Search Results (4,285)

Background/Objectives: Although copper nanoparticles (Cu-NPs) are increasingly explored as food and feed additives, there is still limited evidence on how the commonly consumed dietary fibre matrix modulates their effects on the gut microbiota. This study evaluated whether different dietary fibres (cellulose, pectin, inulin, psyllium) modulate Cu-NP–driven changes in caecal microbiota activity, composition, and bile acid metabolism in rats in a multifactorial design accounting for fibre type, copper dose, and copper form. Methods: Wistar male rats (n = 10 per group, 10 groups) were fed semi-purified diets for 6 weeks. Cu-NPs were provided at 6.5 or 13 mg Cu/kg diet and combined with cellulose (control fibre) or with pectin, inulin, or psyllium. Caecal digesta parameters, microbial enzyme activities, short-chain fatty acids (SCFAs), bile acids, and 16S rRNA sequencing were used to assess microbial diversity. Results: Final body weight did not differ among groups, whereas feed intake decreased most consistently with inulin and psyllium. Inulin and psyllium increased caecal digesta and tissue mass, while pectin increased caecal ammonia. Higher Cu-NPs dose reduced several microbial enzyme activities and lowered major SCFAs across most treatments; pectin most strongly preserved/enhanced glycosidase activities and was associated with increased SCFA levels vs. control, with a 32% rise in acetate, a 47% rise in propionate, and a 61% rise in butyrate. Fibre type dominated bile acid outcomes: psyllium reduced total bile acids by 11.8% vs. control, while inulin increased muricholic acids by 216% vs. control. Microbiota alpha and beta diversity separated primarily by fibre type, with distinct clustering particularly in pectin-fed groups. Across comparisons, Mucispirillum was consistently reduced in fibre-supplemented groups vs. cellulose, alongside recurrent changes in selected genera; functional profiling highlighted shared shifts in carbohydrate, fermentation, transport, and stress-response features under Cu-NPs exposure. Conclusions: The gastrointestinal and microbiota responses to Cu-NPs are strongly fibre-dependent; thus, Cu-NP safety and functionality should be evaluated together with the accompanying dietary fibre matrix, not as a standalone exposure. Implications for humans remain indirect and require confirmation in human-relevant models and clinical settings. Full article

Background/Objectives: Pulmonary tuberculosis (TB) is accompanied by inflammation-driven hypercoagulability and increased venous thromboembolism risk. We investigated whether the natural anticoagulants protein C and free protein S are reduced in active TB and whether baseline levels are associated with bacillary burden, treatment response, CT evolution, and pulmonary embolism (PE). Methods: We conducted a prospective cohort study in Romania, including 63 adults with newly diagnosed, bacteriologically confirmed, drug-susceptible pulmonary TB and 30 TB-free controls (October 2024–December 2025). Venous blood was collected at baseline (before anti-TB therapy) and at 6 months to quantify inflammatory and coagulation parameters, protein C, and free protein S. Sputum AFB smear was assessed at baseline, 2 months, and 6 months; chest CT was performed at baseline and 6 months. Propensity score matching (age, sex, BMI, smoking) and multivariable regression were used to account for confounding. Logistic regression and ROC analyses evaluated the prediction of BK persistence. Results: Compared with controls, TB patients had substantially lower baseline protein C and free protein S levels, and higher D-dimer levels (all p < 0.001). In matched multivariable models, TB status remained independently associated with lower baseline natural anticoagulant levels. Lower baseline protein C and free protein S clustered with higher inflammatory markers and higher bacillary burden, and independently predicted BK persistence at 2 and 6 months (OR per 1%-point increase ~0.93–0.95 for protein C and ~0.92–0.94 for free protein S; all p < 0.001). Discrimination for BK persistence was high (AUCs ~0.88–0.89). Lower baseline levels of natural anticoagulants were also associated with greater residual CT abnormalities at 6 months. PE cases had significantly lower protein C and free protein S than PE-free patients. Conclusions: Active pulmonary TB is associated with marked depletion of protein C and free protein S. Baseline reductions identify patients with higher inflammatory/coagulation activation, higher bacillary burden, delayed microbiological clearance, more residual CT disease, and PE, supporting their potential role as adjunct risk-stratification biomarkers. Full article

Variational Mode Decomposition (VMD) is a powerful formalism for the time-scale analysis of vibration signals from rotating machinery. However, its performance is often compromised by complex parameter configuration, where subjective manual tuning leads to mode mixing or information loss. In this study, we present a physics-guided framework that generalizes VMD optimization across diverse operating conditions. We utilized a meta-dataset combining three distinct sources (CWRU, HUST, UO) to validate the approach. Through a shaft-normalized segmentation strategy and K-Means++ clustering, we identified six distinct signal archetypes based on spectral morphology. Central to this framework is the Energy-Dispersion Index (EDI), a novel physically interpretable metric designed to differentiate between structured fault transients and stochastic noise. Extensive validation via a full-factorial Design of Experiments (8640 trials) confirmed the statistical superiority of EDI over benchmarks like kurtosis and envelope entropy, yielding an 8.3% improvement in modal fidelity. Furthermore, a rigorous ablation study demonstrated that the proposed archetype-based parameterization is highly efficient. This strategy achieved a $392\times$ speedup over online optimization while maintaining statistically equivalent diagnostic accuracy. Additionally, by generalizing parameters from high-quality archetype representatives, the framework reduced spectral leakage (Orthogonality Index) by 51.4% compared to instance-wise optimization. The resulting framework provides a mathematically rigorous, real-time solution for automated vibration signal decomposition. Full article

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

Sub/super-synchronous oscillations induced by the interaction between wind turbines and the grid pose increasing challenges to the dynamic analysis of power-electronics-dominated power systems. For microgrids comprising a large number of distributed direct-drive wind turbines (DDWTs), detailed electromagnetic transient modeling becomes computationally prohibitive, while conventional single-machine equivalent models often fail to capture critical oscillatory characteristics. To address these issues, this paper proposes an impedance-sensitivity-based clustering and equivalent modeling method for DDWT groups in a microgrid. First, a frequency domain impedance model of DDWTs is established, and the impedance sensitivities of key control parameters are analyzed under various steady-state operating conditions. By jointly considering the absolute magnitude of impedance sensitivity and its variation across operating points, a sensitivity-informed criterion is developed to select physically meaningful clustering indices capable of distinguishing wind turbines with different operating conditions. Based on the selected indices, a k-means clustering algorithm is employed to group distributed DDWTs, and a multi-machine equivalent model is constructed accordingly. Simulation studies under impedance disturbances validate the effectiveness of the proposed equivalent model in accurately reproducing the oscillation characteristics of a microgrid with multiple DDWTs. Full article

Background and Objectives: In real-world NSCLC management, prognostic assessment extends beyond tumour staging and molecular profiling, which represent a partial timeframe of disease biology. Routinely collected inflammatory and haematological markers may better reflect the dynamic host–tumour interactions during treatment. This study assessed the prognostic significance of baseline and longitudinal neutrophil-to-lymphocyte ratio (NLR) and haemoglobin levels on survival outcomes in a real-world NSCLC cohort. Materials and Methods: We conducted a retrospective observational cohort study of 615 patients with histologically confirmed NSCLC diagnosed between 1 May 2022 and 30 April 2024 at a tertiary referral centre in western Romania. Survival outcomes, including progression-free and overall survival, were analysed through Kaplan–Meier curves, complemented by 12-month restricted mean survival time estimates. High NLR was defined as ≥3 and low haemoglobin as <12 g/dL. Longitudinal changes were evaluated at 6 and 12 months, with 12-month analyses restricted to patients alive at that landmark. Results: The cohort had a median age of 66 years (IQR 60–72) and was predominantly male (66.3%). Most patients presented with advanced disease (60.3% stage IV, 23.6% stage III). At baseline, 57.1% (n = 351) exhibited high NLR and 39.8% (n = 245) had low haemoglobin. Median PFS was 9.0 months (IQR 4.5–15.5), and median OS was 16.5 months (IQR 8.5–27.0). Stage IV disease was associated with shorter PFS than stages I–II (7.0 vs. 20.8 months; log-rank p < 0.001). High-baseline NLR showed a borderline association with shorter PFS (adjusted HR 1.40; 95% CI 0.98–1.95). Among the 436 patients alive at 12 months, NLR increased in 56.7% of cases, and this increase showed a non-significant trend toward shorter PFS (HR 1.35; 95% CI 0.95–1.90; p = 0.09) in a 12-month landmark analysis. Conclusions: Baseline systemic inflammation and anaemia are highly prevalent in real-world NSCLC patients and cluster with advanced disease. Elevated NLR was associated with poorer survival outcomes, whereas low haemoglobin did not demonstrate a significant independent association in adjusted analyses. These haematological parameters are accessible tools for prognostic assessment in routine clinical practice. Full article

Accurate and efficient screening of nitrogen-efficient rice varieties is crucial for implementing precision agriculture and achieving green and sustainable development. However, traditional screening methods rely on destructive sampling and chemical analysis, which are inefficient and costly, and thus cannot meet the requirements of large-scale breeding applications. Therefore, this study aims to develop a non-invasive, high-throughput screening method for nitrogen efficiency of rice based on unmanned aerial vehicle (UAV) hyperspectral data and machine learning algorithms. Sixty rice varieties were selected as the target, and principal component analysis (PCA) was used to reduce the dimension of seven key agronomic parameters (such as yield, nitrogen utilization rate, etc.). A comprehensive evaluation index for nitrogen utilization efficiency was constructed, and K-means clustering was used to classify the varieties into three categories: nitrogen-efficient, medium-efficient, and low-efficient varieties. On this basis, four machine learning algorithms (decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN)) were used to establish a variety nitrogen efficiency classification model based on spectral indices. The results showed that the indicators constructed based on PCA and clustering could effectively distinguish different nitrogen-efficient varieties; among the four models compared, the DT model achieved the highest overall performance, with an accuracy of 0.75, precision of 0.80, and F1-score of 0.74. This study confirmed the feasibility of combining UAV hyperspectral data with decision tree models, providing a reliable technical solution for the large-scale, rapid, and non-invasive screening of nitrogen-efficient rice varieties. Full article

The two most severe cosmological tensions in the Hubble constant \(H_0\) and the matter clustering amplitude \(S_8\) have the same relative discrepancy of 8.3%, which suggests that they may have a common origin. Modifications of gravity and exotic dark fields with numerous free parameters introduced in the Einstein field equations often struggle to simultaneously alleviate both tensions; thus, we need to look for a common cause within the standard \(\Lambda\)CDM framework. At the same time, linear perturbation analyses of matter in the expanding \(\Lambda\)CDM universe have always neglected the impact of comoving peculiar velocities \(\mathbf{v}\) (generally thought to be a second-order effect), the same velocities that, in physical space, cannot be fully accounted for in the observed late-time universe when the cosmic distance ladder is used to determine the local value of \(H_0\). We have reworked the linear density perturbation equations in the conformal Newtonian gauge (sub-horizon limit) by introducing an additional drag force per unit mass \(-\Gamma(t)\mathbf{v}\) in the Euler equation with \(\Gamma \equiv \gamma(2 H)\), where \(\gamma\ll1\) is a positive dimensionless constant and \(2H(t)\) is the time-dependent Hubble friction. We find that a damping parameter of \(\gamma = 0.083\) is sufficient to resolve the \(S_8\) tension by suppressing the growth of structure at low redshifts, starting at \(z_\star\simeq 3.5\)–6.5 to achieve \(S_8\simeq 0.78\)–0.76, respectively. Furthermore, we argue that the physical source causing this additional friction (a tidal field generated by nonlinear structures in the late-time universe) is also responsible for a systematic error in the local determinations of \(H_0\)—the inability to subtract peculiar tidal velocities along the lines of sight when determining the Hubble flow via the cosmic distance ladder. Finally, the dual action of the tidal field on the expanding background—reducing both the matter and the dark energy sources of the squared Hubble rate \(H^2\), thereby holding back the cosmic acceleration \(\ddot a\)—is of fundamental importance in resolving cosmological tensions and can also substantially alleviate the density coincidence problem. Full article

To address the challenges of traditional methods for monitoring rock dynamic hazards in mines, which struggle to fully characterize the spatiotemporal heterogeneity of damage evolution and the resulting lag in early warning, this paper proposes a dynamic rock damage classification and fracture early warning model driven by acoustic emission data. Based on an improved dynamic K-means algorithm, this model fuses time dependence, energy intensity, and event spatial density characteristics through exponentially decaying weights to construct a spatiotemporal-energy synergistic clustering framework. Furthermore, a nonlinear coupling model for the comprehensive risk index (RI) is established, combining the static damage variable D with dynamic parameters such as energy release rate, ring count, and spatial clustering, to create a five-level early warning threshold. Experimental results demonstrate that the improved algorithm achieves clustering silhouette coefficients exceeding 0.7 for single-source, multi-source, and complex fracture patterns, and the error between cluster regions and actual fracture distribution is less than 1 mm. The RI model accurately identifies the damage state of the test block and effectively predicts critical instability, significantly improving both timeliness and accuracy. This research overcomes the limitations of traditional static evaluation and provides high-precision technical support for real-time monitoring of hidden rock fractures and prevention and control of mine dynamic hazards. Full article

In order to overcome the computational challenges associated with block preconditioners for Krylov subspace methods, particularly those arising from Schur complement systems, this paper proposes an improved new block (INB) preconditioner for solving 3 × 3 block saddle point problems. A detailed semi-convergence analysis of the iterative scheme induced by the INB preconditioner is provided. Moreover, the spectral properties of the preconditioned matrix are analyzed, revealing strong eigenvalue clustering around one. Efficient formulas for selecting quasi-optimal parameters are derived based on Frobenius-norm minimization. Extensive numerical experiments demonstrate that the proposed INB preconditioner significantly reduces iteration counts and CPU time compared with several existing block preconditioners. Full article

Customer Segment Classification is a machine learning task in marketing analytics that involves assigning customers to predefined categories using features derived from historical transactional data. However, conventional approaches, such as statistical and clustering-based algorithms, may face challenges in fully capturing the nonlinear relationships in customer data, which can lead to limited insights and suboptimal segmentation outcomes. This paper introduces RFM-Net, an approach that integrates Deep Learning with Recency, Frequency, and Monetary (RFM) analysis for customer segment classification. By leveraging RFM features as input and labeled customer segments as output, we designed a specialized Convolutional Neural Network (CNN) model tailored for classification tasks. In the proposed method, labels are generated by a rule-based logic from RFM scores and then used as supervised ground truth. Accordingly, learning an expert-defined mapping is employed to model customer segmentation, rather than discovering a new segmentation structure. The proposed method enables businesses to classify customers into strategically meaningful segments such as Champions, Loyal Customers, At Risk, and Hibernating, thereby facilitating effective and targeted marketing strategies. Unlike traditional CNN architectures, RFM-Net offers a more compact, lightweight, and computationally efficient model with fewer layers and parameters, supporting improved interpretability and reduced risk of overfitting. Experimental results conducted on a real-world dataset demonstrated the effectiveness of RFM-Net with an accuracy of 94.33%. The results of this study showed a relative average increase of 13.17% compared to the results reported in previous studies on the same dataset. The core contribution of this research lies in combining the powerful generalization capabilities of deep learning with the effectiveness of RFM analysis, offering a robust solution for data-driven customer relationship management. Full article

Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, enabling robust perception under fast motion and challenging lighting conditions. Nevertheless, event streams are susceptible to background activity, thermal noise, and hot pixels. Their sparse and irregular patterns can corrupt event structures and degrade downstream tasks. We propose MSF, a multi-level spatiotemporal filtering framework that couples motion-compensated aggregation with neighborhood-level verification. In each temporal window, MSF estimates a constant 2D optical flow by maximizing a robust, density-normalized contrast objective on the image of warped events (IWE). We further incorporate polarity–gradient decorrelation to suppress mixed-polarity noise and an explicit peak-suppression regularizer to avoid hot-pixel-induced degeneracy. The motion parameters are optimized via coarse grid initialization followed by gradient-ascent refinement. Based on the estimated motion, MSF performs hierarchical event selection: central events are extracted from high-confidence aggregated regions, local events are recovered through joint spatial–temporal–directional–polarity consistency, and weak border events are identified using a density-normalized probabilistic support model that rewards support from reliable structures while penalizing self-clustering. Experiments on four public benchmarks (DVSNOISE20, DVSMOTION20, DVSCLEAN, and E-MLB) show that MSF consistently improves the Event Structural Ratio (ESR) and outperforms representative baselines across diverse motion regimes and severe low-light noise. Full article

Background/Objectives: Evidence regarding the relationship between thyroid function tests (TFTs) and severe or treatment-resistant depression in euthyroid individuals remains limited. We aimed to investigate thyroid function tests (TFTs) in euthyroid patients with depression undergoing electroconvulsive therapy (ECT), evaluate associations with ECT response and depression severity, and explore whether clinically meaningful subgroups with differential thyroid function patterns can be identified. Methods: In this retrospective cohort study, we screened 107 inpatients who received ECT for severe or treatment-resistant depression (major depressive disorder [MDD] or bipolar disorder [BD]). Seventy-six euthyroid patients were analyzed. Clinical data, Hamilton Depression Rating Scale (HAMD) scores, and TFTs (TSH, free-T3, and free-T4) were assessed. Logistic regression, multiple linear regression and unsupervised hierarchical cluster analyses were performed. The cluster analysis used clinical and demographic variables, excluding TFTs to avoid circularity and allow thyroid parameters to be examined as secondary biological correlates. Results: The TFT results were not significantly associated with ECT response in euthyroid patients. The multiple linear regression revealed that the baseline HAMD scores were positively associated with free-T4 (β = 0.797, p = 0.001). Hierarchical clustering identified two subgroups; one group characterized by male sex, psychotic features, and MDD diagnosis exhibited lower TSH levels (2.12 vs. 1.49 mlU/L, Cohen’s d = 0.56) despite similar ECT response rates. Conclusions: Subtle TFT variations were not associated with ECT response but were related to depression severity and clinical phenotypes. These findings suggest that normal-range thyroid hormone variability may reflect state-related neuroendocrine patterns rather than predictors of treatment outcome. Our results should be regarded as hypothesis-generating and underline the need for prospective studies to clarify the clinical significance of thyroid function variability in severe depression. Full article

Objectives: To analyze speech perception outcomes of a cohort of experienced adult cochlear implant (CI) users to explore whether there is a correlation with electrical dynamic range (EDR) parameters, and to describe speech intelligibility curve morphology according to the degree of CI performance. Methods: A bicentric retrospective observational study. Data were extracted from a cochlear implantation database from a total of 36 CI users implanted with Advanced Bionics devices. Results: Mean age at implantation was 56.61 years. In the majority of cases, hearing loss onset was more than 15 years before implantation (80.55%), and only 11.11% of cases preserved residual hearing. This resulted in a significant relationship between speech therapy and better speech recognition (p = 0.044). At the same time, no correlation was found between age, duration of deafness before implantation, and maximum speech perception achieved (p > 0.05). Mean speech audiometry curves displayed a roll-over phenomenon in poor performers and a plateau effect in average performers. In contrast, the mean curve of high performers exhibited a steeper morphology (p < 0.0001). Speech recognition threshold (SRT) and word recognition score (WRS) were predictors of speech audiogram curves (p = 0.006). No direct correlation was found between the mean T-level, M-level, dynamic range, and maximum recognition score, even after clustering electrodes by position along the cochlea (p > 0.05). Conclusions: EDR parameters did not emerge as independent predictors of speech recognition outcomes within this specific cohort. Speech therapy and rehabilitative efforts showed a significant relationship with improved performance, and speech audiogram curve morphology may offer a more specific clinical tool for assessing global CI performance. Further prospective studies with larger, more homogenous populations are required to validate these findings. Full article

Thermal groundwater resources constitute valuable health-oriented georesources, particularly when integrated into regional strategies for wellness, balneotherapy, and therapeutic tourism. This study presents the first comprehensive and integrated hydrochemical, geospatial, and balneological characterization of thermal groundwater systems in Uruguay, enabling their classification from a medical hydrology perspective and supporting the assessment of their potential use in balneotherapy. Seven thermal groundwater sources located in northwestern Uruguay were investigated, mainly associated with the Guaraní Aquifer System (GAS), together with the singular Almirón spring, which represents a distinct hydrogeological setting. Field measurements and laboratory analyses were conducted to determine physicochemical parameters, major ions, and gases. Hydrogeochemical facies were identified using Piper and Gibbs diagrams, while multivariate statistical techniques, including Principal Component Analysis (PCA) and hierarchical clustering, were applied to discriminate water types and support their balneological classification. The results indicate that most thermal waters associated with the GAS are characterized by sodium–bicarbonate facies, weak to medium mineralization. Dry residue to 180 °C, (311–734 mg/L), and mesothermal to hyperthermal temperatures (36.3–44.5 °C), reflecting deep confined circulation and prolonged water–rock interaction. By comparison, the Almirón spring exhibits a chloride–sodium facies with strong mineralization. Dry residue to 180 °C, (6590 mg/L) and hypothermal (32 °C), consistent with a distinct hydrogeological origin involving crystalline basement and Devonian sedimentary units and reflecting more evolved geochemical conditions. Based on the obtained results, and by analogy with comparable international hydrothermal profiles, the main balneological indications of these waters include musculoskeletal and rheumatic disorders, dermatological disorders, and other emerging indications such as stress, sleep disorders, obesity, and Long COVID. In conclusion, this study reveals the hydrochemical diversity of Uruguay’s thermal groundwater and its possible use in balneology. Future research should focus on controlled clinical and balneological studies to validate specific therapeutic effects. Full article