Enterprise Credit Evaluating Model Study

Accurate enterprise credit evaluating can efficiently avoid the asymmetry of the information which the finance institute transferred ,on the other hand ,the enterprise can bring the financing cost down. According to the loan examine and approve work of our country and stock industry bank, the index system of ECE is constructed. Then, a new Enterprise Credit Evaluating Model Based on Gray theory and fuzzy mathematics is proposed. Finally, a example is given and the result show good reliability. Keywords: ECE (Enterprise Credit Evaluate), Gray System Theory, White function, AHM（Attribute Hierarchical Model）, FCE(Fuzzy Comprehensive Evaluate) Résumé L’évaluation précise du crédit d’entreprises peut d’un côté éviter de façon efficace l’asymétrie de la transmission des informations des institutions financières, de l’autre, elle peut permettre aux entreprises de baisser les coûts financiers. Cette thèse relie ensemble la vérification et l’approbation du crédit de la banque du commerce nationalisée et de la banque industrielle par société anonyme pour créer un système d’évaluation du crédit d’entreprises( ECE). Selon les théories d’évaluation grises basées sur AHM et des méthodes des mathématiques ambigues, elle a fait une proposition d’un nouveau modèle d’évaluation qualitative et quantitative. Enfin, un exemplaire est donné et le résultat nous montre une bonne crédibilité et précision. Mots-clés: ECE (évaluation du crédit d’entreprises), théorie d’évaluation grise, fonction blanche, AHM（modèle d’attribuation hiérarchique）, FCE(méthodes des mathématiques ambigues) 摘 要 準確的企業資信評級一方面可以有效的避免對金融機構資訊傳遞的非對稱性，另一方面企業也可以降低融資成本。文章結合我國國有商業銀行和股份制商業銀行信貸審批工作，構建企業資信評估指標體系，通過基於 AHM的灰色評價理論和模糊數學的方法，提出了一種新的定性和定量相結合的評價模型，實證結果表明，該模型得到的企業資信評級結果有較高的可信度和準確度。關鍵詞：資信評級；屬性層次；白化權函數；灰色評價；模糊綜合評

Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems

Fusing Radar and Lidar sensor data can fully utilize their complementary advantages and provide more accurate reconstruction of the surrounding for autonomous driving systems. Surround Radar/Lidar can provide 360-degree view sampling with the minimal cost, which are promising sensing hardware solutions for autonomous driving systems. However, due to the intrinsic physical constraints, the rotating speed of surround Radar, and thus the frequency to generate Radar data frames, is much lower than surround Lidar. Existing Radar/Lidar fusion methods have to work at the low frequency of surround Radar, which cannot meet the high responsiveness requirement of autonomous driving systems.This paper develops techniques to fuse surround Radar/Lidar with working frequency only limited by the faster surround Lidar instead of the slower surround Radar, based on the state-of-the-art object detection model MVDNet. The basic idea of our approach is simple: we let MVDNet work with temporally unaligned data from Radar/Lidar, so that fusion can take place at any time when a new Lidar data frame arrives, instead of waiting for the slow Radar data frame. However, directly applying MVDNet to temporally unaligned Radar/Lidar data greatly degrades its object detection accuracy. The key information revealed in this paper is that we can achieve high output frequency with little accuracy loss by enhancing the training procedure to explore the temporal redundancy in MVDNet so that it can tolerate the temporal unalignment of input data. We explore several different ways of training enhancement and compare them quantitatively with experiments.Comment: Accepted at DATE 202

Diazido­bis[2,4-diamino-6-(2-pyrid­yl)-1,3,5-triazine-κ2 N 1,N 6]zinc(II)

In the title mononuclear complex, [Zn(N3)2(C8H8N6)2], the ZnII atom, lying on a twofold rotation axis, is six-coordinated in a distorted octa­hedral environment by four N atoms from two 2,4-diamino-6-(2-pyrid­yl)-1,3,5-triazine ligands and two N atoms from two end-on-coordinated azide ions. N—H⋯N hydrogen bonds between the ligand and azide ion link the complex mol­ecules into a three-dimensional network

Analisa Kepuasan Konsumen Di Restaurant “X” Di Surabaya

Penelitian ini ditunjukan untuk menganalisa tingkat kesenjangan antara harapan dari konsumen terhadap Kenyataan yang diterima oleh konsumen dan mengukur tingkat kepuasan konsumen di Restoran “X” dengan menggunakan atribut DINESERV. Penelitian ini menggunakan Importance Performance Analysis(IPA). Hasil dari penelitian ini adalah kesenjangan antara harapan dan Kenyataan yang diukur menggunakan atribut DINESERV adalah Kenyataan yang diterima oleh konsumen sangat tidak sesuai dengan harapan konsumen dan konsumen sangat tidak puas terutama dengan atribut Convenience Restoran yaitu Jarak dari Restoran “X” di Surabay

Federated and distributed learning applications for electronic health records and structured medical data: A scoping review

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations and discusses potential innovations. We searched five databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from three primary perspectives, including data quality, modeling strategies, and FL frameworks. Out of the 1160 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research

The protective mechanism of Dehydromiltirone in diabetic kidney disease is revealed through network pharmacology and experimental validation

Background:Salvia miltiorrhiza (SM) is an effective traditional Chinese medicine for treating DKD, but the exact mechanism is elusive. In this study, we aimed to investigate and confirm the method underlying the action of the active components of SM in the treatment of DKD.Methods: Renal tissue transcriptomics and network pharmacology of DKD patients was performed to identify the active components of SM and the disease targets of DKD. Next, the point of convergence among these three groups was studied. Potential candidate genes were identified and analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The component-target networks were modelled and visualized with Cytoscape. In addition, docking studies were performed to validate our potential target predictions. Lastly, in vitro and in vivo experiments were performed to understand the role of Dehydromiltirone (DHT), the active component of SM, in the phenotypic switching of mesangial cells.Results: Transcriptomics of DKD patients’ renal tissues screened 4,864 differentially expressed genes. Eighty-nine active components of SM and 161 common targets were found. Functional enrichment analysis indicated that 161 genes were enriched in apoptosis, the PI3K-AKT signaling pathway, and the AGE-RAGE signaling pathway in diabetes complications. Molecular docking and molecular dynamic simulations show that DHT can bind to functional PIK3CA pockets, thereby becoming a possible inhibitor of PIK3CA. In vitro study demonstrated that DHT reduced the expression of phenotypic switching markers α-SMA, Col-I, and FN in HMCs by downregulating the over-activation of the PI3K-AKT signaling pathway through the inhibition of PIK3CA. Furthermore, the DKD mouse model confirmed that DHT could reduce proteinuria and improve glomerular hypertrophy in vivo.Conclusion: DHT was identified as the key active component of SM, and its therapeutic effect on DKD was achieved by inhibiting the phenotypic switching of mesangial cells via the PIK3CA signaling pathway

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO