Search Results (219)

The Varnous Mt. area in the northern Pelagonian Nappe is characterized by the intrusion of an Early Permian pluton, with its tectonic setting and igneous petrology well constrained in earlier studies. The metamorphic basement rocks warrant further detailed investigation due to their complex history. These rocks are polymetamorphosed, preserving a sequence of overprinting metamorphic and deformational events. The metapelitic rocks have undergone an initial, pre-Carboniferous regional metamorphism of unknown grade before or during Hercynian Orogeny, followed by a thermal metamorphic event associated with the intrusion of the Varnous pluton at 297 Ma. The assemblage attributed to this event is And + Crd + Bt + Ms (west), while the first assemblage identified at the eastern part is Sil + Bt + Gt. Additionally, three regional tectonometamorphic events occurred during the Alpine Orogeny. For the Alpine events, the assemblages are as follows: first, the development of St + Gt + Chl + Kfs + Pl + Qtz at 150–130 Ma; second, retrograde metamorphism of these assemblages with Cld + Gt + Ser + Mrg + Chl ± Sil (Fi) at 110–90 Ma; and finally, mylonitization of all previous assemblages at 90–70 Ma with simultaneous annealing and formation of Cld + Chl + Ms. Full article

The study investigates the influence of microstructures on fatigue behavior and failure mechanisms of the α-β titanium alloy Ti6246, fabricated via Powder Bed Fusion-Laser Beam (PBF-LB). In particular, the investigation assesses the effect of two post-processing heat treatments, namely α-β annealing at 875 °C (AN875) and solution treatment at 825 °C followed by aging at 500 °C (STA825), on the alloy’s rotating and bending fatigue behavior. The results indicate that the STA825 condition provides superior fatigue resistance (+25%) compared to AN875, due to the presence of a finer bilamellar microstructure, characterized by thinner primary α lamellae (α_p) and a more homogeneous distribution of secondary α lamellae (α_s) within the β matrix. Additionally, an investigation conducted using the Kitagawa–Takahashi (KT) approach and the El-Haddad model, based on the relationship between the fatigue limit and defect sensitivity, revealed improved crack propagation resistance from pre-existing defects (ΔK_th) for the STA825 condition compared to AN875. Notably, the presence of fine α_s after aging for STA825 is effective in delaying crack nucleation and propagation at early stages, while refined α_p contributes to hindering macrocrack growth. The fatigue behavior of the STA825-treated Ti6246 alloy was even superior to that of the PBF-LB-processed Ti64, representing a viable alternative for the production of high-performance components in the automotive and aerospace sectors. Full article

We present an alternative process for production of binary Nb_1−βSn_β superconducting phases using pre- and post-treatment of arc-melted Nb + Sn ingots. This process combines sequential sintering, arc melting, and annealing procedures that provide dense, bulk samples of Nb_1−βSn_β with varying stoichiometry between 0.18 < β < 0.25 depending on annealing time and temperature. We show, through magnetization measurements of these Nb_1−βSn_β bulks, that annealing of arc-melted samples at 900 °C for 3 h significantly enhances J_c values compared with arc-melted Nb_1−βSn_β samples without annealing. Microstructural analyses show that optimum grain size and orientation are achieved by sintering and annealing at lower temperatures (i.e., 720 °C and 900 °C, respectively) with short annealing times (i.e., <10 h). Processing at higher temperatures and for longer times enhances grain growth and results in fewer pinning centres. The optimum process creates effective pinning centres that deliver a J_c = 6.16 × 10⁴ A/cm² at 10 K (and ~0.2 T), compared with J_c = 3.4 × 10⁴ A/cm² for Nb_1−βSn_β subjected to a longer annealing time at a higher temperature and J_c = 775 A/cm² for an arc-melted sample without post-annealing. We suggest that further work addressing post-treatment annealing times between 3 h < t_post < 60 h at temperatures between 900 °C and 1000 °C will provide the opportunity to control stoichiometric and microstructural imperfections in bulk Nb_1−βSn_β materials. Full article

FeCrAl alloys have garnered considerable attention as candidate cladding materials for light water reactors due to their promising mechanical stability and irradiation resistance. However, the response characteristics of these alloys to irradiation and the associated mechanisms remain poorly understood. This study provides atomistic insights into irradiation-induced defect formation and microstructural evolution in polycrystalline FeCrAl. Using the LAMMPS molecular dynamics code, displacement cascades were simulated under irradiation doses ranging from 0.05 dpa to 0.5 dpa while evaluating the dependencies on temperature and grain size. The interaction between pre-existing defects and irradiation-induced microstructures (point defects, dislocations, clusters, etc.) was visualized and analyzed visually and quantitatively. The results indicate that the irradiation dose increases the number of surviving Frenkel pairs, whereas elevated temperatures reduce their stability. The cluster fraction of interstitials increases with both irradiation dose and temperature, while that of vacancies decreases at higher temperatures due to their lower stability. In the initial phase of the displacement cascade, the density and distribution of dislocations evolve continuously until the annealing stage. The dislocation density at the end of the annealing phase decreases with increasing dose and temperature. The thickness of grain boundaries increases with the irradiation dose, and the regions adjacent to grain boundaries transform into an amorphous state at higher dose levels. As both the irradiation dose and temperature increase, the amorphization process accelerates, and smaller grain size leads to a greater degree of amorphization. Full article

The automatic identification of traffic collisions is an emerging topic in modern traffic surveillance systems. The increasing number of surveillance cameras at urban intersections connected to traffic surveillance systems has created new opportunities for leveraging computer vision techniques for automatic collision detection. This study investigates the effectiveness of transfer learning utilizing pre-trained deep learning models for collision detection through dashcam images. We evaluated several state-of-the-art (SOTA) image classification models and fine-tuned them using different hyperparameter combinations to test their performance on the car collision detection problem. Our methodology systematically investigates the influence of optimizers, loss functions, schedulers, and learning rates on model generalization. A comprehensive analysis is conducted using 7 performance metrics to assess classification performance. Experiments on a large dashcam-based images dataset show that ResNet50, optimized with AdamW, a learning rate of 0.0001, CosineAnnealingLR scheduler, and Focal Loss, emerged as the top performer, achieving an accuracy of 0.9782, F1-score of 0.9617, and IoU of 0.9262, indicating a strong ability to reduce false negatives. Full article

The recovery of radiation damage induced by 231-MeV xenon ions with varying fluence (from 5 × 10¹¹ to 2 × 10¹⁴ cm⁻²) in α-Al₂O₃ (corundum) single crystals has been studied by means of isochronal thermal annealing of radiation-induced optical absorption (RIOA). The integral of elementary Gaussians (product of RIOA spectrum decomposition) OK has been considered as a concentration measure of relevant oxygen-related Frenkel defects (neutral and charged interstitial-vacancy pairs, F-H, F⁺-H⁻). The annealing kinetics of these four ion-induced point lattice defects has been modelled in terms of diffusion-controlled bimolecular recombination reactions and compared with those carried out earlier for the case of corundum irradiation by fast neutrons. The changes in the parameters of interstitial (mobile component in the recombination process) annealing kinetics—activation energy E and pre-exponential factor X—in ion-irradiated crystals are considered. Full article

High-Cu-content (Cu-content > 1.3 at.%) nanocrystalline alloys exhibit wide heat-treatment windows and favorable soft magnetic properties due to the presence of pre-existing α-Fe nanocrystals. By fabricating ribbons with varying thicknesses to tailor cooling rates, distinct structural characteristics were achieved in Fe₈₂B_16.5Cu_1.5 alloy ribbons. Notably, the face-centered cubic (fcc) γ-Fe phase was identified in Fe-based nanocrystalline alloys. The precipitation of the fcc γ-Fe phase originates from a phase-selection mechanism under specific cooling conditions, while its retention in the as-quenched ribbon with a thickness of 27 μm is attributed to kinetic suppression during rapid cooling and the nanoscale stabilization effect. The formation of the fcc γ-Fe phase significantly reduced the saturation flux density (B_s) and increased coercivity (H_c), concurrently destabilizing the residual amorphous matrix. By suppressing the precipitation of the γ-Fe and Fe₃B phases through precise control of ribbon thickness and annealing parameters, the alloy ribbon with a thickness of 16 μm achieved an optimal combination of B_s (1.82 T) and H_c (8.3 A/m). These findings on anomalous fcc γ-Fe phase precipitation provide novel insights into metastable phase engineering and offer structural design guidelines for alloys containing pre-existing α-Fe nanocrystals. Full article

Operating room (OR) scheduling problems are often addressed using deterministic models that assume surgery durations are known in advance. However, such assumptions fail to reflect the uncertainty that often occurs in real surgical environments, especially during the surgery and recovery stages. This study focuses on a robust scheduling problem involving a three-stage surgical process that includes pre-surgery, surgery, and post-surgery stages. The scheduling needs to coordinate multiple resources—pre-operative holding unit (PHU) beds, ORs, and post-anesthesia care unit (PACU) beds—while following a strict no-wait rule to keep patient flow continuous without delays between stages. The main goal is to minimize the makespan and improve schedule robustness when surgery and post-surgery durations are uncertain. To solve this problem, we propose a Genetic Algorithm for Robust Scheduling (GARS), which evaluates solutions using a scenario-based robustness criterion derived from multiple sampled instances. GARS is compared with four other algorithms: a deterministic GA (GAD), a random search (BRS), a greedy randomized insertion and swap heuristic (GRIS), and an improved version of GARS with simulated annealing (GARS_SA). The results from different problem sizes and uncertainty levels show that GARS and GARS_SA consistently perform better than the other algorithms. In large-scale tests with moderate uncertainty (30 surgeries, α = 0.5), GARS achieves an average makespan of 633.85, a standard deviation of 40.81, and a worst-case performance ratio (WPR) of 1.00, while GAD reaches 673.75, 54.21, and 1.11, respectively. GARS can achieve robust performance without using any extra techniques to strengthen the search process. Its structure remains simple and easy to use, making it a practical and effective approach for creating reliable and efficient surgical schedules under uncertainty. Full article

There is growing interest in measuring the temperature-dependent dielectric properties of bio-tissues using dual-mode techniques (scattering measurements and thermal treatment). Uncooled coaxial antennas are preferred for their direct contact with the measured medium and reduced complexity; however, they exhibit structural changes during ablation due to the thermal expansion of polytetrafluoroethylene (PTFE). This paper presents an experimental study on PTFE expansion in an uncooled coaxial insulated monopole antenna in response to changes in the tissue’s thermal environment. Furthermore, it presents a methodology to mitigate these effects through coaxial annealing. The investigation consists of two distinct experiments: characterising PTFE expansion and assessing the effects of annealing through microwave ablation. This was achieved by simulating the thermal effects experienced during ablation by immersing the test antenna in heated peanut oil. PTFE expansion was measured through camera monitoring and using a toolmaker’s microscope, revealing two expansion modalities: linear PTFE expansion and non-linear plastic deformation from manufacturing processes. The return loss during ablation and consequential changes in the ablated lesion were also assessed. Antenna pre-annealing increased resilience against structural changes in the antenna, improving lesion ellipticity. Therefore, this study establishes a fabrication method for achieving an uncooled thermally stable antenna, leading to an optimised dual-mode ablation procedure, enabling quasi-real-time permittivity measurement of the surrounding tissue. Full article

Photovoltaic (PV) technology, as a crucial source of clean energy, can effectively mitigate the impact of climate change caused by fossil fuel-based power generation. However, improper use of PV installations may encroach upon agricultural land, grasslands, and other land uses, thereby affecting local ecosystems. Exploring the spatial characteristics of centralized or distributed PV installations is essential for quantifying the development of clean energy and protecting agricultural land. Due to the distinct characteristics of centralized and distributed PV installations, large-scale mapping methods based on satellite remote sensing are insufficient for creating detailed PV distribution maps. This study proposes a model called Joint Semi-Supervised Weighted Adaptive PV Panel Recognition Model (JSWPVI)to achieve reliable PV mapping using UAV datasets. The JSWPVI employs a semi-supervised approach to construct and optimize a comprehensive segmentation network, incorporating the Spatial and Channel Weight Adaptive Model (SCWA) module to integrate different feature layers by reconstructing the spatial and channel weights of feature maps. Finally, a guided filtering algorithm is used to minimize non-edge noise while preserving edge integrity. Our results demonstrate that JSWPVI can accurately extract PV panels in both centralized and distributed scenarios, with an average extraction accuracy of 91.1% and a mean Intersection over Union of 77.7%. The findings of this study will assist regional policymakers in better quantifying renewable energy potential and assessing environmental impacts. Full article

This study investigates the identification of artificial irradiation in thermoluminescence (TL) pre-dose dating of ancient Chinese porcelain to address the challenges posed by sophisticated counterfeiting techniques. While TL pre-dose dating is effective for authenticating ceramics, modern imitations artificially irradiated to mimic ancient doses complicate accurate age determination. By analyzing the TL characteristics of five historical porcelain samples (Song, Yuan, Ming, and Qing Dynasties) and artificially irradiated modern replicas, distinct differences were observed. Natural irradiation samples exhibited lower TL sensitivity, less smooth glow curves, and reduced linear regression fit (R² < 0.97) compared to artificial counterparts, which showed higher sensitivity, smoother curves, and superior linearity (R² > 0.97). The following methodology was proposed: annealing samples to erase natural signals, applying equivalent artificial doses, and comparing TL responses. The results demonstrated significant disparities in TL behavior between ancient and irradiated samples, enabling discrimination. This approach enhances the reliability of TL pre-dose dating for porcelain authentication, offering a practical solution to combat forgery in cultural heritage preservation. Full article

Motors, as the core carriers of pollution-free power, realize efficient electric energy conversion in clean energy systems such as electric vehicles and wind power generation, and are widely used in industrial automation, smart home appliances, and rail transit fields with their low-noise and zero-emission operating characteristics, significantly reducing the dependence on fossil energy. As the requirements of various application scenarios become increasingly complex, it becomes particularly important to accurately and quickly design the sealing structure of motors. However, traditional design methods show many limitations when facing such challenges. To solve this problem, this paper proposes hybrid models of machine learning that contain polynomial regression and optimization XGBOOST models to rapidly and accurately predict the sealing performance of motors. Then, the hybrid model is combined with the simulated annealing algorithm and multi-objective particle swarm optimization algorithm for optimization. The reliability of the results is verified by the mutual verification of the results of the simulated annealing algorithm and the particle swarm optimization algorithm. The prediction accuracy of the hybrid model for data outside the training set is within 2.881%. Regarding the prediction speed of this model, the computing time of ML is less than 1 s, while the computing time of FEA is approximately 9 h, with an efficiency improvement of 32,400 times. Through the cross-validation of single-objective optimization and multi-objective optimization algorithms, the optimal design scheme is a groove depth of 0.8–0.85 mm and a pre-tightening force of 80 N. The new method proposed in this paper solves the limitations in the design of motor sealing structures, and this method can be extended to other fields for application. Full article

Traditional iodine-based polyvinyl alcohol (PVA) polarizers encounter considerable durability challenges, especially in humid conditions, due to poor moisture resistance. This study presents an innovative organic–inorganic composite film composed of poly(methyl methacrylate) (PMMA) and carbon nanotubes (CNTs), fabricated via electrospinning, solvent vapor annealing (SVA), and uniaxial stretching. Pre-aligned PMMA/CNT composite fibers were electrospun and underwent SVA to stabilize the structure and reduce inter-fiber porosity. Further uniaxial stretching aligned the CNTs, enhancing optical anisotropy and polarization performance. The optimized parameters, 45 min of SVA and 75% stretching strain, produced composite films with a polarization degree exceeding 60%, which was combined with exceptional moisture resistance (<2% weight variation under 90% relative humidity). The integration of CNTs enhanced mechanical stability while preserving alignment during post-processing, thereby tackling the crucial challenge of scalable nanomaterial orientation. This study provides a scalable, cost-effective approach for developing durable polarizing materials with enhanced performance for optical devices in demanding environments. Full article

Background/Objectives: Aberrant expression of c-MYC drives aggressive cancers. A guanine-rich promoter sequence (Pu27) folds into a transcriptionally repressive G-quadruplex (G4). Epigallocatechin gallate (EGCG), the main green tea polyphenol, displays anticancer activity, but clear, easily replicated evidence for direct binding to the c-MYC G4 is lacking. We therefore obtained the first biophysical confirmation of an EGCG–c-MYC G4 interaction using routine UV–visible spectroscopy. Methods: A pre-annealed Pu27 G4 (5 µM) in potassium-rich buffer was titrated with freshly prepared EGCG (0–20 µM) at 25 °C. Full-range UV–Vis spectra (220–400 nm) were recorded after each addition, and absorbance variations at the DNA (260 nm) and ligand (275 nm) maxima were quantified across three independent replicates. Results: EGCG induced pronounced, concentration-dependent hyperchromicity at 260 nm, reaching ~8–10% above baseline at a 4:1 ligand/DNA ratio and exhibiting saturable binding behaviour. Concurrently, the 275 nm band displayed relative hypochromicity coupled with a subtle bathochromic shift. These reciprocal perturbations—absent in buffer-only controls—constitute definitive evidence of a specific EGCG•G4 complex most consistent with external π-stacking or groove engagement rather than intercalation. Conclusions: This study delivers the first rigorous, quantitative UV–Vis confirmation that a readily consumed dietary polyphenol directly targets the c-MYC promoter G4. By marrying conceptual elegance with methodological accessibility, it provides a compelling molecular rationale for EGCG’s anti-oncogenic repertoire, inaugurates an expedient platform for screening G4-reactive nutraceuticals, and paves the way for structural and cellular investigations en route to next-generation c-MYC-directed therapies. Full article

The degree of tree curvature exerts a significant influence on the utilization of forestry resources. This study proposes an enhanced quantitative structural modeling (QSM) method, founded upon terrestrial laser scanning (TLS) point cloud data, for the precise extraction of 3D curvature characteristics of tree trunks. The conventional approach operates under the assumption that the tree trunk constitutes an upright rotating body, thereby disregarding the tree trunk’s true curvature morphology. The proposed method is founded on the classical QSM algorithm and introduces two zoom factors that can dynamically adjust the fitting parameters. This improvement leads to enhanced accuracy in the representation of tree trunk curvature and reduced computational complexity. The study utilized 146 sample trees from 13 plots in Jixi, Anhui Province, which were collected and pre-processed by TLS. The study combines point cloud segmentation, manual labeling of actual curvature and dual-factor experiments, and uses quadratic polynomials and simulated annealing algorithms to determine the optimal model factors. The validation results demonstrate that the enhanced method exhibits a greater degree of concordance between the predicted and actual curvature values within the validation set. In the regression equation, the coefficient of the two-factor method for fitting a straight line is 0.95, which is substantially higher than the 0.75 of the one-factor method. Furthermore, the two-factor model has an R² of 0.21, indicating that the two-factor optimization method generates a significantly smaller error compared to the one-factor model (with an R² of 0.12). In addition, this study discusses the possible reasons for the error in the results, as well as the shortcomings and outlook. The experimental results demonstrate the augmented method’s capacity to accurately reconstruct the 3D curvature of tree trunks in most cases. This study provides an efficient and accurate method for conducting fine-grained forest resource measurements and tree bending studies. Full article