Shannon Entropy and Herfindahl-Hirschman Index as Team's Performance and Competitive Balance Indicators in Cyclist Multi-Stage Races.

It seems that one cannot find many papers relating entropy to sport competitions. Thus, in this paper, I use (i) the Shannon intrinsic entropy (S) as an indicator of "teams sporting value" (or "competition performance") and (ii) the Herfindahl-Hirschman index (HHi) as a "teams competitive balance" indicator, in the case of (professional) cyclist multi-stage races. The 2022 Tour de France and 2023 Tour of Oman are used for numerical illustrations and discussion. The numerical values are obtained from classical and and new ranking indices which measure the teams "final time", on one hand, and "final place", on the other hand, based on the "best three" riders in each stage, but also the corresponding times and places throughout the race, for these finishing riders. The analysis data demonstrate that the constraint, "only the finishing riders count", makes much sense for obtaining a more objective measure of "team value" and team performance", at the end of a multi-stage race. A graphical analysis allows us to distinguish various team levels, each exhibiting a Feller-Pareto distribution, thereby indicating self-organized processes. In so doing, one hopefully better relates objective scientific measures to sport team competitions. Moreover, this analysis proposes some paths to elaborate on forecasting through standard probability concepts.

Development of a Mobile App for Self-Care Against COVID-19 Using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) Model: Methodological Study.

BACKGROUND: Mobile apps have been shown to play an important role in the management, care, and prevention of infectious diseases. Thus, skills for self-care-one of the most effective ways to prevent illness-can be improved through mobile health apps. OBJECTIVE: This study aimed to design, develop, and evaluate an educational mobile-based self-care app in order to help the self-prevention of COVID-19 in underdeveloped countries. We intended the app to be easy to use, quick, and inexpensive. METHODS: In 2020 and 2021, we conducted a methodological study. Using the ADDIE (analysis, design, development, implementation, and evaluation) educational model, we developed a self-care management mobile app. According to the ADDIE model, an effective training and performance support tool is built through the 5 phases that comprise its name. There were 27 participants who conducted 2 evaluations of the mobile app's usability and impact using the mobile health app usability and self-care inventory scales. The study design included pre- and posttesting. RESULTS: An Android app called MyShield was developed. The results of pre- and posttests showed that on a scale from 0 to 5, MyShield scored a performance average of 4.17 in the physical health dimension and an average of 3.88 in the mental well-being dimension, thereby showing positive effects on self-care skills. MyShield scored highly on the "interface and satisfaction," "ease of use," and "usefulness" components. CONCLUSIONS: MyShield facilitates learning self-care skills at home, even during quarantine, increasing acquisition of information. Given its low development cost and the ADDIE educational design on which it is based, the app can be helpful in underdeveloped countries. Thus, low-income countries-often lacking other tools-can use the app as an effective tool for fighting COVID-19, if it becomes a standard mobile app recommended by the government.

The Cross-Sectional Intrinsic Entropy-A Comprehensive Stock Market Volatility Estimator.

To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices-open, high, low, and close prices (OHLC)-along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor's 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses an approximate 6000-day reference point, starting 1 January 2001, until 23 January 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.

Mobile-Based Self-Care Application for COVID-19: Development Process Using the ADDIE Model.

During the COVID-19 era, technology-enhanced protection of this disease has saved lives in developed countries in which citizens have the privilege of accessing and using such technologies to fight Coronavirus. In the undeveloped countries, on the other hand, citizens have had no accession or ability to use digital technologies to prevent COVID-19. Having this in front, in the MyShield research project, we aim to address how to teach self-care skills in undeveloped countries in the era of COVID-19 using a mobile low-cost application effectively based on a standard educational model (ADDIE). This paper reports a framework that arises from the results of semi-structured interviews and online workshops conducted in the ADDIE design process for the self-care mobile application. The specialists contributed to indicte the appropriate content for teaching self-care skills while informants contributed to optimize the user experience flow.

Sleeping Beauties of Coronavirus Research.

A "Sleeping Beauty" (SB) in science is a metaphor for a scholarly publication that remains relatively unnoticed by the related communities for a long time; - the publication is "sleeping". However, suddenly due to the appearance of some phenomenon, such a "forgotten" publication may become a center of scientific attention; - the SB is "awakened". Currently, there are specific scientific areas for which sleeping beauties (SBs) are awakened. For example, as the world is experiencing the COVID-19 global pandemic (triggered by SARS-CoV-2), publications on coronaviruses appear to be awakened. Thus, one can raise questions of scientific interest: are these publications coronavirus related SBs? Moreover, while much literature exists on other coronaviruses, there seems to be no comprehensive investigation on COVID-19, - in particular in the context of SBs. Nowadays, such SB papers can be even used for sustaining literature reviews and/or scientific claims about COVID-19. In our study, in order to pinpoint pertinent SBs, we use the "beauty score" (B-score) measure. The Activity Index (AI) and the Relative Specialization Index (RSI) are also calculated to compare countries where such SBs appear. Results show that most of these SBs were published previously to the present epidemic time (triggered by SARS-CoV or SARS-CoV-1), and are awakened in 2020. Besides outlining the most important SBs, we show from what countries and institutions they originate, and the most prolific author(s) of such SBs. The citation trend of SBs that have the highest B-score is also discussed.

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning.

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model.

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

Economic Freedom: The Top, the Bottom, and the Reality. I. 1997-2007.

We recall the historically admitted prerequisites of Economic Freedom (EF). We have examined 908 data points for the Economic Freedom of the World (EFW) index and 1884 points for the Index of Economic Freedom (IEF); the studied periods are 2000-2006 and 1997-2007, respectively, thereby following the Berlin wall collapse, and including 11 September 2001. After discussing EFW index and IEF, in order to compare the indices, one needs to study their overlap in time and space. That leaves 138 countries to be examined over a period extending from 2000 to 2006, thus 2 sets of 862 data points. The data analysis pertains to the rank-size law technique. It is examined whether the distributions obey an exponential or a power law. A correlation with the country's Gross Domestic Product (GDP), an admittedly major determinant of EF, follows, distinguishing regional aspects, i.e., defining 6 continents. Semi-log plots show that the EFW-rank relationship is exponential for countries of high rank (≥20); overall the log-log plots point to a behaviour close to a power law. In contrast, for the IEF, the overall ranking has an exponential behaviour; but the log-log plots point to the existence of a transitional point between two different power laws, i.e., near rank 10. Moreover, log-log plots of the EFW index relationship to country GDP are characterised by a power law, with a rather stable exponent (γ≃0.674) as a function of time. In contrast, log-log plots of the IEF relationship with the country's gross domestic product point to a downward evolutive power law as a function of time. Markedly the two studied indices provide different aspects of EF.

Tsallis Entropy for Cross-Shareholding Network Configurations.

In this work, we develop the Tsallis entropy approach for examining the cross-shareholding network of companies traded on the Italian stock market. In such a network, the nodes represent the companies, and the links represent the ownership. Within this context, we introduce the out-degree of the nodes-which represents the diversification-and the in-degree of them-capturing the integration. Diversification and integration allow a clear description of the industrial structure that were formed by the considered companies. The stochastic dependence of diversification and integration is modeled through copulas. We argue that copulas are well suited for modelling the joint distribution. The analysis of the stochastic dependence between integration and diversification by means of the Tsallis entropy gives a crucial information on the reaction of the market structure to the external shocks-on the basis of some relevant cases of dependence between the considered variables. In this respect, the considered entropy framework provides insights on the relationship between in-degree and out-degree dependence structure and market polarisation or fairness. Moreover, the interpretation of the results in the light of the Tsallis entropy parameter gives relevant suggestions for policymakers who aim at shaping the industrial context for having high polarisation or fair joint distribution of diversification and integration. Furthermore, a discussion of possible parametrisations of the in-degree and out-degree marginal distribution-by means of power laws or exponential functions- is also carried out. An empirical experiment on a large dataset of Italian companies validates the theoretical framework.

Changes in disease burden in Poland between 1990-2017 in comparison with other Central European countries: A systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Systematic collection of mortality/morbidity data over time is crucial for monitoring trends in population health, developing health policies, assessing the impact of health programs. In Poland, a comprehensive analysis describing trends in disease burden for major conditions has never been published. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides data on the burden of over 300 diseases in 195 countries since 1990. We used the GBD database to undertake an assessment of disease burden in Poland, evaluate changes in population health between 1990-2017, and compare Poland with other Central European (CE) countries. METHODS: The results of GBD 2017 for 1990 and 2017 for Poland and CE were used to assess rates and trends in years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs). Data came from cause-of-death registration systems, population health surveys, disease registries, hospitalization databases, and the scientific literature. Analytical approaches have been used to adjust for missing data, errors in cause-of-death certification, and differences in data collection methodology. Main estimation strategies were ensemble modelling for mortality and Bayesian meta-regression for disability. RESULTS: Between 1990-2017, age-standardized YLL rates for all causes declined in Poland by 46.0% (95% UI: 43.7-48.2), YLD rates declined by 4.0% (4.2-4.9), DALY rates by 31.7% (29.2-34.4). For both YLLs and YLDs, greater relative declines were observed for females. There was a large decrease in communicable, maternal, neonatal, and nutritional disease DALYs (48.2%; 46.3-50.4). DALYs due to non-communicable diseases (NCDs) decreased slightly (2.0%; 0.1-4.6). In 2017, Poland performed better than CE as a whole (ranked fourth for YLLs, sixth for YLDs, and fifth for DALYs) and achieved greater reductions in YLLs and DALYs than most CE countries. In 2017 and 1990, the leading cause of YLLs and DALYs in Poland and CE was ischaemic heart disease (IHD), and the leading cause of YLDs was low back pain. In 2017, the top 20 causes of YLLs and YLDs in Poland and CE were the same, although in different order. In Poland, age-standardized DALYs from neonatal causes, other cardiovascular and circulatory diseases, and road injuries declined substantially between 1990-2017, while alcohol use disorders and chronic liver diseases increased. The highest observed-to-expected ratios were seen for alcohol use disorders for YLLs, neonatal sepsis for YLDs, and falls for DALYs (3.21, 2.65, and 2.03, respectively). CONCLUSIONS: There was relatively little geographical variation in premature death and disability in CE in 2017, although some between-country differences existed. Health in Poland has been improving since 1990; in 2017 Poland outperformed CE as a whole for YLLs, YLDs, and DALYs. While the health gap between Poland and Western Europe has diminished, it remains substantial. The shift to NCDs and chronic disability, together with marked between-gender health inequalities, poses a challenge for the Polish health-care system. IHD is still the leading cause of disease burden in Poland, but DALYs from IHD are declining. To further reduce disease burden, an integrated response focused on NCDs and population groups with disproportionally high burden is needed.

Coupled Criticality Analysis of Inflation and Unemployment.

In this paper, we focus on the critical periods in the economy that are characterized by unusual and large fluctuations in macroeconomic indicators, like those measuring inflation and unemployment. We analyze U.S. data for 70 years from 1948 until 2018. To capture their fluctuation essence, we concentrate on the non-Gaussianity of their distributions. We investigate how the non-Gaussianity of these variables affects the coupling structure of them. We distinguish "regular" from "rare" events, in calculating the correlation coefficient, emphasizing that both cases might lead to a different response of the economy. Through the "multifractal random wall" model, one can see that the non-Gaussianity depends on time scales. The non-Gaussianity of unemployment is noticeable only for periods shorter than one year; for longer periods, the fluctuation distribution tends to a Gaussian behavior. In contrast, the non-Gaussianities of inflation fluctuations persist for all time scales. We observe through the "bivariate multifractal random walk" that despite the inflation features, the non-Gaussianity of the coupled structure is finite for scales less than one year, drops for periods larger than one year, and becomes small for scales greater than two years. This means that the footprint of the monetary policies intentionally influencing the inflation and unemployment couple is observed only for time horizons smaller than two years. Finally, to improve some understanding of the effect of rare events, we calculate high moments of the variables' increments for various q orders and various time scales. The results show that coupling with high moments sharply increases during crises.

Seasonal Entropy, Diversity and Inequality Measures of Submitted and Accepted Papers Distributions in Peer-Reviewed Journals.

This paper presents a novel method for finding features in the analysis of variable distributions stemming from time series. We apply the methodology to the case of submitted and accepted papers in peer-reviewed journals. We provide a comparative study of editorial decisions for papers submitted to two peer-reviewed journals: the Journal of the Serbian Chemical Society (JSCS) and this MDPI Entropy journal. We cover three recent years for which the fate of submitted papers-about 600 papers to JSCS and 2500 to Entropy-is completely determined. Instead of comparing the number distributions of these papers as a function of time with respect to a uniform distribution, we analyze the relevant probabilities, from which we derive the information entropy. It is argued that such probabilities are indeed more relevant for authors than the actual number of submissions. We tie this entropy analysis to the so called diversity of the variable distributions. Furthermore, we emphasize the correspondence between the entropy and the diversity with inequality measures, like the Herfindahl-Hirschman index and the Theil index, itself being in the class of entropy measures; the Gini coefficient which also measures the diversity in ranking is calculated for further discussion. In this sample, the seasonal aspects of the peer review process are outlined. It is found that the use of such indices, non linear transformations of the data distributions, allow us to distinguish features and evolutions of the peer review process as a function of time as well as comparing the non-uniformity of distributions. Furthermore, t- and z-statistical tests are applied in order to measure the significance (p-level) of the findings, that is, whether papers are more likely to be accepted if they are submitted during a few specific months or during a particular "season"; the predictability strength depends on the journal.

Data on the annual aggregated income taxes of the Italian municipalities over the quinquennium 2007-2011.

This dataset contains the annual aggregated income taxes of all the Italian municipalities over the years 2007-2011. Data are clustered over the Italian regions and provinces. The source of the data is the Italian Ministry of Economics and Finance. The administrative variations in Italy over the quinquennium have been taken into account. Data are useful to understand the economic structure of Italy at the microscopic level of municipalities. They can serve also for making comparisons between economical aspects and other features of the Italian cities.

Investigating the Configurations in Cross-Shareholding: A Joint Copula-Entropy Approach.

The complex nature of the interlacement of economic actors is quite evident at the level of the Stock market, where any company may actually interact with the other companies buying and selling their shares. In this respect, the companies populating a Stock market, along with their connections, can be effectively modeled through a directed network, where the nodes represent the companies, and the links indicate the ownership. This paper deals with this theme and discusses the concentration of a market. A cross-shareholding matrix is considered, along with two key factors: the node out-degree distribution which represents the diversification of investments in terms of the number of involved companies, and the node in-degree distribution which reports the integration of a company due to the sales of its own shares to other companies. While diversification is widely explored in the literature, integration is most present in literature on contagions. This paper captures such quantities of interest in the two frameworks and studies the stochastic dependence of diversification and integration through a copula approach. We adopt entropies as measures for assessing the concentration in the market. The main question is to assess the dependence structure leading to a better description of the data or to market polarization (minimal entropy) or market fairness (maximal entropy). In so doing, we derive information on the way in which the in- and out-degrees should be connected in order to shape the market. The question is of interest to regulators bodies, as witnessed by specific alert threshold published on the US mergers guidelines for limiting the possibility of acquisitions and the prevalence of a single company on the market. Indeed, all countries and the EU have also rules or guidelines in order to limit concentrations, in a country or across borders, respectively. The calibration of copulas and model parameters on the basis of real data serves as an illustrative application of the theoretical proposal.

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors' workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

Dynamical Systems Theory in Quantitative Psychology and Cognitive Science: A Fair Discrimination between Deterministic and Statistical Counterparts is Required.

This article addresses a set of observations framed in both deterministic as well as statistical formal guidelines. It operates within the framework of nonlinear dynamical systems theory (NDS). It is argued that statistical approaches can manifest themselves ambiguously, creating practical discrepancies in psychological and cognitive data analyses both quantitatively and qualitatively. This is sometimes termed in literature as 'questionable research practices.' This communication points to the demand for a deeper awareness of the data 'initial conditions, allowing to focus on pertinent evolution constraints in such systems.' It also considers whether the exponential (Malthus-type) or the algebraic (Pareto-type) statistical distribution ought to be effectively considered in practical interpretations. The role of repetitive specific behaviors by patients seeking treatment is examined within the NDS frame. The significance of these behaviors, involving a certain memory effect seems crucial in determining a patient's progression or regression. With this perspective, it is discussed how a sensitively applied hazardous or triggering factor can be helpful for well-controlled psychological strategic treatments; those attributable to obsessive-compulsive disorders or self-injurious behaviors are recalled in particular. There are both inherent criticality- and complexity-exploiting (reduced-variance based) relations between a therapist and a patient that can be intrinsically included in NDS theory.

A Universal Rank-Size Law.

A mere hyperbolic law, like the Zipf's law power function, is often inadequate to describe rank-size relationships. An alternative theoretical distribution is proposed based on theoretical physics arguments starting from the Yule-Simon distribution. A modeling is proposed leading to a universal form. A theoretical suggestion for the "best (or optimal) distribution", is provided through an entropy argument. The ranking of areas through the number of cities in various countries and some sport competition ranking serves for the present illustrations.

Fractional Dynamics of Network Growth Constrained by Aging Node Interactions.

In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model.

Review time in peer review: quantitative analysis and modelling of editorial workflows.

In this paper, we undertake a data-driven theoretical investigation of editorial workflows. We analyse a dataset containing information about 58 papers submitted to the Biochemistry and Biotechnology section of the Journal of the Serbian Chemical Society. We separate the peer review process into stages that each paper has to go through and introduce the notion of completion rate - the probability that an invitation sent to a potential reviewer will result in a finished review. Using empirical transition probabilities and probability distributions of the duration of each stage we create a directed weighted network, the analysis of which allows us to obtain the theoretical probability distributions of review time for different classes of reviewers. These theoretical distributions underlie our numerical simulations of different editorial strategies. Through these simulations, we test the impact of some modifications of the editorial policy on the efficiency of the whole review process. We discover that the distribution of review time is similar for all classes of reviewers, and that the completion rate of reviewers known personally by the editor is very high, which means that they are much more likely to answer the invitation and finish the review than other reviewers. Thus, the completion rate is the key factor that determines the efficiency of each editorial policy. Our results may be of great importance for editors and act as a guide in determining the optimal number of reviewers.