Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.Comment: Accepted in the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2023

Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets.

Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach

Study on mechanical properties and microscopic damage mechanism of tight sandstone reservoir under uniaxial compression

Due to the characteristics of low porosity, low permeability and serious anisotropy in tight reservoirs, it is difficult for conventional hydraulic fracturing theory to accurately guide the efficient exploitation of tight reservoirs. It has been shown that the reservoir rock mechanical properties are the key factor impacting the fracturing effect, but the current research on the damage properties of tight reservoir rocks is not comprehensive enough. Therefore, in order to improve the fracturing theory of tight reservoirs, this paper first explores the evolution mechanism of rock fractures through uniaxial compression experiments. Secondly, based on the particle discrete element method, the damage and failure process of tight sandstone under uniaxial compression is simulated from the microscopic scale. The test results show that the rock failure mainly includes tensile failure, shear failure, and tensile-shear failure; Internal micro-fractures will interconnect during rock destruction to form primary fractures through the rock mass, while secondary micro-fractures will also be generated. The numerical simulation results show that when the rock is subjected to tensile-shear failure, with the increase of load, tensile micro-fractures are mainly produced in the specimen, accompanied by a few shear fractures. Under the joint action of shear failure and tensile failure, V-shaped cracks are easily formed in rock. The tensile strength of rock is mainly affected by the microscopic tensile strength, and the cohesive force, modulus, stiffness ratio, friction coefficient and friction angle have significant effects on the compressive strength of rock. Therefore, a reasonable choice of microscopic parameters can realistically simulate the compression-tensile strength ratio of the rock. The research results of this paper can provide the theoretical basis of rock mechanics for the efficient exploitation of tight reservoirs

A novel approach for automatic segmentation of prostate and its lesion regions on magnetic resonance imaging

ObjectiveTo develop an accurate and automatic segmentation model based on convolution neural network to segment the prostate and its lesion regions.MethodsOf all 180 subjects, 122 healthy individuals and 58 patients with prostate cancer were included. For each subject, all slices of the prostate were comprised in the DWIs. A novel DCNN is proposed to automatically segment the prostate and its lesion regions. This model is inspired by the U-Net model with the encoding-decoding path as the backbone, importing dense block, attention mechanism techniques, and group norm-Atrous Spatial Pyramidal Pooling. Data augmentation was used to avoid overfitting in training. In the experimental phase, the data set was randomly divided into a training (70%), testing set (30%). four-fold cross-validation methods were used to obtain results for each metric.ResultsThe proposed model achieved in terms of Iou, Dice score, accuracy, sensitivity, 95% Hausdorff Distance, 86.82%,93.90%, 94.11%, 93.8%,7.84 for the prostate, 79.2%, 89.51%, 88.43%,89.31%,8.39 for lesion region in segmentation. Compared to the state-of-the-art models, FCN, U-Net, U-Net++, and ResU-Net, the segmentation model achieved more promising results.ConclusionThe proposed model yielded excellent performance in accurate and automatic segmentation of the prostate and lesion regions, revealing that the novel deep convolutional neural network could be used in clinical disease treatment and diagnosis

Strong magnon-magnon coupling in an ultralow damping all-magnetic-insulator heterostructure

Magnetic insulators such as yttrium iron garnets (YIGs) are of paramount importance for spin-wave or magnonic devices as their ultralow damping enables ultralow power dissipation that is free of Joule heating, exotic magnon quantum state, and coherent coupling to other wave excitations. Magnetic insulator heterostructures bestow superior structural and magnetic properties and house immense design space thanks to the strong and engineerable exchange interaction between individual layers. To fully unleash their potential, realizing low damping and strong exchange coupling simultaneously is critical, which often requires high quality interface. Here, we show that such a demand is realized in an all-insulator thulium iron garnet (TmIG)/YIG bilayer system. The ultralow dissipation rates in both YIG and TmIG, along with their significant spin-spin interaction at the interface, enable strong and coherent magnon-magnon coupling with a benchmarking cooperativity value larger than the conventional ferromagnetic metal-based heterostructures. The coupling strength can be tuned by varying the magnetic insulator layer thickness and magnon modes, which is consistent with analytical calculations and micromagnetic simulations. Our results demonstrate TmIG/YIG as a novel platform for investigating hybrid magnonic phenomena and open opportunities in magnon devices comprising all-insulator heterostructures.Comment: 45 pages, 18 figures, and 2 table

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The predictive value of the preoperative albumin‐to‐fibrinogen ratio for postoperative hospital length of stay in liver cancer patients

Abstract Background Hepatocellular carcinoma (HCC) is a significant global health burden, with postoperative hospital length of stay (LOS) impacting patient outcomes and healthcare costs. Existing nutritional, inflammatory, and coagulation indices can predict LOS, with particular interest in albumin, fibrinogen, and D‐dimer. This study investigates the predictive value of preoperative albumin‐to‐fibrinogen ratio (AFR) and albumin‐to‐D‐dimer ratio (ADR) for postoperative LOS in HCC patients. Methods This retrospective study involved 462 adult HCC patients who underwent partial hepatic lesion excision between February 2016 and August 2022. We analyzed demographic and clinical data, including preoperative blood samples, surgical approach, and LOS. The primary outcome measure was LOS, calculated from the date of surgery to the date of hospital discharge. Preoperative AFR and ADR were calculated. The ROC curves determined optimal cutoff points. The Cox proportional hazards model, Kaplan–Meier method, and the log‐rank test were used for statistical analysis. Results The study established an optimal AFR cutoff value of 15.474, with a higher AUC value than ADR, indicating superior predictive potential for postoperative LOS. Participants with high‐AFR (AFR > 15.474) had a shorter median LOS (13 vs. 15 days, p 20.5 μmol/L (HR: 0.58; p < 0.001) negatively impacted LOS reduction. Subgroup analysis confirmed AFR's predictive ability for patients experiencing reduced or prolonged LOS due to Child–Pugh score, surgical methods, and total bilirubin concentrations. Even within normal albumin and fibrinogen levels, patients with high‐AFR exhibited a shorter LOS (all p < 0.001). Conclusions Our findings underscore the value of the AFR as a reliable predictor of LOS in HCC patients. An AFR greater than 15.474 consistently correlated with a shorter LOS, suggesting its potential clinical utility in guiding perioperative management and resource allocation in HCC patients

SPS and DPS: Two New Grid-Based Source Location Privacy Protection Schemes in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed to monitor valuable objects. In these applications, the sensor node senses the existence of objects and transmitting data packets to the sink node (SN) in a multi hop fashion. The SN is a powerful node with high performance and is used to collect all the information sensed by the sensor nodes. Due to the open nature of the wireless medium, it is easy for an adversary to trace back along the routing path of the packets and get the location of the source node. Once adversaries have got the source node location, they can capture the monitored targets. Thus, it is important to protect the source node location privacy in WSNs. Many methods have been proposed to deal with this source location privacy protection problem, and most of them provide routing path diversity by using phantom node (PN) which is a fake source node used to entice the adversaries away from the actual source node. But in the existing schemes, the PN is determined by the source node via flooding, which not only consumes a lot of communication overhead, but also shortens the safety period of the source node. In view of the above problems, we propose two new grid-based source location privacy protection schemes in WSNs called grid-based single phantom node source location privacy protection scheme (SPS) and grid-based dual phantom node source location privacy protection scheme (DPS) in this paper. Different from the idea of determining the phantom node by the source node in the existing schemes, we propose to use powerful sink node to help the source node to determine the phantom node candidate set (PNCS), from which the source node randomly selects a phantom node acting as a fake source node. We evaluate our schemes through theoretical analysis and experiments. Experimental results show that compared with other schemes, our proposed schemes are more efficient and achieves higher security, as well as keeping lower total energy consumption. Our proposed schemes can protect the location privacy of the source node even in resource-constrained wireless network environments

Simulation research on motion of three machines on fully mechanized coal mining face

In view of problems that current simulation research on motion of three machines on fully mechanized mining face could not display single-machine motion state in detail, and linkage process of a single unit was rarely involved, geometric model of three machines were established using virtual machine technology. On the basis of attitude analysis, under the Unity3D platform and through script component,object-oriented programming ideas was applied, motions of single machine were restored including rocker arm lifting,coal cutting action of shearer, descending column, moving frame, pushing of conveyer, and bending movement of scraper conveyor. The test results show that the established model can clearly and completely show the structure and operation principle of the three machines and the motion of single machine

Development Strategy of Intelligent Logistics for Agricultural Products

The lagging informatization and intellectualization of the agricultural product postharvest supply chain is the main factor that results in low circulation efficiency and serious quality loss. Improvement on the postharvest added values of agricultural products necessitates the planning of intelligent logistics for agricultural products in China. In this study, we analyze the demand for intelligent logistics of agricultural products, summarize the development status, and investigate the problems existing in informatization, standards and quality, and professionals. Considering China’s conditions, we propose the development goals and key tasks of the intelligent logistics for agricultural products in China by 2035. The study shows that strengthening policy support, improving the standardization system, and promoting personnel training are the foundation for the intelligent logistics of agricultural product. The continuous innovation and extended application of the new-generation information technology has promoted the transformation and upgrading of traditional agricultural logistics toward intelligent agricultural logistics in China. In the future, an intelligent, integrated, and green supply chain will be the major development direction for intelligent agricultural logistics. This study can provide a basic reference for improving overall operation efficiency and upgrading service quality of the agricultural logistics industry