A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Taming Gradient Variance in Federated Learning with Networked Control Variates

Federated learning, a decentralized approach to machine learning, faces significant challenges such as extensive communication overheads, slow convergence, and unstable improvements. These challenges primarily stem from the gradient variance due to heterogeneous client data distributions. To address this, we introduce a novel Networked Control Variates (FedNCV) framework for Federated Learning. We adopt the REINFORCE Leave-One-Out (RLOO) as a fundamental control variate unit in the FedNCV framework, implemented at both client and server levels. At the client level, the RLOO control variate is employed to optimize local gradient updates, mitigating the variance introduced by data samples. Once relayed to the server, the RLOO-based estimator further provides an unbiased and low-variance aggregated gradient, leading to robust global updates. This dual-side application is formalized as a linear combination of composite control variates. We provide a mathematical expression capturing this integration of double control variates within FedNCV and present three theoretical results with corresponding proofs. This unique dual structure equips FedNCV to address data heterogeneity and scalability issues, thus potentially paving the way for large-scale applications. Moreover, we tested FedNCV on six diverse datasets under a Dirichlet distribution with {\alpha} = 0.1, and benchmarked its performance against six SOTA methods, demonstrating its superiority.Comment: 14 page

Source Apportionment and Probabilistic Ecological Risk of Heavy Metal(loid)s in Sediments in the Mianyang Section of the Fujiang River, China

The Mianyang section of the Fujiang River is Mianyang City’s main source of drinking water; therefore, we must ascertain this aquatic ecosystem’s heavy metal(loid)s (HMs) pollution status to protect the health of local residents. We examined 27 surface sediment samples using X-ray fluorescence spectrometry for 10 widely concerned HMs. We applied spatial interpolation, the positive matrix factorization, and a potential ecological risk index to determine the spatial distribution, source, and potential ecological risk of HMs in the sediment, respectively. Our results showed that Mn, Co, Cr, As, Zn, and Pb were disturbed by human activities. The levels of HM content at different sites were different due to the influence of urban human activities. Our source apportionment results showed that As, Cu, Pb, and Mn principally originated from mixed sources of industry and traffic; Ba and Co were chiefly derived from architectural sources; Ni, Zn, and V were mainly from natural sources; and Cr originated from industrial sources. Mixed, architectural, natural, and industrial sources account for 25.62%, 25.93%, 24.52%, and 23.93% of the total HM content, respectively. The HMs were of low ecological risk, which were mainly caused by As and Co. In our study, the mixed source was the priority anthropogenic source, and As and Co were the priority elements for further risk control in the Mianyang section of the Fujiang River

Simple synthesis and characterization of nanoporous materials from talc

Synthetic siliceous mesoporous materials are of great value in many different applications, including nanotechnology, biotechnology, information technology, and medical fields, but historically the resource materials used in their synthesis have been expensive. Recent efforts have focused on indirect synthesis methods which utilize less expensive silicate minerals as a resource material. The purpose of the present study was to investigate talc, a natural silicate mineral, as one such resource. It was used as raw material to prepare two advanced materials: porous silica (PS) and ordered mesoporous silica (MCM-41). The PS, with a specific surface area of 260 m/g and bimodal pore-size distribution of 1.2 nm and 3.7 nm, was prepared by grinding and subsequent acid leaching. The MCM-41, with a large surface area of 974 m/g and a narrow pore-size distribution of 2.8 nm, was obtained using a surfactant, cetyltrimethylammonium bromide (CTAB), by hydrothermal treatment using the as-prepared PS as a source of Si. The two resultant materials were characterized by small angle X-ray diffraction (SAXRD) and wide-angle X-ray diffraction (WAXRD), high-resolution transmission electron microscopy (HRTEM), solid-state magic-angle-spinning nuclear magnetic resonance (MAS NMR), Fourier transform infrared spectroscopy (FTIR), and N adsorption-desorption measurements. Based on these measurements, possible processes of transformation of PS from talc, upon acid treatment, and the formation of MCM-41 were investigated systemically. Acid leaching induced the transformation of a rigid layered structure to a nearly amorphous one, with micropores formed by a residual layered structure and mesopores formed from a condensed framework. The MCM-41 was a mixture of silanol groups (Si(SiO)(OH)) and a condensed Q framework structure (Si(SiO)), with a small amount of remaining Q layered structure (Si(SiO)OMg). The increased Q/Q value confirmed greater polymerization of MCM-41 than of PS. At the low CTAB concentration used (2 wt.%), the highly charged silicate species controlled the surfactant geometry. Charge-density matching, together with the degree of polymerization of the silicates, determined the resultant mesophase

Source Apportionment and Probabilistic Ecological Risk of Heavy Metal(loid)s in Sediments in the Mianyang Section of the Fujiang River, China

The Mianyang section of the Fujiang River is Mianyang City’s main source of drinking water; therefore, we must ascertain this aquatic ecosystem’s heavy metal(loid)s (HMs) pollution status to protect the health of local residents. We examined 27 surface sediment samples using X-ray fluorescence spectrometry for 10 widely concerned HMs. We applied spatial interpolation, the positive matrix factorization, and a potential ecological risk index to determine the spatial distribution, source, and potential ecological risk of HMs in the sediment, respectively. Our results showed that Mn, Co, Cr, As, Zn, and Pb were disturbed by human activities. The levels of HM content at different sites were different due to the influence of urban human activities. Our source apportionment results showed that As, Cu, Pb, and Mn principally originated from mixed sources of industry and traffic; Ba and Co were chiefly derived from architectural sources; Ni, Zn, and V were mainly from natural sources; and Cr originated from industrial sources. Mixed, architectural, natural, and industrial sources account for 25.62%, 25.93%, 24.52%, and 23.93% of the total HM content, respectively. The HMs were of low ecological risk, which were mainly caused by As and Co. In our study, the mixed source was the priority anthropogenic source, and As and Co were the priority elements for further risk control in the Mianyang section of the Fujiang River

Quantitative Detection of Quartz Sandstone SiO2 Grade Using Polarized Infrared Absorption Spectroscopy with Convolutional Neural Network Model

As an independent characteristic of electromagnetic radiation, the polarization of light is sensitive to the scattering and absorption characteristics of the mineral particles. The combination of polarization and infrared absorption spectroscopy is conducive to rapidly and accurately detecting the SiO2 content of metallurgical sandstone deposits. In this study, the 8–14 μm polarized infrared absorption spectra and the grade of the sandstone ore samples were used to analyse the spectral characteristics of the sandstone powder samples. Principal component analysis (PCA) and the successive projection algorithm (SPA) were used to reduce the dimension of the original data, first-order derivative, reciprocal logarithm, and multivariate scattering correction (MSC) data. Then, generalized regression neural network (GRNN), partial least squares regression (PLSR), and convolutional neural network (CNN) were employed to establish a hyperspectral prediction model of SiO2 grade. The results show that the quantitative model by the PCA-CNN algorithm has the better prediction precision for the reciprocal logarithm data, with a coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ) of 0.907, 0.023, and 5.11, respectively. This method indicates that the polarized infrared absorption spectra and the PCA-CNN model can provide a more robust and significant spectral interpretation than single infrared spectra, and it is expected to be applied to any high-purity quartz deposit type for in situ and rapid analysis

Machine Learning Model of Hydrothermal Vein Copper Deposits at Meso-Low Temperatures Based on Visible-Near Infrared Parallel Polarized Reflectance Spectroscopy

The verification efficiency and precision of copper ore grade has a great influence on copper ore mining. At present, the common method for the exploration of reserves often uses chemical analysis and identification, which have high costs, long cycles, and pollution risks but cannot realize the in situ determination of the copper grade. The existing scalar spectrometric techniques generally have limited accuracy. As a vector spectrum, polarization state information is sensitive to mineral particle distribution and composition, which is conducive to high-precision detection. Taking the visible-near infrared parallel polarization reflectance spectrum data and grade data of a copper mine in Xiaoyuan village, Huaining County, Anhui Province, China, as an example, the characteristics of the parallel polarization spectra of the copper mine were analyzed. The spectra were pretreated by first-order derivative transform and wavelet denoising, and the dimensions of wavelet denoising spectra, parallel polarization spectra, and first-order derivative spectra were also reduced by principal component analysis (PCA). Three, four, and eight principal components of the three types of spectra were selected as variables. Four machine learning models, the radial basis function (RBF), support vector machine (SVM), generalized regression neural network (GRNN), and partial least squares regression (PLSR), were selected to establish the PCA parallel polarization reflectance spectrum and copper grade prediction model. The accuracy of the model was evaluated by the determination coefficient (R2) and root mean square error (RMSE). The results show that, for parallel polarization spectra, first-order derivative spectra, and wavelet denoising spectra, the PCA-SVM model has better results, with R2 values of 0.911, 0.942, and 0.953 and RMSE values of 0.022, 0.019, and 0.017, respectively. This method can effectively reduce the redundancy of polarized hyperspectral data, has better model prediction ability, and provides a useful exploration for the grade analysis of hydrothermal copper deposits at meso-low temperatures

Reflection Spectra Coupling Analysis and Polarized Modeling of Optically Active Particles in Lakes

The coupling between optically active substances of algae particles and inorganic suspended solids of water makes the characteristics of reflection spectra of water complex and changeable. This makes modeling and inversion of polarization remote sensing in class II water difficult. In our study, considering the influence of the mixing ratio of algae particles and inorganic suspended solids, the sensor incidence angle, and the solar zenith angle on the polarization reflection spectrum, we analyzed the coupling characteristics of the polarized bidirectional reflectance of particulate matter through control experiments of mixed components of water particles in the laboratory. With Chaohu Lake in China as an example, the polarized reflectance coupling characteristics of water particles was investigated by the water-leaving radiation. The results showed that in the characteristic bands of 570, 675, and 705 nm, the degree of linear polarization (DOLP) was sensitive to the water-leaving radiation of the particles rather than to the reflectance. With the variation of observation angle, the reflection spectra were strongly interfered with by solar flare when the sensor zenith angle was close to 50° on the meridian plane with an azimuth angle of 180°, but DOLP was less affected, while also having a low correlation in the high concentration region. Combined with the coupling characteristics of particles at 675 and 705 nm, the model of DOLP ratio was established by partial least squares regression (PLSR) with a determination coefficient (R2) of 0.91, root mean square error (RMSE) 0.035, and a verification accuracy of 0.959. This shows that the model has better prediction ability for the coupling characteristics of water particles by the polarization reflection spectra and provides good support for mixed spectral unmixing of class II water

Machine Learning Model of Hydrothermal Vein Copper Deposits at Meso-Low Temperatures Based on Visible-Near Infrared Parallel Polarized Reflectance Spectroscopy

The verification efficiency and precision of copper ore grade has a great influence on copper ore mining. At present, the common method for the exploration of reserves often uses chemical analysis and identification, which have high costs, long cycles, and pollution risks but cannot realize the in situ determination of the copper grade. The existing scalar spectrometric techniques generally have limited accuracy. As a vector spectrum, polarization state information is sensitive to mineral particle distribution and composition, which is conducive to high-precision detection. Taking the visible-near infrared parallel polarization reflectance spectrum data and grade data of a copper mine in Xiaoyuan village, Huaining County, Anhui Province, China, as an example, the characteristics of the parallel polarization spectra of the copper mine were analyzed. The spectra were pretreated by first-order derivative transform and wavelet denoising, and the dimensions of wavelet denoising spectra, parallel polarization spectra, and first-order derivative spectra were also reduced by principal component analysis (PCA). Three, four, and eight principal components of the three types of spectra were selected as variables. Four machine learning models, the radial basis function (RBF), support vector machine (SVM), generalized regression neural network (GRNN), and partial least squares regression (PLSR), were selected to establish the PCA parallel polarization reflectance spectrum and copper grade prediction model. The accuracy of the model was evaluated by the determination coefficient (R2) and root mean square error (RMSE). The results show that, for parallel polarization spectra, first-order derivative spectra, and wavelet denoising spectra, the PCA-SVM model has better results, with R2 values of 0.911, 0.942, and 0.953 and RMSE values of 0.022, 0.019, and 0.017, respectively. This method can effectively reduce the redundancy of polarized hyperspectral data, has better model prediction ability, and provides a useful exploration for the grade analysis of hydrothermal copper deposits at meso-low temperatures