Search Results (2,974)

The main quality and safety issue in processing salted jellyfish for food is excessive aluminum. After dealumination, problems such as a low quality and short shelf life may occur. A method for reprocessing dealuminated jellyfish that can maintain quality and yield bactericidal effects is necessary. Alcohol provides astringent protein and bactericidal effects, and ethanol is safe and nontoxic. It can be added as needed in food production. The optimal processing conditions were determined by studying the mass transfer and quality changes in dealuminated jellyfish at different ethanol concentrations. The results revealed that both the ethanol concentration and pickling time significantly affected the mass transfer changes of substances in the pickling process for dealuminated jellyfish. The total mass of dealuminated jellyfish decreased with increasing ethanol concentration, whereas the ethanol content increased. The changes were more obvious at the early stages of pickling, and then tended to flatten out. The diffusion coefficient was the highest for the 45% pickling solution, and the texture characteristics were similar to those of edible jellyfish, thus rendering this solution more suitable for dealuminated jellyfish ethanol soaking. In addition, the mass transfer model for various substances in the pickling process for dealuminated jellyfish exhibited a suitable linear correlation with time, which can be effectively applied. Full article

The present study investigates post-emplacement zeolitization processes in two widespread pyroclastic units from Central Italy: the Cimina Ignimbrite and the Sorano Ignimbrite. A total of seventy-five samples from ten outcrops were analyzed using optical and environmental scanning electron microscopy, electron probe microanalysis, X-ray powder diffraction, and inductively coupled plasma optical emission spectrometry. Analytical results allow the mineral distribution, zeolite composition, textural relationships, and geochemical features of the zeolite-bearing rocks to be defined. In the Cimina Ignimbrite, zeolitization affects the glassy portion of the groundmass, where the glass transforms into a medium- to high-temperature mineral assemblage dominated by clinoptilolite-Ca and cristobalite. This transformation is restricted to the innermost parts of the deposit. In contrast, zeolitization in the Sorano Ignimbrite involves the entire glassy fraction of pumice clasts, with extensive alteration of the glass into medium- to low-temperature zeolites such as chabazite-K and phillipsite-K. The results reveal a significant correlation between the chemical composition of the juvenile material and that of the newly formed zeolites in both types of ignimbrites, particularly in the Sorano Ignimbrite. Zeolitization in Central Italy ignimbrites likely occurs in a natural autoclave-like setting, where hot fluids remain trapped in the deposit for a long time. Full article

Recently, there has been increased interest in NASICON-type electrodes for sodium-ion batteries due to their unique combination of intercalation properties, low cost, and safety. However, their commercialization is hindered by the low electrical conductivity. One strategy to overcome this issue is to integrate NASICON materials with carbon additives. This study shows that carbon additives improve the sodium storage performance of a NASICON-type electrode in various ways, depending on the additives’ functional groups, texture, and conductivity properties. The proof-of-concept is based on a multi-electron phospho-sulphate electrode, NaFeVPO₄(SO₄)₂ (NFVPS) mixed with carbon black (C) and reduced graphene oxide (rGO). Carbon-coated samples are obtained via a simple ball milling procedure followed by thermal treatment in an argon flow. Sodium storage in the composites occurs through capacitive and Faradaic reactions. The Faradaic reaction is facilitated at the carbon black composite, while the capacitive reaction dominates for the rGO composite. NFVPS operates through two-electron reactions at 20 °C, while the increased temperatures favor the three-electron reaction. The rGO composite outperforms the carbon black composite in terms of cycling stability and rate capability at 20 and 40 °C. The role of the rGO and carbon black in electrochemical performance is discussed based on the different reactivity of hydroxyl/epoxide and carbonyl functional groups with the electrolyte salt, NaPF₆, and the solvent, polypropylene carbonate. Full article

In this work, ethyl cellulose nanoparticles loaded with rosemary extract (RCL-NPs) were synthesized and utilized to reinforce high-density polyethylene (HDPE) packaging bags as a nanotechnological alternative for rabbit meat preservation. The synthesized RCL-NPs were characterized by DLS and for their stability. The analyzed variables of rabbit meat packaged samples included drained liquid, weight loss, color, pH, texture, and hardness. The total phenolic content (TPC) and antioxidant capacity of rosemary extract were also investigated. The results demonstrated that RCL-NPs were 117.30 nm in size with a negative surface charge (−24.59 mV) and low PDI (0.12). According to the Higuchi model, the release rate of RCL-NPs was sustained from 0 to 24 h. The encapsulation efficiency of the implemented synthesis route was 99.97%. The TPC of rosemary extract was 566.13 ± 1.72 mg GAE/L, whereas their antioxidant activity utilizing the DPPH and FRAP assays was 27.86 ± 0.32 mM Trolox/L and 0.31 mM Trolox/L, respectively. Contrary to control samples, rabbit meat samples conserved in HDPE packaging bags reinforced with RCL-NPs prevent drained liquid and weight loss, while preserving *L (60 ± 2.5–66.10 ± 2.0) and *b (10.67 ± 2.28–11.62 ± 2.39), pH (5.22 ± 0.05–5.80 ± 0.03), and texture (10.37 ± 0.82–0.70 ± 0.50). In the same regard, the developed material conserved the hardness of rabbit meat samples, exhibiting values that ranged from 27.79 ± 7.23 to 27.60 ± 3.05 N during the evaluated period (0–13 days). The retrieved data demonstrate the efficacy of RCL in preserving the quality of rabbit meat when integrated with additional food packaging materials. Full article

Accurately estimating aboveground carbon storage (AGC) of urban vegetation remains a major challenge, due to the heterogeneity and vertical complexity of urban environments, where traditional forest-based remote sensing models often perform poorly. This study integrates multimodal remote sensing data and incorporates two three-dimensional structural features—mean tree height ($H_{mean}$) and canopy cover ratio (CCR)—in addition to conventional spectral and textural variables. To minimize redundancy, the Boruta algorithm was applied for feature selection, and four machine learning models (SVR, RF, XGBoost, and CatBoost) were evaluated. Results demonstrate that under multimodal data fusion, three-dimensional features emerge as the dominant predictors, with XGBoost using Boruta-selected variables achieving the highest accuracy (R² = 0.701, RMSE = 0.894 tC/400 m²). Spatial mapping of AGC revealed a “high-aggregation, low-dispersion” pattern, with the model performing best in large, continuous green spaces, while accuracy declined in fragmented or small-scale vegetation patches. Overall, this study highlights the potential of machine learning with multi-source variable inputs for fine-scale urban AGC estimation, emphasizes the importance of three-dimensional vegetation indicators, and provides practical insights for urban carbon assessment and green infrastructure planning. Full article

Monocular fisheye camera distance estimation is a crucial visual perception task for autonomous driving. Due to the practical challenges of acquiring precise depth annotations, existing self-supervised methods usually consist of a monocular distance model and an ego-motion predictor with the goal of minimizing a reconstruction matching loss. However, they suffer from inaccurate distance estimation in low-texture regions, especially road surfaces. In this paper, we introduce a weakly supervised learning strategy that incorporates semantic segmentation, instance segmentation, and optical flow as additional sources of supervision. In addition to the self-supervised reconstruction loss, we introduce a road surface flatness loss, an instance smoothness loss, and an optical flow loss to enhance the accuracy of distance estimation. We evaluate the proposed method on the WoodScape and SynWoodScape datasets, and it outperforms the self-supervised monocular baseline, FisheyeDistanceNet. Full article

Growing global concerns over pesticide residues and microbial contamination in plant-derived foods have intensified the demand for sustainable decontamination solutions. Conventional physical, chemical, and biological methods are hampered by inherent limitations, including operational inefficiency, secondary pollution risks, and nutritional degradation. Atmospheric cold plasma (ACP) has emerged as a promising non-thermal technology to address these challenges at near-ambient temperatures, leveraging the generation of highly reactive oxygen/nitrogen species (RONS), ultraviolet radiation, and ozone. This review comprehensively examines fundamental ACP mechanisms, discharge configurations, and their applications within plant-based food safety systems. It critically evaluates recent advancements in inactivating microorganisms, degrading mycotoxins and pesticides, and modulating enzymatic activity, while also exploring emerging applications in bioactive compound extraction, drying enhancement, and seed germination promotion. Crucially, the impact of ACP on the quality attributes of plant-based foods is summarized. Treatment parameters can alter physicochemical properties covering color, texture, flavor, acidity, and water activity as well as nutritional constituents such as antioxidants, proteins, lipids, and carbohydrate content. As an environmentally friendly, low-energy-consumption technology with high reactivity, ACP offers transformative potential for enhancing food safety, preserving quality, and fostering sustainable agricultural systems. Full article

This study utilizes high-resolution X-ray computed tomography (CT) to evaluate the reservoir characterization in heterogenous carbonate rocks. These rocks show a diagenetic alteration that influences the reservoir quality in the Cretaceous Qamchuqa–Bekhme formations in outcrop and subsurface sections (Gali-Bekhal, Bekhme, and Taq Taq oilfields, NE Iraq). The scanning of fifty-one directional line analyses was conducted on three facies: marine, early diagenetic (non-hydrothermal), and late diagenetic (hydrothermal dolomitization, or HTD). The facies were analyzed from thousands of micro-spot analyses (up to 5250) and computed tomographic numbers (CTNs) across vertical, horizontal, and inclined directions. The surface (outcrop) marine facies exhibited CTNs ranging from 2578 to 2982 Hounsfield Units (HUs) (Av. 2740 HU), with very low average porosity (1.20%) and permeability (0.14 mD) values, while subsurface marine facies showed lower CTNs (1446–2556 HU, Av. 2360 HU) and higher porosity (Av. 8.40%) and permeability (Av. 1.02 mD) compared to the surface samples. Subsurface marine facies revealed higher porosity, lower density, and considerably enhanced conditions for hydrocarbon storage. The CT measurements and petrophysical properties in early diagenesis highlight a considerable porous system in the surface compared to the one in subsurface settings, significantly controlling the quality of the reservoir storage. The late diagenetic scanning values coincide with a saddle dolomite formation formed under high temperature conditions and intensive rock–fluid interactions. These dolomites are related to a hot fluid and are associated with intensive fracturing, vuggy porosities, and zebra-like textures. These textures are more pronounced in the surface than the subsurface settings. A surface evaluation showed a wide CTN range, accompanied by an average porosity of up to 15.47% and permeability of 301.27 mD, while subsurface facies exhibited a significant depletion in the CTN (<500 HU), with an average porosity of about 14.05% and permeability of 91.56 mD. The petrophysical characteristics of the reservoir associated with late-HT dolomitization (subsurface setting) show two populations. The first one exhibited CTN values between 1931 and 2586 HU (Av. 2341 HU), with porosity ranging from 3.10 to 18.43% (Av. 8.84%) and permeability from 0.08 to 2.39 mD (Av. 0.31 mD). The second one recorded a considerable range of CTNs from 457 to 2446 HU (Av. 1823 HU), with porosity from 6.38 to 52.92% (Av. 20.97%) and permeability from 0.16 to 5462.62 mD (Av. 223.11 mD). High temperatures significantly altered the carbonate rock’s properties, with partial/complete occlusion of the porous vuggy and fractured networks, enhancing or reducing the reservoir quality and its storage. In summary, the variations in the CTN across both surface and subsurface facies provide new insight into reservoir heterogeneity and characterization, which is a fundamental factor for understanding the potential of hydrocarbon storage within various geological settings. Full article

This study aimed to identify and evaluate yeasts and lactic acid bacteria (LAB) isolated from Mexican cocoa mucilage (Theobroma cacao) and coffee pulp (Coffea arabica) for their potential use as sourdough starter co-cultures to improve bread quality. Functional screens included assessments of amylolytic, proteolytic, and phytase activities, CO₂ production, acidification capacity, and exopolysaccharide (EPS) synthesis. Saccharomyces cerevisiae YCTA13 exhibited the highest fermentative performance, surpassing commercial baker’s yeast by 52.24%. Leuconostoc mesenteroides LABCTA3 showed a high acidification capacity and EPS production, while Lactiplantibacillus plantarum 20B3HB had the highest phytase activity. Six yeast–LAB combinations were formulated as mixed starter co-cultures and evaluated in sourdough breadmaking. The B3Y14 co-culture (LABCTA3 + YCTA14) significantly improved the bread volume and height by 35.61% and 17.18%, respectively, compared to the commercial sourdough starter, and reduced crumb firmness by 59.66%. Image analysis of the bread crumb revealed that B3Y14 enhanced the crumb structure, resulting in greater alveolar uniformity and a balanced gas cell geometry. Specifically, B3Y14 showed low alveolar regularity (1.16 ± 0.03) and circularity (0.40 ± 0.01), indicating a fine and homogeneous crumb structure. These findings highlight the synergistic potential of selected allochthonous yeast and LAB strains in optimizing sourdough performance, positively impacting bread texture, structure, and quality. Full article

Despite the continued growth of the gluten-free food market, there is a dearth of sensory and consumer knowledge on commercial products. The existing research is mostly limited to hedonic measurements and ingredient effects instead of analytical methods for a better understanding of product characteristics of gluten-free crackers specifically. In this work, a semi-trained consumer panel used projective mapping to choose objectively different plain/original crackers from a pool of sixteen commercial gluten-free cracker varieties. The cracker samples represented a widespread sensory space originating from different key ingredients such as brown rice, white rice, flaxseed, cassava flour, nut flour blend, millet blend, and tapioca/potato starch blend. Based on projective mapping results, the crackers that mostly represented the sensory space were selected for characterization by a modified flash profiling method. The consumer panel developed 74 descriptors: 30 aromas, 28 flavors, 15 texture terms, and a mouthfeel attribute. The samples were monadically rated for intensity on a 4-point scale (0 = none, 1 = low, 2 = medium, and 3 = high). Rice, toasted, salt, grain, burnt, flaxseed, bitter, earthy, nutty, seeds, and grass were the prevalent aromas and flavors. Others were specific to cracker type. Some of these attributes can be traced back to the ingredients list. Results suggest that ingredients used in small portions are defining the flavor properties over the major grains/flour blends. All samples had some degree of crunchiness, crispness, and pasty mouthfeel; rice crackers were particularly firm, hard, and chewy; brown rice crackers were gritty; crackers with tuber starches/flours were more airy, soft, smooth, and flaky. Overall, the samples shared more aroma and flavor notes than texture attributes. In comparison to trained panel results, consumers generated a greater number of terms and were successful in finding subtle differences primarily in texture but had many overlapped flavors. The developed consumer terminology will facilitate the gluten-free industry to tailor communication that better resonates with consumer experiences, needs, and product values. Full article

To overcome the limitations of low texture accuracy in traditional sculpture color restoration methods, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) model incorporating a dual attention mechanism (spatial and channel attention) and a channel converter to enhance restoration quality. First, the theoretical foundations of the DCGAN algorithm and its key components (generator, discriminator, etc.) are systematically introduced. Subsequently, a DCGAN-based application model for sculpture color restoration is developed. The generator employs a U-Net architecture integrated with a dual attention module and a channel converter, enhancing both local feature representation and global information capture. Meanwhile, the discriminator utilizes an image region segmentation approach to optimize the assessment of consistency between restored and original regions. The loss function follows a joint optimization strategy, combining perceptual loss, adversarial loss, and structural similarity index (SSIM) loss, ensuring superior restoration performance. In the experiments, mean square error (MSE), peak signal-to-noise ratio (PSNR), and SSIM were used as evaluation metrics, and sculpture color restoration tests were conducted on an Intel Xeon workstation. The performance of the proposed model was compared against the traditional DCGAN and other restoration models. The experimental results demonstrate that the improved DCGAN outperforms traditional methods across all evaluation metrics, and compared to traditional DCGAN, the proposed model achieves significantly higher SSIM and PSNR, while reducing MSE. Compared to other restoration models, PSNR and SSIM are further enhanced, MSE is reduced, and the visual consistency between the restored and undamaged areas is significantly improved, with richer texture details. Full article

High-resolution (HR) remote sensing images contain rich, sensitive information regarding the distribution of geospatial objects and natural resources. With the widespread application of HR remote sensing images, there is an urgent need to protect the data security of HR remote sensing images during transmission and sharing. Existing encryption approaches typically employ a global encryption strategy, overlooking the varying texture complexity across different sub-regions in HR remote sensing images. This oversight results in low efficiency and flexibility for encrypting large-scale remote sensing image data. To address these limitations, this paper presents a texture-adaptive hierarchical encryption method that combines region-specific security levels. The method first decomposes remote sensing images into grid-based sub-blocks and classifies them into three texture complexity types (i.e., simple, medium, and complex) through gradient and frequency metrics. Then, chaotic systems of different dimensions are adaptively adopted to encrypt the sub-blocks according to their texture complexity. A more complex chaotic system encrypts a sub-block with a more complex texture to ensure security while reducing computational complexity. The experimental results on publicly available high-resolution remote sensing datasets demonstrate that the proposed method achieves adequate information concealment while maintaining an optimal balance between encryption security and computational efficiency. The proposed method is more competitive in encrypting large-scale HR remote sensing data compared to conventional approaches, and it shows significant potential for the secure sharing and processing of HR remote sensing images in the big data era. Full article

Plant disease segmentation has achieved significant progress with the help of artificial intelligence. However, deploying high-accuracy segmentation models in resource-limited settings faces three key challenges, as follows: (A) Traditional dense attention mechanisms incur quadratic computational complexity growth (O($n^{2}d$)), rendering them ill-suited for low-power hardware. (B) Naturally sparse spatial distributions and large-scale variations in the lesions on leaves necessitate models that concurrently capture long-range dependencies and local details. (C) Complex backgrounds and variable lighting in field images often induce segmentation errors. To address these challenges, we propose Sparse-MoE-SAM, an efficient framework based on an enhanced Segment Anything Model (SAM). This deep learning framework integrates sparse attention mechanisms with a two-stage mixture of experts (MoE) decoder. The sparse attention dynamically activates key channels aligned with lesion sparsity patterns, reducing self-attention complexity while preserving long-range context. Stage 1 of the MoE decoder performs coarse-grained boundary localization; Stage 2 achieves fine-grained segmentation by leveraging specialized experts within the MoE, significantly enhancing edge discrimination accuracy. The expert repository—comprising standard convolutions, dilated convolutions, and depthwise separable convolutions—dynamically routes features through optimized processing paths based on input texture and lesion morphology. This enables robust segmentation across diverse leaf textures and plant developmental stages. Further, we design a sparse attention-enhanced Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contexts for both extensive lesions and small spots. Evaluations on three heterogeneous datasets (PlantVillage Extended, CVPPP, and our self-collected field images) show that Sparse-MoE-SAM achieves a mean Intersection-over-Union (mIoU) of 94.2%—surpassing standard SAM by 2.5 percentage points—while reducing computational costs by 23.7% compared to the original SAM baseline. The model also demonstrates balanced performance across disease classes and enhanced hardware compatibility. Our work validates that integrating sparse attention with MoE mechanisms sustains accuracy while drastically lowering computational demands, enabling the scalable deployment of plant disease segmentation models on mobile and edge devices. Full article

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

Rapid, accurate, and sustainable methods for assessing soil properties are essential for environmental management. This study proposes a green analytical approach for the direct determination of soil texture and organic matter using benchtop (1250–2500 nm) and portable (900–1700 nm) near-infrared (NIR) spectrophotometers combined with multivariate calibration. Partial least squares (PLS1 and PLS2) regression models were developed using regional calibration samples and applied to additional samples from the same area. Both individual (PLS1) and simultaneous (PLS2) predictions of clay, sand, silt, and organic matter contents were evaluated. Synergy interval PLS (siPLS) algorithms were used to optimize variable selection. For clay, RMSEP was 2.1% (benchtop) and 2.0% (portable), with RPD values around 2.0. Simultaneous prediction of sand content yielded better results (RPD = 1.3 benchtop; 0.8 portable). Silt prediction showed low accuracy (RPD < 1.0). Organic matter was best predicted by siPLS1 using the benchtop device (RPD = 1.5), followed by portable PLS2 (RPD = 1.2). Benchtop and portable NIR approaches proved satisfactory for direct determination of soil properties. PLS1 models offered greater specificity, while siPLS enhanced accuracy through variable selection. PLS2 models enabled efficient simultaneous predictions. Both devices meet white analytical chemistry principles, aligning performance with sustainability, thus demonstrating that accurate and environmentally responsible soil analysis can be achieved without compromising analytical efficiency. Full article