Search Results (47,024)

Completely weathered granite residual soil is a weathering-derived, soil-like geomaterial whose shear strength is difficult to characterize using only conventional small-scale laboratory tests. This study evaluated the effects of specimen size and material structure by comparing in situ direct shear tests, conventional laboratory direct shear tests on undisturbed and remolded specimens, and large-scale laboratory direct shear tests on remolded specimens with box sizes of 150, 200, and 250 mm. The results show that undisturbed specimens exhibited higher shear strength than remolded specimens, indicating a clear structural contribution. With increasing specimen size, cohesion decreased from 41.2 to 31.4 kPa, whereas the friction angle increased from 35.3° to 40.6°. Compared with the conventional undisturbed test, the in situ tests yielded lower cohesion but higher friction angles. These results indicate that both size effect and structural disturbance significantly influence the interpretation of shear strength parameters in completely weathered granite residual soil. For engineering design in weathered-granite terrains, strength parameters derived from larger specimens or in situ tests are likely to be more representative than those obtained from conventional small-scale laboratory tests. Full article

The growing global energy demand and the urgent need to decarbonize the energy sector are driving the search for renewable and low-impact energy sources. Within this context, the conversion of biomass into hydrogen represents a viable pathway to sustainable energy, enabling both carbon mitigation and circular use of agricultural residues. This research focuses on the simulation of an integrated system that converts viticulture residues, vine prunings and grape stalks into biogenic hydrogen through a combination of pretreatment, gasification, and upgrading stages. The analysis of four different supply scenarios shows that the integration of prunings and stalks ensures the highest hydrogen yield (6.61·10⁵ Nm³/year of H₂) and the highest energy self-sufficiency, with 25% of produced syngas used to partially cover internal energy demand. Gasification enables the process to be carbon-negative, saving 1.18 kgCO₂eq for Nm³ of H₂ produced, and economically competitive, with a break-even price of 3.81 €/kg and a return on investment of ten years. The study aligns with the decarbonization goals of the European energy transition, promoting local and circular valorization of agro-industrial waste. Full article

Mining and metallurgical residues represent one of the largest untapped secondary raw-material resources in Europe; however, their critical raw material (CRM) potential remains insufficiently quantified. This study applies a comprehensive mineralogical, geochemical, and geostatistical framework to evaluate nine distinct waste types from the Cartagena–La Unión Mining District (SE Spain), a historically exploited polymetallic system. A total of 79 samples were analysed using X-ray diffraction, wavelength-dispersive X-ray fluorescence, and advanced multivariate statistical techniques (correlation analysis, principal component analysis and hierarchical clustering) to identify geochemical associations controlling CRM distribution. The results reveal strong geochemical heterogeneity, with systematic enrichment in Co, Ni, Cu, Ga, Nb, and rare-earth proxies. Three dominant geochemical controls were identified: (i) a lithogenic silicate association governing Al–Si–Ti–Nb patterns, (ii) a sulphide-derived metalliferous association characterized by Cu–As–Sb, and (iii) an oxidation–adsorption association responsible for Ga–Y affinity. Several CRM concentrations approach or exceed typical global ore grades for secondary resources, particularly in flotation-derived and oxidation-rich residues. Geostatistical modelling confirms spatially coherent CRM hotspots, with base-metal enrichment linked to sulphide relics and Ga–Nb–Y controlled by Fe–Mn oxyhydroxides. Environmental assessment indicates potential metal mobility under acidic conditions, while also highlighting significant remediation benefits associated with residue reprocessing. Taken together, this study provides a robust and reproducible methodology for CRM assessment in legacy mining wastes and identifies priority residue types within the district with the highest strategic recovery potential. Full article

Background: Quiescent cancer cells (QCCs) evade conventional therapies and contribute to minimal residual disease (MRD) and relapse, yet the signaling pathways governing their survival remain poorly understood. Methods: Here, we performed integrative proteomic and phosphoproteomic profiling of triple-negative breast cancer (TNBC) cells transitioning between proliferation and serum removal-induced quiescence, followed by re-stimulation. Results: We identified dynamic remodeling of both proteome and phosphoproteome, with quiescent cells showing downregulation of mitotic drivers and upregulation of extracellular matrix components. Notably, phosphorylation of CK2 substrates was increased during quiescence, and CK2 inhibition using CX-4945 impaired cell survival under nutrient and genotoxic stress, disrupted autophagy, microtubule dynamics, and protein synthesis. Phospho-enrichment and functional assays identified death-associated protein kinase 3 (DAPK3) as a CK2-regulated effector mediating stress-induced apoptosis. In silico analysis confirms a link between high CK2 expression and poor chemotherapy response in basal breast cancer. Conclusions: These findings establish CK2 as a critical survival kinase in QCCs and a potential therapeutic target for MRD eradication in breast cancer. Full article

Efficient calibration is essential for the practical application of option pricing models. The Fractional Stochastic Volatility Jump Diffusion (FVSJ) model can reproduce several stylized features observed in option markets, including the volatility smile, volatility clustering, and long-memory effects. However, its multiple stochastic components make conventional calibration computationally expensive. This paper proposes a two-step calibration framework that combines a neural network with a differential evolution (DE) algorithm. In the first step, we construct a Physics-Informed Kolmogorov–Arnold Network (PCKAN) to approximate the FVSJ pricing map. Specifically, we replace the B-spline basis in KAN with second-kind Chebyshev polynomials and incorporate a Black–Scholes PDE residual as an additional penalty term in the training objective, aiming to improve global approximation and enhance numerical stability and interpretability. In the second step, the trained PCKAN is used as a fast surrogate pricer within the DE algorithm to accelerate parameter estimation. Empirical results show that the proposed method achieves calibration accuracy comparable to direct pricing while substantially reducing computational time. Full article

To address the emerging multidrug-resistance crisis caused by Klebsiella pneumoniae, we expressed the endolysin Lys59 derived from phage VB_KpP_HS106 and performed a comprehensive analysis of its antibacterial activity and structural features. Molecular modeling revealed that Lys59 carries a highly positively charged N-terminus and an amphipathic helix at the C-terminus. In vitro antibacterial assays showed that Lys59 exhibited significant bactericidal activity against K. pneumoniae with an approximately 4 log reduction at 50 µg/mL in 2 h. Meanwhile, Lys59 exhibited potent, broad-spectrum activity against both Gram-negative and Gram-positive bacteria. Stability analysis indicated that Lys59 retained high activity over a pH range of 3–9 and a temperature range of 4–55 °C. Notably, the antibacterial activity of Lys59 was found to be regulated by metal ions. Molecular docking indicated that K⁺ can enhance binding stability by interacting with ASN35 and VAL57. In contrast, Mg²⁺ and Ca²⁺ suppressed catalytic function by binding to the essential GLU17 residue. Furthermore, treatment with 200 µg/mL of Lys59 resulted in a 44.6% reduction in K. pneumoniae biofilm biomass. Overall, this study identified a phage-derived endolysin with broad-spectrum antimicrobial activity and demonstrated its potential as an antibacterial agent against multidrug-resistant K. pneumoniae. Full article

There is growing concern that antimicrobial use (AMU) in livestock may contribute to antimicrobial resistance (AMR) in humans and lead to the consumption of animal-derived foods contaminated with antimicrobial residues. As stakeholders in the livestock industry, farmers must participate in the joint effort to reduce AMU. This cross-sectional study, based on a survey questionnaire, was conducted to evaluate the biosafety measures implemented on small ruminant farms and to assess the knowledge, attitudes and practices (KAP) of small ruminant farmers regarding AMU, AMR and residues. The mean biosafety score obtained was 8.4 points on a 0–17 scale. Some biosafety measures appeared difficult to implement, namely vehicle disinfection, requiring visitors to change clothing and footwear at the farm entrance, cleaning and disinfecting farm facilities, using high-pressure washing equipment, and requiring employees to change clothing and footwear upon entering the farm. Although farmers self-reported moderate levels of knowledge (4.9 points on a 0–7 scale) and positive attitudes (5.8 points on a 0–7 scale), significant gaps in knowledge about antibiotics and antimicrobial stewardship persisted. Practices received lower scores (4.7 on a 0–7 scale), especially regarding medication recording, leftover antibiotic management, and waste disposal. Cluster analysis identified distinct farmer profiles with different patterns of knowledge and practices. These findings underscore the importance of considering farmer heterogeneity when designing interventions aimed at improving AMU. Full article

This study investigates the impact of standby activation strategies on system reliability. The results show that a delayed activation strategy effectively improves system reliability. Additionally, to tackle the difficulty of deriving analytical solutions for reliability metrics under the Weibull distribution, a non-homogeneous Markov model based on the delayed activation strategy is introduced. The system’s residual life is modeled computationally using the state transition method. The numerical results suggest that the proposed method aligns closely with Monte Carlo simulations. It significantly improves computational efficiency while maintaining high accuracy, thus confirming its effectiveness. Full article

Valve stiction remains a persistent nonlinear phenomenon in industrial control loops, often inducing limit-cycle oscillations that degrade control performance, compromise stability, and reduce process efficiency. Reliable detection of stiction is therefore essential for condition-based maintenance and improved operational performance. This study proposes a Hybrid Wavelet–Convolutional Neural Network (HW-CNN) framework for the detection of valve stiction in closed-loop systems. The approach employs the continuous wavelet transform (CWT) to generate time–frequency scalograms that preserve localized energy distributions associated with stick–slip behavior, including transient release events and sustained oscillatory patterns. These representations are subsequently processed using a fine-tuned deep residual neural network to enable automated feature extraction and classification. Unlike conventional signal-based or generic time–frequency learning approaches, the proposed framework is designed to retain control system-specific dynamics within the feature representation, thereby improving the separability of stiction-induced signatures under varying operating conditions. The methodology is evaluated using both simulated control loop data and real industrial datasets obtained from the International Stiction Database (ISDB), ensuring evaluation under controlled and practical conditions. To enhance reliability, performance metrics are reported as averages over repeated experimental runs. The results demonstrate that the proposed HW-CNN framework achieves an accuracy of 96.1% and an F1-score of 96.0% on simulated datasets, and 90.4% accuracy with an F1-score of 90.0% on industrial data. Additional analysis indicates that the model maintains consistent detection capability despite increased variability in real-world conditions. Furthermore, interpretability is supported through Grad-CAM analysis, which shows that the network focuses on physically meaningful regions within the scalograms corresponding to known stiction dynamics. The findings confirm that the integration of wavelet-based feature encoding with deep residual learning provides a robust and interpretable framework for valve stiction detection. Full article

This paper addresses active-mode detection for switched discrete-time Takagi–Sugeno systems from noisy input–output data under candidate-dependent input correction and uncertainty in data-driven observability subspaces. A lifted input–output formulation is developed in which each candidate mode is associated with a mode-dependent forced-response correction and a nominal observability subspace identified offline from representative data. Based on this construction, a practical residual criterion is introduced together with an ideal residual criterion defined by the exact residual projector. An online verifiable sufficient condition is then derived to guarantee consistency between the practical and ideal residual orderings, yielding a conservative but theorem-consistent certification mechanism. To quantify the effect of measurement uncertainty, a component-wise noise-to-signal ratio (NSR) analysis is established, leading to explicit conservative NSR bounds when signal-floor conditions are available offline. These results motivate an adaptive observation-window strategy driven by an explicit online NSR estimate. In addition, an uncertainty-corrected discernibility index based on principal angles between estimated observability subspaces is introduced to assess offline mode separability. Simulations on a switched T–S benchmark show high practical detection accuracy, sound but conservative certification, informative NSR bounds, and stable adaptive-window regulation, including under reviewer-motivated switching stress tests and baseline comparison experiments. Full article

This study aims to improve the accuracy of cruise-phase power consumption prediction for multirotor unmanned aerial vehicles operating under varying wind conditions. Existing parametric energy models typically retain the wind velocity vector in the ground or inertial reference frame, and this representation does not distinguish between axial drag contributions along the fuselage and lateral attitude-correction contributions perpendicular to it. The proposed framework addresses this limitation through a physics-informed coordinate transformation that projects the measured wind vector into the body frame of the aircraft using quaternion-derived heading angles, yielding separate axial and lateral wind components. These components enter the power model as two additional predictors that augment the induced-power baseline, with the axial term following a cubic airspeed–power relationship consistent with parasitic drag formulations and the lateral term following a quadratic relationship consistent with attitude-correction mechanics. The framework is validated on a publicly available flight dataset, which comprises 188 flights of a DJI Matrice 100 quadcopter across payloads of 0 to 0.75 kg, ground speeds of 4 to 12 m/s, and altitudes of 25 to 100 m. Compared with the induced-power baseline, the proposed model reduces the root mean square error by 15.9% and the mean squared error by 29.7% during the cruise phase. The improvement is larger when wind speeds exceed 6 m/s, a regime in which the baseline residuals increase while the proposed model retains a comparatively stable error profile. Residual analysis indicates that baseline errors follow an approximately quadratic trend relative to the axial and lateral wind components, consistent with established parasitic-power and attitude-correction formulations. The closed-form structure of the proposed model is compatible with onboard execution on flight controllers, which suggests a feasible pathway toward its use as the power-prediction module within downstream range-estimation and energy-reserve sizing routines. Full article

To accurately predict the fishing intensity of the Northwest Pacific squid fishing grounds and address the limitations of traditional models in capturing long-term temporal and spatial correlations and neglecting the coupling relationships of deep environmental factors, this study constructs a 3DCNN model and three fusion models incorporating residual, attention, and Transformer mechanisms. Using the 2017–2024 AIS fishing data and ocean environmental variables from the North Pacific squid fishing industry, the models’ performance is compared at 12 different temporal and spatial scales, and key core environmental variables are identified. The results show that the ResNet3D model exhibits the best overall performance, achieving an F1 score of 0.7909 at the 1.0°-7 days temporal–spatial scale. The residual connections effectively mitigate the gradient vanishing problem, balancing prediction accuracy and stability. The optimal spatial resolution is 1.0°, and the key environmental variables include S₁₀₀, Chl-a₁₀₀, PP₁₀₀, and DO₁₀₀. S₁₀₀ is the core driving variable, consistently exhibiting the highest feature importance value at all time scales. It should be noted that Chl-a is considered an indirect indicator of primary productivity, which may influence squid distribution through trophic transfer processes rather than direct biological effects. This study demonstrates the prediction accuracy and applicability of the multi-mechanism fusion 3DCNN model, reveals the temporal and spatial distribution patterns of fishing intensity in the Northwest Pacific squid fishing grounds, and provides scientific methods and technical support for dynamic monitoring, intelligent management, and sustainable utilization of squid resources. Full article

The paper presents an optimisation workflow for modelling of a periodic mechanical structure in the form of a multi-material, axisymmetric beam. The optimisation objective is to prescribe the positions and widths of selected band gaps within a target frequency range for three basic types of structural vibrations: flexural, longitudinal and torsional. The decision variables were geometric parameters of the unit cell and material properties of selected thermoplastics assigned to successive segments of the cell. The frequency characteristics of the beam were determined using the time-domain spectral finite-element method (TD-SFEM). This model was used to perform a sensitivity analysis using the Morris method, which showed the dominant influence of the beam geometry on the position and width of band gaps, with a relatively smaller role of material variability. Due to high computational costs of the global optimisation based on a FEM solver, a surrogate regression model in the form of a residual MLP network was developed to predict the positions and widths of the first five band gaps for each vibration type. The global search was carried out using a genetic algorithm (GA) with the surrogate model and then the results were refined using a deterministic goal-attainment method with a high-fidelity model. Full article

IGF1 plays a critical role in cell proliferation and survival. Previous studies show that IGF1 binds to integrin αvβ3 and induces αvβ3-IGF1-IGF1R ternary complex formation. However, how IGF1 binding to αvβ3 leads to IGF1R activation is unclear. Previous studies showed that Gα13, a guanine nucleotide-binding protein of the G12 class of Gα proteins, binds to the integrin β3 tail through the EEE motif upon fibrinogen binding to integrin αIIbβ3 and induces RhoA activation. We discovered that the EEE/AAA mutation of the β3 tail inhibited IGF1-induced cell survival, suggesting that Gα13 binding to the β3 tail is required for IGF1 signaling. Since RhoA activation may not be directly involved in IGF1R activation, we studied if Gα13 binds to molecules other than RhoA. Since Gα13 binds to several cytoplasmic tyrosine kinases, we studied if Gα13 binds to the IGF1R kinase by a docking simulation. The simulation predicted that Gα13 binds to the IGF1R kinase through a new binding site. Mutating the predicted Gα13 binding site in the IGF1R kinase (residues 1020-1022) or the predicted IGF1R kinase binding site in Gα13 (residues 260-279) inhibited Gα13 binding to the IGF1R kinase, which is consistent with the docking model. Notably, the Gα13(260-279A) mutant inhibited IGF1-induced cell survival. We propose that IGF1 binding to αvβ3 induces Gα13 binding to the β3 tail and subsequent Gα13 binding to the IGF1R kinase, leading to IGF1R activation. Interestingly, Gα13(260-279A) mutation inhibited cell survival due to a constitutively active Gα13(Q226L) mutant. We propose that Gα13(Q226L) induces its effect by binding to the IGF1R kinase. We propose that the Gα13 binding site of the IGF1R kinase or the IGF1R binding site in Gα13 may be a novel therapeutic target. Full article

Background/Objectives: Accurate kidney segmentation from magnetic resonance imaging (MRI) in kidney-transplant patients is essential for quantitative graft assessment, yet it remains challenging due to low tissue contrast, intensity inhomogeneity, and inter-patient anatomical variability introduced by surgical graft placement. Methods: We propose a 3D adversarial segmentation framework that incorporates probabilistic appearance and anatomical shape priors into a residual conditional generative adversarial network (GAN). The framework integrates image-driven and prior-guided information to improve boundary delineation under challenging imaging conditions and is evaluated on 100 kidney-transplant patients across T2-weighted imaging, BOLD-MRI, and DW-MRI using leave-one-out cross-validation. Results: The proposed method achieves mean Dice scores of 90.86% on T2-weighted imaging, 92.02% on BOLD-MRI, and 94.00% on DW-MRI. Consistent performance across all modalities demonstrates robustness under heterogeneous MRI acquisitions. The incorporation of prior guidance improves segmentation stability and anatomical consistency, particularly in low-contrast modalities. Conclusions: The proposed framework enables reliable kidney delineation across multiple MRI sequences, supporting consistent extraction of quantitative imaging biomarkers. This capability facilitates noninvasive assessment of renal graft function and supports longitudinal monitoring of transplant patients. Full article