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Systemic Inflammatory and Oxidative–Metabolic Alterations in Rosacea: A Cross-Sectional Case–Control Study
No full textBackground/Objectives: Rosacea increasingly appears to involve systemic immune and metabolic disturbances rather than isolated cutaneous inflammation. To evaluate inflammatory, platelet, and oxidative–metabolic biomarkers in rosacea and explore their interrelations. Methods: 90 patients with rosacea and 90 healthy controls were evaluated for hematologic inflammatory indices—neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune–inflammation index (SII), pan-immune–inflammation value (PIV), mean platelet volume (MPV), and C-reactive protein (CRP)—along with oxidative–metabolic regulators including sirtuin 1 (SIRT1), sirtuin 3 (SIRT3), visfatin, and irisin. Logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of rosacea, while inter-marker associations were evaluated using Spearman’s rank correlation. Results: Rosacea patients showed higher NLR, PLR, SII, PIV, MPV, CRP, and LDL cholesterol (p < 0.05) and lower SIRT1, SIRT3, visfatin, and irisin (p < 0.01). MPV independently predicted rosacea (OR = 7.24; AUC = 0.827), whereas SIRT1 inversely correlated with disease risk. SIRT1, SIRT3, and visfatin showed inverse correlations with HbA1c and waist-to-height ratio, while fasting glucose and HOMA-IR remained within normal ranges. Conclusions: Rosacea exhibits dual systemic activation, an inflammatory–platelet and an oxidative–metabolic axis bridging immune dysregulation, mitochondrial stress, and vascular dysfunction. Recognition of these pathways highlights the potential of redox-targeted and metabolic interventions beyond symptomatic treatment
The Role of Fintech in Enhancing Financial Innovation in Asia: Sustainable Development Approach
No full textInterest in financial inclusion among academics has grown significantly over the past decade. The Sustainable Development Goals (SDGs), which aim to create enabling policies to mobilize financial resources, highlight key factors in poverty reduction and inclusive economic growth, particularly financial inclusion. This study focuses on 15 selected Asian economies. This research examines the role of fintech in promoting financial inclusion in Asia, employing a mixed-methods research design. The literature review part employs critical analysis based on the SciVal bibliometric tool. Quantitatively, it applies the Moments Quantile Regression (MMQR) technique to country-level panel data for 2011, 2014, 2017, and 2021. This study also uses a comparative analysis of digitalization indices provided by the World Bank (WB), specifically the Global Findex Database. The findings reveal that digital payments have the most substantial effect at higher quantiles (τ = 0.5 and 0.75), reflecting their role in deepening financial engagement. Mobile money exhibits significant influence at the lower quantile (τ = 0.25), indicating its role in facilitating initial access for underserved populations. Internet usage contributes positively, albeit moderately, while GDP per capita shows no strong direct effect. Qualitative insights highlight challenges such as regulatory gaps, cybersecurity risks, and digital inequality
Research on Activation of Solid Waste Through Microbial Desilification
No full textTo investigate the biosilicification capabilities of Bacillus mucilaginosus and Bacillus polymyxa, silicon concentrations in supernatants from quartz and calcium silicate cultures were monitored over a 12-day period using inductively coupled plasma optical emission spectrometry (ICP-OES). Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) were employed to evaluate changes in the absorption intensity of Si–O–Si characteristic peaks, crystalline phase transformations in the reaction products, and the microstructural morphology of quartz and calcium silicate before and after microbial leaching. The results show that after leaching with B. mucilaginosus, the dissolved silicon concentration in the quartz supernatant reached a maximum of 73.868 mg/L on day 8. In contrast, following treatment with B. polymyxa, the silicon concentration in the calcium silicate supernatant peaked earlier, at 149.153 mg/L on day 4. After microbial leaching, both substrates exhibited marked changes in the intensity of the infrared absorption peaks at 1071 cm−1 and 1083 cm−1, suggesting the formation of Si–O–R type organosilicon complexes. Iron tailings (containing inert silica) and fly ash (containing active silica) were selected for experimental validation. Following treatment with B. mucilaginosus for desilication over an 8-day period, the activity index of iron tailings increased from 77.83% to 90.51%, while that of fly ash rose from 66.32% to 85.01%. ICP-OES analysis confirmed that under the action of B. mucilaginosus, the trends in silicon concentration and activity index in the supernatant of silica-containing solid wastes, such as iron tailings and fly ash, were consistent with those observed in quartz, thereby demonstrating effective biological desilication. These findings provide novel insights into the development of environmentally sound disposal methods for a wider range of solid waste types
Annoyance Penalty Model for Steady-State Broadband Noise with Varying Spectra
No full textNoise regulations often apply penalties (e.g., +5 dB) to A-weighted equivalent sound pressure levels (LAeq [dB]) to account for increased annoyance from tonal or impulsive features. Psychoacoustic evidence indicates that spectral characteristics also affect annoyance, with some spectra being substantially more disturbing than others. Yet, no established method exists for determining spectrum-based penalties from measured sound spectra. This study aimed to develop a simple, objective model for assigning penalties to steady-state broadband sounds based on spectral properties. Using experimental data comprising annoyance ratings and penalties for 23 spectrally distinct broadband sounds at three LAeq levels (32, 40, and 48 dB), we evaluated several single-number noise descriptors from the literature. Room The Noise Criterion showed the strongest association with direct annoyance ratings, while the spectral centroid (SC) and sharpness were most closely related to spectrum-based penalties. Due to its simplicity, the spectral centroid was selected for the final model: k=6.9·log10(SC)−16.3. The proposed model is expected to be applicable for broadband sounds within 32–48 dB LAeq and offers a practical approach for incorporating spectral effects into noise assessment
Conceptual Neighborhood Graphs of Discrete Time Intervals
No full textTemporal reasoning is an important part of the field of time geography and spatio-temporal data science. Recent advances in qualitative temporal reasoning have developed a set of 74 relations that apply between discretized time intervals of at least two pixels each. While the identification of specific relations is important, the field of qualitative spatial and temporal reasoning relies on conceptual neighborhood graphs to address relational similarity. This similarity is paramount for generating essential decision support structures, notably reasonable aggregations of concepts into single terms and the determination of nearest neighbor queries. In this paper, conceptual neighborhood graphs of qualitative topological changes, with discretized temporal interval relations in the form of translation, isotropic scaling, and anisotropic scaling, are identified using data generated through a simulation protocol. The outputs of this protocol are compared to the extant literature regarding conceptual neighborhood graphs of the Allen interval algebra, demonstrating the theoretical accuracy of the work. This work supports the development of robust spatio-temporal artificial intelligence as well as the future development of spatio-temporal query systems upon the spatio-temporal stack data architecture
Synovial Joint Fluid Metabolomic Profiles and Pathways Differentiate Osteoarthritis, Rheumatoid Arthritis, and Psoriatic Arthritis
No full textBackground: Distinguishing between osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PsA) remains challenging despite different underlying mechanisms. Synovial fluid reflects metabolic changes within affected joints, yet comprehensive metabolomic comparisons across these conditions are limited. We aimed to identify disease-specific metabolic signatures in synovial fluid that could improve differential diagnosis and reveal therapeutic targets. Methods: We collected synovial fluid from 39 patients (20 OA, 5 RA, and 14 PsA) during routine knee arthrocentesis between January 2023 and February 2024. Following metabolite extraction, we performed untargeted metabolomic profiling using quadrupole time-of-flight liquid chromatography–mass spectrometry (Q-TOF LC/MS). Data underwent multivariate statistical analysis, including principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA), to identify discriminatory metabolites. Results: While unsupervised analysis showed overlap between groups, supervised PLS-DA achieved clear metabolic separation. RA samples showed elevated itaconic acid, indicating inflammatory macrophage activation, and increased O-acetylserine, suggesting altered one-carbon metabolism. Hypoxanthine was decreased, which reflected severe metabolic stress. PsA exhibited the unique elevation of 4,4-dimethylcholestane and 2-oxoarginine. These metabolites have previously been unreported in this disease. OA demonstrated increased hippuric acid and indoleacetic acid, which are both gut microbiota products, supporting the gut–joint axis hypothesis. Conclusions: Each arthritis type displayed distinct metabolic fingerprints in synovial fluid. Candidate discriminatory metabolites, including gut-derived metabolites in OA and specific lipid alterations in PsA, open new diagnostic and therapeutic avenues. Given the limited RA sample size (n = 5), RA-related results should be viewed as exploratory and requiring validation in larger independent cohorts. These metabolites may, after rigorous validation in larger and independent cohorts, contribute to multi-metabolite biomarker panels for earlier diagnosis and to the rational design of targeted therapeutics addressing disease-specific metabolic disruptions
Quantum-Resilient Federated Learning for Multi-Layer Cyber Anomaly Detection in UAV Systems
No full textUnmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE–classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data
Investigating the Impact of Wind Tower Geometry on Ventilation Efficiency in Semi-Enclosed Spaces: A Comprehensive Parametric Analysis and Design Implications
No full textPassive building ventilation features, such as wind towers, can help meet rising cooling and ventilation demands in hot, arid regions. However, most prior studies rely on scaled models or isolate single design parameters, limiting holistic insight. This study conducts a full-scale, validated computational fluid dynamics (CFD) parametric analysis of wind tower geometry and its impact on ventilation efficiency in semi-enclosed spaces. Five geometric properties are investigated: tower shape, roof type, number of shafts, separator height, and number of louvres. Additionally, the sensitivity of the optimal configuration to wind speed, wind direction, and louvre orientation is assessed. Results from 88 CFD cases highlight strong interactions among design parameters and show that straight towers with curved roofs consistently perform best. Compared with a tower with six shafts, a flat internal roof, and downward-facing louvres, an optimized tower with four shafts, a convex internal roof, and upward-facing louvres increases airflow rate by a factor of 2.7 and occupied-zone air velocity by 45%, underscoring the importance of holistic geometric optimization
Transition Metal-Doped ZnO and ZrO2 Nanocrystals: Correlations Between Structure, Magnetism, and Vibrational Properties—A Review
No full textTransition metal (TM)-doped zinc oxide (ZnO) and zirconium dioxide (ZrO2) nanocrystals exhibit complex correlations between crystal structure, defect chemistry, vibrational properties, and magnetic behavior that are strongly governed by synthesis route and dopant incorporation mechanisms. This review critically summarizes recent progress on Fe-, Mn-, and Co-doped ZnO and ZrO2 nanocrystals synthesized by wet chemical, hydrothermal, and microwave-assisted hydrothermal methods, with emphasis on synthesis-driven phase evolution and apparent solubility limits. ZnO and ZrO2 are treated as complementary host lattices: ZnO is a semiconducting, piezoelectric oxide with narrow solubility limits for most 3d dopants, while ZrO2 is a dielectric, polymorphic oxide in which transition metal doping may stabilize tetragonal or cubic phases. Structural and microstructural studies using X-ray diffraction, electron microscopy, Raman spectroscopy, and Mössbauer spectroscopy demonstrate that at low dopant concentrations, TM ions may be partially incorporated into the host lattice, giving rise to diluted or defect-mediated magnetic behavior. When solubility limits are exceeded, nanoscopic secondary oxide phases emerge, leading to superparamagnetic, ferrimagnetic, or spin-glass-like responses. Magnetic measurements, including DC magnetization and AC susceptibility, reveal a continuous evolution from paramagnetism in lightly doped samples to dynamic magnetic states characteristic of nanoscale magnetic entities. Vibrational spectroscopy highlights phonon confinement, surface optical phonons, and disorder-activated modes that sensitively reflect nanocrystal size, lattice strain, and defect populations, and often correlate with magnetic dynamics. Rather than classifying these materials as diluted magnetic semiconductors, this review adopts a synthesis-driven and correlation-based framework that links dopant incorporation, local structural disorder, vibrational fingerprints, and magnetic response. By emphasizing multi-technique characterization strategies required to distinguish intrinsic from extrinsic magnetic contributions, this review provides practical guidelines for interpreting magnetism in TM-doped oxide nanocrystals and outlines implications for applications in photocatalysis, sensing, biomedicine, and electromagnetic interference (EMI) shielding
Stability for Caputo–Hadamard Fractional Uncertain Differential Equation
No full textThis paper focuses on the Caputo-Hadamard fractional uncertain differential equations (CH-FUDEs) governed by Liu processes, which combine the Caputo–Hadamard fractional derivative with uncertain differential equations to describe dynamic systems involving memory characteristics and uncertain information. Within the framework of uncertain theory, this Liu process serves as the counterpart to Brownian motion. We establish some new Bihari type fractional inequalities that are easy to apply in practice and can be considered as a more general tool in some situations. As applications of those inequalities, we establish the well-posedness of a proposed class of equations under specified non-Lipschitz conditions. Building upon this result, we establish the notions of stability in distribution and stability in measure solutions to CH-FUDEs, deriving sufficient conditions to ensure these stability properties. Finally, the theoretical findings are verified through two numerical examples