preferred by the panelists. The soy beverage presented better foaming properties compared to the coconut/soy one and to the oat one. In the case of 65 °C, the foaming properties were in general similar for all samples. Concerning the sensory evaluation, all samples presented similar color, gloss, and texture, with the exception of oat beverage, while the soy beverage was inferior in taste and overall acceptance to all the other four samples. The studied plant-based beverages could not replace milk in the production of freddo cappuccino beverages, but they could in the case of cappuccino beverages, considering that they provide proper taste.

K původu analcimu v mezozoických a kenozoických vulkanitech České republiky a jeho úloze v názvosloví hornin

Magmatic origin of analcime has been discussed for decades (e. g., Karlsson and Clayton 1991, 1993; Pearce 1993). Despite the fact that analcime has been re-classified as zeolite and its secondary (post-magmatic) origin is globally accepted (e. g., Roux and Hamilton 1976, Giannetti and Masi 1989, Wilkinson and Hensel 1994), it is still commonly used in classification of alkaline rocks in the Czech Republic. In these rocks, analcime can be mostly found in the pseudomorphs after leucite (Fig. 1a; see Rapprich 2003), or as a homogeneous anhedral filling in the groundmass (Fig. 1b). In the second case, the boundaries of individual analcime crystals cannot be identified due to the isotropic optical properties of analcime. Therefore, origin and growth of analcime is difficult to reconstruct. For this contribution, we have studied samples of Mesozoic and Cenozoic alkaline rocks from three localities across the Czech Republic, where partly analcimized glass in the groundmass was preserved, with an aim to better understand the origin of analcime in alkaline rocks. Mesozoic augitite from the southwestern slope of the Petřkovice Mt. near Nový Jičín (sample TG05) displays small isometric colourless, optically isotropic domains (analcime) enclosed in originally glassy groundmass (Fig. 1c–f ). This texture suggests the analcime is replacing original glass in the groundmass, enclosing also microcrysts of the stable minerals in groundmass. Very similar texture was observed also in Oligocene augitite from Mětikalov in Doupovské hory Mts. (sample DR338, Fig. 2). In this rock, the analcime domains either mantle larger phenocrysts, or are distributed within the groundmass. The growth of the analcime domains on the edges of larger phenocrysts resembles growth of spherulites, which represent products of silica-rich glass recrystallization (e. g., Breitkreuz 2013). To further investigate this possibility, additional samples were collected from Kamenický vrch near Zákupy (Fig. 3). In individual samples, number and size of analcime domains vary, which suggests, that these domains represent various stages of a continuous growth. The X-ray elemental mapping of Al and Na (Fig. 4) then shows, that the analcime domains are represented by single grains of cubic analcime. As a result, we may conclude that analcime in groundmass of Mesozoic and Cenozoic alkaline rocks originates from devitrification of original glass. These rocks hence should not be classified as “analcimites” or “analcimic …” but rather as “analcimized …” according to Le Maitre et al. (2005).

In Situ Trace Elements in Quartz and K-Feldspar from Felsic Igneous Rocks: A Titanium-in-K-Feldspar Geothermometer for Natural Magmatic Systems

Abstract Scanning electron microscope, cathodoluminescence (CL) imaging and laser ablation–inductively coupled plasma–mass spectrometry analyses were conducted on coexisting pairs of quartz and K-feldspar from 14 samples of various types of felsic igneous rocks. Difference of the concentration of trace elements in quartz and K-feldspar among plutonic, pegmatitic and volcanic rocks is closely related to the rock-forming process and P–T condition. In general, a decreasing Ti concentration and increasing Al, Li, and Ge concentrations from plutonic to pegmatitic quartz suggest a higher degree of magmatic fractionation and lower crystallization temperature. Sensitive elements to magma differentiation in K-feldspar such as Ba, Sr, and LREE show a decreasing trend from various granitic rocks to pegmatite, while Rb, Cs, Li, Ge, and P exhibit increasing trends. The K-feldspar from various types of felsic igneous rocks typically shows similar CL textures but relatively higher luminescence intensity compared with coexisting quartz. Both quartz and K-feldspar phenocrysts in the volcanic rhyolite have a bright rim and a dark core in CL images, corresponding to bimodal Ti concentrations. Among all samples, Ti concentrations in both quartz and coexisting K-feldspar positively correlate with their CL intensities, suggesting the activation of Ti-impurity in these minerals leads to increased CL intensity. Meanwhile, there is a good positive correlation between Ti concentrations in quartz and those in K-feldspar with an R2 value of 0.86. It is considered that Ti concentrations in the both minerals are mainly temperature dependent at relatively constant pressure on basis of a fair aTiO2 restriction. Here, for the first time, we calculated a titanium-in-K-feldspar (TitaniKfs) thermometer in the form of log (XTi, kfs/aTiO2) = −(3430 ± 268)/T(K) + (5.081 ± 0.298) for natural felsic magma systems. The Ti contents of K-feldspar (in ppm by weight) increase exponentially with reciprocal T at temperatures ranging from 500°C to 800°C, at 200–300 MPa. An application of this thermometer to granitic rocks from Xinjiang and Inner Mongolia in China fits well with other geothermometers. In addition, the present TitaniKfs thermometer is expected to be particularly useful in determining the temperature condition of K-feldspar-bearing extraterrestrial materials such as lunar rocks.

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation.

Medical image segmentation is fundamental and essential for the analysis of medical images. Although prevalent success has been achieved by convolutional neural networks (CNN), challenges are encountered in the domain of medical image analysis by two aspects: 1) lack of discriminative features to handle similar textures of distinct structures and 2) lack of selective features for potential blurred boundaries in medical images. In this paper, we extend the concept of contrastive learning (CL) to the segmentation task to learn more discriminative representation. Specifically, we propose a novel patch-dragsaw contrastive regularization (PDCR) to perform patch-level tugging and repulsing. In addition, a new structure, namely uncertainty-aware feature re- weighting block (UAFR), is designed to address the potential high uncertainty regions in the feature maps and serves as a better feature re- weighting. Our proposed method achieves state-of-the-art results across 8 public datasets from 6 domains. Besides, the method also demonstrates robustness in the limited-data scenario. The code is publicly available at https://github.com/lzh19961031/PDCR_UAFR-MIShttps://github.com/lzh19961031/PDCR_UAFR-MIS.

Assessing the Efficacy of Whole-Body Titanium Dental Implant Surface Modifications in Inducing Adhesion, Proliferation, and Osteogenesis in Human Adipose Tissue Stem Cells.

Although the influence of titanium implants' micro-surface properties on titanium discs has been extensively investigated, the research has not taken into consideration their whole-body effect, which may be considered possible using a combinatorial approach. Five titanium dental implants with a similar moderate roughness and different surface textures were thoroughly characterized. The cell adhesion and proliferation were assessed after adipose-tissue-derived stem cells (ADSCs) were seeded on whole-body implants. The implants' inductive properties were assessed by evaluating the osteoblastic gene expression. The surface micro-topography was analyzed, showing that hydroxyapatite (HA)-blasted and bland acid etching implants had the highest roughness and a lower number of surface particles. Cell adhesion was observed after 24 h on all the implants, with the highest score registered for the HA-blasted and bland acid etching implants. Cell proliferation was observed only on the laser-treated and double-acid-etched surfaces. The ADSCs expressed collagen type I, osteonectin, and alkaline phosphatase on all the implant surfaces, with high levels on the HA-treated surfaces, which also triggered osteocalcin expression on day seven. The findings of this study show that the morphology and treatment of whole titanium dental implants, primarily HA-treated and bland acid etching implants, impact the adherence and activity of ADSCs in osteogenic differentiation in the absence of specific osteo-inductive signals.

Controlled feeding experiments with juvenile alligators reveal microscopic dental wear texture patterns associated with hard-object feeding

Dental wear analyses are classically applied to mammals because they have evolved heterodont dentitions for sophisticated mastication. Recently, several studies have shown a correlation between pre-assigned and analytically inferred diet preferences in extant reptiles through dental microwear texture analysis (DMTA), a method using quantitative assessment of microscopic wear marks to reconstruct the diet material properties. The first tentative applications of DMTA to extinct reptiles have followed. However, for large and small mammals, microwear analyses have undergone a long time of ground-truthing through direct feeding observations, stomach content analyses, and feeding experiments. Such data are currently lacking for reptiles, but are necessary to further extend DMTA, especially to Archosauria, as the application to dinosaurs could be of great interest to the scientific community. We herein present data from a pilot feeding experiment with five juvenile American alligators (Alligator mississippiensis). Each individual received a diet of assumed different hardness for ~4 months: crocodylian pellets (control), sardines, quails, rats, or crawfish. All individuals initially received the same pellet diet, and we found them to show similar dental microwear texture patterns before they were switched to their designated experimental diet. From the first feeding bout on, dental microwear textures differed across the diets. The crawfish-feeder showed consistently higher surface complexity, followed by the rat-feeder. Quail- and fish-feeding resulted in similar wear signatures, with low complexity. Fast tooth replacement and selective tooth use likely affected microwear formation, but we were able to detect a general hard (crawfish and rat) versus soft (quail and fish) DMTA signature. Such patterns can support the identification of hard-object feeding in the fossil record.

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning.

Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.

Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.

Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity. The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity.

Non-uniform high-temperature oxidation behavior of twin wire and arc additive manufactured Ni3Al-based alloy

Ni3Al-based alloy fabricated by twin wire and arc additive manufacturing exhibited periodic remelting bands (RB) with unique microstructural characteristics. In this work, the deposited Ni3Al-based alloy was oxidized at 1100 °C for different times to investigate the high-temperature oxidation difference caused by RB. The differences in the microstructure, grain orientation, oxidation behavior, and oxidation mechanism between RB and non-RB were systematically studied. The results showed that these RB, epitaxially grown on the non-RB during each deposition layer, displayed similar Goss textures but a higher proportion of γ + γ′ dual-phase microstructure compared to non-RB. The oxidation of the deposited Ni3Al-based alloy obeyed parabolic growth kinetics at 1100 °C for 10 h and the oxidation scales were consisted of external NiO layer, intermediate NiAl2O4 layer with dispersed Al2O3 and TiO2 particles and internal Al2O3 layer. The outward diffusion of Al and Ti elements during the oxidation process led to the decomposition of γ′ precipitations and the formation of γ′-reduced layer beneath the Al2O3 layer. The cross-section microstructure of oxidation scales indicated that the RB displayed thicker scales and poorer oxidation resistance than non-RB. The oxidation rate of the deposited Ni3Al-based alloy was controlled by the outward transport rate of the total cations (Ni2+, Al3+, and Ti4+) through the Al2O3 layer. Initial oxidation and diffusion interface analysis demonstrated that the γ + γ′ dual-phase had a higher outward transport rate of the cations, which was responsible for the thicker oxide scales on the RB.

Facial optical flow estimation via neural non-rigid registration

Optical flow estimation in human facial video, which provides 2D correspondences between adjacent frames, is a fundamental pre-processing step for many applications, like facial expression capture and recognition. However, it is quite challenging as human facial images contain large areas of similar textures, rich expressions, and large rotations. These characteristics also result in the scarcity of large, annotated real-world datasets. We propose a robust and accurate method to learn facial optical flow in a self-supervised manner. Specifically, we utilize various shape priors, including face depth, landmarks, and parsing, to guide the self-supervised learning task via a differentiable nonrigid registration framework. Extensive experiments demonstrate that our method achieves remarkable improvements for facial optical flow estimation in the presence of significant expressions and large rotations.

Genetic model of the El Laco magnetite-apatite deposits by extrusion of iron-rich melt

Magnetite-apatite deposits are important sources of iron and other metals. A prominent example are the magnetite lavas at the El Laco volcano, Northern Chile. Their formation processes remain debated. Here, we test the genetic hypothesis that an Fe-rich melt separated from silicate magma and ascended along collapse-related fractures. We complement recent analyses with thermodynamic modelling to corroborate Fe-Si liquid immiscibility evident in melt inclusions at El Laco and present viscometry of Fe- and Si-rich melts to assess the time and length scales of immiscible liquid separation. Using a rock deformation model, we demonstrate that volcano collapse can form failure zones extending towards the edifice flanks along which the ore liquid ascends towards extrusion driven by vapour exsolution despite its high density. Our results support the proposed magmatic genesis for the El Laco deposits. Geochemical and textural similarities indicate magnetite-apatite deposits elsewhere form by similar processes.

Investigating a microbial approach to water conservation: Effects of Bacillus subtilis and Surfactin on evaporation dynamics in loam and sandy loam soils

Semi-arid regions faced with increasingly scarce freshwater resources must manage competing demands in the food-energy-water nexus. A possible solution modifies soil hydrologic properties using biosurfactants to reduce evaporation and improve water retention. In this study, two different soil textures representative of agricultural soils in Kansas were treated with a direct application of the biosurfactant, Surfactin, and an indirect application via inoculation of Bacillus subtilis. Evaporation rates of the wetted soils were measured when exposed to artificial sunlight (1000 W/m2) and compared to non-treated control soils. Experimental results indicate that both treatments alter soil moisture dynamics by increasing evaporation rates by when soil moisture is plentiful (i.e., constant rate period) and decreasing evaporation rates by when moisture is scarce (i.e., slower rate period). Furthermore, both treatments significantly reduced the soil moisture content at which the soil transitioned from constant rate to slower rate evaporation. Out of the two treatments, inoculation with B. subtilis generally produced greater changes in evaporation dynamics; for example, the treatment with B. subtilis in sandy loam soils increased constant rate periods of evaporation by 43% and decreased slower rate evaporation by 49%. In comparing the two soil textures, the sandy loam soil exhibited a larger treatment effect than the loam soil. To evaluate the potential significance of the treatment effects, a System Dynamics Model operationalized the evaporation rate results and simulated soil moisture dynamics under typical daily precipitation conditions. The results from this model indicate both treatment methods significantly altered soil moisture dynamics in the sandy loam soils and increased the probability of the soil exhibiting constant rate evaporation relative to the control soils. Overall, these findings suggest that the decrease in soil moisture threshold observed in the experimental setting could increase soil moisture availability by prolonging the constant rate stage of evaporation. As inoculation with B. subtilis in the sandy loam soil had the most pronounced effects in both the experimental and simulated contexts, future work should focus on testing this treatment in field trials with similar soil textures.

Improving textural properties of gluten‐free veggie sausage with egg white proteins

AbstractGluten and gums are usually used in veggie sausage for textural purposes; however, they are not label‐friendly for consumers with gluten sensitivity or consumers looking for clean‐label foods. Therefore, two commercial egg white proteins (P110 and M200) were studied at different levels (0%–10%) to substitute gluten or xanthan gum. The veggie sausages were subjected to color, texture, length, and weight loss evaluations. M200 significantly improved the texture of sausages and exhibited the most similar texture profile as a commercial meat sausage. The overall texture‐enhanced capability of gel materials was ranked in the order of 5% M200 > 5% P110 ≥ 3% Gluten ≥ 1.3% Xanthan Gum. Egg white proteins did not significantly affect the color, length, and weight loss of sausages. Raman spectra revealed that M200 gel had a higher α‐helix structure than P110 gel, which indicated that M200 formed an ordered gel by reforming tyrosine at Raman shift 833 and 860 cm−1 inside while P110 formed a disordered gel by exposing tyrosine. This study demonstrated that M200 would substitute both the gluten and xanthan gum for improving the texture of veggie sausages. The findings may also be applied to other meat analogs targeting gluten‐free and label‐friendly purposes.

Microstructure and texture evolution of nonoriented silicon steel during the punching process

The iron core of a motor is mainly manufactured from rolled nonoriented silicon steel using a punching process that leads to deformation and texture evolution at the cutting edge. According to this process, circular samples of nonoriented silicon steel were prepared by punching using blunt punch tools. In this work, two positions along the rolling and transverse directions at the cutting edge were analyzed. The main mechanisms of deformation for both positions are dislocation slip and formation of shear bands. These two mechanisms lead to similar texture evolutions for both positions. The dislocation slip leads to the formation of the {221} 〈uvw〉 component in the unbending area (200 µm away from the cutting edge) and intermediate continuum-bent area. Additionally, the evolution of the texture from the {111} γ fiber to the {110} fiber was observed at the extremity of the cutting edge with the formation of shear bands.