Efficient Method Research Articles

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

The pharmacokinetic (PK) parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide valuable information for clinical research and diagnosis. However, these estimated PK parameters suffer from many sources of variability. Thus, the estimation of the posterior distributions of these PK parameters could provide a way to simultaneously quantify the values and uncertainties of the PK parameters. Our objective is to develop an efficient and flexible method to more closely approximate and estimate the underlying posterior distributions of the PK parameters. The normalizing flow model-based parameters distribution estimation neural network (FPDEN) is proposed to adaptively learn and estimate the posterior distributions of the PK parameters. The maximum likelihood estimation (MLE) loss is directly constructed based on the parameter distributions learned by the normalizing flow model, rather than pre-defined distributions. Experimental analysis shows that the proposed method can improve parameter estimation accuracy. Moreover, the uncertainty derived from the parameter distribution constitutes an effective indicator to exclude unreliable parametric results. A successful demonstration is the improved classification performance of the glioma World Health Organization (WHO) grading task, specifically in terms of distinguishing between low and high grades, as well as between Grade III and Grade IV. The FPDEN method offers improved accuracy for estimation of PK parameters and boosts the performance of the glioma grading task. By enhancing the precision and reliability of DCE-MRI, the proposed method promotes its further applications in clinical practice.

Inverse Rheological Hysteresis Model and its Efficient Parameter Identification Method

Inverse Rheological Hysteresis Model and its Efficient Parameter Identification Method

User Scheduling in Wireless Networks for Deterministic Service: An Efficient Genetic Algorithm Method

User Scheduling in Wireless Networks for Deterministic Service: An Efficient Genetic Algorithm Method

Exploring the effect of Wuzhi capsule on the pharmacokinetics of regorafenib and its main metabolites in rat plasma using liquid chromatography-tandem mass spectrometry.

Regorafenib is a small-molecule tyrosine kinase inhibitor with severe hepatotoxicity. It undergoes metabolism mainly by CYP3A4 to generate active metabolites regorafenib-N-oxide (M2) and N-desmethyl-regorafenib-N-oxide (M5). Wuzhi capsule (WZC) is an herbal preparation derived from Schisandra sphenanthera and is potentially used to prevent regorafenib-induced hepatotoxicity. This study aims to explore the effect of WZC on the pharmacokinetics of regorafenib in rats. An efficient and sensitive liquid chromatography-tandem mass spectrometry method was developed to quantitatively determine regorafenib and its main metabolites in rat plasma. The proposed method was applied to the pharmacokinetic study of regorafenib in rats, with or without WZC. Coadministration of regorafenib with WZC resulted in a prolonged mean residence time (MRT) of the parent drug but had no statistically significant difference in other pharmacokinetic parameters. While for the main metabolites of regorafenib, WZC decreased the area under the curve and maximum concentration (Cmax ), delayed the time to reach Cmax , and prolonged the MRT of M2 and M5. These results indicate that WZC delayed and inhibited the metabolism of regorafenib to M2 and M5 by suppressing CYP3A4. Our study provides implications for the rational use of the WZC-regorafenib combination in clinical practice.

An Efficient Hybrid CNN-Transformer Approach for Remote Sensing Super-Resolution

Transformer models have great potential in the field of remote sensing super-resolution (SR) due to their excellent self-attention mechanisms. However, transformer models are prone to overfitting because of their large number of parameters, especially with the typically small remote sensing datasets. Additionally, the reliance of transformer-based SR models on convolution-based upsampling often leads to mismatched semantic information. To tackle these challenges, we propose an efficient super-resolution hybrid network (EHNet) based on the encoder composed of our designed lightweight convolution module and the decoder composed of an improved swin transformer. The encoder, featuring our novel Lightweight Feature Extraction Block (LFEB), employs a more efficient convolution method than depthwise separable convolution based on depthwise convolution. Our LFEB also integrates a Cross Stage Partial structure for enhanced feature extraction. In terms of the decoder, based on the swin transformer, we innovatively propose a sequence-based upsample block (SUB) for the first time, which directly uses the sequence of tokens in the transformer to focus on semantic information through the MLP layer, which enhances the feature expression ability of the model and improves the reconstruction accuracy. Experiments show that EHNet’s PSNR on UCMerced and AID datasets obtains a SOTA performance of 28.02 and 29.44, respectively, and is also visually better than other existing methods. Its 2.64 M parameters effectively balance model efficiency and computational demands.

Integrated watershed management strategies for sustainable resource utilization using the SWAT model: case study of the Kalte River watershed, Rift Valley Basin, Ethiopia

ABSTRACT Most people who live in rural areas are highly dependent on shared access to natural resources including land, water, and forests for their food requirements and livelihoods. However, land degradation remains one of the biggest environmental problems worldwide. Therefore, this study proposed to develop integrated watershed management strategies for sustainable resource utilization in the Kalte River watershed. To achieve this, the SWAT model was simulated for 31 years (1992–2022), calibrated and validated at Wajifo and Humbo hydrological stations to determine the sediment and runoff from the watershed, highly sediment erosion-vulnerable part of the watershed was identified from the result of the model and the best watershed management practice was suggested for the study watershed. In total, 87,920 tons/year of sediment are yielded to Lake Abaya from the Kalte River watershed. The sediment yield was reduced by terracing at 64%, strip cropping at 59.32%, grassed waterway at 54.06%, and contour planting at 47.93%. Therefore, the highest efficiency management method in the Kalte River watershed is terracing. The watershed managers and scientific community are beneficiaries of the output of this study. Watershed managers and decision-makers can make use of the information to help them choose appropriate watershed management strategies and ensure sustainable watershed management.

Development of a virus-based affinity ultrafiltration method for screening virus-surface-protein-targeted compounds from complex matrixes: Herbal medicines as a case study.

Herbal medicines (HMs) are one of the main sources for the development of lead antiviral compounds. However, due to the complex composition of HMs, the screening of active compounds within these is inefficient and requires a significant time investment. We report a novel and efficient virus-based screening method for antiviral active compounds in HMs. This method involves the centrifugal ultrafiltration of viruses, known as the virus-based affinity ultrafiltration method (VAUM). This method is suitable to identify virus specific active compounds from complex matrices such as HMs. The effectiveness of the VAUM was evaluated using influenza A virus (IAV) H1N1. Using this method, four compounds that bind to the surface protein of H1N1 were identified from dried fruits of Terminalia chebula (TC). Through competitive inhibition assays, the influenza surface protein, neuraminidase (NA), was identified as the target protein of these four TC-derived compounds. Three compounds were identified by high performance liquid chromatography (HPLC) and liquid chromatography/mass spectrometry (LC/MS), and their anti-H1N1 activities were verified by examining the cytopathic effect (CPE) and by performing a virus yield reduction assay. Further mechanistic studies demonstrated that these three compounds directly bind to NA and inhibit its activity. In summary, we describe here a VAUM that we designed, one that can be used to accurately screen antiviral active compounds in HMs and also help improve the efficiency of screening antiviral drugs found in natural products.

Time-varying mesh stiffness calculation of spiral bevel gear with spalling defect

Spiral bevel gears often operate at high speeds and under heavy loads, which can easily lead to the occurrence of spalling faults on the tooth surfaces, particularly in cases of inadequate lubrication. In this study, we introduce an efficient and accurate numerical calculation method to determine the time-varying meshing stiffness (TVMS) of a spiral bevel gear pair with actual spalling faults, taking into consideration the effects of multi-tooth meshing and the flexibility of the gear flank. In parallel, a finite element model is established to verify the accuracy of the proposed method across various spalling parameters. We delve into the impact of spalling length, width, location, and shape. The results demonstrate that the TVMS values obtained from our proposed method align closely with those obtained using the finite element method. The range of TVMS reduction caused by the spalling fault is primarily determined by the spalling location and width. Moreover, the magnitude of TVMS decline is strongly associated with the spalling length. Furthermore, the clearances between two conjugated tooth surfaces change due to the presence of the spalling fault, resulting in a redistribution of contact forces on the same contact surface and other meshing tooth surfaces.

Gas-phase oxidation of benzene using Cu-loaded MFI binderless zeolite compacts

Phenols are essential raw materials for various fields, and several studies have focused on their synthesis. The development of an efficient one-step synthetic method for the industrial production of phenols is highly important in terms of cost, energy, and environmental impact. In this study, we investigated the formation of phenols via the gas-phase oxidation of benzene using copper-loaded MFI zeolite compacts prepared via a binderless zeolite synthetic method. The catalytic activity of the zeolite in the gas-phase phenol one-step synthetic reaction was investigated, in addition to the effect of benzene diffusion in the molded zeolite and the reaction due to water vapor addition to the reaction gas. The results showed that the activity of the copper-loaded MFI zeolite compacts was almost equivalent to that of powdered copper-loaded MFI zeolites. An effect of diffusion was observed on the external boundary film diffusion where the linear velocity was low. However, no effect of diffusion or internal diffusion was observed in the area where the linear velocity was high. Furthermore, the addition of water vapor to the reaction gas suppressed the decrease in phenol formation activity due to the formation of coke on the catalyst surface.

Microgrid Formation and Real-Time Scheduling of Active Distribution Networks Considering Source-Load Stochasticity

The post-disruption microgrid (MG) formation and the subsequent scheduling are resilience-enhancing measures for active distribution networks (ADNs) against disastrous events. This paper proposes an integrated MG formation and scheduling solution, considering stochastic loads and distributed generators (DGs). Specifically, a first-stage MG formation model and a sec-ond-stage MG scheduling model are proposed to flexibly identify MG boundaries and restore as many critical loads as possible. The MG formation problem is formulated over multiple time steps to address time-varying generation outputs of intermittent DGs. Moreover, in the MG scheduling problem, the stochasticity of load demands and DG outputs is modeled as joint chance constraints (JCCs) to describe the possibility of security violations in the over-all system. An efficient approximation method is next developed to convexify the JCCs into solvable second-order cones. The interac-tion between the MG formation and scheduling by the model predictive control implementation achieves a responsive solution for enhancing the emergency support capability of ADNs by incor-porating power forecasts and real-time scheduling. The performance of the proposed method is verified in a modified real-world 136-node test feeder under catastrophic failures.

Resonant response of a flexible semi-submersible floating structure: experimental analysis and second-order modelling

The dynamics and nonlinear wave forcing of a flexible floating structure are investigated experimentally and numerically. The floater was designed to match sub-harmonic rigid-body natural frequencies of typical floating wind turbine substructures, with the addition of a flexible bending mode. Experiments were carried out for three sea states with phase-shifted input signals to allow harmonic separation of the measured response. We find for the weakest sea states that sub-harmonic rigid-body motion is driven by even-harmonic difference frequency forcing, and by linear forcing for the strongest sea state. The flexible mode was tested in a soft, linearly forced layout, and a stiff layout, forced by second-, third- and fourth-harmonic frequency content, for increasing severity of the sea state. Further insight is gained by analysis of the amplitude scaling of the resonant response. A new simplified approach is proposed and compared with the recent method of Orszaghova et al. (J. Fluid Mech., vol. 929, 2021, A32). We find that resonant surge and pitch motions are dominated by even-harmonic potential-flow forcing and that odd-harmonic response is mainly potential-flow driven in surge and mainly drag driven in pitch. The measured responses are reproduced numerically with second-order forcing and quadratic drag loads, using a recent and computationally efficient calculation method, extended here for the heave, pitch and flexible motions. We are able to reproduce the response statistics and power spectra for the measurements, including the subharmonic pitch and heave modes and the flexible mode. Deeper analysis reveals that inaccuracies in the even-harmonic forcing content can be compensated by the odd-harmonic loads.

LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.

Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this article, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA). In particular, LMGATCDA first incorporates information on circRNA functional similarity, disease semantic similarity, and the Gaussian interaction profile (GIP) kernel similarity as explicit features, along with node-labeling of the three-hop subgraphs extracted from each linked target node as graph structural features. After that, the fused features are used as input, and further implied features are extracted by graph sampling aggregation (GraphSAGE) and multi-hop attention graph neural network (MAGNA). Finally, the prediction scores are obtained through a fully connected layer. With five-fold cross-validation, LMGATCDA demonstrated excellent competitiveness against gold standard data, reaching 95.37% accuracy and 91.31% recall with an AUC of 94.25% on the circR2Disease benchmark dataset. Collectively, the noteworthy findings from these case studies support our conclusion that the LMGATCDA model can provide reliable circRNA-disease associations for clinical research while helping to mitigate experimental uncertainties in wet-lab investigations.

Development And Validation Of Rp-Hplc Method For The Estimation Of Tenofovir And Emtricitabine In Bulk And Pharmaceutical Dosage Form.

A novel, simple, efficient, rapid, and precise reverse-phase high-performance liquid chromatography (RP-HPLC) method was developed for the estimation of Tenofovir and Emtricitabine in the bulk and pharmaceutical dosage form. The currently developed method was subsequently validated according to ICH guidelines in terms of linearity, accuracy, precision, the limit of detection, the limit of quantification, robustness, etc. Methods: The separation of the selected drugs was optimized after several trials including change of mobile phase and its composition, stationary phase, flow rate, column temperature, etc. The separation was performed by using an Inertsil ODS C18 column (250 mm x 4.6 mm, 5μ) and UV absorption was measured at 231 nm. Methanol: Acetonitrile: Water was selected as the mobile phase in the ratio of 50:20:30 (V/V/V) at a flow rate of 1 mL/min. As per International Conference on Harmonization (ICH) Q2 R1 guidelines, several validation parameters were evaluated which include specificity, linearity, precision, accuracy, the limit of detection (LOD), and the limit of quantitation (LOQ). The acceptable degree of linearity range was found to be 40-100 µg/mL. The standard solution exhibited retention times of 3.06 minutes and 5.07 minutes for Tenofovir and Emtricitabine respectively. The LOD and LOQ obtained were 0.05µg/ml and 0.02µg/mL, 15µg/mL, and 0.08µg/mL for Tenofovir and Emtricitabine respectively. The percent recovery was found to be 98 to 102%. Hence, the proposed method is simple, selective, and specifically meets the requirements of ICH guidelines for the validation of the analytical method.

Spectral-Galerkin method for second kind VIEs with highly oscillatory kernels of the stationary point

In this paper, we discuss an efficient spectral approach for solving a class of second kind VIEs with highly oscillatory kernels possessing the stationary point. First, we use one variable transform to convert the highly oscillatory problem into the long-time one, and then split the long-time problem into a linear system of integral equations by using a dilation approach. Next on each interval we study the characteristic of the original solution and then propose an efficient spectral-Galerkin method for solving the linear system of integral equations. We prove that the proposed algorithm requires to solve the number O(log2⁡ω) of linear algebra equations of order n+1 and reaches the convergence order O(n−r), where r denotes the degree of the regularity of the original solution and ω denotes the wave number. Moreover, we give the approach of computing the highly oscillatory integrals produced in the spectral-Galerkin method. At last, two numerical examples are provided to verify the efficiency of our proposed method.

Multi-Feature Decision Fusion Network for Heart Sound Abnormality Detection and Classification.

The heart sound reflects the movement status of the cardiovascular system and contains the early pathological information of cardiovascular diseases. Automatic heart sound diagnosis plays an essential role in the early detection of cardiovascular diseases. In this study, we aim to develop a novel end-to-end heart sound abnormality detection and classification method, which can be adapted to different heart sound diagnosis tasks. Specifically, we developed a Multi-feature Decision Fusion Network (MDFNet) composed of a Multi-dimensional Feature Extraction (MFE) module and a Multi-dimensional Decision Fusion (MDF) module. The MFE module extracted spatial features, multi-level temporal features and spatial-temporal fusion features to learn heart sound characteristics from multiple perspectives. Through deep supervision and decision fusion, the MDF module made the multi-dimensional features extracted by the MFE module more discriminative, and fused the decision results of multi-dimensional features to integrate complementary information. Furthermore, attention modules were embedded in the MDFNet to emphasize the fundamental heart sounds containing effective feature information. Finally, we proposed an efficient data augmentation method to circumvent the diagnosis performance degradation caused by the lack of cardiac cycle segmentation in other end-to-end methods. The developed method achieved an overall accuracy of 94.44% and a F1-score of 86.90% on the binary classification task and a F1-score of 99.30% on the five-classification task. Our method outperformed other state-of-the-art methods and had good clinical application prospects.

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

AbstractThe innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs.Highlights A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs. Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA. SP linkages are established between 2D cross‐sections and 3D macromechanical properties. Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models.

A Computationally Efficient Semi-Analytical Method for Circulating Current Loss of High Speed Permanent Magnet Machines

A Computationally Efficient Semi-Analytical Method for Circulating Current Loss of High Speed Permanent Magnet Machines

Resonances of Fluid‐Filled Cracks With Complex Geometry and Application to Very Long Period (VLP) Seismic Signals at Mayotte Submarine Volcano

AbstractFluid‐filled cracks sustain a slow guided wave (Krauklis wave or crack wave) whose resonant frequencies are widely used for interpreting long period (LP) and very long period (VLP) seismic signals at active volcanoes. Significant efforts have been made to model this process using analytical developments along an infinite crack or numerical methods on simple crack geometries. In this work, we develop an efficient hybrid numerical method for computing resonant frequencies of complex‐shaped fluid‐filled cracks and networks of cracks and apply it to explain the ratio of spectral peaks in the VLP signals from the Fani Maoré submarine volcano that formed in Mayotte in 2018. By coupling triangular boundary elements and the finite volume method, we successfully handle complex geometries and achieve computational efficiency by discretizing solely the crack surfaces. The resonant frequencies are directly determined through eigenvalue analysis. After proper verification, we systematically analyze the resonant frequencies of rectangular and elliptical cracks, quantifying the effect of aspect ratio and crack stiffness. We then discuss theoretically the contribution of fluid viscosity and seismic radiation to energy dissipation. Finally, we obtain a crack geometry that successfully explains the characteristic ratio between the first two modes of the VLP seismic signals from the Fani Maoré submarine volcano in Mayotte. Our work not only reveals rich eigenmodes in complex‐shaped cracks but also contributes to illuminating the subsurface plumbing system of active volcanoes. The developed model is readily applicable to crack wave resonances in other geological settings, such as glacier hydrology and hydrocarbon reservoirs.

Reconfigurable Intelligent Surface Assisted Non-Orthogonal Multiple Access Network Based on Machine Learning Approaches

In the upcoming beyond fifth-generation (B5G), the surging demand on high network capacity and numerous access devices can hardly be satisfied by the scarce spectrum resources. Capable of sharing the same spectrum resource among multiple users, non-orthogonal multiple access (NOMA) provides an efficient method to enhance the spectrum efficiency, serving as a strong candidate for next generation multiple access (NGMA). To achieve a better performance gain of NOMA, reconfigurable intelligent surface (RIS) provides an effective leverage by reconstructing the channel conditions. Since the increasingly computational complexity and elusive communication environment in RIS-aided NOMA networks, we employ machine learning (ML) to improve network performance. In this article, we first present an overview of the NOMA, RIS, and ML. Based on the good compatibility between NOMA and RIS, we then provide a series of emerging application scenarios and associated challenges of RIS-aided NOMA. After that, we discuss the potential of ML approaches to cope with the challenges in RIS-aided NOMA. Finally, evaluation results are provided to illustrate the superiority of the RIS-aided NOMA network based on ML.

A method for determining the locations and configurations of magnetic reconnection within three-dimensional turbulent plasmas

Context. Three-dimensional (3D) reconnection is an important mechanism for efficiently releasing energy during astrophysical eruptive events, which is difficult to be quantitatively analyzed especially within turbulent plasmas. Aims. In this paper, an efficient method for identifying locations and configurations of 3D reconnection from magnetohydrodynamical (MHD) data is developed. Methods. This method analyzes the local nonideal electric field and magnetic structure at an arbitrary position. As only performing algebraical manipulations on the discrete field data and avoiding computationally expensive operations such as field-line tracing and root-finding, this method naturally possesses high efficiency. To validate this method, we apply it to the 3D data from a high-resolution simulation of a Harris-sheet reconnection and a data-driven simulation of a coronal flux rope eruption. Results. It is shown that this method can precisely identify the local structures of discrete magnetic field. Through the information of nonideal electric field and the geometric attributes of magnetic field, the local structures of reconnection sites can be effectively and comprehensively determined. For fine turbulent processes, both qualitative pictures and quantitative statistical properties of small-scale reconnection structures can be obtained. For large-scale solar simulations, macro-scale magnetic structures such as flux ropes and eruption current sheets can also be recognized. Conclusions. We develop a powerful method to analyze multi-scale structures of 3D reconnection. It can be applied not only in MHD simulations but also in kinetic simulations, plasma experiments, and in situ observations.