The Impact of Credit Ratings on Financial Performance (ROA) and Value Creation (Tobin’s Q)

This study employs a bibliometric and systematic approach to examine the impact of credit ratings as a measure of financial performance for companies listed in the S&P 500 index.The study identified a knowledge gap as only two researches were found,one suggesting and another using credit ratings to measure financial performance.Most researches use leverage,profitability,liquidity,and Share Return measures to explain financial performance.The empirical analysis uses the data of 2,398 observations of 240 companies rated by S&P Global Ratings for the period 2009-2013,applying a Generalized Method of Moments(GMM)methodology to estimate the models due to its ability to address potential endogeneity issues.The study considers Return on Assets(ROA)and Tobin’s Q as dependent variables.It incorporates credit ratings(CRWLTA)along with variables such as Total Debt to Total Assets(TDTA),Total Shareholder Return(TSR),EBITDA Interest coverage(EBITDAICOV),Quick Ratio(QR),Altman’s Z-Score(AZS),as well as macroeconomic factors like Gross Domestic Product(GDP)growth,inflation(Consumer Price Index-CPI),and the Federal Reserve Interest Rate(FDRI)as independent variables.The study argues that credit ratings,which incorporate historical data and confidential information about companies’strategies,provide reliable forward-looking creditworthiness assessments to the market.It is supported by specialized rating agencies that employ their methodologies.However,the findings suggested that CRWLTA,had a negative relationship with Q Tobin,although it was not statistically significant,and a negative relationship with ROA that was on the verge of significance.

一种新的P2P系统中基于双ratings的声誉管理机制

声誉系统的效率取决于回馈的质量,现有P2P系统中的声誉模型不能处理合谋、不递交ratings等攻击,并且对无过失节点还存在不公平性.针对这些问题,提出了一个新的基于双ratings的声誉管理机制.采用两个度量来评价节点:服务信任和回馈信任.服务信任反映server提供服务的可靠性;回馈信任反映consumer汇报ratings的可信度.一次交易后,要求服务双方都递交ratings,依据这两个ratings对server的服务信任和consumer的回馈信任进行更新,并且server的服务信任更新与consumer的回馈信任紧密相关.为了防止恶意节点反复实施恶意行为或不递交rating,还提出了一个惩罚机制,作为对声誉模型的补充.仿真表明,提出的声誉管理机制能够抵制上述恶意攻击,减少不公平性.

Prediction of film ratings based on domain adaptive transfer learning

This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is utilized to remove singular film samples,and feature selections are carried out.When solving the problem that film samples of the target domain are unlabelled,it is impossible to train a model and address the inconsistency in the feature dimension for film samples from the source domain.Therefore,the domain adaptive transfer learning model combined with dimensionality reduction algorithms is adopted in this paper.At the same time,in order to reduce the prediction error of models,the stacking ensemble learning model for regression is also used.Finally,through comparative experiments,the effectiveness of the proposed method is verified,which proves to be better predicting film ratings in the target domain.

Red Ratings for Loess-Paleosol Sequences on China’s Loess Plateau and Their Paleo-Climatic Implications

Comparisons of red ratings (RR) with Fe_d, Fe_d/Fet, clay content, andmagnetic susceptibility (x) of two loess-paleosol sequences at Luochuan and Lingtai on China's LoessPlateau were conducted to study the possible relationship between RR and pedogenic degrees of thetwo loess-paleosol sequences, and to discuss whether the RR could become new paleo-climaticindicators. Results showed that the RR of the two loess-paleosol sequences had positive, highlysignificant (P < 0.01) correlations with: 1) citrate-bicarbonate-dithionite (CBD) extracted iron(Fe_d), 2) ratios of CBD extracted iron to total iron (Fe_d/Fet), 3) clay (< 2 mum), and 4) magneticsusceptibility (x). This suggested that the RR of these loess-paleosol sequences could indicatedegreesof loess weathering and pedogenesis and were potential paleo-climatic proxies. The strongcorrelations of RR to Fe_d and x also implied that during pedogenic processes, pedogenic hematite inloess and paleosols were closely related to the amount of total secondary iron oxides and pedogenicferrimagnetic minerals (predominantly maghemite).

Regression Modeling of Individual-Patient Correlated Discrete Outcomes with Applications to Cancer Pain Ratings

Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for modeling of outcome probabilities are considered. Multinomial probabilities are based on different intercepts and slopes for probabilities of different outcome values. Ordinal probabilities are based on different intercepts and the same slope for probabilities of different outcome values. Censored Poisson probabilities are based on the same intercept and slope for probabilities of different outcome values. Parameters are estimated with extended linear mixed modeling maximizing a likelihood-like function based on the multivariate normal density that accounts for within-patient correlation. Formulas are provided for gradient vectors and Hessian matrices for estimating model parameters. The likelihood-like function is also used to compute cross-validation scores for alternative models and to control an adaptive modeling process for identifying possibly nonlinear functional relationships in predictors for probabilities and dispersions. Example analyses are provided of daily pain ratings for a cancer patient over a period of 97 days. Results: The censored Poisson approach is preferable for modeling these data, and presumably other data sets of this kind, because it generates a competitive model with fewer parameters in less time than the other two approaches. The generated probabilities for this model are distinctly nonlinear in time while the dispersions are distinctly nonconstant over time, demonstrating the need for adaptive modeling of such data. The analyses also address the dependence of these daily pain ratings on time and the daily numbers of pain flares. Probabilities and dispersions change differently over time for different numbers of pain flares. Conclusions: Adaptive modeling of daily pain ratings for individual cancer patients is an effective way to identify nonlinear relationships in time as well as in other predictors such as the number of pain flares.

Measurement invariance of maternal ratings of ADHD symptoms across clinic referred children’s with and without ADHD

The study examined the measurement invariance (configural,metric,scalar,and error variances) and factor mean scores equivalencies of a modified version of the Strengths and Weaknesses of ADHDSymptoms and Normal Behavior Scale (SWAN-M) across ratings provided by mothers of clinic-referred children and adolescents,diagnosed with (N = 666) and without (N = 202) ADHD. Confirmatory factor analysis (CFA) of these ratings provided support for the bi-factor model of ADHD [orthogonal general and specific factors for inattention (IA) and hyperactivity/impulsivity (HI) symptoms]. Multiple-group confirmatory factor analysis (CFA) of the bi-factor model supported full measurement invariance. Findings also showed that for latent mean scores,the ADHD group had higher scores than the non-ADHD group for the ADHD general and IA specific factors. The findings indicate that observed scores (based on maternal ratings of the SWAN-M) are comparable,as they have the same measurement properties. The theoretical,psychometric and clinical implications of the findings are discussed.

Increased prevalence of parent ratings of ADHD symptoms among children with bilateral congenital cataracts

AIM: To investigate the behavioral and psychological disorders and the prevalence of parent ratings of attention deficit hyperactivity disorder(ADHD) symptoms among children with bilateral congenital cataracts(CCs). METHODS: This cross-sectional study investigated children with bilateral CC aged 3-8 y(CC group) using Conners’ Parent Rating Scale-48(CPRS-48) from July to December 2016. The abnormal rates of psychological symptoms in CC children and normal vision(NV) children were compared using the Chi-square test. The scores of CC children were compared with those of NV children and the Chinese urban norm using the independent samples t-test and one-sample t-test, respectively. RESULTS: A total of 262 valid questionnaires were collected. The ratio of CC children to NV children was 119:143. The overall rate of psychological symptoms in CC children was 2.28 times higher than that in NV children(46.22% vs 20.28%, Pearson’s χ2=20.062;P<0.001). CC children showed higher scores for conduct problems, learning problems, impulsiveness/hyperactivity, anxiety, and hyperactivity index than NV children and the Chinese urban norm, particularly between the ages of 3 and 5 y. Furthermore, male children aged between 6 and 8 y showed a higher impulsive/hyperactive score than females of the same age(t=6.083, P<0.001). CONCLUSION: Children with bilateral CCs have a higher rate of ADHD symptoms than children with NV. This study provides clinical evidence that screening for psychological symptoms and particularly for ADHD symptoms in children with bilateral CC are recommended for an early diagnosis and timely treatment.

The Influence of Ratings on Choosing Accommodation

Present research is inspired to study the effects and influences of customer ratings and reviews on choosing a hostel accommodation. The hostel industry is one of the fastest growing fields of tourism. Several studies already exist about the influences of ratings, concentrating mainly on hotels. However, using the same results for hostels, might lead to false conclusions. Not only the natures of the two accommodations are different, so as the customers. Hostels target mainly youth travelers, which includes the age group from 20 to 35 years. Youth travelers, often called ＂Generation Y＂ have specific expectations, needs, and budget which indicate the importance of study at this field as well. The research studies the youth traveler＇s booking habits, expectations before arriving to the accommodation, how often they leave reviews and rate hostels, what is the most important aspect when they are choosing the establishment, importance of how high the rating is, whether they read the reviews before booking an accommodation, and what a hostel has to have to give them 100% rating and a good review.

Assessment of laryngologists'ratings on physician review websites

Objective:To assess and characterize online ratings and comments on laryngologists and determine factors that correlate with higher ratings.Methods:All the American Laryngological Association(ALA)members were queried across several online platforms.Ratings were normalized for comparison on a five-point Likert scale.Ratings were categorized based on context and for positive/negative aspects.Results:Of the 331 ALA members,256(77%)were rated on at least one online platform.Across all platforms,the average overall rating was 4.39±0.61(range:1.00-5.00).Specific positive ratings including“bedside manners,”“diagnostic accuracy,”“adequate time spent with patient,”“appropriate follow-up,”and“physician timeliness”had significant positive correlations to overall ratings,by Pearson's correlation(P<0.001).Long wait times had significant negative correlations to overall ratings(P<0.001).Conclusion:Online ratings and comments for laryngologists are significantly influenced by patient perceptions of bedside manner,physician competence,and time spent with the patient.

Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

order to help investors understand the credit status of target corporations and reduce investment risks,the corporate credit rating model has become an important evaluation tool in the financial market.These models are based on statistical learning,machine learning and deep learning especially graph neural networks(GNNs).However,we found that only few models take the hierarchy,heterogeneity or unlabeled data into account in the actual corporate credit rating process.Therefore,we propose a novel framework named hierarchical heterogeneous graph neural networks(HHGNN),which can fully model the hierarchy of corporate features and the heterogeneity of relationships between corporations.In addition,we design an adversarial learning block to make full use of the rich unlabeled samples in the financial data.Extensive experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.

Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators,including platform transaction volume and average expected rate of return.We also consider two qualitative indicators of online loan background,namely platform background and guarantee mode,that reflect Chinese characteristics.Subsequently,a factor analysis was conducted to reduce the 14 indicators dimensions.The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor,fund dispersion factor,security factor,and profitability factor.Finally,a K-means clustering algorithm was employed to cluster the factor scores of each OLP,thereby obtaining credit rating results.The empirical results indicate that the proposed machine learning-based credit rating method effectively provides early warnings of problem platforms,yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China,namely,Wangdaitianyan and Wangdaizhijia.

电力线信道中极化码译码算法的改进

脉冲噪声是影响电力线通信性能的最主要因素。为了提升极化码在电力线信道的性能,文章提出了分段循环冗余校验码辅助串行抵消列表比特翻转(segmented CRC aid SCL-bit flip,SCA-SCL-BF)译码算法,并在加性高斯白噪声(additive white gaussian noise,AWGN)信道和电力线信道下进行了仿真。在构造关键集合时引入Rate1子块,避免了全集搜索的复杂度;根据关键集合中比特的位置进行分段,每段翻转一比特,可以实现多比特翻转。仿真结果表明,SCA-SCL-BF算法的性能相较于公开文献中性能在信噪比(signal-to-noiseratios,SNR)大于0dB时有显著提升。该算法对于提升电力线传输性能有重要意义。

The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information

To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.

The Determination Method of Product Engineering Features Based on Linguistic Variables

To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Anisotropic time-dependent behaviors of shale under direct shearing and associated empirical creep models

Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

Dynamic changes of rumen microbiota and serum metabolome revealed increases in meat quality and growth performances of sheep fed bio‑fermented rice straw

Background Providing high-quality roughage is crucial for improvement of ruminant production because it is an essential component of their feed.Our previous study showed that feeding bio-fermented rice straw(BF)improved the feed intake and weight gain of sheep.However,it remains unclear why feeding BF to sheep increased their feed intake and weight gain.Therefore,the purposes of this research were to investigate how the rumen micro-biota and serum metabolome are dynamically changing after feeding BF,as well as how their changes influence the feed intake,digestibility,nutrient transport,meat quality and growth performances of sheep.Twelve growing Hu sheep were allocated into 3 groups:alfalfa hay fed group(AH:positive control),rice straw fed group(RS:negative control)and BF fed group(BF:treatment).Samples of rumen content,blood,rumen epithelium,muscle,feed offered and refusals were collected for the subsequent analysis.Results Feeding BF changed the microbial community and rumen fermentation,particularly increasing(P<0.05)relative abundance of Prevotella and propionate production,and decreasing(P<0.05)enteric methane yield.The histomorphology(height,width,area and thickness)of rumen papillae and gene expression for carbohydrate trans-port(MCT1),tight junction(claudin-1,claudin-4),and cell proliferation(CDK4,Cyclin A2,Cyclin E1)were improved(P<0.05)in sheep fed BF.Additionally,serum metabolome was also dynamically changed,which led to up-regulating(P<0.05)the primary bile acid biosynthesis and biosynthesis of unsaturated fatty acid in sheep fed BF.As a result,the higher(P<0.05)feed intake,digestibility,growth rate,feed efficiency,meat quality and mono-unsaturated fatty acid concentration in muscle,and the lower(P<0.05)feed cost per kg of live weight were achieved by feeding BF.Conclusions Feeding BF improved the growth performances and meat quality of sheep and reduced their feed cost.Therefore,bio-fermentation of rice straw could be an innovative way for improving ruminant production with mini-mizing production costs.

Body composition and metabolic syndrome in patients with type 1 diabetes

BACKGROUND In recent years,the prevalence of obesity and metabolic syndrome in type 1 diabetes(T1DM)patients has gradually increased.Insulin resistance in T1DM deserves attention.It is necessary to clarify the relationship between body composition,metabolic syndrome and insulin resistance in T1DM to guide clinical treatment and intervention.AIM To assess body composition(BC)in T1DM patients and evaluate the relationship between BC,metabolic syndrome(MS),and insulin resistance in these indi-viduals.METHODS A total of 101 subjects with T1DM,aged 10 years or older,and with a disease duration of over 1 year were included.Bioelectrical impedance analysis using the Tsinghua-Tongfang BC Analyzer BCA-1B was employed to measure various BC parameters.Clinical and laboratory data were collected,and insulin resistance was calculated using the estimated glucose disposal rate(eGDR).RESULTS MS was diagnosed in 16/101 patients(15.84%),overweight in 16/101 patients(15.84%),obesity in 4/101(3.96%),hypertension in 34/101(33.66%%)and dyslip-idemia in 16/101 patients(15.84%).Visceral fat index(VFI)and trunk fat mass were significantly and negatively correlated with eGDR(both P<0.001).Female patients exhibited higher body fat percentage and visceral fat ratio compared to male patients.Binary logistic regression analysis revealed that significant factors for MS included eGDR[P=0.017,odds ratio(OR)=0.109],VFI(P=0.030,OR=3.529),and a family history of diabetes(P=0.004,OR=0.228).Significant factors for hypertension included eGDR(P<0.001,OR=0.488)and skeletal muscle mass(P=0.003,OR=1.111).Significant factors for dyslipidemia included trunk fat mass(P=0.033,OR=1.202)and eGDR(P=0.037,OR=0.708).CONCLUSION Visceral fat was found to be a superior predictor of MS compared to conventional measures such as body mass index and waist-to-hip ratio in Chinese individuals with T1DM.BC analysis,specifically identifying visceral fat(trunk fat),may play an important role in identifying the increased risk of MS in non-obese patients with T1DM.