Producing animal proteins requires large areas of agricultural land and is a major source of greenhouse gases. Cellular agriculture, especially cultured meat, could be a potential alternative for the environment and human health. It enables meat and other agricultural products to be grown from cells in a bioreactor without being taken from farm animals. This paper aims at an interdisciplinary review of literature focusing on potential benefits and risks associated with cultured meat. To achieve this goal, several international databases and governmental projects were thoroughly analyzed using keywords and phrases with specialty terms. This is a growing scientific domain, which has generated a series of debates regarding its potential effects. On the one hand the potential of beneficial effects is the reduction of agricultural land usage, pollution and the improvement of human health. Other authors question if cultured meat could be a sustainable alternative for reducing gas emissions. Interestingly, the energy used for cultured meat could be higher, due to the replacement of some biological functions, by technological processes. For potential effects to turn into results, a realistic understanding of the technology involved and more experimental studies are required. According to the United Nations , the worLivestock breeding requires a significant amount of natural resources and plays a big role in global greenhouse gas emissions . Also, cThis paper aims at an interdisciplinary review of literature focusing on the potential benefits and risks associated with cultured meat analyzed from both environmental and medical points of view. Interventional studies involving animals or humans, and other studies that require ethical approval, must list the authority that provided approval and the corresponding ethical approval code.in vitro meat,” “health,” “gas emissions.”To achieve this goal, several international databases and government projects were thoroughly analyzed using keywords and phrases containing specialty terms such as “cellular agriculture,” “cultured meat,” “In this paper, we have gathered and presented the results of several studies, reviews, reports and government projects. These studies cover a long period of time from 2000 to 2021. Thus, this review will emphasize on several aspects of cultured meat production, including the following issues: bioreactors , current status analysis of cultured meat bioprocessing, as well as advantages and disadvantages of cultured meat consumption.in vitro management of mammalian cells  . It all an cells . The twoIn this respect, high reproducibility, control and automation of bioreactors for specific experimental bioprocesses are key to their transfer toward large-scale applications. In general, bioreactors are used in industrial fermentation processing, wastewater treatment, food processing and the production of recombinant pharmaceuticals and proteins.As a field, tissue engineering has been defined as the application of engineering principles and methods in life sciences to the development of biological substituents to restore, maintain or improve tissue function . Normallin vitro culture of 3D cell-scaffolds is performed under conditions that support efficient cell nutrition, possibly combined with the application of mechanical forces for direct cellular activity and with phenotype. It is thus a paramount step for the development of functional grafts in the treatment of lost or damaged parts of the body ..in vitroNext, this review will present the bioprocesses of cultured meat, by analyzing the current status of this procedure and discussing the possibilities of improvement.Nowadays, cultured meat can be obtained using several methods which include cell cultivation from muscle biopsies, tissue engineering as well as organ printing and nanotechnology , 40. CurCertainly, a further question arises: how can the whole process be transferred onto a large, industrial scale? Furthermore, whether this process will not have negative effects on human health in the short and long term? These questions are closely related to the fact that growth hormone promoters are banned in agricultural systems for conventional meat production in the European Union (unlike other parts of the world).Nevertheless, it is a long way to the real muscle, which consists of orderly fibers, blood vessels, nerves, connective tissue and fat cells . This isHowever, producing a thick piece of meat similar to a real steak is still a dream, due to the need to infuse oxygen inside the meat to mimic the diffusion of oxygen as it appears in real tissue . EverythWhen it comes to the cultivation of cultured meat, a typical material used for scaffolding is represented by the collagen spheres that transform stem cells into myotubes .in vitro meat production was explored many years ago, but has not been industrialized yet. Nevertheless, there are some authors who believe that the development of stem cell and tissue engineering ensures the possibility of large-scale cultured meat production  . However culture .in vitro meat production began to emerge ..in vitroAs such, obtaining some known animal products  is based on the use of these molecules . This type of agriculture is different from cellular tissue engineering because the products related to this technology can be traded in a shorter period and are based on the usual industrial biotechnology. Also, the technology used in cellular tissue engineering agriculture has not been proven efficient for large-scale production .in vitro meat can reduce the carbon footprint and meet all the nutritional needs and desires of the consumers. Some researchers even suggest that cell agriculture is going to have beneficial effects, starting from increasing animal welfare to improving human health  and and60) a volumes . There aCultured meat production using the above-mentioned bioprocesses has obvious advantages regarding the impacts on environment, human health, land usage, water pollution and animal welfare. In the same time, it has some disadvantages referring to its quality, flavor and content. The following sections will show the benefits and drawbacks of cultured meat production and consumption.in vitro meat production systems. CH4 and N2O emissions are relatively small and therefore do not significantly contribute to global warming dynamics. In the case of CO2, the continuous emission rate determines the long-term increase in heating , due to their digestive process . Methane heating .As a result, cultured meat has a clear advantage over classical meat production, making it possible to control the working environment and the disposal of ancillary products.United Nations Sustainable Developments Goals (SDGs) of the 2030 Agenda mention Climate Action as one of the important strategies to be considered. Production of cultured meat could significantly reduce gas emissions and therefore contribute to the decrease of green-house effects .2, NOx, CH4, etc. can be captured using specially designed filters and devices  and micro-nutrients . Currently, fetal bovine serum (FBS) is a key component of the standard culture medium used in biotechnology laboratories worldwide , there is no risk of fecal contamination . Even thIn vitro meat can reduce the pollution and toxicity caused by them. Also, by using cellular agriculture it is possible to avoid the contamination with residues in vegetable farms, too, when these are close to animal farms.Therefore, there are fewer risks for microbial contamination, but more risks for substrate contamination . For indAnother important issue regarding the use of cultured meat refers to the compliance with the United Nations Sustainable Developments Goals (SDGs) of the 2030 Agenda. The production and consumption of cultured meat could represent a sustainable solution to decrease of poverty and hunger. It is well-known that third world countries have an acute need for animal protein which could be replaced by cultured meat, if only it could be produced on a large scale and low cost. Also, good health and well-being could be improved by the consumption of cultured meat. It represents a healthier and non-contaminated source of nutrients, which can even be enriched by adding various other micronutrients which are missing from the diet in some geographical areas .Government measures on travel restrictions have created a scarcity in various economic sectors, including agriculture . HoweverAlthough it is not known that SARS-CoV-2 can be transmitted through food and this has been explicitly emphasized by authorities such as the European Food Safety Authority , a decreThe taste of real meat is influenced by the post mortem processing conditions, such as storage conditions, and pre-slaughter animal conditioning designed to preserve desirable meat quality. It is important to mention the fact that the metabolic transformation after death is represented by tissue hypoxia. The result is a decrease in intracellular pH . MoreoveMoreover, we are left with some questions, namely the nutrition value of cultured meat, because the taste, the texture is related to the nutritional specificity of the animals. In addition, if the animal has more intense physical activity, the meat is much richer in protein and low in fat. Also, the distribution of the type I or type II muscle fibers depends on the physical activity that certain subjects perform. This transition can be produced in the presence of B3 Vitamin . ProbablConventional agriculture is linked to several problems, such as the environmental ones, due to the increase of greenhouse gas emission, pollution of the land surface used in industrialization, with a hint toward carbon footprint and global warming. From this point of view, we propose the calculation of the bioprocess efficiency and according to its adaptation in order to obtain a large-scale production with the lowest possible costs and a minimum degree of pollution. The organoleptic properties of cultured meat should perfectly mimic conventional meat in order to reach the consumer directly.Animal welfare issues need to be taken seriously. Certain aspects related to the impact of this type of agriculture on human health should not be neglected, such as the source of infectious diseases, antibiotics resistance, products with low nutritional value. Also, we recommend improving the culture meat with micronutrients to avoid some pathologies.In this context, alternative solutions should be found, analyzed and verified, in order to find out if they are sustainable. Cultured meat may represent such an alternative, but as it was mentioned, it is in its infancy and for the time being, it cannot be performed on a large scale. For potential effects to turn into results, a realistic understanding of the technology involved and more experimental studies are required.Future studies should be performed taking into consideration the use of novel bioreactor types. New technical solutions should represent an objective of such studies in order to facilitate large scale-production of cultured meat. Moreover, the effect of cultured meat consumption on human health should be assessed, as well as antibiotic resistance, correlated with the One Health objective. Another important goal is to study the impact of cultured meat production on the environment and the feasibility of such production from an ecological perspective. These proposals must comply with the United Nation's Development Goals (SDGs) of the 2030 agenda. Out of the 17 objectives proposed by this strategy, cultured meat production could contribute and bring some benefits to the following: zero hunger, good health and well-being, climate action and life on land .CM, IG, and MC wrote the original draft and contributed substantially to the design of the research, to the collection, and interpretation of the data. VM, AI, CR, and PU determined a critical revision of the article and data curation. DP and AN contributed to the realization of the last version of the manuscript through conceptualization and investigation. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Following the emergence of cryptocurrencies, the field of digital assets experienced a sudden explosion of interest among institutional investors. However, regarding ICOs, there were a lot of scams involving the disappearance of firms after they had collected significant amounts of funds. We study how well one can predict if an offering will turn out to be a scam, doing so based on the characteristics known ex-ante. We therefore examine which of these characteristics are the most important predictors of a scam, and how they influence the probability of a scam. We use detailed data with 160 features from about 300 ICOs that took place before March 2018 and succeeded in raising most of their required capital. Various machine learning algorithms are applied together with novel XAI tools in order to identify the most important predictors of an offering’s failure and understand the shape of relationships. It turns out that based on the features known ex-ante, one can predict a scam with an accuracy of about 65–70%, and that nonlinear machine learning models perform better than traditional logistic regression and its regularized extensions. Nowadays, one may observe a second wave of increasing interest in cryptos, with the market capitalization having reached almost $2.3 trillion in valuation . These products became popular for the first time during the 2017–2018 hype when they reached almost one trillion in capitalization. According to data provider aluation .Unlike centralized electronic money and central banking systems, most digital tokens are independent of central authorities. The control of these decentralized systems operates within a blockchain, which is an open and distributed ledger that continuously expands. The emergence of cryptocurrencies and coins led to the establishment of new disruptive products like DeFiGiven the previous studies related to this issue, we use data on ICOs to observe which characteristics of an ICO lead to its success or failure. In our case, we include a wide range of control variables that consider additional characteristics of ICOs, with our main focus on features known in advance (ex-ante). In this way, we understand in particular which factors are conducive to success. To achieve this, we employ machine learning algorithms—namely, several linear  and nonlinear  classification algorithms. Given the wide set of variables used, we argue that the nonlinear models better capture the relationships between success and its predictors and better perform classification. Moreover, we consider the technological background of the venture  and the ability to raise funds sufficient for the project’s development to lead to success. Additionally, we use novel, explainable artificial intelligence (XAI) tools to identify the most important predictors of an offering’s failure and to uncover the shape of their relationships with the outcome variable.The results of our analysis show that extreme gradient boosting (xgboost) is the best algorithm to identify scam ICOs. Additionally, our findings suggest how investors can evaluate and how ventures can conduct a successful ICO. Factors related to ventures’ technological capabilities  are important determinants of an ICO’s success. This implies that investors should familiarize themselves with DLT and blockchain technology in order to be able to more accurately understand the technical information provided by the ventures . The resInitial Coin Offerings. Predicting the Success of ICOs presents a review of the literature. In Research Framework and Methodology, we explain our methodological framework and methodology, while Data describes the dataset of ICO projects and provides some basic analysis of the data. Empirical Analysis presents the results of our empirical analysis, and this is followed by our conclusions.The rest of this study is organized as follows: we give a brief outline of cryptocurrencies, blockchains, and ICO market conditions in Starting 2016, there was a huge increase in the flow of funds to ventures through a procedure called initial coin offering (ICO), with their amount increasing from 39 total up until 2016 to 256 in 2016 alone . ICO comIn fact, this all derives from the emergence of cryptocurrencies, which goes back to 2008, following the issuance of a Bitcoin white paper by Satoshi NakamotoCompared to similar ways of raising funds for SMEs, ICOs have pros and cons. On the benefits side, the most important features are as follows: disintermediation, network values, liquidity with time savings, inclusiveness, non-conferred ownership, and unlimited investor pool . The disintermediation leads most of these companies to save on the costs of raising funds, which can be noticed in traditional ways of raising funds. Also, the company will have efficiency gains due to the use of blockchain and automation. The inclusive way of financing not only reflects the democratized way of raising funds but also the unconstrained access and active participation of investors. Additionally, there is an ability to invest in a certain portion of tokens and to quickly execute deals coupled with the near-intermediate liquidity of the market. There are usually many investors to these projects, who are very diverse and heterogeneous, as there is direct access to the global pool of investors . In the case of ICOs, ownership is not conferred, meaning that the risk capital is raised without giving up ownership rights. Lastly, there is a valuable network that is being created where the customer base is formed and the value produced through the network’s effects.In addition to crowdfunding, ICOs can be also compared to traditional ways of raising funds. For instance, an ICO resembles an initial public offering (IPO), as in both cases a venture issues digital tokens or shares to raise capital, which is then also traded on the secondary market . One of coinmarketcap.com)—or they do not even exist anymore . Therefore, we have a clear case of ICOs which turned out to be scams after a longer period.In our study, we use the data that were used previously by The main goal of our study is to identify the most important features, ones allowing us to identify scam ICOs ex-ante, as well as to discover additional insights for investors using machine learning techniques. We analyze a wide range of ICO characteristics and check which of them play an important role in scam ICOs. Our study is novel in the literature on the subject in three dimensions. First, we focus purely on the ICO features known in advance. Second, our definition of scams is wider and considers updated information not hitherto considered. Third, by using flexible machine learning algorithms, we can discover complex potentially nonlinear relationships and predict scams with greater accuracy. Finally, the application of XAI tools allows us to disclose the black-box models—i.e., to identify the most important features and uncover the shape of existing relationships between the outcome and individual predictors.A number of regulators have proposed differing approaches to the classification of ICOs. The Swiss Financial Market Supervisory Authority classifies tokens based on their underlying economic function into payment tokens, utility tokens, and asset tokens . The difIn the case of ICOs, most of the tokens that are issued are either security tokens, utility tokens, or cryptocurrencies . In most cases, the companies that employ ICOs for their projects are avoiding supervision from the regulatory institutions by issuing tokens other than security ones . The IPOs which pass the Howey test are identified as securities  and undeFrom the rational point of view, the safest type of tokens for investors is security tokens. However, this type of token makes ICOs more complicated and requires the application of know your customer (KYC) and anti-money laundering (AML) procedures . AnotherNevertheless, when discussing the usefulness of ICOs, one notes the remarkable success of top projects, ones which in fact are still performing well today. One example is Chainlink, which held its ICO back in 2017 and still performs on a top level  and is the second among DeFi ventures based on market capitalization. However, in the case of tokens, it is challenging given the absence of any performance metric, the complexity in value creation and attribution in networks, and the difficulty in applying standard corporate finance theory on blockchain-based networks . Since tRecently, there has been a lot of research related to the characteristics of the ICO market and the classification of ICOs based on differing sets of variables, where each article relied on a different methodology and arrived at insightful results. For instance, icobench.com (which is a platform that rates ICOs based on 20 different criteria). As independent variables, they use features from different groups, including ICO characteristics , market characteristics , and rating characteristics . The authors verify two research hypotheses: 1) the platform used for creating smart contracts matters to potential contributors and has an impact on the probability of a project’s success; 2) the transparency  of the information regarding ICOs impacts potential contributors and has a positive impact on the probability of a project’s success. As a result, the usage of the Ethereum platform (ERC-20) for smart contacts and dependence on the Ethereum volatility appear to be the most significant factors. Additionally, the findings indicate that the indicators of the sector of the project  and the changing influence of the quality of the team are insignificant factors. This outcome may be explained by the lack of transparency, investor uncertainty about the venture , and lack of the regulation and legal framework to control the market.Furthermore, some of the scientific studies dive deeper into the topic and discover more patterns and dependencies using advanced statistical techniques like machine learning and deep learning. trackico.com, daily trading data from coinmarketcap.com, and textual measures from ICO white papers. The white paper in the ICOs is the most important source of information. Above all, it is the best source for deducing how much technology there is in a project. In this study, technology is a natural candidate for measuring the fundamentals of an ICO since all the projects relate to blockchain and also employ this technology most of the time. In particular, a machine learning method—word embedding—is used to capture the importance of technology in ICOs. The authors calculate the Tech Index based on industry-level statistics and data from GitHub. The final sample consists of 2,916 ICOs which together raised more than $17 billion. The dataset includes features as follows: ICO start and end date, total capital raised, trading status, platform, accepted currency, the founder team, industry, links of white paper, GitHub, and Twitter. In this study, there are two measures of success: 1) Trading, equal to 1 if tokens are traded in trackico.com and 0 otherwise; 2) Success, equal to 1 if the ICO has raised any capital and 0 otherwise. To deal with textual analysis, the authors use NLP techniques like bag of words (to capture semantics between words), word2vec (for vector representation of white papers), kNN (for classification), etc. Finally, they calculate the Tech Index which is the percentage of words in a white paper which belongs to a specific set of technological words. The authors first evaluate if Tech Index is related to ICO fund-raising and assume that if entrepreneurs cannot raise any funding, the ICO is not likely to succeed. Thus, one of the most important steps to ICO success is being capable of raising funds. The results show that the Tech Index offers a good proxy for the fundamental of an ICO and a good ICO should have a higher Tech Index. ICOs with a higher Tech Index are more likely to succeed and less likely to be delisted. Although the Tech Index does not affect short-run returns of ICOs, it has a positive impact on ICOs’ long-run performance. Overall, the results suggest that an important driving force is fundamental for the valuation of ICOs. What is more, ICOs with a higher Tech Index are more likely to raise capital and more likely to be traded in the secondary market subsequently. Thus, it appears that investors do care about the technology associated with ICOs. Furthermore, the authors investigate whether the technology aspect of ICOs is associated with its underpricing phenomenon. They find that the Tech Index is positively associated with ICO underpricing, indicating that the underpricing phenomenon is more severe for technologically advanced coins, which offers evidence that investors have difficulty in fully incorporating tech-related information, and thus undervalue the ICO with better underlying technology. This can be the consequence of the complexity of blockchain technology or stem from investor inattention.m percent of its ICO price. The amount of successful ICOs can differ for a different level of m . According to the results, as the value of m increases, the proportion of the scam projects increases, leading to higher precision and lower recall. The features from the white paper and GitHub repository groups are the most important, giving F1 scores of about 0.7 when m is set to 0.1 and 0.5. Adding more features leads to progressively better precision and recall. After labelling 10 LDA topics and calculating the influence score of human-defined topics, ICOs on “gaming, gambling, and entertainment” are more likely to be scams than “exchange, payment, and smart contract”.Based on the above review, we identify a research gap related to the extent of variables taken into account in classification of ICOs. Previous studies dealt with this issue from different approaches, analyzing the ICOs in a deep  or in a In this study, we test three research hypotheses as follows:First, the characteristics of ICOs known ex-ante help to predict that an ICO is a scam. The dataset that we use (gathered from a range of sources) contains data for almost 300 ICOs with approximately 135 features (out of which 92 are known ex-ante). We state that the ex-ante features can help to predict scam ICOs with relatively high accuracy. The essence of ex-ante and ex-post features is also noticed in Second, we state that the nonlinear machine algorithms allow us to predict the probability of a scam more accurately as compared to traditional logistic regression and its regularized extensions. These models may capture potentially existing highly nonlinear relationships between the outcome and its predictors.Third, the technological background of ventures is an important factor determining the success of an ICO. The success of ICO ventures depends on their technological capabilities, where higher technological capabilities may correspond to higher quality in ICOs. Such ventures have a profound interest in signaling these capabilities to potential investors to obtain higher amounts of funding . In addition, one can expect that at least some predictors have a highly nonlinear impact on the probability of scamming and may interfere in complex interactions. To cope with these challenges, we apply several machine learning algorithms of various types. Apart from simple regularized extensions of the basic logistic regression , we use another generalization of the quasi-linear approach—namely, the Support Vector Machine (SVM) with linear and polynomial kernels that allow us to better capture more complex nonlinear relationships assuming that the identified groups are well separated. In the end, we apply a group of homogeneous ensemble learners based on decision trees, both random forest as a representative of the bagging approach and several popular boosting models. The latter include extreme gradient boosting (xgboost), catboost, and lightgbm.LASSO  is appliThe Support Vector Machine  is anothRandom forest  is one oXGBoost  represenCatboost  is a newLightgbm  is a higWe divide our data into the training (70%) and test sample (30%) and perform hyperparameter tuning on the training sample using 10-fold cross validation. All models are estimated on two different sets of variables (explained with more details in the Data part): all potential predictors and a preselected subset of explanatory variables with relative stronger individual relationship with the outcome variable.Most of the machine learning tools are “black boxes” which do not allow for easy interpretation of their results. However, various methods of explainable artificial intelligence (XAI) have recently been developed and can be used to explain or explore complex models. There are plenty of methods developed under the explainable artificial intelligence (XAI) umbrella that can be used to explain or explore complex models . SeveralIn this study, we use the structured data which was prepared and used by icorating.com, smithandcrown.com, icowatchlist.com, and coinschedule.com were used mainly to gather general information about projects, such as its name, start and end dates of the funding, the amount raised during these periods, the amounts of tokens issued, price of a token at the beginning and end of funding phases, the platform used, information about the team, information about the white paper, and social media channels.The information about ICOs was collected from multiple sources–namely, company websites, white papers, social media, and Github pages where on average ICOs have 4,700 contributors . WebsiteAdditionally, the dataset consists of dummy variables such as the availability of a website, white paper, and Github page. According to We furthermore have information about the legal aspect of the ICO, whether it takes appropriate compliance measures as know your customer and anti-money laundering or has any known legal form and jurisdiction. The information about the advisors and the team which are the backbone of the project is helpful in qualifying the ICO as more reliable where we mainly have the information about the number of advisors for each ICO and checking their educational background to assess the quality of the team. The remaining predictors are numerical with different scales.In the case of ICOs, to assess a project’s business prospects and potential, one goes through the white paper, where one can find both technical information, business strategy, and some economic and financial results of the venture. To be successful in investing, one should not avoid synthesizing the business plan which might be a schematic and intuitive interpretation of the investment product. The feature that distinguishes a good plan is the clarity, the synthesis, and the professional description of the project workflow . In our In their study, scam: an ICO which doesn’t exist anymore based on social media activities and delisting from listing platforms; 2) non-scam: an ICO which is still active in the market. The data are almost balanced—according to the above definition, we have 42.5% of scams and 57.5% of non-scams.We applied the updated classification of ICOs into two groups: 1) Before applying classification models, we performed feature engineering and initial feature selection to omit non-informative  or redundant variables. All the initial data analyses and transformations were applied based on the distributions in the training sample, in order to avoid information leakage. In the case of binary variables , we ignored those with a frequency of one of the levels below 5 (2.5% of the training sample). For three numeric features with right-skewed distribution, we applied the log transformation (log (x+1)) in the case of zeros among the values). Several variables were omitted due to a very strong relationship with other predictors . Token_share_producers_miners_ex_ante was omitted due to the large concentration of its values in 0. Finally, the full set of ex-ante features included 62 categorical and 12 numeric variables . In addition, we applied simple variable filtering and selected features based on their relationship with the outcome. This subset included 20 categorical features (with Cramer’s V > 0.1) and seven numeric predictors —it was labeled as “selected” in tables with results. Since the data are comprised of many binary features, for modelling purposes we applied range transformation for all variables, therefore all the predictors have the same range. Hyperparameter tuning was based on maximization of the F1 statistic , but the assessment of all models was based also on other performance measures: area under ROC curve, accuracy, sensitivity , specificity, precision, and balanced accuracy (average of sensitivity and specificity).Therefore, the identification of the most important predictors was based on these models. Nonetheless, all the models were also compared on the test sample, which is summarized in The results in test data show that by using previously selected models, one can still classify ICOs with an accuracy of 63–65%. Nonlinear models based on all features still have higher F1 and balanced accuracy statistics that the linear approaches, but the difference in performance measures is much lower. For LR, a substantial improvement in performance can be noticed. The LR estimated on selected variables consistently performs better for all performance measures considered. Adding backward elimination allows us to further improve ROC, specificity, and F1 statistic. LASSO and ridge regression have the most stable performance of all models as compared with the validation sample.Again, the SVM model with a polynomial kernel estimated on selected variables consistently performs well on all measures applied except specificity. However, it does not perform as well as in the validation set. The remaining nonlinear algorithms are efficient in the automated selection of the most important predictors , except lightgbm, which shows the opposite. Overall, we observe the superiority of nonlinear models in identifying scam projects, especially in the case of validation set, used for model selection, where linear models are being substantially outperformed.To see which features play a crucial role in the success factor of an ICO, we evaluate the permutated feature importance for two selected models—an xgboost and a random forest . We measHowever, to identify the shape of the relationships of these features in a more detailed way, we investigate partial dependence plots (PDP).In In All models indicate that basing the offer on the decentralized platform  or the aICOs may aptly be deemed one of the most controversial phenomena in the modern disruptive financial world. In the case of ICOs, being one of the easiest ways of crowdfunding through the usage of blockchain technology, this makes it vulnerable as a secure source of investment. Obviously, these projects lack transparency, technical understanding, and legality—and this leads to unscrupulous actors launching scam ICOs, something that spawns significant loss and places this infant world of cryptofinance in an unfavorable light.In this study, we used machine learning to verify if a scam can be predicted based on features known in advance (ex-ante) and to identify the most important characteristics that lead to scam ICOs. By integrating different types of information about ICOs, using a wide range of variables showing various characteristics of the ICO market, the system is able to predict whether a project is a scam or not. We confirmed all three research hypotheses. The ex-ante characteristics of ICOs allow us to distinguish between scams and non-scams with a relatively high probability. Taking into consideration all performance measures, we confirmed the superiority, as compared to linear models, of nonlinear machine learning models in identifying warning signs hidden below the surface. However, this superiority was mainly visible in the validation sample and much weaker in the test data. Last but not least, we notice the importance of the technological capabilities of these ventures, which are a crucial factor in the future success of a project. Although it was not obvious that all technical aspects are important, we uncovered a positive relationship with the availability of smart contract code and being decentralized. The patterns and innovations revealed may help investors to identify reliable ICO projects and to make more rational decisions.


The debate continues as to which governance structure is most appropriate for collaborative disaster response, particularly between centralization and decentralization. This article aims to contribute to this debate by analyzing the structural characteristics of a multisectoral network that emerged and evolved under strong state control during the 2015 outbreak of Middle East respiratory syndrome-coronavirus (MERS) in South Korea. This study particularly focuses on the evolution of intra- and inter-sectoral collaboration ties in the network. The data for the study were collected through a content analysis of government documents and news articles. Using social network analysis, the authors found that the network evolved into a centralized structure around a small number of governmental organizations at the central level, organizing the ties between participating organizations rather hierarchically. The network displayed a preponderance of internal ties both among health and non-health organizations and among public and nonpublic health organizations, but under different influences of structural characteristics. This tendency was intensified during the peak period. Based on these findings, the authors conclude that the centralization of disaster management may not or only marginally be conducive to cross-sector collaboration during public health disasters, calling for a careful design of governance structures for disaster response.  The COVID-19 pandemic and the history of infectious diseases have reinforced the importance of cross-sector collaboration in containing and responding to public health disasters. Cross-sector collaboration refers to “the linking or sharing of information, resources, activities, and capabilities by organizations in two or more sectors to achieve jointly an outcome that could not be achieved by organizations in one sector separately” . In counA well-integrated governance structure can make it easier to solve complex problems that arise in disaster situations, by tapping into networks of organizations that provide necessary services in a collaborative manner. Such a structure as a collection of diverse interdependent organizations  can enable early intervention to prevent or reduce the risk of disease spread by pooling and allocating resources . In addiWithin disaster management theory, there has been a debate about the appropriate governance of disaster response activities. This debate centers on the question of whether centralization or decentralization is most appropriate for coordination and collaboration problems in turbulent environments, along the line of research on public governance and management. Centralization  generally refers to the extent to which decision-making authority or power is concentrated on a single point. Some claim that centralized systems are effective in managing a surge in demand for a range of services including health care and public safety services in disaster situations to restore and maintain order and stability ,12,13. TDespite the ongoing shift towards decentralization, centralized structures have still served as the primary framework for disaster management in many countries. In the context of disaster response, centralization can be conceptualized in two forms: functional centralization and intergovernmental centralization. In a functionally centralized structure, response efforts of organizations that operate in a relatively parallel manner are aligned with the commands of a dominating authority. For example, in the incident command system (ICS), which is widely used in North America and in some other parts of the world , an incident commander or unified command team in the public sector is responsible for directing and coordinating response operations ,12,13. AThe capability and effectiveness of centralization, whether functionally or vertically centralized, in managing public health disasters has yet to be conclusively demonstrated. Some have argued, based on evidence from the use of ICS at local incidents, that a centralized system can produce a scalable and flexible structure of collaboration in response to a disaster ,13. ReceHowever, these claims have been subject to the criticism that they lack sufficient evidence . MoreoveIn the present study, therefore, we examine the influence of state-led centralization on cross-sector collaboration, among government, civil society, and private sectors, in response to a public health disaster from an evolutionary perspective, within the context of South Korea ’s response to the 2015 MERS outbreak, by answering the following questions: (1) How was the network of organizations that responded to the epidemic shaped over time?; (2) How did collaborations between health and non-health sectors and between public and nonpublic health sectors unfold in the network? In particular, how did the ratio of intra- and inter-sectoral collaboration ties change during the response phases?; and (3) How did the structural characteristics of the network influence these patterns of cross-sector collaboration?Our aim is to contribute to the literatures on disaster management and public governance by adding empirical evidence to the debate on whether centralized or decentralized governance structure is more effective in facilitating cross-sector collaboration to solve complex problems that arise during public health disasters. In this paper, we particularly focus on the structural dynamics of response networks that emerge under strong state control and on the evolutionary patterns of interorganizational collaboration in the networks within and across sectors in response to disasters caused by infectious diseases. To do so, we employ social network analysis to analyze the data on interorganizational responses to the MERS epidemic in Korea. Social network analysis allows researchers not only to capture and visualize the structure of social networks, but also to analyze and understand the patterns of interactions and relationships between actors underlying the structure. We, therefore, empirically show that a nationwide response network can evolve dynamically during a large-scale public health disaster, such as an epidemic or a pandemic, in a country that adopts a centralized national disaster management system, using social network data and methods. Our findings suggest, however, that strong government control over response activities may constrain the innovative capability to deal with unprecedented threats and challenges through dynamic collaborations among organizations across sectors in the network.MERS, first having emerged in the Arabian Peninsula in 2012, entered Korea through a traveler in May 2015 and rapidly morphed into an epidemic in early June of the same year. Korea has a two-tier system to manage health-related disasters. As of 2015, Korea’s Framework Act on the Management of Disasters and Safety (FAMDS), which defines the country’s emergency management system, granted general authority to control and coordinate disaster management affairs at the national and subnational levels to the Ministry of Public Safety and Security (MPSS) . The Ministry of Health and Welfare (MOHW) had the authority and responsibility to prevent and control infectious diseases under the Infectious Disease Control and Prevention Act (IDCPA). During the epidemic, the MOHW thus took the lead in organizing health-related measures , and the MPSS coordinated non-health-related responses, under the supervision of the Prime Minister. Despite the national emergency plans for infectious diseases, including MERS, considerable coordination challenges arose throughout the response process as the number and diversity of participants and activities sharply increased . This ouWe used social network analysis approaches to examine the emergence and evolution of Korea’s MERS response network, focusing on the patterns of interorganizational collaboration across sectors, from a network structural perspective. A network can be defined as a collection of three or more nodes  that work together to achieve a collective goal . To collTo identify the organizations that participated in each of the response phases and the ties that connected them in each of the relational categories, we conducted a content analysis of government documents and news articles. The data sources were a white paper published by the MOHW in 2016, and 6187 news articles published by four major domestic newspapers  between 20 May and 31 December 2015. The white paper contains 1125 pages that describe intra- and interorganizational activities that occurred during the epidemic, including copies of press releases and interoffice memoranda issued by government authorities. The selected newspapers reflect a diverse political spectrum from right to left. In doing so, we reduced the chances of any groups of organizations being overrepresented in the data by their political stance. Three investigators, including the first author of this paper, collected, coded, and cross-checked the data by manually reading them. First, they independently collected information about “who did what, with whom, when, and how” from the sources. To address the discrepancies between the independently coded data sets such as different organization names, different descriptions of relational events, and missing data entries, two of the three investigators re-coded the problematic sources and double-checked data coding accuracy independently first and collectively later to increase the reliability of the data. Depending on the relational type, data records were labeled as “communication”, “information sharing”, “resource sharing”, or “joint action or decision making”. In this process, we eliminated the data that fell into none of the four categories or did not contain time information. The processed data were converted into sociomatrices using UCINET 6.720  [We employed social network methods to analyze and visualize the data, using UCINET 6.720 . To captKrackhardt’s graph theoretical dimensions of hierarchy (GTD) analysis was carried out to capture the degree of structural hierarchy in the network, that is, the degree of similarity to an out-tree in which any two nodes are connected by one tie pointing towards the root. Krackhardt proposed four GTD measures which assess the extent to which a network structure deviates from pure hierarchy : connectEL = the number of external links between nodes in different groups, and IL = the number of internal links between nodes in the same group. The scale varies from +1  to −1  based on a specific attribute. In this study, nodes were partitioned according to their affiliation with the health and public sectors. It should be noted that the comparisons by public and nonpublic  nodes were performed only on health nodes as collaboration between public health agencies and health care providers and research institutions outside the public sector during the epidemic is of interest of this study. The E-I index model proposed by Krackhardt and Stern, used to determine whether, and the extent to which, collaborations occurred across sectors in Korea’s MERS response network , is as fThe E-I model incorporated in UCINET 6 produces, in addition to the observed value, a range of possible values of the E-I index, based on the number of groups, group size, and the total number of ties in the network . This allowed us to examine in two ways whether state-led centralization of response efforts facilitated or hindered cross-sector collaboration during the MERS outbreak, from a network structural perspective. First, using an expected E-I index, we investigated whether the tendency for within- or cross-sector collaboration was expected on average to appear by chance alone in the network, given the sector size and the network density, when the nodes were dichotomized by sector. In the previous section, we considered that the centralization of governance structures may affect, if not determine, who enters a response network and how flexible it can be to create ties between them. That is, by controlling or influencing the basic conditions of network formation, the government’s policy regarding centralization or decentralization of response may intentionally or unintentionally create an environment in favor of or against cross-sector collaboration in times of disaster. An expected value of E-I index thus measures the degree of “network structural effects”. Second, nodes in the network may show a higher focus on internal or external ties than the random distribution. To test for the existence of “preference effects” that go beyond the network structural effects, we assessed whether an observed E-I value was significantly different from the expected one by a permutation test. Since the E-I index models did not consider the direction of ties, symmetrized networks were used in the two sets of analyses. The network became denser over the phases, but the network density was still less than 1%, indicating a low level of collaboration across organizations. Communication was the primary reason that the nodes were connected to other nodes in all the three phases, followed by information sharing in the early response phase. Only a small proportion of nodes in the network were engaged in resource sharing in the early phase, but resources came to play more of a part in shaping collaboration ties in the network as the epidemic proceeded. The nodes were most active in making decisions or taking joint action in the early phase, whereas it decreased during the peak as well as the fall of the epidemic. A small world phenomenon was also observed in the network. The average geodesic distances indicate that the nodes were connected to one another on average through two to four other nodes. The network average clustering coefficient, which measures the degree to which nodes in a network cluster together, was low (0.25) in the early phase, but was more than doubled in the active response (0.53) and post-response (0.58) phases. These results suggest that although the network shrank in size and showed limited compositional variation between the phases, the network evolved, though slowly, stimulating network cohesion among nodes. Graph centralization and four GTD scores in p < 0.05). A similar pattern was identified in the active response phase, as the observed values for the ties in the whole network and the health only network, except the “resource sharing” ties in the whole network, were negative. In this phase, the models for all types of ties in the whole network and for the “communication” and “information sharing” ties in the health only network detected significant differences between the observed and expected values.The results of the permutation tests in The results show greater variation in the post-response phase. Overall, the prevalence of internal ties among health and non-health nodes remained intact, whereas ties among public and nonpublic health nodes moved into focus on external ties. The observed E-I index value for the whole network, as an aggregated structure of all types of ties, was negative, though the observed values for some subsets of ties  in the network were slightly positive. However, the observed values, except the value for the “resource sharing” ties in the whole network, were not significantly different from the expected ones. Unlike the whole network, the observed values for the ties in the health only network were consistently positive, and the positive value for the aggregated network structure comprising all the four types of ties among the health nodes was significantly different from the expected one. These, combined with the results reported above, suggest that under strong state control, collaboration rarely occurred across organizational lines in the response and post-response periods and that when it did, it tended to occur more within the sectors. This tendency observed in the health only network might have been related to structural properties of the network, particularly to the difference in size between public and nonpublic sectors, while other factors might have played more into the collaborations between the health and non-health nodes. This study has investigated how the structure of a network representing collaborative relationships between organizations to communicate, share information and resources, and coordinate joint actions and decisions can emerge and evolve under centralized control in times of public health disaster. Using social network analysis to analyze the data on interorganizational responses to the MERS epidemic in Korea, we have demonstrated that the network may not be completely rigid, but can show more or less responsiveness to changing situations. However, in the Korean case, strong state control over disaster operations led to the centralization of network structure with limited scalability and flexibility. In the network, cross-sector collaborations particularly between public and nonpublic sectors and between health and non-health sectors were limited whereby sectoral clusters grew as the epidemic progressed. Going one step further, our examination of the association of this preponderance of intra-sector collaboration with the structural characteristics of the network has revealed that the centralization of disaster management can facilitate or inhibit collaboration within or across sectors during disasters by shaping a response network in a particular way, e.g., determining the network size and controlling the entrance or exit of nodes in particular sectors. These findings suggest several theoretical and policy implications. First, a country’s disaster governance structure influences the structural and compositional characteristics of interorganizational networks that emerge during public health disasters. This effect is likely to lead to a centralized network structure in which decision-making authority to coordinate response activities across organizations and sectors is concentrated in a few actors at the central government. The Korean government adopted the bureaucratic command and control model of disaster management in the 1970s, and efforts have progressed towards an integrated and comprehensive nationwide disaster management system since 2004 . DespiteThe network displayed greater variation in composition than in structure across the response phases in the Korean case, as indicated by the differences in the numbers and proportions of sectoral nodes. The network size, however, consistently decreased with increased centralization of control over the response operations. Notwithstanding that the proportion of public hospital beds was only around 10% as of 2015 so that the engagement of healthcare providers and partners in the private sector was essential for successful response to the epidemic , the parSecond, the results of our E-I index analysis support the contention that strong state control over response efforts may limit the capacity of organizations to tackle complex problems through cross-sector collaborations during public health disasters. The results further show that vertical centralization can even exacerbate existing communication, information, and resource disparities between sectors. A hierarchical and centralized model of disaster management may not necessarily hinder interorganizational collaboration under volatile conditions but rather the opposite may be expected, as suggested by the increasing clustering coefficients on the MERS response network over the phases. This effect was, however, seen mostly among in-group participants, and participants were constrained and limited in their capacity to collaborate across health and non-health sectors during the response operations. The same tendency was observed when health participants collaborated across public and nonpublic sectors, and this pattern was accentuated for communication and information sharing during the peak period. Seamless and timely communication among diverse actors has been noted to be of great importance during an epidemic response to reduce the risk of spreading infections in the early stage of the epidemic as well as manage the surge of patients in the active stage through spontaneous collaboration ,21. HoweAlthough some scholars have argued for the scalability and flexibility of interorganizational response to disasters under a centralized system, many others have pointed out that this may occur at best under limited conditions. The positive and significant values of the E-I index for joint action or decision in the early response phase and for resource sharing in the active response phase, in terms of collaboration between health and non-health sectors, may be interpreted in line with this argument. The Korean government has mandated the creation of the Infectious Disease Control Committee, which is cross-sectoral, at the national level since 2010 to better manage infectious diseases through collaborative governance . In addition, Korean public health law stipulates the reasonability of medical institutions and personnel to cooperate with governments at all levels in surveillance and control of infectious diseases . Similarly, the country’s disaster management law also mandates in general terms for governmental and nongovernmental entities to render assistance upon request to local governments that respond to disasters (the FAMDS, Chapter VI, Third, the challenges of collaboration especially between health and non-health sectors under state control in disaster situations may have deeper roots than accounts of network structure would suggest. A possible account is that professional identities provide organizations and individuals with a frame of reference through which to interpret the world and decide on what course of action to take by shaping their logics and priorities . By helpOverall, our findings are consistent with other studies arguing that a centralized governance structure led by strong government leadership is not conducive to cross-sector collaboration in times of disaster. Moving one step further, we demonstrated that this tendency might cause more difficulties for collaborative problem-solving when an emergency situation is in progress, rather than when it has just happened or been resolved, from an evolutionary perspective. We also showed that these problems might be linked not only to the structural conditions of the network imposed by the centralization of disaster response, but also to deeper social, political, and other mechanisms, and thus legal institutional approaches to collaborative disaster management, such as mandating cross-sector collaboration and cooperation, might have only limited impacts. Despite these contributions, our research has some limitations. We dichotomized sectors by coding nodes as “health” if they provided health-related services or programs and “non-health” otherwise. The health nodes were identified as “public health” if they were in the public sector and everyone else identified as “nonpublic health”. This allowed us to reduce analytic complexity, but at the expense of a richer and more nuanced understanding of cross-sectoral collaboration in a centralized system during public health disasters. Future studies may wish to explore the topic with more diverse classifications of sectors. Another limitation is imposed by the nature of our data sources. Although the white paper and news articles enabled us to identify more than 1200 organizations participating in response operations during the epidemic and 2700 ties between them, they tended to focus on formal response activities by government agencies at the central level. This limited our ability to collect information about informal communication and interaction among the organizations as well as formal communication and interaction among local-level agencies and nongovernmental organizations. We suggest that future studies include post-event documents published by local authorities and news articles by local newspapers, though this may not entirely resolve the problem of data comprehensiveness. Since our analysis was conducted on a single case basis, further studies are needed to examine the generalizability to different public health disasters such as COVID-19 and to different countries. Collaboration within and across sectoral boundaries has been identified as essential to the successful management of disasters with greater frequency and intensity. While diverse modalities of disaster governance through the strong state, the entrepreneurial spirit of the public sector, and civil self-government, have been proposed to date, centralized disaster management systems have been widely used in many countries. Their effectiveness in coordinating collaborative responses by streamlining communications, pooling and sharing information and resources, and supporting joint actions and decisions across sectors has, however, been subject to much criticism. This concern is supported by the findings of this study, since vertical centralization strengthened the centralization of network structure around governmental actors who held bureaucratic power. Furthermore, the engagement of nongovernmental actors in the network was limited, and connections between participating organizations was rigid and hierarchical. While our study highlights the structural limitations of centralized, bureaucratic systems within the context of public health disaster response, they do not necessarily mean that centralization is inferior to decentralization. Since our analysis focuses only on a centralized disaster management system in a country, more studies are needed to reveal which governance structure, at least between centralized and decentralized structures, is most effective in managing the growing threats to public health. We also believe that answering the question about whether and under what conditions centralized or decentralized disaster governance works best is equally important for understanding the relationship between governance structure and disaster management performance, as well as creating and sustaining resilient societies prepared for future public health disasters.


Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559. Modern life is being transformed by big data and machine learning, from entertainment to commerce to healthcare. Netflix, Amazon, and Google all know what movies and series people want to watch, what things they like to buy, and what symptoms and ailments they are searching for. All of these data may be used to create extremely complete personal profiles, which can be used for behavioural understanding and marketing, as well as to predict healthcare trends. According to several experts, artificial intelligence (AI) has the potential to revolutionise the healthcare industry, from diagnostics to treatment. An overwhelming amount of evidence suggests that AI algorithms can already perform better than humans in a wide range of activities, such as the analysis of medical pictures and the correlation of illness symptomatology and biomarkers from electronic medical records (EMRs) .Medical professionals, particularly doctors, are in short supply in many nations due to the rising demand for healthcare services. It is not just consumers who are demanding better service and better outcomes from healthcare providers; they are also demanding it from healthcare providers themselves . Using hIt is becoming clear that an artificial intelligence- (AI-) powered tool –10 will Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder that affects the central nervous system. It is the most prevalent kind of dementia and is characterised by severe neuronal and synaptic loss. According to a recent study, AD has a high prevalence, with an estimated 40 million sufferers globally. Mild cognitive impairment (MCI) has traditionally been seen as a transitional stage between normal aging and the start of AD. As a result, AD and MCI, the transitional stage between healthy aging and dementia, which is frequently characterised by mild cognitive abnormalities but generally intact activities of daily living, have piqued the interest of researchers.Changing the healthcare strategy from a reactive to a proactive one, focusing on health management rather than disease treatment, is a major factor in these cost savings in case of cognitive impairments like Alzheimer Disease. AI-based technology will play a major role in helping individuals maintain their health through continuous monitoring and coaching and will ensure earlier diagnosis, treatments, and follow-ups.In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy.The authors investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management systemThe authors develop a real-time health monitoring for classification and assess the response time and accuracyThe authors analyse various classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive levelThe authors develop a framework with a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant classThe main contribution of the work involves the following:Distributed AI can be used to overcome the scalability, cost, and storage concerns of EHR among distributed nodes in the blockchain . SimilarMany academics have come up with effective ways to deal with the security and privacy problems of putting EHRs on blockchain. According to Esposito et al. , the patLogic expressions given by Chen et al.  were useAn IoT-based RFID tag structure that is secure and anonymous at low latency was developed by Gope et al.  to combaA collaborative, distributed, federated deep learning model training for fairness and scalability on blockchain was proposed by Weng et al. . For retBy pretraining the word model word2vec, the authors of  used worEHR deployment in Healthcare 4.0 applications is a topic covered in the aforementioned debates. There have also been discussions on the security concerns of installing EHR on the blockchain and offered solutions including privacy-preserving ones , authentAs a result, this study provides a revolutionary architecture for Healthcare 4.0 applications that incorporates lattices, blockchains, and deep learning algorithms. For this study, the main goal is twofold. In mined transactions, the blockchain guarantees immutability and chronology. In addition, by using DNN prediction on previously recorded EHR feature records, it is possible to classify and display categorised findings to users more quickly. By automating recommendations, it cuts down on feature extraction time and boosts the scalability of mined transactions. Based on previous diagnoses and current health circumstances, suggestions are made. By averting serious sickness, it allows patients to receive treatment and medication on time, potentially saving their lives.In this, we use a blockchain network for faster transaction of data from the seed node to destination node with proper security. The sparse autoencoder is used for prediction of disease from the input collected datasets from IoT devices. x = {x1, x2, x3, x4, x5} shown in As shown in hW,b(x) ≈ x is to learn a given task. This arrangement has the ability to mine data for hidden features that are not readily obvious by restricting the number of neurons in the hidden layer. A 32 × 32 image can be processed by ten thousand twenty-four neurons, for example. Using an autoencoding network with a hidden layer of 50 neurons, it is possible to produce a compact representation of the image. In this way, PCA and dimension reduction methods perform the same function as they do in practise. However, the number of neurons in the buried layer is very low. It is possible to uncover underlying features even if the number of neurons in the hidden layer is large. A limitation on the network can lead it to become sparse if the activation value of each neuron in the hidden layer is optimized. If the input neurons are utilised, then the output neurons are also utilised. m is the input neurons and ρj is the sparse parameter (0.05).To put it another way, the autoencoder network The KL distance formulation is used to optimize the sparse parameter while solving the hidden layer. In the convolution layer, the spatiotemporal convolution kernel simply gives the input of each feature map. When a network is configured manually, it has a limited ability to learn from its own experience and extract the most relevant aspects from the data. As illustrated in The output of the neural network sparse autoencoder is subjected to the network sparse constraint. In this example, the input is constrained by the sparse constraint. These two forms of operation are different, but they accomplish the same things. Useful in a variety of industries and applications, the sparse autoencoder neural network extracts low-level properties from the input data. The output layer of the network is activated by using sparse limitations on the network output side. Accordingly, we will only activate neurons in the output layer that have been trained to respond to certain inputs in order to construct the most compact representation of input data—in other words, extract the most compact representation of data.It is important to note that the previous layer input feature maps are used as inputs for each of the output feature maps in this framework spatiotemporal convolution. However, the number of feature maps that may be input into the output feature map is highly constrained because of the sparse restrictions.In this section, we present the results of the proposed DNN-BC model and the existing smart contract with DNN, distributed blockchain with DNN, and multifactor authentication with DNN classification.The results are compared in terms of different performance metrics that include packet delivery rate, throughput, delay, and energy efficiency. It is further tested in terms of classification accuracy and loss. The simulation is conducted in Python3.8, where the CNN is modelled in TensorFlow2.2. The simulations are conducted on a high-end computing engine with 16 cores and 32 threads CPU, 16 GB of primary memory, and a graphics acceleration unit: GTX 1060. The simulation is conducted on a high-end computing platform that supports the implementation of blockchains and healthcare management system with a classification module.The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI.In this paper, the study uses blockchain modelling-based DNN for optimal transmission of captured data from the sensing IoT node to the destination based on the user query. The study offers higher speed and delivery of data using a healthcare management framework. The testing on real-time data shows that the health monitoring and its classification provide increased accuracy with optimal response time based on the user query. The results of simulation show that the proposed method accurately responds to the query with high speed retrieval of classified results compared to other existing blockchain data transfer mechanisms. In future, the modelling of blockchain in a distributed ledger can be improvised with the utilization of machine learning or deep learning optimization that could be aimed at bringing effective ledgering of the addresses.


To describe the current status of regulatory reliance in Latin America and the Caribbean (LAC) by assessing the countries’ regulatory frameworks to approve new medicines, and to ascertain, for each country, which foreign regulators are considered as trusted regulatory authorities to rely on.reference (trusted) regulators defined as such by the corresponding national legislation.Websites from LAC regulators were searched to identify the official regulations to approve new drugs. Data collection was carried out in December 2019 and completed in June 2020 for the Caribbean countries. Two independent teams collected information regarding direct recognition or abbreviated processes to approve new drugs and the Regulatory documents regarding marketing authorization were found in 20 LAC regulators’ websites, covering 34 countries. Seven countries do not accept reliance on foreign regulators. Thirteen regulatory authorities  explicitly accept relying on marketing authorizations issued by the European Medicines Agency, United States Food and Drug Administration, and Health Canada. Ten countries rely also on marketing authorizations from Australia, Japan, and Switzerland. Argentina, Brazil, Chile, and Mexico are reference authorities for eight LAC regulators.Regulatory reliance has become a common practice in the LAC region. Thirteen out of 20 regulators directly recognize or abbreviate the marketing authorization process in case of earlier approval by a regulator from another jurisdiction. The regulators most relied upon are the European Medicines Agency, United States Food and Drug Administration, and Health Canada. Harmonihorities , 6. Relihorities , 7. The During the last 15 years, several countries of Latin America and the Caribbean (LAC) have reformed their MA processes, aiming to directly recognize or abbreviate the MA of new chemical entities or biotherapeutic products earlier approved by the European Medicines Agency (EMA), the United States Food and Drug Administration (FDA), and a few others, including some regulators from among the countries of the Americas . In 2009The reliance of LAC regulators on MA decisions taken by regulatory authorities from other jurisdictions has seldomly been studied. Cañás et al.  have shoIn this study, we aimed to describe the current status of regulatory reliance in LAC by assessing the countries’ regulatory frameworks to approve new medicines, and to ascertain, for each LAC country, which foreign regulators are considered as trusted regulatory authorities to rely on.We performed a cross-sectional study using official documents from the LAC regulatory authorities. All LAC countries were included. Official websites of the competent national regulatory authorities were systematically searched in order to identify the official regulation to approve new drugs.A template for data extraction was developed. We documented information regarding the legal acceptance of any kind of direct recognition or abbreviated process for approval of new drugs at the country level, the exceptions to the mechanism, and whether the country accepts these rules regardless of the MA procedure followed by the foreign regulator , the list of regulatory bodies recognized as trusted regulators to apply the rule, and name and year of the regulation. Data collection was carried out in December 2019 and completed in June 2020 for the Caribbean countries. The information was collected, analyzed, and summarized by two independent teams (authors CED and MC/MAU). Two consensus meetings were used to review the collected information and solve the observed discrepancies. These discrepancies varied in relevance. In three cases , the differences were related to the actual regulatory reliance status. For five countries  the differences referred mainly to the identified trusted regulators and the existence of regulations other than those initially identified. Country experts from Honduras and Peru were contacted, as specific unclear information persisted after the consensus round.direct recognition or abbreviated process as when the country’s law explicitly mentioned one of these rules and the process was explained. The direct recognition of MA was defined as the approval of a new drug by a national regulator if the drug had been previously approved by a regulator from another jurisdiction. Direct recognition implies the omission of the revision of the clinical information dossier; however, other legal requirements at registration time could be kept, according to the country’s legislation. We defined abbreviated process as cases where the country regulator explicitly allows a fast-track registration for the drugs previously approved by another regulator. In such cases the clinical information dossier will be revised quickly.We judged reference—or trusted—regulatory authorities for MA reliance were collected as stated in the national legislation. If the regulation mentioned one or more European countries belonging to the European Economic Area, and therefore under the umbrella of the EMA’s central approval procedure for new drugs, we summarized them as EMA. If the regulation explicitly referred to PAHO’s list of national regulatory authorities of reference, we displayed them according to the information contained in the official PAHO webpage on the System for Evaluation of the National Regulatory Authorities of Medicines   are signatories of a supranational regulatory unit, the Caribbean Regulatory System (CRS), and therefore were analyzed as a single regulatory framework in this study. After the consensus meetings and the advice from the country experts, the same 20 regulators were confirmed as the pool of regulators to be analyzed. Seven of the 20 included LAC regulators do not accept any form of MA reliance . Thirteen LAC regulators either recognize or abbreviate the MA process in case of earlier approval by a regulatory authority from another jurisdiction . In 11 countries there are no exceptions to this mechanism. In Ecuador, the rule can only be applied if the active ingredient is already included in the national list of essential medicines, and never if the product was previously approved by a foreign regulatory authority under an accelerated (fast-track) procedure. The CRS registers a new drug when it has been approved by the designated trusted authority and only if the product is named in the most recent update of the WHO Model List of Essential Medicines or in the PAHO Strategic Fund list. The CRS recommends the reviewed drug to the Member States, and thereafter they have 60 days to confirm the MA.n = 15) and Colombia (n = 14) are the countries that rely on the highest number of foreign regulatory authorities. Paraguayan regulation explicitly allows reliance on MA issued by any of the Member States of the Southern Common Market (MERCOSUR by its abbreviation in Spanish), PAHO’s national regulatory authorities of reference, EMA, and some others. Colombia is the only LAC country abbreviating the approval pathway in case of earlier approval by any of 36 Member States of the Organisation for Economic Co-operation and Development (OECD). A second group of six regulators  all rely on EMA, FDA, and Health Canada’s MA, as well as on the authorizations issued by any of PAHO’s national regulatory authorities of reference. Finally, a third group of five countries  rely on regulatory authorities from Europe, the United States of America, Canada, Oceania, and Asia, but not on any LAC regulator.EMA, FDA, and Health Canada are the trusted regulatory authorities for all 13 of the LAC regulators included. Ten of them also rely on the regulatory decisions taken by Australia, Japan, and Switzerland. Argentina, Brazil, Chile, and Mexico are reference authorities for eight countries. Colombia and Cuba are trusted parties for seven regulators, and finally, Israel, Republic of Korea, New Zealand, Turkey, and Uruguay are each cited as trusted regulatory authorities in one to three countries’ regulatory frameworks see .In this study, we systematically reviewed and documented the current state of regulatory reliance for the approval of new medicinal products in the 34 countries of LAC. To the best of our knowledge, this is the first study exploring the normative status allowing MA reliance in the region. Our results show that LAC countries have adopted the concept of MA reliance, particularly in the last 10 years. Indeed, 65% of LAC regulators (out of 20) are currently relying on other regulators’ decisions. In other words, decisions around MA taken mainly by EMA, FDA, or Health Canada are fully recognized by two-thirds of regulators in the LAC region.Most regulatory authorities in the region have taken this decision based on the idea of having limited resources to deal with a complete and critical appraisal, particularly of the new biotechnological drugs. In 2016, Ahonkhai et al.  observedHowever, the practice of fully relying on regulatory authorities from another jurisdiction to approve new drugs engenders at least three potential problems. First and most important is the growing evidence about flaws in the EMA and FDA’s appraising of the supporting evidence to approve new drugs. For instance, up to 49% of the clinical trials underpinning the authorization of new cancer drugs by EMA scored as having a high risk of bias when those were independently evaluated . EMA as Second, both EMA and FDA have implemented specific programs to abbreviate the approval process to obtain quicker access to certain new drugs. Under these programs, selected new drugs, mostly biotechnological products, are approved on the basis of non-randomized trials, surrogate outcomes, and usually on phase II of the clinical development of the drug –25, withsecondary reliance can occur. This could happen when one of the LAC regulators assessed by PAHO has approved a given drug by relying on a trusted party, and then one of the LAC non-trusted regulators decides to approve the same drug by relying on that particular PAHO reference authority. This is not easy to detect on regulators’ databases, given the difficulties in identifying which products were approved based on reliance mechanisms and which ones were not.Third, when an LAC regulator relies on a national regulatory authority of reference under the PAHO qualification process  a chain reference regulators was brought by Argentina, Brazil, Chile, Colombia, Cuba, and Mexico (together known as the Oaxaca Group). The qualification process to become a reference authority was launched in 2009  . It is pReliance may be a double-edged sword. While optimizing MA to speed access to essential medicines may be desirable, regulatory efficiency must not jeopardize in-depth and careful evaluation of new medicines. Reliance may foster cooperation and resource optimization, but it may also decrease transparency of approval pathways, induce loss of national control over approvals of doubtful new medicines, and promote quick approvals where capacities to supervise them after MA, and even to cancel the MA, is almost nonexistent.A potential limitation of our study is that most countries which have reformed their legal frameworks to allow reliance did this only in recent years and, therefore, the implementation could differ greatly among them. We did not evaluate the level of implementation of such policies at country level; it could be the case that a changed legal framework had not yet been implemented, either partly or completely, due to particular local reasons. This aspect deserves more attention in future research.Regulatory reliance has become a common regulatory practice among LAC regulatory authorities. Thirteen out of 20 regulators, covering 34 countries, directly recognize or abbreviate the MA process in case of earlier approval by a regulator from another jurisdiction. The regulatory authorities that are most relied upon are EMA, FDA, and Health Canada.Reliance might be a good regulatory practice if i) decisions taken by trusted regulators are based on the highest quality evidence and critically appraised, ii) decisions are adapted to the local context, and iii) strong teams exist within the national regulatory authorities to deal with new contrasting evidence that could emerge and change the initial appraisal of a drug. Unless these conditions are met, reliance can also lead to indiscriminate approval of products with unknown benefit.RPSP/PAJPH and/or PAHO.Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the


Pregnancy is a time of significant changes occurring in the composition of a woman’s body in order to provide support for the growth and development of the foetus. Bioelectrical impedance analysis (BIA) is used to assess the body composition and hydration status. This technique represents a non-invasive, reliable, and fast clinical approach, which is well tolerated by patients. A segmental impedance measurement might be advantageous in pregnant women, particularly in late pregnancy. The purpose of this paper is to provide a review of different applications of BIA in pregnant and postpartum women. It seems that BIA has a better prognostic potential for gestational and post-partum outcomes than body mass index. The BIA method can be successfully used to study the effect of excessive gestational weight gain in pregnancy on the development of obstetric complications. Studying the mother’s body composition and correlating it with her energy balance could facilitate the development of dietary recommendations for women. Evaluation of the body composition can provide important clues for diagnosis of gestational diabetes mellitus in pregnant women with a low risk of this disease. BIA is also used as one of the additional tests in assessing the risk of developing gestational hypertension and preeclampsia. Pregnancy is a time of significant changes occurring in the composition of a woman’s body in order to provide support for the growth and development of the foetus. Gestational weight gain (GWG) observed during pregnancy is associated with an increase in the maternal, foetal and placental tissue, changes in the amniotic and extracellular fluid, and blood volume expansion . DespiteBioelectrical impedance analysis (BIA) is used to assess the body composition and hydration status. This technique represents a non-invasive, reliable, and fast clinical approach, which is well tolerated and widely accepted by patients ,10,11,12The higher the fat mass, the higher or greater the electrical impedance . BIA invThe electrical conductivity is proportional to the amount of water or electrolytes, which allows BIA to quantify intracellular (ICW) and extracellular water (ECW) as well as the fat and muscle mass. This method is used to assess body composition ,14. The It can be successfully used in both malnourished ,23,24,25Our interest in reviewing the use of the BIA method in obstetrics and perinatology stems from our own use of this method to assess the body composition and hydration status in postpartum women diagnosed with gestational diabetes mellitus (GDM) or excessive gestational weight gain (EGWG) in pregnancy ,49,50,51The purpose of this paper is to provide a review of different applications of BIA in pregnant and postpartum women. Scientific articles in the Pubmed database, Wiley Online Library and Google Scholar on the use of BIA in pregnancy and puerperium have been reviewed.Even though BIA cannot distinguish between the maternal and foetal tissues , it is cHowever, there are methodological issues regarding limitations of the use of BIA in pregnancy. Total body water plays a key role in determining body composition, thus appropriate preparation is needed to minimize the effect of daytime total water change in the body. This “pretest” preparation involves performing the BIA procedure always at the same time of day, with no food or beverage intake for 4 h before testing and with the bladder emptied immediately before the measurement. All these requirements may be a serious disadvantage and constitute significant limitations for clinicians who wish to apply the BIA test in their pregnant patients .A complete body change during pregnancy is a potential confounding factor affecting the results of BIA measurements. In the work of Sween et al., pregnancy-specific equations were used during the BIA measurements  and theyBIA has been used in various clinical situations and pre-pregnancy and pregnancy conditions, including oedema , GDM 48,51,61,62The ICW/ECW ratio can also be used to diagnose polyhydramnios as it is supposedly low before the onset of the disorder ,79,80.The BIA method can be successfully used to study the effect of excessive gestational weight gain in pregnancy on the development of obstetric complications. The incidence of EGWG in pregnancy is increasing worldwide and it is associated with complications of pregnancy, including GDM, PE, preterm labour, foetal macrosomia and obesity in the offspring . GWG is It seems that BIA has a better prognostic potential for gestational and post-partum outcomes than BMI ,50,65. WHigher rates of newborns with a low Apgar score have been observed in obese women compared to women with normal BMI ,88. The A study by Zhang et al. revealed that bone minerals in early pregnancy were a significant risk factor for GDM . PatholoThe amount and composition of a healthy GWG varies greatly among women who are underweight, normal weight, overweight and obese . StudyinEGWG and GDM can have an important effect on the foetal development, which is manifested by an increased predisposition to obesity, insulin resistance, diabetes, hypertension and other diseases in the later life of the offspring. The “foetal programming” hypothesis suggests that adverse effects early in the development lead to permanent changes in the structure, physiology and metabolism of the foetus ,93,94,95GDM causes both short- and long-term complications for mothers and foetuses, so it is important to identify the risk factors as early as possible and implement appropriate measures to prevent their development.Liu et al. investigated a relationship between the body composition measured by BIA at 13 weeks of gestation and GDM diagnosed at 24–28 weeks of gestation . They obBIA is also used as one of the additional tests in assessing the risk of developing GH and PE ,69,75,9175% of PE patients had excessive GWG . The Da In the study of McLennan et al., attention was drawn to the early postpartum period in women who developed PE . The stuMeta-analyses revealed a two- to three-times higher risk of chronic cardiovascular disease and T2DM in women after PE compared to women with normotension during pregnancy ,105,106.Levario-Carrillo et al. investigated body composition in four groups of patients: women with uncomplicated pregnancy, women with GH, women with mild PE and women with severe PE . It was In a study by Yeboah et al., maternal serum leptin and lipid profile were analysed, also, body fat percentage was determined during the first trimester . The stuIt seems that the BIA method can be extensively used in puerperium. In early puerperium, after giving birth and before being discharged from the hospital, conducting BIA analysis of both body composition and hydration may give the opportunity of detecting certain abnormalities. On the one hand, the risk of complications in late puerperium, and on the other hand it can also be useful to predict the development of civilization diseases in the future. Being diagnosed with GDM, EGWG, PE and GH during pregnancy increases the risk of developing pathologies such as T2DM, obesity, cardiovascular diseases with ischaemic heart disease and strokes in the future. This is due the fact that obesity is characterised by low-grade chronic inflammation with persistently elevated OS; additionally, chronic inflammation and OS contribute to the pathophysiology of many so called civilization diseases . What is2, but also of TBW and ECW, where the latter consists of the interstitial water, plasma water and transcellular water [In 2018 Kimber-Trojnar et al., observed that mothers with GDM in the early puerperium, when compared with the healthy controls, presented higher levels of not only fat tissue index (FTI), which is defined as the adipose tissue mass divided by the square of the body height and expressed in units of kg/mar water . Anotherar water .As a result of the research work of Bzikowska-Jura et al., who used the BIA method, it became known that it is not the diet but the composition of the maternal body that may be related to the total protein concentration in breast milk. A positive correlation with the maternal weight, BMI, FM and muscles, and a negative correlation with TBW were reported . MaternaAdvanced body composition assessment methods in pregnant and puerperal women are an important tool in revealing any interactions between FM and inauspicious effects on the mother and her offspring. Body composition assessment helps to better understand the significance of maintaining appropriate body weight during pregnancy, which will in turn help reduce the incidence of diseases related to EGWG in women and their children. BIA could also help to identify patients at increased risk of developing different clinical phenotypes of hypertensive diseases of pregnancy in early gestation. Evaluation of the body composition can also provide important clues for diagnosis of GDM in pregnant women with a low risk of this disease. The use of the BIA method in puerperium may help in the prediction of civilization diseases in the future, such as obesity, T2DM, cardiovascular diseases with ischemic heart disease and strokes. However, more research is needed to shed more light on the correlation between the body composition and pregnancy development, which could thoroughly explain pathologies of pregnancy and puerperium.