Search Results (194)

Plant diversity is a key indicator of ecosystem structure, function, and restoration status, yet its rapid assessment remains challenging in sandy ecosystems where vegetation is sparse, spatially heterogeneous, and strongly affected by exposed soil backgrounds. In such environments, conventional greenness-based spectral indices may not adequately capture species-level variation because plant communities are controlled not only by photosynthetic biomass but also by soil moisture, micro-topography, and dune-related habitat heterogeneity. This study evaluated the potential of Sentinel-2-derived spectral indices for estimating plant α-diversity in the Hunshandak Sandland, northern China. Based on field observations from 888 plots collected during 2017–2024, four α-diversity metrics—species richness, Shannon–Wiener index, Simpson index, and Pielou evenness index—were calculated and compared with 21 spectral indices using correlation analysis, partial least squares regression (PLSR), and random forest (RF) models. The results showed that model performance varied substantially among diversity metrics. Species richness was estimated with the highest accuracy, whereas Shannon–Wiener, Simpson, and Pielou indices showed weaker predictability, indicating that remotely sensed spectral indices were more sensitive to species number than to abundance distribution and evenness. Moisture- and soil-background-sensitive indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BSI/BRI), and Chlorophyll Absorption Ratio Index (CARI), showed relatively stable relationships with plant diversity across different vegetation gradients. Although the overall explanatory power was moderate rather than high, the results demonstrate the practical value of Sentinel-2 spectral indices for regional screening of plant diversity patterns in sandy ecosystems. This study provides empirical evidence for biodiversity monitoring and ecological restoration assessment in semi-arid sandy landscapes and highlights the need to integrate environmental covariates, multi-source remote sensing, and phenological information in future studies. Full article

Background/Objective: Myocardial fibrosis (MF) is a prevalent pathological endpoint in various heart diseases, characterized by extracellular matrix (ECM) dysregulation and oxidative stress. Hyperoside (Hyp) plays a role in regulating cardiac oxidative stress and fibrosis. This study aimed to elucidate whether Hyp regulates isoproterenol (ISO)-induced MF in mice by modulating the GATA4/HIF-1α signaling pathway and reducing oxidative stress. Methods: The binding affinity of Hyp to GATA4 and HIF-1α was assessed through molecular docking and dynamics simulation. The MF model of mice was established by subcutaneous injection of ISO. Cardiac function was measured by echocardiography. Myocardial injury and collagen deposition were examined using H&E and Sirius red staining. Levels of fibrosis markers, oxidative stress indicators, and GATA4/HIF-1α pathway indicators in serum and heart tissue were quantified by ELISA, Western blot, RT-qPCR and flow cytometry. The distribution of myocardial marker proteins was visualized by immunofluorescence and immunohistochemistry. Results: Molecular docking revealed high binding affinity of Hyp to GATA4 and HIF-1α (binding energies < −5.0 kcal·mol⁻¹), and dynamics simulation showed that the complex’s structure remained stable over 100 nanoseconds (RMSD < 0.1 nm). High-dose Hyp (36 mg/kg) significantly improved cardiac function, myocardial injury, collagen deposition, and inflammatory infiltration in MF mice. Molecularly, Hyp effectively reduces oxidative stress and fibrosis through upregulating GATA4 and downregulating HIF-1α. Conclusions: Hyp suppresses oxidative stress by activating the GATA4/HIF-1α pathway, presenting a promising therapeutic target for the treatment of MF. Full article

Creating secondary flows that encourage fluid interchange between hot and cold regions is frequently necessary to improve convective heat transfer in compact channels. A well-known passive method for enhancing mixing and boosting thermal performance in laminar regimes is the use of vortex generators (VGs), which create streamwise and transverse vortices. Laminar forced convection in a rectangular channel with rectangular wing vortex generators with triangular tips is investigated numerically in this work. The primary goal is to assess the impact of the number of tips per wing on pressure drop and heat transfer enhancement at a fixed angle of attack (α). This study examines a single row of rectangular wing vortex generators (VGs) with triangular tips and systematically evaluates how variations in tip number influence not only the global Nusselt number and friction factor but also the three-dimensional vortex structure distribution along the channel. This approach contrasts with many previous studies that primarily focus on global performance indices or on classical delta-type VGs. ANSYS Fluent’s finite volume method is used to solve three-dimensional stable, laminar, incompressible flow and heat transfer. Two Reynolds numbers, Re = 456 and Re = 911, are simulated for different triangular-tip configurations at a fixed angle of attack of α = 30°. To connect flow structures to heat transfer behavior, area-averaged Nusselt numbers and friction factors are calculated for each case, and vortex cores and their spatial locations are examined. The findings demonstrate that heat transfer improvement is directly and significantly impacted by the VG tip arrangement. The trade-off between heat gains and pressure losses is highlighted by the fact that some tip configurations produce stronger, more persistent vortices and higher Nusselt numbers at the expense of an increased friction factor. The conclusions are limited to laminar flow conditions at α = 30°, Reynolds numbers of 456 and 911, and the investigated one-, two-, and three-tip configurations. Full article

Time–frequency analysis (TFA) facilitates the extraction of instantaneous frequency (IF) features from non-stationary signals. Transient Extraction Transform (TET) produces highly concentrated time–frequency representations (TFRs) for pulse-like fault vibration signals. Nevertheless, in noisy environments characterized by strong impulsive-like infinite-variance processes with a low characteristic exponent α, the performance of TET degrades significantly. First, an adaptive fractional-order low-order statistical function (AFLOF) is defined, which adaptively determines optimal parameter values based on noise impulse level. Subsequently, a robust transient extraction transform (RTET) based on AFLOF is proposed to accommodate various α-stable distribution environments defined by characteristic metrics, thereby facilitating the efficient acquisition of time–frequency features from rapidly varying signals. To mitigate the blurring effects faced by conventional TET algorithms in linear TFA-like scenarios, a robust generalized instantaneous extraction transform (RGTET) is further constructed. The computational framework of RGTET is rigorously derived, along with its corresponding inverse transform expression. Compared with existing methods, RGTET demonstrates superior adaptive performance in parameter adaptation. Finally, the proposed methods are applied to analyze pulse-like mechanical fault vibration signals and conduct time–frequency analysis of continuous signals under complex background noise conditions. The results demonstrate the robustness and adaptability of the proposed RTET and RGTET methods. Full article

Aspartylglucosaminidase (AGA) is an amidohydrolase that can hydrolyze the amide bond between N-acetylglucosamine (GlcNAc) and asparagine (Asn), producing N-acetylglucosamine and aspartic acid (Asp). AGA is distributed in both eukaryotes and prokaryotes. In this study, we identified the sequence of AGA in Elizabethkingia meningoseptica (EmAGA), cloned and expressed it in Escherichia coli, and recharacterized its properties, confirming its substrates as aspartylglucosamine (Asn-GlcNAc) and Asn. Key residues affecting its enzymatic activity were predicted through molecular docking and conserved site analysis, and 10 key residues that affected enzymatic activity were verified, eight of which regulated activity by interfering with EmAGA’s autoproteolysis, indicating that autoproteolytic cleavage into α/β subunits was essential for EmAGA maturation. Molecular dynamics simulations were performed on autohydrolysis-impaired mutants, which showed a more stable conformation and lower energy. In summary, EmAGA’s functional characterization provided novel evidence for elucidating its molecular mechanism. Clinically used Asparaginase (ASNase) exerts its therapeutic effect on acute lymphoblastic leukemia (ALL) through its asparaginase activity, but it is limited by glutamine off-target side effects, while EmAGA also has asparaginase activity but no glutaminase activity, rendering its potential as a basis for novel anti-leukemic enzymatic therapeutics. Full article

Background/Objectives: Dyspnoea in heart failure with reduced or mildly reduced ejection fraction (HFrEF/HFmrEF) is multidimensional, yet conventional unidimensional scales do not capture its sensory and affective components. The Multidimensional Dyspnea Profile (MDP) addresses this gap; however, its psychometric properties have not been established in a dedicated HFrEF/HFmrEF cohort. We assessed structural validity, internal consistency, test–retest reliability, and construct validity of the MDP using COSMIN methodology. Methods: In this prospective, single-centre psychometric validation study, 101 clinically stable adults with HFrEF or HFmrEF were enrolled at a tertiary outpatient cardiac clinic in Riyadh, Saudi Arabia. Participants completed the MDP alongside Dyspnea-12, modified Medical Research Council scale, Kansas City Cardiomyopathy Questionnaire-12, Fatigue Severity Scale, and 6 min walk test. Test–retest data were obtained at 12 days in patients confirmed stable by the Global Rating of Change (n = 87). Psychometric evaluation included Cronbach’s α, intraclass correlation (ICC₂,₁), standard error of measurement, minimum detectable change (MDC₉₅), confirmatory factor analysis (comparative fit index [CFI], root mean square error of approximation [RMSEA], standardised root mean square residual [SRMR]), and 12 a priori construct hypotheses. A preliminary minimal clinically important difference (MCID) was estimated using anchor- and distribution-based methods. Results: The mean age was 55 ± 11 years and 80% were male. CFA supported the two-factor model (CFI = 0.96; RMSEA = 0.061; SRMR = 0.058). Cronbach α was 0.92 for the full scale, 0.88 for immediate perception, and 0.91 for emotional response. ICC_2,1 was 0.94 (95% CI: 0.91–0.96), and MDC₉₅ was 4.2 points. All 12 hypotheses were confirmed. The preliminary MCID was 8 points. Conclusions: The MDP is a reliable, valid, and clinically interpretable multidimensional dyspnoea measure in HFrEF/HFmrEF. The 8-point MCID is preliminary and requires confirmation in larger longitudinal intervention studies. Full article

The yak (Bos grunniens), a unique bovine species that is endemic to the Qinghai–Tibet Plateau and adjacent mountainous regions, exhibits remarkable adaptations to chronic high-altitude hypoxia. However, the molecular mechanisms underlying yaks’ adaptation to this extreme environment remain poorly understood. This study aimed to elucidate the spatiotemporal expression dynamics of hypoxia-inducible factor 1α (HIF-1α) and 2α (HIF-2α) in major tissues of yaks across developmental stages (0.5, 1.5, 2.5, and 4.5 years; n = 3 per group). The tissues (heart, liver, spleen, lungs, kidneys, blood vessels and skeletal muscles) were analyzed using hematoxylin and eosin (H&E) staining and immunohistochemistry. The results revealed significant differences in the expression levels of HIF-1α and HIF-2α between tissues and at different ages. In cardiac tissue, both HIF-1α and HIF-2α are localized to the myocardial interstitium, with HIF-1α expression peaking at 1.5–2.5 years and HIF-2α expression reaching its maximum at 2.5 years. Hepatic HIF-1α showed perivenous hepatocytes enrichment and peaked at 2.5 years (p < 0.01 vs. other ages), while HIF-2α was uniformly distributed across lobules without age-related changes. Splenic HIF-1α and HIF-2α levels increased progressively with age, both peaking at 4.5 years (p < 0.01), and age was strongly correlated with expression levels (HIF-1α: r = 0.430; HIF-2α: r = 0.493). In pulmonary tissues, HIF-1α in bronchial smooth muscle peaked at 2.5 years, whereas alveolar septal HIF-2α peaked at 1.5 years (p < 0.05). In the kidney, HIF-1α was primarily localized to tubular epithelial cells and HIF-2α was diffusely distributed in the glomerular interstitium; neither factor showed significant variation across ages. In vascular tissues, HIF-1α expression remained stable across all ages and was predominantly observed in the smooth muscle layer, while HIF-2α exhibited a significant peak in endothelial cells at 2.5 years (p < 0.01). These findings suggest that HIF-1α predominates during early development stages, while HIF-2α becomes dominant as yaks approach maturity. Full article

This study evaluates how titanium and polymethyl methacrylate (PMMA) Boston Keratoprosthesis backplate substrates influence human corneal fibroblast proliferation, cytotoxicity, morphology, activation phenotype, and mechanotransductive signaling. Human corneal fibroblasts were cultured on titanium and PMMA, with tissue culture plastic or glass as controls. Proliferation was assessed over 7 days using metabolic assays, and cytotoxicity was measured by lactate dehydrogenase release. Cell morphology and surface coverage were examined by scanning electron microscopy, while immunofluorescence quantified fibroblast-specific protein 1 (FSP-1) and α-smooth muscle actin (α-SMA). Gene expression of α-SMA, collagen I, FSP-1, and focal adhesion kinase (FAK) was analyzed by quantitative PCR. Cells cultured on both substrates maintained stable viability with modest increases in estimated cell numbers and comparable proliferation curves, indicating preserved metabolic activity without growth suppression. Cytotoxicity remained low and similar between groups. SEM demonstrated broader and more continuous cell spreading on titanium, whereas cells on PMMA were more sparsely distributed. Immunofluorescence showed higher FSP-1 expression on titanium and increased α-SMA on PMMA. Gene expression analysis revealed higher FAK transcripts on PMMA, with no significant differences in α-SMA, FSP-1, or collagen I. These results confirm the cytocompatibility of both titanium and PMMA backplates with human corneal fibroblasts and support their use with the Boston Keratoprosthesis. Full article

Impulsive alpha (α)-stable noise, characterized by heavy tails and intense outliers, is a key ingredient in simulating financial, medical, seismic, and digital communication technologies. It poses versatile challenges to conventional machine learning (ML) algorithms in predicting noise parameters for multidisciplinary artificial intelligence (AI)-embedded devices. In this study, we adopted a two-phase methodology to investigate the complexity and performance of supervised ML algorithms while classifying impulsive noise parameters. We generated synthetic datasets of α-stable noise distributions for experimentation in a controlled environment. It was followed by experimental evaluation to derive the complexity and performance of ML classifiers—k-nearest neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and Random Forest (RF). Moreover, we employed a very high channel noise level of −15 dB in the test datasets to ensure that the derived analysis applies to real-world devices. The results demonstrate the high performance of DT and RF in structured binary classification of the α regime and the sign of skewness, while incurring satisfactory computational costs. However, SVM and kNN are comparatively more robust for multi-class classification, albeit with higher memory and training costs. On the contrary, NB fails to address the skewed and impulsive behavior of α-stable noise. We observed that even the most effective classifiers struggle to achieve perfect accuracy in multi-class classification. Overall, the experimental results reveal significant trade-off relationships between the complexity and performance of ML classifiers. Conclusively, simple models are well-suited for coarse-grained tasks, such as α-approximation and sign-of-skewness classification. In contrast, sophisticated models can be deployed to predict noise parameters to some extent. Our study provides a clear set of trade-offs for future applied AI devices that address adversarial and impulsive noise. Full article

This paper develops a rigorous inferential framework for a class of Gaussian stochastic processes driven by white noise with constant drift, whose temporal evolution is governed by a Caputo fractional derivative of order ${\displaystyle \alpha \in (1/2,1)}$. The model belongs to the family of fractional Volterra processes, where memory is generated by the dynamics themselves rather than by correlated noise. We derive explicit analytical expressions for the mean, variance, and covariance structure of the solution, thereby characterizing in a precise manner how the fractional order ${\displaystyle \alpha }$ governs both variance growth and the strength of temporal dependence. In particular, the process exhibits correlated increments and a power-law variance scaling of order ${\displaystyle t^{2\alpha -1}}$, highlighting the dual role of ${\displaystyle \alpha }$ as a regularity and memory parameter. Building on this structural analysis, we address the statistical problem of estimating the parameter vector ${\displaystyle (\mu ,\sigma ,\alpha )}$ from discrete-time observations. Two complementary procedures are proposed for the estimation of the fractional order: a variance-growth method based on log–log regression of empirical variances, and a wavelet-based estimator exploiting multi-scale scaling properties of the process. For the drift and diffusion parameters ${\displaystyle (\mu ,\sigma )}$, we construct explicit Gaussian pseudo-maximum likelihood estimators derived from the Volterra covariance structure of the increment process. We establish unbiasedness, ${\displaystyle L^2}$-convergence, strong consistency, and asymptotic normality for all estimators. Furthermore, we derive Berry–Esseen type bounds that quantify the rate of convergence toward the Gaussian law, providing sharp distributional approximations in a genuinely fractional and non-Markovian setting. A Monte Carlo study is carried out, using high-resolution Volterra discretizations, large-scale simulation budgets, covariance-structured linear algebra, and multi-scale diagnostic tools. The numerical experiments confirm the theoretical convergence rates, demonstrate the finite-sample reliability of the estimators, and illustrate the sensitivity of the process dynamics to the fractional order ${\displaystyle \alpha }$: smaller values of ${\displaystyle \alpha }$ produce stronger memory effects and higher variability, while values closer to one lead to smoother and more stable trajectories. The proposed methodology unifies statistical inference for long-memory Gaussian processes with fractional differential stochastic dynamics, offering a coherent analytical and computational framework applicable in areas such as quantitative finance, anomalous diffusion in physics, hydrology, and engineering systems with hereditary effects. Full article

Anthocyanins are naturally occurring flavonoid pigments widely distributed in plants and are recognized for their antioxidant and anti-inflammatory activities. However, the molecular mechanisms underlying their potential immunomodulatory effects remain poorly characterized, particularly regarding their direct interactions with key signaling cytokines. In this study, a set of selected anthocyanins was investigated using a hierarchical computational workflow targeting three major pro-inflammatory cytokines: interleukin-2 (IL-2), interleukin-17 (IL-17), and tumor necrosis factor-α (TNF-α). Molecular docking analyses identified primulin and antirrhinin as the most favorable binders, forming stabilizing hydrogen bonds and hydrophobic interactions within predicted cytokine interaction interfaces. To further assess the stability of these complexes, molecular dynamics simulations were performed under near-physiological conditions. Trajectory analyses demonstrated stable ligand–protein interactions and persistent intermolecular contacts throughout the 100 ns simulation period. These findings provide molecular-level insights into anthocyanin–cytokine interactions and highlight their potential relevance for modulating inflammatory signaling pathways. Full article

Background and Objectives: Soft denture lining materials improve stress distribution and patient comfort but can lose mechanical stability under routine chemical cleansing. This study aimed to evaluate the time-dependent effects of two alkaline peroxide-based denture cleansing tablets (i.e., Efferdent and Protefix) on Shore A hardness and weight change of three soft lining materials (i.e., Ufi Gel P, Ufi Gel SC, and Visco-gel) at days 1, 7, and 30. Materials and Methods: Ninety specimens (n = 10/group) were assigned to a 3 × 3 factorial design. Specimens were immersed in cleansing solutions for 8 h daily and stored in artificial saliva for 16 h; controls remained solely in artificial saliva. Shore A hardness was measured using a durometer, and weight was recorded with a precision scale. Data were analyzed by mixed-design ANOVA and linear regression (α = 0.05). Results: Material type significantly affected hardness and weight change (p < 0.001). Visco-gel showed a marked increase in Shore A hardness (from about 15–16 to 26–27 HA) and greater weight loss (approximately 0.04–0.06 g), whereas silicone-based materials (Ufi Gel P and Ufi Gel SC) demonstrated more stable hardness values (from about 24–25 to 31–32 HA) with minimal weight variation (generally below about 0.02 g). The type of cleansing tablet had a smaller but significant effect (p = 0.004), with Protefix causing greater alterations. Weight change was negatively correlated with hardness increase (R² = 0.33, p < 0.001). Conclusions: Within the limitations of this in vitro study, material composition was identified as the main determinant of degradation resistance, with silicone-based liners demonstrating greater durability under the tested conditions, while Efferdent may be considered a milder option for long-term cleansing. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article

Tourism 4.0 integrates Industry 4.0 technologies into tourism services to enhance visitor experiences and improve destination management. This study presents the design, implementation, and pilot validation of an integrated IoT–Augmented Reality (IoT–AR) cyber-physical urban node developed for smart tourism infrastructure in Baños de Agua Santa, Ecuador. The system combines distributed environmental sensing, LoRa-based communication, edge-level preprocessing, cloud data management via RESTful services, and immersive visualization through a cross-platform augmented reality mobile interface. The development followed the TDDM4IoTS methodology, adapted into five phases covering requirements analysis, technological design, modeling, validation, and deployment. The architecture supports contextual real-time information delivery while maintaining low power consumption and robustness under heterogeneous connectivity conditions. Field tests confirmed stable communication between sensor nodes and the gateway, as well as reliable AR marker recognition under varying light and distance conditions. Usability evaluation using the System Usability Scale (SUS) yielded a mean score of 84.38, classified as excellent, with high internal consistency (α ≈ 0.89). The results demonstrate technical feasibility and strong user acceptance, providing a scalable and replicable model for interactive IoT–AR urban systems in smart tourism environments. Full article

We study a parsimonious constrained two-component Gaussian mixture with symmetric locations $\pm \lambda$ and unequal weights controlled by $\alpha \in [-1,1]$; we refer to this family as the asymmetric bimodal normal. The constraint eliminates label switching and yields an identifiable parametrization for $\lambda >0$, while noting the boundary degeneracy at $\lambda =0$ where $\alpha$ is not identifiable. We derive closed-form analytical expressions for the density and distribution functions, an equivalent constructive representation (useful for simulation and interpretation), explicit moment formulas, and conditions distinguishing unimodality from bimodality. For inference, we develop maximum likelihood estimation with observed information standard errors and provide numerically stable fits via a block-coordinate quasi-Newton routine using method of moments initial values. A Monte Carlo simulation study across representative parameter settings evaluates bias and root mean squared error, and examines the behavior of Hessian-based standard error estimates, highlighting regimes where the observed information becomes ill-conditioned under weak separation. Empirical analyses, chemical calibration deviations from the National Institute of Standards and Technology and a regression example with asymmetric errors, show competitive or superior fit and interpretability relative to skewed normal alternatives, asymmetric Laplace models, and unconstrained Gaussian mixtures, with consistent advantages under model comparison using the Akaike information criterion and the Bayesian information criterion. Full article