Search Results (7,159)

This review examines various methods for the design and optimization of horizontal-axis hydrokinetic turbines. A detailed analysis is presented of the results from numerical and experimental studies on small axial hydrokinetic turbines optimized through different methodologies. The influence of individual components of the flow passage on the turbine’s efficiency is emphasized. The energy performance of the studied turbines is compared with that of modern commercial hydrokinetic turbines. It is demonstrated that Computational Fluid Dynamics (CFD) can be used to optimize the geometry of the flow passage, achieving a higher power coefficient compared to commercial hydrokinetic turbines. All of this contributes to the future development of more efficient axial hydrokinetic turbines suitable for operation at lower flow velocities. Full article

Liquid biopsy through the analysis of circulating tumor DNA (ctDNA) is emerging as a powerful and non-invasive tool complementing tissue biopsy in lymphoma management. Whilst tissue biopsy remains the diagnostic gold standard, it fails to detect the molecular heterogeneity of the tumor’s multiple compartments and poses challenges for sequential disease monitoring. In diffuse large-B-cell lymphoma (DLBCL), ctDNA facilitates non-invasive genotyping by identifying hallmark mutations (e.g., MYD88, CD79B, EZH2), enabling molecular cluster classification. Dynamic changes in ctDNA levels during DLBCL treatment correlate strongly with progression-free survival and overall survival, underscoring its value as a predictive and prognostic biomarker. In Hodgkin’s lymphoma, characterized by a scarcity of malignant cells in tissue biopsies, ctDNA provides reliable molecular insights into tumor biology, response to therapy, and relapse risk. In primary central nervous system lymphoma, the detection of MYD88 L265P in ctDNA offers a highly sensitive, specific, and minimally invasive diagnostic option. Likewise, in aggressive T-cell lymphomas, ctDNA supports molecular profiling, aligns with tumor burden, and shows high concordance with tissue-based results. Ongoing and future clinical trials will be critical for validating and standardizing ctDNA applications, ultimately integrating liquid biopsy into routine clinical practice and enabling more personalized and dynamic lymphoma care. Full article

Background: Continuous rise of type 2 diabetes mellitus (T2DM) global prevalence, has led to a subsequent increase in the prevalence of diabetic kidney disease (DKD). DKD is associated with higher levels of inflammation and impaired kidney function. Many patients do not receive adequate treatment for this condition. This research aims to evaluate the therapeutic impact of autologous dendritic cell transfer by examining its effects on renal microstructural changes as assessed through Diffusion Tensor Imaging (DTI) MRI, alongside the analysis of key inflammatory biomarkers, namely Matrix Metalloproteinase-9 (MMP-9) and Intercellular Adhesion Molecule-1 (ICAM-1). Methods: A clinical trial with an open-label design was performed with 25 DKD patients receiving outpatient care at Gatot Soebroto Army Hospital. Each participant was administered a single injection of autologous dendritic cells. Evaluations were conducted both prior to and one month following the treatment. The primary measurements included Diffusion Tensor Imaging (DTI) MRI-derived Fractional Anisotropy (FA) scans and the inflammatory biomarker MMP-9. Results: A notable increase in FA was observed, rising from 242.57 ± 63.97 at baseline to 305.61 ± 152.32 one month after the dendritic cell injection. However, there were no significant changes in MMP-9 and ICAM-1 levels. Additionally, a negative correlation was found between FA and MMP-9 (r = −0.324, p = 0.025). Conclusion: The transfer of autologous dendritic cells significantly enhanced FA, which correlates with a reduction in the inflammatory biomarker MMP-9, suggesting a potential impact on renal repair in DKD. Full article

The effects of proline co-solvent on helix folding are explored through the single discrete coordinate of the number of helical hydrogen bonds. The analysis is based on multi-microsecond length molecular dynamics simulations of alanine-based helix-forming peptides, (ALA)n, of length n = 4, 8, 15 and 21 residues, in an aqueous solution with 2 M concentration of proline. The effects of addition of proline on the free energy landscape for helix folding were analyzed using the graph-based Dijkstra algorithm, Optimal Dimensionality Reduction kinetic coarse graining, committor functions, as well as through the diffusion of the helix boundary. Viewed at a sufficiently long time-scale, helix folding in the coarse-grained hydrogen bond space follows a consecutive mechanism, with well-defined initiation and propagation phases, and an interesting set of intermediates. Proline addition slows down the folding relaxation of all four peptides, increases helix content and induces subtle mechanistic changes compared to pure water solvation. A general trend is for transition state shift towards earlier stages of folding in proline relative to water. For ALA5 and ALA8 direct folding is dominant. In ALA8 and ALA15 multiple pathways appear possible. For ALA21 a simple mechanism emerges, with a single path from helix to coil through a set of intermediates. Overall, this work provides new insights into effects of proline co-solvent on helix folding, complementary to more standard approaches based on three-dimensional molecular structures. Full article

Drone object detection serves as a fundamental task for more advanced applications. However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. Traditional object detection networks struggle to achieve the required accuracy and efficiency under these conditions. In this paper, we propose DAU-YOLO, a novel object detection method tailored for drone imagery, built upon the YOLOv11 framework. To enhance feature extraction, a Receptive-Field Attention (RFA) module is introduced in the backbone, allowing adaptive convolution kernel adjustments across different local regions, thereby addressing the challenge of dense object distributions. In the neck, we propose a Dynamic Attention and Upsampling (DAU) module, which incorporates additional low-level features rich in small-object information. Furthermore, Scale-Diffusion Attention and Task-Aware Attention are employed to refine these features, significantly improving the network’s ability to detect small objects. To maintain an extremely lightweight architecture, the bottom-most Bottom–Up layer is removed, reducing model complexity without compromising detection accuracy. In the experiments, the proposed method achieves state-of-the-art (SOTA) performance on the VisDrone2019 dataset. On the validation set, DAU-YOLO(l) attains an $mA{P}_{50}$ of 56.1%, surpassing the baseline YOLOv11(l) by 9.1% and the latest similar-structure method Drone-YOLO(l) by 4.8%, while maintaining only 28.9M parameters, almost half those of Drone-YOLO(l). In the discussion, we provide a detailed analysis of the improvements in small object detection as well as the trade-off between detection accuracy and inference speed. These results demonstrate the effectiveness of DAU-YOLO in addressing the challenges of drone object detection, offering a highly accurate and lightweight solution for real-time applications in complex aerial scenes. Full article

Water pollution is a significant threat for human health, particularly in developed countries. This study advances the mathematical understanding of WP transmission dynamics by developing a fractional–fractal derivative framework with non-singular kernels and the Mittage–Leffler function, which successfully preserves the non-local behavior of pollutants. The fractional–fractal derivatives in sense of the Atangana–Baleanu–Caputo formulation inherently captures the non-local and memory-dependent behavior of pollutant diffusion, addressing limitations of classical differential operators. A novel parameter, $\gamma$, is introduced to represent the recovery rate of water systems through treatment processes, explicitly modeling the bridge between natural purification mechanisms and engineered remediation efforts. Furthermore, this study establishes stability analysis, and the existence and uniqueness of the solution are established through fixed-point theory to ensure the mathematical stability of the system. Moreover, a numerical scheme based on the Newton polynomial is formulated, by obtaining significant simulations of pollution dynamics under various conditions. Graphical results show the effect of important parameters on pollutant evolution, providing useful information about the behavior of the system. Full article

The low-symmetry Weyl semimetallic Td-phase WTe₂ exhibits both a distinct out-of-plane damping torque (${\tau }_{\mathrm{DL}}$) and exceptional charge–spin interconversion efficiency enabled by strong spin-orbit coupling, positioning it as a prime candidate for spin–orbit torque (SOT) applications in two-dimensional transition metal dichalcogenides. Herein, we report on thickness-dependent unconventional out-of-plane ${\tau }_{\mathrm{DL}}$ in chemically vapor-deposited (CVD) polycrystalline Td-WTe₂ (t)/Ni₈₀Fe₂₀/MgO/Ti (Td-WTN-t) heterostructures. Angle-resolved spin-torque ferromagnetic resonance measurements on the Td-WTN-12 structure showed significant spin Hall conductivities of σ_SH,y = 4.93 × 10³ (ℏ/2e) Ω⁻¹m⁻¹ and σ_SH,z = 0.81 × 10³ (ℏ/2e) Ω⁻¹m⁻¹, highlighting its potential for wafer-scale spin–orbit torque device applications. Additionally, a detailed examination of magnetotransport properties in polycrystalline few-layer Td-WTe₂ films as a function of thickness revealed a marked amplification of the out-of-plane magnetoresistance, which can be ascribed to the anisotropic nature of charge carrier scattering mechanisms within the material. Spin pumping measurements in Td-WTN-t heterostructures further revealed thickness-dependent spin transport properties of Td-WTe₂, with damping analysis yielding an out-of-plane spin diffusion length of λ_SD ≈ 14 nm. Full article

Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., “HMML-BP”, based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring. Full article

Electrocatalytic nitrate reduction reaction (NO₃⁻RR) to ammonia (NH₃) presents an alternative, sustainable approach to ammonia production. However, the existing catalysts suffer from poor NH₃ yield under lower concentrations of NO₃⁻, and the kinetic understanding of bimetal catalysis is lacking. In this study, a Co₃O₄–modified Cu₂₊₁O nanowire (CoCuNWs) catalyst with a high specific surface area was synthesized to effectively produce NH₃ from a 10 mM KNO₃ basic solution. CoCuNWs demonstrated a high NH₃ yield rate of 0.30 mmol h⁻¹ cm⁻² with an NH₃ Faradaic efficiency (FE) of 96.7% at −0.2 V vs. RHE, which is 1.5 times higher than the bare Cu₂₊₁O NWs. The synergistic effect between Co₃O₄ and Cu₂₊₁O significantly enhanced both the nitrate conversion and ammonia yield. Importantly, it is revealed that the surface of CoCuNWs is kinetically more easily saturated with NO₃⁻ (NO₂⁻) ions than that of Cu₂₊₁O NWs, as evidenced by both the higher current density and the plateau occurring at higher NO_x⁻ concentrations. In addition, CoCuNWs exhibit a higher diffusion coefficient of NO₃⁻, being 1.6 times higher than that of Cu₂₊₁O NWs, which also indicates that the presence of Co₃O₄ could promote the diffusion and adsorption of NO₃⁻ on CoCuNWs. Moreover, the ATR–SEIRAS analysis was applied to illustrate the reduction pathway of NO₃⁻ to NH₃ on CoCuNWs, which follows the formation of the key intermediate from *NO₂⁻, *NO, *NH₂OH to *NH₃. This work presents a strategy for constructing dual–metal catalysts for NO₃⁻RR and provides an insight to understand the catalysis from the perspective of the kinetics. Full article

Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward. Full article

The S oilfield has adopted polymer flooding technology, specifically using partially hydrolyzed polyacrylamide (HPAM), to enhance oil recovery. During the production process, the S oilfield has generated a substantial amount of stable polymer flooding-produced liquid, in which oil droplets are difficult to effectively coalesce, presenting significant challenges in demulsification. This article focuses on the produced fluids from S Oilfield as the research subject, developing a molecular dynamics model for the stability analysis of production liquid, including the molecular dynamics model of an oil–pure water system, an oil–mineralized water system and an oil–polymer–mineralized water system, using the principle of molecular dynamics and combining it with the basic molecular model for analyzing the stability of polymer flooding-production liquid. Through the molecular dynamics simulation of the stability analysis of the extracted liquid, the changing rules of the molecular diffusion coefficient, radial distribution function (RDF), interfacial interaction energy, and interfacial tension under the action of ions as well as polymers in water were investigated. The simulation results demonstrate that the presence of all three inorganic salt ions (Na⁺, Ca²⁺, and Mg²⁺) reduces the interfacial tension between oil and water and stabilizes the interface. Following the addition of polymer, the interfacial tension of the system decreases and the interfacial interaction energy increases significantly, indicating that the stability of the system is significantly enhanced by HPAM. Full article

This study investigated the spatiotemporal dynamics, regional disparities, and spatial spillover effects of carbon emissions in China’s livestock sector from 2003 to 2022. By integrating carbon accounting, decoupling elasticity analysis, kernel density estimation, Theil index decomposition, and the Spatial Durbin Model, the research revealed a 6.5% reduction in national livestock carbon emissions alongside intensified spatial polarization. The decoupling relationship evolved dynamically, with strong decoupling dominating but regional fluctuations persisting, particularly in resource-dependent areas. The distribution of emission intensity shifted from unimodal right-skewness to bimodal concentration, indicating technological diffusion barriers and structural divergence across regions. Spatial econometric analysis confirmed significant emission interdependence (ρ = 0.214, p < 0.01), where neighboring economic growth increased local emission intensity. These findings highlighted the limitations of uniform policy approaches and emphasized the need for region-specific governance, market-based incentives, and localized technological innovation. The study provided empirical evidence and a policy framework to address cross-regional coordination and sustainable low-carbon transitions in agriculture. Full article

The manufacturing industry serves as critical carrier for the empowerment of digital technologies and is the cornerstone of digital innovation and development. Smart transformation (ST), propelled by technological advancements, has become a prominent area of academic research, but its role in fostering the co-development of manufacturing industries has been overlooked. This study employs an empirical approach to examine the causal mechanisms linking ST with regional economic development (RED), particularly emphasizing the mediating effects exerted by regional innovation capacity (RIC). Leveraging panel data from 29 Chinese provinces spanning the period from 2009 to 2023, we constructed an econometric model for analysis. The findings reveal that ST has a direct effect on RED, knowledge innovation capacity (KIC), and innovation infrastructure (II) partially mediated, while technology innovation capacity (TIC) completely mediated the relationship. Theoretical contributions manifest in three dimensions: First, drawing on the sociotechnical system theory and technology diffusion theory, this paper establishes a multidimensional framework of ST, deepening the theoretical underpinnings of smart technology application in the manufacturing industry from three aspects: smart base input, smart applications, and smart market benefits. Second, it extends regional innovation theory and endogenous growth theory by conceptualizing RIC in three sub-capabilities (KIC, TIC, and II). Third, it contributes to the RED literature by exploring the coupling effect between manufacturing industry clusters and the development of RIC and ultimately concludes with targeted policy recommendations for optimizing ST strategies to foster RED in different manufacturing industries. Full article

This work investigated the optical characteristics of BaWO₄ nanoparticles that were produced through direct mechanochemical synthesis at varying speeds and times. This research expands upon our previous study. We demonstrated that the mechanochemical activation of the precursor of BaCO₃ and WO₃, at elevated milling speeds (850 rpm), facilitates the formation of tetragonal BaWO₄ in a reduced reaction time. The final products were characterized by scanning electron microscopy (SEM), as well as Raman, infrared (IR), UV-Vis diffuse reflectance, and photoluminescence spectroscopies. The crystallite sizes and particles shapes were determined by X-ray diffraction and SEM analysis. Round particles with a size below 50 nm formed under different milling conditions. The Raman spectra of the synthesized samples confirmed the presence of a scheelite-type structure with the typical six distinct vibrational peaks. The symmetry of the structural WO₄ groups was determined by IR spectroscopy. The absorption spectra of both samples exhibited intensive peaks at 210 nm, and the calculated optical band gaps of BaWO₄ were 5.10 eV (3 h/500 rpm) and 5.24 eV (1 h/850 rpm). A strong (400 nm) and weak (465 nm) emission were observed for the BaWO₄ that was obtained at a higher milling speed, while wider emission at 410 nm was visible for the BaWO₄ that was prepared at a lower milling speed. The CIE coordinates of the mechanochemically synthesized BaWO₄ were located within the blue area, exhibiting various positions. Full article

Background/Objectives: Cryotherapy involves the insertion of cryoprobes into tumors to induce cell destruction through exposure to extremely low temperatures over several minutes. This localized treatment modality may enhance the efficacy of established therapies, such as radiotherapy, particularly for glioblastomas. Our study aimed to provide proof-of-concept for the efficacy of combining cryotherapy and radiotherapy in the treatment of subcutaneous murine brain tumors (GL-261) in immunocompetent C57BL/6 mice. Methods: Tumor growth, survival and response were evaluated using MRI and histological analysis. Results: Partial cryotherapy alone showed no therapeutic efficacy. However, combining cryotherapy with radiotherapy significantly potentiated treatment outcomes. A statistically significant survival benefit was observed in the combined therapy group compared to control, cryotherapy and radiotherapy groups. Notably, 40% of mice receiving the combined treatment exhibited complete responses, with no detectable tumor cells on MRI or histological analysis. Furthermore, MRI-based monitoring revealed that the Apparent Diffusion Coefficient (ADC) map could predict complete response 14 days post-treatment, unlike caliper-based measurements. Conclusions: These findings suggest that cryotherapy may enhance radiotherapy efficacy, resulting in complete tumor regression in 4 out of 10 cases. ADC distribution may serve as a predictive marker for therapeutic response. However, given the limitations of the model, further studies in orthotopic models are needed to validate these findings and assess their clinical relevance. Full article