Search Results (30,083)

Accurate traffic prediction is crucial for optimizing taxi demand, managing traffic flow, and planning public transportation routes. Traditional models often fail to capture complex spatial–temporal dependencies. To tackle this, we introduce the Spatio-Temporal Embedding Fusion Transformer (STEFT). This deep learning model leverages attention mechanisms and feature fusion to effectively model dynamic dependencies in traffic data. STEFT includes an Embedding Fusion Network that integrates spatial, temporal, and flow embeddings, preserving original flow information. The Flow Block uses an enhanced Transformer encoder to capture periodic dependencies within neighboring regions, while the Prediction Block forecasts inflow and outflow dynamics using a fully connected network. Experiments on NYC (New York City) Taxi and NYC Bike datasets show STEFT’s superior performance over baseline methods in RMSE and MAPE metrics, highlighting the effectiveness of the concatenation-based feature fusion approach. Ablation studies confirm the contribution of each component, underscoring STEFT’s potential for real-world traffic prediction and other spatial–temporal challenges. Full article

Betablockers are one of the most frequently used medications in cardiology. They can lead to fatal drops in blood pressure and heart rhythm disturbances. Death is functional, and poisoning with this group of drugs can be difficult to detect. The liquid–liquid extraction (LLE) method developed using ethyl acetate at pH 9 successfully identified 18 β-blockers in human blood. The method’s limit of quantification (LOQ) was in the range of 0.1 to 0.5 ng/mL. No carryover of substances between samples was detected, and no interfering ion current signals were observed in the biological samples at the retention times of the compounds or internal standards. All compounds had a coefficient of determination (R²) above 0.995. Intraday and interday precision (RSD%) and accuracy (RE%) for low and high QC levels were within 1.7–12.3% and −14.4 to 14.1%, respectively. Very good recovery (80.0–119.6%) and matrix effect (±20.0%) values were achieved for all compounds. In addition, fragmentation spectra were collected for all the examined substances, and high-resolution spectra were presented for landiolol and metipranolol, because they are not available in commercial HRMS spectra databases. The developed method was applied in authentic postmortem samples. Full article

Chondrules are tiny particles that occur in stony meteorites and are considered as the building blocks of early asteroids and planets. It is believed that they were formed by the fast heating of the dust in the solar nebula. To date, there is no lab-scale experimental study of the formation of chondrules from the initial gas phase precursors following fast heating and crystallisation. The motivation of this work is a pre-trial study of the formation of chnodrule-like particles. The formation of meteorites in the space environment is associated with the aggregation of small particles or molecular clouds under the influence of shock waves or high-energy gas discharges in the solar nebula. In this work, the properties of product formation at the nanoscale-level were investigated using different feedstock materials which are the dominant elements in the meteorite. The structural and morphological properties of the synthesised Si-Fe nanomaterials were analysed by scanning/transmission electron microscopy (SEM/TEM), and chemical composition was analysed by X-ray energy-dispersive spectroscopy (EDS). The identification of crystalline phases was carried out by X-ray diffraction (XRD), whereas the presence of an Fe-Si system in the synthesised particles was demonstrated by Mössbauer spectroscopy. The obtained materials were exposed to the relatively high-energy pulsed plasma beam on the substrate with the aim to emulate the possible fast heating and melting of the formed nanoparticles. The formation steps of growing synthetic (engineered) chondro-like particles and nanostructures in laboratory conditions is discussed. Full article

Enhancing the understanding of the behavior, optimizing the design, and improving the predictability and reliability of manufactured unidirectional (UD) FRP plies, which serve as primary building blocks for structural FRP laminates and components, are crucial to achieving a safe and cost-effective design. This research investigated the influence of fiber volume fraction ($v_f$) on the predictability and reliability of the homogenized elastic properties and damage initiation strengths of two different types of UD FRP plies using validated micromechanical virtual testing for representative volume element (RVE) models. Several sources of uncertainties were included in the RVE models. This study also proposed a modified algorithm for microstructure generation and explored the effect of $v_f$ on the optimal sizes of the RVE in terms of fiber number. Virtual tests were systematically conducted using full factorial DOE coupled with Monte Carlo simulation. The modified algorithm demonstrated exceptional performance in terms of convergence speed and jamming limit, significantly reducing the time required to generate microstructures. The developed RVE models accurately predicted failure modes, loci, homogenized elastic properties, and damage initiation strengths with a mean error of less than $5\%$. Also, it was found that increasing $v_f$ led to a concurrent increase in the optimal size of the RVE. While it was found that the $v_f$ had a direct influence on homogenized elastic properties and damage initiation strengths, it did not significantly affect the reliability and predictability of these properties, as indicated by low correlation coefficients and fluctuations in the coefficient of variation of normalized properties. Full article

This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two techniques: the Wavelet Kernel Network (WKN) for noise reduction and the Convolutional Block Attention Module (CBAM) for feature enhancement. The wavelet function in the WKN reduces noise, while the attention mechanism in the CBAM enhances features. The Transformer module then processes the feature vectors and sends the results to the softmax layer for classification. To validate the proposed method’s efficacy, experiments were conducted using acoustic emission datasets from NASA Ames Research Center and the University of California, Berkeley. The results were compared using the four key metrics obtained through confusion matrix analysis. Experimental results show that the proposed method performs excellently in fault diagnosis using acoustic emission signals, achieving a high average accuracy of 99.84% and outperforming several baseline models, such as CNN, CNN-LSTM, CNN-GRU, VGG19, and ZFNet. The best-performing model, VGG19, only achieved an accuracy of 88.61%. Additionally, the findings suggest that integrating noise reduction and feature enhancement in a single framework significantly improves the network’s classification accuracy and robustness when analyzing acoustic emission signals. Full article

Background: DNA-dependent protein kinase (DNA-PK) is a validated cancer therapeutic target involved in DNA damage response (DDR) and non-homologous end-joining (NHEJ) repair of DNA double-strand breaks (DSBs). Ku serves as a sensor of DSBs by binding to DNA ends and activating DNA-PK. Inhibition of DNA-PK is a common strategy to block DSB repair and improve efficacy of ionizing radiation (IR) therapy and radiomimetic drug therapies. We have previously developed Ku–DNA binding inhibitors (Ku-DBis) that block in vitro and cellular NHEJ activity, abrogate DNA-PK autophosphorylation, and potentiate cellular sensitivity to IR. Results and Conclusions: Here we report the discovery of oxindole Ku-DBis with improved cellular uptake and retained potent Ku-inhibitory activity. Variable monotherapy activity was observed in a panel of non-small cell lung cancer (NSCLC) cell lines, with ATM-null cells being the most sensitive and showing synergy with IR. BRCA1-deficient cells were resistant to single-agent treatment and antagonistic when combined with DSB-generating therapies. In vivo studies in an NSCLC xenograft model demonstrated that the Ku-DBi treatment blocked IR-dependent DNA-PKcs autophosphorylation, modulated DDR, and reduced tumor cell proliferation. This represents the first in vivo demonstration of a Ku-targeted DNA-binding inhibitor impacting IR response and highlights the potential therapeutic utility of Ku-DBis for cancer treatment. Full article

Improving the energy performance of buildings is critical in the construction sector. This study investigates the effects of incorporating coconut mesocarp fibers (F = Fiber) and bovine manure (M = Manure) on the thermal conductivity and compressive strength of concrete blocks. Bovine manure and coconut fiber replaced the block sand at maximum concentrations of 10 and 1.5%, respectively. Thermal conductivities were measured according to the ASTM C177 (2013) standard, compression tests were performed using the ASTM C140 standard, and characterization assays such as Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed to determine the morphological properties of the final material and its constituents. The results showed a 50% reduction in the thermal conductivity coefficient of the blocks when 10 and 1.5% of the sand was replaced with manure and coconut fiber, respectively. Similarly, incorporating coconut fiber at percentages of 0.5, 1, and 1.5% improved compressive strength results. Blocks comprising 0.5, 1, and 1.5% fiber or a mix of 3% manure and 1.5% fiber attained the compressive strength requirements established by the standard. This study demonstrated the feasibility of using coconut fiber mixed with cattle manure as a substitute for up to 2.5% of the sand in non-structural wall elements manufacturing, attaining a decrease in thermal conductibility of around 10%. Full article

Asynchronous injection–production cycle (AIPC) in a horizontal–vertical well pattern is an efficient strategy for enhancing water injection in tight reservoirs. However, current studies lack consideration of waterflood-induced fractures (WIFs) caused by long-term water injection. This paper takes block Z in the Ordos Basin, China, as the research object and first clarifies the formation conditions of WIFs considering the horizontal principal stress and flow line. Then, the pressure-sensitive permeability equations for the induce-fracture region between wells are derived. Finally, the WIFs characteristics in a horizontal–vertical well network with different injection modes are discussed by numerical simulation. The results show that WIFs preferentially form where flow aligns with the maximum principal stress, influencing permeability distribution. Controlling the injection rate of vertical wells on the maximum principal stress and flow line and cyclically adjusting the production rate of horizontal wells can regulate the appropriate propagation of WIFs and expand the swept areas. The parallel injection mode (PIM) and the half-production injection mode are superior to the full-production injection mode. This study can provide theoretical support for the effective development of tight oil reservoirs. Full article

Classification and identification of eye diseases using Optical Coherence Tomography (OCT) has been a challenging task and a trending research area in recent years. Accurate classification and detection of different diseases are crucial for effective care management and improving vision outcomes. Current detection methods fall into two main categories: traditional methods and deep learning-based approaches. Traditional approaches rely on machine learning for feature extraction, while deep learning methods utilize data-driven classification model training. In recent years, Deep Learning (DL) and Machine Learning (ML) algorithms have become essential tools, particularly in medical image classification, and are widely used to classify and identify various diseases. However, due to the high spatial similarities in OCT images, accurate classification remains a challenging task. In this paper, we introduce a novel model called “OCTNet” that integrates a deep learning model combining InceptionV3 with a modified multi-scale attention-based spatial attention block to enhance model performance. OCTNet employs an InceptionV3 backbone with a fusion of dual attention modules to construct the proposed architecture. The InceptionV3 model generates rich features from images, capturing both local and global aspects, which are then enhanced by utilizing the modified multi-scale spatial attention block, resulting in a significantly improved feature map. To evaluate the model’s performance, we utilized two state-of-the-art (SOTA) datasets that include images of normal cases, Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). Through experimentation and simulation, the proposed OCTNet improves the classification accuracy of the InceptionV3 model by 1.3%, yielding higher accuracy than other SOTA models. We also performed an ablation study to demonstrate the effectiveness of the proposed method. The model achieved an overall average accuracy of 99.50% and 99.65% with two different OCT datasets. Full article

In this paper, a coupled model is built to research the space-fractional magnetohydrodynamic (MHD) flow and heat transfer problem. The fractional coupled model is solved numerically by combining the matrix function vector products method in the temporal direction with the spectral method in the spatial direction. A fast method based on the numerical scheme is established to reduce the computational time. With the help of the Bayesian method, the space-fractional orders of the coupled model are estimated, and the problem of multi-parameter estimation in the coupled model is solved. Finally, a numerical example is carried out to verify the stability of the numerical methods and the effectiveness of the parameter estimation method. Results show that the numerical method is stable, which converges with an accuracy of $O({\tau }^2+N^{-r})$. The fast method is efficient in reducing the computational time, and the parameter estimation method can effectively estimate parameters in the space-fractional coupled model. The numerical solutions are discussed to describe the effects of several important parameters on the velocity and the temperature. Results indicate that the Lorentz force produced by the MHD flow blocks the movement of the fluid and prolongs the time for the fluid to reach a stable state. But the Hall parameter m weakens this hindrance. The Joule heating effects play a negative role in heat transfer. Full article

FMS-like tyrosine kinase 3 (FLT3) internal tandem duplication (ITD) mutations in acute myeloid leukemia (AML) are associated with poor prognosis and therapy resistance. This study aimed to demonstrate that inhibiting the deubiquitinating enzymes ubiquitin-specific peptidase 14 (USP14) and ubiquitin C-terminal hydrolase L5 (UCHL5) (USP14/UCHL5) with b-AP15 or the organogold compound auranofin (AUR) induces apoptosis in the ITD-transformed human leukemia cell line MV4-11 and mononuclear leukocytes derived from patients with FLT3-ITD-positive AML. This study included patients diagnosed with AML at Tokyo Medical and Dental University Hospital between January 2018 and July 2024. Both treatments blocked downstream FLT3 pathway events, with the effects potentiated by USP14 knockdown. Both treatments inhibited FLT3 deubiquitination via K48 and disrupted translation initiation via 4EBP1, a downstream FLT3 target. FLT3 was downregulated in the leukemic cells, with the associated activation of stress-related MAP kinase pathways and increased NF-E2-related factor 2. Furthermore, the overexpression of B-cell lymphoma-extra-large and myeloid cell leukemia-1 prevented the cell death caused by b-AP15 and AUR. These results suggest that inhibiting USP14/UCHL5, which involves multiple regulatory mechanisms, is a promising target for novel therapies for treatment-resistant FLT3-ITD-positive AML. Full article

With the frequent occurrence of natural disasters and the acceleration of urbanization, it is necessary to carry out efficient evacuation, especially when earthquakes, fires, terrorist attacks, and other serious threats occur. However, due to factors such as small targets, complex posture, occlusion, and dense distribution, the current mainstream algorithms still have problems such as low precision and poor real-time performance in crowd person detection. Therefore, this paper proposes EAAnet, a crowd person detection algorithm. It is based on YOLOv5, with CBAM (Convolutional Block Attention Module) introduced into the backbone, BiFPN (Bidirectional Feature Pyramid Network) introduced into the neck, and combined with a loss function of CIoU_Loss to better predict the person number. The experimental results show that compared with other mainstream detection algorithms, EAAnet has achieved significant improvement in precision and real-time performance. The precision value of all categories was 78.6%, which was increased by 1.8. Among these, the categories of riders and partially visible person were increased by 4.6 and 0.8, respectively. At the same time, the parameter number of EAAnet is only 7.1M, with a calculation amount of 16.0G FLOPs. Therefore, it is proved that EAAnet has the ability of the efficient real-time detection of the crowd person and is feasible in the field of emergency management. Full article

Focusing water surface waves is a promising approach for enhancing wave power in clean energy harvesting. This study presents a novel method that simplifies the wave-scattering problems of large-scale three-dimensional (3D) focusing blocks by decomposing them into scattering problems of two-dimensional (2D) phase regulators. The phase lags of transmitted waves over such 2D structures of various heights and thicknesses are investigated using both linear potential flow theory and numerical simulations based on smoothed-particle hydrodynamics (SPH). Due to propagation path differences of a converging wave, our approach compensates for circular phase differences within a maximal collection angle by optimizing the geometries of 2D phase regulators. Based on this concept, we designed three types of submerged structures and tested them in a 3D numerical water tank. All three structures successfully converted monochromatic plane waves into circular waves, which then converged at the designated focal point. This study offers a potential method to enhance the collection efficiency of monochromatic and regular waves for wave energy converters. Full article

This study was conducted to investigate the protective effects of xylooligosaccharide (XOS) on the growth performance and intestinal health of broilers challenged by avian pathogenic Escherichia coli (APEC). A total of 144 newly hatched male Lingnan yellow-feathered broilers were randomly divided into three groups (six replicates/group): a control (CON) group, an APEC group and an XOS group (APEC-challenged broilers supplemented with 1600 mg/kg XOS). Birds in the APEC and XOS groups were orally challenged with APEC from 7 to 12 d of age. Growth performance and intestinal health-related parameters were determined on d 13 and 17. The reductions (p < 0.05) in final body weight, average daily gain and elevation (p < 0.05) in intestinal APEC colonization in challenged broilers were counteracted by the XOS addition, which also alleviated the APEC-induced reductions (p < 0.05) in jejunal goblet cell count and density in broilers on d 17. Supplementing with XOS increased (p < 0.05) jejunal villus height and crypt depth, coupled with occludin and zonula occluden-1 expression, on d 17, and diminished the change (p < 0.05) in the jejunal inflammatory cytokine expression profile in a time-dependent manner. Moreover, cecal counts of total bacteria and Lactobacillus in challenged broilers were augmented (p < 0.05) by the XOS addition, which also mitigated APEC-induced reductions (p < 0.05) in cecal acetate, butyrate and valerate concentrations in broilers on d 13 or 17. Supplementing with XOS blocked the increases (p < 0.05) in the expression of cecal E. coli virulence genes relA and ompR on d 13 along with the expression of fimH and csgA on d 17. XOS alleviated APEC-induced growth retardation and intestinal disruption in broilers partially by restraining the intestinal colonization of APEC. Furthermore, the improvements in cecal microbiota and fermentation pattern, along with attenuation of cecal E. coli virulence resulting from XOS supplementation, could also support the maintenance of intestinal health in APEC-challenged broilers. Full article

Insulator and defect detection is a critical technology for the automated inspection of transmission and distribution lines within smart grids. However, the development of a lightweight, real-time detection platform suitable for deployment on drones faces significant challenges. These include the high complexity of existing algorithms, limited availability of UAV images, and persistent issues with false positives and missed detections. To address this issue, this paper proposed a lightweight drone-based insulator defect detection method (LDIDD) that integrates data augmentation and attention mechanisms based on YOLOv8. Firstly, to address the limitations of the existing insulator dataset, data augmentation techniques are developed to enhance the diversity and quantity of samples in the dataset. Secondly, to address the issue of the network model’s complexity hindering its application on UAV equipment, depthwise separable convolution is incorporated for lightweight enhancement within the YOLOv8 algorithm framework. Thirdly, a convolutional block attention mechanism is integrated into the feature extraction module to enhance the detection of small insulator targets in aerial images. The experimental results show that the improved network reduces the computational volume by 46.6% and the mAP stably maintains at 98.3% compared to YOLOv8, which enables the implementation of a lightweight insulator defect network suitable for the UAV equipment side without affecting the detection performance. Full article