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The primary goal of developing new mark–release–recapture (MRR) methods is to make MMR more cost-effective and able to provide more information. To test the effectiveness of UV fluorescence marking, a case study was carried out on an Isophya costata population occurring in a mown grassland with a heterogeneous vegetation pattern. The marked individuals were examined daily, from the emergence of imagos until the mowing of the habitat. The 40 marked insects (20 males and 20 females) were recaptured in 206 cases over 16 days (32.2% of the potential maximal recaptures). The finding of males was 26.2% successful, and the finding of females was 38.1% successful. Females released in shorter and sparse vegetation were recaptured more than those released in dense vegetation or than males. Recaptures showed a significant decrease during the study period in almost all groups based on Mann–Kendall trend tests. Detections were made on the upper third of the vertical structure of the vegetation in 88% of cases. In most of the detections (males: 70%, females: 62%), the axis of the insect’s body was more likely to be located vertically. The results showed that the chance of detection is significantly reduced when the position of the insect’s body is facing the axis of UV illumination. Thus, the visibility of individuals can be greatly increased by marking all sides of the body.

Steppes form large zonal habitats in Asia but only consist of localised outposts in Europe. An ideal subject for the study of differences within species between the main steppe zone and the localized more western outposts is the Orthopteran Stenobothrus eurasius, widespread across the Siberian and Central Asian steppes but present only in isolated relic populations at the western edge of its area. We used genetic and morphological analyses to detect possible differences among these relic populations. We carried out a study on morphological parameters of wings in parallel with the comparison of four DNA fragments (cytochrome c oxidase subunit I, 12S rRNA and the mtDNA control region, cytochrome B, nuclear internal transcribed spacers plus the 5.8S rRNA region) involving 15 extrazonal populations of the species. St. nigromaculatus was used as an outgroup taxon in the genetic analyses. Variability of the morphological characters of St. eurasius individuals was higher within the regions than amongst the regions. The two Stenobothrus species were not separated based on the CR gene. Samples of both Stenobothrus species were separated on the COI, cytB and ITS1-5.8S-ITS2 phylogenetic trees with high support (PP = 1) in Bayesian analyses but clear genetic lineages were not revealed, and populations of the focal species were not grouped according to their geographic locations. The similarity of this species in different steppe outposts supports the hypothesis that St. eurasius was widespread in the more extensive steppe areas that were once present, but the extension of agricultural landuse reduced the steppe habitats resulting in the current patchy distribution of St. eurasius limited to the remaining habitats.

Abstract Background Results of spatial and temporal comparison of malaria hotspots and coldspots could improve the health measures of malaria control and eradication strategies. The study aimed to reveal the spatially and temporally independent correlations between the potentially most effective background variables and the number of autochthonous malaria cases. Methods Relationships between malaria cases and background variables were studied in 2 km × 2 km sized quadrates (10 Central European and 10 African). In addition to the current habitat structure of the African sites, annual precipitation, and annual mean temperature, data of the above parameters detected in the nineteenth and twentieth centuries and currently in the Central European sites were included in the analyses (n = 40). Mann–Whitney tests, Principal Component Analysis, and Generalized Linear Models were used for the examinations. Results In addition to the apparent significant positive correlation of malaria cases with annual rainfall and mean temperature, several correlations were found for habitat parameters. The cover of marshlands in the 19th-century habitat structure of Central European quadrates was considerably the same as in the recent African ones. The extent of rural residential areas was significantly smaller in the 19th-century habitat structure of Central European quadrats than in present-day African ones. According to the revealed correlations, the surface cover of rural residential areas is the main driver of the number of autochthonous malaria cases that we can directly impact. Conclusions The study confirmed with historical comparison that not only the annual rainfall and mean temperature, the cover of marshlands and other habitats with breeding sites, but also the elements of the rural human environment play a significant role in the high number of autochthonous malaria cases, probably through the concentration and enhancing sites for vector mosquitoes. The latter confirms that a rapid urbanization process could reduce malaria cases in the most infected areas of Africa. Until the latter happens, extensive biological control of Anopheles larvae and chemical control (both outdoor and indoor) of their imagoes, further mosquito nets, repellents, and carbon dioxide traps will need to be applied more widely in the most heavily infested areas.

The harmful political, economic and health effects of fake news on social media are well known. The present study examines the impact of two socio-cultural variables (political orientation and parents' educational attainment), one media literacy variable and two media use variables (news control, platform usage frequency and media addiction) on fake videos detection. The ability to detect fake videos among Hungarian secondary school students (N = 507) was assessed using a 16-item video test. The results of the online survey are partly consistent and partly contradictory to the literature. There is no gender difference in the ability to detect fake videos in the age group studied, and media literacy and media use do not influence the ability to detect fake videos. However, a more conservative worldview and higher parental education are associated with better detection of fake videos. The paper concludes with recommendations.

This study highlights how particle filter optimization (PFO) algorithms can explore objective functions and their robustness near optimums. Improvements of the general algorithm are also introduced to increase search efficiency. Population-based optimization algorithms reach outstanding performance by propagating not only one but many candidate solutions. One novel representative of these methods is the PFO concept, which was created as an analogue of the particle filter state estimation algorithm. The PFO algorithm results in a probability distribution of the sample elements, which can represent the shape of the objective function. In the literature, several variants of the PFO can be found, but its elements are not clearly fixed because of its novelty. In the present study, a method is introduced to gain information on the shape of the objective function by following the propagation of the particles along the iterations. The contributions of the paper: 1) comparative study is proposed examining the different variants of the algorithm, and some improvements are introduced (e.g., weight differentiation) to increase the efficiency of the general PFO algorithm; 2) propagation of the particles is investigated to explore the shape of the objective function; 3) clustering-based technique is proposed to get information about the local optimums (e.g., robustness). The results verify that the proposed method is applicable to find local optimums and evaluate their robustness, which is a promising prospect for robust optimization problems where often not the global, but a more stable local optimum gives the best solution.

This study presents a model-based parameter estimation method for integrating and validating uncertainty in expert knowledge and simulation models. The parameters of the models of complex systems are often unknown due to a lack of measurement data. The experience-based knowledge of experts can substitute missing information, which is usually imprecise. The novelty of the present paper is a method based on Monte Carlo (MC) simulation and importance sampling (IS) techniques for integrating uncertain expert knowledge into the system model. Uncertain knowledge about the model parameters is propagated through the system model by MC simulation in the form of a discrete sample, while IS helps to weight the sample elements regarding imprecise knowledge about the outputs in an iterative circle. Thereby, the consistency of expert judgments can be investigated as well. The contributions of this paper include an expert knowledge-based parameter estimation technique and a method for the evaluation of expert judgments according to the estimation results to eliminate incorrect ones. The applicability of the proposed method is introduced through a case study of a Hungarian operating waste separation system. The results verify that the assessments of experts can be efficiently integrated into system models, and their consistency can be evaluated.

The number of records of Acrida ungarica in novel habitats and in places where the species was considered extinct, has markedly increased in recent years. We hypothesized that the newly revealed occurrences, on the northern margin of the species range, were not due to an increase in survey effort, but rather a result of the warming climate in the Carpathian Basin. We studied the occurrence data of Acrida ungarica and the intensity of Orthoptera surveys over the period of 2002-2022 in 1,840 6 × 5.5 km grid cells of the Central European Flora Mapping System. As background variables, we included macroclimatic data and the vegetation cover of the main potential habitats. The number of grid cells containing A. ungarica was significantly higher than the corresponding increase in cells surveyed for orthopterans and the presence of sand and salt steppe habitats, respectively. Furthermore, from 2012 to 2022, significant increasing trends were revealed in effective heat summation above 10°C in the summer months. That the increase in the known distribution of A. ungarica is unrelated to the rise in survey intensity indicates that the species distribution seems to be increasing, making it one of the winners from global warming. At the same time, regional rising levels of disturbance (highway networks, large fallow areas) can contribute to the successful horizontal expansion of a species related to open habitats and this species tolerance of disturbance.

Objective Follicular lymphoma (FL) is an indolent, lymphoproliferative disease of B-cell origin that has a heterogeneous disease course with varying outcomes. Certain patients may undergo autologous stem cell transplantation. We investigated the outcome of autologous stem cell transplantation in patients with FL. Methods Patients who received autologous stem cell transplantation at the University of Debrecen’s Department of Hematology between 2004 and 2021 were retrospectively analyzed. The overall survival (OS) and progression-free survival (PFS) after transplantation of patients with FL were examined. Prognostic factors that may influence the course of the disease were chosen. Results Data were collected from 49 patients. OS was influenced only by age, whereas PFS was affected by age and the lymphocyte/monocyte ratio. The combination of age and lymphocyte/monocyte ratio defined a patient population with a particularly unfavorable prognostic risk profile: patients over 47 years of age with a pre-transplant lymphocyte/monocyte ratio greater than or equal to 2.675. Conclusion Age and lymphocyte/monocyte ratio were identified as useful prognostic factors for PFS in patients with FL following autologous stem cell transplantation.

This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system.

Introduction: Ischemic conditionings (ICon) were intensively investigated and several protective signaling pathways were identified. Previously, we have shown the role of matrix metalloproteinases (MMP) in myocardial ischemia/reperfusion injury (MIRI) and the cardioprotective role of biglycan (BGN), a small leucine-rich proteoglycan in vitro. Here, we hypothesized that cardiac MMP and BGN signaling are involved in the protective effects of ICon. Methods: A reverse target-microRNA prediction was performed by using the miRNAtarget™ 2.0 software to identify human microRNAs with a possible regulatory effect on MMP and BGN, such as on related genes. To validate the identified 1289 miRNAs in the predicted network, we compared them to two cardioprotective miRNA omics datasets derived from pig and rat models of MIRI in the presence of ICons. Results: Among the experimentally measured miRNAs, we found 100% sequence identity to human predicted regulatory miRNAs in the case of 37 porcine and 24 rat miRNAs. Upon further analysis, 42 miRNAs were identified as MIRI-associated miRNAs, from which 24 miRNAs were counter-regulated due to ICons. Conclusions: Our findings highlight 24 miRNAs that potentially regulate cardioprotective therapeutic targets associated with MMPs and BGN in a highly translatable porcine model of acute myocardial infarction.

The present paper aimed to open a debate about the increasing needs of collaborative learning in digital learning contexts. Collaborative teaching and learning are effective approaches to enhance learning and facilitate students’ satisfaction. Digital platforms may help teachers develop collaborative learning environments and provide ways of building various teaching scenarios, promoting autonomous and structured learning contexts. ZUSAMMENNFASSUNG. Dieser Beitrag soll eine Debatte über den wachsenden Bedarf an kollaborativem Lernen in digitalen Lernkontexten eröffnen. Kollaboratives Lehren und Lernen ist ein effektiver Ansatz, um das Lernen zu verbessern und die Zufriedenheit der Studierenden zu fördern. Digitale Plattformen können Lehrkräften dabei helfen, kollaborative Lernumgebungen zu entwickeln und bieten Möglichkeiten, verschiedene Unterrichtsszenarien aufzubauen, die autonome und strukturierte Lernkontexte fördern. Schlüsselwörter: kollaboratives Lehren und Lernen, digitales Lernen, digitale Plattformen, Unterrichtsgestaltung

Abstract Background and aims Internet gaming disorder (IGD) is an emerging problem. Rarely, media reports about people, who have died during playing video games, but thus far no systematic, scientific study is available about the topic. We investigated such cases, looking for common characteristics, connection between gaming and death, and the possible reasons leading to death. Methods Cases were collected through internet search with general keywords, with ones specific to identified cases, and by working along cross references. Results 24 cases were found: one from 1982, the others between 2002 and 2021. Twenty-three of the victims were male, age ranged from 11 to 40 years. More than half of the cases originated from Southeast Asia, and 12 deaths happened in internet cafes. Gamers played action-rich multiplayer games. In 18 cases the gaming session before death was extremely long (around a day or even several days) with minimal rest. The cause of death was pulmonary embolism in 5 cases, cerebral hemorrhage in 2 cases, most of the rest was presumably due to fatal cardiac arrhythmia. Discussion Long sedentary position and dehydration may precipitate thromboembolism, acute blood pressure elevation during gaming may promote cerebral hemorrhage, and several factors (including acute and chronic sleep deprivation, exhaustion, stress) can lead to acute autonomic dysfunction and fatal arrhythmia. Conclusion Incidence of non-violent death cases linked to playing video games is presumably very low. It mostly occurs in young males and it is often characterized by extremely long gaming time.

The term “multi-agent system” is generally understood as an interconnected set of independent entities that can effectively solve complex and time-consuming problems exceeding the individual abilities of common problem solvers [...]

Consensus-based data aggregation in d-regular bipartite graphs poses a challenging task for the scientific community since some of these algorithms diverge in this critical graph topology. Nevertheless, one can see a lack of scientific studies dealing with this topic in the literature. Motivated by our recent research concerned with this issue, we provide a comparative study of frequently applied consensus algorithms for distributed averaging in d-regular bipartite graphs in this paper. More specifically, we examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs. In the experimental part, we apply the number of iterations required for consensus to evaluate the performance of the algorithms in randomly generated regular bipartite graphs with various connectivities and for three configurations of the applied stopping criterion, allowing us to identify the optimal distributed consensus algorithm for this graph topology. Moreover, the obtained experimental results presented in this paper are compared to other scientific manuscripts where the analyzed algorithms are examined in non-regular non-bipartite topologies.

The current educational paradigm implemented worldwide is the development of competence comprising knowledge, skills, and attitudes. This research aims to investigate the acquisition of procedural knowledge and competencies targeting this type of knowledge through the study of Biology in middle and high schools in Romania. The paper first provides a brief overview of the history and existent usage of the concepts of competence and procedural knowledge. Moreover, it describes the Romanian educational system from a diachronic perspective. Following this, a careful analysis of middle and high school Biology syllabi is conducted. Particularly, development of procedural knowledge and competencies targeting procedural knowledge in these documents is examined critically. The results of this analysis indicate that, even though syllabi mention competence and procedural knowledge, they need more concrete activity guidance and assessment to train them. Finally, an online survey targeting high school ninth-graders and first-year university students is applied and analyzed. The survey assesses the implementation of instruction based on procedural knowledge. The results reveal the need for more logistics (laboratories and instruments) provision in schools and for carrying out practical activities. A need for reconsidering Biology syllabi, educational investment, and teacher preparation is necessary to succeed in developing procedural knowledge and competencies associated with this, through the study of Biology.

It has been long known that blood health heavily influences optimal physiological function. Abnormalities affecting the physical properties of blood have been implicated in the pathogenesis of various disorders, although the exact mechanistic links between hemorheology and clinical disease manifestations remain poorly understood. Often overlooked in current medical practice, perhaps due to the promises offered in the molecular and genetic era, the physical properties of blood which remain a valuable and definitive indicator of circulatory health and disease. Bridging this gap, the current manuscript provides an introduction to hemorheology. It reviews the properties that dictate bulk and microcirculatory flow by systematically dissecting the biomechanics that determine the non-Newtonian behavior of blood. Specifically, the impact of hematocrit, the mechanical properties and tendency of red blood cells to aggregate, and various plasma factors on blood viscosity will be examined. Subsequently, the manner in which the physical properties of blood influence hemodynamics in health and disease is discussed. Special attention is given to disorders such as sickle cell disease, emphasizing the clinical impact of severely abnormal blood rheology. This review expands into concepts that are highly topical; the relation between mechanical stress and intracellular homeostasis is examined through a contemporary cell-signaling lens. Indeed, accumulating evidence demonstrates that nitric oxide is not only transported by erythrocytes, but is locally produced by mechanically-sensitive enzymes, which appears to have intracellular and potentially extracellular effects. Finally, given the importance of shear forces in the developing field of mechanical circulatory support, we review the role of blood rheology in temporary and durable mechanical circulatory support devices, an increasingly utilized method of life support. This review thus provides a comprehensive overview for interested trainees, scientists, and clinicians.

Isophya costata Brunner von Wattenwyl, 1878, commonly called the Keeled Plump Bush-cricket, is an endemic Natura 2000 species in the Carpathian Basin and is included in the IUCN Red List of Threatened species. We used extensive data collection from Hungary retrieved between 2004 and 2019 from 700 sampling sites spread over an area of 12,700 km2 to examine the occurrence of the species in different regions in grasslands of similar structure but different origin, naturalness, and character. The results confirmed that I. costata currently occurs with the highest number of populations and highest density in regularly mowed, mesophilic hayfields rich in dicotyledonous plants (Arrhenatheretalia). The species also appears in smaller numbers in grasslands adjacent to hayfields, such as wetland meadows (Molinion coeruleae), marsh meadows (Deschampsion caespitosae, Alopecurenion pratensis), and edge habitats dominated by herbaceous plants. However, the results show that the extension of these habitats has a negatively significant correlation with both the occurrence of the species and its density. Isophya costata occurs in steppe meadows much less frequently than in mesophilic hayfields. The species is endemic to the Pannonian Steppe, and the key to their conservation is by maintaining stocks of hayfields in the species’ area of distribution. According to this study, overseeding of mowed grasslands leads to the decline of the species. To preserve I. costata, it is necessary to eliminate trampling in its areas of occurrence (prohibition of grazing) and encourage late-season mowing adapted to the phenology of the species (not as early as mid-July) or, if this is not feasible, mosaic-type treatment leaving unmown patches (e.g., 1/3 of the plot).

Introduction Follicular lymphoma (FL) is an indolent, yet heterogeneous, B-cell lymphoproliferative disorder. Although most FL patients respond well to treatment, few with specific traits have a poor prognosis; the latter are difficult to define. Patients and methods We retrospectively analyzed data from 143 FL patients treated at the University of Debrecen since 2009 and investigated prognostic factors that may influence the survival of FL patients. Results A maximum standardized uptake value (SUVmax) cut-off of 9.85 at the staging positron emission tomography/computed tomography (PET/CT) (p = 0.0001, hazard ratio [HR]: 0.2535, 95% confidence interval [CI]: 0.1118–0.4878) and a lymphocyte/monocyte (Ly/Mo) ratio of 3.41 (p = 0.0027, HR: 2.997, 95% CI: 1.463–6.142), drawn at diagnosis, significantly predicted FL patients’ progression-free survival (PFS). A staging SUVmax >9.85 with Ly/Mo 9.85 in conjunction with interim SUVmax >3.15 predicted poor prognosis (pConclusion Biological prognostic factors, such as the Ly/Mo ratio, may improve the prognostic assessment of staging PET/CT. The survival advantage observed in PFS is translated into OS when determined using a combination of staging and interim SUVmax. We recommend investigating additional biological prognostic factors while highlighting the role of PET/CT in FL.

As a result of the increasing human population, the availability of resources per capita has been vastly diminished in the last decades. Naturally, the depletion of valuable environmental assets such as water and arable land, poses a threat to mankind’s sustainable development. In this regard, various novel ideas have been proposed for processing agricultural products ecologically and sustainably; one of such ideas is vertical farming (VF). VF is a novel production technology that aims at enhancing both the yield and the product quality, by growing them in highly packed, high energy-density systems with high mass-flow rates and in a controlled environment. The technologies required for VF have been developed and successfully tested, thereby producing crops that meet the requirements of food safety, adequate nutrient content, and maximum yield. However, the extremely high biomass densities and high turnover rates employed to give rise to challenges regarding to energy efficiency and homogeneity patterns. In this work, a P-graph model is presented for the integration of VF systems. The algorithmic approach is employed to evaluate options for process integration and intensification of VF with plausible synergetic production processes into a dense urban environment. As a result, 115 integrated process alternatives are identified for the base case, with the best structure exhibiting a total cost of 41,920 EUR/y, thereby yielding reductions up to 11% for the total cost of the integrated network. The pareto front of economic performance and CO2 emission is presented to illustrate the potential benefits of integration, and the capability of the methodology to evaluate alternative designs.

One of the most crucial aspects of an algorithmdesign for the wireless sensors networks is the failure tolerance.A high natural robustness and an effectively bounded executiontime are factors that can significantly optimize the overall energyconsumption and therefore, a great emphasis is laid on theseaspects in many applications from the area of the wireless sensornetworks. This paper addresses the robustness of the optimizedBest Constant weights of Average Consensus with a stoppingcriterion (i.e. the algorithm is executed in a finite time) and theirfive variations with a lower mixing parameter (i.e. slowervariants) to random communication breakdowns modeled as a stochastic event of a Bernoulli distribution. We choose threemetrics, namely the deviation of the least precise final estimatesfrom the average, the convergence rate expressed as the numberof the iterations for the consensus, and the deceleration of eachinitial setup, in order to evaluate the robustness of various initialsetups of Best Constant weights under a varying failureprobability and over 30 random geometric graphs of either astrong or a weak connectivity. Our contribution is to find themost robust initial setup of Best Constant weights according tonumerical experiments executed in Matlab. Finally, theexperimentally obtained results are discussed, compared to theresults from the error-free executions, and our conclusions arecompared with the conclusions from related papers.

Orthoptera is a good indicator taxon of macroclimate changes. In our case study, we analyzed data of orthopterans, vegetation, and macroclimate collected yearly from 2002 through 2017 in Central European humid grasslands. During the study period, the annual mean temperature increased, while the relative abundance of moderately hygrophilic orthopteran species decreased significantly. On the other hand, the species richness and diversity of the assemblages increased due, mostly, to an increase of graminicole/thermophilic species. According to our results, the conservation of the hygrophilic orthopteran assemblages of Central European humid grasslands under global warming can only be ensured by adequate land management, which can at least mitigate the effects of climate change resulting in the warming and drying of humid habitats.

Concentrating on some important literary and theoretical works, this article examines how Hungarian identity has been given different meanings and various forms of expression over the past few centuries. Hungarian identity is an ever-changing concept and the question of belonging and what is Hungarian has been subject to numerous interpretations. Linked to periods of victory and prosperity, and their opposite, defeat, humiliation and poverty, as well as revolt, revolution, defiance, resignation and even silence, the alternating forms of national identity can all be found in literature. The fact that Hungarians have more than one text (the “Hymn,” the “Appeal” and, recently, the “Székely Hymn”) that assumes the status or quasi-status of a national anthem itself indicates the multiplicity of Hungarian identity. These texts have become part and parcel of national identity due to the frequency with which individuals encounter them from their early childhood on. This is coupled with a strong Hungarian literary canon, which also plays an essential role in identity building. In the absence of a solid literary canon, literary works are less significant in the identity structure of many other nations; in larger countries a diversity of texts are taught at school due to substantial regional differences. In contrast, the Hungarian school system has traditionally placed and still places emphasis on a stable and only slowly changing literary canon which, in turn, leads to a body of literature known to most members of society.

In recent decades, distributed consensus-based algorithms for data aggregation have been gaining in importance in wireless sensor networks since their implementation as a complementary mechanism can ensure sensor-measured values with high reliability and optimized energy consumption in spite of imprecise sensor readings. In the presented article, we address the average consensus algorithm over bipartite regular graphs, where the application of the maximum-degree weights causes the divergence of the algorithm. We provide a spectral analysis of the algorithm, propose a distributed mechanism to detect whether a graph is bipartite regular, and identify how to reconfigure the algorithm so that the convergence of the average consensus algorithm is guaranteed over bipartite regular graphs. More specifically, we identify in the article that only the largest and the smallest eigenvalues of the weight matrix are located on the unit circle; the sum of all the inner states is preserved at each iteration despite the algorithm divergence; and the inner states oscillate between two values close to the arithmetic means determined by the initial inner states from each disjoint subset. The proposed mechanism utilizes the first-order forward and backward finite-difference of the inner states (more specifically, five conditions are proposed) to detect whether a graph is bipartite regular or not. Subsequently, the mixing parameter of the algorithm can be reconfigured the way it is identified in this study whereby the convergence of the algorithm is ensured in bipartite regular graphs. In the experimental part, we tested our mechanism over randomly generated bipartite regular graphs, random graphs, and random geometric graphs with various parameters, thereby identifying its very high detection rate and proving that the algorithm can estimate the arithmetic mean with high precision (like in error-free scenarios) after the suggested reconfiguration.

Distributed algorithms for an aggregate function estimation are an important complement of many real-life applications based on wireless sensor networks. Achieving a high precision of an estimation in a shorter time can optimize the overall energy consumption. Therefore, the choice of a proper distributed algorithm is an important part of an application design. In this study, we focus our attention on the average consensus algorithm and evaluate six weight models appropriate for the implementation into real-life applications. Our aim is to find the most suitable model in terms of the estimation precision in various phases of the algorithm. We examine the deviation of the least precise estimate over iterations for a Gaussian, a Uniform and a Bernoulli distribution of the initial states in strongly and weakly connected networks with a randomly generated topology. We examine which model is the most and the least precise in various phases. Based on these findings, we determine the most suitable model for real-life applications.

An energy-efficient estimation of an aggregate function can significantly optimize a global event detection or monitoring in wireless sensor networks. This is probably the main reason why an optimization of the complementary consensus algorithms is one of the key challenges of the lifetime extension of the wireless sensor networks on which the attention of many scientists is paid. In this paper, we introduce an optimized weight model for the average consensus algorithm. It is called the Biphasically configured Metropolis-Hasting weight model and is based on a modification of the Metropolis-Hasting weight model by rephrasing the initial configuration into two parts. The first one is the default configuration of the Metropolis-Hasting weight model, while, the other one is based on a recalculation of the weights allocated to the adjacent nodes’ incoming values at the cost of decreasing the value of the weights of the inner states. The whole initial configuration is executed in a fully-distributed manner. In the experimental section, it is proven that our optimized weight model significantly optimizes the Metropolis-Hasting weight model in several aspects and achieves better results compared with other concurrent weight models.

Objectives. Several beneficial effects of resveratrol have already been published. This study evaluated the effect of resveratrol on the hemorheological parameters in patients with heart failure with reduced ejection fraction. Methods. In our double-blind, placebo-controlled human clinical trial, we enrolled 60 outpatients with heart failure. Patients were randomized into two groups: receiving either 100 mg resveratrol capsule daily or placebo for 3 months. Hematocrit was determined by microhematocrit centrifuge. Plasma and whole blood viscosity was evaluated by capillary viscometer. Erythrocyte aggregation was measured by both LORCA and Myrenne aggregometers. LORCA ektacytometer was used for measuring erythrocyte deformability. Exercise capacity was assessed by a 6-minute walk test. Results. Resveratrol treatment did not have any significant effect on hematocrit and viscosity. The erythrocyte deformability also remained unchanged. However, significant improvement of red blood cell aggregation was observed in the resveratrol group compared to baseline after 3 months. Furthermore, positive correlation was found between the exercise capacity and the hemorheological properties (Hct, WBV, and RBC aggregation and deformability) as well. Conclusion. These findings indicate that resveratrol can significantly reduce red blood cell aggregation, which may positively influence microcirculation, which may contribute to the improvement of tissue perfusion and oxygen supply in heart failure.

This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12–0.23 to 0.40–0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model).

Determining the network size is a critical process in numerous areas (e.g., computer science, logistic, epidemiology, social networking services, mathematical modeling, demography, etc.). However, many modern real-world systems are so extensive that measuring their size poses a serious challenge. Therefore, the algorithms for determining/estimating this parameter in an effective manner have been gaining popularity over the past decades. In the paper, we analyze five frequently applied distributed consensus gossip-based algorithms for network size estimation in multi-agent systems (namely, the Randomized gossip algorithm, the Geographic gossip algorithm, the Broadcast gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol). We examine the performance of the mentioned algorithms with bounded execution over random geometric graphs by applying two metrics: the number of sent messages required for consensus achievement and the estimation precision quantified as the median deviation from the real value of the network size. The experimental part consists of two scenarios—the consensus achievement is conditioned by either the values of the inner states or the network size estimates—and, in both scenarios, either the best-connected or the worst-connected agent is chosen as the leader. The goal of this paper is to identify whether all the examined algorithms are applicable to estimating the network size, which algorithm provides the best performance, how the leader selection can affect the performance of the algorithms, and how to most effectively configure the applied stopping criterion.

The effect of grazing on Orthoptera assemblages has long been the focus of research worldwide due to the high sensitivity of orthopterans to changes in vegetation structure. According to previous studies, grazing has individual, spatially-different effects on orthopteran assemblages. The current case study was carried out between 2012 and 2016 in a subarea dominated by open sandy grasslands in the Carpathian Basin. The ~70 ha study area was grazed by 250–300 sheep in 2012. In the beginning of 2014, the overgrazing pressure was overall reduced, for the most part, in the examined grassland patches. The study aimed to answer how the complete abandonment of grazing and moderate grazing influences the species richness, diversity and density of the orthopteran assemblages. Investigations in Central European sand steppes confirmed that both intense grazing and the abandonment of grazing have a detrimental effect on the structure of orthopteran assemblages: (a) the Shannon diversity index was higher on moderately grazed sites than on grazed and ungrazed ones; (b) the number of habitat specialists of sandy grasslands was higher on moderately grazed patches than in grazed habitats; and (c) the frequency of geophilic species was higher on grazed patches than on moderately grazed and grazing-abandoned ones.

Diabetes mellitus influences several important hemorheological parameters including blood viscosity, erythrocyte aggregation and deformability. In the present study, 159 type-2 diabetic patients and 25 healthy controls were involved. Patient’s age, body weight, body mass index (BMI), smoking habits, physical activity, history of cardiovascular diseases, current antidiabetic therapy and concomitant medication were recorded. Patients were grouped according to their antidiabetic treatment with insulin, or with one or more of the following antidiabetic drugs: metformin, sulfonylureas, acarbose, or no antidiabetic therapy. Hemorheological measurements (hematocrit, erythrocyte aggregation, plasma fibrinogen, whole blood and plasma viscosity), von Willebrand factor activity, and platelet aggregation measurements were performed. Platelet aggregation was investigated with the method of Born. Plasma viscosity and red blood cell aggregation were significatly higher in diabetes. No significant difference was found in hemorheological parameters between different antidiabetic regimens. Whole blood and plasma viscosity and red blood cell aggregation correlated with glucose levels but not with HbA1C levels. In conclusion, plasma and whole blood viscosity, as well as red blood cell aggregation appear to be associated with concurrent hyperglycemia, but not with the quality of glycemic control or the applied antidiabetic treatment. Platelet aggregation induced by ADP or epinephrine does not seem to be associated with diabetes even at subthreshold doses.

We investigated peripartum maternal red blood cell (RBC) properties in early-onset preeclampsia (PE). Repeated blood samples were taken prospectively for hemorheological measurements at PE diagnosis (n = 13) or during 26–34 weeks of gestation in healthy pregnancies (n = 24), then at delivery, and 72 h postpartum. RBC aggregation was characterized by M index (infrared light transmission between the aggregated RBCs in stasis) and aggregation index (AI—laser backscattering from the RBC aggregates). We observed significantly elevated RBC aggregation (M index = 9.8 vs. 8.5; AI = 72.9% vs. 67.5%; p < 0.001) and reduced RBC deformability in PE (p < 0.05). A positive linear relationship was observed between AI and gestational age at birth in PE by regression analysis (R2 = 0.554; p = 0.006). ROC analysis of AI showed an AUC of 0.84 (0.68–0.99) (p = 0.001) for PE and indicated a cutoff of 69.4% (sensitivity = 83.3%; specificity = 62.5%), while M values showed an AUC of 0.75 (0.58–0.92) (p = 0.019) and indicated a cutoff of 8.39 (sensitivity = 90.9% and specificity = 50%). The predicted probabilities from the combination of AI and M variables showed increased AUC = 0.90 (0.79–1.00) (p < 0.001). Our results established impaired microcirculation in early-onset PE manifesting as deteriorated maternal RBC properties. The longer the pathologic pregnancy persists, the more pronounced the maternal erythrocyte aggregation. AI and M index could help in the prognostication of early-onset PE, but further investigations are warranted to confirm the prognostic role before the onset of symptoms.

The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality.

Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained. Over recent years, the average consensus algorithm has found a wide application in this technology. In this paper, we present a weight matrix simplifying the average consensus algorithm over mobile wireless sensor networks, thereby prolonging the network lifetime as well as ensuring the proper operation of the algorithm. Our contribution results from the theorem stating how the Laplacian spectrum of an undirected simple finite graph changes in the case of adding an arbitrary edge into this graph. We identify that the mixing parameter of Best Constant weights of a complete finite graph with an arbitrary order ensures the convergence in time-varying topologies without any reconfiguration of the edge weights. The presented theorems and lemmas are verified over evolving graphs with various parameters, whereby it is demonstrated that our approach ensures the convergence of the average consensus algorithm over mobile wireless sensor networks in spite of no edge reconfiguration.