Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments. For many years, electrocardiography (ECG) has been considered the gold standard in the field of cardiac testing. It is an essential tool for diagnosing various cardiac diseases. It is also commonly applied to derive another signal—heart rate variability (HRV), whose high diagnostic ability has been demonstrated in recent years ,2,3. In The above-mentioned noticeable interest in the PRV signal acquisition by wearable devices and its reliable processing to extract encoded information has prompted many research groups to sophisticated work on reducing artifacts in PPG. The vast majority of these studies has been summarized in recent review articles ,16,17,18The aim of this study is to identify the nature of artifacts caused by various types of factors under the conditions of precisely planned experiments as well as to propose and validate methods for artefact reduction based solely on the PPG signal. Their goal is to improve the accuracy of PRV derivation, paying simultaneously special attention to preserving the frequency content of PRV.A big role in the selection and effectiveness of artifact removal methods is knowledge about linking their character with specific conditions. For this reason, in many studies, the PPG signal was recorded during experiments inducing artifacts. Most often, they included: (1) baseline measurements ,21,22,23The main contribution of this study is to propose a series of methods for the reduction of artifacts affecting PPG based solely on this signal and focusing on the proper derivation of PRV, thus maintaining its frequency content, which is crucial for subsequent PRV analyses. The methods were tested and selected on the basis of carefully designed experiments with an increasing level of artifacts. In particular, the advantage of PPG amplitude demodulation in determining the duration of pulse cycles was demonstrated as well as the benefits of PRV post-processing, including statistical correction of outliers and prediction of PRV samples using the autoregressive (AR) model. We also demonstrated the possibility of quantifying the correctness of the extracted PRV against the reference HRV derived from the simultaneous ECG using dynamic time warping for data of unequal length or classical measures for the uniformly resampled signals.The organization of the paper is as follows. The first part of The study was conducted in accordance with the Declaration of Helsinki and ethical guidelines provided by the National Science Centre in Poland. All participants were informed about the study before, and all provided signed written consent.We collected data from 11 participants  aged between 22 and 34 . All participants were students or university employees. Only one person reported being diagnosed with left ventricular hypertrophy in the past. Other participants did not report any heart-related problems.We exploited three wearable devices simultaneously worn by all participants: (1) Samsung Galaxy Watch 3, (2) Polar H10, and (3) Empatica E4. The smartwatch Samsung Galaxy Watch 3 was placed on a non-dominant hand to gather the photoplethysmograph—PPG (with 25 Hz sampling rate) and accelerometer—ACC 50 Hz) signals. The chest strap Polar H10 was utilized to obtain the reference signals: electrocardiogram—ECG (130 Hz) and ACC (200 Hz). The E4 Manager desktop application was used to download the data stored in the Empatica E4, while for the acquisition of signals from the Samsung and Polar, we had developed our own mobile application working in real time with Bluetooth transfer. The Polar H4 appears to be a good and comfortable alternative to the Holter monitors when performing intense activities ,50. Addi Hz signaThe experiments were divided into three sessions : (1) a lNoMove. Participants were sitting still in a comfortable position on a chair, with hands resting on a table, normally breathing, and without talking. The room lighting (a lamp) was constant. With this setup, we intended to have the minimal number of artifacts in the signal.Light. Participants were sitting still as in the NoMove experiment. The room lighting was being switched on and off every ten seconds .Tap. While in a sitting position, the participants were tapping the Samsung Watch 3 with a finger, imitating a typical smartwatch usage. The rhythm of the tapping was determined with the metronome, i.e., with one-second periods during the first 150 s of the experiment and two-seconds periods during the remaining 150 s.Arm. While sitting, the participants were constantly raising and putting down a straight arm, possibly in one plane. The movement pace was guided with a sine wave displayed on a monitor—ten seconds for each direction of movement. The goal of this experiment was to analyze, i.a., the effect of changes in hydrostatic blood pressure on artifacts.Breath. While sitting, the participants were performing a controlled breathing, which was guided with a sine wave, i.e., five breaths per minute during the first 150 s of the experiment (very slow and deep breath), and 30 breaths per minute during the remaining 150 s (fast breathing).The laboratory session consisted of the following five activities:Each experiment lasted five minutes. The interval between experiments was 20 s, during which the participant should not move but relax. The entire laboratory session lasted about 30 min.The running session included three experiments, each with different pace, i.e., 1, 5, and 9 km/h; equivalent to about 40, 110, and 150 steps per minute, respectively. Each activity lasted five minutes, and the interval between them was one minute. The pace was selected based on the assumption that the lower and higher number of steps per minute will be significantly different from the recorded HR value, whereas the middle value will be similar to the observed HR value.The everyday life session covered at least seven hours of participants’ regular day as well as a minimum of five hours of their sleep.This paper focuses on analyzing the PPG and ECG signals from the laboratory session only, with the data from the treadmill and everyday life sessions postponed for use in further research. This is related to the goal of this study, which is the use of artifact reduction methods based solely on the PPG signal. Meanwhile, removing artifacts from the treadmill and everyday life sessions would require taking into account the ACC signals as well, since then, the PPGs contain even stronger motion artifacts than in the analyzed “dynamic” experiments.Processing of the PPG and ECG signals for PRV and HRV extraction has been divided into the following stages : pre-proIn general, the ECG obtained from the Polar H10 device was of a good quality. To reduce high-frequency noise and baseline wander, occurring especially at the beginning of the measurements, we applied the discrete wavelet transform (DWT)  but in aSymlet 4 wavelet was chosen to decompose the ECG signal because of its similarity to the QRS complex . The timestamps corresponding to the taps were manually obtained from such ACC signals. Since the Polar H10 does not provide timestamps for all samples, the timestamps from the Samsung Watch 3 were used to create a time vector for a particular session, and the samples within a given session were distributed equally across the time vector. Both signals, ECG and PPG, were then upsampled by interpolation with cubic splines to 1 kHz to enhance the HRV and PRV quantization levels, standardize, and facilitate the analysis between the signals .The alignment of the ECG and PPG signals was tuned manually by comparing and adjusting their waveforms, i.e., R-waves and systolic peaks.After computing the HRV and PRV waveforms, post-processing was applied to possibly increase their reliability, taking into account both a priori information about HRVs series and their local properties . First, r) for HRVs after their aligning were also computed. The next comparisons were made for the evenly sampled signals (before and after autoregressive correction) by calculating the relative root mean squared errors (RRMSE): n is the length of the HRVs vectors and i is the element index. Then, these results were averaged (mRRMSE) across the study participants for a particular experiment: N is the number of participants for each experiment, and Complete processing of the ECG and PPG signals returned HRVs and PRVs for the three methods of PPG processing after several stages: raw signals, HRVs and PRVs fixed according to physiological and statistical premises, data uniformly resampled and low-pass filtered, as well as PRVs with locally corrected spectra. Appropriate metrics should have been applied to assess the impact of the subsequent signal processing stages on the overall quality of final PRV. It is possible to compare two associated time series with a different number of samples using a dynamic time warping (DTW) technique that aligns the corresponding signals ,65. ThanFinally, the effect of post-processing on the PRV frequency content was examined by calculating the power spectral density (PSD) of the HRV as well as PRV signals—before and after pre-processing, and then comparing them. PSD was estimated for 5 min waveforms of data sampled at 1 Hz using the Welch method with a window length of 30 s and 25 samples overlapped.Arm experiments, which were characterized by hard-to-remove artifacts. By applying the DWT/IDWT to that signal, we managed to decrease the wandering effect, as shown in The raw PPG signals collected by the Samsung Watch 3 on Person 6 (P6) during the further analyzed experiments are shown in On the other hand, the ECG signal obtained with the Polar H10 device presented satisfactory quality, even when participants were performing intense body movement. To reduce the baseline wander and high-frequency content of the signal without affecting the QRS complex, we applied the DWT/IDWT denoising method. The comparison of HRV obtained from the ECG signal  and PRVs from the PPG signal  before and after signal processing is presented in NoMove and Light), PRVs obtained from the PPGs are very similar to HRVs obtained from the ECGs even without additional signal processing—similarity measures (RRMSE and DTW) have values lower than 3.6% and 4.9, respectively, and the Pearson correlation coefficient is close to 1. For experiments with movements (Tap and Arm), the PRV and HRV signals derived from the PPG and the ECG differ significantly. The proposed signal filtering helped to obtain more proper PRV from the PPG in such a case, although the Pearson correlation coefficient was still at the 0.62–0.77 level. For the Breath experiment, the similarity of the PRV and HRV from the PPG and ECG is rather satisfactory, achieving r > 0.85.More detailed results are available in Breath experiment, P7 and P8 in the Arm experiment, and P5, P8, P9, and P10 in the Tap experiment. Since there are only a few cases like this, and the majority of them are related to the experiments introducing movement, we conclude these are outliers caused by insufficient adhesion of the sensor to the skin (loosely worn device). Breath activity (reduction of the RRMSE by about 75%). Additionally, we performed a statistical paired Student’s t-test with the 5% significance level for the PRV data to check for differences in the performance of the PRV extraction algorithms. It stems that the SSF indicates more errors in the Tap and Arm experiments than other algorithms.We noticed that for some participants, the HRVs similarity measures were significantly lower in case of a few experiments, i.e., P8 (participant no. 8) and P9 in the To determine the computational complexity, we measured the execution time of each PRV algorithm applied to the same signal fragment lasting five minutes. The computation was repeated ten times. On average, the AMPD required 20.6 s to process the signal fragment and provide peaks, whereas it took only 0.08 s for the SSF and 0.01 for the PDA algorithm to compute the results.The primary aim of this study was to propose a procedure for reducing artifacts caused by various factors induced during the planned experiments based solely on the PPG signal. Particular attention was paid to improving the accuracy of PRV derived from the PPG compared to HRV derived from the concurrent ECG. It referred to maintaining its frequency content, as this is the main feature used in subsequent analyses. The PSDs presented in Experiments included in this study were of various types, some of them replicating the ones in papers mentioned in the Introduction. We focused our research on their potentially progressive impact on PPG quality to successively deal with new artifacts. Most of the articles published until now have included baseline measurements and controlled arm movements, typically lasting 1 min or less ,25,26,36Light. The exception is [In studies carried out by others, the ambient light was not changed, especially cyclically, compared to our experiments conducted as ption is , where cption is ,34, the The decision to temporarily set aside data related to the treadmill and everyday life in this first approach to the analysis of the collected experimental results is associated with the observed relationship of the induced artifacts to the rhythm of steps and their overlapping with the heartbeat, so that their reduction without an additional signal such as ACC  seems to be unlikely. On the other hand, due to the measurements of PPG on different parts of the body presented in the literature , differeSome previous publications ,50 compaArtifact removal with DWT/IDWT is one of the methods based on signal decomposition, which is often used interchangeably with empirical mode decomposition (EMD) . HoweverOne of the motion-related artifacts is the amplitude modulation of the PPG, which is undoubtedly a nonlinear process. There are some other papers that have dealt with this problem, for example in order to train a neural network  or for nThree algorithms were used to derive the PRV waveforms from the PPGs, which differ significantly in the way they determine the duration of pulse cycles. The first difference noticed is the computational effort: the most greedy is the AMPD, then SSF, and the fastest is PDA because of its simplicity. On the other hand, the AMPD algorithm is convenient to use due to the lack of hyper-parameters and no restrictions on the frequency of the tested signal. The most influential factor in the results between used algorithms was the selection of appropriate hyper-parameter values. As shown in Tap and Arm, but it sometimes worsened slightly in simple cases, such as Light. This is mainly due to the effect of the AR model, because when it operates on a very good quality PPG signal, then sometimes, the correct PRV values are replaced by the prediction outcomes. This leads to the conclusion that post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments. Nevertheless, it improves the PRV spectra compared to HRV  and are associated mainly with the SSF algorithm. For the rest of the experiments, no significant differences were noted, which shows that the AMPD, SSF, and PDA algorithms can be used interchangeably and do not significantly affect the resulting PRV for non-complex lab measurements. It is worth adding that the statistical tests marked in From the statistical tests performed regarding the methods for extracting PRV from PPG , the difAn important element of this work was the verification of the quality of PRVs extracted from PPG in comparison with reference HRVs derived from ECG. This comparison is problematic as the two original waveforms often have different lengths due to artifacts affecting the PPG analyzing algorithms. This means that it is difficult to apply benchmarks that require the same amount of data to be compared, such as Pearson’s correlation, which is very often used in research but usually ignored when assessing the raw PRVs and HRVs, or the signals are locally averaged over time to HRs and only then compared. The approaches used in this study that are known from the literature, i.e., DTW, allowed comparing two fully corresponding signals represented, however, by a different number of samples. Spline interpolation resulted in uniform sampling, which successfully solved this problem.Arm experiments are to be expected. It is different at night, when body movements are infrequent and happen irregularly.However, this study also has some limitations. The proposed methodology, focusing on using only the PPG signal, resolved the problem of deriving PRV in the presence of artifacts only to a certain extent. It should be taken into account that for measurements in everyday life scenarios, artifacts in PPG affecting the correctness of PRV determination will be visible even during the spontaneous arm or body movement, and these will increase in the case of more intense activities. These effects have been well illustrated in experiments with regular tapping on the smartwatch or arm movements . In everThe fact that the reduction of artifacts was restricted to PPG processing only, without the use of other sensors such as an accelerometer or gyroscope, resulted in the abandonment of the analysis of signals from the treadmill or everyday life, even though they were recorded at the experimental stage. Nevertheless, in this study, it was possible to check how much the quality of PRV can be improved without the use of additional signals, and thus, it will be useful in moving to the second stage of research on the currently omitted recordings.There are also some shortcomings related to the selection of hyper-parameter values, which could be conducted more precisely with a greater amount of work, as well as with the selection of wavelets in the DWT/IDWT method, where it is worth carefully checking which of them have the greatest impact on improving the results.Regarding the dataset, the signals collected from the Empatica E4 and during the treadmill and everyday life sessions were not analyzed in this article for reasons explained earlier. However, it is worth emphasizing here that those rich in content data are to be used in the next stage of our research for the additional optimization of some signal processing procedures, especially after supplementing them with methods that also employ ACC signals. Another limitation concerns the number of participants, which was just 11, including mostly male subjects of a similar age. Future experiments should involve more volunteers with greater diversity. The above facts prove that although the current solution improves the quality of PRV, it is still not quite sufficient for application in everyday life scenarios.r coefficient) indicate that PRV derived solely from the PPG without the use of other sensors appears to be a reasonable alternative to HRV at rest, but it is unreliable, even after post-processing, during exercise.The main contribution of this article is to show the effective chain of procedures for processing the PPG signal from a smartwatch to eliminate artifacts without the use of any additional sensors, e.g., an accelerometer. The PPG signal is usually of poor quality, which is mainly due to the susceptibility to motion artifacts. Therefore, their reduction allows improving the quality of the extracted PRV with attention to its frequency content, which is crucial in its further analyses. Discrete wavelet transform and its inverse, followed by amplitude demodulation, enable the proper preparation of the PPG signal for the use of PRV extraction algorithms. In the tests, the SSF gave statistically more errors than the PDA or AMPD algorithms. It was found that the correction of outliers based on local statistical measures is only important when PPG is of low quality, and it has no effect under good signal quality. The same conclusion applies to post-processing using the AR model. The biggest problem turned out to be the interference from tapping the smartwatch and vigorous hand movements, which introduced additional peaks and modulation of the PPG amplitude. This suggests that it would be beneficial to use other information to further reduce such artifacts. For example, accelerometer signals can be utilized for that purpose. Nevertheless, our proposed procedures coped well with the artifacts in the remaining cases examined. Summarizing, the accuracy measures gathered in Future work will mainly focus on the further studies on the impact of the PPG amplitude demodulation methods on the quality of the derived PRV. Additionally, the application of appropriate methods (including the nonlinear ones) exploiting the accelerometer signal will be investigated. This should allow for the efficient analysis of the results also from the treadmill and then from the everyday life records divided into nighttime and daytime activities.


Cystic fibrosis is the most common life-limiting recessive genetic disorder in Caucasian populations, characterized by the involvement of exocrine glands, causing multisystemic comorbidities. Since the first descriptions of pancreatic and pulmonary involvement in children, technological development and basic science research have allowed great advances in the diagnosis and treatment of cystic fibrosis. The great search for treatments that acted at the genetic level, despite not having found a cure for this disease, culminated in the creation of CFTR modulators, highly effective medications for certain groups of patients. However, there are still many obstacles behind the treatment of the disease to be discussed, given the wide variety of mutations and phenotypes involved and the difficulty of access that permeate these new therapies around the world. Cystic fibrosis (CF) is an autosomal recessive genetic disease that affects at least 100,000 people worldwide . This caThis review aims to provide a summary of developments on diagnostics, proteomics and translational advances including CFTR therapeutics. Recent directions on therapeutics are also discussed in order to present how the novel modulators may impact on life expectancy for almost all individuals with CF.Although there are archaic records of some of these clinical repercussions of CF—described as witchcraft —the charF508del is the most frequent, especially in Caucasian population  phenotypes. Individuals with CF may nevertheless carry different CFTR mutations on the two alleles, leading to thousands of possible combinations of CF genotypes. Noteworthy, clinical phenotypes and therapeutic responses may differ between individuals carrying the same CF genotypes , 17. In Numerous libraries of compounds have been screened by distinct high-throughput screening (HTS) methods and using several cell models. These experimental approaches contributed to the identification of molecules from different chemical series –21.CFTR modulators enhance or even restore the expression, function, and stability of a defective CFTR channel by distinct mechanisms. CFTR therapeutics have been classified into five main groups depending on their effects on CFTR mutations: potentiators, correctors, stabilizers, read-through agents, and amplifiers . To dateOnly after clarifying the pathophysiology and identifying the mutations that cause the disease, the arduous search for treatments that acted at the genetic level began . Up to tAlthough it is already possible to observe the great clinical impact of these drugs, there are still many economic, social, and clinical challenges to be faced to enable their large-scale use. One of the biggest obstacles is the cost of these medications, which are around €150–200,000 per patient per year, causing many countries to not fund the therapies through their health systems . FurtherThe impact of these discoveries over the years is evident, achieving advances not only in the pulmonary function of patients, but also in its quality and life expectancy . HoweverThe CF Foundation Registry was created in 1966 to track the health of people with cystic fibrosis who receive care at CF Foundation-accredited care centers and agree to share their data to inform continued quality improvement in treatment and specialized care. Each year, the CF Foundation analyzes these data and shares this information with the CF community through the Patient Registry Annual Data Report. Based on 2019 Registry data, the life expectancy of people with CF who are born between 2015 and 2019 is predicted to be 46 years. Data also show that of the babies who are born in 2019, half are predicted to live to be 48 years or older . The latIn conclusion, advances in diagnosis and treatment, as well as improvements and expansion in multidisciplinary care centers, have changed the situation for CF patients, resulting in a significant increase in life expectancy. Expanding the use of new drugs may result in improved mortality rates, life expectancy, and quality of life for CF patients.Therefore, we conclude that advances in basic science related with CF (genetics and proteomics) has been essential for the developments of recent achievements in the survival and quality of life in CF. Advances in CF are a real and unique example in translational medicine. It is essential to continue the efforts in the search for new treatment strategies for CF to minimize its effects over time, optimize treatment, increase the quality of life of patients, reduce inequalities in access between them, and extend their survival.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The present work was carried out with the support of the Coordination of Improvement of Personnel Higher Education - Brazil (CAPES) - Financing Code 001. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Whether families using big data-based digital payments will increase household healthcare expenditure is a subject that needs to be investigated in the era of big data. Based on the data from China Family Panel Studies (CFPS), 24,126 samples from 2014 to 2018 are used to examine the impact and mechanism of big data-based digital payments on household healthcare expenditure. The empirical results of this paper show that the use of digital payments by households can significantly increase household healthcare expenditure with the empowerment of big data. This research employs the instrumental variable method to verify and produce consistent estimation results in order to address potential endogeneity issues such as measurement error and missing variables. We learn via mechanism analysis that household adoption of big data-driven digital payments can remove credit limitations and build social capital, resulting in higher household health-care spending. We also perform a heterogeneity analysis. The findings reveal that when a family's traditional financial accessibility is high, the head of the household is young or middle-aged, and the head of the household has a higher level of education, digital payment will play a larger role in encouraging household healthcare expenditure. The conclusions of this paper are still solid after changing the indicators of household healthcare expenditure substituting the indicators of digital payment, and adjusting the variables. As a result, this article provides micro-evidence for the usage of digital payments by households to enhance healthcare spending.D12 G21 O30 O53 I12 Household healthcare expenditure have risen to become the third greatest family expense after housing and education, and the phenomenon has been attracting the attention of many academics. Economic strife , family Health problems are growing more serious as China's economy grows, and households are paying greater attention to medical treatment. The share of families working overtime and staying up late in Beijing is substantially higher than the general average level, according to the survey results of 2021 Chinese Urban Family Health Life Power White Paper. Families in Beijing and Shanghai are burdened by a greater economic load, while families in Zhengzhou are plagued by mental illness and financial strain. Families are increasingly confronted with acute mental sub-health and intense pressure in an era when individuals are more concerned about their health. Health security is becoming an increasingly significant aspect of family health management. For example, 65 percent of families agree that customizing commercial health insurance is vital. When compared to traditional payment methods, digital payment based on big data is more favorable to breaking down time and space barriers and lowering the threshold for consumers to enjoy financial services, allowing them to do so more conveniently and swiftly. Digital medicine has become an active choice to alleviate the shortage of medical resources and limited medical conditions, thanks to the empowerment of digital technologies like big data, cloud computing, and blockchain. Digital payment has a bigger role to play in the growth of digital healthcare. As a result, from the standpoint of big data, this research investigates the effect of big data-based digital payments on household healthcare expenditure.On the one hand, households' use of digital payment based on big data can broaden available financial services, increase sources of income, and alleviate credit constraints; on the other hand, we can accumulate social capital, broaden information channels, and reduce transaction costs, which may lead to an increase in household healthcare expenditure. However, existing research does not provide a clear response to the question of how digital payments based on big data affect household health care spending. The majority of studies look at the impact of family income , age andAs a result, utilizing the China Family Panel Studies (CFPS) database and 24,126 family sample data from 2014 to 2018, this research investigates the influence of digital payment on household healthcare expenditure from a big data viewpoint, yielding the following findings. First, this research shows that digital payment enabled by big data can boost household health care expenditures using OLS estimation. Second, we use the instrumental variable technique to verify and get consistent estimation results in order to address potential endogenous issues such as measurement errors and missing data. Third, the mechanism analysis reveals that digital payments enabled by household big data applications can reduce credit limitations and build social capital, resulting in an increase in household health-care spending. Fourth, the heterogeneity analysis reveals that the use of digital payment based on big data is more effective in encouraging household healthcare expenditure in families headed by middle-aged and young individuals with a high level of education and financial accessibility. The conclusions of this research are still solid after replacing the indicators of household healthcare expenditure, replacing the indicators of digital payment, and tail reduction. As a result, this article presents micro evidence encouraging families to adopt digital payment to boost their household healthcare expenditure.The following are the contributions of this paper. First, this paper adds to the body of knowledge about the factors that influence household healthcare expenditure , 6, 8, 9Second, this research adds to the body of knowledge about the influence of digital payments on household spending. The majority of existing work focuses on the possible benefits and risks of digital payment , the infThird, this study adds to the body of knowledge about the impact of Fintech on family economic behavior. The impact of consumer finance , 20 and This paper's structure is as follows: The theoretical analysis of digital payment on household healthcare expenditure under big data empowerment is summarized in Section Theoretical Analysis and Research Hypotheses; Section Data and Methods provides the data, variables, and models used in empirical analysis; Section Results presents the major result, which includes a mechanism and robustness analysis; Section Further Discussion: The Heterogeneity of Household Healthcare Expenditures presents the paper's further analysis; and Section Conclusion presents the conclusion.Financial institutions gain access to a large amount of household data after households use big data-based digital payments. On the one hand, financial institutions can use big data from households to understand households' consumption preferences and accurately deliver personalized information by mining households' consumption data in order to increase households' demand for financial services ; on the On the demand side of financial services, households' use of big data-based digital payments helps them increase their financial and entrepreneurial incomes , while hOn the supply side of financial services, on the one hand, financial institutions analyze households' credit, consumption, social, and other behaviors based on big data generated by digital payments  to accurIn conclusion, households' use of big data-based digital payments can boost household healthcare expenditure by increasing income sources from both the demand and supply sides. As a result, this paper proposes hypothesis below.H1: Households use big data-based digital payments to alleviate credit constraints and thus boost household healthcare expenditure.Digital platforms in China, such as WeChat and Alipay, offer not only digital payment services but also virtual social features. When households use big data-based digital payments, these digital platforms tend to use big data, based on a MapReduce-based paradigm, to perform social data analysis through the most popular framework, Apache Hadoop, to drive the creation and strengthening of social networks among different households on digital platforms to increase the viscosity of households' use of digital platforms to increase the viscosity of households' use of digital platforms to increase the viscosity of households' use of digital platforms . The forHouseholds' use of big data-based digital payments expands access to information. Households are connected to digital platforms and their social capital grows when they use big data-based digital payments. Following that, digital platforms like WeChat and Alipay can push more healthcare-related information to households, increasing their access to information. Households can purchase products such as health insurance and health consultation based on information pushed to the Internet by big data. Furthermore, digital payments make it easier for households to buy healthcare-related products and services by streamlining the transaction process by allowing payments and transfers to be made via the Internet , 38.Furthermore, households can reduce transaction costs in healthcare visits by using big data-based digital payments. When households build social capital through big data-based digital payments, they become more connected to subjects like family and friends, other households, and hospitals, giving them more opportunities and resources while lowering transaction costs , 39. HosIn conclusion, households' use of big data-based digital payments can reduce widening information channels, lower transaction costs, increase social capital, and thus boost household healthcare expenditure. Thus, this paper proposes hypothesis below.H2: households' use of big data-based digital payments can accumulate social capital, boosting household healthcare expenditure.Furthermore, the use of big data-based digital payments by households has a heterogeneous effect on household health care expenditures, according to our study. Financial literacy is influenced by socio-demographic characteristics  in households , and becThe use of big data-based digital payments is more prevalent in promoting household health care expenditures among households living in areas with greater financial accessibility on a regional level. Financial exclusion has long been a problem in rural areas of the country with distinct geographic characteristics , and theH3: The use of digital payments contributes more to household healthcare expenditure if the household has higher traditional financial accessibility, the household head is young or middle-aged, and the household head is more educated.Referring to Liu et al.  and ZhanExpenditureijt is household healthcare expenditure from the t th year and j th province and i th household. The household healthcare expenditure in the past year is obtained by logarithmic treatment. The core explanatory variable Digital paymentsijt is the big data-based digital payment, which is represented by the frequency of Internet business activity of the household head in the usual case. We focus mainly on the coefficient β0 to see the impact of digital payments on household healthcare expenditure. A positive coefficient indicates that big data-based digital payments boost household healthcare expenditure. If the coefficient is negative, it indicates that digital payments reduce household healthcare expenditure. Controlijt is the household head-level and household-level control variables, φi denote household fixed effects, and δt denote time fixed effects. Since households may be correlated in the same province, we cluster the standard errors to the provincial level.The explanatory variable The explanatory variable is household healthcare expenditure. Healthcare expenditures are divided into two categories in the CFPS data: household medical expenditure and household fitness expenditure. Specifically, the CFPS questionnaire addresses household healthcare costs by asking, “What medical expenditure were paid directly by your household in the last 12 months? Of these, fitness expenditure do not include expenses that have been repaid or are expected to be reimbursed, but they do include certain expenses borrowed from friends and relatives or previously paid.” “How much did your family pay for healthcare in the past 12 months? Healthcare expenditures include, among other things, the cost of physical activity and the purchase of related equipment and healthcare items.” It can be observed that the first question indicates the household's medical care spending, while the second question reflects the household's healthcare spending, which combined show the significance of healthcare to the household. To get the logarithmized result, we add up the medical and healthcare costs paid directly by households in the previous 12 months to get a total, then add 1 to the total and take the natural logarithm of the value after adding 1 to get the result.The frequency of our household head's Internet business activity is used as a proxy variable for the extent to which the household uses digital payments. The household head is the primary decision-maker in the household, so the behavior of the household head has a significant impact on household activities. Specifically, this variable is addressed in the CFPS questionnaire by the question, “In general, how often do you use the Internet network for business activities ?” To provide a more visual representation of the extent to which households use digital payments, we assign a value to the frequency of the household head's response. We use 6 if the home head utilizes the Internet virtually every day for work purposes. We use 5 if the household head uses it 3 to 4 times a week; 4 if the household head uses it 1–2 times a week; 3 if the household head uses it 2–3 times a month; 2 if the household head uses it once a month; 1 if the household head uses it once every few months; and 0 if the household head never uses digital payments.In terms of the selection of control variables, this paper draws on existing literature , 50–52 tSpecifically, the larger the household, the higher the household healthcare expenditure ; the larThe data we utilize comes from the China Family Panel Studies (CFPS) database, which is updated every 2 years. Because the most recent released household data from this survey are available up to 2018, and digital payments have been widely utilized since 2013, we selected to use data from 2014 to 2018. Referring to existing research methods , we procAfter the above processing, we obtained 24,126 samples between 2014 and 2018, and the descriptive statistical results of the relevant variables are shown in We present the results of estimating the impact of digital payments on household healthcare expenditure in Next, we observed the coefficients of the control variables and obtained the following results. The positive coefficient on marriage indicates that married heads of households value family protection and are more willing to spend on family healthcare; the coefficient on the number of household members is also positive, indicating that the larger the family size, the higher the household healthcare expenditure; and the coefficient on net household income is also significantly positive, indicating that households with higher income also value household healthcare, and increase expenditure more.Due to omitted variables, this study may have endogeneity issues. In other words, variables other than those already controlled for in this paper may be associated with the use of digital payments or with households' health care expenditure, but we do not control for these other variables. In addition to this, there may be some error between the CFPS survey data and the actual household sentiment data, so there is also the possibility of measurement error in this paper.Therefore, this paper uses the instrumental variable method to avoid the endogeneity problem. In this paper, the spherical distance from the capital city of the household's province to Hangzhou is taken as an instrumental variable. If Alipay originates in Hangzhou, the further the household is from Hangzhou, the less the use of digital payment. Therefore, we consider that the distance from the provincial capital city to Hangzhou is highly correlated with digital payment usage of households. In addition, the spherical distance is strongly exogenous and is not affected by digital payment use of households. The specific results are shown in The results show that the F-value of the weak instrumental variable test is >10 and passes the Wald test, indicating that there is no problem of weak instrumental variables and the instrumental variables we selected are valid. In addition, driving distance is an instrumental variable similar to spherical distance. Specifically, we use the driving distance from the capital city of the household's province to Hangzhou as the instrumental variable, and the results are still robust.Considering that the frequency of household heads using the Internet for commercial activities is not linear, in the robustness analysis, we choose to use whether household heads conduct commercial activities via the Internet as a proxy variable for digital payments in order to eliminate the effect of uneven frequency distribution. If the household head usually does not ever conduct business activities on the Internet, then we consider that the household head has not used digital payments and the core explanatory variable is equal to 0. Conversely, if the household head usually conducts business activities on the Internet, then we consider that the household head has used digital payments and the core explanatory variable is equal to 1. We know from Household healthcare expenditure can be divided into two components: household medical expenditure and household fitness expenditure, and both components can represent to some extent the degree to which households value their health. To conduct robustness tests, we split the household healthcare expenditure into two sub-dimensions, medical expenditure and fitness expenditure. In addition, we also replace the explanatory variables with dummy variables based on the median and mean of household healthcare expenditure. If the household healthcare expenditure exceeds the median or the mean, the explained variable equals 1. As seen in To prevent some extreme values and outliers from affecting the study, we next apply winsorizing to the sample data to correct for all variables and address possible measurement errors in the regression. We can see from the results in columns (1) to (3) in It can be found from the previous section that the use of big data-based digital payments by households significantly contributes to household healthcare expenditure. We further discuss and analyze the mechanism by which digital payments affect household healthcare expenditure using a mediating effects model, which is set up as follows.Inter are the mediating variables, and the Control is the control variable, which is consistent with the baseline regression. The core coefficient of the mediation model is β, if the coefficients in models (2) and (3) are significant, and pass the mediating effect test and are consistent with the theoretical hypothesis, then the mediating effect exists.The We use the household's loan balance to determine credit availability and thus measure the household's credit constraint status. If the household has any debt, then credit availability is equal to 1. If the household has no debt, then its credit availability is equal to 0. In the CFPS questionnaire, the relevant questions related to credit availability are: “Does your family have any outstanding bank loans to buy or build or renovate your house?”, “Apart from your mortgage, does your family have any other outstanding bank loans at present?” “Do you have any outstanding loans from organizations or individuals other than banks, such as private credit institutions, relatives, friends, acquaintances, etc., for the purchase or construction or renovation of your home?” and “Pending loans from friends, relatives and private individuals” Does your household owe money to organizations or individuals other than banks  for reasons other than borrowing for the purchase or construction of a house, and has it not been repaid?” If the household head answers “yes” to any of the above questions, the household is certified as having a loan month and their credit availability is 1.As we can see from column (2) in The reason is that households generate big data when they use digital payments, and these big data can empower both the supply side and demand measurement of financial services, reducing households' credit constraints and ultimately benefiting the increase of households' healthcare spending. Specifically, on the one hand, financial institutions use big data to analyze the payment behavior activities of households, which helps financial institutions accurately assess the risk status of households, thus the risk of financial institutions providing credit to households is reduced, and financial institutions have more incentives to provide loans to households. On the other hand, big data can provide households with precise information of various types, including not only information about credit services that are suitable for them, but also information about the household's financial income and employment. As a result, households are more likely to increase their applications for credit services, and their incomes are likely to increase. And higher-income households are more likely to have their credit applications approved. In summary, household use of big data-based digital payments promotes household healthcare spending by easing household credit constraints.We use household spending on human gifts to measure the social capital of the household. With respect to favor gift expenditure, the CFPS questionnaire addresses the question, “Including in-kind and cash, how many total favor gifts did your household give in the past 12 months?” In order to correct for bias, we talk about the expenditure on favors to be logarithmic. The specific regression results can be seen in In other words, when households use digital payments based on big data, digital platforms exert social attributes. The digital platform uses big data to mine families' usage data and promotes subjects on the digital platform to families, such as relatives, friends, and medical institutions, in order to strengthen their dependency on the digital platform while also increasing their social capital. On the one hand, increasing a family's social capital allows them to share knowledge with other people and expand their information channels. Families may be able to receive more healthcare information via their social networks, lowering the cost of looking for healthcare information and allowing households to increase healthcare spending. On the other hand, increasing households' social capital allows them to be more closely connected with medical institutions, banks, and other subjects, allowing medical institutions and banks to better understand households' situations and provide health care services to them, thereby increasing household health care expenditures.The previous analysis found that households' digital payment use increases households' healthcare spending by alleviating credit and information constraints. So, does this effect differ for households with different endowments? We examine the heterogeneous impact of digital payment use on households' health care expenditures separately from differences in financial availability, age of the household head, and education level of the household head.In this paper, we refer to Beck et al.  and use The household head has a substantial impact on the household's decision-making, and age is an essential aspect of the household head. Based on whether the household head is <60 years old, we split the sample into a young and middle-aged group and an older group. Young and middle-aged household heads usually have a higher level of awareness, recognize the advantages of digital payments, and have a stronger willingness to use them. In addition, young and middle-aged heads of households usually have a higher risk tolerance. They are also more open-minded, and they are more willing to try new things. Most older heads of household may have a relatively lower level of awareness and are more traditional and conservative in their thinking. They will be more concerned about the risks associated with digital payments, such as suffering losses due to fraud, than the benefits that come with them. So they may be reluctant to use digital payments.As seen in We separated the sample into two groups, less educated and more educated, depending on whether the household head has fewer than 6 years of school, and then regressed them, because around 43.37 percent of household heads had <6 years of education. The results in This article investigates the effect of big data-based digital payments on household healthcare expenditure from a big-data perspective. This research analyzes 24,126 sample data from 2014 to 2018 from the China Family Panel Studies (CFPS) to investigate the effect of big data-based digital payments on household healthcare expenditure. This research uses benchmark regression to examine the intermediary effect and heterogeneous influence of household digital payment on household health care expenditure, in order to improve knowledge of household health care expenditure and assist families in making better use of digital payment and maximizing its role in promoting a healthy lifestyle.The following are the findings of this study. First, big data-based digital payments can dramatically raise household healthcare expenditure; that is, the higher a family's mobile spending, the higher the household healthcare expenditure. Second, the mechanism analysis demonstrates that by reducing household credit limitations and collecting social capital, the household application of big data-based digital payments can enhance household health care expenditure. Third, in families headed by middle-aged and young individuals with high education and financial access, the use of digital payment has a larger role in promoting household healthcare expenditure. Fourth, the conclusions of this work are still robust when using the instrumental variable technique, substituting the indicators of household healthcare expenditure, replacing the indicators of digital payment, and shortening the tail of the variables.This paper, however, may have the following flaws. First, this article only looks at the influence of digital payments on household healthcare expenditure, but it ignores the impact on individual health outcomes, which is a finding that needs to be explored more in the future. Second, because China's digital payment includes a variety of types, including mobile payment, bank electronic payment, digital RMB payment, etc., this paper examines the impact of digital payment in general from the perspective of big data, rather than providing a more detailed classified discussion on digital payment. As a result, researching the effects of various digital payment methods is an area that has to be addressed in the future.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.CL and DL contributed to conception and design of the study. SH organized the database. SS performed the statistical analysis. YT wrote the first draft of the manuscript. CL, DL, and ZW wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A challenge for education and teaching in universities is posed by “Internet plus,” which has made numerous educational resources at universities richer and more accessible. The development of a professional Japanese teaching resource base should be centered on the needs and characteristics of Japanese teaching in universities, as well as establish and enhance the mechanism for resource base construction. All forms of instructional resources should also continuously be updated and improved in order to realize the diversified, systematic, open, and long-term development of Japanese instructional resources. In light of the current state of the information technology industry's rapid expansion, this essay examines a few issues with the building of a Japanese teaching resource database. A fundamental Japanese teaching resource database built on DNN was created as a result. The CNN technology is used in this study to create the Arduino device identification application. Utilizing gadgets in the learning process, learners can obtain learning resources using the Arduino device identification program before engaging in learning activities. The experimental findings also demonstrate that the precision rate and recall rate of the Japanese teaching resource database system developed in this study may achieve about 93 and 94 percent, respectively. Its performance is better than the conventional teaching resource system, and it can offer top-notch teaching resources for teaching fundamental Japanese. In recent years, universities have actively improved and reformed information teaching, exploring the establishment of more efficient and convenient teaching forms that keep pace with the times and promoting the sharing of high-quality instructional resources. The informatization process of education has achieved fruitful results and improved the efficiency of teaching and management . HoweverThis article analyzes the requirements, theoretical underpinnings, users, characteristics of the content, and issues that should be taken into consideration when building a basic Japanese teaching resource database to improve the level of application of contemporary information technology and the autonomy of mobile network learning for Japanese learners and to create more high-quality basic Japanese instructional resources. The findings demonstrate that this methodology has the potential to significantly and successfully increase the rate at which Japanese instructional resources are used.This paper develops the Arduino device identification program using CNN technology. Learners can use the Arduino device identification program to find learning materials related to devices during the learning process, then engage in learning. The creation of a Japanese teaching resource database has made it possible to publish, search for, and maintain educational materials. The platform implements the data integration function proposed in this paper and creates an operational model for seamless connection between various systems.The core purpose of a professional teaching resource database is to fully utilize the benefits of network resources, share resources, and establish a high-capacity, open, interactive, and information-based teaching service system with teaching content as the primary content that adapts to network development. In order to adapt to the information era, the creation of Japanese teaching resource databases is a new trend in school reform and a new type of Japanese teaching in universities . The devThe emerging network technology, represented by the Internet and mobile Internet, is driving the traditional teaching mode, learning resources, and learning methods towards digitalization, networking, and intelligence, and driving the traditional paper-based teaching materials towards integration and three-dimensional development. The construction of a professional teaching resource database is imminent, and many scholars have studied it at present.Wei designed an XML-based university teaching resource database-sharing platform system. In this system structure, XML is used as a data exchange format to solve the problem of shared data among heterogeneous systems, and the data of three existing systems in universities is integrated . Zhe et This paper highlights the necessity, theoretical foundation, users, content characteristics, and problems that should be taken into consideration in the construction of a basic Japanese teaching resource database from the perspective of concept discrimination, on the basis of a thorough analysis of concepts. It also examines a few issues to the creation of a database of Japanese teaching resources. On the basis of this, a fundamental DNN-based database of teaching resources for Japanese is built. In this study, CNN technology is used to create the Arduino device identification application. The Arduino device identification program enables learners to find learning materials about the devices used in the learning process before engaging in learning activities. The creation of a Japanese teaching resource database has made it possible to publish, search for, and manage educational materials. The platform implements the data integration function provided in this work and creates an operational model for seamless interaction between various systems.Deep learning has drawn the attention of more and more scholars in recent years as a brand-new discipline with rapid progress. Deep learning provides clear advantages in modeling and feature extraction over the shallow models of prior years. Deep learning, a novel technology in machine learning algorithm research, is descended from artificial neural network research . Each neConvolution networks put a more efficient feature learning component on the foundation of multi-layer deep neural networks, giving them a higher capability for feature learning. In particular, the original full connection layer is inserted before the convolution layer and pool layer are connected via local displacement. The pooling layer lowers the resolution of feature surfaces in order to acquire spatially invariant features. Each neuron in the pooling layer pools the local receptive field in order to perform the second feature extraction function. The most notable distinction between CNN and shallow NN is the partial connection between neurons of two neighboring layers in CNN as opposed to a full connection in shallow NN. CNN is a type of deep NN, though, and so it will not experience the insurmountable issues that conventional shallow NN does. The use of CNN for small text has a positive impact on gathering local information. When recognizing images, we can move via sliding windows from left to right and up to down to detect local information. The most popular technique for modifying the parameter weight in a CNN model is the gradient descent method, which minimizes the loss function. The precision of the network is continually increased through thousands, and even tens of thousands, of repeated training sessions, layer by layer.Under the current information background, Japanese teachers will use multimedia technology to assist in teaching, and the use of information platforms and Japanese teaching resource databases can effectively save teachers' teaching time. For example, saving teachers' time to write Japanese blackboard writing will increase the time to explain more useful Japanese knowledge points to students in disguise. Some schools and teachers have made many attempts at teaching of network resources before, but due to various reasons, relevant personnel have not correctly recognized the real significance and value of the method of network resources in teaching practice . This kiThe design of Japanese instructional resources should pay attention to language input and output from form to content, strengthen learners' skills training in Japanese listening and speaking by using mobile networks, and learn Japanese cultural knowledge through various resources . The JapThe overall construction goal of the Japanese teaching resource database is to fully utilize the benefits of the network, and provide all types of information including instructional resources for basic core courses of the Japanese major, Japanese exam question banks, professional qualification certification, and enterprise industry trends, etc., pay attention to the improvement of students' self-learning ability, and integrate employment information and interactive communication. The development of the fundamental Japanese teaching resource base was theoretically underpinned by constructivism learning theory. According to the constructivism philosophy, learning is a process of actively accumulating knowledge. Students with certain social and cultural backgrounds actively absorb and digest information by utilizing quality learning tools to create their cognitive structures. The creation of a platform interface is crucial for the development of a Japanese teaching resource database, which aims to actualize students' autonomy in learning. To create a learning platform with distinct educational qualities that can interact with one another, the resource pool should be clear and user-friendly, simple to use when looking for Japanese learning content, and clear when conducting searches. It should also be able to demonstrate the wisdom and teaching accomplishments of front-line Japanese teachers. The creation of high-quality digital basic Japanese instructional resources based on DNN is one of the goals of the construction of basic Japanese teaching resources, which also aims to raise the application level of modern information technology and foster independent learning among Japanese students. The foundational database of teaching materials in Japanese should be open, adaptable, interactive, useful, and scalable. The teaching resource database module can supply teachers and students with rich educational resources thanks to the aforementioned qualities, but it can also give them access to a larger learning environment. Students can fully utilize the teaching resource database's contents to increase their interest in studying Japanese. The properties of CNN are the basis for this paper's decision to use CNN to mine the data for an educational resource. For training and problem-solving in joint probability matrix decomposition models, implicit characteristic matrices that represent resources using CNN weight parameters are constructed. The primary function of the CNN framework is to extract implicit feature vectors from resources and mine their potential features from basic Japanese instructional materials. The identification of an Arduino device and model training process is shown in Cross entropy is a widely used loss function. This paper also uses cross entropy as the loss function. The formula of cross entropy is as follows:p represents the correct answer and q represents the predicted value. The smaller the value of cross entropy, the closer the two probability distributions are, which means the better the constructed NN model. For all m samples, use the forward propagation algorithm to calculate the activation values of all neurons; for the L layer network, calculation is as follows:When it is used as the loss function of NN, l starts from the L − 1 layer and decreases to 2, and calculates as follows:Compute the partial derivatives of the error function with respect to the network parameters of each layer as follows:Using the gradient descent method, execute as follows:Tj ∈ Rp×l by connecting the word vectors in the test question information, as shown in the following formula:Calculate the error in accordance with the error function, then stop, if the error is less than the predetermined threshold or the iteration times exceed the predetermined threshold. The majority of the model developed in this paper is composed of five layers, including two convolution layers and three full connection layers. The output layer is the last complete connection layer. In the convolution layer, ReLU is used as the activation function. Each convolution layer is followed by a pool layer, which is pooled and locally normalised. The output layer's full connection layer, which is the last layer, employs the Softmax regression model. The test question is represented as a dense digital matrix p is the dimension of the vector; l represents the number of word vectors. A test item context feature cij ∈ R is extracted by the j th shared weight Wcj ∈ Rp×ω, and its window size ω represents the number of surrounding words, which satisfies the following equation:Among them, bcj ∈ R is the deviation corresponding to Wcj; fun is a nonlinear excitation function. This paper uses ReLU to avoid the vanishing gradient problem. Then, the context feature vector cj ∈ R l − ω+1 with weight Wcj is constructed by the following formula:Among them, “⊗” represents the convolution operation, Information about platform users, subsystem connection configuration, XML data format, and teaching resource retrieval are the main components of the basic Japanese teaching resource database. Students' learning is centered on the knowledge points in instructional materials, which are also a key vehicle for reflecting the unique qualities of each student. The Arduino device identification program is used by students as they learn. It is based on CNN. Learners are informed of the names of the identified devices by the identification program, and learning materials, including text and video resources, are pushed to them that are pertinent to the devices. As instructional resources generally occupy more storage space, this paper adopts cloud storage mode. The main content stored in the resource table is the storage path of instructional resources uploaded by administrators and teachers. In addition to the storage path of the resource, the size of the resource, the query and comment permission of the resource are also saved in the table. The resource table is shown in Because the level of students' cognitive ability is constantly improving and changing with the deepening of study, only real-time feedback and updating of students' cognitive ability can truly realize the recommendation of resources that are compatible with the real level. The content of this basic Japanese teaching resource database is constantly updated and improved, carefully selected by Japanese teachers, and can be directly linked with Japanese learning websites and Japanese exhibitions, further enriching students' autonomous learning resources.The data in this study are randomly dispersed, with each data sample having the same likelihood of being either training data or testing data. The data are separated into training data sets and testing data sets in accordance with a specific proportion. The simulation experiment from this chapter is performed in MATLAB in this publication. The server side of this system's test environment consists of the following components: the Windows Server operating system, the SQL SERVER database management system, and the Net Framework. During the model's training phase, its parameters are iteratively adjusted and optimized. This process continues until either the output data value converges and tends to a stable state, the model training is complete, or the specified number of iterations has been reached. The classification of images can now be done by acquiring and using a classification model. In this study, the modeling impact of the model is tested using two widely used assessment criteria: the pattern accuracy rate and the RMSE (Root mean square error) index. Among these, the following formula illustrates the computation formula for the pattern accuracy index:t th experiment is the same as the real value M and T represent the number of experiments and the number of experiments, respectively. The formula for calculating the RMSE indicator is shown in the following formula:Among them, t th experimental estimate and the real attribute mode r in the j th question, respectively. The estimated value is closer to the real value if the model accuracy value is greater and the RMSE value is smaller; in other words, the smaller the error, the more accurate the modeling algorithm.Among them, Every step of the network structure in this paper will reduce the size of input images by convolution or pooling. This reduces the amount of computation needed to train and test a network, and at the same time makes the output more general. In this chapter, 10%, 30%, 50%, 70%, and 90% of the data sets are selected as test data sets, and the results of the algorithms are compared when the data sparseness is different. The experimental results of pattern recognition rate values of different methods under various test data sets are shown in It can be seen that compared with other methods, the accuracy rate of this method is larger and the RMSE value is smaller.Teachers' uploading resources is the embodiment of the data integration function provided by the platform. After logging in, teachers' users in the teachers' teaching management system can directly upload an instructional resource to the basic Japanese teaching resource management system. This function is the superior function embodiment of the data integration subsystem of the basic Japanese teaching resource pool sharing platform. The comparison results of the retrieval efficiency of different methods are shown in The test environment is an intranet with a bandwidth of 100 M, and the query operation of a single resource is performed to ensure that the system performance is unaffected by the network speed. The experimental findings demonstrate the great retrieval efficiency of this technology. In this chapter, various approaches are contrasted in order to better test the impact of the model put forth in this study. The experimental results of precision and recall are shown in In the adjusted network model of this paper, the image data is input from the bottom of the network, and after three convolution processing and corresponding pooling processing, the data is converted into a one-dimensional vector and input to the full connection layer, where finally, the test accuracy and error loss function values are output. It can be seen that the accuracy of this network model is high. This verifies the feasibility and effectiveness of the basic Japanese teaching resource database system based on DNN, thus achieving the expected effect of this paper. The experimental results of this chapter show that the precision rate and recall rate of the Japanese teaching resource database system constructed in this paper can reach about 93% and 94%, respectively. Its performance is superior to the traditional teaching resource system, and it can provide a high-quality instructional resource for basic Japanese teaching.The social expectations for talent in Japanese education have not been met by traditional teaching methods. The use of network resources in contemporary Japanese instruction must be taken into consideration in order to broaden students' knowledge and raise their actual Japanese proficiency. The need, theoretical underpinnings, users, characteristics of the content, and issues that should be taken into consideration when building a basic Japanese teaching resource database are all covered in this essay. In light of the current, rapid development of information technology, this paper examines some issues with the creation of a database of Japanese teaching resources. Based on this, a fundamental DNN-based database of teaching resources for Japanese is built. The creation of a Japanese teaching resource database has made it possible to publish, search for, and maintain educational resources. The platform creates the operational form of seamless connection between various systems and executes the data integration function suggested in this paper. The experimental findings indicate that the precision rate and recall rate of the Japanese teaching resource database system developed in this paper can reach about 93 percent and 94 percent, respectively. Its performance is better than the conventional teaching resource system, and it can deliver high-quality teaching materials for the teaching of fundamental Japanese. We can further explore the new Japanese teaching mode, professional talent cultivation mode, and self-learning ability cultivation mode based on the professional teaching resource database mode by continuously developing and perfecting the professional teaching resource database. Contribute appropriately to developing highly qualified and creative higher-vocational talent that satisfies market demand.


We established a MCDA methodology and result-interpretation approach that can assist in prioritizing alternatives to cope with AMR in the French dairy industry or in other livestock systems. Our MCDA framework showed that consumers and public health representatives preferred alternatives that consider the restriction of AMU, whereas farmers preferred to maintain baseline policy.To respond to the antimicrobial resistance (AMR) threat, public health entities implement policies aiming to reduce antimicrobial use (AMU) in livestock systems, in which policy success and sustainability might be subject to the social acceptability of the novel regulatory environment. Therefore, consistent methods that gather and synthesize preferences of stakeholder groups are needed during the policy design. The objective of this study was to present a methodology for evaluating the acceptability of potential strategies to reduce AMU using multi-criteria decision analysis (MCDA) using French dairy industry as a model. Preference-ranking organization methods for enrichment evaluations were applied to rank stakeholders’ acceptance of four different potential AMU reduction strategies: 1. Baseline AMU regulations in France; 2. Total interdiction of AMU; 3. Interdiction of prophylaxis and metaphylaxis AMU; and 4. Subsidies to reduce AMU by 25%. A total of 15 stakeholders (consumers,  Antimicrobial resistance (AMR) is one of the most serious threats for public health . By the In livestock systems, antimicrobials play a significant role in treating and preventing disease that cause animal suffering and decrease in productivity ,11,12. IThere is consensus that the AMR threat must be approached from a One Health perspective ,15. In tFarmers might face challenges to adopt policy against AMR when regulations do not consider their interests and needs. These challenges range from a lack of cost-benefit analyses of using antimicrobials or not, to farmers’ experience detecting and treating infectious diseases, and to welfare considerations of not using antimicrobials ,20. On tThe preference weights of different criteria that gauged the impact of the potential AMU policy scenarios were estimated based on stakeholders’ interviews with consumers, farmers, and public health representative groups. After the interview invitations were distributed to potential participants, a total of 10 responders from the consumers group , 2 from the farmers group , and 3 from the public health representatives group ) responded to the interview. A model of the interview is presented in The acceptability rankings of strategies to reduce AMU were performed within each stakeholder group using the overall performance score (phi) of each strategy, determined by criteria weighing. The consumers’ group score ranged from −0.09 to 0.15 . In thisA similar interpretation can be employed for preference ranking of stakeholders from the farmer B and theTo assess group acceptability, an overall decision map was developed considering the preferences of the three groups of stakeholders. Therefore, in The action profile tool on the VP software was used to represent graphically the impact of each strategy on each criterion considering all stakeholder groups . AccordiTo evaluate whether changes in criteria weighing could have an impact on the analysis, a sensitivity analysis was performed using the stability intervals window of the VP software. We aimed to present a MCDA framework including implementation and result interpretation that could be used to assess the social acceptability of a novel policy environment against AMR in the French dairy industry. Although we performed comparisons between strategies to reduce AMU to exemplify our MCDA approach, we did not intent to test the viability of these strategies in real settings. Moreover, we acknowledge the limitations of the strategy comparison due to the low response rate. Therefore, we encourage readers to use the information presented here as a framework to perform MCDA around the AMU policy development. The number of AMR microorganisms is rising at high levels in all parts of the world ,3,21. ThIn the dairy industry, antimicrobials are critical for the control and treatment of mastitis, which produces significant economic loses at the farm level worldwide, and other disorders that cause economic losses and animal suffering . In FranWhen public policies are formulated, the complexity of the disease, the diversity of farm environments, a farm’s microbial ecology, and the idiosyncrasy of farmers should be considered. Nonetheless, it is common to observe that AMU policy only considers one side of the problem, such as total restriction, which might affect other management practices and not be sustainable in the long-term. In this study, we proposed to participants four potential strategies that have not been fully implemented. However, these strategies synthesize potential scenarios of AMU policy and allowed us to develop our MCDA framework. Regarding these strategies, no country has yet established a regulation that totally bans AMU in animal agriculture. Nonetheless, special productive settings, such as voluntary organic dairy production, have been already implemented posing new challenges to dairy farmers regarding health and reproduction management . AdditioThe introduction of new regulations for controlling AMU affects production costs and product prices. For instance, in the USA, it has been estimated that the interdiction of prophylaxis AMU would cause loses of $1.8 billion USD for the beef industry  and, in To perform sustainability policy assessments, decision support tools are frequently used, in which the most common are risk analysis, cost-benefit analysis, system dynamics, and MCDA. In the context of AMR, risk analyses are useful before the implementation of policies for assessing and managing human and animal health risks associated with the development of resistance, including appropriate communication measures. Nonetheless, the implementation of risk analysis is costly and time consuming because it requires several steps to be completed, including hazard identification, risk assessment, risk management, and risk communication . In thisDespite the strategies designed by the French administration, there has been an increase in exposure to antimicrobials in dairy cows between 2017 and 2018 . TherefoThe PROMETHEE procedure is based on the pair-wise comparison of strategies. In this case, the deviation between the evaluations of two alternatives on one particular criterion is considered. Small deviations indicate weak preference or no preference, as the stakeholders will consider this deviation small or negligible. For larger deviations, higher preference levels are expected. With PROMETHEE, preference levels are measured on the degree of preference flow (phi) between −1 to 1, where −1 means no preference and 1 means total preference . AdditioFrom our MCDA approach, we determined that the first-ranked alternative for consumer and public health representative groups was related to the total interdiction of AMU . This agOther strategies presented in this study had lower scores and, therefore, were less preferred by consumers and public health representatives . In the The methodology presented in this study is highly flexible in incorporating more stakeholders and blocking stakeholders by demographic features, relevant criteria, and other productive contexts to take better decisions regarding AMU and AMR. Nonetheless, future research using our MCDA approach will require a larger number of participants, considerations of demographic bias, and a comparable number of stakeholders in each group to assure the external validity of conclusions made from MCDA in AMU policy design.This study was conducted from January to July 2020 with the general objective of creating a MCDA framework for assessing potential scenarios of AMU policy, allowing one to rank strategy preference from consumers, farmers, and public health representatives in the French dairy sector. Within the MCDA methodology, the PROMETHEE approach was used. The study started planning a general framework, which is necessary for carrying out the PROMETHEE technique.To begin this framework, we selected criteria which can be assessed under environmental, economic, social, and political dimensions of AMR and AMU. The environment dimension had two criteria: AMU assessed by the animal level of exposure to antimicrobials (ALEA) and AMR assessed by the fraction of AMR human infections attributable to livestock. The economic dimension had three criteria: production costs, farmer’s revenues, and product prices (meat and milk). The social dimension was assessed using animal welfare metrics, including herd culling rate and mortality. Finally, the political dimension criteria consisted of the number of policies and investments to fight AMR. Following this, we identified stakeholders in AMU policies comprising three groups of consumers of dairy products, dairy farmers, and public health representatives in the region of Occitanie, France. The proposed MCDA framework for criteria and stakeholder selection is depicted in We translated the issues related to AMR and AMU into four dimensions with measurable assessment criteria within them. In the environmental dimension, we used the ALEA indicator as an estimation of antimicrobial sales and animal exposure to antimicrobials . The ALEThe four strategies used in this study were created with the purpose of developing a MCDA approach. The baseline strategy corresponds to the current situation of antimicrobial use in French dairy farms. The current French strategy against AMR has led to a 37% reduction in the veterinary AMU between 2012 and 2016 . This stThe general framework depicted in To address the general problem of identifying, evaluating, and ranking different strategies to decrease AMR in dairy farms in France, three groups of stakeholders  were invited to participate in the weighing-criteria process. We aimed a convenience sample size of 10 responders from each stakeholder group. A stakeholder was defined as a person representing an organization or a group with direct responsibilities or with specific interests in AMU in dairy cattle .Criteria were identified to evaluate the effectiveness of AMU strategies. These criteria were nested into four dimensions: environmental, economic, social, and political. Defined criteria had scaled values derived from our experience and literature to calibrate the PROMETHEE model that might determine decision making of stakeholders under each strategy .After the criteria and the strategies to be evaluated have been defined, a total of 10 residents of Toulouse as part of the consumer group, 10 dairy farmers from the Occitanie region, and 10 public health representatives from the French Ministry of Agriculture, OIE, the National Veterinary School of Toulouse, the French National Agency for Food, and the Environmental and Occupational Health Safety Agency were invited to participate in the interviews using the interview model shown in The Visual PROMETHEE software  was used to perform MCDA and obtain comparative rankings of a set of alternatives. Overall numerical scores assigned by the participants were entered for each criterion on VP. Following this, results were visualized using a visual model performed by the GAIA  tool provided in VP. Group rankings of strategies yielding the best to worst alternatives were performed using the individual stakeholder’s values expressed via criteria weighing.Each stakeholder was asked to weight each criterion after observing the impact of each strategy on criteria . PrefereTo model the way the stakeholder perceives the criterion measurement scale, the PROMETHEE procedure requires associating a preference function with each criterion. The V-shape preference function was used to analyze quantitative criteria . This prA sensitivity analysis was performed using the Visual Stability Intervals tool in VP for assessing the impact of a stakeholder’s weighing preferences on their individual and group rankings. This analysis gives indications of the robustness of the results and can be generated for each stakeholder for all criteria.Our MCDA framework offers a methodology to assess potential strategies to reduce AMR development in the French dairy industry. In our approach, we observed that the most accepted strategy for consumers and public health representatives was total interdiction of AMU, whereas farmers opted to maintain the baseline strategy. Although the strategies considered in this study might not be plausible in the current dairy industry and due to the low number of responders, this study provides a MCDA framework that can be applied with an ample range of strategies for assessing their acceptability by relevant stakeholders in the food-animal supply.