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Abstract Objective To stratify patients with copy-number low (CNL) endometrial cancer (EC) by clinicopathological characteristics. Methods EC patients who underwent surgery between June 2018 and June 2022 at Peking University People’s Hospital were included and further classified according to TCGA molecular subtyping: POLE ultramutated, microsatellite instability high (MSI-H), CNL, and copy-number high (CNH). Clinicopathological characteristics and prognosis of CNL patients were retrospectively reviewed. The Cox proportional hazards regression model was applied to perform univariate and multivariate analysis, and independent risk factors were identified. Differentially expressed genes (DEGs) according to overall survival (OS) were screened based on the transcriptome of CNL cases from the TCGA program. Finally, a nomogram was established, with an accuracy analysis performed. Results (1) A total of 279 EC patients were included, of whom 168 (60.2%) were in the CNL group. A total of 21 patients had recurrence and 6 patients deceased, and no significant difference in recurrence-free survival (RFS) was exhibited among the four molecular subtypes (P = 0.104), but that in overall survival (OS) was statistically significant (P = 0.036). (2) CNL patients were divided into recurrence and non-recurrence groups, and significant differences (P

Bayesian networks are a class of probabilistic graphical models used to characterize the relationships between variables, and structure learning is a key step in Bayesian network modeling, which is the basis for network parameter learning and probabilistic inference. In this study, we introduce Bayesian networks and propose a model for tracking vocabulary knowledge based on BKT. In order to test the performance of the model, the vocabulary knowledge tracking model, including the Bayesian network model, is compared with other models by combining the learning structure algorithm and students’ learning characteristics, and the accuracy, recall, and mean square error of the model are explored to study the accuracy of the vocabulary knowledge tracking model in predicting students’ vocabulary mastery is close to 0.9, and the precision is in the interval of (0.5-0.6). In teaching, the model that predicts students’ mastery of English vocabulary in different datasets performs better than other models in practice.

The development of contemporary college English teaching methods and practice should respect the subjective will of college students; fully tap their wisdom and potential; constantly innovate college English teaching methods, teaching practice activities, classroom teaching, and teaching evaluation models; create a good teaching atmosphere; and establish the concept of developmental evaluation. Through the innovation of teaching methods, college students’ actual language communication ability is cultivated, the teaching objectives and teaching methods of college English reading are innovated, and interactive, body language, role play, situational teaching, communicative teaching, and other teaching methods are used. The College English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this article, the triangular Pythagorean fuzzy sets (TPFSs) are introduced, and the MADM problem is investigated under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean operator and power average operator, the triangular Pythagorean fuzzy dual generalized power Bonferroni mean (TPFDGPBM) operator is proposed. Accordingly, the TPFDGPBM operator is employed to construct the triangular Pythagorean fuzzy MADM method. Finally, a numerical example for College English teaching quality evaluation is constructed to verify the TPFDGPBM technique.

It is important to quickly strengthen and repair earthquake-damaged buildings to restore the normal life after earthquake. Therefore, it is necessary to study the seismic performance of widely-used assembled buildings repaired with different approaches. In this paper, two different methods are designed to repair seismically damaged shear walls assembled with grouted sleeves. Based on different damage scenarios, the first method uses high-strength grout to replace damaged concrete, and the second method uses grout jacketing with reinforcement implantation and reinforcing mesh to enhance the damaged cross-section. The repaired walls are compared with the original walls on the damage mode, bearing capacity, ductility, energy dissipation capacity, and stiffness degradation. The test results verify that both repair methods can restore the load-bearing capacity of the assembled shear walls. Further, the seismic performance of the two repaired seismic-damaged shear walls with different axial pressure ratios is analyzed using finite element analysis. The seismic loss indices of the original assembled and the repaired shear walls are calculated using the Park-Ang two-parameter seismic damage model based on the results of the experimental studies and the finite element analysis.

As one of the important way for constructing smart agriculture, unmanned farms are the most attractive in nowadays, and have been explored in many countries. Generally, data, knowledge and intelligent equipment are the core elements of unmanned farms. It deeply integrates modern information technologies such as the Internet of Things, big data, cloud computing, edge computing, and artificial intelligence with agriculture to realize agricultural production information perception, quantitative decision-making, intelligent control, precise input and personalized services. In the paper, the overall technical architecture of unmanned farms is introduced, and five kinds of key technologies of unmanned farms are proposed, which include information perception and intelligent decision-making technology, precision control technology and key equipment for agriculture, automatic driving technology in agriculture, unmanned operation agricultural equipment, management and remote controlling system for unmanned farms. Furthermore, the latest research progress of the above technologies both worldwide are analyzed. Based on which, critical scientific and technological issues to be solved for developing unmanned farms in China are proposed, include unstructured environment perception of farmland, automatic drive for agriculture machinery in complex and changeable farmland environment, autonomous task assignment and path planning of unmanned agricultural machinery, autonomous cooperative operation control of unmanned agricultural machinery group. Those technologies are challenging and absolutely, and would be the most competitive commanding height in the future. The maize unmanned farm constructed in the city of Gongzhuling, Jilin province, China, was also introduced in detail. The unmanned farms is mainly composed of information perception system, unmanned agricultural equipment, management and controlling system. The perception system obtains and provides the farmland information, maize growth, pest and disease information of the farm. The unmanned agricultural machineries could complete the whole process of the maize mechanization under unattended conditions. The management and controlling system includes the basic GIS, remote controlling subsystem, precision operation management subsystem and working display system for unmanned agricultural machineries. The application of the maize unmanned farm has improved maize production efficiency (the harvesting efficiency has been increased by 3－4 times) and reduced labors. Finally, the paper summarizes the important role of the unmanned farm technology were summarized in solving the problems such as reduction of labors, analyzes the opportunities and challenges of developing unmanned farms in China, and put forward the strategic goals and ideas of developing unmanned farm in China.

The pseudorabies virus (PRV) gene encoding thymidine kinase (tk) is an important virulence-associated factor. Attenuation of PRV in susceptible animals is a frequent result of tk deletion. The aim of the study was to assess the pathogenicity of tk-deleted PRV in rats.

Abstract Background To explore the safety and effectiveness of a modified surgical technique which could shorten the time of renal ischemia in left renal cancer and Mayo level II to IV inferior vena cava (IVC) tumor thrombus. Method We retrospectively analyzed the clinical data of 14 cases with left renal cell carcinoma (RCC) and Mayo level II to IV IVC tumor thrombus from February 2015 to July 2019. Preoperative imaging showed that there was no obvious sign of tumor thrombus invading the blood vessel wall. During the surgery, after the right renal artery, the right renal vein and the distal end of IVC were blocked, the balloon catheter was used and the tumor thrombus was removed completely from the IVC. The incision of IVC was closed by Satinsky clamp to make IVC partially blocked. Then the right renal artery and right renal vein were released. The incision of IVC was sutured continuously. At last, the Satinsky clamp and the blocking band at the distal end of the IVC were released. Result There were 8 cases (57.1%) of Mayo level II, 3 cases (21.4%) of Mayo level III and 3 cases (21.4%) of Mayo level IV. The operation was successfully completed in all 14 patients. There were 2 cases (14.3%) operated by complete laparoscopic approach, 8 cases (57.1%) by open approach, and 4 patients (28.6%) by laparoscopic conversion to open approach. The occlusion time of right renal artery and vein (renal ischemia time) was 3 to 15 min, with an average of (6.8 ± 3.2) minutes. The mean time of IVC occlusion was (19.4 ± 4.9) min. Preoperative creatinine was 66 to 130 μmol/L, with an average of (96.6 ± 21.2) μmol/L. One week after operation, serum creatinine was 64 to 632 μmol/L, with an average of (132.4 ± 144.9) μmol/L. Among the 14 cases, 5 (42.9%) had early postoperative complications. Besides one of the 14 patients died in perioperative period, the median follow-up of other 13 cases was 10 months (range: 4–29 months). The 5 (35.7%) of the 14 cases were died of disease. Conclusion This modified procedure was relatively safe and effective in shortening the time of renal ischemia in left RCC patients with Mayo II to IV IVC tumor thrombus.

Through the study of the logic obfuscation circuit and the physical level camouflage technology, a scheme of the domino logic obfuscation circuit is proposed based on dummy contacts, which can prevent reverse engineering. Firstly, the scheme adopts the same circuit structure to realize the NAND, NOR, INV logic functions by contacts′ reality. Then, the TSMC 65 nm is used to compile the ISCAS-89 benchmark circuit and the domino logic obfuscation circuit is applied to the circuit netlist. Finally, the netlist performance test after replacement design is completed. Compared with the existing circuits, the power consumption of the designed circuit is reduced by 4.79%, the area overhead is reduced by 2.16%, and the delay time is reduced by 16.66%, which effectively improves the circuit′s safety.

Based on remanence magnetic field of ferromagnetic materials,a finite element model for leaking magnetic field of broken wire of bridge cables was established. Using this model the magnetic field distribution of fracture surface of the wire and the air leak were analyzed,the leaking magnetic field strength was calculated. At the same time,a testing platform of broken wire of cables by using three-dimensional magnetoresistive sensor was build. Experimental results confirmed the existence of leakage magnetic field of broken wire and it can be detected. The leakage magnetic flux signal characteristics varies from different number and different locations of broken wire,which verify the correctness,effectiveness and feasibility of micro-magnetic detection methods.

Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. The key to successful cross-view geolocalization lies in extracting crucial spatial structure information across different scales in the image. Recent studies improve image matching accuracy by introducing an attention mechanism to establish global associations among local features. However, existing methods primarily focus on using single-scale features and employ a single-channel attention mechanism to correlate local convolutional features from different locations. This approach inadequately explores and utilizes multi-scale spatial structure information within the image, particularly lacking in the extraction and utilization of locally valuable information. In this paper, we propose a cross-view image geolocalization method based on multi-scale information and a dual-channel attention mechanism. The multi-scale information includes features extracted from different scales using various convolutional slices, and it extensively utilizes shallow network features. The dual-channel attention mechanism, through successive local and global feature associations, effectively learns depth discriminative features across different scales. Experimental results were conducted using existing satellite and drone image datasets, with additional validation performed on an independent self-made dataset. The findings indicate that our approach exhibits superior performance compared to existing methods. The methodology presented in this paper exhibits enhanced capabilities, especially in the exploitation of multi-scale spatial structure information and the extraction of locally valuable information. [ABSTRACT FROM AUTHOR]

Electrical Discharge Machining (EDM) is a machining method commonly used to produce complex shapes and deep holes by eroding hard metals with an electric arc. There is a growing demand for process simulation using finite element models in order to improve the quality and efficiency of EDM, to reduce costs, to improve resource efficiency, and to facilitate its application in critical areas such as aerospace and mechanical engineering. Finite element models have greatly improved the prediction accuracy of EDM processes, simulated complex hybrid machining processes, and provided important guidance for the optimization of EDM processes. This paper systematically reviews the research progress of finite element modeling for EDM. Finite element method modeling is evaluated mainly in terms of four indicators: material removal rate, surface roughness, tool wear ratio, and recast layer thickness. Firstly, the importance and application of EDM are described, and the EDM finite element method modeling and its advantages are summarized. Then, the single-spark simulation model and the multi-spark simulation model of EDM are compared and discussed. Among the mainstream finite element models, the prediction error of the material removal rate for single-spark simulation ranges from 8.2% to 14.75%, while the prediction error of the recast layer thickness for multi-spark simulation can be as low as 1.98%. Finally, the applications of finite element modeling in EDM hybrid machining processes' performance prediction and new material machining are summarized, and future research directions and trends in EDM finite element modeling are predicted. [ABSTRACT FROM AUTHOR]