The production of some alcoholic beverages very often requires the use of wood from various tree species to improve the quality parameters  of the drink. The review discusses the types of wood used in the production of wines, beers, and flavored vodkas. Changes occurring in wood during the process of toasting barrels or wood chips are described. The compounds derived from wood that shape the aroma, taste, and color of alcoholic beverages are presented. Depending on their origin, they were classified into compounds naturally occurring in wood and those formed as a result of the thermal treatment of wood. Next, the influence of the presence of wood on the quality of alcoholic beverages was described, with particular emphasis on wine, beer, whisky, and brandy. The final part of the article presents innovative techniques using wood to impart qualitative features to alcoholic beverages. The taste and aroma of most alcoholic beverages depend on many factors, including climatic and soil conditions, the type of yeast used for fermentation, the quality of the raw material subjected to fermentation, and beverage production technology. Building the sensory characteristics of alcoholic beverages is really about obtaining a variety of desired chemical compounds. These compounds come from various stages of liquor production, but it is important that they are obtained in the right proportions . One of Alcoholic beverages have been produced by man since ancient times and have been known in all civilizations. Initially, people made only non-distilled beverages, mainly beer and wine. Amphorae and clay jars were most often used for fermentation, maturation, and storage, but due to their high weight and impermanence, they have been replaced by more technically advanced wooden barrels. The beginnings of barrel production date back over 2000 years. Originally, barrels were produced mainly in northern parts of Europe, where climatic conditions prevented the use of clay as a raw material for the production of jars and other vessels used initially in the production of alcoholic beverages . In the The original use of wooden barrels, i.e., the production, storage, and transport of beverages, has now been significantly limited. Nowadays, tanks and containers made of acid-resistant steel are used for these purposes, and wood is mainly used as a factor supporting the sensory qualities  of alcoholic beverages . The maiQuercus genus, whether or not toasted, may be used for the production and aging of wine. The most frequently chosen types of wood for the production of both barrels and wood chips are oak and chestnut, which are approved for use in wine production by the OIV  ..2].It was shown ,15 that The consumer value of wine consists of many factors, primarily sensory parameters, i.e., aroma, taste, and color. The quality of wines is largely shaped by oenological processes performed both during fruit cultivation and wine production ,45. The In the case of red wines, compounds derived from wood, such as furfural, guaiacol, whisky lactones, eugenol, vanillin, and syringinaldehyde, have the greatest impact on their aroma parameters. Furfural, as one of the most important compounds of woody origin, is responsible for giving wine the flavor of dried fruit and roasted almonds . Bautistcis and trans, which give the wine different aromatic notes. The cis form is responsible for weak coconut, earthy, or sometimes moldy notes, while the trans form gives the wine spicy, celery, or coconut notes. In winemaking, whisky lactones are usually associated with coconut, vanilla, and spicy aromas, but they may change depending on the grape variety used [Whisky lactones derived from oak wood occur in two stereoisomers, ety used . Guaiacoety used .The concentration of guaiacol and 4-vinylguaiacol depends mainly on the time of maturation, as well as on the type and dose of oak chips. These compounds can also be used as an indicator of the degree of wood toasting due to their chemical origin. In addition, 4-vinylguaiacol and 4-vinylphenol can be formed in the process of enzymatic or thermal decarboxylation from cinnamic acids . Esters The approval of chestnut wood for winemaking has resulted in an increase in the variety of aromas in wines. The use of chestnut in the aging of wines may promote the formation of more phenols and gallic acid compared to oak wood. Chestnut wood is also characterized by greater oxygen permeability than oak wood, which results in a low content of catechins, epicatechins, and anthocyanins in the wine aged with its use, which most likely undergo accelerated polymerization. Chestnut gives the wine fruity notes, and in the case of oak wood, the aromas of vanilla, smoky, and spicy are more noticeable .The typical color of red wines is due to the presence of many anthocyanins and their derivatives. Unfortunately, the total anthocyanin content decreases diametrically with wood aging. The reason for this gradual decrease in monomeric pigments is the reaction of the native grape anthocyanins with the constituents of the wood and with the oxygen passing through the micropores of the wood. These changes are manifested by a decrease in the intensity of the color of the wine and differences in shade compared to wines not aged in the presence of wood .White wines were traditionally rarely aged in barrels or with oak chips. It was believed that this procedure lowered their quality due to the masking of the original fruit and floral notes found in white wines. Esters, alcohols, and terpenes are responsible for these specific floral and fresh notes of white wines ,65. TherThe production of beer became a part of the culture and tradition in many countries . Before the advent of stainless steel, barrels were a typical vessel used for the storage and transportation of beer. Some styles of beer, e.g., porter, often had to be aged for about 6–12 months in wooden tanks in order to soften their harsh taste .Some types of beer are closely associated with aging in barrels. The Belgians used, and continue to use, the process of enriching the flavor composition with barrels. Currently, around the world, there is a so-called beer revolution, where beer production in small, craft breweries is rapidly developing. The increased development of this branch of industry has forced producers to compete more, which is why breweries try to distinguish themselves primarily by the originality of their products . For thiToday, in search of new, interesting, and attractive flavor compositions for beers, these beverages are barreled or other methods are used to obtain a flavor composition derived from wood. In the case of beer, malt and hops are the ingredients responsible for providing the aromatic compounds associated with the character of a particular style of beer ,80. ContBrettanomyces yeast and Lactobacillus and Pediococcus bacteria. They reside mainly in the pores of barrel wood, where they are difficult to remove during washing, but their presence is necessary during the production of traditional sour beers. This is also the reason for maintaining appropriate sterility in the production of commercial beers. In relation to other styles, these organisms are responsible for beer defects, which makes wood chips an interesting alternative to barrels. The use of chips allows for faster maturation effects associated with taste sensations that would not be possible to obtain in barrels [In addition, the use of wood in the production of beer may be associated with the loss of some compounds as a result of their absorption by wood or as a result of evaporation . Quite p barrels .Whisky is produced mainly from barley in a process that includes stages such as mashing, fermentation, distillation, and maturation. It is thanks to this last stage that the whisky acquires its final character, which is appreciated all over the world. It is from the wooden barrel that the compounds responsible for the aroma and specific color pass into the drink. Aging in oak barrels also increases the antioxidant potential of whisky by increasing the concentration of phenols, furans, and acidic compounds ,86,87. SThe method and time of maturation vary greatly and are often determined by local or international laws depending on the final product. Another factor influencing this process is the ratio of wood surface to volume of liquid, so the use of smaller barrels can have a significant impact on increasing the extractivity of compounds contained in wood . Compouncis isomer is responsible for sweetness and coconut notes, while the trans isomer is responsible for floral sensations. The highest intensities of caramel and burned notes were found in the brandies aged in chestnut wood. The toasting level of the barrels had a significant effect on the majority of the aroma descriptors, namely fruity, vanilla, woody, spicy, caramel, burned, smoke, green, tails, and glue. Brandies aged in heavily toasted barrels presented the highest intensities of vanilla, woody, spicy, caramel, burned, and smoke and the lowest intensities of fruity, green, tails, and glue [The types of brandy include flavored vodkas made from many different raw materials, primarily from grapes, but also from other fruits. What raw material will be used to produce brandy depends mainly on the region, its availability, and its price. For this reason, there are significant differences in quality, taste, and smell between particular types of brandy . Brandy and glue . Accordiand glue  the degrand glue .A specific type of brandy is Brandy de Jerez. It is a spirit produced in Southern Spain and obtained from wine spirits and distillates that were aged in <1000 L oak barrels previously seasoned with Sherry wine. Brandy de Jerez, in addition to typical compounds extracted from wood, also contains other compounds that come from sherry wine, such as tartaric, lactic, or succinic acids . In thosMicro-oxygenation accelerated the changes taking place in cider distillates when compared to traditional aging in barrels. Furthermore, a higher degree of brandy oxidation led to a higher concentration of benzoic derivatives, total acetaldehyde, oak lactones, and gallic acid and a more pronounced decrease in the levels of 3-methyl-1-butyl acetate and 2-phenylethyl acetate . CaldeirThe qualitative changes of alcoholic beverages  described above that occur under the influence of contact with wood during maturation are influenced to some extent on the concentration of ethanol in these beverages. The drink penetrates the wood and washes out various compounds that shape the aroma, taste, and color of the drink. Variables here include the use of new or used barrels, toast levels, char levels, how the wood was seasoned, as well as the storage conditions during maturation. Water and alcohol penetrate the wood structure and initiate the hydrolysis of the hemicelluloses and lignin. The possibility of compound migration from wooden barrels or chips is related, among others, to the concentration of alcohol and the method of wood processing ,103. TabThe aging of alcoholic beverages without the addition of wood, with some exceptions, had almost no effect on the level of volatile compounds . The preApart from extraction of volatile compounds, compound losses inherent to subtractive ageing were also observed as a direct result of wood application. Concentrations of ethyl acetate and ethyl decanoate decreased in all beverages after contact with wood. In the distillate, hexyl acetate, 2-phenylethyl acetate, and ethyl dodecanoate were adsorbed on the wood, mainly due to hydrophobic interactions .Ultrasound is defined as sound waves with a frequency exceeding the limit of hearing for the human ear. In the food industry, waves with frequencies from 20 kHz to 10 MHz are used. The most important task of ultrasonic treatment is to modify the structure of the raw material, thanks to which the intensity of mass exchange is strengthened and chemical and biological changes occur that affect the properties of the final product . Ageing 2 in wine production. Santos et al. [In many cases, efforts are made to shorten the maturation time of alcoholic beverages and to reduce costs by replacing barrels with wood chips. A technique for reformulating alcoholic beverages can be the use of high pressure (HHP). Tao et al.  showed ts et al.  treated s et al. . The uses et al. . It alloPulsed electric field (PEF) is usually used for non-thermal inactivation of microorganisms . In wine


With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China. Traditional Chinese medicine (TCM) is a complete medical knowledge system, which is based on the philosophy of Yin-Yang and Five Phases and essential Qi. It has accumulated rich theoretical and practical experience for thousands of years after long-term clinical practice and test –3. TCM rArtemisia annua, so it can be considered a chemical medicine. Tu Youyou, a researcher in TCM, has successfully extracted artemisinin and saved millions of lives around the world and won the 2011 Lasker Award in clinical medicine and the 2015 Nobel Prize in Physiology or Medicine. In general, TCM is a medical system whose main content is to study the treatment of diseases and adapt to nature [On the other hand, with the dramatic increase in the prevalence of chronic conditions, the chemical medicines cannot fully meet the needs of health preservation, disease prevention, and treatment. Chemical medicines usually refer to the active ingredients extracted from natural minerals, animals, and plants as well as drugs prepared through chemical synthesis or biosynthesis. The experiences and knowledge in TCM can play a great role in human health and the large-scale development and application of natural medicines. TCM not only contains herbal plants, but also includes Chinese herbal extracts and minerals. Chinese herbal medicine is considered as an important supplement and substitute medicine in the Western countries as they are generally extracted from natural products with mild therapeutic effects, few side effects, and no artificial additives , 11. Arto nature –14. In rWestern medicine was mainly introduced into China in the 19th century and gradually dominated the Chinese market. At present, TCM and Western medicine are two major schools of medicine in China. The Chinese have realized that TCM and Western medicine have different characteristics and functions, which manage to combine the best practices of TCM with Western medicine , 16. GenOn the other hand, the Chinese medicine of minorities, such as Miao medicine, Hui medicine, and Tibetan medicine, are also formally practiced in the Chinese health-care system –19. The Artificial intelligence is a comprehensive interdisciplinary subject based on the computer science, cybernetics, information theory, neuropsychology, philosophy, etc., which is a new emerging and developing frontier subject with new ideas, concepts, theories, and technologies , 21. NowAs an important branch of computer science, AI has laid an important theoretical foundation and made great progress after more than 60 years of development. Nowadays, AI is widely used in the diagnosis of common clinical diseases in modern medicine. For example, Hirasawa et al. designed a CNN system capable of automatically identifying gastric cancer from a large number of endoscopic images, which is promising in aiding the diagnosis of gastric cancer . Wang etThe combination of TCM and AI began in the 1970s, with the birth of the first international expert system of TCM, which was named “TCM Guan Youbo Hepatitis Diagnostic and Treatment Procedures,” as a landmark event. In the decades since, TCM expert system has made progress with the help of clinical practice guidelines, database, and the knowledge of specialists, assisting in standardizing TCM diagnostic models. For example, Weng et al. explored the quantitative diagnosis of the blood stasis syndrome, wherein the changes in tongue bodies of 352 patients of the blood stasis syndrome and 218 patients of non-blood stasis syndromes were observed by the expert system of tongue inspection of TCM . Wang etChina's population aging has the characteristics of large population, fast aging speed, and long duration of aging peak. In 2019, China's 60-year-old population has exceeded 248 million, accounting for 17.8% of the total population , 33. TheRealize the “improvement in the service ability of community-level Chinese medicine” proposed in the 13th Five-year plan of TCM. According to the plan, more than 85% of the community health service centers and 70% of the township health centers will set up comprehensive service areas of TCM. The informatization need to be strengthened and the proportion of TCM diagnosis and treatment volume will strive to reach about 30% in the total medicine diagnosis and treatment. All community health service institutions, township hospitals, and 70% of village clinics will have the capacity of TCM service by 2021.Accelerate the cultivation of TCM talents. At present, the doctors graduated from medical colleges are more inclined to identify diseases from Western medicine perspective in the process of disease diagnosis and treatment. In addition, the general practitioners in primary health institutions are most from Western medicine. Even many doctors who graduated from TCM colleges also have embarked on the road of westernization of TCM. In short, the Westernization of TCM is to replace the thinking of TCM with the thinking of Western medicine and then guide TCM to prescribe for patients. Therefore, the whole primary medical environment usually lacks a real atmosphere of TCM diagnosis and treatment. With the help of TCM intelligent system, it can shorten the training cycle of TCM talents, improve the supply speed of TCM professionals, and reduce the cost of training young doctors who graduated from a college of TCM. They usually focus on classroom learning, theoretical learning of TCM and Western medicine, and adopt the methods of learning from teachers, and oral and personal teaching. Students can follow teachers for consultation, prescription, and ward rounds. With the combination of theory and practice, teachers can demonstrate and guide students at any time, which help students better establish diagnosis and treatment thinking and use actual cases to understand the obscure theoretical knowledge in books.Improve the service level of TCM hospitals' diagnosis and treatment. With the help of TCM intelligent medical association, the diagnosis and treatment level of medicine students, general practitioners, and TCM students will be greatly improved. The improvement in TCM diagnosis level not only meet the people's medical needs, but also enhance the confidence in TCM diagnosis and treatment. In particular, it is necessary to accelerate the construction and development of TCM, so TCM can better serve the health of the community-level people in China.Expand the coverage of TCM services. The intelligent system of TCM has broad prospects and huge demands in clinical assistant diagnosis, telemedicine, and personal health management. The establishment of regional TCM intelligent medicine association cloud platform will help students to realize intelligent prescription and directly transmit diagnostic information to users as well as expand the coverage of TCM services. This online intelligent diagnosis modes of TCM are of great significance to the people who live in remote areas. Obviously, there is a broad prospect in theory and practice, which the demand of TCM intelligent system is getting more and more attention in China. The total production value of TCM industry in recent years is shown in Recently, the Chinese government has undertaken enormous efforts to modernize TCM by investing capital in scientific research and the economic value exploitation of TCM. The report of the 19th Session of National Congress of the Communist Party of China, held on October 18, 2017, once again put forward that “We will support both traditional Chinese medicine and Western medicine, and ensure the preservation and development of traditional Chinese medicine,” and upgrade to the level of “healthy China initiative,” which means that TCM has entered a new era from supporting development to inheriting development. The law of the People's Republic of China on TCM clearly points out that encourages scientific research institutions, colleges and universities, medical institutions, and pharmaceutical manufacturers to use modern science and technology and TCM methods to carry out scientific research on Chinese medicine, strengthen the integration exploration of Chinese and Western medicine, and promote the inheritance and innovation of TCM theories and technical methods. Meanwhile, the State Council of China issued the current situation and the strategic plan of the TCM development in 2016–2030. According to the survey, the establishment of intelligent TCM is mainly based on the following reasons:AI is often used in expert medical system that is mainly a computer program designed by using the principle and method of expert clinical diagnosis to simulate the diagnosis process of TCM practitioners. It can help doctors to solve many complex medical problems, which effectively avoids misdiagnosis caused by human factors and improves the speed and accuracy of TCM diagnosis. For example, TCM intelligent robots can imitate human finger massage, precisely measure the hardness, achieve precise sensing and control of flexible soft tissues, and then calculate the pressure required for the massage by AI algorithm. They generate visual reports that allows doctors to use empirical data to measure the rehabilitation of patients and avoid misdiagnosis caused by human factors. In the field of TCM materials, the identification accuracy of TCM is improved by AI technology, near-infrared spectroscopy analysis, deep learning model, and multimodal fusion technology. Compared with traditional manual identification, multimodal fusion identification of TCM has shorter cycle and lower cost by image technology.On the other hand, intelligent TCM inherits and develops the medical theory and enrich clinical experience, which make the intelligent TCM diagnosis system very close to the diagnosis and treatment level of TCM experts. Furthermore, the process of intelligent diagnosis for the disease with single condition greatly promotes the inheritance of the experience of the famous Chinese medicine practitioner. The famous Chinese medicine practitioner refers to the outstanding representatives of the academic development of contemporary TCM. Their academic thoughts and clinical experience are the concentrated embodiment of the academic characteristics and theoretical characteristics of TCM. So, the application of AI technology is an intelligent treatment method integrating treatment and evaluation. The main achievements of the combination of TCM and AI can be divided into four categories: multisource data integration and construction, medical model change, experience inheritance, and intelligent instruments. The intelligent analysis process of TCM is shown in Yi=f(Xi).TCM syndrome differentiation is a comprehensive description of patients' pathological state. The syndrome covers a large amount of dynamic information about human body, such as physical signs, tongue pulse, age, and constitution. It has multisource, multilevel, nonlinear, and strong uncertainty characteristics. Moreover, the big data of TCM also include the monograph of ancient Chinese medicine, literature journal, medical record monograph of famous and old Chinese medicine, medical record, community health file, wearable equipment data, astronomical and geographical data, etc. These data are scattered in different spatial locations, so it is difficult to collect and integrate them. The long and tedious process must be gradually integrated and accumulated. However, AI technology, such as text mining, web crawler, intelligent matching, etc., can quickly collect valuable knowledge in the text file and timely find the disease consistent with the symptoms of TCM. According to the characteristics of knowledge, different recording methods were taken. People will classify and manage the existing prescriptions, ancient books, and encoded TCM knowledge; comprehensively collect; and centrally edit. For some who are difficult to express, such as oral expression and personalized knowledge, we usually retain and input their original treatment process and retain some details in the treatment to the greatest extent. TCM doctors used AI models to establish standardized and unified diagnosis and treatment terms and realize language conversion. These intelligent methods are often suitable for converting symptom combination to diagnosis and treatment plan combination, so as to complete the function relation construction of These lines of information of different groups people can be collected, stored, and analyzed, then relevant diagnosis and treatment reports are given, forming a huge human symptom database. It can not only enrich and improve the exist clinical database, but also better promote the development of TCM digitalization. Large database is of great significance for the accuracy and sensitivity of TCM intelligent diagnosis. To some extent, TCM researchers can provide explanations for the scientific nature of TCM disease diagnosis and treatment by mining its potential objective laws from a large number of clinical data. The independent recognition, independent learning, independent judgment, and intelligent clinic treatment and diagnosis will be ultimately achieved with AI technology, database, and advanced algorithms. In addition, the construction of the database requires the collection and input of data as well as the characteristics of TCM.The research of TCM should aim at the key points of scientific and technological innovation, such as biomedicine and system science, clarify the scientific connotation of TCM theory, and innovate and develop the theory and technology of TCM. New technologies such as information, biology, new materials, and emerging new methods are organically combined to strengthen the circular demonstration of the scientific nature of TCM data. The database construction should also take root in the clinical front line, strengthen the scientific nature of data, and explore the construction of evidence-based medicine system with the characteristics of TCM.2O medicine delivery, health management, Chinese medicine media, Chinese medicine education, intelligent equipment etc. Specifically, the intelligent diagnosis equipments of TCM mainly use AI algorithms to simulate the diagnosis and treatment thinking of TCM to make a diagnosis and give a corresponding diagnosis and treatment plan. The different prescriptions can be provided for different diseases according to the analysis results of literature data, severity degree of disease, and patient situation. The doctor's task is to make choices and judgments based on their own clinical experience. For instance, Zhang et al. extracted the diabetes feature parameters from standard tongue image and established diagnosis model based on support vector machine algorithm [With the maturity of information technology, the online and offline data fusion is effectively realized, which changes the traditional diagnosis and treatment mode of TCM. AI integrates a multidisciplinary integrated medical model and multipath diagnosis and treatment and makes up for the deficiencies of TCM data, information, technology so that personalized, fragmented, and fuzzy TCM clinical experience and knowledge have a more scientific expression. At present, the development of the field of “TCM + AI” mainly has several models: Chinese herbal medicine e-commerce, online consultation, Olgorithm . Hu et algorithm . Allwoodlgorithm . The facAI technology can discover the correlation between the data in the massive TCM data and conduct training, learning, and optimization. Moreover, AI converts TCM classics and clinical diagnosis and treatment experience into data and form a large number of TCM databases. With the popularity of the Internet, the accumulation of data has accelerated. It provides a foundation for the application of industrial intelligence technology in the field of TCM. We should mine information from past data, especially the theories and clinical experience of well-known veteran TCM experts, and use scientific research methods to upgrade traditional experience to scientific law, so as to provide basis and ideas for further basic research. At the same time, the government has strengthened the supervision of data quality of TCM, improved the evaluation system, established internal and external linkage database, and comprehensively evaluated all kinds of data. In this way, the personalized and fragmented clinical experience of TCM has a scientific expression, and personal experience can be transmitted and exchanged. The model is adopted to determine the clinical diagnosis elements of prominent TCM experts, the structured data were transformed, and the diagnosis and treatment information database of TCM was summarized and established. For example, Cai et al. established TCMIO, a comprehensive database of TCM on Immuno-Oncology, which can be used to explore the molecular mechanisms of TCM in modulating the cancer immune microenvironment. All of these data, along with cheminformatics and bioinformatics tools, were integrated into the publicly accessible database . Zhang eThe intelligent instruments are necessary to realize accuracy and rapid diagnosis of disease in the AI era. TCM attaches great importance to individual differences and needs to give syndrome differentiation diagnosis opinions according to the constitution of human body and the drug efficacy, but the diagnosis and treatment modes are easily affected by individual subjectivity or external factors. Compared with TCM diagnosis and treatment, precision medicine has seemingly different aspects. Syndrome differentiation and treatment is the essence of TCM diagnosis and treatment and the basic principle of TCM in understanding and treating diseases. Precision medicine includes precise detection, precise conditioning, precise diagnosis, and precise treatment. Under the guidance of the holism, it studies the precise correspondence and laws of human symptoms and signs, diseases, symptoms, etc. Therefore, the diagnosis and treatment concept of syndrome differentiation and precision medicine have similar starting point and focus. It also reflects the great connotation and breadth and depth of understanding of TCM in the process of disease diagnosis and treatment. The application of AI equipment can well solve this problem in TCM diagnosis aspects. The possible medical accidents caused by external factors can be avoided by objective and rigorous data processing.Pulse diagnosis and tongue diagnosis, as the basic links in the diagnosis of TCM, have attracted more and more attention. Pulse instruments mainly collect pulse information of patients through high precision sensors and conduct independent analysis and diagnosis according to unified standards. For example, professor Cui Ji has developed the pulse map identification standards of flat, smooth, and chord pulse by measuring the index range of pulse map characteristic parameters . Zhang eTCM workers are responsible for disseminating TCM knowledge and promoting Chinese culture. TCM hospitals account for the vast majority of TCM medical institutions and play a key role in providing TCM medical services and promoting TCM culture as well as technology. AI technology can detect the characteristics of most diseases by the massive TCM database, which make the ancient and modern TCM data be an endless precious resource rather than an empirical “garbage data” that are difficult to deal with. At present, the multitype database of TCM has become a solid information for TCM with its abundant data. The TCM stroke prevention database contains data on the treatment of stroke with TCM, acupuncture, and massage from 1996 to 2006. It has established epidemiological information, diseases, syndromes, and other related content and realized the analysis of four categories of factors including clinical research, diagnosis, treatment, and disease relationship as well as the purpose of disease-related data sharing services and data mining. Ai et al. screened the literature on TCM treatment of diabetic retinopathy through the Web of Science, ScienceDirect, PubMed, Google scholar, and CNKI online databases. In addition, they summarized and analyzed the prescriptions, herbs, and identified compounds of TCM on the treatment of diabetic retinopathy from the aspects of anti-inflammation, antioxidative stress, antiangiogenesis, and antiapoptosis .With the improvement in TCM informatization and the development of AI technology, intelligent TCM is imperative for the construction of modern medical system with Chinese characteristics. The development of intelligent TCM system not only covers information release, medical service, health management, medical organization cooperation, and doctor-patient communication, but also innovates the medical service mode, builds the information sharing service system, and makes the medical service move toward the real sense of intelligence and convenience. Importantly, the advance of intelligent TCM should be organically combined with the construction of grassroots TCM and the development of smart city, such as building a “healthy TCM hut” model that promotes the improvement in grassroots TCM service level, which will bring new development opportunities for the overall industry of TCM.AI is the representative of the latest round of technological and industrial revolution. At present, it has developed to the fifth generation, which can handle massive information, carry out cloud computing and reasoning, and simulate human thinking with logic reasoning function. AI technology is mainly used in research and development of intelligent syndrome differentiation system and intelligent diagnosis and treatment equipment in TCM. The utilization of intelligent instruments and the medical records based on patients' condition can be used to determine the syndrome type of the disease and then select the responding treatment method. It expands the research scope of TCM and increases the research means, as well as enriches the methods of TCM.Ancient and modern TCM classics and experience are digitized, and AI is used in data mining and processing. All data are stored in the cloud system, including TCM classics, TCM clinical diagnosis and treatment data, human health status data, etc., which can be searched and obtained at any time and anywhere. With the help of TCM database, TCM doctors can quickly grasp the existing TCM experience and realize intelligent retrieval and consultation of TCM diagnosis and treatment plans.The scale of TCM databases has gradually increased, and the level of intelligence has become more in-depth and proficient. The wide application of intelligent sensor technology, such as wearable devices, mobile phones, and smart terminals, makes patients use intelligent diagnostic instruments to diagnose and analyze independently user's physical state. At this stage, all kinds of intelligent diagnostic instruments will be developed and used to help people solve many practical problems.Intelligent TCM will be integrated into daily life and become a part of human life. The modernization intelligent TCM of multidisciplinary, multilevel, and multimethods is established based on the cross cooperation. The defects of TCM dialectics' subjectivity and fuzziness are well avoided by various intelligent treatment models and advanced algorithms. In the process of disease diagnosis and treatment, intelligent TCM will be recognized and accepted by doctors and patients and gradually popularized throughout the country.On the other hand, the thinking ways and scientific theory of TCM are similar to the complex scientific thinking mode. Therefore, it is the classic representative of complex science, which is in accordance with the development direction of future science in some extent. AI has brought many opportunities for China's economic and social development, as well as great chances and challenges for TCM in future. We can learn and improve diagnostic means with the help of TCM big data, but also constantly inherit and innovate human experience. In terms of the development of intelligent TCM, I consider that it will probably go through three stages: digitalization, intellectualization, and popularization.Despite its limitations, all medicine is a form of local knowledge that is a relevant part of human medicine. The medical knowledge of different nationalities and countries are digitized, which can gather the vast ocean of medical big database. Furthermore, the medicine systems also need to integrate the medicine wisdom of different nationalities and countries. In the era of AI technology and advanced algorithms, TCM, as an ancient science and art, is brought to a greater height for its ultimate mission of benefiting human health. There is no doubt that TCM will not only provide health services for the Chinese nation, but also will make an important contribution to the health of people all over the world.


X enterprise. Through the questionnaire survey, it is found that through the comprehensive application of artificial intelligence technology, the new system has a higher degree of intelligence than the existing system, and its construction can effectively improve the timeliness and accuracy of financial decision-making, while reducing the cost of financial decision-making. It is conducive to promoting the integration of management accounting and financial accounting.The quality of financial decision-making is very important to the future development of an enterprise, but it is often affected by the completeness of useful information for decision-making and the subjective factors of decision makers, and is often unstable. With the development of computer technology, the financial decision support system came into being, which improved the quality of financial decision to some extent. However, although the existing financial decision support system has achieved dataization to a certain extent, it still faces problems such as artificial leadership, insufficient intelligence, and poor decision-making efficiency, and cannot fully meet the needs of decision-makers. The explosion of artificial intelligence technology in recent years has provided potential improvements to financial decision support systems. In this article, we conduct a detailed analysis of the deficiencies in the current financial decision support system, build the mechanism and implementation path of the financial decision support system under artificial intelligence, and design a digital and intelligent financial decision support system. At the same time, we apply the proposed financial decision support system to the financial practice of  The quality of financial decision-making is the life source of an enterprise and is of great significance to the sustainable and stable development of the enterprise. For a long time, the construction of a financial decision support system has been the concern of scholars. The financial decision support system realizes scientific and effective financial decision-making by comprehensively managing the upstream and downstream financial information of the enterprise, integrating the actual financial situation of the enterprise in the whole process, and integrating internal and external information –3. In reThe traditional financial decision support system cannot effectively mine the financial information of enterprises, resulting in the inability to distinguish the value of information, including information and usage information. Judging from the decision analysis system and big data technology implemented by some listed companies and Internet companies, the full mining of data value can greatly improve the efficiency and quality of decision-making . HoweverIn order to effectively improve the quality of corporate financial decision-making and promote the deep integration of artificial intelligence technology and the financial field, it is the general trend to build a financial decision support system under artificial intelligence. In order to lay a foundation for the construction of the financial decision support system under artificial intelligence in theory, so as to better guide its construction and application in practice, this article conducts in-depth research on its mechanism and implementation path. Based on artificial intelligence technology, this article investigates the application status of the existing financial decision support system and the necessity and feasibility of the elements contained in the mechanism and implementation path of the financial decision support system under artificial intelligence. On this basis, this article constructs the mechanism and implementation path of the financial decision support system under artificial intelligence. Finally, through the case analysis of the A group that applied the system, the results prove the effectiveness of our proposed system.The study of decision support systems (DSSs) began in the 1970s, when Scott et al. first proposed the term management decision systems and proposed the application of computers in the decision-making process . The devAt the same time, with the development of computer and Internet technology, foreign scholars are also deepening the research on new financial decision support systems, and they have put forward suggestions for improving financial decision support systems from the perspective of specific technical applications. Rupnik and Kukkar proposed the application of data mining technology in decision support systems . Tang etThe existing system mainly assists financial decision-making by providing information, which makes it actually mainly play the function of a calculator, and cannot directly provide decision-making suggestions to managers –18, as sThe financial decision support effect of the existing system is not good –21. In tOverall structure of our proposed system is shown in The data layer mainly performs data collection, cleaning, data mining, and storage. With the help of automatic data transmission programs and natural language processing technology, it is possible to quickly obtain useful information for internal decision-making such as business and financial information, audit information, and credit information stored in the local database, as well as government policy information, tax information, exchange rate information, and market information published on the Internet information, legal information, macroeconomic information, and other external information. These massive heterogeneous data are cleaned and mined to form multidimensional useful information for decision-making, and are classified and stored in the data warehouse. The data warehouse lays a strong data foundation for the deep learning of the new system and financial decision-making. At the same time, the advance processing and classification of data also provides a guarantee for the timeliness of financial decision-making.The analysis layer is responsible for carrying out financial analysis, financial forecasting, and financial decision-making activities. Financial analysis is the basis for carrying out financial forecasting and decision-making, and financial decision-making depends on the results of financial analysis and financial forecasting. The analysis layer includes knowledge base, method base, model base, and their respective management systems and artificial intelligence analysis system. The knowledge base stores various financial knowledge, common sense and reasoning rules, and other data, the method base stores financial analysis, forecasting, and decision-making methods, and the model base stores financial analysis models. On the one hand, the management systems of the three databases are responsible for receiving the instructions of the artificial intelligence analysis system and fetching the required knowledge, methods, and models from the corresponding libraries, and on the other hand, they embed deep learning algorithms to automatically carry out new knowledge, new methods, and models in the background. The establishment of new models and the improvement of existing knowledge, methods, and models, so as to update the knowledge base, method base, and model base in time. The artificial intelligence analysis system is responsible for receiving the financial decision-making goals conveyed by the human-computer interaction system and accordingly sends instructions to the database management systems and data warehouses, receives data for analysis, and finally feeds the results back to the human-computer interaction system. The artificial intelligence analysis system includes several inference engines embedded with deep learning algorithms. Some of these inference engines are responsible for determining the types of knowledge, methods, models, and portraits required according to financial decision-making goals, and the other is responsible for financial analysis to generate various portraits. A part is responsible for the generation of financial forecasts and decisions.The interaction layer is the link between the new system and decision-makers. Human-computer interaction systems use speech recognition and natural language processing technology, so decision-makers can use natural language to communicate with new systems. In the process of financial decision-making, the human-computer interaction system forms financial decision-making goals through the processing of natural language and at the same time communicates the financial decision-making goals to the artificial intelligence analysis system. After completing the financial decision, through the human-computer interaction system, output the financial analysis report, the financial forecast report and the financial decision report that integrates the above report information, or the customized report prepared according to the needs of the decision-maker.With the help of the Internet, the new system can obtain massive amounts of structured, semi-structured, and unstructured data such as financial statement information, supply chain information, market information, industry information, securities market information, and online public opinion information in real time. These raw data describe the company's own financial situation and the external financial decision-making environment it faces from many aspects, but these data are chaotic in structure and uneven in quality, and cannot be directly used for financial analysis, so data cleaning and data mining are required. The raw data processed by big data technology have become multidimensional decision-making useful information and is classified and stored by subject. Taking a certain type of product as an example, through multidimensional decision-making useful information, we can extract relevant information from multiple dimensions such as product model, output, sales volume, and main market and quickly obtain the sales of the product at a certain time and place. Just as the liquidity of assets can be judged by indicators such as current ratio and quick ratio, and through these multidimensional decision-making useful information, deep learning algorithms will determine the solvency, profitability, operating ability, growth ability, risk tolerance, risk appetite, and other factors should be evaluated and judged. Compared with the previous evaluation results obtained by solidified indicators, the results obtained by artificial intelligence technology based on exponential indicators are more accurate, thus ensuring the appropriateness of financial decisions. Further carry out financial analysis, financial forecasting, and financial decision-making based on useful information for decision-making. With the help of existing financial analysis methods and corresponding deep learning algorithms, it is possible to analyze and evaluate the solvency, development, profitability, and operational capabilities of enterprises. The financial analysis data together with the enterprise characteristic data constitute the enterprise portrait.Similarly, through the analysis and processing of useful information for multidimensional decision-making on other topics, various types of financial decision-making information groups such as external environment portraits, asset portraits, and customer portraits can be obtained. When the financial decision target is generated, according to the model obtained by the deep learning algorithm during training, the new portrait is customized, and the various portraits are matched with each other and the future under different matching paths.Predicting and analyzing the results of financial activities is to obtain financial forecast data. On this basis, the action path that can meet the financial decision-making objective to the greatest extent is selected as the financial decision. Decision-makers can revise the output financial decision through man-machine dialogue, and the revision process will affect the final decision model to improve the quality of the next decision. After obtaining a satisfactory financial decision, decision-makers can choose to output a general financial decision report or customize a personalized report. Useful information for decision-making is the starting point for financial decision-making. Therefore, the new system does not screen information when collecting information, so as to ensure the comprehensiveness of useful information for decision-making. Further processing of these data is required to improve the relevance and availability of useful information for decision-making. For unstructured data, it uses natural language processing technology for structured processing, extracts key entity information, and mines the data relationships contained in this information.Combining data mining with data-cleaned structured data, the new system can obtain high-quality decision-making useful information including the data itself and the complex relationship between the data. Financial decision-making methods and models are the link between useful information for decision-making and financial decision-making, reflecting the logical relationship and causal relationship between the two. Therefore, financial decision-making methods and models have a significant impact on the quality of financial decision-making. Here, we introduce two classical deep learning methods as decision models, namely, recurrent neural network (RNN) and long short-term memory. The equation for RNN is denoted asxt is the input features, which consist of price features and technical indicator. ft, it, and ot represent forget gate, input gate, and output gate, respectively. ct and ht represent cell state and hidden state, respectively.For LSTM, the equation is denoted asBecause our model is deployed on a financial system, computational complexity is a consideration, we improve the LSTM so that it can be deployed on high-traffic financial decision support systems. Specifically, the improved LSTM formula is as follows:Meanwhile, considering the black-box characteristics of the deep learning model, we added attention mechanism in the improved LSTM to improve the interpretability of the model. The attention mechanism enables models to adaptively discover potential patterns in input data, thereby improving model performance by identifying important features. Besides, the identification of important features also provides guidance for financial personnel to explain the decision-making process of the model. The formula for the attention mechanism is as follows:Using deep learning algorithms, we feed the new system with decision-making information, and if it makes the right financial decision, we give the neural network that made the right decision more weight, and less weight if it does not. This process is the training process of the new system. After training enough times, the new system will sum up its own financial decision-making methods and models, making financial decisions without human involvement. These financial decision-making methods and models may be different from the current solidified models. Compared with solidified models, these methods and models are more complex functional systems and have a higher degree of fitting to the data. And as the number of training increases, these methods and models will become more complex, and the quality of financial decisions will continue to improve. When the new system receives the financial decision objective, the financial decision support process is initiated. The new system will select the financial decision-making methods and models that have been trained according to the financial decision-making objectives and select useful information for decision-making according to the methods and models. After calculation and analysis, a financial decision is finally generated.X company and collect data for assessment in order to verify the efficacy of the suggested financial decision support system. Internal demands, rules, objectives, and external pressures are all factors that financial decision support systems consider. As a result, before the update, each category is assessed against the planned structure and compared to Enterprise X's decision support system: S1 for internal pressure, T1 external pressure, U1 goals and financial criteria, with subgoals, JA1 job satisfaction , JA2 cognitive skills, JA3 employee contribution to the company, JA4 employee strength, JA5 individual effort, JA6 timely reaction, JA7 accessible resources, JA8 organizational characteristics, and JA9 maturity to company activities. The classification results are grouped into U1 and U2 groups, and the final classification result is generated as an output of the organizational development decision support system.We execute real applications at The priority function value is obtained based on the consistency of the clusters and thresholds. If the value is below 0.1, it is acceptable, and others are considered unacceptable. Based on the weights, the priority value of the proposed system is measured and compared with the previous system, as shown in The priority values of our proposed system are assessed and compared to the existing system using the weights, as illustrated in X enterprise shows that the system we propose not only inherits the digital characteristics of the existing system but also better utilizes intelligent technology to organize the flow of data, and realizes the improvement of decision-making efficiency.The application of the financial decision support system can have a positive impact on enterprise financial decision-making, including making financial analysis more comprehensive and accurate, improving the comprehensiveness of useful information for decision-making, and improving the timeliness of financial decision-making. However, the existing system is not widely used, and its functions are not comprehensive enough to meet all the decision-making needs of decision makers. The main reason for this situation is that the existing systems generally have the problems of low degree of intelligence, high cost of system construction and operation, insufficient timeliness of decision-making support, and poor decision-making effect. By realizing the extensive application of artificial intelligence technology in financial decision support systems, a financial decision support system under artificial intelligence can be constructed. The application of digital and intelligent systems can provide decision-makers with more comprehensive and accurate decision-making useful information, and under the condition of ensuring the principle of cost and benefit, expand the scope of financial decision support, improve the scientificity and objectivity of financial decision-making, and reduce the probability of irrational decision-making. It will improve the overall financial decision-making quality of the enterprise and ensure the long-term stable and healthy development of the enterprise. This article proposes a financial decision support system composed of data layer, analysis layer, and interaction layer. Driven by digitalization and intelligence, it provides financial analysis, forecasting, and decision support services including financing decisions, investment decisions, cost decisions, dividend distribution decisions, and special financial decisions. AI technology is applied throughout the financial decision-making process. First of all, the new system mines and organizes the data collected in each basic database, draws various portraits, and stores them in the data warehouse. When the decision-making target is received, it supplements the decision-making useful information according to the decision-making target, and performs portrait matching, so as to obtain financial decision-making. Subsequently, the scheme can be modified and improved with the help of human-computer interaction to form the final scheme. The empirical research on the actual application of


Modern medicine must move from a wait-and-see and remedial system to a preventive and interdisciplinary science that aims to provide patients with personalised and precise treatment planning .Personalised medicine (PM) is becoming increasingly important in the clinical and research settings of all medical disciplines, including pediatrics. PM focuses on the phenotyping of individual patients at the same clinic, allowing tailored screening, diagnostics, and treatment ,3. ChildTo achieve and implement the PM approach, the digital twin system (DTS) has been proposed. The DTS is composed of a physical element (the patient), a cybernetic element (the patient’s DT), and two-way interactions between the two elements. The sensors transform the signals of the patient into the DT of the patient. Artificial intelligence software processes the signals to act through recommendations or automatic adaptations of the patient’s management .Due to the high complexity of the human body and its functional mechanisms (not fully elucidated), no DT of the whole human body has actually been designed . HoweverAn artificial pancreas for children with type 1 diabetes, combining a closed-loop glucose control system and insulin infusion algorithm, opened the way for DTS in the management of paediatric chronic diseases .In the near future, DTS are likely to be developed for other complex chronic diseases of paediatric age.A future application may be for paediatric obesity, one of the most critical public health challenges. Childhood obesity is a multisystem condition that has various complications . GeneticThis information, combined with longitudinal metabolomic, immunological, biochemical, behavioural, and gut microbiota parameters, could define a digital replica of oneself used to implement a personalized nutritional program, offering a revolution in obesity management .In childhood asthma, likewise, where different determinants of asthma symptoms need to be considered, including the treatment and the environment , DTS could be used to define the appropriate treatment in real time and/or to adopt the appropriate mitigation measures in subjects at high risk of asthma, and to determine an optimal medication dose and treatment plan, leading to a decrease in both the costs and the difficulties involved in clinically managing the disease ,8. AssocThe role of DTS in the management and treatment of other non-communicable diseases, such as preventable cancers, neurodegenerative disorders , and rarAn interdisciplinary approach is mandatory for DTS proposals, as artificial intelligence, data science, and engineering concepts can identify risk factors over the course of the patient’s life, potentially enabling personalized simulation of life-course multimorbidity risk and thus improving health outcomes ,11.The first stage of the DTS development for healthcare is the training of a predictive model, using machine learning, by identifying key factors over the patient’s lifespan that predict the risk of later multimorbidity associated with disease . ThroughA DTS in the health system offers a virtual disease representation, offering the possibility to test scientific hypotheses and predict the interaction of pathogenic components and their effect on children and adolescents .The DTS produces highly realistic models of real systems ,5,11. InThe complexity and costs of DTS will be comparable to those of projects such as the Human Genome Project; in addition, DTS may lead to an improvement in health and access to healthcare, performing early diagnosis of diseases, performing personalized treatment, and offering innovative research directions ,12,13.However, for the clinical implementation of DTS, a wide range of technical, medical, ethical, and theoretical challenges , particuThe topic of DTS represents current and future health challenges in pediatrics as a tool which promises to promote and protect the health of children, maximize the efficiency and efficacy of PM in the healthcare system, and to offer an innovative perspective for research.


The COVID-pandemic context is characterized by a global ambiguity, reflecting the unexpected onset of pandemic, unpredictability and amplitude of the related dangers, questionability and the side effects of the policy measures used to protect people.The development of theoretical framework for understanding variability in the reactions to COVID situation and self-regulatory mechanisms.Conceptualization and analysis of individual psychotherapy cases in the framework of psychodynamic approach.Five typical patterns of anxiety, defensive functioning and mentalization structures were identified: Paranoial pattern is marked by flooding with persecutory anxiety, exaggerated subjective uncertainty. reflected in chaotic boundlessness, incoherence, fantasies of hostility, splitting and polarization of self and others. Depressive pattern reflects inability to sustain ambiguity due to deficiency of internal supports, overly dependence on others, conformity, obedience to authority and denial of personal standards and individuality. Noogenic pattern refers to negative affective states, generated by the ambiguity, contradictions and ambivalence of information. The means to regulate it include the lowering of the level of psychical functioning, with cognitive simplification, preference for order, routine and predictability. Transgression pattern suggests maniacal fascination with the dissipation of limits, normative restraints and rules, and the triumph of the narcissistic-perfectionist permissiveness. Constructivity pattern consists of the pleasure from explorations and insights, creation of new meanings, creative reappraisal and reconstruction of ambiguous situations.The typical patterns of experiencing ambiguity that were singled out may have diagnostic and prognostic significance in evaluating the individual resource potential in situations of COVID related dangers and isolation.


The corrected Author contributions statement appears below.EP, T-TP, LV, QR, HP and SK performed the experiments. EP and QR analyzed the data. PVdS, EP and GO conceived the study. EP and PVdS wrote the first drafts of the manuscript. EP, T-TP, LV, HP, SK, GO and PVdS critically read and edited the manuscript. All authors contributed to the article, read the article and approved the final version.Furthermore, one picture of the lungs in The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.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.


Converciencia, a program designed to connect Guatemalan scientists residing abroad with their country of origin. Converciencia has been a flagship practice for over 15 years. This program involves three main groups of participants: (i) science and technology (S&T) policy agents, (ii) the scientific community , and (iii) host institutions . This article presents a comprehensive and balanced overview of the Converciencia program applying an in-depth analysis of its creation, evolution, leading trends, and legacies. Using a qualitative methodology and conducting a four-level analysis  allowed for the identification of nuances of this S&T practice in the context of a scientifically lagging country such as Guatemala. The detailed data collected through documentary and desk review, gray literature, focus group discussions, and semi-structured interviews resulted in a framework to highlight the strengths, weaknesses, opportunities, and threats (SWOTs) in the planning, organization, implementation, monitoring, and perception of the results achieved by Converciencia. Findings portray the contrasting views and perceptions from a single S&T practice, depending on the participating parties' roles and responsibilities. Direct participants examined how Converciencia has achieved its objectives while questioning the effectiveness and impact that the resources allocated to the initiative have yielded over time. Evidence indicates that despite the design, coordination, and evaluation limits of Converciencia, the GSD, the scientific community in Guatemala, and the host institutions are interested in the continuity of the practice. Indeed, the main recommendation involves restructuring and turning Converciencia into a robust S&T policy. Converciencia as a policy engaging the GSD could produce greater results and impacts by involving all the key actors in co-designing activities, clearly determining roles and responsibilities, and establishing performance and impact indicators for evaluation.In 2005, the Guatemala National Secretariat of Science and Technology (Senacyt) introduced  It was founded in 1991 and managed by Colciencias . Its purpose was to channel the research potential of Colombians living abroad to the country and link them to research institutions for the creation of scientific capacity. R-Caldas relied on connections among Colombian diplomatic missions abroad to identify and reach out to the Colombian SD. The engagement activities of the members of the R-Caldas were classified into four categories: (i) identification of research areas with possibilities of cooperation and formulation of joint projects, (ii) training of human resources and return of researchers, (iii) internships and mobility, and (iv) communication and scientific dissemination. In Argentina, Programa Raices (a network of Argentine researchers and scientists) was designed by the Ministry of Science, Technology, and Productive Innovation in 2003. It became a national policy in 2008 managed by the National Agency for Scientific and Technological Promotion and the National Council for Scientific and Technical Research. The objectives included permanent or temporary repatriation of Argentine researchers residing abroad, development of networks linking local researchers and those of the diaspora by realization of short stays, dissemination of job opportunities agreed upon with the private sector and foundations, and dissemination of scientific and technological activities , a project developed between 2013 and 2016, as a series of engagement strategies related to the Brazilian diaspora of science, technology, and innovation promoted by the Brazilian government. This project was designed and implemented by the Brazilian Industrial Development Agency (Abdi) and was funded by the Brazilian Trade and Investment Promotion Agency (Apex-Brasil). The project aimed to set up a “network of networks,” engaging active initiatives and Brazilian professionals  and foreign professionals with ties with Brazil. Some of the project's main components included building partnerships and research cooperation to develop business and projects in intensive knowledge and technology areas. The case of Uruguay involves the program Circulación de Uruguayos Altamente Calificados . The program was created in 2005 to “actively and effectively link highly qualified Uruguayans residing abroad with national institutions”  disciplines. The diversity would facilitate communication between colleagues to implement collaboration mechanisms in multiple fields. Several of them intersect with aspects related to the country's development process.From the policy perspective, some cases can be highlighted in the context of Latin America, particularly the cases of Colombia, Argentina, Mexico, Brazil, Uruguay, and Costa Rica. Chaparro et al.  show thaConverciencia as the single identified initiative designed and implemented by the Guatemala National Secretariat of Science and Technology (Senacyt) aimed at connecting Guatemalan scientists living and working abroad with their country of origin. This initiative has incorporated the participation of grassroots actors, including members of the GSD and host institutions (co-organizers). Converciencia has also mobilized broader sectors of the society, such as the media, the industry/productive sector, other public institutions, organizations from the civil society, and students in middle, high school, and universities. Converciencia has used valuable and scarce human and financial resources, which should be analyzed against the achieved results. The review of this S&T practice follows a four-level analysis adapted from Patton et al.  or significant policies addressing the relevance of the migration of skilled individuals and the eventual connection among them and with their country of origin. This research intends to bridge such gaps. This article presents a comprehensive review of n et al. , p. 31 tConverciencia since its origin.This study was carried out using a qualitative methodology in which three types of methods were used to collect data: (i) focus group discussions, (ii) semi-structured interviews with key respondents, and (iii) documentary and gray literature review. Primary and secondary data were collected to incorporate the perspectives of the three main parties involved in Converciencia. This category emphasized the selection criteria . The review of relevant literature contributes to a deeper understanding of the policy and practice analysis. Complete details of the participants can be found in the supplementary data. Following data collection, a multi-layered policy analysis was conducted.In addition, the authors developed a body of gray literature for review using annual reports from Senacyt from 2005 to 2020. This was complemented by media coverage and published news related to This section presents the discussion and findings following a four-level policy and practice analysis. This level of analysis refers to the general characteristics of the practice. It includes the most relevant features, namely, vision, parties involved, roles and responsibilities, and resources. It is essential to include the perspectives reported by all the parties involved.Converciencia as an S&T practice has taken place for over 15 years. One of its main objectives has been to connect the GSD with local scientists, the industrial sector, the academic community, and society. Institutions from the public sector involved in organizing Converciencia include the National Council for Science and Technology (Concyt) and Senacyt. Other institutions with indirect involvement in different editions have included the Ministry of Education, the Congress of Guatemala, and the Technical Training and Productivity Institute (Intecap). From the scientific community, the main representation has been the RedCTI. However, researchers and scientists working in local universities and research centers have also been part of Converciencia activities. Host institutions co-organizing Converciencia have mainly included those with presence in the capital city of Guatemala. However, in various editions, host institutions have participated in co-organizing/hosting Converciencia in a few departments (provinces) of Guatemala. Other actors with sporadic participation include international organizations, non-governmental organizations, and industry and private firms. In this level of analysis, it is crucial to describe the involvement of the main actors and stakeholders that have consistently participated in Converciencia. Converciencia are illustrated in different colors, nodes, and veins.Concyt formulates the country's science, technology, and innovation policy. It aims to direct and coordinate the national scientific and technological development with the executing functions of Senacyt  provides a detailed summary of the main highlights of Converciencia reported each year. Converciencia is referred to each year. Interestingly, some patterns emerge in terms of the absence of performance indicators and difficulties in conducting a comparative/monitoring analysis. Also, a period with no activities related to Converciencia was confirmed from 2013 to 2016.Senacyt is the government institution that, in response to a mandate by Concyt, designed Converciencia, RedCTI was founded. This was promoted by the National Secretary of Science and Technology, the Presidential Commissioner for Science and Technology, eleven members of the SD of Guatemala, and eight scientists residing in Guatemala representatives of the Council of Notables . The main objective of this network was to generate academic coordination between different Guatemalan institutions and to engage Guatemalan scientists with RedCTI. Also, part of its goals was to identify academic content of diffusion, teaching, research, innovation, science communication, and knowledge transfer for the development of Guatemala. RedCTI constitutes one of the main actions supported by Senacyt to promote S&T for national development. As of February 2022, RedCTIlicentiate), 29% (57) master's degree, and 65.9% (129) hold doctoral degrees (Ph.D.). The reported country of residence shows that 54.6% (107) indicated their country of residence as Guatemala and that 45.4% (89) reside abroad. In this aspect, a more comprehensive concentration of GSD is found in the United States with 18.3% (36), followed by Mexico at 5.6% (11) and 4.6% in France (9). Other destinations with less presence include Argentina, Chile, Canada, Germany, England, the United Kingdom, South Korea, Taiwan, Japan, etc.In 2005, with the framework of the first RedCTI were invited to participate in Converciencia. Likewise, within the framework of the event, until 2019, participation was in-person and required mobility. Members of RedCTI held a general assembly in which they declared the importance of the continuity of Converciencia. RedCTI considered Converciencia an essential space for the Guatemalan scientific community and their interaction with society, sharing the country's scientific developments.Each year, members of Converciencia, the role of the host institution was designed as a co-organizer sharing with Senacyt logistic, organizational, and follow-up responsibilities. The level of involvement of each host institution and the number of participants (single or recurrent) have changed over time. Universities, research centers, and a few public institutions have played a recurring role in co-organizing Converciencia. Among their functions, host institutions provide infrastructure (their facilities host the activities) and audience . The activities, in the context of Converciencia, include academic events  and visits to laboratories and different facilities. Host institutions have expressed their intention to have more influence on planning and organizing Converciencia. In other words, they seek to act as true co-organizers and not as passive bystanders. However, as it will be discussed later, their involvement has been limited to logistics , with no influence on the organizational aspects of the event.Since the first edition of Converciencia activities and speakers. The coverage has been focused on highlighting the academic background of the speakers, the activities in the context of Converciencia, and the roles and contributions of the host institutions, especially in cases with interactions between scientists, children, and young students. Frequently, the coverage has transcended the informative perspective incorporating a critical or reflective approach. News and reports have highlighted the importance of increased investment in science, technology, and innovation for Guatemala. Also, media posts written by scientists living in Guatemala have described the need to rethink Converciencia toward GSD engagement with local scientists beyond dissemination activities.Senacyt has reached out to the media to provide coverage for Converciencia in the leading national newspapers  means. Notwithstanding, the reviewed media coverage included three private newspapers, Prensa Libre, El Periódico, and La Hora, one public newspaper, Diario de America Central, and one digital portal, Soy502. These news reports were the ones found in online search engines using the keyword “Converciencia Guatemala.”Reviewing the news coverage and publications addressing pers see  makes itThis level of analysis refers to the argumentation regarding elements that impact or cause the analyzed policy/program/practice.Converciencia. The activities related to this practice have changed, reflecting the vision and priorities set by the institution's top authority. Converciencia's key events. The following analysis was developed according to interviews with each National Secretariat of Science, interviews with host institutions, and public report results from the annual activities of Senacyt. In countries such as Guatemala, where re-election is impossible, governmental officials may show a terminal logical behavior and strategic defection during transitions that affect policy sustainability. Also, new government officials dissociate from the outgoing government to fit in with the new government, leading to a lack of continuity  strengthening science and technology in the country, (ii) exchanging experiences with the GSD, and (iii) seeking solutions to latent problems in Guatemalan society.Secretary Rosa Maria Amaya was among the co-founders of Converciencia went beyond founding RedCTI. Not all established local researchers participated in the conception of Converciencia, only awardees of the National Science Medal. This created continuous friction over time. Various researchers working and residing in Guatemala with established careers describe Converciencia as an elitist event where they were not invited, and their work in the country was not valued.Additionally, to be the first program of its kind searching for collaboration with GSD and local scientists, the results from the first Converciencia was suspended , with a background in industrial engineering and academic and industry trajectory, d Cobar, . Rubio CConverciencia was not valuable. He did not identify a positive impact of bringing the scientists' diaspora to Guatemala for one week a year to Guatemala City to share experiences, especially when 80% of the participants were the same each year. Pokus Yanquian launched the National Week of Science and Technology in different regional locations of the country. His goal was to engage the youth to pursue science careers in other departments of Guatemala. Local researchers liked the regional approach, but the program was focused on dissemination to young generations and there were no other potential collaborations. The GSD was not invited to this event, and RedCTI was inactive. This period shows a steep decrease in the participation of scientists. The general political situation in Guatemala was unstable, reflected in the consecutive transition between three vice presidents in this period. Cobar  led him to envision linking the GSD with the scientific community in the country. He was interested in bridging the academic sectors from Guatemala with the GSD for potential collaborations. In addition, his priority was to promote science for the Guatemalan new generations. One of the first modifications of Converciencia during that period was to locate some activities in different regions of the country and different host institutions using the universities as part of the scenarios to develop collaborations. Converciencia had public open calls to apply as a speaker, making it easier for other scientists interested in the event. Host institutions selected their preferred speaker and activity from a list provided by Senacyt. The GSD participating in Converciencia felt a significant affinity to the vision of the head (related to his scientific background). Most of the scientists from the diaspora felt that their voices were heard. Nevertheless, some local scientists considered they were not the protagonists of these events, causing conflicts in their perception. Host institutions also felt left out of the planning, limiting the outcomes.Oscar Cobar Pinto was the Secretary who relaunched Converciencia as “one of 20 activities implemented by Senacyt each year.” Her vision for Converciencia was to restructure the activity to be more connected to the Guatemalan population, the country, and the industry's needs. She considered the past events of Converciencia as activity occasions disconnected from the harsh realities of Guatemala. Her perception of Converciencia was of a time and resource-consuming event with limited results and impact. In 2020, Converciencia was developed without significant changes in organization and scope; however, due to the COVID-19 pandemic, the event was entirely implemented through remote/digital online formats with no in-presence activities. Researchers appreciated that the event was online, and there were no significant expenses involved . The GSD participating in the event had a larger audience, but interactions with peers and networking opportunities were constrained.At the time of this research, Ana Judith Chan was the incumbent National Secretary of Science, who has a master's degree in International Business Law. She is a former President of the Guatemalan Mexican Chamber of Commerce and Industry (Camex). She considered This level of analysis refers to the set of ideas and the worldview held by the actors that influence (justify) their behavior, actions, and decisions. It is based primarily on their interpretation of the ideas that shape the policy/program/practice under analysis.Converciencia's limitations found in its design, coordination, and evaluation processes. These three aspects show that although Senacyt gave greater relevance to the participation of the scientific diaspora during the days of the event, they were neither included in the previous stages (planning) nor in the posterior stages . The priorities of Converciencia topics were driven by the government's vision and did not engage the GSD with the rest of the scientific system. Converciencia limitations.The analysis of Senacyt's annual reports released between 2005 and 2020, contrasted with the assessments derived from the focus groups, allows for the emergence of Converciencia was conceived as a program contemplated in the National Plan for Science, Technology, and Innovation (2005–2014), over time, the policy perspective shifted and reduced it to a yearly event. The annual reports repeated knowledge exchange as a critical characteristic of Converciencia, but according to Participant . Researchers appreciate the GSD attitude toward learning and collaboration. Media coverage of the importance of science during the event has been one of the strengths highlighted by local researchers. Moreover, Converciencia is well-established and well-known, and has been running for many years, facilitating marketing and promotion strategies. The joint doctoral program between Unam and Usac organized by one of the researchers visiting the event has been one of the outcomes of Converciencia that have generated measurable results, i.e., various Guatemalans who graduated from doctoral programs currently occupy leading positions in academia, mobility, and research exchange between Unam and Usac, and other collaboration agreements with Cambridge University and Ictp organized by the other co-founders of Converciencia.Converciencia is a one-time a year event; for most participants, it does not have continuity in collaborations with other researchers. Many activities have been organized without defining the target public; it has not been clear to the audience or the scientists participating. Stakeholders from the industry had identified the lack of participation of industry and other sectors in these events. Also, the profiles of scientists have been repetitive every year. Participants have also specified bad logistics in event planning, timing, and booking, including lack of regional participation and inclusion of indigenous scientists or indigenous participants. None of the events were available in indigenous languages. Most of the researchers residing in Guatemala consider that the program has more weaknesses than strengths. They have described the program as unidirectional without room for collaboration. Additionally, there is no evidence of the benefits achieved with the funds expended in the program. Guatemalan scientists, including the members of the RedCTI, have not been considered for co-designing the program. This group should be fundamental to Converciencia planning due to their seminal contribution to the origin of the program.Lack of engagement of local scientists with the event has been identified in all the interviews. There have not been enough incentives for local scientists to participate. There has not been a match among scientists working in the same area to generate spaces for collaboration. The number of events assigned to each scientist has been extensive, limiting only to disseminating research results and not allowing communication between different sectors to evaluate collaboration opportunities. Converciencia in collaboration with universities, research centers, RedCTI, and local scientists has been identified as an opportunity to create longer and established relationships between the participating scientists. According to the local researchers, The Converciencia funds' allocation in the Senacyt annual operation plan should continue but under a collaborative approach focusing on Guatemalan researchers' needs. Converciencia has an assigned annual budget that can continue over time and is an advantage to consider with a better restructuring and with a more collaborative approach to listening to the Guatemalan researcher's needs. Likewise, there is an opportunity to invite scientists who carry out scientific dissemination to participate in the event.One of the main opportunities identified is that the platform could allow for follow-up and concrete collaborations between scientists residing abroad and in the country. It can also promote connections among scientists, NGOs, civil society, and industries. Also, a new cohort of scientists has been identified as an opportunity. This is because of the renewed energy and innovative ideas that they can potentially bring. Designing Converciencia has been changing with every transition in the top direction of Senacyt. Moreover, there is not enough participation of established local scientists who consider Converciencia an elitist event. They perceive that the event only highlights the work of Guatemalan scientists residing abroad, along with local results. Another threat identified by researchers living in Guatemala is the potential message that science can only be done outside Guatemala. According to researchers, politicians on duty have been another threat since they guide the event for their propaganda interests. The most significant danger mentioned by all scientists is the unwillingness of decision-makers to allocate a budget to science, technology, and innovation.The main threats involve changes in policy direction, budget allocation, and structure of the event which occur when the government administration changes. The focus of Converciencia. The perspective of the host institutions is critical for explaining different challenges and opportunities experienced in their roles as co-organizers of This level of analysis refers to the “ideal” version of the policy/program/practice, for which actors and stakeholders present a set of recommendations to improve.Converciencia offers opportunities to assess its relevance as a policy/practice. Participant  in Italy. In addition, the transfer of environmental virology with the University of Barcelona was promoted. These three initiatives were promoted by three scientists from the GSD who co-founded Converciencia, Concepción Toriello, Fernando Quevedo, and Oscar Cobar Pinto. These examples shared as a common feature the need to follow up and engage in sustained collaborations as opposed to episodic, isolated activities.The evidence of successful results from ticipant  providedConverciencia should focus on inter-institutional exchanges and transfer knowledge by co-creating postgraduate programs with GSD institutions and local institutions. Another participant provided an example of results from Converciencia, a joint publication derived from the 2019 edition in which a member of the GSD working in Chile collaborated with scholars in the host institution and presented the research results in international conferences and journals.According to Participant , a visioConverciencia has responded, according to some comments from the Participant  How does Converciencia policy impact the GSD? (ii) How does it feel to the GSD, local researchers, and the research, technology, and innovation system representatives? (iii) How does it work for the GSD and local researchers?The policy cycle model has a sequential development rational approach. The model first defines the problem, then formulates the policy and implements it, and finally evaluates it  the number of dissemination activities, (ii) the number of participants, and (iii) the number of speakers. Nevertheless, some of the expected indicators from scientists and host institutions for an efficient Converciencia policy are: (i) the number of research visits from local Guatemalan researchers, professors, and students to foreign GSD affiliated institutions, (ii) the number of exchange research visits from the GSD to Guatemala, (iii) the number of co-written research proposals between the GSD and local researchers, (iv) the number of scientific publications that resulted from collaborations between Converciencia participants, (v) the number of scholarships for local students, professor, or researchers that resulted from interaction with the GSD, (vi) the number of equipment donated through the GSD management, (vii) the number of joint Master or Ph.D. programs co-created with the GSD and local scientists, (viii) the number of young Guatemalan inspired to pursue a scientific career due Converciencia, (ix) the number of research or development projects funded and approved because of the collaboration of Converciencia participants, (x) the amount of investment from the Guatemalan industry for research projects with Converciencia participants, and (xi) the number of new spin-offs or start-ups generated through research results of Converciencia collaborations, among others. Converciencia.The KB: conceptualization (lead), data curation (lead), project administration (lead), methodology (lead), resources (lead), validation (lead), and writing the original draft (lead). SA: conceptualization , data curation , supervision , project administration , writing the original draft , and visualization (lead). LGVP: investigation (supporting), visualization (supporting), conceptualization , and writing . All the authors contributed to the article and approved the submitted version.SA was employed by New Sun Road. The remaining 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. 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