Search Results (77,890)

This study aimed to investigate the production of nanoemulgels structured with flaxseed fiber, designed to simulate salad dressings. For this purpose, the influence of microfluidizer passes (from one to four) on physicochemical and rheological properties was determined, followed by an assessment of thermal behavior. Rotor–stator homogenization followed by microfluidization were employed to produce nanoemulgels, which were characterized using laser diffraction, multiple light scattering, and rheological measurements. The resulting systems exhibited monomodal particle size distributions with mean diameters below 220 nm. Increasing the number of microfluidizer passes from one to four led to slight reductions in particle size, although they were not statistically significant. The formulation with two passes demonstrated superior physical stability during aging studies. Rheological evaluation indicated enhanced gel-like behavior with up to three passes, whereas excessive energy input (four passes) slightly compromised structural integrity. The linear viscoelastic region decreased notably after the first pass but remained relatively stable thereafter. The two-pass nanoemulgel, identified as the optimal formulation, was further tested for thermal stability. Temperature increases (5–20 °C) led to minor decreases in viscosity and firmness, yet the structure remained thermally stable. These findings support microfluidization as an effective strategy for developing stable flaxseed fiber-based nanoemulgels, with potential applications in functional food systems. Full article

As the urbanization and industrialization processes in developing countries continue to advance, environmental issues caused by carbon dioxide emissions (CDEs) have become a significant research topic in the field of sustainable development. However, existing research has primarily focused on macro and meso scales such as global, national, and urban levels, and due to limitations in data precision, in-depth exploration of spatial heterogeneity within cities remains insufficient. To address this, this study utilizes China high-resolution emission gridded data (CHRED) to establish a theoretical analytical framework for spatial zoning of urban carbon emissions. The main innovations of this study are as follows: first, a stepwise analysis method matching carbon emissions with spatial patterns was designed based on CHRED data; second, by establishing a “ring-layer and direction” model, the study systematically revealed the spatial differentiation characteristics of carbon emissions within cities. Empirical research using Shenzhen as a case study shows that the city’s CDE intensity (CDEI) is generally at a medium-to-low level, but exhibits significant spatial heterogeneity, with Nanshan District and Kuiyong District forming two major high-emission core areas. Further analysis reveals that during the processes of urbanization and industrialization, population density, nighttime light intensity index, and the proportion of construction land are the key drivers influencing the spatial pattern of carbon emissions. This study provides scientific basis and decision-making references for optimizing urban spatial layout to achieve the “dual carbon” goals. Full article

Chlorophyll biosynthesis is essential for photosynthesis and plant development. Disruptions in this pathway often manifest as pigment-deficient phenotypes. This study characterizes the morphological, anatomical, and physiological consequences of a chlorophyll-deficient rice mutant (yellow seedling, YS) caused by a loss-of-function mutation in the OsCHLI gene, which encodes the ATPase subunit of magnesium chelatase. Comparative analyses between YSs and wild-type green seedlings (GSs) revealed that YSs exhibited severe growth retardation, altered mesophyll structure, reduced xylem and bulliform cell areas, and higher stomatal and papillae density. These phenotypes were strongly light-dependent, indicating that OsCHLI plays a crucial role in light-mediated chloroplast development and growth. Transcriptome analysis further revealed global down-regulation of photosynthesis-, TCA cycle-, and cell wall-related genes, alongside selective up-regulation of redox-related pathways. These results suggest that chlorophyll deficiency induces systemic metabolic reprogramming, prioritizing stress responses over growth. This study highlights the multifaceted role of OsCHLI in plastid maturation, retrograde signaling, and developmental regulation, providing new insights for improving photosynthetic efficiency and stress resilience in rice. Full article

Background/Objectives: Pompia is an ancient, endemic citrus ecotype native to Sardinia (Italy), characterized by distinctive morphology and high content of bioactive compounds. Despite increasing interest, several aspects of this fruit, including its histological characteristics, remain poorly understood. This study aims to address this gap by investigating the anatomical features and spatial distribution of secretory cavities involved in essential oil (EO) production and accumulation, while also evaluating the EO’s chemical profile and associated biological activity. Methods: Pompia peel (flavedo and albedo) was subjected to histological analysis through fixation, dehydration, resin inclusion and sectioning. Sections were stained with 0.05% toluidine blue and observed under a light microscope to measure different parameters of secretory cavities. Essential oil (EO) was obtained from Pompia peel by hydrodistillation and characterized by gas chromatography–mass spectrometry (GC–MS) analysis. The biological activity of Pompia EO was assessed in vitro using NIH/3T3 fibroblasts, where wound-healing was evaluated by scratch assay and anti-senescence effects by β-galactosidase and γH2AX activity. Results: Microscopic analysis of the peel revealed pronounced variability in depth and size of the secretory cavities, along with the presence of lenticel-like structures in the epidermis. GC–MS analysis showed that Pompia EO is dominated by limonene (89%), with minor compounds including myrcene, geranial and neral. In vitro biological assays demonstrated that the EO promotes cell migration in a wound-healing model at concentrations ≥ 12.5 µg/mL and reduces markers of cellular senescence, including β-galactosidase activity and γH2AX foci, in etoposide-induced senescent fibroblasts. Conclusions: Overall, this study provides the first histological characterization of Pompia peel and confirms the bioactive potential of its EO. These findings support future applications in skin regeneration and anti-aging strategies and contribute to the valorization of this underexplored Citrus ecotype. Full article

Many cases have been described where the combination of semiochemicals and light sources in traps cause an increase in adult insect attraction. In this context, we tested different treatments using Delta plastic traps to catch DBM adults: (1) dispensers containing DBM SSA; (2) UV (365–370 nm) LEDs; (3) a combination of a dispenser containing DBM SSA and LEDs (SSA + LED); and (4) no lures (Control). The trials were conducted in northwestern Russia (the vicinity of St. Petersburg) during the period of 2022–2024 on cabbage crops. The results showed a highly significant interaction between SSA and LEDs with respect to their attractiveness to male DBM adults, as evidenced by an average 15-fold increase in DBM captures after the traps containing SSA were equipped with a second lure, an LED. This article discusses the prospects for using the identified synergistic effect of interaction between SSA and LEDs to enhance the catch of DBM adults for practical purposes, such as improving monitoring and developing more effective mass-trapping technologies. Full article

With the rapid development of advanced machining technologies such as high-speed cutting, dry cutting, and ultra-precision cutting, as well as the widespread application of various difficult-to-machine materials, the surface degradation problems such as wear, oxidation, and delamination faced by tools in the service process have become increasingly prominent, seriously restricting the performance and service life of tools. Nanocoatings, with their distinct nano-effects, provide superior hardness, thermal stability, and tribological properties, making them an effective solution for cutting tools in increasingly demanding working environments. For example, the hardness of the CrAlN/TiSiN nano-multilayer coating can reach 41.59 GPa, which is much higher than that of a single CrAlN coating (34.5–35.8 GPa). This paper summarizes the most common nanocoating material design, coating deposition technologies, performance evaluation indicators, and characterization methods currently used in cutting tools. It also discusses how to improve nanocoating performance using modulation analysis of element content, coating composition, geometric structure, and coating thickness. Finally, this paper considers the future development of nanocoatings for cutting tools in light of recent research hotspots. Full article

It is well known that for low-intensity incident light within a certain frequency range, the stopping voltage of the photoelectric effect is independent of the intensity but dependent on the frequency of the light, which is described by the equation $V=h\nu /e-W_0/e$, where V is the stopping voltage, h is the Planck constant, $\nu$ is the frequency of incident light, e is the basic charge, and $W_0$ is the work function. This implies that the stopping voltage increases with the frequency of the incident light. However, our experiments reveal that for non-monochromatic incident light, the stopping voltage is not determined by the maximum frequency component of the incident light, but by the maximum center frequency among all wave train components (with different center frequencies) involved in the incident light; that is to say, in the photon energy expression $h\nu$, the physical quantity $\nu$ does not refer to the frequency of monochromatic light, but represents the center frequency of a wave train spectrum. The spectral bandwidth of a wave train component can be as large as 122 nm in the visible and near-infrared regions. These findings highlight the need for greater attention to such effects in photoelectric detection and the study of energy exchange between light and matter. Full article

In this perspective article, we introduce Ecocebo as a novel concept describing the modulatory effects of physical environments, whether natural or built, on drug effect. Positioned as a spatial component of the placebo effect, Ecocebo is grounded in evidence-based design principles and proposes that environmental features such as natural light, greenery, spatial geometry, and calming esthetics can significantly influence sensory, emotional, and cognitive processes. These environmental factors may enhance or modify pharmacological responses, especially for analgesics, anxiolytics, and antidepressants. We highlighted how exposure to restorative spaces can reduce pain perception, stress, and the need for medication, paralleling findings in placebo research where contextual and sensory cues influence brain regions linked to emotion and pain regulation. We propose virtual reality (VR) as the most suitable methodological tool to study Ecocebo in controlled and ecologically valid settings. VR allows for the precise manipulation of spatial features and real-time monitoring of physiological and psychological responses. We also propose integrating VR with neuromodulation techniques to investigate brain–environment–drug interactions. Finally, we addressed key methodological challenges such as defining control conditions and standardizing the measurement of presence. This perspective opens new directions for the integration of non-pharmacological and pharmacological interventions and personalized therapeutic environments to optimize clinical outcomes. Full article

This paper is a result of the search for design assumptions for a sensor to detect oil dispersed in the sea waters (oil-in-water emulsions). Our approach is based on analyzing changes in the underwater solar radiance (L) field caused by the presence of oil droplets in the water column. This method would enable the sensor to respond to the presence of oil contaminants dispersed in the surrounding environment, even if they are not located directly at the measurement point. This study draws on both literature sources and the results of current numerical modeling of the spread of solar light in the water column to account for both downward and upward radiance (Es). The core principle of the analysis involves simulating the paths of a large number of virtual solar photons in a seawater model defined by spatially distributed Inherent Optical Properties (IOPs). The IOPs data were taken from the literature and pertain to the waters of the southern Baltic Sea. The optical properties of the oil used in the model correspond to crude oil extracted from the Baltic shelf. The obtained results were compared with previously published spectral analyses of an analogous polluted sea model, considering vertical downward radiance, vertical upward radiance, and downward and upward irradiance. It was found that the optimal wavelength ratio of 555/412, identified for these quantities, is also applicable to scalar irradiance. The findings indicate that the most effective way to determine this index is by measuring it using a sensor with its window oriented in the direction of upward-traveling light. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

Self-healing coatings can replace conventional coatings and are capable of self-healing and continuing to protect the substrate after coating damage. In this study, two types of self-healing resins were synthesized as coatings: Type-A via Diels–Alder crosslinking of furfuryl-modified diglycidyl ether bisphenol A with bismaleimide, and Type-B through epoxy blending/curing to form a semi-interpenetrating network. FTIR and Raman spectroscopy confirmed the formation of Diels–Alder (DA) bonds, while GPC tests indicated incomplete monomer conversion. Both resins were applied to glass and wood substrates, with performance evaluated through TGA, colorimetry (ΔE), gloss analysis, and scratch-healing tests (120 °C/30 min). The results showed that Type-A resins had a higher healing efficiency (about 80% on glass substrates and 60% on wood substrates), while Type-B resins had a lower healing rate (about 65% on glass substrates and 55% on wood substrates). However, Type-B is more heat-resistant, has a slower decomposition rate between 300 and 400 °C, higher gloss retention, and less color difference (ΔE) between wood and glass substrates. The visible light transmission of Type-B (74.14%) is also significantly higher. Full article

Blood glucose monitoring is crucial for the daily management of diabetic patients. In this study, we developed a differential absorbance and photoplethysmography (PPG)-based non-invasive blood glucose measurement system (NIBGMS) using visible–near-infrared (Vis-NIR) light. Three light-emitting diodes (LEDs) (625 nm, 850 nm, and 940 nm) and three photodetectors (PDs) with different source–detector separation distances were used to detect the differential absorbance of tissues at different depths and PPG signals of the index finger. A spatiotemporal multimodal fused long short-term memory (STMF-LSTM) model was developed to improve the prediction accuracy of blood glucose levels by multimodal fusion of optical spatial information (differential absorbance and PPG signals) and glucose temporal information. The validity of the NIBGMS was preliminarily verified using multilayer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XG Boost) models on datasets collected from 15 non-diabetic subjects and 3 type-2 diabetic subjects, with a total of 805 samples. Additionally, a continuous dataset consisting 272 samples from four non-diabetic subjects was used to validate the developed STMF-LSTM model. The results demonstrate that the STMF-LSTM model indicated improved prediction performance with a root mean square error (RMSE) of 0.811 mmol/L and a percentage of 100% for Parkes error grid analysis (EGA) Zone A and B in 8-fold cross validation. Therefore, the developed NIBGMS and STMF-LSTM model show potential in practical non-invasive blood glucose monitoring. Full article

Background: Neurofilament light chain (NfL) is a promising biomarker of neuroaxonal injury, increasingly used to monitor neurodegeneration in Alzheimer’s disease (AD). Multiple analytical platforms are available for NfL quantification in cerebrospinal fluid (CSF), but data on cross-platform consistency remain limited. Objective: This pilot study aimed to provide CSF NfL concentrations measured using Simoa and Lumipulse immunoassays in patients with biologically confirmed AD. Methods: Twenty-eight patients with cognitive impairment fulfilling the biological criteria for AD were enrolled. CSF NfL levels were measured using both Simoa and Lumipulse immunoassays. Statistical analyses assessed intra-individual agreement, correlation between platforms, and associations with cognitive status. Results: NfL concentrations measured with Simoa and Lumipulse showed a strong positive correlation between platforms (Spearman’s ρ = 0.965, p < 0.001), demonstrating excellent analytical concordance. Conclusions: In this pilot study, Simoa and Lumipulse yielded strongly correlated CSF NfL measurements, providing initial evidence of cross-platform consistency. However, these findings require confirmation in larger and diverse cohorts before definitive validation. Full article

Hydrogen-fueled internal combustion engines present a promising pathway for reducing carbon emissions in urban transportation by allowing for the reuse of existing vehicle platforms while eliminating carbon dioxide emissions from the exhaust. However, operating these engines with lean air–fuel mixtures—necessary to reduce nitrogen oxide emissions and improve thermal efficiency—leads to significant reductions in power output due to the low energy content of hydrogen per unit volume and slower flame propagation. This study investigates whether integrating a mild hybrid electric system, operating at 48 volts, can mitigate the performance losses associated with lean hydrogen combustion in a small passenger vehicle. A complete simulation was carried out using a validated one-dimensional engine model and a full zero-dimensional vehicle model. A Design of Experiments approach was employed to vary the electric motor size (from 1 to 15 kW) and battery capacity (0.5 to 5 kWh) while maintaining a fixed system voltage, optimizing both the component sizing and control strategy. Results showed that the best lean hydrogen hybrid configuration achieved reductions of 18.6% in energy consumption in the New European Driving Cycle and 5.5% in the Worldwide Harmonized Light Vehicles Test Cycle, putting its performance on par with the gasoline hybrid benchmark. On average, the lean H₂ hybrid consumed 41.2 kWh/100 km, nearly matching the 41.0 kWh/100 km of the gasoline P0 configuration. Engine usage analysis demonstrated that the mild hybrid system kept the hydrogen engine operating predominantly within its high-efficiency region. These findings confirm that lean hydrogen combustion, when supported by appropriately scaled mild hybridization, is a viable near-zero-emission solution for urban mobility—delivering competitive efficiency while avoiding tailpipe CO₂ and significantly reducing NO_x emissions, all with reduced reliance on large battery packs. 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