An asset value evaluation for docking finance lease problems in the peer-to-peer platform

The convenience and rapidity of financial leasing modes in the peer-to-peer (P2P) platform enable small and medium-sized enterprises (SMEs) to solve financing problems. The core of risk management in the P2P platform is to improve the quality of the docking assets. Therefore, the purpose of this paper is to establish a financial leasing value model of debt cession with an optimal economic pattern and an analysis of the risk assessment to improve the management of the asset value docking quality of both parties. For the transaction price of the leased assets in a P2P platform, this paper establishes multi-periodic, continuous, and variable models of the leased assets value evaluation, taking rent, lease term, and interest as independent variables. The paper proves that the price of the leased assets is related to the interest force, the rent per period, and the numbers of payments and changes in rent when other factors remain unchanged. Our results prove that the risk of the P2P platform docking finance lease and the transfer of the creditor’s rights investment mode are low. The proposed scheme is verified through hypothesis testing and model simulation. When the lease term is longer and the interest rate is higher, the difference between the two function surfaces is larger. Thus, the business model of financial leasing in the P2P platform has more obvious business advantages. It provides better business macro direction and business micro-management guidance for the leasing industry, P2P platforms and financial leasing companies. First published online 21 December 202

The effects of pseudo-relevance feedback on MT-based

In this paper, we identify factors that affect machine translation (MT) of a source query for cross-language information retrieval (CLIR) and empirically evaluate the effect of pseudo relevance feedback on crosslanguage retrieval performance. Our experiments demonstrate that, by using pseudo relevance feedback, we can significantly improve cross-language retrieval performance and achieve the level of monolingual retrieval. 1

Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting

Crowd Counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the new-coming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks to handle multiple domains. 1) The models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies of intrinsic data distributions from various domains, which is called catastrophic forgetting. 2) The well-trained model in a specific domain achieves imperfect performance among other unseen domains because of the domain shift. 3) It leads to linearly-increased storage overhead either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigate a new task of crowd counting under the incremental domains training setting, namely, Lifelong Crowd Counting. It aims at alleviating the catastrophic forgetting and improving the generalization ability using a single model updated by the incremental domains. To be more specific, we propose a self-distillation learning framework as a benchmark~(Forget Less, Count Better, FLCB) for lifelong crowd counting, which helps the model sustainably leverage previous meaningful knowledge for better crowd counting to mitigate the forgetting when the new data arrive. Meanwhile, a new quantitative metric, normalized backward transfer~(nBwT), is developed to evaluate the forgetting degree of the model in the lifelong learning process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability

Notes on Experiments with Pseudo Relevance Feedback and Data Merging In Cross-Language Retrieval

. In the TREC-8 cross-language information retrieval (CLIR) track, we adopted the approach of using machine translation to prepare a source-language query for use in a target-language retrieval task. We empirically evaluated (1) the effect of pseudo relevance feedback on retrieval performance with two feedback vector length control methods in CLIR, and (2) the effect of multilingual data merging either before or after retrieval. Our experiments show that, in general, pseudo relevance feedback significantly improves cross-language retrieval performance, and that post-retrieval merging of retrieval results can outperform pre-retrieval merging of multilingual data collections. 1 Introduction TREC-8 marks the first occasion for CLARITECH to participate in the CLIR track. For commercial reasons, we have developed technology for English, Japanese, and Chinese CLIR. With our TREC-8 submission, we are in a position to assess how well our techniques extend to European languages. Our approach..

Numerical Simulation of Bridging Ball Plugging Mechanism in Fractured-Vuggy Carbonate Reservoirs

Pores, fractures, caves, and other storage spaces are commonly distributed in fractured-vuggy carbonate reservoirs. During the drilling process, more than half of all drill-in fluid loss issues are caused by developed caves. Cave scales range from centimeters to meters, making leak prevention increasingly difficult through the use of traditional technologies. Currently, there is still high demand for the understanding of feasible loss control techniques, especially in fractured-vuggy carbonate reservoirs. Multistage Bridge Plugging (MBP) technology has facilitated pioneering experiments in many oilfields, but the success rate of plugging is less than 50%, and the effects of plugging are uncontrollable and difficult to predict. This is due to a lack of clarity regarding the plugging mechanism and the key controlling factors. In this study, we used the Discrete Element Method (DEM) simulation method to investigate the controlling factors of MBP technology, and analyzed its applicable conditions. We found that the prerequisite for the success of MBP is the presence of a constricted throat near the wellbore when drilling the well hole; the first-stage bridging ball is the key to the success of MBP. Larger ball radius, cave inclination and initial flow rate, and lower ball velocity are beneficial to the first-stage bridging. All discussion in this research is based on the ideal situation. However, the cave pattern is difficult to describe using several models, let alone by one ideal model. With the progress of seismic fine description technology and mud logging, more accurate characterization of caves in carbonate reservoirs will help to accurately formulate the plugging scheme and greatly improve the success rate of plugging technology. Additionally, the engineering risks of this technology, such as plugging the coiled tubing, need to be further studied

Adsorption Characteristics of Carbon Monoxide on Ag- and Au-Doped HfS2 Monolayers Based on Density Functional Theory

A large amount of power equipment works in closed or semi-closed environments for a long time. Carbon monoxide (CO) is the most prevalent discharge gas following a fault in the components. Based on the density functional theory of first principles, the adsorption behavior of CO gas molecules on intrinsic, Ag-doped and Au-doped hafnium disulfide (HfS2) monolayers was systematically studied at the atomic scale. Firstly, the intrinsic HfS2 monolayer, Ag-doped HfS2 (Ag-HfS2) monolayer and Au-doped HfS2 (Au-HfS2) monolayer, with different doping sites, were created. The structural stability, dopant charge transfer, substrate conductivity and energy band structure of different doping sites of the Ag-HfS2 and Au-HfS2 monolayer structures were calculated. The most stable doping structure was selected with which to obtain the best performance on the subsequent gas adsorption test. Then, the CO adsorption models of intrinsic HfS2, Ag-HfS2 and Au-HfS2 were constructed and geometrically optimized. The results show that the adsorption energy of the Ag-HfS2 monolayer for CO gas is −0.815 eV, which has good detection sensitivity and adsorption performance. The adsorption energy of CO on the Au-HfS2 monolayer is 2.142 eV, the adsorption cannot react spontaneously, and the detection sensitivity is low. The research content of this paper provides a theoretical basis for the design and research of gas sensing materials based on HfS2, promoting the development and application of HfS2 in gas sensing and other fields

Immunohistochemical Study of NR2C2, BTG2, TBX19, and CDK2 Expression in 31 Paired Primary/Recurrent Nonfunctioning Pituitary Adenomas

This study investigated potential markers for predicting nonfunctioning pituitary adenoma (NFPA) invasion and recurrence by high-throughput tissue microarray analyses. We retrospectively studied two groups of patients: 60 nonrecurrent NFPA cases that included noninvasion and invasion subtypes and 43 recurrent cases that included primary NFPA. A total of 31 paired patient samples were evaluated (12 patients with one surgery and 31 who had undergone two operations, with both tumors analyzed). Expressions of nuclear receptor subfamily 2 group C member 2 (NR2C2), B cell translocation gene 2, T-box-19 (TBX19), and cyclin-dependent kinase 2 (CDK2) in surgically resected specimens were assessed by immunohistochemistry. The relationships between marker expression and clinical characteristics including age, sex, tumor volume, and follow-up time were analyzed. Tumor volume and invasion as well as follow-up time were significantly associated with invasion and recurrence (P < 0.01). Of the 60 nonrecurrent samples, 15/41 and 13/19 showed high NR2C2 expression in the noninvasion and invasion groups, respectively (χ2 =5.287, P = 0.021). NR2C2 was also overexpressed in 43 primary recurrent cases (χ2 =5.433, P = 0.02), whereas CDK2 (χ2 = 11.242, P = 0.001) and TBX19 (χ2 = 4.875, P = 0.027) were downregulated. In the 31 paired samples, NR2C2 was more highly expressed in the recurrent as compared to the primary tumor. High NR2C2 expression was associated with NFPA invasion, recurrence, and progression, while TBX19 and CDK2 were associated with NFPA recurrence

Adsorption Characteristics of Carbon Monoxide on Ag- and Au-Doped HfS₂ Monolayers Based on Density Functional Theory

A large amount of power equipment works in closed or semi-closed environments for a long time. Carbon monoxide (CO) is the most prevalent discharge gas following a fault in the components. Based on the density functional theory of first principles, the adsorption behavior of CO gas molecules on intrinsic, Ag-doped and Au-doped hafnium disulfide (HfS2) monolayers was systematically studied at the atomic scale. Firstly, the intrinsic HfS2 monolayer, Ag-doped HfS2 (Ag-HfS2) monolayer and Au-doped HfS2 (Au-HfS2) monolayer, with different doping sites, were created. The structural stability, dopant charge transfer, substrate conductivity and energy band structure of different doping sites of the Ag-HfS2 and Au-HfS2 monolayer structures were calculated. The most stable doping structure was selected with which to obtain the best performance on the subsequent gas adsorption test. Then, the CO adsorption models of intrinsic HfS2, Ag-HfS2 and Au-HfS2 were constructed and geometrically optimized. The results show that the adsorption energy of the Ag-HfS2 monolayer for CO gas is −0.815 eV, which has good detection sensitivity and adsorption performance. The adsorption energy of CO on the Au-HfS2 monolayer is 2.142 eV, the adsorption cannot react spontaneously, and the detection sensitivity is low. The research content of this paper provides a theoretical basis for the design and research of gas sensing materials based on HfS2, promoting the development and application of HfS2 in gas sensing and other fields