A country’s ability to become a prominent knowledge economy is tied closely to its ability to acquire skilled people who can compete internationally while resolving challenges of the future. To equip students with competence that can only by gained by being immersed in a foreign environment, outbound mobility is vital.To analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy.The proposed approach was examined using a dataset of outbound Taiwanese student mobility to ten countries between 1998 and 2018. Without the requirements of any special conditions for the proprieties of the objective function and constraints, the FSDESVR model retained the advantage of FS, SVR, and DE. A comparison of the FSDESVR model and other forecasting models revealed that FSDESVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) results for all the analyzed nations. The experimental results indicate that FSDESVR achieved higher forecasting accuracy than the compared models from the literature.With the recognition of outbound values, key findings of Taiwanese outbound student mobility, and accurate application of the FSDESVR model, education administration units are exposed to a more in-depth view of future student mobility, which enables the implement of a more accurate education curriculum. For decades, internationalization has been high on the agenda of global education. The value and benefits of a study abroad experience for students, institutions, communities, and economies have long been recognized. Researchers and administrators in tertiary education have described student mobility as a critical aspect of the internationalization of higher education and a key institutional response to the "globalization" and regionalization imperatives –3. Due tIn this research, the feature selection (FS), differential evolution (DE), and support vector regression (SVR) had been combined and developed as integrated algorithm called FSDESVR model, to create a reliable forecasting method for outbound student mobility in Taiwan. In time series data, FS is applied to verify the most relevant input features. To construct an effective SVR model, DE algorithm was applied to distinguish a combination of optimal parameters for SVR. A feature selection using random forest method was proposed. The method creates a subset of the most important features from the original feature set, thereby improving the accuracy and training efficiency of its forecasting. SVR has three hyperparameters, and differences among these parameters greatly affect the accuracy of SVR forecasting. Through an optimized algorithm, DE was used s to search for the best hyperparameters in a limited timeframe and further optimized the accuracy of SVR for time series forecasting.Data sets provided by the Taiwan Ministry of Education from 1998 to 2018 were used as an example , where N is the dimension of the solution space, DE is based on the population of the candidate solutions xk, k = 1, 2, …, K, (K is the population size). These candidate solutions repeatedly search the global minimum of f(x) in the solution space. After the initial population is generated, the candidate solutions are optimized by repeated operations, such as mutation, intersection, and selection. For each individual solution during the mutation process xk, three different solutions  that are different from each other and different from xk are randomly selected to generate a new solution yk.β is the mutation factor. According to the aforementioned scheme, the crossover results in the generation of the final offspring uk, which can be formulated as follows:n = 1, 2, …, N, hn is a random number uniformly distributed within , and H∈  is a predefined crossover probability. The term uk inherits at least one component from yk. During the selection process, the offspring uk competes with the initial candidate solution xk. If the offspring has a superior cost function, it replaces xk in the next generation.DE is a population-based random function optimizer that was proposed by Storn and Price . DE has The SVR parameters are selected using DE, as presented in N), mutation factor (F), and crossover rate (R) of the DE algorithm are defined. The chromosome consists of three parameters—C, ν, and σ—for SVR.Step 1. Initialization parameters: The population numbers , the mean absolute percentage error (MAPE) function is used for load forecasting as follows:Ai is the actual value, Fi is the forecast values, and n is the sample size.g = g + 1.Step 4. Offspring generation. Offspring are generated and then input into SVR to calculate the fitness value again. Set stop criterion is satisfied. If g is equal to the maximum number of generations, the optimal solution chromosome is obtained; otherwise, go to step 3.Step 5. Circulate until the A high-dimensional feature set can contain enough state-related information, butalso comes at a cost: the addition of feature set dimensions may lead to sparsity in the feature space, and overfitting problems may occur when the training samples are scarce. Feature selection is a necessary operation to ensure the accuracy of forecasting outbound student mobility. In response to the above problems, we adopted a supervised feature selection method called the recursive feature elimination method (RFE) , which ci is the impurity of the node, the importance of the MSE of the node is defined as follows:RF is an integrated learning application for classification and regression problems . RF featEmse of each possible feature split were calculated, and features with the largest Emse impurity reduction were selected. The output value of all samples under the tree node minus the sample of the node’s mean was squared. The value was then divided by the number of samples under the node; n is the number of samples under the node, yi is the label value of one of the samples, and The xi, yi}, i = 1, …, N, where xi is a feature vector sample with S features and yi is the label value of one of the samples. The selected features are used in the training samples to train the RF model. According to the trained RF model, the feature ranking criteria can be calculated by s(q)k from the feature set with the lowest standard is removed as:T1 is the feature subset with S−1 features in the first iteration of feature selection. RF-RFE repeats the above operations until all the features are removed from the feature set, and then the S feature subsets can be obtained as:Given a set of training samples {The feature subset under each iteration with the best accuracy is selected as the optimal feature subset for forecasting outbound student mobility.First proposed by Brown and Meyer , ETS conThe ARIMA method is a popular time series forecasting method proposed by Box and Jenkins . Data arIn the VARMA model, the next step in each time series is modeled using the ARIMA model. The VARMA model is a summary of multiple parallel time series of ARIMA. The order of the AR(p) and MA(q) models are specified as parameters of the VARMA function .The VARMAX model is an extension of the VARMA model and includes the exogenous variable model. Exogenous variables, also known as covariates, can be considered parallel input sequences observed at the same time step as in the original sequence . The proTo evaluate the forecast performance of the FSDESVR model, two common statistical measures were used in this study, namely the root mean square error (RMSE) and MAPE , for comAi is the actual value, Fi is the forecast values, and n is the sample size.To evaluate the proposed method, the proposed FSDESVR method was applied to the extracted data to determine the trend. The annual data from 1998 to 2014 were used as a training set to train the proposed method. A testing set, which consisted of the annual outbound student data from 2015 to 2018, was used to test forecast accuracy. One of the goals of this study tested the ability to obtain the optimal parameters of GRIDSVR and DESVR to prove the effectiveness of the SVR system, which relies on the parameters selected, was successful. GRIDSVR employs a grid search to aim for the most effective parametric combination. The calculation of each grid's fitness value is involved in a grid search. A Grid quest involves measuring each grid's fitness value. Since this approach may suggest that the most appropriate combinations of parameters do not exist in the grid, DE has been used to enhance the problem analysis. The optimal values of the three SVR parameters for each SVR model are presented in yt-i indicates the number of students from i years ago. We found that the better subsets in all countries under RF-RFE approach are the two and three lagged variables as the acceptable number of features.The selected six years of data that are applied as lagged variables are considered for forecasting outbound student mobility . This study used the RF-RFE approach  to selecThe variations among the actual data and the predicted results in The United States. Being the top destination for outbound students in Taiwan, mobility forecasting for the United States was essential to the exploration conducted in this study. Some MAPE values for various forecasting models were over 10. However, the lowest MAPE and RMSE values were observed for the FSDESVR method. The largest gap between the experimental findings and the real data emerged in the 2018 data. This gap may be correlated with US President Trump’s policies, which caused a decline in the demand for US higher education. Due to the political and social climate in the United States, student visa delays and denials may be the main reason for prospective outbound students to select alternative options.The United Kingdom. Much like the results for the United States, overall MAPE results for the United Kingdom indicated high accuracy. Although the DESVR model achieved results that were close to the actual numbers in 2016 and 2017, which led their RMSE to decrease, the overall outcome remained slightly below the actual results. This result indicates that the DESVR model failed to concisely reflect the actual trend of outbound mobility. A fluctuation occurred in 2016, which may be related to changes in the political environment .Australia. For the data from Australia, the FSDESVR model exhibited the lowest MAPE and RMSE. Moreover, this model was the only one to reach a standard of high accuracy. The largest gap occurred in 2015 when Western Australia had a major upturn in international student enrolment. The possibility of migration was one of the greatest incentives offered to foreign students to select Australia as their destination. Australia itself provides flexibility and financial incentives to international students in hopes of having them train and potentially contribute to the Australian economy post-study.Japan. The data from Japan exhibited the lowest MAPE results and high accuracy possibly because the number of outbound Taiwanese students traveling to Japan has steadily increased over the past decade. Gaining competitiveness in global rankings, Japanese universities have been aiming to attract an increased number of international students with the help of their government. Since 2008, Japan has had the following goals: to increase the number of foreign students in their country to 300000 by 2020 and have ten Japanese universities rank among the top 100 universities in the world by 2023 [ by 2023 . MultiplCanada. Similar to Japan, all the MAPE results for Canada indicated high accuracy. Canada has been well known for its strong growth in international student enrolment in recent years. In 2018, the number of foreign students with a Canadian study permit was 572,415, which is a substantial rise from the previous year's 492,545. This increase was comparable to that over the same year for Australia, who hosted 690,468 foreign students, and the United Kingdom, who welcomed 458,490 EU and non-EU students during the 2017/18 academic year [mic year . Due to France. The FSDESVR model provided the lowest MAPE and RMSE results for the data for France. The noticeable changes that occurred in 2018 may be the outcome of the French Government's launch of advanced international education policy, which aims to host 500000 international students in France by 2027. To achieve this goal, France must welcome an additional 5% of foreign students per year. Aiming to improve international student service in French universities, The new policy, Bienvenue en France, is now supported by a € 10 million (US$ 11.4 million) government funding grant [ng grant . To elimng grant .Germany. For the data for Germany, the FSDESVR exhibited a high accuracy with lower MAPE and RMSE values than the other models. In the last decade, Germany's international recruiting attempts have been driven by a strategy of no-tuition for foreign graduates. In addition, the number of foreign students in Germany has continued to increase, with increased job prospects during and after graduation and the ongoing expansion of English-language programs within German universities. The fluctuation in 2018 may be the result of prospective students selecting France over Germany, which may have caused the gap between the predicted and actual numbers.New Zealand. The FSDESVR model provided the lowest MAPE and RMSE results for the data for New Zealand and was the only model that achieved high accuracy. As illustrated in Figs Spain. The FSDESVR model provided the lowest MAPE and RMSE results for the data for Spain. The outbound mobility to Spain has remained steady over the past few years. Even though Spain is not currently a leading global study destination, its Mediterranean climate and welcoming local atmosphere have always been its most attractive features. Like many other countries, Spain continues to strive to attract international students to its domestic universities; however, its main prospective inbound market lies in countries that also speak Spanish. Therefore, language barriers are often reservations for students coming from Asia.South Korea. Similar to the results for most countries analyzed in this study, all MAPE results for South Korea indicated high accuracy. The MAPE and RMSE results from all the other models were markedly larger than the actual values. This result was obtained because the FSDESVR model can learn from historical data, therefore ensuring accurate predictions. With the advantage of geographical location and familiarity within pop culture, South Korea has always been one of the top study destinations among young Taiwanese students. South Korea recently announced that 2019 was the sixth consecutive year of international enrolment growth for the nation, which is significantly a new record high.Total. From 2015, outbound student mobility from Taiwan increased more slowly than it did during the previous decade possibly because many students did not find an increase in employment opportunities with an overseas diploma. Hence, the propensity to study abroad declined. It can also be speculated that many students study abroad because high-quality education and suitable higher education capacities are not available domestically; however, with countries like Taiwan investing more in developing their own higher education systems, outbound mobility will consequently suffer. With the number of foreign students in higher education rapidly increasing today, Taiwan ranks seventh of top senders, which is two percent of the total foreign student enrollment. Nevertheless, before China emerged as the dominant supplier of foreign students, Taiwan was the leading sender of students to the United States, the destination with the highest foreign enrollment of foreign students [students .Our study revealed that FSDESVR performs superior forecasting regarding outbound student mobility. In FSDESVR, based on feature importance scores, features with higher importance are selected to proceed with the SVR time series forecasting. The inclusion of fewer features can reduce the training time for a forecasting model and increase forecasting accuracy to a substantial extent. In outbound student mobility forecasting, the strength of feature space representation is that the mean squared error is equal to the loss function. The use of RF-RFE for feature selection can remove features recursively to improve overfitting problems and reduce sparsity in the feature space. Therefore, the RF-RFE in FSDESVR is capable of reducing noise which helps in improving accuracy when forecasting. In addition, interpretable features can help to clarify essential features. Thus, SVR can ensure the accuracy of forecasting outbound student mobility. Furthermore, DE can effectively optimize the three SVR hyperparameters due to its superior implementation in solving optimization of continuous domains . WithoutWithout the requirement of special conditions for the proprieties of the objective function and constraints, the FSDESVR model retains all the advantages of FS, DE, and SVR. This model can be applied to both continuous and combinatorial problems and can also be extended to multimodal and multi-objective optimization ,48. In aAccurate forecasting of outbound student mobility is critical to ensure that outbound students are equipped with supportive preparation upon leaving a country. FSDESVR integrates the function of feature selection (FS) and support vector regression (SVR) with differential evolution (DE). The FS function is used to identify features of higher significance that are chosen to demonstrate SVR time series prediction. Based on recursive feature elimination, FS as an iterative operation process, uses the mean-square error (MSE) evaluated from the random forest model to evaluate features, then uses specific parameters to recursively remove features. DE is used to adjust appropriate parameters for SVR to more effectively forecast the designated task. According to the results, FSDESVR obtained the lowest MAPE and RMSE when compared to the following methods: ARIMA, ETS, VARMA, VARMAX, GRIDSVR, and DESVR on the forecast of outbound student mobility for all analyzed nations. Based on the results of the proposed FSDESVR method on outbound student mobility forecasting, governments, educational institutions, and decision makers can more effectively customize comprehensive policies, which may benefit many participating stakeholders. Based on the successful method performance in this study, the newly proposed FSDESVR model can be applied to other complex forecasting problems in the future.S1 File(TIF)Click here for additional data file.S2 File(TIF)Click here for additional data file.


Start-up companies are confronted with various risks and many uncertainties, and professional auditing can fully analyze start-up companies. In this way, both parties may maximize their interests through auditing the market activities. Based on the characteristics of start-up companies, this study explores the impact of customer psychological prices on audit pricing. The impact of customer psychological prices on audit pricing decisions was systematically analyzed from various angles, thereby determining whether it will affect the development of the product market. The results show that product market dominance reduces the agency costs between the owner and manager of the customer company. In other words, if the customers have greater control over the product market, they will have a lower business risk, and the auditor risk will be reduced accordingly, hence a lower audit fee. In the start-up company market, even if the financing dilemma restricts the survival and development of the company, customers still have psychological expectations for audit pricing. When their psychological price of products is different from the market price of products, it may affect the market advantage of products with lower audit fees, and further expands the previous research. In the market, customers also have certain psychological expectations for “auditing” products. Therefore, strengthening the relationship between auditors’ pricing and customers’ psychological prices has a positive effect on enhancing the competitiveness of product markets, which also increases the operating efficiency of start-up companies. Since China joined the World Trade Organization (WTO) in 2001, Chinese companies have not only faced competition from the domestic market but also participated in the fierce competition in the international market . IncreasThe audit pricing strategy of a company is a key issue often considered in the marketing field. When a company highlights a new product, the company will price it and may need to adjust the product price as the market changes. These are the basic issues to be considered in the marketing process . HoweverAlthough the competitiveness of the industry is the same as all companies in the same industry, companies with stronger product market strength may protect themselves from the effects of fierce competition. By using the estimated profit-cost-margin (EPCM) values, this study follows previous research to measure the specific product market strength of a company in the industry. In this study, the existing research is extended to a developing country market such as China. From the perspective of customer psychology, the relationship between product market competitiveness and audit pricing under different institutional backgrounds is explored. This study aims to expand the information of customers on the establishment of audit prices, systematically analyze the impact of customer psychological prices on company pricing decisions from various angles, and enrich the role of customer bargaining power in the relationship between product market power and audit pricing.Traditional audit pricing theory believes that in the competitive audit market, both auditors and clients should consider audit costs and audit risks during the negotiation of audit pricing. Most of the research on the influencing factors of audit pricing have selected the company size as the explanatory variable of audit pricing, and all have confirmed that the company size has the strongest explanatory power to audit pricing . The infIn addition to the company size and governance, audit pricing is also affected by customers’ psychological expectations of audit quality. Some scholars pointed out that the customer’s demand for audit quality depends on their motivation to choose audit and the firm’s industry expertise; at the same time, they also discussed the impact on audit quality from the legal system environment faced by auditors . The entIn summary, there are many studies on the factors affecting audit pricing in this field, and their respective focuses are also different, including company size, audit quality, and market competition. However, as an important party of auditing process, customers also have a very important influence on audit pricing. Therefore, from the perspective of customers’ psychological prices, the correlation between product market competitiveness and audit pricing under different institutional backgrounds is explored.Auditing is a commodity and the result of auditing accounting statements. Currently, for the relationship between audit report products and non-audit report products, there are two main views theoretically. One is that the relationship between the two types of audit products is irreconcilable; the audit report product reflects the essential content of the audit. If the auditor provides non-audit report products at the same time, it will lead to loss of independence and reduce the quality of audit products. Another view holds that the relationship between the two types of audit products can be reconciled; the auditor can provide non-audit report products at the same time. If the auditor is professional enough, the audit quality will not be affected. In a market environment, an accounting firm can be regarded as a party providing audit services, and a listed company can be regarded as a party receiving audit services. Both parties have their goals in market activities and maximize their interests through audits . The audThe audit market refers to a special transaction environment formed by the “audit” business. The main bodies of the two parties to the transaction are listed companies and accounting firms. In any market, the price system is an important factor in maintaining the balance of interests of all parties to the transaction. Price is also one of the key factors that determine the characteristics of the market structure. The premise of the orderly operation of the audit market is to have a complete audit system to provide protection, thereby regulating the behavior of the accounting firm and the individual auditor. Product market power symbolizes the comparative ability of a company to determine prices in the market by manipulating the levels of supply, demand, or both . TherefoIn recent years, with the continuous development of the accounting firms in China, the concentration of the audit market has increased, and studies have confirmed that the concentration of the audit market will increase due to the intensification of market competition . On the Due to the limited funds and few financing channels, the size of start-up companies is limited inevitably, which will lead to slower business development and lower product market share. Audit services can maintain the financial laws and regulations of the companies, improve business management, and increase economic efficiency. Generally, once problems are found during the auditing process, improvement opinions will be received in time to help the company make financial statements, which is of vital importance to the legal and positive development of the company. In the process of audit pricing, listed companies as customers who receive audit services, their psychological prices will affect the final audit pricing. Audit pricing is a common concern of the company’s internal financial and auditing industries, which is affected by various company characteristics such as company size and property rights. Customers will also set their own audit pricing according to their business development and operation, so there is a pricing game between the psychological price of customers and the audit pricing of accounting firms . The triThe joining of regulatory agencies such as the Securities Regulatory Commission will further review listed companies based on audit reports provided by accounting firms. In the tripartite game model, a listed company and accounting firm are two elements that seek to maximize benefits in the cooperation process . TherefoAuditing services can be seen as a special product on the market. When a company pays for the audit service as a customer, it will usually make a value prediction of the audit service as a commodity based on the service prices of other items that the customer refers to. Moreover, after the real service price is made clear, the service selection is determined and the customer psychological price is formed . If the Existing research has tested whether the commercial risks of customers have an impact on audit costs. Johnstone has built a model to describe customer acceptance decisions for risk assessment and risk adaptation. The auditor evaluates the customer’s risk and uses their evaluation results to decide on the audit plan to avoid or adapt to the risk . CompaniStudies have confirmed that customers have an impact on service providers’ revenue and revenue realization. In turn, service providers may influence the psychological pricing of customers. For both parties of the audit supply chain, there is a contractual relationship between the auditor and the customer on whether to disclose audit fees. As a result, the third-party supervision of audit fees may exacerbate the game between the two . Since tIndustrial competitiveness is the same for all companies in the same industry, while the market competitiveness of a product represents the market position of a company itself. On the one hand, audit companies may undermine their audit independence and use a “low-profile” strategy in exchange for retaining important customers. On the other hand, due to the large reputational risk capital of large customers, auditing companies tend to provide higher levels of assurance for important customers. Therefore, auditors will increase their auditing efforts and provide differentiated services. At this time, the customer psychological pricing of audit services will have a greater impact on audit pricing .Hypothesis 1: For customers with greater product market influence, the psychological price of the customer will have a greater impact on audit pricing. Audit service providers often charge fewer audit fees to retain important customers and enhance their influence.Hypothesis 2: When the internal competition in the auditor industry is more intense, the psychological price of customers for audit fees will tend to decrease. At this time, customers with strong competitiveness in the product market will often be charged fewer audit fees.To reduce the huge changes in sample selection after China’s accession to the WTO in 2001, this study selects samples since 2005. The samples include 21,154 company annual observations from 2005 to 2017. First, the listed companies of Growth Enterprise Board are excluded. Because the market competition of listed companies of Growth Enterprise Board is distinct from the market competition of listed companies of the Main Board, the included samples exclude financial companies (using the industry code of the 2012 listed company classification guide issued by the United States Securities and Exchange Commission). Besides, the company’s annual data with missing dependent, independent, and control variable data are excluded. To mitigate the effects of extreme values on the results of this study, all continuous variables are ranked at the head and tail of the 1% level each year.Based on the existing research, this study selects some control variables that affect audit costs to control the fixed effects: customer company size (SIZE), customer leverage (LEV), financial distress (LOSS), inventory asset ratio (INVREC), return on assets (ROA), property, plant, and equipment asset ratio (PPE), operating cash flow (CASH_FLOW), market to book ratio (MB), and annual dummy variables and industry dummy variables . The folFor the ability of auditors and customers to compete with each other’s psychological prices, the auditor industry competition (HERF_AUDITOR), experts (SPECIALIST), and customer importance (OFFICEIMPORT) are used as moderating variables. The industry competition of auditor (HERF_AUDITOR) is calculated by the relative value of the sum of the squared sums of all customers and audit fees for the same industry in the same year. An expert is an indicator. If the auditor has at least 10% of the total market share of an industry, SPECIALIST is set to 1; otherwise, it is set to 0. Customer importance (OFFICEIMPORT) is a measure of the ratio of customer fees to total fees paid by all public customers to the auditing firm. For customer bargaining power, the Pearson correlation coefficient of the percentage change of industry members is used to test the customer industry homogeneity (HOM). The customer industry competition (LNN) is defined as the number of companies in the same year and industry, and customer business complexity (COMPLEXITY) is also being considered. In the research on customer bargaining power based on their psychological price, the size of the company, company leverage, financial distress, inventory asset ratio, return on assets, property, plant, and equipment asset ratio, operating cash flow, market to book ratio, and annual and industry fixed effects are also controlled .Panel B contains univariate Pearson and Spearman correlations between audit fees, interest variables (EPCM), and other control variables. It is found that companies with stronger product market power often charge lower audit fees. There is a significant correlation between LAF and most control variables. EPCM and the natural logarithm of audit fees are negatively correlated. Therefore, Hypothesis 1 is verified.t-test and significance level of the two subsamples of the mean, the standard errors of heteroscedasticity are clustered at the company level.Model (2) is used to test Hypothesis 2 from the bargaining power between the auditor and the customer. The subsample regression results are shown in P < 0.01), which supports that the fiercer the audit competition is, the weaker the auditor’s bargaining power over audit fees is. The moderating effect of different expert values of panel B is compared. The EPCM difference coefficient of the two subsamples is 0.156 (P < 0.05), which indicates that customers with stronger market strength are often be charged them fewer audit fees. Panel C compares the different levels of customer competition for the company’s product market power, i.e., the advantage of being charged fewer audit fees. The coefficient of difference between the two subsamples of EPCM is 0.304 (P < 0.01), indicating that increased competition in the customer industry will reduce the disciplinary mechanism of company potential risk and further decrease the audit costs.Panel A compares the differences in the coefficients of the two subsamples. The EPCM difference coefficients of the two subsamples are 0.304 (The product market power symbolizes the comparative ability of a company to determine prices in the market by manipulating the levels of supply, demand, or both. Therefore, a company with a stronger market strength will have a competitive advantage in the product market, and thus bring stable profits to the company . This stThis study selects the capital market of China, a developing country, as the research object, and examines the relationship between product market power and audit costs in various industries. In the market, customers also have certain psychological expectations for products. When their psychological price of products is different from the market price of products, it may affect the market advantage of products with lower audit fees, which further expands the previous research . StrengtFaced with a highly uncertain competitive environment, startup founders will make organizational changes. The founders influence the commitments of their subordinates and strengthen the behaviors of opportunity identification and advantage search, in order to realize the entrepreneurial vision. Under such circumstances, professional auditors will have a preliminary understanding of the company, review whether there are loopholes in internal control, and identify abnormal problems timely after reviewing the financial statements, thereby laying a solid foundation for the stable development of start-up companies. For customers with greater product market influence, the psychological price of the customer will have a greater impact on audit pricing. Audit service providers often charge fewer audit fees to retain important customers and enhance their influence. When the internal competition in the auditor industry is more intense, the psychological price of customers for audit fees will tend to decrease. At this time, customers with strong competitiveness in the product market will often be charged fewer audit fees. Due to the limitation of the data, the author may only choose the listed companies in China as the research object. Because there is no market competitiveness of private companies from the same industry, the measurement of China’s overall product market competitiveness is not comprehensive. In addition, the specific impact of audit pricing and customer psychological prices is not clarified in this study. Therefore, a more in-depth analysis is needed in the subsequent study.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Nanjing University of Science and Technology Ethics Committee. The patients/participants provided their written informed consent to participate in this study.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases. Therefore, in this study, we developed and evaluated several methods for identifying plant diseases with little data. Convolutional Neural Networks (CNNs) are used due to their superior ability to transfer learning. Three CNN architectures  were used to build two baseline models, a Triplet network and a deep adversarial Metric Learning (DAML) approach. These approaches were trained from a large source domain dataset and then tuned to identify new diseases from few images, ranging from 5 to 50 images per disease. The proposed approaches were also evaluated in the case of identifying the disease and plant species together or only if the disease was identified, regardless of the affected plant. The evaluation results demonstrated that a baseline model trained with a large set of source field images can be adapted to classify new diseases from a small number of images. It can also take advantage of the availability of a larger number of images. In addition, by comparing it with metric learning methods, we found that baseline model has better transferability when the source domain images differ from the target domain images significantly or are captured in different conditions. It achieved an accuracy of  Since plant diseases may cause crop damage, they pose a major threat to food security and sustainability and may lead to food shortages. Therefore, early detection and control of plant diseases is very important. This process often requires trained human expertise to make the correct diagnosis. However, this expertise is not always available, especially in remote locations and small farms in developing counties. With the advancement of consumer devices, such as smartphones, that can capture high-quality images, the development of an effective image-based diagnostic system can greatly contribute to early disease diagnosis and waste reduction . In the A number of the techniques that have been proposed focus on diagnosing diseases of a single crop. Esgario et al.  proposedeases in . The tomeases in ,6,7. An eases in . A dataseases in , grape peases in , and poteases in .Another class of methods aims to provide general solutions that can be used to diagnose several diseases simultaneously. Too et al.  performeposed in , based oposed in , to idenented in ,19. Lee ented in  approachIf we look at the literature, we can realize the effectiveness of deep learning methods for diagnosing plant diseases. However, on the other hand, the success of these techniques depends greatly on the availability of a good amount of data to train the models, and the lack of data may hinder the development of these techniques. This data collection and labeling are often challenging, either because of the novelty of the disease or because of the high cost or lack of appropriate expertise. Therefore, the focus is on learning techniques that can learn from small datasets, formally called few-shot learning (FSL) ,22,23. TRecently, some FSL-based approaches have been proposed for identifying plant species and diagnosing crop diseases. Hu et al.  generateDevelopment and evaluation of several FSL approaches to classify plant diseases based on Triplet network, Deep Adversarial Metric Learning (DAML)  and tranWe investigate the impact of model complexity on the performance of the FSL approaches, ResNet18, ResNet34, and ResNet50 were used in this study. The results showed that fine-tuning using linear classifiers benefits from increased model complexity while this cannot be confirmed for other approaches.We examined two different formulations in this study. In the first, we classify plants and diseases together, while in the second we focus on diseases only. The second formulation helped the model to achieve better results in identifying new diseases and to better adapt to images captured under different conditions.Since selection of an appropriate learning strategy and models, as well as the right formulation of the problem, are key factors to the success of the FSL approach, the main purpose of this study is to provide an effective plant disease diagnosis approach that can learn from little data. The contributions of this study can be summarized as follows:Two datasets were used for training and evaluating all few-shot learning algorithms in this study, namely the PlantVillage dataset  and the milar to . In thismilar to , resultiThe coffee leaves Dataset  containsC novel classes using K sample from each class. The idea behind all FSL algorithms is to build a generic feature extractor or embedder, f, to map the image In this study, we aim to develop an approach that can learn from little data. This problem in the community is known as few-shot learning (FSL), which can leverage the information learned from a large source domain dataset, f and the classifier In transfer learning, a CNN model is trained using the source domain dataset and then fine-tuned using few samples from the target domain dataset as shown in milar to , in thisd is the dimension of the feature vector and c is the number of classes. This classifier has a linear layer followed by a Softmax function The Baseline model uses a linear classifier Equation .(1)Y˜=σc, d-dimensional weight vectors In the Baseline++ classifier, the weight matrix rs ws1,ws,…,wsc. Em is the margin.The Triplet network  uses thrfunction  defined After training from source domain dataset, to classify plant diseases with little data, a metric learning approach based on  is utiline (SVM)  classifiDAML  attemptsEquation . AccordiEquation  is utiliSimilar to the metric-learning using Triplet network, feature embedder and generator were trained with the source domain data and a multiclass SVM was used for FSL in the target domain.X is the input feature and In all experiments, we used three CNN models having different complexities, namely, ResNet18, ResNet34, and ResNet50 . We use All models were trained using the entire source domain dataset, after that each model was fine-tuned using a small set of target domain images ranging from 5 to 50 in increments of 5. Baseline and Baselineptimizer  with a lIn target domain, the baseline and baseline++ models were fine-tuned using Stochastic Gradient Decent (SGD) optimizer with a learning rate of 0.01, momentum of 0.9, and weigh decay of 0.001 for 100 epochs. SVM with linear kernel was used for triplet loss and DAML methods and the soft margin parameter was set to  PyTorch  deep leaIn this study, the performance of four learning approaches and three backbone models for classifying crop diseases from limited data was evaluated. We use two different formulations; one considers both the classification of crops and diseases as a single task as  and the In this set of experiments, 32 classes of the PlantVillage dataset  were useThe results of the coffee leaf dataset are shown in Since the same disease may affect more than one crop, the focus of these experiments will be on classifying diseases regardless of which plant is affected. This formulation is more realistic in the real world, where the farmer knows which crops he is growing but wants to learn about the diseases that afflict them. Eighteen classes of the rearranged PlantVillage dataset, as in The results shown in The results presented in this study show that we can use a good baseline model trained from a large source domain dataset to build a model that can learn from little data. Compared to the other approach, the baseline model can take advantage of the complexity of the backbone model to build a good representation of the features. Choosing the right backbone model and learning strategy is also important. Formulating the problem based on the name of the common disease can lead to better and more consistent results and enhances generalization of the model.In a previous study , ArguesoIn this study, we developed several approaches for classifying plant diseases that can learn from little data. Transfer learning, Triplet network, and Deep Adversarial Metric learning (DAML) were used to build these approaches. The evaluation of these approaches demonstrated the efficiency of transfer learning using a good baseline model. It achieved a very high accuracy of


Contaminated sites are recognized as the “hotspot” for the development and spread of antibiotic resistance in environmental bacteria. It is very challenging to understand mechanism of development of antibiotic resistance in polluted environment in the presence of different anthropogenic pollutants. Uncertainties in the environmental processes adds complexity to the development of resistance. This study attempts to develop mathematical model by using stochastic partial differential equations for the transport of fluoroquinolone and its resistant bacteria in riverine environment. Poisson’s process is assumed for the diffusion approximation in the stochastic partial differential equations (SPDE). Sensitive analysis is performed to evaluate the parameters and variables for their influence over the model outcome. Based on their sensitivity, the model parameters and variables are chosen and classified into environmental, demographic, and anthropogenic categories to investigate the sources of stochasticity. Stochastic partial differential equations are formulated for the state variables in the model. This SPDE model is then applied to the 100 km stretch of river Musi (South India) and simulations are carried out to assess the impact of stochasticity in model variables on the resistant bacteria population in sediments. By employing the stochasticity in model variables and parameters we came to know that environmental and anthropogenic variations are not able to affect the resistance dynamics at all. Demographic variations are able to affect the distribution of resistant bacteria population uniformly with standard deviation between 0.087 and 0.084, however, is not significant to have any biological relevance to it. The outcome of the present study is helpful in simplifying the model for practical applications. This study is an ongoing effort to improve the model for the transport of antibiotics and transport of antibiotic resistant bacteria in polluted river. There is a wide gap between the knowledge of stochastic resistant bacterial growth dynamics and the knowledge of transport of antibiotic resistance in polluted aquatic environment, this study is one step towards filling up that gap. Continuous presence of traces of antibiotics in environment lead to the rapid development and spread of antibiotic resistance, consequently threatening the effectiveness of antibiotics. Antibiotics enter aquatic environment majorly through effluent discharges from wastewater treatment plants56. After entering the natural environment antibiotics get subjected to various ecological/environmental processes such as advection, dispersion, diffusion, degradation, settling, resuspension, pH, sorption, sunlight, temperature, presence of organic compounds/minerals, and population of bacteria. Ecological factors affect the fate of antibiotics significantly, hence play important role in the occurrence of antibiotic resistance in environment. For example, sorption dictates the uptake and degradation of antibiotics over the reaches of streams as antibiotics are very much susceptible to getting adsorbed to bed material. Sorption behavior of antibiotics is dependent on their molecular structure and physicochemical properties of sediments and water, such as pH, organic matter and mineral contents50. Sunlight is another example of ecological factor which affects the presence of antibiotics through photodegradation, which is again dependent on other environmental factors such as pH, temperature, presence of salts, organic compounds etc.51.Rampant use of antibiotics caused antibiotic pollution in natural aquatic environment such as rivers, lakes, groundwater, seawater, sediments, plants and aquatic animals44. There are mathematical models for predicting antibiotic concentrations in aquatic environment both spatially and temporally38. Predicting antibiotic resistance in the hospital environment has been studied by55. However, very few studies have attempted to model the transport of antibiotics and its resistance in aquatic environment. There is a limited knowledge of the prediction of antibiotic resistance associated with microbes in an aquatic environment, as it is a complex ecosystem considering the wide range of selection pressures and transmission pathways. Hellweger et al.19 developed a simple model of tetracycline resistance in application to Poudre River (Colorado) to find the cause of resistance while considering the effect of tetracycline on growth rate of bacteria. We have also developed a conceptual and mathematical model for steady state one dimensional advection–dispersion dominated transport of fluoroquinolone and its resistant culture in application to the Musi river, South India13. These models comprise several partial differential equations representing physical processes such as advection, dispersion, adsorption, degradation, settling, re-suspension, diffusion, bacterial growth, plasmid conjugation and segregation. The models include state variables such as fluoroquinolone, total suspended particles, organic matter, heavy metals, antibiotic resistant bacteria, and susceptible bacteria.Mathematical models play a key role in understanding and predicting natural phenomena along with their uncertainties33. Similarly, other variables are also dependent on the number of natural processes. All the variables which affect a model should be considered for accurate prediction of model results. However, the variables in the model are also the sources of randomness due to which uncertainty creeps in the model. Uncertainty in a model may be attributed to randomness in model parameters, initial boundary and end boundary conditions. It is important not to ignore uncertainty in the model in order to obtain predictable outcomes of the studied system. In this study, our goal is to analyze the impact of stochasticity in the model parameters on temporal and spatial prediction of antibiotic resistance in aquatic environment.In a transport model the variables are affected by several natural phenomenon. For example, total suspended particles are affected by hydrological conditions such as river bed slope, type of bed material, flow of water, depth of water and rainfall, and also, due to ecosystem of the river as well as anthropogenic factorsThe deterministic model represents one-dimensional advection dispersion transport of fluoroquinolones and its resistant bacteria in the aquatic environment of Musi river (India). Schematic diagram of the model is shown in Fig. The partial differential equations are developed from mass balance around a control volume of A Δx. All the contaminant concentrations in sediment and water are referred to the mass per unit of total environmental volume (liters). The mass balance equations are comprised of the transport terms and reaction terms. The transport terms are advection, dispersion, settling, re-suspension and diffusion. Reaction terms in the model are adsorption, hydrolysis, bacterial growth, bacterial growth inhibition, death of bacteria, degradation, resistance gene transfer (conjugation) and loss of resistance gene (segregation).The chemical, physical, and ecological reactions are affected due to reaction rate constants. The reaction rate constants used in the model are enlisted in Table FluoroquinolonesFluoroquinolones are majorly degraded due to photolysis and a little due to oxidation, hence first ordered decay was assumed for antibiotics in this model.MetalsMetals utilization is considered for the growth of bacteria up to a certain concentration and is treated as inhibitor above that inhibition level of concentration. No other decay was assumed for the metals.Total suspended solidsTotal suspended solids comprise of organic and inorganic fractions and only organic fraction is subjected to the hydrolysis.Particulate organic matterParticulate organic matter is the fraction of total suspended solids and acts as a source for dissolved organic matter due to hydrolysis.Dissolved organic matterDissolved organic matter acts as a substrate for the bacteria. In this model the total DOM is assumed to be available for the bacteria.Susceptible and resistant culture19. The death rate of bacteria is represented by ‘a’.46.Apart from transport terms the concentrations of bacterial culture changes with the function of growth and respiration. The concentration of bacteria is defined on a biomass basis (mg/l) which is a usual practice in biogeochemical transport modelsMass balance equations are presented without transport terms and single term is used to represent resistant bacterial culture as 35.Stochastic process or random process is a collection of random variables representing the evolution of random values over time in some system. In a deterministic process the same trajectory of outcome is observed for a given set of initial conditions and parameter values, but in a stochastic process there is some indeterminacy, and the process may evolve in several directions. Hence, stochastic partial differential equation arises when randomness is introduced into the phenomena represented by a deterministic differential partial equation in a meaningful way, and relevant parameters are modeled as a suitable stochastic process54.The SPDE model is comprised of two parts: first is deterministic part, and the other is probabilistic part whose dynamics are partly driven by noise terms. Deterministic part of the variable and its stochastic fluctuation controlled the process rate at time step Δt. SPDEs are solved similarly as PDE, but the noise term 24 demonstrated that the frequency of cell counts of bacterial culture follows a Poisson distribution. Steven45 discussed how the random stochastic fluctuations in the microscopic processes follow a pattern of Poisson distribution and how it affects the patterns of nature in disease onset, rates of amino acids substitutions and composition of ecological communities. Mucha et al.37 presented a model for sedimentation of thin cells where velocity fluctuations are predicted by independent-Poisson-distribution estimates. Radioactive decay rates are reported to follow the Poisson distribution6. In ecological theory and practices, Poisson distribution usually represents a baseline against which other spatial patterns are compared, and its tractability helped in the development of the mathematics of population dynamics47.In this study, Poisson process is employed to model the noise to formulate SPDE for transport of antibiotic and its resistant culture in the aquatic environment of the river. Poisson distribution could be employed in the processes where: a definite number of times an event occurs; the occurrence of one event is independent of the other; the average rate of the event is also independent of other the occurrences; and two events can not occur at the same time. Koyama et al.The processes involved in the model are considered with a given number of events occurring in a fixed interval of time and/or space, these events occur with a known average rate and independently of the last time since the last event. Hence, the processes in the model possess the characteristics which make them appropriate to employ Poisson distribution for diffusion approximation. Another reason to employ Poisson distribution is that it has the unusual characteristics that both mean and variance are given as the same value which makes the calculations simple . For example, an event such as losing the plasmid carrying resistance gene (segregation) at a small Δt, is expected to be a rare random event in the population of bacteria. Hence, bacteria segregated at Δt are assumed to follow a Poisson distribution, and the variance in the number of loss equaled the mean.41. The model is sensitive to a parameter if the changes in that parameter causes a large change in the model results. In this study, sensitivity analysis of the model is investigated for the variations in the concentration of plasmid-mediated resistant culture  noise model formulation: The source of stochasticity in the population of resistance culture is due to fluctuations in the concentration of upstream boundary values such as antibiotic and dissolved organic matter.Sources of stochasticity in the model are categorized into three parts:deterministic rate). Similarly, stochasticity is applied in the other processes rates. Randomness in individual parameter is represented with the stochastic part which is drawn   2) repreMetalsEquations  and 4)  4) repreTotal suspended solidsSimilarly, Eqs.  and 6)  6) are rParticulate organic matterThe concentration of particulate organic matter in the river is represented by Eqs.  and 8),,8), the Dissolved organic matterEquations  and 10)10) are rSusceptible and resistant bacteriaEquations from  to 17)  17) are The SPDEs for the change in concentrations of fluoroquinolone (A), heavy metal (M), total suspended solid (TSS), particulate organic matter (POM), dissolved organic matter (DOM), population of susceptible bacteria  variables were found not to be significantly influencing the amount of resistance (only in multiples of μg/l). Stochasticity in environmental parameters was not at all influencing the population of resistant culture in the sediments.Musi river is polluted with high concentrations of antibiotics and there is an urgent need to perform a risk assessment of the nearby aeras which are impacted by its water. Hence developing a refined model for the transport of antibiotics and transport of antibiotic resistant bacteria in the polluted river is a necessity. A robust model is required for precise temporal-spatial prediction of resistant bacteria. Two-dimensional and three-dimensional stochastic transport model be could be developed to obtain high accuracy results. However, for a better understanding of the behavior of stochasticity in the system one-dimensional model can be used, and with more field data for a sophisticated validation. The availability of site-specific data can facilitate an improved calibration and verification of model, which could play a huge role in making the model robust and successful. There is a substantial gap between the theory of stochastic resistant bacterial growth dynamics and the theory of transport of antibiotic resistance in polluted aquatic environment, this study is an effort towards bridging this gap.Supplementary information.


Universities around the world, including Malaysia, have attracted many international students from different countries. Research has reported that acculturative stress resulting from international students’ attempts to adjust to the cultures of host countries is one of the most challenging issues that affects their lives in general and academic lives in particular.This study aims to examine the effectiveness of an educational intervention on acculturative stress among new postgraduate international students joining Malaysian public universities.A cluster randomized controlled trial design with Malaysian public universities as the unit of randomization will be used in this study. Public universities will be randomized in a 1:1 ratio to be either in the intervention  or control group (waiting list). Participants in the intervention group will receive 7 sessions in 9 hours delivered by an expert in psychology and the researcher. The control group will receive the intervention once the 3-month follow-up evaluation is completed.P<.05. The results of the study underlie the effectiveness of educational program in decreasing acculturative stress of new international students and enabling them to cope with a new environment. The results of this study will contribute to previous knowledge of acculturative stress, acculturation, and adjustment of international students. Furthermore, such results are expected to play a role in raising university policy makers’ awareness of their postgraduate international students’ acculturative stress issues and how they can help them avoid such stress and perform well in their academic life.The data will be analyzed using the generalized estimation equation with a confidence interval value of 95%; significant differences between and within groups are determined as We expect that the intervention group will score significantly lower than the wait-list group on the immediate and 3-month postintervention evaluation of acculturative stress and achieve a higher level of adjustment. Results will have implications for international students, policy makers at universities, the Malaysian Ministry of Higher Education, and future research.Clinical Trials Registry India CTRI/2018/01/011223; http://ctri.nic.in/Clinicaltrials/showallp.php?mid1= 21978&EncHid=&userName=Muhamad%20Hanafiah%20JuniPRR1-10.2196/12950 Previous research has identified the challenges and needs of international students during their higher education outside their native countries -6. The mA causality relationship between a high level of stress experienced by international students and deteriorating health conditions and even more serious health problems has been reported ,19. The Malaysia has attracted many international students, which has positioned it 11th in world ranking in hosting international students . The incAlthough the above studies in Malaysia are useful for investigation of international students’ acculturative stress, these studies have focused on describing stress merely from student perception and self-reported reflections, and they are scant and general in comparison with those studies carried out in other host countries . For insThe literature highlights several acculturation models developed by scholars over time, one of which is the bidirectional model of acculturation widely accepted in previous research . SpecifiThe second view emphasizes acculturation as a cultural change initiated by the conjunction of two or more autonomous cultural systems. Thus, acculturative change can result from direct cultural transmission or it can be a result of noncultural aspects, including modification of ecological or demographic characteristics by an impinging culture, and it may be delayed. Hence, acculturation can appear in the form of an internal adaption after the individual accepts alien cultural patterns or behaviors or it can emerge in the individual as a kind of reactive adaptation of traditional modes of life ,55. In aBased on the model of stress developed by Lazarus and Folkman , Berry  and BerrIn their systematic review of the theoretical frameworks used in research on international students’ sociocultural adaptation to host countries between 2012 and 2017, Sarmiento et al  found thBerry’s  acculturThis study aims to develop, implement, and evaluate the effectiveness of an educational intervention in reducing acculturative stress and improving adjustment among new international postgraduates joining Malaysian public universities. Specifically, the study aims to (1) describe participants’ sociodemographic characteristics, acculturation, acculturative stressors, and social support at the baseline; (2) determine the level of acculturative stress, adjustment , and intention to drop out of university among new international postgraduates at the baseline; and (3) determine the effect of the educational intervention on acculturative stress, adjustment, and intention to drop out between and within the control and intervention groups at the immediate and 3-month postintervention follow-up.In order to achieve the above research objectives, the study attempts to test the following null research hypotheses: (1) the intervention group will score significantly higher than the wait-list group on the immediate and 3-month postintervention evaluation of acculturative stress, adjustment, and intention to drop out measures (between groups) and (2) the acculturative stress, adjustment, and intention to drop out scores for the intervention group at the immediate and 3-month postintervention follow-up will be significantly higher than scores of the baseline or preintervention (within groups).The design, conduct, and reporting of the study will adhere to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) , ConsoliMalaysia is the context of this study since it is the host country for many international students coming from different countries. The target participants in this study will be those new international postgraduate students joining Malaysian public universities. The study includes 20 well-known public universities that attract large numbers of new international students who will be screened according to the inclusion criteria.This study is quantitative in nature and experimental in design. Specifically, the study employs a cluster randomized controlled trial (RCT) design. Public universities that will be eligible to participate in the study should satisfy the following criteria: acting or serving as the main campuses for international students in Malaysia and having more than 40 new international postgraduates in their first semester in Malaysia. For students, only new international postgraduates entering the selected universities in their first academic semester will be eligible to participate.For the purpose of eligibility assessment, the researcher will approach all public universities in Malaysia and invite only those universities that are eligible through formal invitation letters issued by the researcher’s university. The letters will be directed to the high authorities of the selected universities through emails seeking their permission for the researcher to conduct his study among international postgraduates. Moreover, the researcher will obtain lists of new international postgraduates joining these universities, including necessary information about the students including contact details, faculty, nationality, and academic program.Procedures for recruiting public universities will be based on several strategies that have been reported to be useful for maximizing research participation, including prenotifying universities of the study, providing opportunities for participation, visiting persons in charge at the universities, contacting potential participants using telephone and other contact methods, accessing research staff for further clarification on participation, and using dedicated research staff to manage the recruitment process. Specifically, an invitation letter will be directed to a university contact person  in each eligible university.Once the letters are sent, the above-mentioned persons in the universities will be contacted by telephone to confirm eligibility based on inclusion criteria and assess interest in participating in the study. This will be followed by several calls until the decision on informing international postgraduates of participation in the study is made and the researcher is informed of such decision. However, such decision, in some cases, will be made through meeting among academic and research staff and consultation with the management committee of a given university. At the end of this process, a confirmation email will be sent to each university, confirming and thanking them for agreement to participate in the study. Each participating university will be given hard and electronic copies of the information and invitation letters for the selected postgraduates in that university.Student recruitment will use a random sampling procedure to select students from the list of the new postgraduates for each selected university based on probability proportional to size method in order to reduce possible sampling bias. The respondent’s information and consent sheets will be given to the sampled students to get their agreement to participate. In order to maximize response rates, representatives of the international students will be requested to seek the selected postgraduates’ consent.Prior to randomization, the researcher will start collecting baseline data. This will be followed by assigning the universities as the unit of randomization. Universities will be randomized in order to reduce contamination between groups ,74. HoweThe total sample size needed will be calculated by applying the formula for two population means  as seen To factor in 20% attrition, if (n) is the sample size required as per formula and (d) is the dropout rate, adjusted sample size N new is obtained as N1=n/(1–d) . TherefoThe intervention is developed based on the acculturation and acculturative stress models integrating with a cognitive behavioral theory with the aim of focusing on cross-cultural sessions and social and behavioral issues in order to improve social and psychological adjustment and reduce acculturative stress and intention to drop out among international students, especially those who have just arrived at the new hosting environment. Most of these models highlight important variables, including acculturation experience, acculturation strategies, and social support, which have an effect on decreasing acculturative stress and a positive impact on the adjustment of students in host cultures ,85,86. TThe educational intervention was developed based on previous experimental studies on the implementation of intervention programs such as Excellence in Experiential Learning and Leadership developed by Mak et al , a cultuBerry’s model  for accuThe proposed program will cover a period of 7 sessions and will be conducted over a period of 9 hours in each university of the intervention group. Core modules of the Acculturative Stress Educational Intervention Program will include (1) introduction to the intervention program, (2) introduction to Malaysia and Malaysian culture and customs, (3) study abroad in Malaysia, (4) understanding acculturation, (5) acculturative stress, (6) coping strategies, and (7) adjustment strategies. In each session, participating international students will be offered different topics with experiential and interactive methods of delivery and materials. Websites; videos; demonstrations; in-group activities and interaction; interchanging of cultural background, experience, and intracultural communication; and critical incidents with analysis, reflection, and discussion on different cases and scenarios of international students’ experience with acculturative stress and adjustment will be used for enabling students to engage with the program.The aim of the intervention program is to transfer knowledge and gain skills, and the program will cover two parts: (1) a seminar session with PowerPoint (Microsoft Corp) presentation covering the 7 sessions, providing knowledge about different aspects of Malaysia and Malaysian culture, acculturation, and acculturation strategies and (2) knowledge and activities will be provided about acculturative stress, the sources for acculturative stress, adjustment, coping strategies, and social support. In this part, group exercises and activities will be used for enabling the students to engage with the program and gain knowledge and skills. Therefore, the intervention program will be delivered through seminar sessions, a PowerPoint presentation, videos, group activities, a printed booklet, and other printed materials that are available on the website during the intervention and study but can be accessed only by the intervention group using codes. Website, email, and WhatsApp are the primary methods to deliver the educational materials to the intervention group. These technological tools will be used to send short reminder messages and encourage participants to take part in the intervention and control groups. Participants in the study will be provided useful information about the topic and how to cope and adjust in the new environment. The second phase focuses on participants’ independent processing and implementing of information provided in the first phase as they begin to use the information in their personal lives and develop the skills necessary for enhancing their situation.This study will use a self-administered questionnaire consisting of 8 sections measuring the sociodemographic, independent, and dependent variables (study outcomes). The questionnaires will be completed at time 1, time 2, and time 3.Stress is defined as a state of a person that results from their interaction with their surroundings and is regarded as a threat to the well-being of the individual. Since this study focuses on international students’ acculturative stress as the primary outcome, we conceptualize acculturative stress as stress that a person suffers from reaction or response to stressful events of life relevant to the process of acculturation  as well This study uses the Acculturative Stress Scale for International Students (ASSIS) for measuring the primary outcome, international students’ acculturative stress. The measure is effective in examining international students’ cross-cultural adjustment, specifically the extent to which they find their involvement in everyday social situations as difficult due to cultural differences. ASSIS is widely used in studies on international students’ acculturative stress in Malaysia, and the scale is also highly reliable and valid as reported in early research ,101-106.Adjustment is known as a short-term and dynamic process experienced by the individual in relation to the new cultural environment; it also refers to the degree to which a certain university meets the demands or requirements of international students -109. AccFirst, sociocultural adjustment is a process in which individuals with different cultural backgrounds become skilled and able to engage in negotiating the host culture and effectively interacting with its local people . AssessmSecond, the Satisfaction with Life Scale (SWLS) is used to measure psychological adjustment and the cognitive component of subjective well-being  through Finally, the international students’ intention to drop out of the university is also a secondary outcome. This variable describes the international students’ intended behavior concerning their persistence of studies in the host country. It will be measured using this single item: How likely is it that you will withdraw from university (for whatever reason)? This question underlying the dependent variable of students’ intention to drop out is known to be the best predictor of students’ actual behavior and can be used in the absence of behavioral data ,121. In All participants will be asked to provide sociodemographic information including their gender, age, marital status, educational level, duration of stay in Malaysia, country of origin, prior traveling experience, native language, and self-reported proficiency of language. Self-reported proficiency will also be assessed using a composite score from the following questions: (1) What is your present level of English fluency? (2) How comfortable are you communicating in English? (3) How often do you communicate in English? This method of measuring and assessing English language proficiency has been documented previously by several researchers -125. ForThis study focuses on other independent variables that directly or indirect affect study outcomes: acculturation, social support, and acculturative stressors.Acculturation is a process of adaptation or adjustment to a new cultural context that imposes behavioral, cultural, and psychological changes on the individual in an attempt to be in contact with other individuals or even groups coming from diverse cultural backgrounds ,127. ParWhile acculturative stressors are those related to international students’ acculturative process at the biological, social, functional, cultural, and physical/environmental levels , other rPerceived social support is another dependent variable, defined as the individual’s impression of the available resources that are supportive for them during their experience of stress and symptoms experienced. Social support encompasses three different dimensions: family, friends, and significant others. Whereas family and friends are recognized as self-explanatory, significant others might be a supervisor, peer, colleague, or any other person with whom the stressed person has constant contact ,132,133.Prior to distributing the questionnaires to the targets of the study, the researcher conducted a pilot study as recommended by scholars to reduce ambiguity, validate the instrument, and evaluate the educational intervention program before conducting the study ,139. ForAfter the educational module and materials were designed, a group of experts in psychiatry and education  rated the content validity of the educational module and materials from the proposed educational intervention in terms of its accuracy, currency, and appropriateness of content using a 5-point scale: 1=extremely unsuitable to 5=extremely suitable. They will be also asked to identify areas of items that need to be improved, removed, or modified ; we willP value of less than .05. Analysis of the data will take into account clustering of students within universities. The study will use statistical analyses, including descriptive statistics of the respondents’ sociodemographic characteristics, chi-square test for nonparametric variables, and 2-tailed t test for continuous variables, to compare between the control and intervention groups on sociodemographic variables and primary and secondary outcome measures. Complete case analyses, intent-to-treat analyses, and completer status analyses will be conducted according to the initial allocation of universities to either intervention or control groups. In relation to the missing data, we will follow the guidelines for analyzing and reporting cluster RCTs with missing data established by Fiero et al  2017). The trial was registered at Clinical Trials Registry India [CTRI/2018/01/011223]. Approval for participation in the study was obtained from the authorities of the selected public universities. The study results will be reported at the cluster and individual levels, including information about the level of acculturative stress, adjustment, intention to drop out of the university, effectiveness of the intervention, estimated effect size and its precision, and ICCs for each primary and secondary outcome. Initial results are expected to be submitted for publication by the end of the first semester 2019/2020, and the paper will be presented at national and international conferences.This study investigates acculturative stress, adjustment, and intention to drop out among new international postgraduates joining Malaysian public universities. Specifically, the focus of the study is on the effectiveness of a proposed educational intervention in reducing new international postgraduates’ acculturative stress in the host country of Malaysia. Due to the fact that Malaysia is becoming one of the important host countries for postgraduates in the Asian region, it is important to investigate acculturative stress and adaptation of new international postgraduates to the culture and environment. The study is expected to contribute to previous studies and existing knowledge about the challenges and barriers faced by postgraduates entering higher education in Malaysia. As an experimental study, the results will inform us of the stressors that cause acculturative stress and strategies that can be used by international postgraduates to adapt to the new environment. Another contribution of this study is the implemented intervention program that can be used as a guide for further studies among new international students. These results will provide good insight into this important research topic and be of value for international postgraduates, academics, authorities, policy makers in higher education, and researchers interested in exploring issues and challenges among postgraduates in different higher educational contexts.This trial is expected to provide valuable insight into the implementation and effectiveness of educational intervention programs in reducing new international postgraduates’ acculturative stress and improving their adjustment to the host culture. The aim of designing the initial cluster RCT is also to provide information regarding the feasibility of potential future full-scale trials in order to optimize the intervention and design approach. Process evaluation outcomes will provide contextual information about implementation decisions of international students, researchers, and higher education stakeholders.


Diabetes mellitus is a chronic disease characterized by high social, economic and health burden, mostly due to the high incidence and morbidity of diabetes complications. Numerous studies have shown that optimizing metabolic control may reduce the risk of micro and macrovascular complications related to the disease, and the algorithms suggest that an appropriate and timely step of care intensification should be proposed after 3 months from the failure to achieve metabolic goals. Nonetheless, many population studies show that glycemic control in diabetic patients is often inadequate. The phenomenon of clinical inertia in diabetology, defined as the failure to start a therapy or its intensification/de-intensification when appropriate, has been studied for almost 20 years, and it is not limited to diabetes care, but also affects other specialties. In the present manuscript, we have documented the issue of inertia in its complexity, assessing its dimensions, its epidemiological weight, and its burden over the effectiveness of care. Our main goal is the identification of the causes of clinical inertia in diabetology, and the quantification of its social and health-related consequences through the adoption of appropriate indicators, in an effort to advance possible solutions and proposals to fight and possibly overcome clinical inertia, thus improving health outcomes and quality of care. The phenomenon of clinical inertia is defined as the failure to start a therapy or its intensification/non-intensification when appropriate, in diabetology.Despite the introduction of many glucose-lowering therapies that have proved to be efficacious in clinical trials, glycaemic control remains suboptimal in many patients globally, at all stages of treatment intensification.There is a clear need to encourage earlier intensification and address issues around therapeutic inertia to make health systems more sustainable and improve the quality of life of diabetic patients.Three classes of factors emerge as causes of clinical inertia: factors related to the healthcare professionals, to the patients and to the National Healthcare System.Through adequate training, clinicians can evaluate their own performances, identify critical areas and adopt suitable strategies, in a virtuous quality cycle able to increase knowledge, and modify behaviors.A structured and continuous educational activity, able to improve patients’ self-management abilities and responsibility, is fundamental.The creation of multi-professional teams able to work with a common and shared language, the planning of educational activities, the presence and implementation of specific and shared diagnostic-therapeutic paths, the creation of pathology registers, and the evaluation of performances with the use of indicators, are all plausibly effective organizational strategies to improve the clinical-care outcomes and reduce clinical inertia.BackgroundDiabetes mellitus is a chronic degenerative disease characterized by high risk of complications and high social, economic and health burden. To date it is a public health problem, as the incidence and prevalence of diabetes are constantly increasing worldwide, particularly in developing countries. Globally, in 2017, people with type 2 diabetes were about 425 million and it has been estimated that in 2045 there will be about 629 million diabetics .Numerous clinical studies have shown that optimizing metabolic control in patients with diabetes may reduce the risk of micro and macrovascular complications related to the disease. The UKPDS Post Trial Monitoring Study, comprising 5102 patients, showed that intensive control of glycated hemoglobin (HbA1c) from the time of diagnosis can reduce the risk of myocardial infarction, and mortality in general . These dApproximately 20 years have passed since the UKPDS report, and the current Guidelines and Recommendations adopted by the various international and national Scientific Societies advocate for personalized but stringent metabolic targets, also suggesting pharmacological intervention algorithms to guide and facilitate their achievement. In all these documents, the metabolic targets are always identified based on patient’s characteristics, and the algorithms expand this concept taking into account not only the glycemic target, but also comorbidities and cardio-nephro-vascular risk profile of the patients.Despite these excellent efforts, the phenomenon of clinical inertia  is an undeniable reality of diabetology Boxes  and 2, sprimordial prevention , able to reduce the percentage of people at risk of diabetes;primary prevention (IFG/IGT), capable of reducing the percentage of people with IGT or IFG who progress to diabetes by up to 30% –18:secondary prevention ;tertiary prevention that identifies complications in their early stages.The discrepancy between Guidelines and clinical practice is defined in the literature as “clinical inertia” or “therapeutic inertia”. Although these terms are usually employed in diabetology to indicate the lack of insulin initiation, according to Khunti and Davies  the concimplicit or explicit guidelines exist;the doctor is aware of the guidelines;the doctor believes that the guidelines apply to the patient;the doctor has the resources to apply the guidelines;all these conditions have been met, but the doctor does not apply the guidelines to the patient.It is therefore possible to define inertia as every instance in which no action is taken to promptly address each of these phases . To avoiWhen talking about therapeutic inertia, overtreatment  and “failure to de-intensify diabetes therapy” cannot be overlooked, since they constitute “the other face” of inertia and a large problem in diabetes care . OvertreInertia relating to diabetes management has been reported for over a decade with Shah and colleagues showing that less than half of a Canadian cohort of 2502 patients with type 2 diabetes and high HbA1c had received intensification of their treatment in 2005 . In 2011In 2012, the SOLVE study across North America, Europe and Asia , documented that the average HbA1c reached 8.9% before insulin was initiated, and nearly half the patients had HbA1c ≥ 9.0% despite treatment with combinations of oral hypoglycemic agents .In Europe, in 2013 and 2014, respectively, the GUIDANCE and PANORAMA studies reported that only 53.6% and 62.6% of patients achieved HbA1c ≤ 7% , 37. MorFinally, a retrospective cohort study investigating whether clinical inertia existed also in Japanese clinical practice, demonstrated that the estimated probability of intensifying treatment during the 12 months after recording HbA1c ≥ 7.0% (≥ 53.0 mmol/mol) was only 22.8%, and 27.5% after 17 months, evidence of clinical inertia in basal insulin-treated patients with type 2 diabetes in Japan .The adoption of more “moderate” personalized objectives taking into account age and clinical fragility  does not justify the persistently poor metabolic control, nor the considerable proportion of people with HbA1c levels higher than 9.0%, as reported in many international studies . The algBecause clinical practice is often linked to highly complex situations, it is fundamental to distinguish true from apparent inertia, especially when trying to assess its causes and to identify solutions or improvement strategies. Indeed, it is difficult to establish when a therapeutic decision is appropriate for a particular patient without having information on the underlying clinical condition or intermediate traits.Several studies clearly denounce the condition of “non-adherence” of the clinicians to the guidelines as a behavioural problem, because making therapeutic decisions is a complex task that involves a variety of cognitive processes. Indeed, uncertainty is one of the principal reasons that contribute to maintaining the status quo , 54. FurIn the present manuscript, we will deal with the issue of inertia in its complexity, assessing its dimensions and its burden over the effectiveness of care. Our main goal is the identification of the causes of clinical inertia in diabetology, and the quantification of its social and health-related consequences through the adoption of appropriate indicators. In conclusion, we will try to advance possible solutions and proposals to overcome clinical inertia or at least reduce it, thus improving health outcomes and quality of care Box .Health professionals-related barriers It is fundamental to identify the subjects at higher risk of delay in the intensification of the treatments , carried out in France, showed that the HbA1c targets set by French doctors for their patients reflected a good knowledge of type 2 diabetes care guidelines. Nevertheless, over two-thirds of patients failed to reach their intended goal, and this issue was attributed to the reluctance of the patients to intensify their treatments : a phenoThe patient’s perception of non-adherence may contribute to clinical inertia in intensifying oral hypoglycemic agents. According to an analysis carried out in the United States, in fact, the clinician is led to make changes in therapy , in patients who show greater compliance . It is aOther factors inherent to specific treatments used for type 2 diabetes can also contribute to clinical inertia by affecting compliance. These factors are mainly related to the side effects of a treatment , to the perceived complexity of treatment administration or to poor efficacy of treatment on glycemic control .Another reason why patients do not achieve their goals is called “educational inertia”, defined as the learning of clinically inaccurate or obsolete information by doctors and health professionals . This miThe evaluation of the barriers generated by the National Healthcare System cannot be generalized, because it variates according to the individual legislation and realities of each Country/Region. Among the numerous possible indicators of therapeutic inertia, a pivotal role is played by a poor coordination between planning and data exchange between the members of a health team, inadequate support technologies, the need for reimbursements, insurance coverage, and the great difference in regional norms . The burThe analysis of the literature does not clearly outline the indicators of therapeutic inertia, but it rather identifies the methods for measuring inertia itself. In particular, some authors measure it by evaluating the appropriateness of the care process in relation to reference standards  and therefore through a methodology that can be equated with the use of process indicators Table . In otheIn several cases the appropriateness of the care process has been evaluated as “failure to intensify the therapy in the presence of a clinical situation that made it necessary”. As part of diabetes therapy, intensification was intended as the addition of a new drug, the increase in the dosage of the existing therapy, or the initiation of insulin in the presence of non-target HBA1c values. As part of the intensification, failure to titrate the basal insulin after initiation was also included. This method of measuring inertia was also used for hypotensive and normolipemic therapy. In some instances, the time elapsed before intensification was evaluated, instead of the percentage of patients assigned to a specific care process.The consequences of therapeutic inertia have been evaluated more frequently on clinical care outcomes as a percentage of target subjects for glycemic, lipid and blood pressure parameters or as time elapsed before clinical optimization. The impact of therapeutic inertia on more severe outcomes such as life expectancy and progression of retinopathy was also assessed. For the economic aspect, the focus was on the impact of therapeutic inertia on the costs of complications.Therapeutic inertia, particularly in the management of chronic diseases such as diabetes, is a very complex phenomenon that recognizes multiple causes, largely dependent on the health professionals, but also on the patient and on national healthcare, with significant impact on health outcomes, welfare and social costs. In the past, the balance between hypoglycemia and strict metabolic control was difficult to assess with a limited therapeutic arsenal, and inertia was somewhat explained by fear of hypoglycemia. Nowadays, we have drugs with a very-low risk of hypoglycemia, able to minimize cardiovascular and renal burden. Therefore, we have entered an era where inertia is ethically unacceptable. In this manuscript we have analyzed the whole phenomenon, also trying to identify indicators to quantitatively and qualitatively measure inertia.One of the possible approaches identified in the literature is to adopt monitoring systems to assess the quality of care as a whole. Through an analysis of process and outcome indicators and above all through a comparison among diverse care realities, clinicians can evaluate their own performances, identify critical areas and adopt suitable strategies, in a virtuous quality cycle. An attentive evaluation of behaviors and results could be instrumental for a real evolution of the entire class of professionals. From a practical point of view, it is crucial to implement discussions and comparisons in the various local realities to analyze the results obtained in each clinical practice and to make corrective actions.Among the possible causes of therapeutic inertia, the main observations regarding health operators are the lack of familiarity with the new drugs, which are associated with a very-low risk of hypoglycemia and cardiovascular and renal events, the inadequate knowledge of Guidelines and up-to-date scientific evidences and the uncertainty about clinical objectives. From here it clearly emerges how training is fundamental to increase knowledge, but also for behavior modification: frontal readings, workshops and in-depth peer reviews, should be supplied alongside more accessible and interactive hands-on experiences combined with distance learning and evaluations.Ideally, the therapeutic decision making could be guided by the implementation of software or algorithms embedded in the informatics clinical folders, able to stratify patients according to previous cardiovascular events, presence of chronic kidney disease, age, fragilities, etc., therefore identifying the most appropriate decision aligned with the current recommendations and candidates for new therapies .Training courses at all levels of care  and specialization  involved in the treatment of diabetes and its complications are essential to reach the greatest number of potentially treatable patients with new drugs since the early stages of illness. This, in addition to raising awareness among professionals and creating a common cultural base, would reassure patients who would receive uniform information shared by various professionals. Large-scale training projects might also help to overcome cognitive biases, indeed, it is necessary to identify the mental maps underlying therapeutic choices in order to recognize and reduce the inappropriate behavior .Alongside a structured and continuous educational activity, able to improve patients’ self-management abilities and responsibility, all strategies able to increase adherence to therapy are important for antagonizing inertia: the simplification of therapeutic schemes, the preference for using drugs burdened by a lower impact on weight and hypoglycemic risk, the choice of therapy taking into account the patient’s preferences and lifestyle, sharing of the therapeutic objectives, the recognition of the cognitive, emotional and behavioral obstacles put in place by the patient as conscious and unconscious defenses to the therapy, an effective communication.The clinical skills and up-to-date scientific knowledge of the professional must therefore be associated with relational, communicative and pedagogical skills. Recently, the skills required for a chronicity professional were examined and described in a Core Competence Curriculum,  through Other effective organizational strategies to improve the clinical-care outcomes and reduce clinical inertia would be the creation of multi-professional teams able to work with a common and shared language, the planning of educational activities, the presence and implementation of specific and shared diagnostic-therapeutic paths, the creation of pathology registers, and the evaluation of performances with the use of indicators.In conclusion, only through a multifactorial approach able to affect all the elements at the basis of therapeutic inertia and through complex and complementary organizational, educational and training strategies, it will be possible to reduce this phenomenon and thus improve care outcomes, with a significant impact on health outcomes, on reduction of complications and health costs of diabetes.