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This paper aims to discuss the application of Faff’s pitch template to a research proposal on client importance and audit quality.

The “cocktail glass” approach and “3-2-1” design provide junior researchers with an effective tool to develop, evaluate and communicate research ideas in a simple way.

The author further provides a commentary and reflections based on his own experience in this letter.

Based on my pitching experience, pitching requires significant preparation. Further, the pitching template is a useful means to deposit research ideas and as a control and supervisory tool.

It is a PRL submission.

This study aims to examine whether analyst revisions on peer firms have information transfer effects on focal firms in the same industry, and whether firm-level rivalry and common analysts affect such information transfer.

This study uses a large sample of US data on listed companies and financial analysts from 1996 to 2021. The authors use ordinary least squares and a short-window event study to test the formulated hypotheses.

The findings show that, on average, focal firm stock returns are positively associated with peer firms’ analyst revisions. However, information transfers from nonrival (rival) peer firms’ analyst revisions are positive (negative). Revisions by common analysts covering both peer and focal firms drive more positive transfers. Furthermore, peer firms’ revisions by common analysts, compared to noncommon analysts, trigger more reactions from focal firm analysts, consistent with investor reactions. Finally, common analyst coverage raises short-term return synchronicity around revisions.

This study adds to the information transfer literature by examining the information released by noncorporate entities (i.e. analyst revisions). It also extends our understanding of the roles of analysts, particularly the peer effect and common analysts, in the capital markets. Findings on analyst-driven return interdependencies among peers may interest investors in portfolio construction.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

The purpose of this study was to prepare carboxylated attapulgite (APT-COOH) and then be used as one of the ligands to prepare metal organic framework (MOF) hybrid materials to reduce the cost of MOF materials and improve the dispersed condition of APT. And then the materials were used to enrich anionic dye Congo red from aqueous solution.

The MOF hybrid materials were designed by means of facile reflux method rather than hydrothermal method, characterized by Thermogravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) Spectrometer and pore structure. The dispersed degree of APT-COOH in the MOF materials was validated according to adsorption efficiency for Congo red.

Due to introduction of APT-COOH, the microenvironment of the MOF materials changed, leading to different adsorption behaviors. Compared to the MOF material without APT-COOH, the adsorption capacities of the hybridized MOF materials with different amounts of APT-COOH introduced increased by 4.58% and 15.55%, respectively, as the initial concentration of Congo red solution of 300 mg/L. Meantime, hybridized MOF materials were suitable to remove Congo red with low concentration, while the MOF material without APT-COOH was appropriate to enrich Congo red with high concentration.

The microstructure of MOF hybrid materials in detail is the further and future investigation.

This study will provide a method to reduce the cost of MOF materials and a theoretical support to treat anionic dyes from aqueous solution.

APT-COOH was prepared and used as one of the ligands to synthesize MOF material to improve the dispersed degree of APT-COOH and reduce the cost of the MOF materials. The adsorption efficiency was greatly enhanced with low concentration of Congo red solution, and the results indicated that hydrogen bonding, electrostatic interaction, and p-p conjugation were involved in the adsorption process. The prepared MOFs materials exhibited excellent adsorption efficiency, which made the present materials highly promising and potentially useful in practical application as adsorbents to enrich anionic dyes such as Congo red from aqueous solution.

Board faultlines, which reflect intrinsic divisions of board members into relatively homogeneous subgroups, are associated with poor firm performance. This paper aims to extend the existing board faultline research by examining how acquisition deal size moderates the negative implications of board faultlines.

This paper uses a sample of acquisitions and a quantitative research approach to conduct statistical analysis.

Using a sample of acquisitions announced between 2007 and 2016, this paper finds evidence suggesting that strong faultlines are associated with poorer acquisition outcomes in the long-term, but not in the short term. Further, this paper finds that the effect of faultline strength on long-term acquisition outcomes is weaker for larger acquisition deals than smaller acquisition deals. The findings are consistent with deal size moderating the relation between faultlines and acquisition outcomes.

This paper addresses possible endogeneity through firm fixed effects and instrumental variable analysis. Although this paper provides evidence on the moderating role of deal size in the context of faultlines, future research could examine the role of additional moderators, such as pro-diversity, trust, board leadership and board and task characteristics.

The findings suggest that boards need to be aware of situations where the negative effects of faultlines are more likely to come to the fore. For example, faultlines are more likely to play a role in more routine, obscure monitoring than for high-profile strategic decisions.

The study is multidisciplinary as it draws on the management, organizational behaviour and psychology and finance literature. It contributes to the developing literature on faultlines in several important ways. First, this paper supports their view that faultlines have adverse effects on board performance by showing that faultlines negatively impact discrete strategic investment decisions. Second, this paper provides evidence that deals size moderates the faultline-acquisition performance relation, indicating that the role of faultlines is contextual. Third, this paper finds evidence that suggests investors do not factor in board faultlines when responding to acquisition announcements.

This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection.

First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure.

The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform.

The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.

In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law.

First, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend.

This approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach.

For the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.

Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.

Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.

In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.

The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.

Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

This paper theoretically and empirically analyzes the effects of the elderly’s physical health status on their need for care and the choice of care models in China.

Empirically, the estimation results of a large-sample randomized intervention trial with chronic obstructive pulmonary disease (COPD) patients through the difference-in-difference method indicated the following: (1) After the COPD intervention trial, the physical health status of the elderly in the treatment group improved significantly, the need for care was substantially reduced and the health improvement led to a 35.5% reduction in the probability of using elderly care. (2) The reduction in the need for care regarding the treatment group occurred mainly in social care. The probability of using social care decreased by 67.8% due to the elderly’s health improvement, while that of home care remained unchanged generally. (3) Further heterogeneity tests suggested that families with fewer potential internal resources for caregiving had a more pronounced decline in the need for social care.

Theoretically, these empirical results support the existence of the “pecking order” theory in the family’s choice of elderly care model, that is, families tend to employ all internal resources for caregiving before resorting to social care, resulting in a higher sensitivity of social care to health.

The main policy implication of this paper is that ex ante preventive health intervention policies can significantly alleviate the burden of care, especially social care, on families. And preventive health intervention policies are particularly effective in reducing the burden of the families with relatively few resources for informal internal care.

Following Cheng et al. (2012) and Tan et al. (2015), this paper aims to investigate how does the forward-looking information disclosure quality affect the investors’ decisions and then the investment efficiency.

The authors obtain the information disclosure quality rating data from the official website of the Shenzhen Stock Exchange (SZSE), and firm financial information is mainly from the China Center for Economic Research (CCER) and China Stock Market and Accounting Research Database (CSMAR). The authors choose firms that publicly traded on the SZSE during the period from 2004 to 2010, and the final sample consists of 2,415 firm-year observations for 345 unique firms.

The authors find that a firm with a high information disclosure quality rating is trusted by investors more. Forward-looking non-financial information (FNFI) disclosure alleviates financial constraints and improves investment efficiency, including alleviating underinvestment and preventing overinvestment to a larger extent for firms with high information disclosure quality rating, especially for the firms rated A (excellent) or B (good) every year since 2001, when the rating began. Moreover, this study proves that investors trust the firms rated high more but do not guard against the firms rated low enough.

The authors only considered the quantity of FNFI disclosed by firms and ignored other characteristics of FNFI. Limited by the data of information disclosure quality rating, the research sample is just from the SZSE.

This paper extends the research of Cheng et al. (2012) and Tan et al. (2015) to show that one of the reasons behind the extant mix results of the relationship between FNF disclosure and investment efficiency is different information disclosure quality. High-quality FNFI disclosure can alleviate underinvestment and prevent overinvestment at same time.