Search Results (5,613)

High alkalinity facilitates copper–sulfur flotation separation but also leads to issues such as high reagent consumption, pipeline scaling, and gold loss in tailings. The ore from a copper mine in Serbia contains 2.86% copper, 1.64 g/t gold, and 20.39% sulfur, with copper occurring mainly in covellite and enargite. To achieve efficient separation and recovery of copper–sulfur, a systematic study was conducted using micro-flotation, Scanning Electron Microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and contact angle analysis to investigate the inhibition and activation patterns of pyrite under low and high alkalinity conditions. The results indicate that the combined use of hydrogen peroxide and lime as inhibitor enables efficient separation of pyrite and covellite under low-alkalinity conditions. This effect is attributed to its ability to enhance oxidation of the pyrite surface, which generates more hydrophilic substances. Under low-alkalinity conditions (slurry pH = 10) regulated with hydrogen peroxide and lime in a covellite flotation cycle, and under acidic conditions (slurry pH = 6) in the pyrite flotation cycle, satisfactory results are obtained in both flotation cycles in comparison with industrial data. The copper flotation index was similar, but pyrite and gold recovery increased by 2.3% and ~4%, respectively, over those using lime alone. This process reduced the activator dosage required for pyrite activation substantially, while improving gold recovery. Results demonstrate an efficient method for copper–sulfur separation and recovery, providing theoretical guidance or industrial production processes. Full article

This study evaluated the optical and mechanical properties of two single-shade composite resins compared with a conventional multi-shade composite. Omnichroma (OM), Metafil Bulk Fill ONE (BO), and Filtek Z350XT (Z350) were tested. Color adjustment was assessed using A3, B1, and C4 background cavities, and ΔE00 values were calculated. The translucency parameter (TP) was measured, and the flexural strength, flexural modulus, and depth of cure (B/T ratio) were determined. OM and BO showed better color adjustment performance on brighter (B1) backgrounds and decreased matching on darker (C4) ones. OM maintained stable color adjustment across cavity depths, while BO showed improved adjustment in shallower cavities. Both exhibited higher TP values than Z350. The control group (Z350) had the highest flexural strength and modulus, though BO’s flexural strength was comparable. OM and BO showed sufficient mechanical strength and a greater depth of cure compared to Z350. Our study indicated that the one-shade composite resins OM and BO exhibited better color adjustment performance compared to conventional composite resins due to the influence of the surrounding shades, with a better adjustment ability on brighter backgrounds. Additionally, OM and BO demonstrated sufficient strength and a higher depth of cure compared to the control group. Full article

Medical anomaly detection is challenged by limited labeled data and domain shifts, which reduce the performance and generalization of deep learning (DL) models. Hybrid convolutional neural network–Vision Transformer (CNN–ViT) architectures have shown promise, but they often rely on large datasets. Multistage transfer learning (MTL) provides a practical strategy to address this limitation. In this study, we evaluated parallel hybrids, where convolutional neural network (CNN) and Vision Transformer (ViT) features are fused after independent extraction, and sequential hybrids, where CNN features are passed through the ViT for integrated processing. Models were pretrained on non-wrist musculoskeletal radiographs (MURA), fine-tuned on the MURA wrist subset, and evaluated for cross-domain generalization on an external wrist X-ray dataset from the Al-Huda Digital X-ray Laboratory. Parallel hybrids (Xception–DeiT, a data-efficient image transformer) achieved the strongest internal performance (accuracy 88%), while sequential DenseNet–ViT generalized best in zero-shot transfer. After light fine-tuning, parallel hybrids achieved near-perfect accuracy (98%) and recall (1.00). Statistical analyses showed no significant difference between the parallel and sequential models (McNemar’s test), while backbone selection played a key role in performance. The Wilcoxon test found no significant difference in recall and F1-score between image and patient-level evaluations, suggesting balanced performance across both levels. Sequential hybrids achieved up to 7× faster inference than parallel models on the MURA test set while maintaining similar GPU memory usage (3.7 GB). Both fusion strategies produced clinically meaningful saliency maps that highlighted relevant wrist regions. These findings present the first systematic comparison of CNN–ViT fusion strategies for wrist anomaly detection, clarifying trade-offs between accuracy, generalization, interpretability, and efficiency in clinical AI. Full article

This work analyzes the hydrodynamic behavior of a magnetorheological valve, considering the microscopic fluid characteristics to generate a damper force. The magnetorheological fluid is composed of ferromagnetic particles dispersed in a non-magnetic carrier fluid, whose mechanical resistance depends on the magnetic field intensity. In the absence of a magnetic field, the magnetorheological fluid behaves as a liquid whose viscosity depends on the particle volume fraction. Conversely, the presence of a magnetic field generates particle chain-like structures that inhibit fluid motion, thereby regulating flow in the control valve. The mathematical model employs the continuity and momentum equations, the Bingham model, and the boundary conditions at the solid–liquid interfaces to determine the flow field. The results show the fluid hydrodynamic response under different flow conditions depending on dimensionless parameters such as the pressure gradient, the field-independent viscosity, the yield stress, the particle volume fraction, the Bingham number, the Mason number, and the critical Mason number. For a pressure gradient of $\Gamma =-10$, the flow rate inside the valve (with particle volume fraction $\varphi =0.2$) results in ${\overline{Q}}_{T,x}=0.34$, $0.06$, and 0 when the magnetic field is 80, 120, and 160 kA m⁻¹, respectively. Likewise, when the magnetic field increases from 80 to 160 kA m⁻¹, the damping capacity increases by $88\%$ when $\varphi =0.2$ and $128\%$ when $\varphi =0.3$ compared to the Newtonian viscous damping. This work contributes to our understanding of semi-active damping devices for flow control. Full article

We re-examine the recent claim that a Dirac particle freely falling in a uniform gravitational field exhibits a spin-dependent transverse deflection (gravitational spin Hall effect). Using a circulating mass model, we show that hidden momentum arises in uniform fields when an object carries angular momentum. On the quantum side, we analyze the Dirac Hamiltonian in a uniform potential, construct its Foldy–Wouthuysen form, and evaluate the Heisenberg evolution of spin-polarized Gaussian packets. The state used previously, with $⟨p⟩=0$, is not at rest: because canonical and kinetic momenta differ, the packet carries a spin-dependent hidden momentum from $t=0$. Imposing $⟨x\left(0\right)⟩=⟨v\left(0\right)⟩=0$ requires a compensating spin-dependent $⟨p\left(0\right)⟩$; with this preparation $⟨x\left(t\right)⟩=0$ to leading order in the gravitational acceleration g. Generalizing, an exact Foldy–Wouthuysen transformation (linear in g but to all orders in $1/c$) shows that spin-dependent transverse motion begins no earlier than at $O\left(g^2\right)$ for a broad class of wave packets. We conclude that a uniform field does not produce a gravitational spin Hall effect at linear order; the previously reported drift stems from inconsistent initial states and misinterpreting canonical momentum. Full article

Consider regression models where the response variable Y only depends on the $p\times 1$ vector of predictors $\mathit{x}={(x_1,\dots ,x_p)}^T$ through the sufficient predictor $SP=\alpha +{\mathit{x}}^T\mathit{\beta }$. Let the covariance vector $\mathrm{Cov}(\mathit{x},Y)={\mathbf{\Sigma }}_{\mathit{x}Y}$. Assume the cases ${({\mathit{x}}_i^T,Y_i)}^T$ are independent and identically distributed random vectors for $i=1,\dots ,n$. Then for many such regression models, $\mathit{\beta }=\mathbf{0}$ if and only if ${\mathbf{\Sigma }}_{\mathit{x}Y}=\mathbf{0}$ where 0 is the $p\times 1$ vector of zeroes. The test of $H_0:{\mathbf{\Sigma }}_{\mathit{x}Y}=\mathbf{0}$ versus $H_1:{\mathbf{\Sigma }}_{\mathit{x}Y}\ne \mathbf{0}$ is equivalent to the high dimensional one sample test $H_0:\mathit{\mu }=\mathbf{0}$ versus $H_A:\mathit{\mu }\ne \mathbf{0}$ applied to ${\mathit{w}}_1,\dots ,{\mathit{w}}_n$ where ${\mathit{w}}_i=({\mathit{x}}_i-{\mathit{\mu }}_{\mathit{x}})(Y_i-{\mu }_Y)$ and the expected values $E(\mathit{x})={\mathit{\mu }}_{\mathit{x}}$ and $E(Y)={\mu }_Y$. Since ${\mathit{\mu }}_{\mathit{x}}$ and ${\mu }_Y$ are unknown, the test of $H_0:\mathit{\beta }=\mathbf{0}$ versus $H_1:\mathit{\beta }\ne \mathbf{0}$ is implemented by applying the one sample test to ${\mathit{v}}_i=({\mathit{x}}_i-\overline{\mathit{x}})(Y_i-\overline{Y})$ for $i=1,\dots ,n$. This test has milder regularity conditions than its few competitors. For the multiple linear regression one component partial least squares and marginal maximum likelihood estimators, the test can be adapted to test $H_0:{({\beta }_{i_1},\dots ,{\beta }_{i_k})}^T=\mathbf{0}$ versus $H_1:{({\beta }_{i_1},\dots ,{\beta }_{i_k})}^T\ne \mathbf{0}$ where $1\le k\le p.$ Full article

With the global low-voltage power market expanding rapidly, lead-free dielectric ceramics exhibit excellent stability and environmental friendliness, but their strong field-dependence limits low-field applications. There is an urgent need to develop lead-free ceramic systems with outstanding energy-storage performance under modest electric fields to meet the rapidly expanding global low-voltage power market for bulk ceramics. In this study, high-entropy ceramics (1 − x%)(NaBiBa)_0.205(SrCa)_0.1925TiO₃-x%La(Zr_0.5Mg_0.5)O₃ (x = 0–8) were successfully prepared. The introduced La(Zr_0.5Mg_0.5)O₃ not only dissolves well in the high-entropy elementary lattice but also effectively improves its relaxation characteristics. High-entropy ceramics show optimal energy-storage characteristics, as indicated by an excellent energy-storage density of 4.46 J/cm³ and an energy-storage efficiency of 94.55% at 318 kV/cm. Moreover, its power density is as high as 92.20 MV/cm³, and the discharge time t_0.9 is only 145 ns. Full article

Reliable differentiation of crops and weeds is essential for precision agriculture, where real-time detection can minimize chemical inputs and support site-specific interventions. This study presents the large-scale and systematic benchmark of 19 YOLO-family variants, spanning YOLOv3 through YOLOv11, for cotton–weed detection using the Cotton–8 dataset. The dataset comprises 4440 annotated field images with five categories: broadleaf weeds, grass weeds, and three growth stages of cotton. All models were trained under a standardized protocol with COCO-pretrained weights, fixed seeds, and Ultralytics implementations to ensure reproducibility and fairness. Inference was conducted with a confidence threshold of 0.25 and a non-maximum suppression (NMS) IoU threshold of 0.45, with test-time augmentation (TTA) disabled. Evaluation employed precision, recall, mAP@0.5, and mAP@0.5:0.95, along with inference latency and parameter counts to capture accuracy–efficiency trade-offs. Results show that larger models, such as YOLO11x, achieved the best detection accuracy (mAP@0.5 = 81.5%), whereas lightweight models like YOLOv8n and YOLOv9t offered the fastest inference ( 27 msper image) but with reduced accuracy. Across classes, cotton growth stages were detected reliably, but broadleaf and grass weeds remained challenging, especially under stricter localization thresholds. These findings highlight that the key bottleneck lies in small-object detection and precise localization rather than architectural design. By providing the first direct comparison across successive YOLO generations for weed detection in cotton, this work offers a practical reference for researchers and practitioners selecting models for real-world, resource-constrained cotton–weed management. Full article

Island building change detection is a critical technology for environmental monitoring, disaster early warning, and urban planning, playing a key role in dynamic resource management and sustainable development of islands. However, the imbalanced distribution of class pixels (changed vs. unchanged) undermines the detection capability of existing methods and severe boundary misdetection. To address issue, we propose the MSDT-Net model, which makes breakthroughs in architecture, modules, and loss functions; a dual-branch twin ConvNeXt architecture is adopted as the feature extraction backbone, and the designed Edge-Aware Smoothing Module (MSA) effectively enhances the continuity of the change region boundaries through a multi-scale feature fusion mechanism. The proposed Difference Feature Enhancement Module (DTEM) enables deep interaction and fusion between original semantic and change features, significantly improving the discriminative power of the features. Additionally, a Focal–Dice–IoU Boundary Joint Loss Function (FDUB-Loss) is constructed to suppress massive background interference using Focal Loss, enhance pixel-level segmentation accuracy with Dice Loss, and optimize object localization with IoU Loss. Experiments show that on a self-constructed island dataset, the model achieves an F1-score of 0.9248 and an IoU value of 0.8614. Compared to mainstream methods, MSDT-Net demonstrates significant improvements in key metrics across various aspects. Especially in scenarios with few changed pixels, the recall rate is 0.9178 and the precision is 0.9328, showing excellent detection performance and boundary integrity. The introduction of MSDT-Net provides a highly reliable technical pathway for island development monitoring. Full article

Objectives: There is currently a lack of research on coronal Computed Tomography (CT) attenuation measurements. This study aimed to evaluate a coronal CT attenuation measurement method for osteoporosis screening at the proximal femur and compare its performance with the conventional axial approach. Methods: A retrospective analysis was conducted on 708 patients who underwent both proximal femur CT and dual-energy X-ray absorptiometry (DXA) within a 6-month period between January 2013 and December 2023. The axial and coronal CT attenuation values for these patients were measured. The correlation, agreement, and efficacy in screening osteoporosis of the two methods were further evaluated and compared. Results: Both measurement methods demonstrated excellent inter-observer reliability. Axial measurements yielded slightly higher HU values than coronal measurements (mean difference: 3.93 Hounsfield unit (HU), p < 0.001), and the Bland–Altman analysis showed a Coefficient of Repeatability (CR) = 9.09 HU (95% CI: 8.63 to 9.61) between the two methods. A strong correlation was observed between the two measurements (Pearson’s coefficient (r) of 0.919 (95% CI: 0.907–0.930, p < 0.001)). Coronal measurements showed comparable correlation with femoral T-scores to axial measurements (0.766 (95% CI: 0.730–0.808) vs. 0.736 (95% CI: 0.697–0.771), p = 0.195). Coronal and axial measurements exhibit good predictive performance for osteoporosis diagnosis, with no statistically significant difference in AUC values between the two methods (0.850 vs. 0.864, p = 0.097). Conclusions: The coronal CT attenuation measurement method provides a reliable and complementary diagnostic tool for opportunistic osteoporosis screening, demonstrating strong correlation with axial measurements and comparable diagnostic accuracy. Full article

Bulk metallic glasses (BMGs), classified as metastable materials, necessitate melt quenching at critical cooling rates higher than 10² K/s to kinetically bypass crystalline phase formation during solidification. Owing to this rapid quenching, the microstructure of BMGs can be regarded as melt quenched. This study examines how their melt state governs the thermal stability, structural characteristics, and plasticity behavior of Zr₅₀Cu₄₀Al₁₀ BMG. Rod samples were prepared via injection casting at controlled melt temperatures and suction casting. Experimental observations demonstrated a positive correlation between elevated melt temperatures and enhanced glass forming ability (GFA) along with improved thermal stability (T-A) in BMGs during processing. Structural analyses confirmed the glassy nature of the prepared BMGs with different melt states and revealed their temperature-dependent atomic-scale heterogeneity: the samples quenched at low melt temperatures exhibited significant Cu-rich clustering as determined via energy-dispersive X-ray spectroscopy (EDS) mapping, and those at high melt temperatures formed homogeneous structures. This structure heterogeneity was directly correlated with good plastic deformation behavior, i.e., the rod sample prepared at the lowest melt temperature achieved 9.7% plastic strain. The transition is attributed to liquid-liquid phase transition (LLPT): below the LLPT threshold, metastable Cu-rich clusters persist in the melt and are retained upon quenching, creating structural defects that facilitate shear band multiplication. These findings highlight melt temperature as a crucial factor in tailoring the structure characteristic and mechanical behavior of Zr₅₀Cu₄₀Al₁₀ BMGs. Full article

Background/Objectives: Fragile X-associated tremor/ataxia syndrome (FXTAS) is characterized by tremor, gait ataxia, and cerebellar white matter degeneration, along with possible cognitive and cerebral changes. Although diagnostic criteria were originally developed in males, emerging evidence suggests that FXTAS may present differently in females. The present study examined sensorimotor and memory features of aging in female premutation carriers with (FXTAS+) and without FXTAS (FXTAS−). Methods: We studied 51 female premutation carriers (FXTAS+ = 16, FXTAS− = 35) and 24 age-matched female controls. Participants ranged in age from 47–80 years. All participants completed genetic testing, clinical evaluations, T2-weighted MRIs, and quantitative assessments of sensorimotor (precision grip force task) and memory (reading span; visual paired associates task) functions. Results: During precision grip testing, FXTAS+ carriers showed higher sustained force regularity than FXTAS− carriers (p = 0.03, d = 1.0) and controls (p = 0.004, d = 1.1) at low gain levels only. FXTAS+ participants were slower than controls on motor reaction time (p = 0.009, d = 0.82). Initial force output was higher in FXTAS+ than FXTAS− carriers (p = 0.03, d = 1.0) and controls (p = 0.03, d = 1.0) but at low gain only. FXTAS+ carriers exhibited poorer working memory than FXTAS− carriers (p = 0.03, d = 0.91) and controls (p = 0.02, d = 1.0). During a long-term memory task, FXTAS+ participants were less accurate than FXTAS− carriers (p = 0.04, d = 0.86) and controls (p = 0.004, d = 1.1) and showed increased reaction times relative to FXTAS− carriers (p = 0.03, d = −0.82) and controls (p = 0.01, d = −1.2). Conclusions: Together, these findings indicate that FXTAS+ females exhibit distinct motor and cognitive impairments, underscoring the value of quantitative behavioral measures for detecting and tracking neurodegenerative progression in female premutation carriers. Full article

An isothermal hot compression test of GH3230 was carried out under deformation conditions with deformation temperatures ranging from 1020 to 1110 °C and strain rates ranging from 1 to 0.001 s⁻¹. On this basis, the corresponding constitutive equation of the alloy was established. $\dot{\epsilon }=exp\left(36.123\right){\left[sinh\left(0.00587\sigma \right)\right]}^{4.7946}exp\left[-451.507/\left(RT\right)\right]$. At the same time, a power dissipation diagram and thermal processing diagram were created. The peak value η can reach 0.36, and the optimum hot working parameter window of the GH3230 superalloy is 1020~1110 °C/0.1~0.001 s⁻¹. The microstructure evolution of the alloy under different conditions was studied by EBSD. With an increase in deformation temperature and a decrease in strain rate, the grain size significantly improved; the average grain size of the GH3230 alloy increased from 16.86 to 35.06 μm, and the degree of recrystallization of the alloy also improved. The maximum recrystallization volume fraction is 75.2%. At low temperature and high strain rate, the recrystallization mechanism of the microstructure is mainly CDRX, and DDRX is the auxiliary mechanism. At high temperature and low strain rate, the main corresponding recrystallization mechanism gradually transforms into DDRX. Full article

This paper investigates a new class of two-dimensional fuzzy difference equations that integrate logarithmic nonlinearities with interaction effects between system variables. Motivated by the need to model complex dynamical systems influenced by uncertainty and interdependencies, we propose a system that extends existing one-dimensional models to capture more realistic interactions within a discrete-time framework. Our approach employs the characterization theory to transform the fuzzy system into an equivalent family of classical difference equations, thereby facilitating a rigorous analysis of the existence, uniqueness, and boundedness of positive solutions. To support the theoretical findings, two numerical examples are provided, illustrating the model’s capacity to capture complex dynamical patterns under fuzzy conditions. An application to a fuzzy population growth model illustrates how the model captures both interaction effects and uncertainty while ensuring well-defined and stable solutions. Numerical simulations show that, for instance, with $\alpha =0.10$, $\beta =\delta =1.0$, $\gamma =0.08$, and ${\rho }_x={\rho }_y=0.10$, the trajectories of $(x_t,y_t)$ rapidly converge toward a stable fuzzy equilibrium, with uncertainty bands confirming the positivity and boundedness of the solutions. Full article

The study at the Sanctuary of Eukleia in Aigai (Vergina, Greece) evaluates the planimetric and vertical accuracy of Digital Surface Model (DSM) generated by a Hesai XT32M2X LiDAR system mounted on UAS WingtraOne GEN II. The paper begins by outlining the evolution of UAS-LiDAR, then describing the acquisition of RGB, multispectral (MS) images and LiDAR data. Twenty-two Check Points (CPs) were measured using an RTK-GNSS receiver, which also served to establish the PPK calibration base point. This is followed by processing the images to generate DSMs and orthophotomosaics, as well as processing the LiDAR point cloud to produce both DSM and DTM products. The DSMs and orthophotomosaics were evaluated by comparing field-measured CP coordinates with those extracted from the products, computing mean values and standard deviations. RGB images yielded DSMs and orthophotomosaics with planimetric accuracy of 1.4 cm (with a standard deviation σ = ±1 cm) in X, 0.9 cm (with σ = ±0.9 cm) in Y and a vertical accuracy of 2.4 cm (with σ = ±1.7 cm). The LiDAR-derived DSM achieved similar planimetric accuracy and an overall vertical accuracy of 7.5 cm (with σ = ±6 cm). LiDAR’s ability to penetrate vegetation enabled near-complete mapping of a densely vegetated streambank, highlighting its clear advantage over images. While high-precision RGB-PPK products can surpass LiDAR in vertical accuracy, UAS-LiDAR remains indispensable for under-canopy terrain mapping. Full article