The work of general practitioners (GPs) is increasingly characterized by digitalization. Their progress in digitalization can be described by the concept of digital maturity and measured using maturity models. The aim of this scoping review is to provide an overview of the state of research on digital maturity and its measurement in primary care, specifically for GPs. The scoping review was conducted according to Arksey and O’Malley, considering the reporting scheme for PRISMA-ScR. For the literature search, we used PubMed and Google Scholar as the main sources of information. A total of 24 international, mostly Anglo-American studies, were identified. The understanding of digital maturity varied widely. In most studies, it was understood in a highly technical way and associated with the adoption of electronic medical records. More recent, but mostly unpublished, studies have attempted to capture overall digital maturity. So far, the understanding of digital maturity of GPs is still very diffuse—the research literature is still in its infancy. Future research should therefore aim to explore the dimensions of digital maturity of GPs to be able to develop a consistent and validated model for measuring digital maturity. The potential applications of digitalization in general practices and primary care organizations are vast . ExampleGeneral practitioners (GPs) are the most important actors in primary care in European countries . AccordiWorking in general practices is increasingly characterized using digitalization. Digitalization is understood as digital transformation in the context of this study. Ebert and Duarte describe it with the adoption of “disruptive technologies to increase productivity, value creation, and the social welfare”  p. 1). . 1]. It Providing all stakeholders involved in the digitalization of primary care with a path to successful implementation would offer orientation in the transformation process. Maturity models have established themselves as strategic elements for organizations, showing them the direction along a development path . They haSeveral maturity models have recently been developed for the area of inpatient care, especially hospitals. The most widely used maturity model internationally in this context is the Electronic Medical Record Adoption Model (EMRAM) of the Healthcare Information and Management Systems Society (HIMMS). HIMMS describes it as a “strategic roadmap for [...] digital transformation journey toward optimizing organizational performance outcomes”  (p. 1). The large number of articles published for the area of digital maturity of hospitals underscores the progress of science in the inpatient sector. We take this as an opportunity to provide an overview of the state of research on digital maturity in primary care. GPs should be given equal consideration in terms of their level of digitization due to their position in the healthcare system. We want to understand how a maturity measurement can look as a roadmap for GPs in digital transformation. To this end, we examine how digital maturity of GPs has been described and measured in studies, how maturity models are applied, and whether there is evidence of correlations between digital maturity and outcomes within primary care. The aim of this work is to lay a foundation for further work on the digital maturity of GPs. To examine the state of research to date, we used a scoping review according to Arksey and O’Malley , consideThe identification of the research question was guided by the objective of our study. Primarily, our goal with the scoping review was to provide an overview of the state of the research literature on digital maturity in primary care, specifically among GPs. In particular, we aimed to shed light on the authors’ understanding of digital maturity and how it is operationalized. Therefore, we formulated our research question as follows: What does digital maturity mean for GPs in the context of digital transformation; how is it measured? On this basis, we secondarily looked at the measured results on digital maturity among GPs and whether there are signs of evidence of postulated benefits of a digital general practice in the literature.After defining the research question, we identified the studies relevant to answering it. According to Arksey and O’Malley , it is iFirst, we searched the PubMed literature database, which contains a large proportion of all internationally published studies in the health and medical sector. However, the number of hits was very low, with fewer than 50 studies per search performed. For this reason, we expanded the search using the Google Scholar search engine. Google Scholar is a search engine specifically for academic literature and displays unpublished papers. The disadvantage of a search with Google Scholar, however, is the very high, often unmanageable number of hits, that contain a lot of gray literature. Therefore, the number of hits was limited to 100 results in this study using the software “Publish or Perish” . The sofFor the definition of search terms, the challenge was the different terminology used internationally to describe GPs. We therefore oriented ourselves to the European Health Information Portal . An initFor the literature selection, we defined inclusion and exclusion criteria in advance. To answer our research question, we only included papers that reported on digital maturity and its measurement among GPs. Furthermore, included works had to show a development plan towards a higher level of digitalization. In this way, we wanted to avoid the inclusion of papers that, for example, only reported on an adoption rate of specific technologies by GPs. Thus, for inclusion, the studies had to show differentiated levels of digitalization, e.g., by using a tiered maturity model or scale. In summary, therefore, we only included studies that provided information about what digital maturity is among GPs and how it is measured. That allowed GPs to be classified in terms of their level of digitalization. Formally, we excluded only papers that were not available in German or English, as well as reviews. We did not make any restrictions about the type of organization in which GPs work. The retrieved papers were first examined independently by both authors based on their titles and abstracts and using the inclusion and exclusion criteria. The full texts were then examined. If the full texts were not available, attempts were made to request them from the authors via ResearchGate. Any disagreements between the authors at any stage of the selection process were resolved through discussion. The study selection process is shown in a PRISMA flow diagram . The diaFollowing the recommendations of Arksey and O’Malley , we nextReporting is critical for a scoping review to provide readers with an understandable overview of the state of the research literature to date . We repoThe first part of our literature search, which we performed using PubMed and Google Scholar, yielded 1111 search results. After excluding duplicates, this number was reduced to 533, which was again reduced to 29 papers after screening the abstracts. A total of 10 of these papers met our requirements according to our predefined inclusion criteria after analysis of the full texts. In the second part, we were able to identify 5 papers through websites found using Google and 19 more through reference lists. After 3 papers could not be found as a full text and further papers were excluded, we included 14 additional papers in our study pool. In total, we included 24 papers in our scoping review. The included studies were published in the period 2000–2022, with most papers (n = 5) published in 2022. In terms of study location, the majority of included studies were from the Anglo-American-speaking world. These included the United States (n = 6) and the United Kingdom (n = 6), followed by Canada (n = 5). Two papers examined the digital maturity of GPs in different countries. This included, for example, a benchmarking exercise commissioned by the European Commission on the use of eHealth by GPs in 27 EU member states. Finally, nine of our included papers were gray literature. These included unpublished papers from initiatives by governmental institutions (n = 4) and dissertations (n = 2). In the included papers, the digital maturity of GPs was understood and examined differently. As a result of our review, digital maturity could be categorized into nine context categories . In most of the included studies (n = 10), the measurement of digitalization was equated with the adoption and use of electronic medical records (EMR) and electronic health records (EHR), respectively. These included the HIMSS Outpatient Electronic Medical Record Adoption Model (O-EMRAM), an adaptation of the classic EMRAM for ambulatory healthcare settings . Other aWithin the different context categories, the method in which digital maturity was operationalized differed . These usually included standardized surveys with questionnaires. Twenty included papers measured the maturity of GPs or general practices, which varied by context (see . These uIn the included papers, a direct relationship between digital maturity and outcomes within primary care was sporadically examined. In the classification of electronic health record user types described by Miller et al. , “SystemMaturity measurements enable a roadmap for the digitalization of GPs. While the focus of maturity measurements to date has been on the inpatient sector, this study deliberately looks at outpatient care. We addressed the research question of how digital maturity among GPs is understood and measured in previous research. Our results can be interpreted as follows: First, the research literature on digital maturity in this area is still in its infancy. We found only one scientifically published study describing the measurement of digitalization of GPs as a whole . In addiResearch on digital maturity in the context of expected process improvements and healthcare improvement seems to have been more valuable in the past. With the guiding principle of “outpatient before inpatient,” countries with a strong focus on inpatient care, such as Germany, want to strengthen outpatient care . It is tFollowing on from this, the results showed that, despite initial attempts, there is still no uniform understanding of the digital maturity of GPs. The context of the maturity studied varied widely. Maturity was examined in general, as well as more specifically, by the maturity of technologies such as the physician information system or the electronic health record. The latter took center stage in our review. Looking at the publication data, it was noticeable that more recent work was devoted to a holistic understanding of digital maturity, while older work focused primarily on technologies such as electronic health records. We attribute this change to a shift in the fundamental understanding of digitalization. Digitalization—or in this case digitization—stems from a technical understanding that was described by the transformation of analog into digital processes . The useIn connection with the section above, our findings showed parallels to the level of digital maturity in inpatient care. We found that, so far, primarily the use and capabilities of technologies have been equated with maturity. Similar findings can be seen in maturity models of hospitals. Representative examples include the EMRAM of the HIMSS or the Digital Maturity Self-Assessment of NHS England ,28. AlthFurthermore, we would like to discuss the last part of our research question, which asks about signs of evidence of postulated benefits of a digital general practice. This question requires researchers to ask whether there are already existing effect measures for digital health in general. There is a lot of controversial discussion, e.g., to use health technology assessment for digital health ,67. One Finally, our scoping review showed that there is a need for research on the expected relationship between digital maturity and impact on primary care. Overall, few studies have addressed the question of whether high levels of digital maturity are associated with positive patient care outcomes. Consequently, there is a lack of studies on the evidence of a digitally advanced general practice. Evidence of improvements in care is important above all, as it motivates all relevant stakeholders to increase their digitalization efforts. For the future, it will be important not only to understand digital maturity and operationalization, but also to study the impact on care.The conclusions are to be classified against the background of limitations of our scoping review. It is possible that, despite our broad search strategy and the use of search engines such as Google Scholar, we did not find all literature relevant to the topic area. Similarly, the selection of search terms used and the inclusion and exclusion criteria may have led to limitations. Flott et al.  noted thThis review provides an overview of the state of research on digital maturity in primary care and, specifically, among GPs. Our findings revealed that research on digital maturity is nascent. Research gaps exist regarding a unified understanding of digital maturity, its operationalization and measurement, and its impact on primary care outcomes. Future research should aim to develop a consistent and validated model for measuring the digital maturity of GPs. This requires a deeper understanding of the meaning of GPs’ digital maturity. Future work should therefore focus on the dimensions of digital maturity. To this end, for example, surveys could be conducted among relevant primary care stakeholders on the one hand. On the other hand, dimensions of digital maturity from the inpatient sector should be examined for transferability. Comparisons should therefore be made between the digital maturity of a hospital and a general practice. In particular, the human factor, which has been little represented to date, but is considered essential, should be given attention for future models. Finally, the relationship between digital maturity and positive effects on patient care needs to be investigated more intensively. Until effectiveness is proven, digitalization efforts will have little success. Scientific evidence of improved patient care and financial benefits, on the other hand, can give physicians incentives to embrace digitalization. They also legitimize policymakers to allocate more resources to digitalization in primary care to improve healthcare in the long term.


Lagovirus europaeus virus. So far, there have been no studies on the potential of defensins in rabbits infected with Lagovirus europaeus virus.Acute liver failure (ALF) is a rare and severe disease, which, despite continuous advances in medicine, is still characterized by high mortality (65-85%). Very often, a liver transplant is the only effective treatment for ALF. Despite the implementation of prophylactic vaccinations in the world, the viral background of ALF is still a problem and leads to many deaths. Depending on the cause of ALF, it is sometimes possible to reverse this condition with appropriate therapies, which is why the search for effective antiviral agents seems to be a very desirable direction of research. Defensins, which are our natural antimicrobial peptides, have a very high potential to be used as therapeutic agents for infectious liver diseases. Previous studies on the expression of human defensins have shown that increased expression of human α and β-defensins in HCV and HBV infections is associated with a better response to treatment. Unfortunately, conducting clinical trials for ALF is very difficult due to the severity of the disease and the low incidence, therefore animal models are important for the development of new therapeutic strategies. One of the best animal models that has real reference to research on acute liver failure (ALF) is rabbit hemorrhagic disease in rabbits caused by the  Although it is a relatively rare clinical entity, available data indicate an incidence of 1-6 cases per million people per year, accounting for 6% of liver-related deaths and 7% of orthotopic liver transplants in the United States . It is eOryctolagus cuniculus) with Lagovirus (L.) europaeus virus, which causes severe Rabbit Hemorrhagic Disease (RHD)  present in almost all organisms that are integral to innate immunity. The current state of knowledge about defensins shows that they are characterized by a broad spectrum of antimicrobial activity against bacterial, viral and fungal pathogens, but also exhibit multifaceted immunomodulatory functions. These features of defensins made them a subject of research for the development of therapeutic preparations for infectious diseases . Literatse (RHD) .L. europaeus infection in research on ALF, we also review the state of knowledge about defensins in rabbits. In this way, we outline the need for further research to use the data obtained in the future to develop therapeutic strategies for the treatment of ALF in humans.In this review, we summarize the key information regarding ALF induced by viral infections, and also shed light on the use of defensins as potential therapeutic agents. Considering the usefulness of the rabbit model of 2Acute liver failure (ALF) is a form of severe liver dysfunction in a patient with no previous history of liver disease. ALF is characterized by severe acute liver injury with encephalopathy in a short time. Defining the root cause of ALF is ambiguous. In developing countries, viral infections  are the main cause. In turn, in developed countries, in the United States and most Western European countries, drug-induced damage is considered to be the dominant cause of ALF. This difference results from definitely better sanitary conditions in these countries, as well as the availability of preventive vaccinations , 11. In Estimates indicate that HAV (Hepatitis A Virus) accounts for 3% of ALF . HoweverAnother important factor of ALF is HEV (Hepatitis E Virus), as it is indicated to be a common cause in Africa and Asia, and accounts for 0.4% of ALF cases in the USA . In turnData on HCV (Hepatitis C Virus) infection with ALF show only isolated cases of this association. In a study by Rao et al.  only thr3Lagovirus europaeus infection, which causes Rabbit Hemorrhagic Disease (RHD). Terblanche and Hickman , which together with cathelicidins are included in the category of antimicrobial peptides (AMPs). Defensins are produced by the innate immune system and have a broad spectrum of antibacterial, antiviral and antifungal activity . They arThe mechanisms of antiviral action of defensins also focus on direct defensin-virus interaction. They interact with the lipid bilayers of enveloped viruses, and some are also capable of binding to glycoproteins and glycolipids. They can also engage in protein-protein or protein-DNA interactions, and defensin oligomerization and conformational stability can also affect binding. Moreover, the antiviral activity of these peptides is relatively dependent on the type of defensin and virus that we are considering in a given case . Defensi5The rabbit is a special organism in the context of defensins. The first mammalian defensin, then called a bactericidal cationic protein, was isolated for the first time from rabbit lung macrophages in 1980 by Lehler et al. , 67 in 1In vitro studies have shown that defensin NP-1 has a broad spectrum of antimicrobial activity against bacteria , therefore the search for effective antiviral drugs seems to be a desirable direction of research. Defensins, being natural antimicrobial peptides, have considerable potential for use as therapeutic agents in infectious liver diseases. Previous studies on the role of human α and β defensins in liver diseases caused by viral infection confirmed the significant role of defensins in the course of the disease. In patients infected with HCV and HBV, a significantly increased expression of defensins is observed, which translates into greater effectiveness of treatment of patients and confirms that defensins are powerful antiviral agents with high therapeutic and prognostic potential in liver diseases. In the case of ALF, conducting clinical trials is quite problematic due to the low incidence and severe course of the disease, due to this fact, studies on the role and therapeutic potential of defensins in liver diseases are scarce, therefore the use of animal models is important for the development of new therapeutic strategies. While searching for an appropriate animal model for ALF, it was observed that the course of rabbit hemorrhagic disease in rabbits is very similar to the course of Acute liver failure. Therefore, experimental infection with All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


The reviewed measures are used to assess predictability across different objects, starting from time series  to network links. Through experiments, we establish a noticeable relationship between measures of realized and intrinsic predictability in both generated and real-world time series data . The discovered correlation in this research holds significant value for tasks related to evaluating time series complexity and their potential to be accurately predicted.Driven by the variety of available measures intended to estimate predictability of diverse objects such as time series and network links, this paper presents a comprehensive overview of the existing literature in this domain. Our overview delves into predictability from two distinct perspectives: the  Nowadays, the task of forecasting is a crucial topic with wide-range applications and significance in various fields. In fact, forecasting provides valuable insights into the future, helping individuals, organizations, and societies plan and adapt in a dynamic and uncertain world. Time series ,2 and neDespite the extensive range of developed forecasting methods, researchers rarely address questions concerning the overall predictability of an object and its upper bound. Nevertheless, there are studies that delve into this matter, and the predictability measures they propose encompass a wide range, reflecting diverse viewpoints from various scientific domains, including dynamical systems  and inforealized predictability) and as a data property not depending on a certain model (intrinsic predictability). A significant part of this research is dedicated to the analysis of the correlation between realized and intrinsic predictability measures. Despite the extensive body of work on estimating object  predictWe formalize the predictability measure concept, specifying measures of realized and intrinsic predictability;We provide a comprehensive survey of predictability measures for time series and network links, encompassing both realized and intrinsic predictability measures;We provide the correlation analysis results between intrinsic and realized time series predictability measures, offering valuable insights for assessing achievable forecast accuracy levels prior to the forecasting moment.Motivated by the above-mentioned issues, we provide an overview of existing predictability measures developed for various types of time series  as well as for network links. Moreover, we conduct an analysis of the correlation rate between intrinsic and realized predictability measures, utilizing both artificial and real-world time series. Our findings reveal statistically significant correlations between specific pairs of these measures. In short, the impact of this study is as follows:The task of predicting the future has become a fundamental practice for many scientists across various disciplines. As a result, a multitude of forecasting methods have been devised, catering to a diverse array of objects and phenomena. This extensive range of objects has led researchers to explore predictive techniques in fields as varied as economics, climate science, medicine, social sciences, and more.Among the various entities whose behaviors have been subject to prediction, time series ,2 and neWe can highlight certain methods that are widely utilized across diverse fields. Among these, linear and nonlinear regression models stand out as some of the most straightforward techniques. What makes them particularly appealing is their simplicity, as they do not demand significant computational resources and can be constructed without reliance on specialized tools or software. Regression models continue to find applications in research and practice, as evidenced by studies such as ,19,20.The autoregressive model ranks among the most popular forecasting methodologies, largely due to its well-defined algorithmic framework for model construction and parameter selection . The resNeural networks represent a specific type of nonlinear functional architecture that involves iteratively processing linear combinations of inputs through nonlinear functions . ArtificWell-established classification methods, including decision trees, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM), among others, have demonstrated their applicability in predicting links within a network , achieviThe approaches discussed in the referenced papers are oriented towards the development of methods capable of making predictions with the desired level of quality on test datasets. However, it has been observed that authors often overlook certain questions: What is the overall predictability of this object? Can a model be devised that exhibits superior performance on this dataset?To validate this concept, we introduce a citation network comprised of the regarded papers focused on forecasting and predictability assessment methods for various objects, such as time series and network links. The network and its connections are depicted in In this section, we aim to clarify the distinction between prediction accuracy and predictability. We provide a comprehensive survey within the field of predictability and the measures employed for its assessment.m is the forecasting model and S is the object or class of objects. For example, different forecast quality metrics, such as MSE, MASE, RMSE, etc., are nothing but realized predictability measures. The second type, intrinsic predictability, is independent of the model used:S is the object or class of objects. Calculating theoretical estimates of intrinsic predictability for objects can be challenging, even in the case of simple data classes [To evaluate the quality of predictive models on data, methods of predictability analysis for the modeling object can be employed. According to , the con classes . TherefoLow values of realized predictability could indicate not only the inherent complexity of the modeled object but also potentially inadequate quality of the selected model. Conversely, intrinsic predictability is independent of the forecasting model and can, therefore, be utilized to assess the data quality.One of the central questions that motivated the experimental aspect of this study is how to establish a connection between quality measures (RPr), Researchers have developed various approaches to measure the predictability of diverse objects, including univariate and multivariate time series, categorical time series (event sequences), network links, and more. It is important to note that there is not a singular methodological approach in this field, as different researchers approach this issue from the perspectives of various scientific domains, such as information theory, dynamical system theory, approximation theory, and others. In the following sections, we provide an overview of the existing approaches used to assess the predictability of different types of objects.Prior to discussing time series predictability measures, we focus on the notion of intrinsic unpredictability (IPr) concerning a random variable. The definition of an unpredictable random variable was introduced in reference . A randoSpecifically, when Equation  suggestsMethods for estimating univariate time series predictability can be categorized into two groups: methods for estimating sample predictability and methods for estimating intrinsic predictability. The first group of methods aims to evaluate realized predictability (RPr) and encompasses measures that analyze forecast quality. The second group comprises intrinsic predictability (IPr) estimation methods, which are often rooted in information theory approaches, particularly those involving Shannon entropy. We will now delve deeper into each of these groups.The starting point for developing sample predictability measures is the work of , in whicFurthermore, this group includes the work , which iThe second group of methods for analyzing the predictability of univariate time series is based on (but not limited to) estimating various forms of entropy for the series, thereby allowing the assessment of data properties rather than models. This group of methods, in turn, is further divided into two subgroups: methods for assessing the predictability of the entire series and methods for assessing predictability on specific scales. Different scales of the series refer to series obtained from the original by retaining only those values that occur at specific intervals of time. Investigating the predictability of the series at different scales can provide valuable information about long-term correlations in the data, which can subsequently be utilized in training predictive models.k, such that the subsequence starting from i with length k does not appear as a continuous subsequence of In , the ShaThe authors of  made slim with a time delay m elements, A.In Reference , permutaThere are also methods for measuring the complexity of a time series that take into account not only the order in which its elements are arranged, but also the amplitude of the series values. To incorporate the amplitude of the series values in assessing its complexity, weighted permutation entropy (WPE) was developed . The comIn Reference , the relAnother measure of intrinsic predictability for a time series, based on entropy, is the Wavelet Energy Entropy Measure (WEEM) . WaveletIn Reference , a methoThe obtained power spectrum In , the autX for this series, which can be written as follows:X and X, calculated as To calculate the SVD entropy value for a time series Furthermore, in , the autAll the mentioned approaches to measuring the predictability of a series are aimed at analyzing the entire series. However, it can be useful not only to answer the question of how predictable a particular series is, but also to analyze in what scale this series exhibits high or low predictability (IPr).m (chosen length of subvectors) and r (chosen filtering level or neighbourhood radius) consists of the following steps:m-dimensional space, defined by Form a sequence of vectors i, Use the sequence Define Calculate the Approximate entropy value:In , one of m (chosen length of subvectors) and r (chosen filtering level or neighbourhood radius) can be found by the following expression:Sample entropy, proposed in , expressThe authors of  conducteFurthermore, for the analysis of predictability in different scales, approaches based on multiscale entropy are employed. Multiscale Entropy (MSE)  is widelAs mentioned earlier, different researchers approach the question of predictability assessment from various scientific perspectives. In , five meThe goal of assessing the predictability of components within a multivariate time series  is to select a set of features that best describe the behavior of the object and enable the model to make forecasts of the desired quality. In fact, there may be situations where using a particular feature as a predictor does not improve the forecast quality, complicating the search for patterns in the data. When dealing with large volumes of input data, there arises a need to match the original features with a specific subset of smaller-sized features that can make the model’s forecasts more stable and effective. However, standard dimensionality reduction methods are focused on preserving data properties unrelated to predictability, which introduces the risk of losing important information contained within the data .There is an entire group of methods ,80,81,82Indeed, in , a methoThe Forecastable Component Analysis (ForeCA) method  also addA similar task of finding a set of features is considered in  (PredictThe method from  (Graph-bAll the methods discussed in this section (apart from  that theThis section is dedicated to the predictability of (often short) univariate time series consisting of categorical values. In practice, these time series can be referred to as events or event sequences. For instance, in , sequencIn Reference , the autIn ,88, the Most of the studies on network link predictability discussed in this section are focused on assessing intrinsic predictability. In Reference , a netwoFurthermore, to assess the predictability of network structure, the normalized shortest compression length of the network structure can be employed . Any netAnother work focusing on the assessment of the intrinsic predictability of network links is . By consThe authors of  assessedFurthermore, there are two studies ,92 focusMoreover, there are methods that involve converting time series into networks. These methods can be categorized into three classes based on the type of resulting network : (a) proHowever, to the best of our knowledge, there is only one study  that anaTo assess the predictability of data, there is a multitude of measures that allow working with various objects: univariate and multivariate time series, categorical time series (sequences of events), and network links. In the case of time series, measures are developed to evaluate the predictability of the series as a whole, as well as at specific scales. The diversity of measures presented in the studies is due to researchers expressing their perspectives on predictability assessment from various scientific domains, such as information theory, dynamical systems, approximation theory, and so on. However, despite the variety of existing predictability measures, the challenge of proper analysis of connection between intrinsic and realized predictability remains unresolved. In the next section, we will try to shed a light on this problem.The objective of the second part of the paper is to investigate the correlation between intrinsic and realized predictability. Exploring the presence of a connection between the measure of forecast accuracy and the measure of intrinsic predictability proves valuable in addressing the challenge of evaluating forecast quality prior to the forecasting moment. It is worth noting that similar work was conducted in , focusinN time series of a certain length, denoted as m forecasting models, represented as The problem can be formulated as follows: consider a set of The task of examining the correlation between intrinsic and realized predictability is accomplished through four key steps  is a type of recurrent neural network designed to capture long-term dependencies within data sequences. The LSTM model was introduced in  and contThe LSTM architecture employed for predicting the next observation in the time series comprises a single hidden layer of 50 LSTM units and an output layer tasked with predicting a single numerical value. The input shape is determined by the number of time steps and the number of features. In this context, the number of time steps is set to 10 (further elaboration on the selection of time steps is provided). Given that the experiments utilize univariate series, the number of features is one. To train the model, the Adam optimization algorithm and the Mean Squared Error (MSE) loss function are employed.Two tree-based machine learning algorithms are applied: Random Forest and XGBoost (eXtreme Gradient Boosting). Decision trees are a supervised learning method that works by recursively splitting the input data into subsets based on the values of different features. Each split is determined by a decision rule, which is chosen to maximize the separation of the data into different classes or to minimize the prediction error.Random Forest is an algorithm for solving classification and regression problems based on decision trees and ensemble learning. In fact, the algorithm is based on bagging, so all trees of the ensemble are constructed in parallel on independent samples.Similar to Random Forest, Gradient Boosting is based on ensemble learning, but in this case, the type of ensemble method is boosting. This means that each tree in the ensemble is constructed in such way as to focus on instances where the previous tree failed. Thus, one tree learns from the mistakes of another tree.In this study, the Random Forest algorithm is employed with the following parameters: an ensemble consisting of 1000 trees, and the squared error function is utilized to evaluate the quality of splits. Similar to the LSTM model, the input shape consists of the number of time steps and the number of features, following similar data processing steps. XGBoost is also employed, utilizing 1000 gradient boosted trees, with the squared error serving as the corresponding metric for performance evaluation.Convolutional Neural Networks (CNNs), originally developed for image data, are increasingly being applied to address time series forecasting problems. As a result, we also incorporate this model into our study. A typical CNN consists of three types of layers: a convolutional layer, a pooling layer, and a fully connected layer. However, the architecture can vary depending on the specific application and design choices.The CNN model used in this study follows the architecture outlined below. The initial layer is a convolutional hidden layer that operates over sequences. This layer consists of 64 filter maps, and the kernel size is set to two. The ReLU activation function is applied. Following the convolutional layer, a pooling layer is employed to filter the output of the previous layer, capturing the most important features. Subsequently, a flatten layer is used to transform the feature maps into a single one-dimensional vector. A fully connected dense layer interprets the features extracted by the convolutional segment of the model. The final dense layer is designed to predict a single value, representing an observation from the time series. Similar to the LSTM model, the Adam optimization algorithm and MSE-loss function are utilized for model fitting.Entropy and its various modifications are employed to assess the intrinsic predictability of the series, namely, Entropy estimated via Lempel–Ziv data compression , PermutaIt is worth mentioning that measures of intrinsic predictability are computed on the training set, whereas forecast errors are computed on the test set after the model’s predictions are obtained. The assumption is that by calculating measures such as entropy for the series, we can gain additional insights into the intrinsic complexity before applying the forecasting model.https://github.com/Anthony-Cov/Timesergraph/blob/main/ArtSerGenerator.py, accessed on 7 September 2023).To ensure the reliability of the results, both real-world and artificial series are utilized in the experiments. Artificial series are created by combining various components, such as periodic components, white noise, and random walks, in different proportions. The general equation for the generated series is as follows:https://github.com/Anthony-Cov/Timesergraph/tree/main/RealWeekly, accessed on 7 September 2023), while the rest is taken from the Kaggle website .To assess the correlation on real-world time series, another set of 250 series is collected, reflecting the dynamics of various socio-economic indicators. This dataset primarily includes stock prices of various companies, along with dynamics of consumer loans, crude oil prices, deposits, unemployment rates, capital expenditures, inflation rates, and many others. Part of the series is available on our GitHub . Subsequently, all the series are normalized to ensure that each value falls within the range from 0 to 1. This normalization is necessary to facilitate the comparison of forecast errors on an absolute scale. Finally, the dataset is split into training (800 observations) and test (200 observations) samples, starting from the last observation of each series in reverse order.Not all the forecast models used in this study can directly accept a nontransformed training sample as input. Therefore, the time series are transformed to be represented as a supervised learning problem, allowing them to be compatible with the models used.n is the number of steps considered for learning, and i is the index of the time series: In The transformed data are divided into train and test periods based on the selected threshold (80% of the data for training and 20% for testing). As mentioned earlier, five models are employed to generate forecasts . Using the forecasts generated by these models, the measures of realized predictability are computed. Additionally, based on the training dataset, measures of intrinsic predictability are calculated. Consequently, a dataset is compiled for each of the five models, containing values of predictability measures  for each time series.In In x and y. The p-value is employed for this purpose. Essentially, the p-value signifies the likelihood of committing an error in rejecting the null hypothesis (Type I error). The chosen significance level, denoted as Subsequently, all the datasets are merged, encompassing all forecasting models and predictability measures. Pearson pairwise correlation coefficients are then computed, and a correlation matrix is compiled . The obtp-values for these pairs are all below Upon analyzing the correlation matrix in p-values are considerably below 0.05, indicating that we can reject the null hypothesis at the 5% significance level.The Pearson correlation test is parametric, requiring the assumption of normal distribution for each of the compared variables. When assessing correlations among variables with nonnormal distributions, including those measured on an ordinal scale, the Spearman rank correlation coefficient is more appropriate. The plots for several pairs of measures are displayed in Hence, in the case of the generated series, a strong correlation between measures of intrinsic and realized predictability is evident. The significance test outcomes affirm that the computed correlation coefficients for the mentioned pairs hold statistical significance.Similar experiments are also conducted on real-world time series. However, it is crucial to acknowledge a limitation that has been identified at this stage of the study.Among the forecasting models used for the experiment, two models based on random forests are included: Random Forest and eXtreme Gradient Boosting. The nonlinear nature of random forests can provide an advantage over other algorithms, which is why these models are frequently employed for forecasting tasks. Nevertheless, it is essential to be aware that random forests are not capable of extrapolation. They can solely provide predictions based on the average of labels previously observed. This behavior can present challenges when the range of inputs during training and testing periods differs.Considering this limitation, models based on random forests are excluded from the experiment involving real-world time series. This decision is made because the range of values in the test sample significantly diverges from the range of values in the training sample. As a result, analyzing forecast errors  under such circumstances would be incorrect.In the correlation matrix with Spearman correlation coefficients , severalIt is worth noting that in some cases, the pair correlation for certain models  in the case of generated time series , and a nHence, the connection between forecast errors  and the measures of intrinsic predictability is investigated. The conclusion about the presence of correlation proves highly beneficial in scenarios where evaluating time series complexity and its potential to be predictable with high level of accuracy is crucial. Estimating predictability using intrinsic predictability measures prior to the forecasting moment could notably streamline the process, minimizing time spent on model selection, parameter tuning, and other stages, especially for time series with evidently low predictability.intrinsic predictability, which represents a data property independent of the chosen forecasting model and serves as the highest achievable forecasting quality level, and the realized predictability, which is a selected quality metric for a specific data–model combination. We regard existing in the literature measures of intrinsic and realized predictability, applicable to univariate, multivariate, and categorical time series, as well as network link predictability estimation.Motivated by the multitude of existing measures for estimating the predictability of different objects , this paper proposes an overview of existing works in this field. Specifically, we investigate predictability from two distinct perspectives: the In the second part of our paper, we provide an analysis of the correlation between measures of realized and intrinsic predictability, which employs five forecasting models, five measures of realized predictability (forecast errors), and six measures of intrinsic predictability. Furthermore, to ensure the reliability of our findings, the experiments are conducted using both generated and real-world time series data. The presence of a strong statistically significant relationship (at a significance level of 5%) between predictability measures is obvious in the generated series. This relationship is observed not only across specific forecast models . Secondly, in the case of time series with changing over time nature, predictability measures should be applied carefully .The results of this research regarding the observed correlation are highly valuable for tasks that involve assessing the complexity of time series and their potential to be accurately predicted. Utilizing intrinsic predictability measures for predictability estimation prior to the actual forecasting moment can considerably reduce the time and effort required for tasks such as selecting an appropriate forecasting model and tuning parameters, especially for time series with evident low predictability. Further elaboration of this research is possible in the direction of inclusion of case studies illustrating how the obtained results can be applied to enhance the time series forecasting process. It will be also useful to expand the results of the experimental study on the network links predictability measures. This task is planned as a part of our future work.


The studies of hospital resilience have been of increasing importance during the last decade due to disasters and pandemics. However, studies in developing the domain and indicators of hospital resilience were limited mainly on disaster response. A few studies of hospital resilience focused on how to deal with disruptions such as environmental turbulence, rapid technological changes, and changes in patient preferences. This study aims to develop domains and indicators of hospital resilience in facing the disruption era.This qualitative study focused on exploring the domains and indicators to face disruptions that have been identified in the first exploratory phase of the studies. Key informants included hospital experts from the government, medical practitioners, and academics. A total of 20 key informants were involved in semi-structured interviews which were conducted face-to-face, via telephone and Zoom. Data was analyzed using a grounded theory approach to discover domains for a resilient hospital.The study identified a number of domains that are fundamental for a hospital to become a resilient in the face of disruption. These include readiness to face digital transformation, effective leadership, and flexibility in managing resources among others. Situation awareness and resilience ethos, implementation of marketing management, networking, and disaster anticipation are found to be equally important. These domains focused on the hospital’s ability to deal with specific shocks from different perspectives as the result of changes from disruptions which are inevitable within the organizational business environment.The domains identified in the study are able to respond to the limitations of the concept of hospital resilience, which is currently more focused on hospital disaster resilience. They can be used to measure hospital resilience in the context of the volatility, uncertainty, complexity, and ambiguity (VUCA), which are relevant to the context of the Indonesia hospital industry.The online version contains supplementary material available at 10.1186/s12913-023-10416-8. The concept of hospital resilience has gained popularity due to the high number of health crises caused by natural and non-natural disasters in the past decade –5. HospiDuring the last decades, resilience concepts in health studies have focused on how to develop domains and indicators –13 and hDisruption in health care could include technological, economic, and societal changes , 29. AddTo date, comprehensive hospital resilience concepts are still being developed. In 2022, the WHO developed a toolkit regarding health system resilience in various contexts, including technical resources of resilience concepts at the operational level . Most ofResilience concept has been used across a number of disciplinary fields –46 and hThe resilience concept has been implemented at each level of healthcare system in the health sector, particularly in hospitals. Studies of hospital resilience as an organization has developed domains and indicators to measure hospital resilience , 49–52. In business and management literature, there are two views of organizational resilience theory, namely static perspective –56 and dIndonesia has a healthcare system comprising public and private providers. The public system in Indonesia follows the decentralized governance system, where the responsibilities are divided among the central, provincial, and district governments . There aThe development of the hospital industry in Indonesia has accelerated over the past decade. In 2021, the total number of hospitals in Indonesia increased by 169.73% compared to 2011. Based on ownership, private hospitals in Indonesia amounted to 1496 units out of 2522 units in 2021 . This nuThe COVID-19 pandemic has made all stakeholders in the health sector realize that health resilience policies are a priority issue, so the concept of resilience becomes one of the six pillars of the transformation of the health system discussed in the Minister of Health’s Regulation No. 13 of 2022 regarding the Strategic Plan of the Ministry of Health for the year 2020–2024. Unfortunately, the transformation of the health resilience system in this policy only discusses improving the health system’s resilience for the pharmaceutical and medical device sectors. This policy does not address the role of hospitals in the context of disruptions era .In 2020, the COVID-19 pandemic in Indonesia became a turning point in adapting the concept of resilience. Hospital services struggled to keep functioning during the pandemic. At the same time, various policies for organizing hospitals in a new normal way, the growth of online start-up health consulting, digitization in health care, consumer behavior changes, and improvement of hospital quality standards were established. These responses to the pandemic changed the landscape of the healthcare in the country in general and hospital industry in particular.Before the COVID-19 pandemic, the hospital resilience concept was recognized as an indicator in The National Hospital Accreditation Standard Edition-1 . It was Based on national hospital accreditation data in 2022, of the 3072 hospitals in the country, 78% of them received full accreditation, while the remaining 22% are not accredited . This reIt is important to acknowledge the fact that one of the weaknesses of using HSI to measure hospital resilience is that this tool is only used to deal with emergencies and disaster shock conditions . At the Experience in the COVID-19 pandemic showed the need for health system transformation even at the hospital service level. The world is rapidly evolving, creating a sense of urgency for organizations to adapt for their sustainability. This change has been labeled as VUCA , referring to the future world on the brink of transformation . The heaDue to health system transformation policies that focus on the agenda of strengthening hospital resilience. The substantial changes in the landscape of the hospital industry in Indonesia could not be accommodated by the Hospital Safety Index Concept. There is a need to develop comprehensive hospital resilience that focuses on organizational processes that can face the complexity of disruption in the health care system. This study is a part of the exploratory phase of Hospital Resilience which aims to identify the domains and indicators to tackle the shocks identified in the first phase of the study.This current study is the second phase of the Conceptual Model of Hospital Resilience study. Interviews are conducted in two phases. The first phase was to explore the shocks that hospitals face in the disruption era in the context of the Indonesian hospital industry. The results of this phase are presented in a separate manuscript that is submitted for publication. The second phase consists of interviews regarding capacities needed to tackle the shocks that are reported by informants in the first phase, which focused on capacities to be resilient hospitals. The interviews of two phases of studies were conducted using interview questionnaire that is provided as a supplementary file  as preferred by informants. 15 of the 20 informants were interviewed by face to face. The remaining five informants were interviewed over the phone or via Zoom. Interviews lasted from 45 to 120 minutes.Interviews were conducted using semi-structured questionnaire guide. All informants received an explanation letter about the study and filled out informed consent before the interview began. All interviews were conducted in Indonesian by the first author from August – November 2022 using audio recorders. This research was carried out in accordance with ethical clearance approved by the Ethics Council under number 9860/UN4.14.1/TP.01.02/2022.This study used the grounded theory approach to discover resilience domain capacities. The analysis began with data reading and memos writing by forming a coding scheme to explore data, identify links between themes, and develop broader categories of themes. The first author made a transcript of the data soon after the first interview was completed. The transcription process was conducted while data were still being collected. Each transcription was labeled with a unique identifier as interview number (1.1–1.20). After that, we developed initial codes to identify issues raised by interviewers. We coded hospital capacities mentioned by interviewers by identifying verb words and nouns that describe hospital capacities. After completing the initial coding, the first author discussed them in a regular meeting. This was followed by focused coding. The initial codes were summarised in notes to enable the first author to categorize the focused coding. All process during initial codes and focused codes was written in memos. Regular meetings to compare data with codes, codes with codes, codes with categories, and analyzed with relevant theory and previous research were conducted during the analysis. After regular meetings, categories were grouped into domain and subdomain of hospital resilience capacities.Table Hospital resources readiness in facing digital transformationEffective leadershipFlexibility in managing existing resourcesSituation awareness and resilience ethosImplementation of marketing managementNetworkingDisaster anticipationBased on interviews, seven domains and 33 indicators to measure hospital resilience in facing the era of disruption were identified. Table The readiness of resources in facing digital transformation included the readiness of human resources in dealing with technological changes and the readiness of information system infrastructure to facilitate decision-making, be updated, be compatible with current technology, especially digital transformation. From the human resources aspect, capacity building of human resources in hospitals is necessary as the digitization of operational activities will be a mandatory policy in all hospital systems in Indonesia. The management should be aware that the digitization will change the workflow. All health workers will adapt to the new way of working, for instances, the ability to adapt with the Electronic Medical Records (EMR) system. The roles of hospital information systems that can collect and transform data into information from decision-making is also important. Besides that, the capacity to upgrade the digital infrastructure is also vital to facilitate interoperability of the hospital information system with the BPJS system, registration, payment information system, integrated medical records, data collection, and services.Effective leadership includes leaders who are able to read the current situation and see changes as opportunities. This capacity is important to reflect how leaders see the context of every situation and how the organization responds to the changes, whether it is a threat or an opportunity. Besides that, it is necessary to have leaders who have a vision, are able to mobilize members to achieve this vision, and have excellent communication skills. Communication skills would entail building interpersonal relationships with members within and outside the organization, both health and non-health organizations.This domain highlights the importance of flexibility in planning, procuring, and organizing resources. In facing disruptions, the hospital must be flexible in managing finances and procuring resources, for instance, procurement of Personal Protective Equipment (PPE) in facing a sudden significant increase in patient demand during the pandemic. This procurement is out of allocation in financial planning but essential to purchase. Under certain conditions, hospitals also need to be able to procure some medical equipment that is urgently available, redesign rooms, and reconfigure their medical devices for different purposes. This domain also focused on a flexible organizational structure that may enable each manager to coordinate not in a bureaucratic way, such as a director being directly able to discuss with the CEO without the need to go through the hospital board.Situation awareness includes the ability to read organizational situations to identify what should be done, be aware of environmental conditions, and be prepared for potential risks. Besides that, hospitals must be able to manage every change and challenge. To address the threats, learning organization capacities that allow continuous learning and open-mindedness are also important. Besides that, the hospital has a resilient ethos. This capacity could be reflected in individual or team capacities, mindset, and organizational culture. For instance, at the individual or group level, the hospital has teamwork that can learn and operationalize government policy changes into a hospital regulation mindset in doing simple research for hospital innovation. At the organizational level, the hospital has tactical funds that can only be used in times of crisis and a habit of conducting regular briefings to identify the current conditions and even contingency plan mechanisms within the organization.This domain represents marketing management functioning in hospitals. In Indonesia, marketing management is still shown as a very modern approach in the transition era of hospitals seen as a social organization to industry managed as a business. Some informants highlighted that the function of marketing management is important. This includes the hospital’s ability to conduct simple research based on patient characteristic data, assess market needs and create services according to community needs, diversify products to improve patient experience, rebranding capabilities for focus on centers of excellence, and have a team to apply marketing and branding function. Besides that, implementing marketing management in hospitals could assist hospitals in finding their unique, excellent value and their centers of excellence.Networking capabilities include how hospitals have a collaborative network with other hospitals, for example, collaboration in hospital resources procurement, collaboration in the use of medical equipment, discussions in determining centers of excellence in each hospital. Networking with non-health organizations would also be beneficial and could include collaborating with the government, academics, business actors, the community, media, and patient groups.Indonesia is located in a disaster-prone area. It is crucial to have the ability to understand potential disaster risks in work areas and human resources should be exposed to disaster literacy. Hence, they understand what to do when a disaster occurs, prepare resilient hospital infrastructure that can withstand a disaster, and have disaster-related standard operational procedures.We have presented seven domains for resilient hospitals in facing a disruption era. The domains highlighted the capacities hospitals should have in order to face the disruptions era which is characterized by a rapidly evolving environment that requires capacities to adapt to any changes in all given periods. These domains contrast the past hospital resilience concept, which mainly focused on the hospital’s ability to deal with specific shocks, such as disasters , 51, 90.One of the domains that stands out is the readiness to face digital transformation. This domain highlighted specific conditions encountered in the disruption era characterized by digital disruption technology. Several trends in the development of digital transformation that affect health facilities are the use of big data in health services, treatment of patients with Virtual Reality (VR) and Augmented Reality (AR) technology, wearable medical devices, blockchain, and electronic medical records . DigitizFrom the seven domains identified in our study, three domains have been identified in previous health system resilience studies such as leadership , 93, 94,Another domain identified is situation awareness and resilience ethos. This domain is developed in the organizational resilience concept in management studies , 68. SitAs a result of societal changes, hospitals face changes concerning customer needs and lifestyles that influence patient expectations, hospital revenue, and the hospital market . TherefoFinally, this study found one domain that focuses on disasters, namely disaster anticipation. This is due to Indonesia’s condition, which is a disaster-prone area for earthquakes, tsunamis, volcanic eruptions, floods, forest fires, droughts, landslides, and flash floods , 101. ThHowever, this study has some limitations. As part of the Conceptual Model of Hospital Resilience Study in Indonesia, informants such as policymakers, hospital practitioners, and academicians involved are only from Indonesia. The capacity of hospital resilience could be specific based on the context of disruption faced by the hospital Industry in Indonesia. The domain of hospital resilience in the context of the global hospital industries in the disruption era could be highlighted if the informants also involved collaborators to gain a global perspective. Besides, this exploratory study could not show which domain significantly contributes to hospital resilience. Therefore, future studies need to show the influence of each domain on hospital resilience and how each domain correlates.This paper addresses comprehensive hospital resilience domains and indicators that do not only focus on disaster aspects but also the disruptions faced by hospitals. The domain of hospital resilience in the era of disruption is more to prepare hospitals to face digitalization and compete with the market through centers of excellence, organizational structure flexibility, effective leadership, and the ability to redesign resources. In addition, in the context of a continuously changing hospital environment, hospitals need to have situation awareness capabilities, implement marketing management, expand networking, manage hospitals with modern management, and remain prepared in the face of disasters. This domain is a resilience capability that is developed according to experts from the perspective of the hospital context in Indonesia. The results of this qualitative research need to be continued for future research by verifying the construct indicators for the domains that have been identified. These findings give us insight into how resilience could be operationalized at the level of health facilities and could be used as self-assessment tools for hospitals to determine the level of hospital resilience.Additional file 1.


A recently-surfaced virus called Monkeypox virus (MPXV) has gained widespread attention as it dominates the news, creating a sense of panic among people due to the potential threat it poses to their health.To evaluate knowledge about this virus and its disease, and to raise consciousness among the members of the Faculty of Natural and Life Sciences and Earth and Universe Sciences at the University of Tlemcen, we launched an online web-based survey for a twenty days’ period that contained sociodemographic and perceptiveness questions about the emergent virus, its disease, and vaccination.Our findings showed that the majority of the respondents of our study have a satisfactory level of knowledge about this emerging virus and its disease. Moreover, most participants showed a positive attitude towards the vaccine, considering it the best preventive means to fight against Monkeypox disease.Although the MPXV may not become a pandemic, but knowing the various ways that contribute to its spread is essential to avoid any possibility of a new one, especially in Algeria. The purpose of our study is to evaluate knowledge of teachers, administrative workers and students of the Faculty of Natural and Life Sciences and Earth and Universe Sciences of University of Tlemcen (NLS/EUS) about this contagious virus in order to promote awareness about the disease and its transmission for anticipating any outbreak in Algeria.The emergence and the growing number of Monkeypox virus (MPXV) cases worldwide pose a serious threat to human life, especially in non-endemic countries like Algeria. Additionally, the general poor public understanding of Monkeypox, which makes it a prerequisite for controlling and preventing this zoonotic virus and its disease  version 25 and Microsoft Office Excel 2007, as well as the creation of tables and graphs.In this section, results obtained from the survey were interpreted according to the following criteria.. These results may be explained by the fact that younger generations use more of social media than others, and more specifically, females that are interested in learning about the latest developments in the field of health.The results indicate that the majority of respondents were women and belonged the 21-40 years’ age group (46.4%) et al., 2022), Nigeria , China , and Jordan .Results show that most of the participants (90%) previously knew that this disease is related to a virus. These results are consistent with precedents studies carried out in Indonesia  also found the same result in their study from Lebanon. It could be explained by the fact that this virus caused several outbreaks in African and non-African countries so it becomes a topic of news in social media.Among the 125 participants, more than two-thirds (61%) have heard about the emergence of the new Monkeypox virus before completing this questionnaire, Youssef et al., 2022). As a result, all of these transmission routes may be present in combination, for that it is difficult to distinguish between them in any given individual exposure or outbreak scenario . In our study we found that among the 66 respondents who declared knowing the sources of transmission of the virus, the majority strongly agreed that Monkeypox could be transmitted through contact with an infected animal or person  .The answers of the respondents about the modes of the Monkeypox virus transmission show that just 47% of acknowledge knowing them. This relatively limited information can be explained by the fact that Algeria has not yet been exposed to an epidemic of this virus. This zoonotic virus can transmit through many routes . Historically, the virus did not spread efficiently between humans, requiring direct contact with infected body fluids or fomites (bed sheets) or sustained respiratory droplet exposure within 6 meters for 3 hours or more , but recently this emerging virus has proven its great ability to spread among people, and the biggest evidence was in May 2022 where more than over 17,300 confirmed and suspected cases were identified, and over 40.000 infected people in 87 countries that were not MPXV endemic .Almost half of participants (43%) did not know if MPXV does spread easily between people or not, and only 39% of them were aware that this virus does spread easily between people, which is in agreement with a previous study conducted in Saudi-Arabia in 2022, where only 41.9% of the participants unanimously agreed on the ease of its transmission between people . Besides, it is very important to note that homosexuality plays a major role in the emergence of this virus. The CDC Data suggest that most patients are amongst the homosexual or bisexual men in the current outbreak .According to the participants, there are several factors that promote their emergence and a large part of them (32.2%) consider that the decrease in vaccine protection against smallpox is the first responsible reason for this phenomenon et al., 2022 ; Harapan et al., 2022 ; Hraib et al., 2022 ; Titanji et al., 2022).Among the 24 respondents, they indicated that they may be exposed to this disease by carrying associated symptoms, like fever (39.5%), muscle pain and backache (23.2%), skin eruption (23.2%), and swollen lymph nodes (14%). This might be due to the high similarity between the symptoms of Monkeypox, Smallpox, Measles and Chickenpox which may also explain by the fact that Algeria has not yet recorded any official cases of this virus , and to maintain a distance of at least 1 meter from suspected individuals, as well as wearing a well-fitted mask and disposable gloves around them .The results show that 48% of the participants knew the preventive measures to avoid transmission of this disease. This result can indicate the consciousness of our participants to follow the news and be updated on the hygienic status concerning emerging viruses in order to avoid repeating the same mistakes made previously in the COVID-19 pandemic era. Whereas according to different studies, there are many preventive measures that can be considered to avoid MPX infection including, avoiding direct contact with animals that are suspected of harboring MPXV, especially in geographical locations where Monkeypox disease is prevalent, to avoid contact with any material that has been in contact with a sick animal or human . On top of that, those with suspected or confirmed MPV infection should isolate themselves at home or at another location covering all lesions, wear a mask around others, wash or disinfect their hands after touching lesions, and not share any personal items .Furthermore, cooking all foods containing animal meat or components thoroughly .In our study, 71 participants have declared that the best ways to prevent the transmission of this virus were to avoid contact with infected animals and sick people, wash hands frequently with soap and avoid illegitimate sexual relations  77.3% of the participants knew the therapeutic and preventive means against this disease, and most of them considered that a vaccine is the best way to prevent and treat this disease with 33.3%, followed by antiviral drugs (21.7%). To date, there are no specialized vaccines and antiviral drugs available for MPXV, although other previous vaccines for small Chickenpox viruses, Cidofovir, ST-246, and VIG can be used to control the Monkeypox epidemics . There are categories of people who are more concerned by this treatments including, those whom are suffering from serious diseases such as hemorrhagic diseases, confluent lesions, sepsis, encephalitis, or other diseases requiring hospitalization, also, the immunocompromised population, the pediatric population, especially those under 8 years old, without forgetting pregnant or breastfeeding women, those with a history of allergic dermatitis, and individuals with other active exfoliative skin diseases, in addition to those with one or more complications like, secondary bacterial skin infection ,bronchopneumonia, concurrent diseases, or other co morbidities .et al., 2021a,b ; Kaler et al., 2022). also as both of them share some common symptoms, like, fever, headache and vomiting ). But we also notice some differences between the two of them like the incubation period which is between 5 and 15 days for SARS-CoV-2 virus and between 5 to 3 weeks for Monkeypox virus, further, the difference in terms of the key symptom, like the classic rash of Monkeypox and ARDS (Acute Respiratory Distress Syndrome) for COVID-19 .Among all of our participants, 58% indicate that there are no similarities between the two infections, while the rest of them (42%) do distinguish that, there are some common ones . By comparing many studies, we notice that there are some similarities between the two infections in terms of methods of transmission, as both can be transmitted to people through close, direct contact, with infected person’s secretions and droplets, or in terms of the preventive measures that must be taken in order to limit their spread . Adding to that, the difficulty to eradicate this virus since it remerges and exists in multiple animal reservoirs . For that every moment there is a possibility and risk of exposure to this new pandemic.Our data shows that 52% of participants think that there is a risk of having a new pandemic like the one of SARS-CoV-2. However, the WHO director stated that the infectivity of MPXV is relatively low and this virus may not become a pandemic. Despite these allegations, MPX outbreak has become endemic in more than 20 countries since May 2022 (Gong In our study which aimed to evaluate knowledge of Teachers, Administrative-Workers and Students of the Faculty of Natural and Life Sciences and Earth and Universe Sciences of University of Tlemcen about Monkeypox virus in order to spread awareness about the disease and its transmission for anticipating any outbreak in Algeria, we found that our participants had a satisfactory level of knowledge for the mode of transmission, and the preventives measures to fight against the spread of this emerging virus. Furthermore, most of the participants showed a positive attitude towards vaccines, considering them as the best therapeutic means to combat the spread of MPX disease. Although there are still many unanswered questions about Monkeypox disease, animal reservoirs, and the virus itself, through this humble work we tried to answer and clarify some important points that all people should know about emerging viruses in general and Monkeypox virus and its disease in particular, in order to prepare in advance for any outbreaks in Algeria.The data that support the findings of this study are available from the corresponding author upon request.The authors declare no conflict of interest.MPXVMonkeypox VirusMPXMonkeypox diseaseARDSAcute Respiratory Distress SyndromeCOVID-19Coronavirus Disease 2019SARS-CoV-2Severe Acute Respiratory Syndrome Coronavirus 2NLS/EUSNatural and Life Sciences and Earth and Universe Sciences


Joachim Küchenhoff, Martin TeisingSich selbst töten mit Hilfe Anderer. Kritische Perspektiven auf den assistierten SuizidPublisher: Psychosozial-VerlagYear of publication: 2022, 275 pages, price: € 34,90ISBN: 978-3-8379-3171-6In 2020, the German Federal Constitutional Court deemed the prohibition of businesslike assisted suicide unconstitutional. In doing so, it made a judgment of historic singularity. Its reasoning echoes the increasingly individualistic orientation of both individual and social life. Two years later, Joachim Küchenhoff and Martin Teising published a book that critically examines this verdict and its significance for the individual and the “others” considered in the title from different perspectives.A contextualizing preface by the editors is followed by a total of 14 separate contributions in four parts. The most comprehensive one is the first part, which deals with the framework of the discussion on assisted suicide. Here, the focus is particularly on the addressed verdict and its “misinterpreted” understanding of the concepts of autonomy and freedom as a central point of criticism. This is followed by reflections on assisted suicide in medicine in the second part. In addition to a broad plea by physician and philosopher Giovanni Maio to society in general and medicine in particular for more commitment to not give people a reason to consider suicide, the other two contributions focus primarily on the psychiatric context. The third part addresses the relationship between suicidal persons and their helpers, which has been little discussed in the public debate so far. This is done primarily from a psychoanalytic perspective, which repeatedly traces the psychodynamic process to the root of suicidality. Finally, the fourth part with its last two contributions is devoted to social and cultural aspects of assisted suicide. Noteworthily, the contribution by Lisa Werthmann-Resch, in which she analyzes the dynamics of suicide in “Winterreise” by Franz Schubert and in the contemporary same-titled film by Hans Steinbichler, stands out due to its unique approach.The broadness of perspectives as well as the resulting solutions and demands (in the sense of a more or less constructive criticism) vary between the contributions from broad and general to focused and concrete: powerful philosophical argumentations stimulate far-reaching thoughts, but may leave solution-oriented readers unsatisfied due to the lack of a practicable outlook. In other contributions, the discussed aspects and concrete possibilities of dealing with them are vividly illustrated by means of case reports rooted in history or the authors‘ own experiences.conditio humana resulting in a lifelong dependency on others. Consistently, the repeated emphasis on this conflict is true to the title of the book, which pays particular attention to those others: the suicide assistants, therapists, relatives, and society.The cover blurb promises a broad interdisciplinary approach to the topic. However, at first glance, the 17 authors appear to be quite homogeneous due to their mostly psychiatric and psychotherapeutic, especially psychoanalytic backgrounds. This fact is also mentioned in the preface of the editors. Indeed, redundancies of some central aspects in the various contributions cannot be denied. For instance, given the psychoanalytic focus it is not surprising that Freud appears regularly in the contributions, both as the forefather of psychoanalytic thought as well as a prominent historical example of a person dying with the help of others. Furthermore, multiple contributions elaborate on the paradox of two incompatible aspirations: autonomy understood as absolute independence from others on the one hand, and on the other hand the fundamentally social Despite the clear psychoanalytical emphasis, the book offers interested readers a variety of approaches and lines of argumentation to engage themselves in selected aspects of assisted suicide. This diversity of reflections on such an existential topic evokes in the readers different degrees of feeling addressed and being able to comprehend the individual contributions. Thus, the book can provoke in them what it has also demanded of its authors: a critical examination of several points of discussion surrounding assisted suicide and the search for one's own inner attitude towards it. Sentiments of approval or disapproval induced during the reading can provide helpful clues on the way to one's own position.However, the reader needs to pay close attention to terminology, since in some contributions the distinction between assisted suicide and voluntary euthanasia is not consistent. Also, sudden jumps in the discussion between different years, nations and thus also different legal and social frameworks can quickly confuse, especially when dealing with the topic for the first time. Here, consistent definitions of terms across the contributions and explicit declarations of temporal and geographical boundaries would have been desirable for the benefit of clarity.Therefore, the book is rather less suitable as a general overview or introductory reading on the discussion about assisted suicide. Practitioners, educators, students, and other interested parties who wish to (further) explore the topic of assisted suicide, fundamental questions of life and death, and the role of physicians and therapists in particular, can find stimulating thoughts and impulses for reflection and discussion in the book. An interest in psychoanalysis and at least a rudimentary understanding of its theoretical principles are advantageous.The author declares that she has no competing interests.


Since China announced that it will strive to achieve carbon peak by 2030 and carbon neutrality by 2060, it has become the focus of the whole society. The implementation of carbon peaking and carbon neutrality goals requires a range of revolutionary technologies and involves an array of key scientific questions. A great deal of research has focused on the development of new concepts and innovative technologies on carbon science and sustainable development.NSR recently interviewed Professor Jing-Hai Li about the topic of the future of carbon neutrality science and technology from a scientific research perspective. Professor Li is an academician of the Chinese Academy of Sciences (CAS) at the Institute of Process Engineering, CAS. He is a scientist who has been working on exploring complex systems in chemical engineering by multi-scale methodology for more than 30 years. He proposed the concept of Mesoscience, a new interdisciplinary discipline. NSR: The implementation of carbon peaking and carbon neutrality goals will undoubtedly usher in a restructuring of the energy sector. It requires a range of revolutionary technologies and involves an array of key scientific questions. Looking ahead, could you please share with us your views about what these major scientific questions are underlying the carbon peaking and neutrality goals that await breakthroughs and solutions?Li: The precondition of achieving the carbon peaking and carbon neutrality goals is the structural transition from fossil energy to renewable energy, in addition to structural transformation of other industries. Energy transformation and industrial transformation are indispensable parts of the whole process, complementing each other. These transformations, given their complexity, scope, scale and urgency, have presented tough challenges to the scientific community, involving each and every discipline, and have in turn generated tremendous impetus for science and technology progress. We are facing a number of technical bottlenecks in various fields. I believe there are three crucial levels of research in the relevant fields.In the first level, we need to create new substances and develop energy transformation processes that are efficient, low-carbon and low-cost to meet carbon peaking and carbon neutrality goals. This will require more profound discoveries in Fundamental Sciences in terms of the law of substance transformation, as well as the quantification and formulation of the relation between multiscale material structure and performance.In the second level, we will expect Engineering Sciences to create new processes and equipment for industrializing emerging techniques and upgrading and transforming the existing ones, which then gives rise to intelligent and green industrial processes with higher energy efficiency, lower carbon emissions and reduced costs.In the third level, we will seek technically feasible system optimization at System Sciences level, optimization of various essential productive factors, consumption patterns, social sectors and industrial structures under the framework of carbon peaking and carbon neutrality goals, so as to pinpoint the optimal pathway to carbon neutrality.The three levels are mutually bound to each other for system optimization. It is a key issue and should be brought to the scientific community's attention. Despite the striking diversity and complexity in different areas at each level, their common challenge would be structure complexity. From a scientific perspective, the key to achieving carbon peaking and carbonFrom a scientific perspective, the key to achieving carbon peaking and carbon neutrality goals lies in the discovery of the common ‘Simplexity’ out of such diversity and complexity of the world.—Jing-Hai Lineutrality goals lies in the discovery of the common ‘Simplexity’ out of such diversity and complexity of the world. Therefore, interdisciplinary research is imperative.NSR: To address the challenging scientific issues of carbon peaking and carbon neutrality goals, Chinese scientists introduced a key notion of green carbon science. What do you think of its role and importance?Li: I am not so familiar with the concept, but believe that the notion of green carbon science falls into the first level of challenge we just discussed. It reflects leading edge research in Fundamental Sciences under the framework of carbon peaking and carbon neutrality goals, and shows the importance of oxidation-reduction reactions among carbon, hydrogen and oxygen in the transformation of energy resources, carbon reduction and energy efficiency. I believe this notion touches the core of the carbon reduction process. We will need the synergy of relevant disciplines, especially focusing on the quantification and formulation of green carbon science theories, the breakthrough of which would exert a profound influence on carbon peaking and carbon neutrality goals.To be more specific, it may include ways to enable quantification, regulation and optimization of transformation ratio and selectivity in physiochemical processes, to develop the directional and structural design of relevant materials based on functional requirements and knowledge accumulated through existing experiments, and especially to enhance the overall efficiency in energy transformation, storage and utilization, among others. I personally believe that we’ve primarily met the conditions to solve these problems through interdisciplinary studies that integrate knowledge of various disciplines and focus on scientific advances in formulating dynamic complex structures. Perhaps this is the value of the notion of green carbon science.NSR: As mentioned earlier, realization of carbon neutrality involves system engineering that involves various aspects. Which aspects do you think need to be specially addressed?Li: This is a crucial question that requires our full attention. Featuring multiple levels, objectives, stages and disciplines, the realization of carbon neutrality presents itself as a complex system problem that involves natural sciences, engineering, humanities and society, data, management and policy et al. The three levels mentioned earlier make up one facet of this system problem, namely science and technology.As shown in Fig. In this way, the implementation of carbon peaking and carbon neutrality goals can be formulated as a multi-objective optimization problem subject to the constraints of the above three parts. To achieve such multi-objective optimization, the key lies in the quantification of the complex network structure of energy, industry, society, ecology and technology, and the formulation of its dominant mechanisms. The solutions may fall into three scenarios. Neither minimum emissions nor maximum economic growth satisfies the requirement of carbon neutrality. Only a reasonable balance between emission and economic growth canCarbon neutrality is a common challenge faced by humanity. No single nation, region or industry can detach itself from it. We must call for openness, collaboration, inclusiveness and wisdom.—Jing-Hai Lilead to sustainable development. As shown at the top of the figure, this is characterized by three regimes and two critical points. The optimal path should be chosen between the two critical points given the specific conditions of the different levels.Carbon neutrality is a common challenge faced by humanity. No single nation, region or industry can detach itself from it. We must call for openness, collaboration, inclusiveness and wisdom. If we could join hands and establish a common model for the analysis and synthesis of different levels , it will help countries and even the entire world to determine their routes to achieve carbon neutrality.NSR: As an advocate of mesoscience, what mesoscale-related scientific problems do you think need to be addressed in carbon neutrality research?Li: The complexity of nature reveals multilevel features, with each level showing multiscale structures. The complexity of each level is manifested in the mesoscale structure in between the unit scale and the system scale in this level. Many scientific, technological and engineering challenges are attributed to the complexity of mesoscale structures. In research on any structure, defining the stability conditions of that structure satisfies the prerequisite for quantified design, regulation and optimization. This is why we started the complexity research on mesoscale structure in the first place.Many mesoscale cases we’ve studied have shown that: the complexity of such mesoscale structures is derived from the compromise in competition between different dominant mechanisms, which can be expressed as multi-objective optimization in mathematics. A system with two dominant mechanisms may find solution results in three regimes: the mechanism A-dominated regime, the regime with mechanisms A/B in compromise in competition, and the mechanism B-dominated regime. Regimes dominated exclusively by mechanisms A or B feature a relatively simple structure, while the A/B meso-regime features the most complex structure. It is quite challenging to find an integrated single mechanism directly in this regime. Mechanisms A and B can be identified separately and then synthesized into the stability conditions expressed by a single objective through physical or mathematical principles.We believe that there is such ‘Simplexity’ existing behind diversity and complexity, so we proposed the idea of mesoscience in hopes that we would be able to verify this common ‘Simplexity’ in more case studies of complex systems.Apparently, mesoscale structures can be found in many studies on carbon neutrality, which involves various disciplines and levels. If mesoscience is employed to analyze these systems through transdisciplinary studies, we might be able to provide a new angle to solving these challenges, while we can test out ‘Simplexity’, a common attribute of mesoscale structures from diversity and complexity.Such analysis is crucial in identifying the problem-related level and its corresponding unit and system, and quantifying the dominant mechanisms at the relevant level. We must be mindful of the hierarchical division, as levels should not be misaligned or simply combined. Traditional knowledge, accumulated by a long history, can play a vital role in formulating dominant mechanisms. That is, mesoscience is a possible new angle to view the complexity of the world.NSR: In the course of scientific research and technological development on carbon neutrality in China, profound changes are taking place in the paradigm of research. Where do you believe these changes are headed?Li: This question involves crucial global trends in scientific and technological development. It also echoes a characteristic of modern science, which is the interwovenness of significant challenges and paradigm shifts. On the one hand, such key issues as carbon peaking and carbon neutrality goals mount major challenges to traditional theory and methodology, driving the paradigm shifts of science. On the other hand, only paradigm shifts can lead to solutions to those major challenges that existing theories fail to resolve, such as quantifying multilevel complex systems. This is a two-way, interdependent progress, as significant challenges bring about crucial scenarios for application, while paradigm shifts provide new means of breakthrough. This may require the global scientific community to embrace new thinking patterns and pay special attention to development of complexity science, AI, data and computing sciences. We need to enhance open cooperation and interdisciplinary studies, embracing all kinds of new ideas, and encouraging meaningful explorations of all sorts.