Search Results (14,943)

Titanium dioxide (TiO₂) is a widely studied semiconductor whose interfacial redox properties strongly influence its photocatalytic and biological performance. In this work, TiO₂ nanomaterials were surface-functionalized with green tea waste extract (GT) and subsequently impregnated with silver to obtain redox-active nanocomposites with tunable optical and biological properties. HPLC, FTIR, diffuse reflectance spectroscopy (DRS), and density functional theory (DFT) analyses demonstrated the formation of an organic–inorganic interface through adsorption of green tea-derived ligands. DFT calculations revealed complementary interfacial roles of the adsorbed constituents, with epigallocatechin gallate (EGCG) inducing interfacial charge-transfer (ICT) states that enable visible-light absorption, as reflected by the decrease in the apparent optical bandgap from ~3.48 eV for pristine TiO₂ to ~1.83 eV for TiO₂/EGCG. ICP-OES analysis further quantified the Ag loading in TiO₂/GT/Ag at 5.1 wt%. Electron paramagnetic resonance (EPR) experiments demonstrated that GT functionalization shifts the interfacial redox balance toward radical scavenging by suppressing excessive radical generation, whereas silver incorporation partially restores oxidative pathways, particularly under irradiation. These differences in interfacial redox behavior directly translate into distinct biological responses. TiO₂/GT/Ag showed the strongest antimicrobial activity, with visible-light enhancement observed predominantly against Staphylococcus aureus, while TiO₂/GT/Ag reduced H₂O₂-induced oxidative stress in non-malignant cells and promoted intracellular reactive oxygen species generation in cancer cells. These findings demonstrate that engineering the organic–inorganic interface through plant-derived ligands and silver incorporation provides an effective strategy for tuning the interfacial redox properties and light-responsive biological performance of TiO₂-based nanomaterials, thereby expanding their potential for antimicrobial and biomedical applications. Full article

Generalization under deployment shifts remains a major challenge for visuomotor robot manipulation, especially when changes in camera placement and control behavior are unknown to the policy. This paper introduces the System-Context-Conditioned Diffusion Policy (SCE-DP), a history-conditioned manipulation policy designed to adapt its latent context to simulated changes in camera extrinsics, action scale, and bounded control delay without explicit recalibration or test-time model-weight updates. SCE-DP encodes a short history of issued commands and their subsequent visual and proprioceptive responses into a latent system context, which conditions the diffusion-based action denoising process. Auxiliary system-parameter regression and one-step response prediction further encourage the context to capture deployment-relevant system properties and their behavioral consequences. We evaluate SCE-DP on five independently trained ManiSkill manipulation tasks across held-out in-range configurations, a withheld camera-delay composition, and mild extrapolation settings. SCE-DP improves the five-task macro-average over domain-randomized Diffusion Policy by 14.5 percentage points on held-out configurations and 15.7 points on the withheld camera-delay composition. Across four shifted evaluation suites, it achieves an average success rate of 64.3%, compared with 48.9% for the domain-randomized baseline, while preserving nominal performance. Command-only, response-only, previous-action, recovery-source, and matched explicit-parameter controls indicate that the gain is not explained solely by command statistics, redundant command input, or recovery-data composition. These results show in simulation that command–response history is an effective source of online latent system context under coupled visual and control shifts; real-robot generalization remains to be established. Full article

(1) Background: Information diffusion models developed for graph-based platforms such as Reddit and broadcast architectures such as Telegram identify temporal features—particularly the timing of peak spread—as dominant predictors of coverage. Whether these predictors generalise to platforms where content is distributed through algorithmic recommendation rather than social-graph contagion remains an open question. (2) Methods: We analyse the YouNiverse dataset, comprising 133,364 English-language YouTube channels observed weekly from January 2015 to September 2019 (18.9 million observations). We derive channel-level diffusion features—including time-to-peak, post-peak decay rate, diffusion volatility, and upload frequency—and train three machine learning models (Linear Regression, Random Forest, and LightGBM) on two tasks: predicting peak weekly view growth (regression) and identifying viral channels (classification). A single-feature naive baseline (subscriber count alone) establishes the marginal contribution of the broader feature set beyond subscriber count alone, and a temporal split experiment (training on channels peaking before 2018, testing on 2018–2019) assesses cross-temporal stability. Because subscriber count and subscriber rank are measured at the October 2019 crawl, this is a retrospective characterisation rather than a strict real-time forecasting design. (3) Results: LightGBM achieves $R^2=0.776$ (5-fold CV: $0.778\pm 0.003$) compared with $R^2=0.548$ for the naive baseline, a net gain of $+0.228R^2$. Because subscriber rank and subscriber count are near-perfectly collinear, we interpret them jointly as a channel-size dimension (42.2% of total mean absolute SHAP attribution), rather than as independent effects. Time-to-peak ranks fourteenth (1.1%), in contrast to its dominant role on Reddit ($r=0.995$, rank #1). For virality classification, LightGBM achieves ROC-AUC $=0.967$. Under the temporal split, Random Forest ($R^2=0.703$) outperforms LightGBM ($R^2=0.683$), showing greater cross-temporal stability within this retrospective split. (4) Conclusions: Within the 2015–2019 data, the results are consistent with algorithmic recommendation weakening the relationship between temporal diffusion dynamics and coverage magnitude at the channel level. Time-to-peak is weakly informative in this setting, while generalisation to the current recommendation system requires validation on newer data. Full article

As information technology evolves rapidly, image data is exposed to growing risks of security breaches and privacy leaks during transmission and storage. Therefore, image encryption has attracted significant attention as an effective protection measure. Nevertheless, most chaos-driven image encryption schemes suffer from inferior chaotic randomness, making them prone to cryptanalytic cracking in practice. To solve this problem, a new image encryption scheme is proposed by integrating the fireworks algorithm with a convolution operation. First, the original image is permuted via the Arnold transform and an improved permutation strategy. Then, the classical Logistic map is iterated to generate an initial pseudo-random sequence, which is further optimized by the fireworks algorithm. Finally, a reversible convolution operation is integrated with a bit-level diffusion mechanism to achieve image encryption. Experimental results confirm that the proposed scheme exhibits superior performance in terms of statistical analysis, robustness analysis, and image-quality assessment, and it possesses remarkable security against various cryptanalytic attacks. Full article

Drawing on Agency Theory, Institutional Theory, and the Diffusion of Innovation (DOI) framework, this study examines the relationship between mandatory adoption of the eXtensible Business Reporting Language (XBRL) and earnings management in an emerging market. Jordan introduced compulsory XBRL reporting for listed firms in 2020, providing a natural setting to evaluate its governance implications. The analysis is based on firm-level data for 40 industrial companies listed on the Amman Stock Exchange over 2016–2023 (320 firm-year observations). Accrual-based earnings management is measured by absolute discretionary accruals from the cross-sectional Modified Jones Model,. Firm fixed-effects regressions with firm-clustered standard errors, an event-study specification with year fixed effects, and an extensive robustness battery (performance-adjusted accruals, pooled estimation, balance-sheet accruals, exclusion of the pandemic years, and a placebo adoption date) consistently show no statistically detectable change in accrual-based earnings management after adoption. By contrast, absolute abnormal production costs increase significantly after the mandate, an effect that strengthens when the COVID-19 years are excluded and disappears under a placebo date, a pattern consistent with partial substitution from accrual-based towards real-activities manipulation. The findings suggest that digital reporting mandates alone do not discipline reporting behavior in environments with limited institutional enforcement and may redirect rather than reduce managerial opportunism. Implications for regulators, auditors, and standard setters are discussed. Full article

Copper nanoparticles possess broad-spectrum antibacterial activity, and aerogels with 3D interconnected porous networks can trap bacteria and sustain metal ion release to boost bactericidal effects. Zinc is another low-toxicity antibacterial metal, and the Cu–Zn combination is predicted to generate synergistic inhibition. Herein, monometallic Cu aerogel and CuZn alloy aerogel were fabricated by a one-step method, and comparative experiments were performed to verify whether Zn alloying improves the antibacterial performance of Cu aerogel. TEM and XRD suggest the probable formation of Cu–Zn substitutional solid solution; Zn addition refined nanoparticles and relieved particle aggregation. Quantitative viability tests, agar diffusion and biofilm inhibition assays proved that CuZn alloy aerogel exhibited superior bactericidal and anti-biofilm activity against E. coli and S. aureus. Mechanistic investigations revealed that the bimetallic alloy induced strain-dependent intracellular ROS accumulation and disrupted bacterial membrane potential to cause irreversible bacterial death. DC2.4 cell tests validated its good cytocompatibility, with cell viability over 70% at 100 ppm, the concentration delivering excellent antibacterial capacity. This work explores the combined antibacterial advantages of Cu-Zn bimetallic alloy aerogel and offers a facile strategy to fabricate biocompatible metal aerogels for biomedical antibacterial applications. Full article

Background/Objectives: Cerebral small vessel disease (SVD) is a heterogeneous condition in which similar conventional MRI findings may be associated with different clinical manifestations and pathogenetic mechanisms. Previously, hierarchical clustering of structural MRI features in patients with severe white matter hyperintensities (Fazekas 3) identified two MRI phenotypes, designated MRI Type 1 and MRI Type 2. Diffusion MRI (dMRI) may provide additional information about the microstructural differences between these phenotypes. To compare white matter microstructure between MRI Type 1 and MRI Type 2 of sporadic age-related SVD using signal-based and biophysical dMRI models. Methods: This cross-sectional study included 75 patients with SVD and 36 age- and sex-matched healthy controls. Among the patients with SVD, 43 had MRI Type 1 and 32 had MRI Type 2. All participants underwent structural and multi-shell dMRI on a 3 Tesla MRI scanner. Diffusion metrics were derived using multiple models: Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), White Matter Tract Integrity (WMTI), and the Multi-compartment Spherical Mean Technique (MC-SMT). Tract-profile analysis was performed in three corpus callosum segments: the forceps major, forceps minor, and body. Group differences were assessed using age- and sex-adjusted general linear models with correction for multiple comparisons. The combined discriminative value of dMRI metrics was evaluated using regularized Elastic Net logistic regression with repeated nested five-fold cross-validation. Results: After adjustment for age and sex, the overall group effect remained significant for 45 of 48 global dMRI measures following Benjamini–Hochberg correction. Compared with MRI Type 2, MRI Type 1 showed lower fractional anisotropy (FA), neurite density index (NDI), intra-axonal volume fraction (INTRA), axonal water fraction (AWF), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK), and higher mean diffusivity (MD), radial diffusivity (RD), extra-axonal mean diffusivity (EXTRA_MD), extra-axonal transverse diffusivity (EXTRA_TRANS), and extra-axonal radial diffusivity (radEAD). These differences were generally most pronounced in the body of the corpus callosum. In the segmental analysis, 131 of 144 values showed a significant overall group effect after correction, and 108 demonstrated significant differences between MRI Type 1 and MRI Type 2. The largest effects were observed in the 60–80% interval of the corpus callosum body, particularly for AWF, MK, INTRA, EXTRA_TRANS, RK, FA, RD, radEAD, and MD. An Elastic Net model combining age, sex, and 48 global dMRI measures discriminated MRI Type 1 from MRI Type 2 with an internally validated area under the curve of 0.866 (95% CI, 0.762–0.953), accuracy of 86.7%, sensitivity of 75.0%, and specificity of 95.3%. Ten dMRI features showed a selection frequency of at least 70% across repeated model construction. Conclusions: MRI Type 1 is characterized by more severe and spatially extensive corpus callosum microstructural abnormalities than MRI Type 2, despite broadly similar vascular risk-factor profiles. The findings support the heterogeneity of sporadic age-related SVD and indicate that combined signal-based and biophysical dMRI metrics may improve MRI phenotyping. The observed associations should be interpreted as indirect markers of tissue microstructure and require confirmation in larger, independent, and longitudinal cohorts. Full article

Antimicrobial resistance is a growing global health challenge, highlighting the need for new bioactive compounds from medicinal plants. Vepris nobilis is traditionally used in East Africa for treating infections and respiratory disorders; however, its essential oil (EO) composition and antimicrobial mechanisms remain poorly characterized. This study investigated the chemical composition and antimicrobial activity of V. nobilis EO, along with an in silico evaluation of its major constituent, germacrene D. The EO was extracted by hydrodistillation and analyzed using gas chromatography-mass spectrometry (GC–MS). Its antimicrobial activity was evaluated against 26 bacterial and 4 fungal strains using disc diffusion, broth microdilution, and MBC/MFC (Minimum Bactericidal Concentration/Minimum Fungicidal Concentration) assays. Germacrene D showed stronger activity than the EO, particularly against both multidrug resistant (MDR) and non-MDR Gram-negative bacterial strains (MIC = 10 µg/mL), with bactericidal and fungicidal effect. Molecular docking of germacrene D against two clinically relevant enzymes—dehydrosqualene synthase (CrtM) from Staphylococcus aureus and SWISS-modeled sterol 14-α-demethylase (CYP51) from Penicillium funiculosum—suggested potential interactions with both targets, with a favorable predicted binding affinity for CrtM (−7.654 kcal/mol) and for CYP51 (−5.898 kcal/mol). These findings provide preliminary insights into a possible antimicrobial mechanism, although experimental validation is needed to confirm this hypothesis. ADMET analysis suggested favorable drug-like properties despite limited solubility. These findings provide scientific support for the traditional use of V. nobilis leaves in the treatment of respiratory infections and highlight germacrene D as a promising lead compound for further antimicrobial development. Full article

Accurate forecasting of Convective Available Potential Energy (CAPE) is critical for severe weather prediction. However, the operational GFS model exhibits a persistent low-CAPE bias. In this work, we apply a two-step regression–diffusion model (NVIDIA CorrDiff) to address this issue. Our results indicate that while a standard U-Net can successfully reduce the bulk systematic bias, the generated output remains overly smoothed. This occurs because, for data with long-tailed statistical distributions such as CAPE, standard models trained on mean squared error fail to capture rare, high-magnitude events. In contrast, generative diffusion models can reproduce realistic, small-scale features similar to the ground truth by learning to reverse a noise-corruption process through a series of iterative denoising steps. Our study begins with bias correction for the 24 h forecast. We then extend this by applying the model—trained solely on 24 h data—to correct forecasts of up to 120 h. This strategy leverages our finding that the GFS forecast bias is highly persistent over time. Furthermore, our examination of CAPE’s joint Probability Density Functions emphasizes the necessity of matching machine learning models to the target variable’s statistical properties. Ultimately, the effectiveness of CorrDiff highlights its potential for other challenging applications involving small-scale phenomena with long-tailed distributions. Full article

Ultra-lean hydrogen flames under terrestrial gravity are governed by a coupled interaction among preferential diffusion, thermal diffusion, heat loss, and self-induced convection. This study numerically examines combustion in a quiescent 6 vol.% H₂–air mixture using detailed chemistry and a low–Mach–number formulation. A complete set of calculations was considered, with Soret diffusion and optically thin radiative heat loss independently enabled and disabled. One-dimensional spherical calculations were used to isolate the initial post-ignition flame kernel growth, while two-dimensional planar and axisymmetric simulations described its subsequent buoyant rise, deformation, and breakup. Over the analyzed interval, the spherical flame-front radius followed an extended $R_f^2\approx Kt$ regime rather than constant-speed expansion. Soret diffusion increased the effective growth coefficient K, whereas radiation reduced it. The axisymmetric calculations reproduced the experimentally measured leading-point trajectory substantially better than the planar formulation. Soret diffusion produced larger, faster-rising kernels and maintained a more nearly circular upper cap, whereas radiation had a weaker effect on trajectory but increased relative lateral flattening. In all cases, a toroidal vortex stretched the flame segment and caused local extinction and fragmentation. Soret diffusion delayed breakup, while radiation advanced it; their combined effect on breakup time was nearly compensating. The results show that Soret transport and radiation primarily alter kernel growth and resistance to vortex-induced extinction, while the qualitative breakup pathway remains hydrodynamically controlled. Full article

Systemic sclerosis (SSc) is a heterogeneous autoimmune connective tissue disease characterized by immune activation, vasculopathy and progressive fibrosis of the skin and internal organs. Immunosuppressive treatment is commonly used in early inflammatory disease and in SSc-associated interstitial lung disease (SSc-ILD), yet the optimal first-line agent depends on the dominant clinical phenotype. Methotrexate (MTX) and mycophenolate mofetil (MMF) are two widely used conventional immunomodulatory options. This review summarizes the clinical trial evidence from the last four decades and places it into the context of contemporary guideline recommendations and real-world comparative effectiveness data. Two randomized placebo-controlled trials support a modest role for MTX in early diffuse cutaneous SSc, particularly for skin and musculoskeletal manifestations, but evidence for lung benefit is limited. MMF has stronger evidence for SSc-ILD, principally from the Scleroderma Lung Study II, a randomized double-blind trial showing comparable efficacy to oral cyclophosphamide with better tolerability, and from subsequent pilot, open-label, and real-world studies. Overall, the current evidence supports a phenotype-driven approach: MTX may be considered when skin or joint disease predominates without clinically relevant ILD, whereas MMF is generally preferred for SSc-ILD and systemic inflammatory disease. Direct head-to-head MTX versus MMF trials are lacking and remain an important research priority. Full article

Persistent late-time variation can remain in physics-informed neural network (PINN) solutions after the governing transient has effectively decayed, while conventional error norms do not reveal whether this variation has a systematic temporal–frequency structure. This study develops an offline temporal–spectral diagnostic and postprocessing workflow for a one-dimensional advection–diffusion benchmark. A high-accuracy analytical reference and three fixed-resolution finite-difference baselines are used to assess a PINN whose architecture is selected by a fully supervised neural architecture search and whose parameters are trained with progressive temporal windowing. Candidate late-time intervals are selected without using the reference solution by applying the Bayesian information criterion (BIC) to a breakpoint model for the inter-reconstruction sensitivity; the selected field is subsequently reconstructed by retaining a prescribed fraction of its temporal spectral energy and is evaluated independently through reference-error and physics-consistency measures. For $[t_{\mathrm{cut}},t_{max}]=[1.8,5]$, the zero-frequency component contains $0.9999996$ of the raw-field energy, so the $q=0.95$ reconstruction retains only the temporal mean. This projection reduces the final-time spatial error norm from $2.70\times {10}^{-3}$ to $1.06\times {10}^{-3}$, a factor of approximately $2.5$, while changing the discrete governing-equation residual by less than $0.3\%$ over the filtered window. Mean-removed tests for $q=0.90,0.95,0.99$ show that the discarded fluctuation is dominated by low-frequency approximation error rather than high-frequency noise. The result supports the proposed selection–validation workflow for this controlled benchmark but does not establish a universally transferable filter. Full article

Zn₂SnO₄ is a promising anode for lithium-ion batteries owing to its high theoretical capacity, yet its practical utilization is severely limited by sluggish reaction kinetics, large volume expansion, and unstable electrode/electrolyte interfaces. Here, we introduce a dimensionality-reduction strategy that simultaneously boosts capacity and cycling stability. Through surfactant-directed crystal growth, acid-etching reconstruction, and hydrothermal carbon coating, compact Zn₂SnO₄ octahedra are controllably transformed into sheet-assembled structures and finally into a core–shell composite with a continuous carbon layer (C@M-Zn₂SnO₄ (H⁺)). The continuous structural evolution shortens Li⁺ diffusion paths, buffers mechanical stress, and stabilizes the solid–electrolyte interface without altering the intrinsic lithium-storage mechanism of Zn₂SnO₄. As a result, the optimized C@M-Zn₂SnO₄ (H⁺) electrode delivers a reversible capacity of 650 mAh g⁻¹ after activation and retains 620 mAh g⁻¹ after 600 cycles at 200 mA g⁻¹, with Coulombic efficiency approaching 100% throughout. This work demonstrates that dimensionality-reduction-assisted structural engineering is an effective strategy for developing high-capacity, long-cycle-life anode materials. Full article

Introduction: Heavily exuding wounds can macerate perilesional skin and favour infection. Information on the properties and use time of dressings should be clear and available to healthcare providers. Methods: Our aim was to evaluate the updated series of the AQUACEL^® dressing family, because their evolution from a single layer of carboxymethylcellulose (NaCMC) to a multicomponent antiseptic or its combination with polyurethane covered with a silicon layer has developed the technology beyond simple exudate absorption. Using gravimetric analysis, we evaluated the porosity, water uptake, and water vapour transmission rate (WVTR) of AQUACEL^® Ag+Extra, AQUACEL^® Foam, and Foam Pro. By modifying the method of measuring WVTR, we assessed this outcome during the progressive saturation of the dressings. We also performed a disc diffusion assay on agar to determine the antimicrobial effects of the dressings. Results: Independently of porosity, a second layer of cellulose in AQUACEL^® Ag+Extra doubles water uptake and quadruples WVTR compared to foam forms. When the different dressings were evaluated for WVTR under progressive saturation, we did not observe any statistically significant changes, indicating that retained liquids do not alter dressing properties, which is a more biologically suitable approach. Despite the acidic character of the cellulose hydrofibre contained in the three dressings, the lack of any antiseptics in the foam forms makes them unsuitable for use in colonised or infected wounds, although they can act as a physical barrier for microorganisms and mechanical damage. The opposite results were observed for the AQUACEL^® Ag+Extra dressing, which contains silver, EDTA, and benzethonium chloride. Discussion: Data on the physicochemical composition of dressings can enable healthcare providers to choose the appropriate dressing series to use during wound bed preparation and beyond. Full article

The agro-industrial valorization of unripe plantain peels through flour production represents a sustainable strategy for waste reduction and nutrient recovery. This study investigated the process intensification of plantain peel drying by evaluating the combined effects of UV-C-assisted hot-air drying with ultrasound and oxalic acid pretreatments. A 2³ full factorial design was employed to evaluate the effects of UV-C lamp distance, ultrasound time and oxalic acid concentration on drying kinetics, effective moisture diffusivity, and the retention of bioactive compounds. The combination of the most intense levels of the pretreatments with a 9 cm distance between the radiation source and the sample achieved a 40.68% reduction in drying time compared to the control (without pretreatments). Among the mathematical models tested, the Logarithmic model provided the most accurate fit (R² > 0.99), effectively describing the falling-rate period and mass transfer phenomena. Scanning electron microscopy revealed structural modifications, including microchannels and surface pores, consistent with enhanced moisture transport and increased effective moisture diffusivity. The accelerated drying kinetics also led to higher contents of bioactive compounds, including total phenolics, ascorbic acid, and carotenoids, while maintaining adequate water activity and color stability. These findings demonstrate the combined potential of UV-C radiation, ultrasound, and oxalic acid to intensify drying efficiency while improving the functional quality of unripe plantain peel flour. Full article