Search Results (542)

Reducing cake fat while maintaining aeration, crumb softness, and consumer acceptance remains challenging because fat crystals contribute to interfacial stabilization and structure development. This study evaluated an interfacial processing strategy in which oil dispersion is refined by pre-emulsification to evaluate whether refining oil dispersion by pre-emulsification modulates the functional impact of lipase (via in situ formation of surface-active lipolysis products). A D-optimal design (16 formulations) quantified the effects of fat type (shortening vs. sunflower oil), fat level (100% vs. 50%), pre-emulsification (absent/present), and lipase dose (0, 50, 100 ppm; flour basis) on batter and baked-cake quality. Responses included moisture, color, volume/visual structure, texture and hedonic sensory evaluation for selected formulations. Lipase improved structure and texture, with the strongest benefits in reduced-fat samples, where hardness-related parameters decreased and volume/crumb refinement improved. Pre-emulsification modulated lipase performance in a formulation-dependent manner, indicating significant interactions. In sensory tests, the combined approach improved low-fat acceptance compared with the low-fat control. Overall, pre-emulsification-enabled lipase action offers a route to recover key quality attributes in low-fat cakes without conventional emulsifiers. Full article

Dissolved-air flotation (DAF) is widely used for surface-water pretreatment but remains insufficiently explored for chemically complex groundwater. This study develops a thermodynamic and bubble-dynamics modeling framework to evaluate the feasibility of DAF pretreatment for groundwater containing elevated arsenic, natural organic matter (NOM), and color. The study is theoretical and model-based; no experimental dissolved-air flotation tests were performed. Air solubility was calculated at pressures of 4–6 bar and temperatures of 13–17 °C, while microbubble size, rise velocity, and bubble–floc interaction efficiencies were estimated using established physical models. Laboratory coagulation–flocculation jar tests with FeCl₃ and FeCl₃/PAC were used to define realistic floc properties prior to flotation modeling. No experimental dissolved-air flotation tests were conducted; all flotation-related results presented in this study are derived from thermodynamic and hydrodynamic modeling. Results show that a temperature decrease from 17 to 13 °C increases effective gas supersaturation by ~15% and shifts predicted microbubble diameters from ~60–90 µm to ~35–60 µm under identical operating conditions. The qualitative consistency between modeled flotation-relevant parameters and previously observed coagulation–flocculation trends for color, total organic carbon, and arsenic removal supports the proposed mechanistic framework. The study demonstrates how coupling coagulation chemistry with thermodynamically optimized air dissolution can enhance DAF applicability for arsenic- and NOM-rich groundwater. Full article

To evaluate the runoff pollution characteristics of roofs in an arid region, this study focused on Yinchuan City, China. It analyzed the runoff properties of various roof materials, including tile, asphalt, and color steel plate. Five rainfall events were monitored during 2024, with samples collected manually at roof pipe outlets and analyzed for suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH₃-N). The results indicated that the concentration of pollutants in runoff from these roofs decreased as rainfall duration increased. The event mean concentration (EMC) of TN and COD in runoff from all three roof materials exceeded the Class V surface water quality standards in China. The first flush of pollutants in roof runoff followed a descending order: SS > COD > TP > TN > NH₃-N. Cluster analysis of three rainfall parameters—dry period, precipitation, and rainfall intensity—revealed that dry period exerted the strongest influence on runoff quality, indicating that the overall quality of roof runoff was primarily influenced by the cumulative effects of atmospheric deposition, with rainwater scouring being the secondary factor. These findings provide critical insights for designing stormwater management strategies and rainwater harvesting systems in arid and semi-arid cities, emphasizing the need to prioritize first-flush control and consider local climatic conditions. Full article

We revisit the extensively debated star-forming main sequence (SFMS)—a tight correlation between the star formation rate and stellar mass in both kiloparsec-resolved and integrated galaxies. We statistically explore the fundamental drivers of star formation at global scales, using a large volume-limited sample of 24,954 local star-forming galaxies to overcome the limitations of previous works. Based on the mid-infrared 12 µm luminosity, stellar mass, and $g-r$ color, we estimate the molecular gas mass for the considered sample. At galaxy-wide scales, we establish global relations between the surface densities of the star formation rate (${\mathrm{\Sigma }}_{\mathrm{SFR}}$), stellar mass (${\mathrm{\Sigma }}_{*}$), and molecular gas mass (${\mathrm{\Sigma }}_{\mathrm{mol}}$). These global density relations are connected with and follow similar trends as the resolved SFMS, the Kennicutt–Schmidt (KS) relation, and the molecular gas main sequence (MGMS). Taking advantage of this large catalog, we show that the scatters in the global KS and MGMS relations are smaller than that of the global relation between ${\mathrm{\Sigma }}_{\mathrm{SFR}}$ and ${\mathrm{\Sigma }}_{*}$, and their Pearson correlation coefficients are higher. More importantly, multivariate regression and partial correlation analyses demonstrate that the apparent ${\mathrm{\Sigma }}_{\mathrm{SFR}}-{\mathrm{\Sigma }}_{*}$ correlation is entirely mediated by ${\mathrm{\Sigma }}_{\mathrm{mol}}$, with its best-fit parameters directly derivable from those of the KS and MGMS relations. Overall, our findings suggest that the correlation between stellar mass and molecular gas, as well as that between molecular gas and star formation, are more direct and fundamental. The star-forming main sequence, thus, appears to be a natural by-product of these two tighter relations. Full article

Beetroot by-product (BBP), an industrial residue rich in bioactive compounds, offers a sustainable solution to reduce food waste while enhancing the nutritional profile. The aim of the study was to evaluate the effect of different leavening agents (baking powder and baker’s yeast) and geometry (rectangular and oval) on bioactive compound stability and antioxidant capacity when incorporating beetroot by-products into gluten-free bread formulations. Rectangular and oval-shaped gluten-free breads were produced using 3D printing. Moisture content, pH, color parameters, bioactive compounds (betalains and phenolic compounds), and antioxidant activity were analyzed in both crust and crumb. BBP addition significantly increased total phenolic content, antioxidant capacity, and betalain content in all formulations. Breads with baker’s yeast exhibited higher bioactive retention due to acidic pH levels that favor phenolic and betanin stability. Bread with baking powder showed a higher retention of betaxanthins (yellow pigments), while those with baker’s yeast retained betacyanins (red-violet pigments). Oval geometry improved moisture retention and bioactive preservation due to reduced surface exposure. This research demonstrates the feasibility of developing nutritionally enhanced gluten-free products using additive manufacturing. Bread enriched with beetroot by-product and baker’s yeast represents a suitable option to improve functionality and pigment retention while valorizing industrial waste. Full article

The global expansion of genetically modified (GM) crop cultivation has increased the demand for analytical platforms that can provide rapid, reliable, and cost-effective detection of GM-derived ingredients to support traceability, regulatory compliance, and accurate labeling. Conventional molecular assays such as polymerase chain reaction (PCR) and isothermal amplification are highly sensitive and specific but depend on sophisticated instrumentation and trained personnel, limiting their applicability in field settings. Here, we present a label-free and amplification-free nanobiosensor based on citrate-capped gold nanoparticles (AuNPs) for the direct colorimetric detection of the Cry1Ac gene associated with the MON87701 soybean event, without the use of polymerase chain reaction (PCR) or any enzymatic nucleic acid amplification step. The assay relies on the localized surface plasmon resonance (LSPR) of AuNPs, which induces a red-to-purple color transition upon hybridization between complementary DNA strands. Critical reaction parameters, including NaCl concentration, AuNP size, and ionic strength, were optimized to enable selective and reproducible aggregation. Integration with a Support Vector Machine (SVM) algorithm enabled automated spectral classification and semi-quantitative discrimination of GM content levels. The optimized AuNP–SVM system achieved high sensitivity (limit of detection ≈ 2.5 ng μL⁻¹, depending on nanoparticle batch), strong specificity toward Cry1Ac-positive sequences, and reproducible classification accuracies exceeding 90%. By eliminating enzymatic amplification steps, the proposed platform significantly reduces assay time, operational complexity, and instrumentation requirements, making it suitable for rapid on-site GMO screening. Full article

Accurate and timely classification of skin lesions is critical for early cancer detection, yet current deep learning approaches suffer from high computational costs, limited interpretability, and poor transparency for clinical deployment. This study presents HCHS-Net, a lightweight and interpretable multimodal framework for six-class skin lesion classification on the PAD-UFES-20 dataset. The proposed framework extracts a 116-dimensional visual feature vector through three complementary handcrafted modules: a Color Module employing multi-channel histogram analysis to capture chromatic diagnostic patterns, a Haralick Module deriving texture descriptors from the gray-level co-occurrence matrix (GLCM) that quantify surface characteristics correlated with malignancy, and a Shape Module encoding morphological properties via Hu moment invariants aligned with the clinical ABCD rule. The architectural design of HCHS-Net adopts a biomimetic approach by emulating the hierarchical information processing of the human visual system and the cognitive diagnostic workflows of expert dermatologists. Unlike conventional black-box deep learning models, this framework employs parallel processing branches that simulate the selective attention mechanisms of the human eye by focusing on biologically significant visual cues such as chromatic variance, textural entropy, and morphological asymmetry. These visual features are concatenated with a 12-dimensional clinical metadata vector encompassing patient demographics and lesion characteristics, yielding a compact 128-dimensional multimodal representation. Classification is performed through an ensemble of three gradient boosting algorithms (XGBoost, LightGBM, CatBoost) with majority voting. HCHS-Net achieves 97.76% classification accuracy with only 0.25 M parameters, outperforming deep learning baselines, including VGG-16 (94.60%), ResNet-50 (94.80%), and EfficientNet-B2 (95.16%), which require 60–97× more parameters. The framework delivers an inference time of 0.11 ms per image, enabling real-time classification on standard CPUs without GPU acceleration. Ablation analysis confirms the complementary contribution of each feature module, with metadata integration providing a 2.53% accuracy gain. The model achieves perfect melanoma and nevus recall (100%) with 99.55% specificity, maintaining reliable discrimination at safety-critical diagnostic boundaries. Comprehensive benchmarking against 13 published methods demonstrates that domain-informed handcrafted features combined with clinical metadata can match or exceed deep learning fusion approaches while offering superior interpretability and computational efficiency for point-of-care deployment. Full article

Fish are nutritionally valuable foods but are also highly perishable, representing a major research focus for the development of effective preservation strategies to delay spoilage while maintaining microbiological acceptability. In this context, edible coatings have gained increasing attention as clean-label tools to extend the shelf life of perishable foods. In this study, an alginate-based emulsion containing oregano essential oil (OEO) was applied to commercial frozen–thawed ready-to-cook (RTC) hake fillets, intentionally selected as a reproducible model system to evaluate coating performance under refrigerated conditions. Coated and uncoated fillets, stored at 4 °C for up to 7 days, were monitored over time for microbiological and physicochemical parameters, including microbial loads, pH, weight loss, and lipid oxidation (TBARS). Compared to uncoated samples, fillets treated with the alginate–OEO emulsion exhibited a marked delay in spoilage-related microbial growth, with a consistent inhibition of Pseudomonas spp. throughout the experimental period, while maintaining microbiological acceptability. Emulsion-based coated fillets also exhibited reduced lipid oxidation, a more uniform surface appearance with only minor visible color changes, and the absence of unpleasant off odors during the refrigerated storage. Overall, the results demonstrate that the alginate–OEO coating could represent an effective strategy for improving the microbiological and oxidative stability of RTC fish fillets under refrigerated conditions, with potential implications for extended shelf life and a reduction in food waste. Full article

Indoor environmental quality significantly affects human perceptions of comfort and well-being due to the fact that most daily activities are spent indoors. However, surface colors are generally considered to be aesthetic choices rather than environmental factors. The purpose of this research is to assess the effect of surface colors on visual comfort, thermal intent, and plant-supportive lighting conditions. This study uses a controlled experimental method and four easily interpretable parameters: surface reflectance (albedo), illuminance, correlated color temperature, and photosynthetic photon flux density. The experiment uses a miniature enclosed chamber to standardize the geometry and lighting conditions to test a set of carefully chosen printed and painted color surfaces. The lighting parameters were directly measured using consumer-level spectral and illuminance meters. The surface reflectance parameter is estimated to be red, green, and blue color codes. The novelty of this research is that it provides a preliminary screening method that can convert color choice into quantifiable implications on indoor environments, with clear assumptions and limitations. The results can be used to inform design decisions that link color choice to specific task-oriented lighting requirements, climate-oriented thermal intent (cooler vs. warmer), and plant-rich interior environments. Full article

Recent developments in all-ceramic restorative materials have enhanced the clinical applicability of lithium disilicate for aesthetic dental restorations. The current study explores the influence of sintering parameter modulation over the translucency and color change of Amber Mill and IPS e.max CAD ceramics at baseline and following simulated aging through thermocycling and simulated toothbrushing. Sixty discs of lithium disilicate were assigned to two groups according to material type (Amber Mill; E-max), then according to translucency level (HT: high translucency; LT: low translucency). All specimens were sintered, polished, and ultrasonically cleaned in accordance with manufacturer guidelines. Optical properties—including translucency parameter (TP), color difference (ΔE), and color coordinates (L*, a*, b*)—were measured using a spectrophotometer at baseline and after a five-year clinical simulation. Scanning electron microscope (SEM) analysis was performed. Amber Mill-HT demonstrated the highest initial translucency (15.48 ± 0.89), followed by Amber Mill-LT (12.52 ± 0.61). Aging increased TP values in Amber Mill groups, while a slight reduction was observed in E-max groups. Amber Mill-LT exhibited the lowest color change (ΔE = 0.80 ± 0.10), reflecting superior color stability, whereas E-max-LT showed the highest ΔE (1.43 ± 0.21). SEM analysis demonstrated distinct microstructural differences between materials and translucency levels. High-translucency ceramics exhibited finer, more uniform lithium disilicate crystals and continuous glassy matrices, whereas low-translucency groups showed larger crystals, increased heterogeneity, and greater surface irregularities after aging. Overall, Amber Mill-LT displayed the greatest color stability, whereas E-max groupsretained translucency more consistently. All evaluated ceramics showed optical alterations within clinically acceptable limits, confirming their suitability for aesthetic restorative applications. Full article

Microbial contamination of table eggs remains an important food safety concern, largely due to the presence of Salmonella spp. on eggshell surfaces and the potential for cross-contamination along the collection, grading, and packing chain. Conventional sanitation practices, including chlorinated-water washing, can reduce surface microbial loads but may also present limitations related to cuticle alteration, process variability, water use, and the risk of recontamination when operational conditions are not tightly controlled. This review synthesizes evidence on non-thermal and selected mild thermal technologies for the surface decontamination of intact table eggs, including ultraviolet-C (UV-C) irradiation, pulsed light, ozone-based treatments (gas and microbubble systems), non-thermal plasma, plasma-activated water, and gas-phase hydroxyl radical processes. For each approach, antimicrobial performance is discussed alongside effects on eggshell integrity, cuticle preservation, and key quality indicators (e.g., Haugh unit, albumen pH, yolk color, and shell strength). Particular attention is given to industrial constraints that influence real-world performance, such as treatment uniformity and shading effects, humidity dependence, line speed, equipment integration, and validation criteria. A shared limitation of surface treatments is their inability to inactivate pathogens that have penetrated shell membranes or contaminated egg contents, underscoring the need to align technology selection with the targeted hazard and the regulatory context. Thus, available data indicate that non-thermal technologies can contribute to reducing eggshell contamination when properly optimized, although broader implementation will depend on standardized operating parameters, robust process validation, and regulatory acceptance within existing egg processing systems. Full article

The study concerns the influence of some basic parameters (speed, raster step, scan overlap coefficient, and surface modification at different processing angles) on the process of color laser marking with a fiber laser on AISI 304 stainless steel samples. Different surface morphology was obtained for single-shot marking; double-shot marking at angles 0° and 90°; and triple-shot marking at angles 0°, 60°, and 120°. According to the created methodology, dependencies for surface roughness, resulting color, color difference, and chromatic distance from the parameters raster step and scanning speed were established. The resulting colors and color differences for different values of these parameters for the three resulting morphological surfaces were compared. Trends in color saturation changes were established for single-shot, double-shot, and triple-shot color marking, as well as for changes in technological parameters in the studied intervals. Full article

The long-term esthetic performance of indirect restorations is closely related to the color stability and surface integrity of the restorative materials used. With the increasing use of CAD/CAM technologies, a wide range of ceramic- and resin-based materials have been developed for indirect restorative applications. These include feldspathic ceramics (VITA Mark II, VITA Zahnfabrik), hybrid ceramics (VITA Enamic, VITA Zahnfabrik), resin nanoceramic CAD/CAM blocks (Lava Ultimate, 3M), and indirect microhybrid resin composites (GC Posterior, GC Corporation). However, these materials are continuously exposed to chemical and mechanical challenges in the oral environment, such as staining from beverages and daily toothbrushing, which may compromise their optical and surface properties over time. The purpose of this in vitro study was to evaluate and compare the color change (ΔE) and surface roughness (Ra) of these materials after repeated coffee immersion and simulated toothbrushing. A total of 240 disk-shaped specimens were fabricated and subjected to three aging cycles consisting of storage in coffee or distilled water, followed by simulated toothbrushing with or without toothpaste. The color parameters were measured using a spectrophotometer according to the CIE Lab* system, surface roughness was assessed using a contact profilometer, and surface topography was qualitatively analyzed by atomic force microscopy. The results demonstrated that coffee immersion significantly increased both color change and surface roughness for all tested materials, with more pronounced effects observed in resin-based materials. Ceramic-based CAD/CAM materials (VITA Mark II and VITA Enamic) showed greater resistance to discoloration and surface degradation, whereas the resin nanoceramic material (Lava Ultimate) and the microhybrid resin composite (GC Posterior) exhibited clinically perceptible color changes and higher roughness values, particularly after toothbrushing with toothpaste. Full article

Neutral atomic hydrogen (H I) regulates galaxy growth and quenching, but direct 21 cm measurements remain observationally expensive and affected by selection biases. We develop Bayesian neural networks (BNNs)—a type of neural model that returns both a prediction and an associated uncertainty—to infer the H I mass, ${log}_{10}\left(M_{\mathrm{HI}}\right)$, from widely available optical properties (e.g., stellar mass, apparent magnitudes, and diagnostic colors) and simple structural parameters. For continuity with the photometric gas fraction (PGF) literature, we also report the gas-to-stellar-mass ratio, ${log}_{10}(G/S)$, where explicitly noted. Our dataset is a reproducible cross-match of SDSS DR12, the MPA–JHU value-added catalogs, and the 100% ALFALFA release, resulting in 31,501 galaxies after quality controls. To ensure fair evaluation, we adopt fixed train/validation/test partitions and an additional sky-holdout region to probe domain shift, i.e., how well the model extrapolates to sky regions that were not used for training. We also audit features to avoid information leakage and benchmark the BNNs against deterministic models, including a feed-forward neural network baseline and gradient-boosted trees (GBTs, a standard tree-based ensemble method in machine learning). Performance is assessed using mean absolute error (MAE), root-mean-square error (RMSE), and probabilistic diagnostics such as the negative log-likelihood (NLL, a loss that rewards models that assign high probability to the observed H I masses), reliability diagrams (plots comparing predicted probabilities to observed frequencies), and empirical 68%/95% coverage. The Bayesian models achieve point accuracy comparable to the deterministic baselines while additionally providing calibrated prediction intervals that adapt to stellar mass, surface density, and color. This enables galaxy-by-galaxy uncertainty estimation and prioritization for 21 cm follow-up that explicitly accounts for predicted uncertainties (“risk-aware” target selection). Overall, the results demonstrate that uncertainty-aware machine-learning methods offer a scalable and reproducible route to inferring galactic H I content from widely available optical data. Full article

This paper investigates the global polynomial synchronization (GPS) problem for quaternion-valued neural networks (QVNNs) featuring proportional delay, parameter uncertainty, and external disturbance. A combined approach of sliding mode control (SMC) and a non-separation strategy is adopted to achieve this goal. First, an integral-type sliding surface is designed for the system. Then, by constructing a delay-free Lyapunov functional and leveraging the properties of the quaternion vector norm and inequality techniques, sufficient conditions are derived to achieve GPS for the sliding mode dynamics. Furthermore, both a SMC law and an adaptive SMC law are designed, with a reachability analysis confirming that the system trajectories reach the predefined sliding surface in finite time. Finally, numerical examples with graphical analysis are provided to verify the obtained results, along with their application in color image pattern storage and retrieval. Full article