Since December 2020 vaccines against the SARS-CoV-2 virus have been available. However, little is known regarding their effects on infections and on hospitalizations. To gain insight into this topic we empirically analyze the effects of the vaccinations against SARS-CoV-2 for European countries beginning 2021 to February 2022 with weekly data. We perform panel fixed effects estimations, GMM estimations and nonlinear penalized spline estimations. We find a statistically significant and positive relationship between the share of infections with the SARS-CoV-2 virus and the share of vaccinated people in nine estimations while one estimation output was insignificant. Regarding hospitalizations, six out of ten estimations yielded a statistically insignificant relationship, and three estimation results were weakly statistically significant with a negative coefficient and one indicated a statistically significant negative relation. Hence, there is empirical evidence for a positive relationship between infections and the share of vaccinated people whereas we find weak empirical evidence for a negative relation between vaccinations and hospitalizations. The implication of our analysis is that vaccinations alone cannot end the pandemic. Rather developing effective medicines should be seen as an additional measure.The online version contains supplementary material available at 10.1007/s43546-023-00445-0. The COVID-19 pandemic started in Wuhan/China in 2019 and is still going on. In the European Union (EU) vaccinations against the SARS-CoV-2 virus have begun in December 2020. Until March 2022, the 5 vaccines Comirnaty, Nuvaxovid, COVID-19 Vaccine Janssen, Spikevax and Vaxzevria have received authorization in the EU  estimation and penalized spline estimation.The rest of the paper is organized as follows. In the next section, we present some literature dealing with the effects of COVID-19 and we point out how our study helps to fill a gap in the literature. Section Meanwhile, there exists an exhaustive literature dealing with the effects of COVID-19. Hence, it is virtually impossible to give a complete survey of the existing studies. Therefore, we confine our presentation to some papers that provide insight into the topic.On the one hand, there are studies analyzing COVID-19 as regards its economic effects. For example, Agrawal and Bütikofer  illustraures. Bo  studies On the other hand, studies have been written analyzing which factors determine the evolution of the COVID-19 pandemic over time. Doroshenko  presentsLooking at the empirical literature dealing with the SARS-CoV-2 pandemic and the ways how to overcome it, one realizes that there does not exist a fully-fledged econometric study analyzing the effects of vaccinations on the infection rate and on the rate of hospitalization, to our knowledge. The study by Rzymski et al.  is similIn this section, we briefly look at the theoretical relationship between vaccinations, the control variables and the infection rate, on the one hand, and the rate of hospitalization, on the other hand. Furthermore, we give a brief description of the data used for the research.Firstly, we are interested in the effect of vaccinations against COVID-19 on the rate of infections. To do so we perform a panel data analysis where we regress the number of infections over the last 7 days (given as a rolling 7 day average) per 100 people in the total population, which we call the infection rate or briefly the infections.As a control variable we include lockdown measures that governments have taken to reduce the number of contacts between people and, consequently, the number of infections. As regards the efficacy of lockdown measures, Alfano and Ercolano  have foui et al. . We haveFurther, we allow for the seasons winter, spring, summer and autumn. We do so because infection rates are typically lower in warmer periods than in colder ones How have the vaccinations impacted COVID-19 related cases?ii)Have the vaccinations reduced the COVID-19 related hospitalization?Hence we will estimate two main general regression models given byi represents the individual countries in the panel, t is the time dimension and I represents our dependent variable of interest. We estimate two regression models; in the first model I is the number of COVID-19 related infections whereas in the second model it represents the number of hospitalizations due to COVID-19. We do this to answer the above-mentioned research questions. We resort to a one-way error component model such that This section presents the empirical methodology, the estimation and the discussion of our results. Our empirical estimation follows a panel data analysis of countries that are considered geographically as a part of Europe. We attempt to answer the following research questions with empirical data to understand the role of vaccinations and boosters in ending the COVID-19 pandemic: V is the number of people fully vaccinated per hundred of the population whilst Z represents all control variables in the model, D represents the seasonal dummies namely: winter, spring and summer. It should be noted that we omit the dummy for autumn from our estimation to avoid multicollinearity between the intercept and one of the dummy variables, a fact also known as the dummy variable trap  of equation  enter thSince the regressors exist in different units and, therefore, have different scales, the interpretation of the regression coefficients is sensitive to the scale of input variables Gelman . Hence, 3To begin our analysis, we compute the correlations that exist between all the variables. Table 9 in the Appendix reveals that the chance of multicollinearity is very small since none of the correlation coefficients exceeds 70%. This fulfills our assumption that each variable carries unique information and, therefore, the absence of multicollinearity is assured. A positive relationship is observed between infections and vaccinations and boosters, respectively. The same holds for the relation between hospitalizations and boosters, while we observe a negative correlation between hospitalizations and vaccinations. Further, a pairwise scatter plot (figure 1 in the appendix) does not explicitly reveal the directions of the relationship between the variables. To obtain deeper insight into the relations between those variables we next estimate several regression models.We begin by testing all variables for stationarity to avoid making inferences from spurious regression. We employ two panel stationarity tests, the Im, Pesaram and Shi (IPS) test and the Levin, Lin and Chu test (LLC) which are widely used in panel data studies. Both tests overwhelmingly reject the null hypothesis of a unit root in favour of the alternative hypothesis of stationarity. Table 6 in the Appendix provides a report of both tests. All variables are stationary irrespective of whether we consider a model with a constant or slope.In the first linear specification, we estimate  with theOur justification for using panel fixed effects stems from the fact that we consider a sample of European countries with individual characteristics, which can be cultural effects for instance or country-wise political effects. We believe these individual country characteristics could have an impact on the response variable. Hence, using the fixed effect model enables us to control for these individual country-wise characteristics.Secondly, we provide an econometric justification for using fixed effects over the alternative random effects using the so-called Hausman test according to Hausman’s Given the presence of a lagged regressor, one could argue that the model is dynamic which implies a possible correlation between the lags of the regressors and the error term. Secondly, in the presence of potential endogeneity arising as a result of correlation between any of the regressors and the error term, static estimators such as the fixed effects estimator could provide biased estimates. Hence we consider an alternative estimator suitable for dynamic models in the like of the generalized method of moments (GMM) where we instrument the model using lags of the regressors to circumvent a possible endogeneity bias. Exploiting moments conditions via GMM has proven to deal with the issue of endogeneity emanating from dynamic models more efficiently and has been largely used in most of the empirical econometrics literature. We specifically resort to the system GMM propounded by Blundell and Bond . Table 14It can be seen that, except for model 4 in table Our conjecture for that outcome is that the vaccinations cannot prevent infections and the spread of the SARS-CoV-2 virus in the community, as shown by Acharya et al. (2022) and by Singanayagam et al. . In addiAnother fact that can contribute to this result is the following. It turned out that vaccinated persons and previously infected individuals can be just as likely to transmit the virus as unvaccinated and not previously infected ones. Hence, infection-induced immunity is associated with protection against infection, but, not necessarily with the absence of the transmission of the virus  without any linearity assumptions. We rely on the estimated degrees of freedom (edf) of the penalized spline estimations to determine the true functional form of the relationships Below is the link to the electronic supplementary material.


Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin. Drinking coffee is a part of daily life for many people. The aroma and taste of coffee are mainly produced after high-temperature roasting. During the roasting process, the moisture of the green coffee beans is slowly released and a large amount of the chlorogenic acid originally contained in green coffee beans will gradually disappear during the process of high-temperature roasting. The taste of coffee also varies with the length of time it has been roasted. More recently, the electronic nose (e-nose)  has beenThe specific aroma of coffee is determined by the variety and the high-temperature roasting process. The roasting process variables are set one by one to improve the reproducibility of the quality of single-origin coffees. During roasting, the green coffee beans produce a large amount of carbon dioxide and moisture from heating which starts the caramel chemical reaction (the oxidation and browning reaction of sugar) and the Mena reaction (the browning reaction of amino acids and carbohydrates). These are key to the aroma that is generated. To maintain the quality of the roasted coffee beans, it is first necessary to trace the origin of the green beans because it is difficult to maintain a consistent quality of green coffee beans. Even the sunshine, rainfall, temperature, and related conditions of the same farm cannot be consistent every year, which indirectly affects the aromatic ingredients of the harvested green coffee beans. In addition, the temperature, humidity, and weather conditions during the roasting process affect the aroma. In order to obtain accurate data during the collection of gas data for the odor analysis, the system must be isolated from the ambient air. However, the roasted coffee beans need to emit carbon dioxide to release aromas to make the flavor more pronounced, and the data preprocessing and classifier type also affect the gas classification and identification results. In the past, the data preprocesses the specific signal data from the gas response signals as features and uses machine learning algorithms such as k-nearest neighbor (K-NN)  or suppoThe motivation behind this work is to prevent the incidents where unscrupulous merchants mislabel inferior coffee beans as high-end beans to deceive consumers. Due to their different origins, coffee producers usually classify their own products, especially with regard to the main aromas that affect coffee quality; this process ensures that consumers associate the specific aroma of coffee with its origin, which is beneficial for increasing market sales. Therefore, it is useful to develop a method to easily verify the origin of coffee beans to ensure quality. In this work, we utilized our developed electronic nose (e-nose) system to collect aroma sensory data and then used several machine learning methods for the recognition task. In particular, the method allows us to cumulatively extract coffee aroma fingerprints in order to build a scalable database containing a wider variety of coffee bean aromas, thereby improving the recognition accuracy of the system.To use the extracted aroma data of coffee beans and machine learning techniques to ensure the authenticity of the origin of coffee beans.To establish a method for creating individual coffee aroma signatures  to identify their provenance.The main contribution and goal of this work can be divided into the following:In this study, we utilized the above-mentioned approaches to create an e-nose system for the aroma recognition of freshly roasted coffee beans. The data processing method involves converting the signals detected by each gas sensor into an aroma dataset but it lacks aroma digital fingerprints. Our algorithms measure and quantitatively represent the raw data in the form of digital fingerprints. These digital fingerprints have the potential to be stored in an extensive coffee aroma database, similar to a comprehensive library of specific coffee bean aroma characteristics, for the traceability and reconfirmation of their origin.The rest of the paper is organized as follows. Several data fusion and machine learning algorithms for identifying aromas in coffee, beverages, and food have been discussed in the literature ranging from basic statistical techniques to sophisticated artificial-intelligence-based approaches. In particular, the emerging artificial intelligence method of deep learning has a better capability for learning hierarchical features than previous machine learning models. Deep-learning-based models provide an end-to-end learning framework to jointly learn feature representations while performing classification or recognition tasks. The use of deep learning (DL) has been applied to different industries such as deep learning in neural networks ,15, mediWakhid  used sevTo sum up, in recent years, with continuous advancements in artificial olfactory sensor technology and applications, electronic nose technology has made significant progress in its quality assessment and aroma identification of coffee, beverages, and food. However, little attention has been paid to developing electronic nose technology and efficient coffee aroma feature extraction methods to achieve traceability. Therefore, in this work, we developed a method to generate a digital aroma fingerprint for each type of tested coffee bean, which can be used as a biochemical marker of the aroma characteristics.The e-nose system uses olfactory gas sensors to mimic human olfactory cells as the basic unit of olfactory gas sensing. The major concept in e-nose system design is that each sensor in the olfactory sensor array has a different sensitivity to the same sample under test, which can make different gases produce different patterns in the detection of the olfactory sensor array. The system framework and configuration of the e-nose system we developed, which are consistent with the underlying principles of the electronic nose system, are described in detail below.Light to light-medium roasts can bring out the original characteristics of coffee. Therefore, for better differentiation, the coffees tested in this did should not exceed light-medium roasts. The system framework diagram is shown in The experimental process was divided into three stages see . The firThe aroma and taste of coffee are mainly produced after high-temperature roasting. During the roasting process, the moisture of the green coffee beans is slowly released and the weight is reduced. Meanwhile, the color deepens, the volume expands, and the aroma-containing oil is slowly released. The large amount of chlorogenic acid originally contained in the green coffee beans gradually disappears during the high-temperature roasting process, releasing delicious fruity acids, and its taste varies according to the duration of roasting. Coffee bean-producing areas have a unique smell. Different olfactory gas sensors form an array configuration. When odor molecules of different compositions encounter the olfactory sensor, each sensor has varying degrees of resistance. Different odors and intensities also cause the sensing array to generate different resistance data. We collected the front-end data of each single coffee odor and observed that the olfactory gas sensor corresponded to different coffee odors. The smell receptors of the system are activated when exposed to a coffee odor sample in the system’s sensors. Our e-nose deciphered the activation patterns of these receptors when exposed to the samples.In this work, our e-nose system employed a sensor array with the advantages of small size and fast response but also low power consumption and low costs. The sensors sensed volatile organic compounds and smells with an easy divergence under normal temperatures and pressures. The metal–semiconductor oxide material was used as the component and the measurements were carried out by reacting with the gas. The e-nose system contained eight olfactory gas sensors. The system function was connected to the data collector  through the olfactory sensor array. The acquisition of samples included collecting all sensor data every second. After the sample data set was collected, data pre-processing, feature extraction, model training, parameter adjustment, evaluation, and analysis were performed. The gas was captured by the sensor array to be processed by the Arduino board into a digital signal. The e-nose system is shown in Humidity affected the output of our developed e-nose system in several ways, as discussed below.First, as mentioned earlier, our system framework began with the process of roasting green coffee beans so the output was naturally affected by environmental factors including humidity. In previous studies, it was clear that the humidity and temperature of the environment affected the moisture content of the coffee beans and the gas sensor response. Rao  highlighSecond, on the sensor side, when the humidity and temperature in the environment increased, so did the mobility of the water molecules. This made it easier for water molecules to bind to the sensing area of the sensor. As a result, the adsorption capacity was decreased by the thin metal oxide layer on the target gas and sensor surface . DecreasThird, in the aspect of the aroma recognition of an e-nose, Brudzewski  mentioneTherefore, in this study, our system for performing coffee aroma recognition experiments was controlled at an appropriate humidity to maintain the accuracy of the output results.Although more than 850 volatile compounds have been identified in roasted and ground coffee , only abGas sensors are widely used to form sensor arrays for odor measurement. Gas sensor operation involves interactions between gas molecules and sensor coating materials that modulate the current through the sensor, which the sensor then detects and converts the modulation into a recordable electronic signal . For exaAs shown in In addition to ensuring the accuracy of the aroma identification, achieving the traceability of the coffee’s origin is also critical. In this work, we aimed to identify the origin of coffee using an e-nose system and machine learning modeling. Therefore, the learning model was developed using the collected sensor data as input to verify the type (origin) of the coffee based on what was reported on the label. As a possible solution to the double-check function of coffee bean origin traceability, we developed a method to establish a digital fingerprint for each tested coffee bean, which could be used to represent its aroma characteristics and be permanently stored in a database. The visual fingerprints extracted from the coffee aromas provided unique traces for identifying their origins.By capturing the e-nose signals and sending them to the computer, the signals are processed into patterns for specific gases, which are used as the basis of the calculations to identify and confirm the different aromas. Using a suction device to vacuum the odor collector, we took 50 g of freshly roasted coffee beans from the sample. After an appropriate time of exhaust, the data collector passed through the gas sensors TGS2600, TGS2602, TGS2610, TGS2611, TGS2620, MQ135, MQ3, and MQ7 to sample the data in sequence. The measurement time of each sample was 2000 s and the sample was measured again in 2000 s intervals. In this work, we mainly utilized the Principal Component Analysis (PCA) method for the feature extraction. As an efficient dimensionality reduction method, PCA projects the original variables onto new coordinates to obtain the values of a set of linearly uncorrelated variables called “principal components” (PCs), thereby eliminating the overlapping parts of the existing information. Thus, multivariate statistics were employed to discriminate and identify the origins of the coffee samples by performing Z-score normalization and PCA techniques. The original response data of the olfactory sensor were converted into 128 × 128 RGB images in units of 100 data sets. A sample digital fingerprint of coffee aroma is shown in According to the system framework described previously, the system model experiments with the extracted feature data and several machine learning methods. In the first-generation system experiments, we performed a manual extraction of gas to obtain the odor data. Before performing the aroma recognition experiments, the data of the coffee samples were divided into training data (80%) and test data (20%). Thus, in this experiment, for each coffee category, we used 144 samples as training data and 36 samples as test data, adding up to a total of 180 samples. A similar approach was used in previous studies. Given that the dataset was small enough to avoid having enough power to overfit the model, the number of samples used for the model was sufficient.In this experiment, we employed six machine learning techniques  to perform the category predictions of the coffee aromas. For each coffee category, two classes of predictions were used to compare two types of coffee . Nine types of coffee beans, including La Flor del, Kona, Mandheling, Sigri, Bunum Wo, Ipanema, Yirgacheffe, Dongshan, and Tolima were used in this experiment. The classification results were measured in terms of the accuracy achieved by each machine learning method. The accuracy value indicates how accurately the system performed its classifications. The accuracy value was obtained by comparing the correct classification results with the overall data. In Then, the experiment using nine-class predictions was conducted by combining all classes into one for the classification. In the nine-class prediction experiment, the accuracy results ranged from 66% to 97%, as shown in To evaluate the proposed coffee aroma recognition system, we employed an e-nose approach with different system configurations. Thus, in the second-generation system experiments, we used a vacuum pump to automatically collect coffee odors instead of manually pumping the gas as in the first-generation experiments. We used the BMP280 pressure sensor to detect the pressure changes in experiments 1 and 2. The changes in pressure affected how much concentration we pumped into the container. The vacuum pump could clearly extract the coffee smell more stably. However, the manual extraction of coffee gas could affect the amount of coffee odor obtained due to the different force and extraction rates, resulting in very obvious pressure changes. To verify the performance improvements, aroma recognition experiments were performed using an e-nose system with a vacuum pump. In this experiment, for each coffee category, the data for the coffee samples were divided into training data (80%) and test data (20%). We used 320 samples as the training data and 80 samples as the test data, for a total of 180 samples. The experimental results are shown in As mentioned previously, the accuracy of the aroma recognition was not the ultimate goal of this work. We also wanted to develop a method to enable the traceability of coffee origin and allow the quality of coffee to be assessed by identifying its aroma features. As a result, we developed a method to generate digital aroma fingerprints for each tested coffee bean, which can be used as the biochemical markers of the aroma signatures. The data extraction method was described in In this work, we combined techniques from olfactory sensor arrays and machine learning techniques to identify the aroma of freshly roasted single-origin coffees from different origins. The experimental results are discussed in detail below.(1) The experimental results shown in (2) It is worth mentioning that most existing e-nose applications have a common condition, that is, the e-nose is treated as a black box and has no sense of the chemical processes going on. Thus, experimental validation relies heavily on the sample sets. In reality, it is very important to be aware of the fact that a person could sometimes have a limited sample set and as a result, the initial results could be misleading . However(3) As mentioned earlier, the goal of this research work is to develop a method to create individual coffee aroma signatures  to identify their provenance. Meanwhile, the developed aroma fingerprint features of coffee beans could be used to ensure the authenticity of the stated origin of coffee beans using machine learning techniques. The resulting digital fingerprints of the tested coffee bean samples were shown in (4) In order to prevent unscrupulous merchants from mislabeling inferior coffee beans as high-priced beans to deceive consumers, the digital fingerprints extracted from the tested coffee aromas can be used as the features of coffee smells to re-identify the beans. Alternatively, these digital fingerprints can be incorporated into the biography  of the coffee bean products to enable traceability and reconfirm their origins. At the same time, these fingerprints could be stored in an aroma database, which is a comprehensive library of the aroma characteristics of specific coffee beans. The more aroma fingerprints are collected, the more accurate the identification of various coffee aromas.(5) Still, we should take a closer look at the technical limitations of electronic noses. In recent years, much work has been done to understand the principles of odorant receptors and the organization of the olfactory system. There is only one odorant receptor on each olfactory receptor cell and it can detect a limited number of substances. For complex odorants composed of multiple odorant molecules, several receptors are activated. The resulting receptor patterns determine our impressions of smells . TherefoIntegrating AI-powered e-nose and digital fingerprint generation methods are shown to be appropriate techniques for digitizing coffee aromas and identifying the authenticity of the origins of coffee beans. From a machine learning perspective, the results are promising, with e-nose systems showing high value in terms of sensing accuracy and the aroma fingerprinting features of coffee beans that can be used to ensure the authenticity of their origins. This means that the method enables us to build a scalable database containing a wide range of coffee aromas by storing the extracted coffee aroma data, thereby improving the recognition accuracy of the system. The more coffee varieties and samples collected, the better the identification accuracy of the system. In this study, our contributions are summarized as follows. We developed an e-nose system to successfully discriminate the aroma of freshly roasted coffee in different production regions. In the experiments, we employed the e-nose system to experiment with various machine learning models on several collected coffee beans such as coffee from Yirgacheffe and Kona. The performance results were good in terms of recognition accuracy. Second, we developed a method to create individual coffee aroma signatures  to identify their provenance. For our future work, in order to increase customer trust, the traceability of coffee will be combined with the digital fingerprints and blockchain technology services that have been developed. This is to ensure that the profile data of the coffee supply chain is authentic and to enhance the quality assurance of the coffee. Thus, customers can be assured of the origins of their coffee. This approach can help to ensure that farmers are paid fairly for their crops and maintain sustainable practices. On the other hand, combining a coffee aroma data cloud with edge computing is another promising approach to processing the collected coffee aroma data for real-time aroma discrimination.


This work is devoted to establishing a comparatively accurate classification model between symptoms, constitutions, and regimens for traditional Chinese medicine (TCM) constitution analysis to provide preliminary screening and decision support for clinical diagnosis. However, for the analysis of massive distributed medical data in a cloud platform, the traditional data mining methods have the problems of low mining efficiency and large memory consumption, and long tuning time, an association rules method for TCM constitution analysis (ARA-TCM) is proposed that based on FP-growth algorithm and the open-source distributed file system in Hadoop framework (HDFS) to make full use of its powerful parallel processing capability. Firstly, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans, so as to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct an evidence-based medical evaluation of TCM effects in constitution-related chronic disease health management. Secondly, experiments were applied on a self-built TCM clinical records database with a total of 30,071 entries and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. What is more, there are obvious promotions in the precision and recall rate compared with the Apriori algorithm, which indicates that the proposed method is suitable for the classification of TCM constitutions. This work is mainly focused on uncovering the rules of “disease symptoms constitution regimen” in TCM medical records, but tongue image and pulse signal are also very important to TCM constitution analysis. Therefore, this additional information should be considered into further studies to be more in line with the actual clinical needs. The diagnosis and treatment methods of TCM are the crystallization of the wisdom of the Chinese nation over thousands of years, and the precious wealth of the TCM industry and the world health system. Guided by the worldview and methodology of ancient Chinese philosophy, TCM carries the experience and theoretical knowledge of Chinese people's struggle against diseases, forming a holistic medical system through long-term practice and accumulating amount of clinical data. Meanwhile, TCM has played an extremely important role in the prevention and control of major infectious diseases in New China. Especially, it is proved once again that TCM is a treasure to protect people's health and lives to combat COVID-19 since 2019 [TCM has paid great attention to individual syndrome differentiation. In the theoretical system of TCM, the constitution of the Chinese people is divided into nine types, namely, balanced constitution (BC), yang-deficiency constitution (YADC), yin-deficiency constitution (YIDC), qi-deficiency constitution (QDC), qi-stagnation constitution (QSC), phlegm-dampness constitution (PDC), dampness-heat constitution (DHC), blood stasis constitution (BSC), and inherited special constitution (ISC). Each physique has a series of different health preserving and conditioning programs including diets, sports, meridians, and so on. Therefore, the conditioning program should be recommended according to the current specific physical state. For example, yang-deficiency constitution is mainly characterized by fear of cold, pale face, fatigue, and cold body. So, the patients with yang-deficiency constitution are usually advised to keep warm, such as warm diet, special acupoints  massage, more exercise, and so on. Yin-deficiency constitution is mainly characterized by fear of hot, dry mouth and throat, hot hands and feet, so the patients with yin-deficiency constitution are normally advised to pay special attention to their lungs and kidneys, such as eating more nourishing and refreshing food, and avoiding that makes ones feel hot and dry. Similarly, the other seven constitutions also have their own unique health conditioning programs.TCM constitution analysis takes people's constitution as the cognitive object and tried to grasp the overall elements of individual health and disease according to the characteristics of different constitutions to formulate a unique prevention and treatment principle, grasping the overall elements of individual health and disease differences from the characteristics of different constitution to formulate the principle of prevention and treatment. Therefore, its automation has become a research hotspot in modern Chinese medicine. TCM constitution automatic analysis is a very practical application for the exploration of TCM standardization. Modern Chinese medicine constitution analysis system combines Internet and artificial intelligence technologies, adopts deep learning model and backpropagation algorithm and data mining methods to extract TCM constitution features from the fused multimodal data that derived from tongue, face, plus diagnoses and so on, and dig out the correlations between syndrome, regimen, and constitution to realize TCM constitution automatic analysis.However, the TCM constitution is still faced with severe challenges due to the lack of knowledge model and objective evaluation criteria with normative diagnosis and treatment mode, which makes it difficult to adapt to the current “Internet +” diagnosis and treatment mode. For example, the existing TCM constitution analysis systems on the market only provide a personal report with some simple food therapy, exercise, and recuperation information, which is lack of the correction between symptoms and prescriptions. Fortunately, modern computer technology brings light on new research ways of upgrading TCM constitution analysis. One effective method that has been widely used in the TCM constitution field is data mining, which is used to extract useful, hidden, and unknown inner connections and knowledge from a large number of TCM clinical records stored in an electronic database to discover the rules implicit in data and predict future trends of TCM diagnosis and treatment , 3.Data mining techniques  have been widely used in the field of health care, such as heart disease diagnosis , DNA patIn recent years, association rule algorithms have also been used in the field of TCM and achieved many gratifying research results. For instance, Yadong et al.  used AprIn this work, a TCM constitution automatic analysis method (ARA-TCM) based on FP-growth algorithm in the Hadoop framework is proposed, which is used to establish a comparatively accurate classification model between the 9 kinds of constitutions and their typical clinical symptoms and treatment plans. Then the experiments were applied to a self-built medical database with 30,071 pieces of records and the results have demonstrated several advantages as follows: Firstly, dozens of medical laws for each physical constitution have been excavated to build an association rule library of “disease symptoms constitution regimen” to provide preliminary screening and decision support for TCM clinical health management. Secondly, new treatment rules for chronic disease and new combinations of TCM herbs appeared in the mined rules, which will have a certain promoting effect on the study of Chinese medicine tablets. Finally, the proposed method has shortened the overall cluster working response time and has higher computational efficiency, precision, and recall rates compared with the traditional Apriori and FP-growth algorithms at the same time.The rest of the article is organized as follows: Compared with Apriori algorithm, FP-growth algorithm uses a clever and compact data structure called FP-tree to extract frequent item sets directly and reduce the time and operation cost, avoiding scanning the whole database and generating the candidate frequent item queue constantly. It only needs to scan the whole transaction database twice to get a frequent 1-item set and create an FP-tree, respectively. The main idea of FP-growth algorithm is to map every transaction in the database into an FP- tree and retain the association information between the item sets at the same time. The primary task of data mining with FP-growth algorithm is to construct a header table to construct the conditional FP-tree. The compressed database is divided into a set of conditional databases to be associated with a frequent item or a pattern segment. After creating the root node, each transaction in the database is processed as follows to generate an FP-tree to mine the frequent item set recursively. The whole framework of FP-growth algorithm is shown in Flist.Build a frequent item list Flist.At first, scan the entire database and count the frequency of each data item to filter out the data items with a lower frequency than the minimum support threshold, and then sort all the rest of the data items in the order of decreasing frequency to obtain Create the FP-tree.Flist, and get rid of the transaction that corresponds to the data item with a low frequency less than the minimum support degree; Secondly, insert the sorted transactions into the prefix tree and record the frequent number of the shared tree nodes with a common prefix, which is used to represent the number of occurrences of each node. To facilitate tree traversal, the header table is established and linked to the node where the data item appears in the tree for the first time [Firstly, sort each transaction according to the position of data item in rst time .The process of constructing an FP-tree is described as follows:Then the process of frequent item set mining is realized through the FP-growth algorithm in the following steps: (i) Generate a conditional pattern base for a given suffix item. Firstly, find the set of leaf nodes IDs of the suffix item from the constructed FP-tree, and then start from each leaf node in the set, search the path from the leaf node to the root node recursively. All the combined paths of the set form the conditional pattern base of the given suffix. Especially, in the process of building the conditional pattern base of the given suffix, it is necessary to update the count that refers to the suffix item to accommodate the case of multiple leaf nodes superimposed. (ii) Frequency is determined based on the updated count of the item of conditional pattern base. FP-growth algorithm is a memory-based frequent pattern mining algorithm. Although FP-growth algorithm saves the scanning time and operational costs compared with the Apriori algorithm, it also is ineffective and memory-consuming when coping with massive clinical data to dig out its frequent patterns. What is more, the traditional multipoint interface (MPI) parallel programming model is facing the problems of node and network failures communication at the same time . For theHadoop is a famous distributed system infrastructure that allows users to develop distributed applications without knowing their underlying details and takes full advantage of the great power of clusters for reliable, efficient, and scalable computing and storage. Hadoop has been widely used in big data processing because of its natural advantages in data extraction, transformation, and loading (ETL). The distributed architecture of Hadoop keeps the big data processing engine as close to storage as possible, for example, ETL like batch operations can be directly oriented to storage.Hadoop consists of many elements. At the lowest bottom is an open-source Hadoop distributed file system (HDFS), which stores files on all storage nodes in the Hadoop cluster. On top of HDFS is the MapReduce engine, which consists of JobTrackers and TaskTrackers two parts. Hadoop's MapReduce is a parallel computing framework that consists of two phases: Map and Reduce , 23. In MapReduce includes a job control module, programming model, and data processing engine. Here only the job control module is described in the following text. As the main control node of MapReduce, Master is mainly used to manage and execute scheduling tasks. As the slave node of Master, Worker is used to managing the computing tasks performed on each node. The Master node NameNode used for data storage and parallel computing can be set on either one node or different nodes . The WorIn this article, frequent item sets for a transaction are obtained by accumulating the item sets in this TCM constitution database which is deployed on a distributed server based on Hadoop framework. The basic idea of clinical data decomposition is divide-and-conquer, that is, the whole clinical data for TCM constitution is divided into several subparts and stored in different server nodes. Fortunately, this step is implemented by Hadoop automatically and what needs to do is only just set the copy of transaction data in the database to the framework of the open-source HDFS. And then Hadoop framework will classify the clinical data and divide it into several blocks automatically. For each block, a copy is saved when the block is stored in an assigned node to avoid node loss caused by fault damage files , 26.Since it is necessary to scan the entire transaction database, the computation speed of traditional association rules algorithms, whether Apriori or FP-growth, will decrease to a certain extent in the face of large amounts of distributed clinical data. Even though some researchers have put forward the method of multithreading to relieve the pressure of calculating experimental data, the memory of a single computer will become the bottleneck to limit the data volume to a certain level that FP-growth algorithm can cope with. It is well known that the most prominent advantage of Hadoop framework based on a cloud platform is its parallel processing capability. Therefore, in order to make the association rules algorithms can be better adapt to the requirements of large-scale data processing problems in various fields and take full use of big data technology, the distributed parallel processing capability of Hadoop is used to promote the accuracy and recall rates and reduce the time consumption in the process of data processing to a large extent. The whole framework of distributed FP-growth algorithm based on HDFS is shown in Step 1: Database sharding and parallel computingThe local transaction database on each node of HDFS needs to be scanned twice in the improved FP-growth algorithm. In order to promote the scanning efficiency, the database is decomposed into several data sets, which are processed in parallel to obtain the local frequent 1-item set list for each data set at the first time scan. Here, the horizontal partitioning strategy is used for data set decomposition.Step 2: Generating the Local FP-treeThe transactions in the local data sets are sorted for each node according to the frequent 1-item set. And then, a local FP-tree is constructed and mined. In the process of mining the frequent item set for a given transaction, it is not necessary to mine the data and information stored in other nodes. Therefore, there is no need to keep communication between nodes to save resources and time. At the end of this step, local frequent items are gained, especially, those local frequent items that do not meet the minimum support threshold will not be removed in this procedure.Step 3: Parallel FP-growthEach node of HDFS counts the frequency of data items of all data sets that are located in the right node and obtains the local frequent item set counts. Then the global frequency of each item is can be calculated through communication between each node and the nonfrequent items are deleted according to the minimum support threshold. Eventually, the global 1-item set of the whole database is acquired.Step 4: Aggregating to generate global FP-treeAfter the above steps, local frequent item sets will be transmitted to the Master node. Local FP-trees are traversed iteratively instead of generating a global FP-tree, and then a large number of conditional FP-trees are generated according to the frequent 1- item sets. In the end, the global frequent item sets can be gained through deleting those frequent items whose count and confidence of the support degree are not satisfied with the minimum threshold.The basic idea of the improved FP-growth method is to search the local frequent item sets in a local FP-tree that is constructed in a local node of HDFS parallelly instead of generating a global FP-tree to mine the clinical data. The specific steps of the proposed algorithm are described in brief as follows:In this article, the traditional association rules algorithm FP-growth in big data mining has been combined with the open-source HDFS in machine learning to promote the computation speed as far as possible to meet the requirements of solving the problem of TCM constitution automatic analysis. In the end, a classification model for TCM constitution was established to observe the accuracy of category prediction to provide preliminary screening and clinical decision support for physicians.K terms was proposed in this article, which is described in flow chart The proposed ARA-TCM algorithm for constitution analysis can be divided into four processes according to the execution order and function subject mentioned in abovementioned section. In the MapReduce programming framework of the Hadoop cluster, the proposed method is implemented in four steps: acquiring table chain, constructing local FP-tree, mining local frequent item sets, and searching the global frequent item set. Firstly, the data blocks in HDFS are read in parallel and the occurrence times of data items are counted to retain those data items that meet the minimum support threshold. Sort the remained data items in descending order of their counts of occurrences and insert the sorted data items into a chain table with a header. Secondly, each storage node in HDFS has a sorted header chain, which is easy to set up Map and Reduce functions to create a local FP-tree. Fortunately, it is the same way to build a local FP-tree through FP-growth algorithm by creating a chain table in Hadoop framework. Thirdly, mining a local frequent item sets of a local FP-tree is also similar to the way to mine a global FP-tree. Both of them delete the infrequent items according to the support and confidence degrees. Finally, the global frequency item sets are calculated through communication between each distributed node. What is more, in order to realize the parallel MapReduce model on the cloud platform to take full advantage of the high capability of Hadoop in processing big data, a method for producing frequent sets of As it is well known that in the theories of TCM, dietary regimens vary from person to person. That is, different symptoms collection corresponds to the different constitutions which has their unique health and conditioning schemes. Furthermore, dietary regimens of different people with the same constitution also can be different due to their individual differences such as ages, genders, health conditions, and so on. In other words, the relationships between individual symptoms and conditioning programs are so close that the dietary regimen should be carried out according to the individual symptoms. Therefore, training on the relationship between constitution and symptoms should be followed by training on constitution and health conditioning. This specific training process is mainly divided into two stages. The first stage is to perform two scans of the constitution data set to dig out the relationships of constitutions in relation to symptoms and health conditions, respectively, and the second stage is to link the symptoms, constitution, and dietary regimen.The specific algorithms for training on the relationships of constitutions in relation to symptoms and health conditions, respectively, are described as follows Algorithms 1 and 2, which are used to generate and mine the local frequent item sets, respectively.T);Step 1: Input ;I in the physical characteristic data training set T;Step 3: In the Map stage, output  for each symptom Ii and its count number Ni; at the end of this step, the occurrence times of data items are counted.Step 4: In the Reduce phase, the sum of initialized frequent physical symptoms is 0. When value.hasNext counts, the sum is increased by 1. And then output  for each symptom T);Step 5: Input ;L is equal to the sum of all the items of physical symptoms; And then data training set T is sorted in descending order Sort; Search the groupings of rightmost item sets of frequent item sets, if Qi = seachGroup(t.get(t.length)- 1), print ;Step 7: In the Map stage, Step 8: In the Reduce phase, when FP is equal to LocalFPGrowth , output the occurrence count of the local frequent item ;Step 9: In the processing stage, calculate the sum of all the item sets of physical symptoms. If FPcount is greater than the minimum support, output the frequent item FP and its occurrence number ;Step 10: End of the algorithm.Therefore, the whole searching process for the quantitative relationships between TCM symptoms and the feature vectors of physical analysis of clinical records is described as follows:T that stored in the Hadoop platform was scanned in parallel for the first time. As mentioned above, a shorter scanning time will be acquired if parallelization is used to process the clinical TCM database. The constitution item with the highest occurrence frequency can be obtained after the first scan, which is recorded as constitution-1 and regarded as the header of the chain table. The frequency items whose occurrence counts are below the minimum support threshold will be deleted. The left constitution items are sorted in descending order of their count numbers to create a local FP-tree. In this article, the minimum support threshold is set to 20. This is because there are at least 20 symptom items according to the judging standards of the symptom physiques for TCM constitution analysis.The physical symptom data training set Firstly, these constitutions with high occurrence frequency are divided into balanced groups. The group number is set to 9 because there are nine constitutions altogether in the theoretical system of TCM. The constitution-1 was divided into 9 groups according to the way of balanced grouping, and the constitution table with high occurrence frequency was obtained from these 9 groups. Secondly, the groupings of the rightmost item sets of 9 different constitutions are searched in Map phase. Then the packet number of each grouping is used as the primary key and the body is used as the value to transmit the grouping to the Reduce node by means of key value. In the Reduce phase, the local minimum support is screened according to the data items received in the Map phase, and a local FP-tree is generated on the basis of local minimum support. Thirdly, the local FP-tree is accessed in an ab initio table to obtain the conditional mode. The frequent constitutive item sets are mined by a recursive method based on the combination of multiple branching and single branching. After mining, the local frequent body item sets are stored in HDFS files. Finally, the global frequent constitution item sets were generated by merging all the local frequent constitution item sets. Here, the frequency constitution item sets in the HDFS file are obtained and added to local ones with the same frequent item to gain global support. In the end, the frequent constitution item sets whose overall supports are greater than the overall minimum support are retained and saved in an HDFS file.The main task of the second stage is to link the symptoms, constitution, and regimen. As it has been mentioned in the above text, it is very important to find the relationships between symptoms and constitution, constitution, and health conditioning programs to TCM clinical diagnosis and treatment. Generally speaking, there are two ways to find this relationships. The first method is to put the training for physical symptoms and health conditioning programs into the Hadoop platform to find the correlations between them. The second approach is based on ontology rules between symptoms, TCM constitution, and dietary regimen since there are already known relationships between the three parameters which are obtained in the first stage.In this article, the data partitioning idea is combined with the working mechanism of Hadoop platform to simplify the flow of mining the TCM constitution frequent item sets. Compared with the classical FP-growth algorithm based on the distributed architecture of MapReduce, the proposed method has more advantages in data volume dispersion and load balancing. Unlike the traditional parallel FP-growth algorithm to generate a global FP-tree, there is no internode communication when generating and mining a local FP-tree on the node of HDFS. Therefore, the parallel FP-growth algorithm based on Hadoop not only has the advantages of shorter computation time and larger data processing capacity under the same amount of data but also solves the memory overflow problem of big data mining under certain conditions.n storage nodes involved in the calculation, p Maps and Reduces can be processed simultaneously on each node, q transaction records in the entire database totally and m data items for each record on average. Then the time complexity of generating the header chain is O(q∗m/n∗p). Supposing r iterations are needed to mine the local maximum frequent item set for each storage node, then the time complexity of this process is O(q∗m∗r/n∗p). Finally, all the local frequent item sets are merged to find the global maximum frequent item set according to the support and confidence degrees together. Supposing the merged item set composes of s different frequent item sets, the time complexity is O (s). Especially, if the key of the local frequent item sets on n storage nodes are the same, the time complexity of the merging process is O(s/n). There is no need to communicate between nodes during the processes of generation and mining of local FP-trees. The larger number of Hadoop nodes and the more times of the MapReduce can be processed at a time, the lower time complexity and higher the implementation efficiency of the proposed algorithm.Usually, the time consumption of the distributed FP-growth algorithm is mainly divided into three parts: (1) counting the number of the data items to obtain the global support number and generating the header chain according to the support degree at the same time; (2) mining the local maximum frequent item set for each storage node; and (3) integrating the global maximum frequent item set that conforms the global support and confidence degrees both. The proposed ARA- TCM algorithm for TCM constitution analysis is based on HDFS. Supposing there are Can the proposed method reflect the TCM constitution patterns accurately?Can the proposed method find the rules between patients' constitution and effect of TCM herbs?Can the proposed ARA-TCM method achieve better precision and recall rates than other typical models?Can the proposed method reduce the training and actual losses dramatically?In this section, the proposed ARA-TCM algorithm for constitution analysis in Hadoop framework is evaluated on the following tasks. Especially, we want to determine:In General, the proposed ARA-TCM method has been compared with typical Apriori and FP-growth algorithms. Both methods have been widely applied to data mining fields and achieved good results in reflecting the TCM constitution patterns and finding the treatment rules from the medical records. However, the proposed method has higher precision and recall rates, lower training, and actual losses comparing with Apriori algorithm.The medical records mainly come from Guangzhou Xiangxue Internet Hospital and classic medical records in history, which contain a large amount of reference medical cases. There are a total of 30,071 pieces of data, each piece of data represents a patient's complete diagnosis and treatment process, including the psychological and physiological symptoms, primary and secondary constitutions, treatment and medication plan, and so on.In this work, these rec medical records are divided into 9 groups as it has been well known that there are 9 TCM constitutions, namely, balanced constitution (BC), yang-deficiency constitution (YADC), yin-deficiency constitution (YIDC), qi-deficiency constitution (QDC), qi-stagnation constitution (QSC), phlegm-dampness constitution (PDC), dampness-heat constitution (DHC), blood stasis constitution (BSC), inherited special constitution (ISC). At the same time, in order to make the experimental results are more robust and accurate, cross-validation method was used in this article. In the training process, the whole data is split into ten parts randomly. Nine parts were used as training samples and the left was used as test samples for each learning. And all ten test results were recorded to evaluate whether the average accuracy of ten test results meets expectations. If not, adjust the hyperparameters until optimal ones were obtained, and then, the whole data set is learned to achieve only the final expected model.(i)Step 1: Data Preprocessing  Text processing stage: To avoid taking unnecessary words as features, it is a common way to descend the total number of entities by removing “irrelevant” words based on stops, these stagnation words usually include articles, auxiliary verbs, pronouns, prepositions, conjunctions and so on. For example, words such as “this,” “he/she/it,” “and,” and “or” occur frequently in the text are not able to distinguish or predict and can be removed from the input entities of the training module.  Noise removing stage: Cluster method is used in this part. The noisy data refers to the same symptoms that will occur in different constitutions. Even if the number of the same symptoms is small, they should be removed to avoid the impact on the final results in the process of training. For instance, there is the same characteristic: pale red tongue for BC and QDC and this characteristic should be discarded when recognizing the two constitutions. Here clustering analysis was used to detect this kind of outliers by organizing similar features into groups (clusters). Intuitively, values that fall outside the cluster are considered outliers.Noise filtering is the first necessary task before training module to make the learned model more accurate. A large number of literature mining data processing is required for the real clinical data before removing noisy symptom data. Therefore, preprocessing process can be divided into two stages: text processing and data noise removal. Generally speaking, the two types of preprocessing allow the continuous sentence in the text set to be represented as feature vectors, which are more suitable for constructing sample text data and subsequent word processing in the training process.(ii)Step 2: Training moduleA <=> B” indicates an interaction between constitution A and symptom B. For example, BC <=> “complexion and moist skin color” means that one of the symptoms corresponding to BC is “complexion and ruddy skin color,” which will be regarded as a kind o expression that someone's constitution is balanced (BC).The main aim of this step is to find out the correlation between constitution and physical symptoms. It has been known that each one corresponds to different physical features. The proposed ARA-TCM method was used to dig out the relationships between symptoms and physical rules based on the support and confidence of the association rules analysis method. Here “support” expresses the probability that a rule will occur and is used to measure the statistical characteristics of a rule; “confidence” represents the conditional probability that a rule will occur and is used to measure the accuracy of a rule. From (iii)Step 3: Constitution analysis moduleThe step is used to classify the obtained mining results in step 2. In the process of classification, it can be found that each kind of frequent item set of the constitution has its own representative frequent items of the physical symptoms, corresponding to the highest frequency of symptoms set, which is also known as the symptoms of frequent items.(iv)Step 4: Testing moduleIn this step, some diseases were input into the learned TCM constitution analysis model to obtain the corresponding classification results for test. And then a reasonable regimen plan is recommended according to the analysis results.The overall functional modules of TCM constitution analysis process are shown in There are two things worth noting: (1) In the actual clinical syndrome differentiation, the collected data always suffers from low quality. That is not all of the information in the provided data set is useful for constitution analysis and health planning. Therefore, it is necessary to pre-process the input data set before sample training such as noise filtering, feature selecting, and so on to reduce the dimension, volume, and interference information of data to be trained. This operation is very crucial to shorten the training time and promote the classification accuracy and effectiveness for the finally TCM constitution analysis and health regimen recommendation results. (2) In the process of TCM constitution analysis, personal characteristics of a given patient are necessary to judge the types of patient's constitution to customize a reasonable health regimen based on individual physical health details. That is, even though there are nine types of constitution totally, doctors will also plan different health regimens for the people with the same constitution because of their gender, age, physical conditions, personal preferences, and so on under the guidance of the theory of TCM health theories. Therefore, the key to TCM constitution analysis is to grasp the main symptoms related to constitution identification directly, and distinguish the specific physical health status of patients clearly to make an accurate judgment.P, recall rate R, and F-measure F1 are used to evaluate the experimental results and verify the ability of the proposed model to identify valuable data for TCM constitution analysis. Supposing TP and TN represent the numbers of true examples and false counterexamples, respectively, and TF and FN represent the false positive examples and true counterexamples, respectively. And AIR = TP + FN represents the total number of samples truly related to TCM constitution in the whole test data set, CDR = TP + TN is regarded as the number of data records that are consistent with their actual categories during the data training and DR = TP + FP represents the number of samples that the constructed models found from the whole test data. Therefore, these criteria for model evaluation can be defined as follows:Usually, classification precision rate F1 value is the harmonic mean of precision and recall rate. The errors between the real result and the predicted results of the training set and testing set, respectively, refer to the training loss and actual loss of the model. In order to make the experimental results of feature training and prediction more stable and robust, cross-validation method was used to acquire the average values of the above criteria. In this article, the entire clinical data set was divided into ten subsets randomly. Nine subsets were regarded as training sample sets and the left one was used as the test sample set to obtain the values of precision rate, recall rate, and F-measure. After 10 rounds of training and testing, the average values of the criteria were obtained. F1 valueIn this work, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct evidence-based medical evaluation of TCM effects in the constitution-related chronic disease health management. In the end, a knowledge set “disease symptoms constitutions treatments/regimen” was conducted during the training process and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. Rules of typical symptoms for each constitution contained in the specific data set of “symptoms constitution” are shown in There are 13 kinds of diseases are related to PDC. The top 5 diseases are The top five diseases were hypertension, nonalcoholic fatty liver disease, obesity/overweight, dyslipidemia, and type 2 diabetes, which are mainly chronic diseases. Basically, it can be concluded that diseases related to PDC are mainly cardiovascular and metabolic diseases, among which, cardiovascular diseases are mainly hypertension, and metabolic diseases are mainly abnormal blood lipid/glucose and obesity. In view of these chronic diseases, TCM prescriptions are mainly used to improve physical constitution with the overall guiding principle to eliminate phlegm and remove dampness. In this work, It is mainly focused on Chinese medicinal drugs.There are more than 300 kinds of drugs used to treat PDC-involved diseases in the medical records and 30 kinds of drugs are used more than 3 times in TCM prescriptions. According to the theory of “Chinese materia medica,” the 30 drugs can be classified as tonifying deficiency drugs, invigorating water and permeating dampness drugs, reducing phlegm, relieving cough and antiasthmatic drugs, promoting blood circulation and removing stasis drugs, reducing dampness drugs, relieving surface drugs, clearing heat drugs, closing astringency drugs, regulating qi drugs, calming liver and dispelling wind drugs, appetizing and eliminating food drugs and purging medicine. The most frequently used drugs were tonics for deficiency, including atractylodes, Chinese yam, licorice root, and white lentil beans. The second is water infiltration medicine, including tuckahoe, coix seed, and Alisma; then the antitussive pinellia; Salvia miltiorrhiza is the main medicine for promoting blood circulation and removing blood stasis. Dehumidification medicine is atractylodes bark, and magnolia bark; the remedies are Pueraria root and vegetable household. Antipyretics include rhizoma coptidis and cassia seed. These drugs belong to the spleen meridian, lung meridian, stomach meridian, and liver meridian, which also shows that the clinical treatment of PDC is to follow the theory of traditional Chinese medicine.As can be seen from F1-measure are three important parameters to judge the quality of models. Especially in medical data mining, it is crucial to find the right information as much as possible to avoid misdiagnosis. In this article, precision rates and recall rates acquired by the three methods for comparison with the three methods under the same numbers of samples are shown in Figures F1 measures are shown in In the field of information retrieval and natural language processing, precision rate, recall rate, and comprehensive evaluation index F1 of the three algorithms in TCM constitution analysis, although the ARA-TCM algorithm was lower than the FP-growth algorithm at the beginning, the overall situation in the later period was relatively in line with expectations, which means the proposed method is more suitable for processing large volume data and consistent with characteristics of HDFS.From Whether assuming symptoms used for TCM constitution analysis to be independent of each other or making Apriori algorithm start from the quantitative relationship between constitution categories and symptoms, is very impractical in the actual clinical TCM constitution identification. Therefore, it is carried out by calculating the prior probability and class-conditional probability to realize the TCM constitution classification. However, when the occurrence frequency of some symptoms is very low in an unbalanced data set, the sparsity problem is likely to occur and the probability calculated will be very small, resulting in relatively a large deviation of results in the actual classification prediction process. But in fact, the proposed ARA-TCM method can avoid this problem be-cause its distributed processing model for training in Hadoop framework. According to the physical symptoms and their corresponding optimal parameters of prescription, the physical type can be calculated and predicted. The proposed ARA-TCM method predicts that there is a 42.3% probability of being mild constitution (BC), 31.3% probability of being hot and humid (DHC), and 26.2% probability of being a special constitution (ISC). Although such prediction may have some deviations, it can undoubtedly provide important decision support for TCM physique analysis. In addition, because the training set given may belong to different constitutions simultaneously, the proposed method based on FP-growth algorithm and HDFS is more suitable for multicategory recognition than Apriori algorithm. Therefore, ARR-TCM method can be more and adapted for TCM constitution analysis.In this article, an improved association rules algorithm (ARA-TCM) based on FP-growth algorithm in Hadoop framework has been proposed for TCM constitution analysis and regimen recommendation, which is able to acquire better experimental results with higher precision and recall rates in comparison to the classical Apriori algorithm. However, there is still room left for improvement in the future TCM constitution recognition and analysis. Inquiring about symptoms in the clinical data set involves a lot of subjective feelings from patients such as painful, energetic, fatigued, excessive sweating, and so on. In other words, the TCM constitution identification results will be affected by the patients'subjective feelings, resulting inaccurate diagnosis. A better solution would be adding tongue image and pulse signal on the basis of inquiring symptoms to discover more accurate knowledge for TCM constitution analysis in data mining process to make the proposed method more suitable for this specific application.


The endwall effect has a great impact on the aerodynamic performance of compressor blades. Based on three conventional near-endwall blade modeling methods of bowed blade, endbend blade and leading-edge strake blade (LESB), two combined optimization design methods of highly loaded blades have been developed considering the endwall effect in the current study, i.e., the bowed blade combined with the LESB (bowed LESB blade) and the endbend blade combined with the LESB (endbend LESB blade). Optimization designs were conducted for a compressor cascade with low solidity by using the two combined modeling methods and the three conventional modeling methods, and the optimization results were compared and analyzed in detail. The results showed that the five optimization modelling methods could all improve the performance for the original cascade, and the optimized cascade with the bowed LESB modeling method has the best aerodynamic performance. The total pressure loss of the optimal bowed LESB cascade was only 40.3% of that in the original cascade while reducing the solidity of the original cascade from 1.53 to 1.25 and keeping the static pressure rise and diffusion factor at the same level as the original one. Among the optimal cascades, the radial migration height of the low-energy fluid and the corresponding vortex have great effects on the aerodynamic performance, and the optimal bowed LESB cascade is superior to the other optimal cascades in this aspect. The designers of modern aero-engine compressors continuously seek to increase the stage loading and improve the aerodynamic limit on the basis of ensuring high efficiency and wide stable working range. However, achieving this goal is a time-consuming and experience-dependent process. The introduction of an optimization design method can greatly improve the compressor aerodynamic design efficiency and save design cost ,2.The choice of optimization design method is very important. Generally, an optimization method with stronger searching ability, combined with a reasonable amount of computing duration, can not only fulfill aerodynamic designs efficiently but also explore the compressor aerodynamic design limit to some extent. Optimization methods can be roughly divided into two categories, i.e., local optimization and global exploration. In recent years, researchers have conducted a lot of research on local optimization techniques and made a lot of progress ,5,6,7,8.Global exploration is another group of optimization methods, such as the genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). There is another new optimization method named surrogate-based optimization (SBO) ,10,11, wThe global exploratory method has a long history of development, and it has played an important role in the field of compressor aerodynamic design as an effective optimization design method ,17,18,19In the design process of compressor blades, the endwall effect has a huge influence on the aerodynamic performance of compressors. As the compressor load increases, the boundary layer near the endwall grows rapidly, which can lead to significant secondary flow loss and flow blockage and bring a significant impact on aerodynamic performance . So far,Although various optimization algorithms tend to be mature, the powerful global exploration ability of the global exploration method cannot be replaced. In recent years, few studies have used the global exploration algorithm to perform aerodynamic optimization design for three-dimensional (3D) blades, so relevant research is meaningful. In this paper, an optimization design platform for highly loaded cascades was developed, including five near-endwall blade modeling methods, and it is used to optimize the design of highly loaded cascades to reduce the endwall effects. The paper is organized as follows. The numerical simulation method is introduced in In the present optimization study, NUMECA software was used to perform the cascade flow field simulation. Because of its high computational accuracy and efficiency, it has been widely used in the numerical simulations of turbomachinery.All computation grids were generated by AutoGrid5 module in NUMECA. In order to ensure the high quality of grids, a block grid structure  was adopted, and the grid was refined at the near-endwall of the cascade. The steady flow model was chosen, and flow simulation was set up as follows. The Spalart–Allmaras (S–A) turbulence model, a one equation model, was adopted. The inlet boundary had been set to an evenly distributed condition, inlet total temperature was 288.15 K and inlet total pressure was 101,325 Pa. On account of the inlet Mach number and the cascade’s inlet flow angle should be fixed in the process of optimization, the outlet boundary was given a fixed outlet mass flow rate and initial pressure. Adiabatic and non-slipping condition were set on the walls. EURANUS solver was utilized, which uses steady time-advancing method to solve Reynolds time-averaged Navier–Stokes equations.Grid dependency study was performed, where 0.65, 0.9, 1.1, 1.5, 2.0, 2.5, 3.0 and 3.5 million grid nodes were calculated under design condition . In order to promote the aerodynamic performance at the endwall area, we need to modify blade shape and blade stack law near the endwall. The original cascade in this study is a compressor stator cascade, and we call this original cascade a straight cascade, which will be modified to improve its aerodynamic performance. This section will introduce five different kinds of endwall modeling methods, respectively, i.e., bowed blade, endbend blade, leading-edge strake blade (LESB) blade, bowed blade combined with LESB blade (bowed LESB blade) and endbend blade combined with LESB blade (endbend LESB blade). These modeling methods will be introduced in detail.Compared to a straight cascade, a bowed blade means changing the cascade stack line along the pitch-wise direction as shown in The endbend blade completes the endwall modeling by adjusting the geometric inlet angle or stagger angle of the blade near the endwall, and this study chooses to adjust the geometric inlet angle. The LESB blade first originated from the strake wing in the aircraft airfoil, which can postpone airfoil stall at a high angle of attack. Applying the idea of the strake wing on compressor blades can effectively improve the flow conditions at the endwall area and enhance the aerodynamic performance of the cascade . In the After specifying the axial chord length of the blade profile at a different span position, how to trim the shape of the airfoil according to that chord length is also a problem. A new LESB blade modeling method that is different from the traditional modeling method is proposed. In the traditional modeling process of an LESB blade, after ascertaining the increment of chord length, two straight lines are used to smoothly connect the blade leading edge. This connection method is simple, but it is difficult to always ensure the connection with the blade leading edge is smooth, especially when the airfoil bends too much, or the pressure side curve is too steep. However, the proposed new method does not simply use straight lines to connect the blade profile but completes the modeling of the LESB and the airfoil in a unified manner to ensure the continuity of the airfoil curves. The new modeling process can be seen in The above three blade modeling methods are all able to improve the flow condition in the endwall region of the high-loaded cascade. Therefore, this study conceives that using a combination of two different modeling methods may have a more positive impact. There are two ways of combining; one is the bowed LESB cascade, and the other is the endbend LESB cascade. Take the bowed LESB cascade for an example. As stated in the previous chapter, H_Bow means the bowed height in the bowed blade, and H_LESB means the height of the LESB in the LESB blade. So, there are three situations when combining two modeling methods, i.e., H_Bow > H_LESB, H_Bow < H_LESB and H_Bow = H_LESB. In different cases, different spanwise ranges of blade will have different modeling methods in order to work. Some ranges only need one modeling method to work, while other ranges require both bowed blade and LESB blade modeling methods to work at the same time. In this case, when these two modeling methods are required to act at the same time, the modeling order is not important because the bowed blade only affects the blade stacking line, and the LESB blade only changes the shape of the blade section. However, things are different when it comes to the endbend LESB cascade. When two modeling methods (endbend blade modeling method and LESB blade modeling method) are required to act at the same time at a certain spanwise location, the modeling order is important because both of the modeling methods will affect the blade section. In order to ensure the rationality of the modeling process, the endbend blade modeling was completed first, followed by the LESB blade modeling.The straight cascade studied in this paper is a compressor stator cascade, and the main target design parameters are given in The preceding part of this paper introduced five near-endwall blade modeling methods, and this study combined these methods within an optimization design platform for highly loaded cascades. The optimization design method first selects the blade modeling method at the near-endwall region, including the five methods described in the previous chapter. Next, it needs to parameterize the cascade and initialize the bee colony algorithm. It mainly includes two parts to parameterize the cascade: the two-dimensional (2D) airfoil on the spanwise section and the blade near the endwall. In the process of optimizing the highly loaded cascade, this study not only optimizes the endwall area but also optimizes the 2D airfoil simultaneously, so it can better match the flow field near the endwall and effectively decrease the loss of cascade. The 2D airfoil parameterization method was introduced in detail in a former study , namely As described in the previous chapter, there are five different methods of endwall modeling. In order to ensure that the generated blades have considerable structural strength and avoid generating unreasonable blade shapes, it is necessary to limit the disturbance range of parameters during the optimization process. Through previous design experience and some attempts, the constraint range of parameters is set in After the 3D cascade parameterization is completed, the program enters the optimization algorithm module. An improved artificial bee colony (IABC) algorithm is used as the optimization algorithm because of its good performance ,31. TablThe construction of the optimization objective function also plays an important role in the optimization design. During the optimization design process in this study, the aerodynamic characteristics of the highly loaded cascade are fully considered, and the total loss obtained by averaging the mass flow is taken as the main optimization objective. At the same time, the load is limited, that is, the diffusion factor and static pressure rise of the optimized cascade cannot be smaller than the straight cascade. Formula (1) gives the optimization objective function used in the optimization design, where the subscript lim denotes the value in the straight cascade. As shown in Formula (1), it introduces a penalty function about the diffusion factor and static pressure ratio into the optimization objective function; once either of them is lower than the value in the straight cascade, fitness will be set to a maximal value. This ensures the optimized cascade losses are minimized while the aerodynamic loads are kept at the corresponding level.In Formula (1):Loss—cascade total pressure loss factor.DF—cascade diffusion factor, defined in Formula (2).In Formula (2):Using the straight cascade as the initial design cascade for optimization and using five different near-endwall blade modeling methods to shape the highly loaded cascade, respectively, we ultimately obtained five cascades with optimal aerodynamic performance.In summary, compared to the straight cascade, the five optimal endwall modeling cascades all deteriorate their flow field in the mid-span position to a certain extent, and the mid-span load on the blade will be slightly reduced. It is the optimal bowed cascade that has the relatively most significant impact on the flow in the middle section.The position of the separation line in the five optimal cascades is different. The axial position of the separation line in the bowed cascade is more forward than the other four, that is to say, the separation region on the suction side in the optimal bowed cascade is bigger than the others. The other four optimal cascades have basically the same of axial position of the separation line. Comparing the radial initial position of the separation line, the endbend cascade is closest to the endwall, followed by the LESB cascade. The bowed LESB cascade has the closest radial initial position of the separation line, which means it has the smallest separation effect region along the spanwise direction.A higher outlet static pressure can be observed in the five optimal cascades than in the straight cascade. Furthermore, the high static pressure areas of the cascades using two combined modeling methods are obviously bigger than that of the other three traditional modeling methods. In other words, a stronger pressure diffusion ability can be obtained by using two combined modeling methods.Preceding parts of the text found that many vortexes exist in the passage of the straight cascade. In order to make the vortex structure observable, we can observe the spatial streamline diagram. In the spatial streamline of the straight cascade and optimal cascade, we can find that the optimal cascades have effectively decreased the strength of the passage vortex and improved the flow condition at the endwall area. Comparing the five optimal cascades, the optimal bowed cascade and optimal bowed LESB cascade have the most significant radial migrations of the endwall boundary layer. In other words, they move more low-energy fluids from the endwall area to the mid-span area of the cascade, which can improve the flow condition near the endwall area and cause a detrimental effect at the middle section of the cascade. This phenomenon can also be observed in This study proposes two approaches for shaping the cascade at the endwall area, namely the bowed LESB cascade and endbend LESB cascade. Among all endwall modeling methods, the bowed LESB cascade has the best aerodynamic performance in this study. In order to comprehensively understand this modeling method, this section further investigates the effect of various parameters in endwall modeling on its aerodynamic performance. This paper has studied the optimization design method of high-loaded cascades considering the endwall effect and proposed two new near-endwall modeling methods: the bowed LESB method and endbend LESB method. At the same time, the modeling method of the LESB blade has been improved so that the generated airfoil has better continuity. This paper developed a high-loaded cascade optimization design platform containing five near-endwall blade modeling methods, and it is capable of coupled optimization toward airfoil and near-endwall modeling parameters. Based on this optimization design platform, a low solidity optimization design of the compressor stator cascade was carried out. The solidity of the optimized cascade decreases from 1.53 to 1.25 while the total pressure loss is only 0.0389, and the diffusion factor reaches roughly 0.6. In this study, five different near-endwall blade modeling methods, i.e., bowed blade, endbend blade, LESB blade, bowed LESB blade and endbend LESB blade, are used to conduct a full three-dimensional optimization design study on a high-loaded cascade. The following conclusions can be drawn:(1) Synthetically comparing five different optimal near-endwall modeling cascades among design and off-design conditions, they all could improve the performance for the original cascade, while the optimal bowed LESB cascade possesses the minimum total pressure loss and maximum aerodynamic load at the same time. Except for the optimal bowed cascade, the aerodynamic performance of the other four optimal cascades is highly consistent. For the endbend blade, the positive endbend has better performance, and for the endbend LESB blade, the negative endbend has better performance.(2) Comparing the flow field of the five optimal cascades, the optimal bowed cascade is the one wherein the axial position of the separation line is the nearest to the blade leading edge, while the other four optimal cascades’ axial positions of the separation line are similar. The separation line in the optimal bowed LESB cascade has the narrowest radial influence range.(3) The straight cascade will form intense passage vortexes in the cascade passage, which will deteriorate the aerodynamic performance. After the near-endwall optimization design, the structure of the channel vortex was completely changed and weakened a lot.(4) The radial migration of the low-energy fluid in the endwall boundary layer will have a great impact on the performance of the cascade. Among the five different optimal cascades, the optimal bowed cascade and the optimal bowed LESB cascade had the largest radial migration height of the low-energy fluid in the endwall boundary layer, so the loss of the two at the near-endwall area is the smallest.(5) For the bowed LESB cascade, the bowed angle has the most significant influence on the cascade aerodynamic performance, while the bowed height has the weakest influence. Meanwhile, the leading-edge strake angle and the leading-edge strake height have almost the same influence strength on its performance.