Heterogeneous Parameters Research Articles

Analytical and numerical study on radius of investigation in composite reservoirs

Radius of investigation (ROI) is of great significance in well testing and reserve estimation. The studies on ROI over the past decades were mainly focusing on homogeneous reservoirs. In heterogeneous formations, such as composite reservoirs and fractured reservoirs, the problem remains unsolved. Few ROI evaluation methods for composite reservoirs have been validated and commonly accepted. This paper conducts extensive analytical and numerical analysis on the pressure propagation of composite reservoirs, analyzing the limitations and misunderstandings of previous methods on the evaluation of ROI. The pressure solution of a two-zone composite reservoir is presented in the Laplace domain and is used to analyze the impact of heterogeneity parameters on pressure transient propagation and the evaluation of radius of investigation. A new ROI formula for composite reservoirs is proposed, which is in great agreement with the analytical solution. The study reveals that the inner zone of a composite reservoir cannot be treated as homogeneous when the pressure front approaches the interface of the two zones with high heterogeneity. A large mobility ratio will slow pressure propagation in composite reservoirs, while a low storativity ratio will increase the value of ROI. Reservoir simulation and real case studies are used to demonstrate the findings of the analytical study. The new study sheds insights into the pressure propagation and eases the calculation of ROI in composite reservoirs.

Efficacy of melatonin vs. midazolam for oral premedication in children: a systematic review and meta-analysis.

Achieving appropriate levels of premedication and parental separation is needed for smooth induction of anesthesia and prevention of perioperative complications. Both melatonin and midazolam are used for the premedication in children, but we do not have consensus on which premedication is superior among them. A systematic review of randomized controlled trials comparing the efficacy and safety of use of midazolam and melatonin as premedication in children aged 1-15 years was conducted. Patients who received drugs other than melatonin or midazolam as premedication were excluded. PubMed, Embase, Scopus, Google Scholar were searched and the last search was done in December 2022. Full text of ten articles with a total of 774 participants (442 melatonin, 332 midazolam) were eligible. The data extracted were synthesized after quality assessment. The outcomes appraised included: sedation, anxiety in preoperative room and during induction. Among four studies examining sedation, there were no significant differences between melatonin and midazolam (SMD=0.03, 95% CI - 0.35 to 0.40, P=0.88, I2=81%). There were no significant differences between melatonin and midazolam among two studies examining anxiety in pre-operative room (SMD=-0.04, 95% CI -4.58,4.50, P=0.99, I2=0%) and anxiety during anesthesia induction as an outcome (SMD=-1.38, 95% CI -4.81 to 2.05, P=0.43, I2=0%). The review showed that melatonin is comparable to midazolam in achieving sedation for facilitating inhalational induction in pediatric patients. The review showed no significant difference in reduction of anxiety in the preoperative room and during induction of anesthesia when either melatonin or midazolam is used as premedicants. Heterogeneity in premedication doses, parameters assessed, outcomes measured, and scales that quantify efficacy resulted in the inconsistencies in how the medications were compared and hence resulted in difficulties in data synthesis. Future studies comparing efficacy of premedication need to consider the proposed standardizations in methodology for achieving optimal results that are a fair comparison of the two medications.

Sensor attack online classification for UAVs using machine learning

Unmanned Aerial Vehicle (UAV) sensors play a vital role in maintaining flight safety and stability. However, the increasing frequency and complexity of sensor attacks have emerged as a critical threat to UAV systems. The current lack of robust multi-classification methods for detecting sensor attacks limits the effectiveness and completeness of existing defense strategies. This research addresses these challenges by leveraging machine learning (ML) techniques to classify various sensor attacks using heterogeneous sensor data and control parameters, thereby enhancing UAV system security. In this study, we design and implement multiple sensor attack scenarios targeting gyroscopes, accelerometers, barometers, and GPS. Comprehensive datasets are collected during UAV flight, integrating diverse sensor readings, flight states, and control parameters. By analyzing the characteristics of sensor attacks and their impact on position estimation and attitude control, we identify and extract key features. To optimize the classification model, we employ feature importance analysis, correlation analysis, and ablation experiments, significantly reducing data dimensionality and enhancing model training efficiency. The experimental results demonstrate the proposed ML-based multi-classification model’s superior performance, achieving a detection rate of 89.38%, significantly outperforming traditional single-attack detection methods in terms of generalization capability. Our approach efficiently handles complex multi-sensor attack scenarios. Moreover, deploying the optimized model on UAV firmware enables real-time monitoring and classification, achieving an online detection rate of 74% with a response time of approximately 0.495ms per detection. The model’s lightweight design, requiring only 48KB of storage, makes it ideal for resource-constrained UAV environments. These contributions highlight the potential of our approach to enhance real-time anomaly detection and improve UAV system resilience against diverse sensor attacks.

Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt

The prediction of highly heterogeneous reservoir parameters from seismic amplitude data is a major challenge. Seismic attribute analysis can enhance the tracking of subtle stratigraphic features. It is challenging to investigate these subtle features, including channel systems, with conventional-amplitude seismic data. Over the past few years, the use of machine learning (ML) to analyze multiple seismic attributes has enhanced the facies analysis by mapping patterns in seismic data. The purpose of this research was to assess the efficiency of an unsupervised self-organizing map (SOM) approach supported by multi-attribute analysis that could improve gas channel detection and seismic facies classification in Serpent Field, offshore Nile Delta, Egypt. As well as evaluates the importance of several available seismic attributes in reservoir characterization rather than analyzing individual attribute volumes. In this study, the single attribute (spectral decomposition attribute) highlighted the gas channel spatial distribution using three distinct frequency magnitude values. Subsequently, we employ principal component analysis (PCA) as an attribute selection method, discovering that combining seismic attributes such as sweetness, envelope, spectral magnitude, and spectral voice as input for SOM reflects an effective method to determine facies. The clustering results distinguish between shale, shaly sand, wet sand, and gas-saturated sand and identify gas–water contact on a 2D topological map (SOM), where each pattern indicates certain facies. This is achieved by associating the SOM outputs with lithofacies determined from petrophysical logs. Reducing exploration and development risk and empowering the geoscientist to generate a more precise interpretation are the ultimate objectives of this multi-attribute analysis.

Occurance and pollution risk assessment of emerging contaminants in groundwater in the vicinity of a typical municipal landfill in northeastern China

Emerging contaminants (ECs) present a significant risk to both the ecological environment and human health. However, there is currently limited knowledge regarding the presence of ECs in leachate and the surrounding groundwater environment of landfills. The heterogeneity of aquifers introduces additional uncertainty into the transport of ECs, thereby impacting the accuracy of pollution risk prediction. In this study, the types and concentrations of ECs in the leachate and surrounding groundwater were firstly investigated in a typical landfill in northeastern China. Results show that 6 different ECs were detected in groundwater monitoring wells around the landfill, with concentrations ranging from 1.25-1471 ng/L, higher than most investigated landfills in China. Leachate contained 18 different ECs with concentrations ranging from 0.25-30414 ng/L. Based on the statistical characteristics of lithology reflected by borehole data, random lithology fields were generated and transformed into heterogeneity parameter fields using Markov chain analysis to facilitate the risk assessment of ECs. Following a simulation period of 100 years, it was observed that due to the low permeability of the aquifer, pollutants only spread up to 700 m northward. While pollution plumes may disperse towards residential areas, the probability of exposure to EC in these regions is minimal. Conversely, areas with high pollution risk are predominantly located on the eastern and northern sides of the landfill. This study contributes to a deeper understanding of the impact of landfills on surrounding groundwater environments, and our proposed pollution risk assessment model can serve as a valuable reference for controlling and treating ECs.

Risk propagation in supply-chain network considering node heterogeneity

In the current highly interconnected global economy, risk propagation within supply-chain networks has drawn significant attention from researchers because of its profound impact. Given the varying risk-propagation capabilities of different firms within the supply chain, we propose a risk-propagation model that considers the heterogeneity between nodes, referred to as the Degree-Dependent Risk Propagation (DDRP) model. We analyze the effects of different heterogeneity parameters on the performance of risk propagation in the supply-chain network and further explore how these effects influence the efficiency of logistics within the supply-chain network. The results indicate that the heterogeneity between nodes significantly increases the vulnerability of the supply-chain network, making it less efficient when facing risk propagation. In a highly heterogeneous network, more nodes become infected, leading to a notable decline in logistics-transportation efficiency, which severely disrupts the normal functioning of the entire supply chain. Our research not only provides a novel theoretical model for risk propagation in supply-chain networks, but also offers valuable practical insights for managers and decision-makers. By identifying and understanding the influence of heterogeneity on risk propagation, decision-makers can formulate more effective risk-management strategies, thereby enhancing supply-chain resilience and efficiency.

Boiling heat transfer enhancement on heterogeneous copper foams in the presence of transverse flow

Open cell copper foams are widely employed to enhance boiling heat transfer, but homogeneous foams face a trade-off between promotion in wetted area and reduction in resistance to bubble departure. In this work, heterogeneous copper foams with horizontal gradient are proposed to assure both enlarged wetted area and facilitated bubble departure. Boiling of HFE-7100 on the heterogeneous foams consisting of 30 pore per inch (PPI) square core and 60 PPI frame with different projection area fractions and thicknesses is conducted under the conditions of forced transverse flow. A peak heat transfer coefficient (HTC) of 5.4 W cm−2 K−1 and a critical heat flux (CHF) of 91.4 W cm−2 are obtained on the heterogeneous foam having a core thickness of 5 mm and a frame thickness of 2 mm. The difference in thickness delays the formation of vapor blanket on the foam to promote CHF and the difference in pore density favors balanced surface wetting and vapor venting to enhance HTC. Vapor bubbles on the copper foams are visually observed and a semi-empirical model correlating CHF with heterogenous geometric parameters of the foam is proposed. This work offers an optional strategy to further enhance boiling heat transfer on porous mediums.

Radiomics Analysis for the Identification of Invasive Pulmonary Subsolid Nodules From Longitudinal Presurgical CT Scans.

Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers. Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔRAB= (RB-RA)/RA) and delta volumes (ΔVAB= (VB-VA)/VA) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs1) and delta radiomics signatures (ΔRs31+ ΔRs21+ ΔRs32). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV31+ ΔV21+ ΔV32), and clinical variable (smoking status, BMI) models (train test split (2:1)). In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test). Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity.

Investigation on the transmission of Love waves due to an impulsive line source in a heterogeneous double porous rock structure

The region around the reservoir sometimes encounters the seismic response of ground. The phenomena of Love waves propagation also caused by seismic activity which can further significantly influenced by an impulsive line source present in the rock structures of ground. The current study elucidates the propagation behavior of Love waves induced by an impulsive line source within the anisotropic layered heterogeneous double porous rock structures commonly found in proximity to the reservoir areas. The expression of the dispersion relation for the Love waves propagation due to an impulsive line source has been derived by using the Fourier transformation and Green’s function technique. The above-mentioned rock composite structure is consisted of two different portions: upper transversely isotropic double porous (TIDP) layer medium and lower heterogeneous isotropic double porous half-space (IDP) medium. In the aforesaid half-space medium, both types of heterogeneities viz. linear and quadratic have been considered. The impacts of heterogeneity parameters associated with the lower heterogeneous IDP half-space, porosity parameter (corresponds to both upper TIDP layer and lower IDP half-space) and anisotropic parameter of TIDP layer on Love waves phase velocity have also been studied. Some special outlines have also been depicted graphically.

Probabilistic seismic hazard analysis in Northern Algeria using the Parametric-Historic method

AbstractIn this study the seismic hazard in Northern Algeria is analyzed by using a probabilistic approach, and specifically the parametric-historic method. This method enables the incorporation of the entire accessible seismic history into the analysis and effectively addresses both the spatial heterogeneity and temporal variability of the seismicity parameters. The recently compiled earthquake catalog covering the region and spanning the period from 1658 to 2018 was used for estimating the seismicity parameters. The seismic hazard maps in terms of peak ground acceleration (PGA) were calculated for return period of 475 years for rock, stiff soil, and soft soil conditions. The uniform hazard spectra (UHS) for the major cities in Northern Algeria were calculated for the same conditions. The largest PGA values are observed near the cities of Chlef, Algiers, Blida, Medea, and Tipasa. Arguably the most important obtained result is evident in the seismic hazard estimates for the capital city of Algiers, which significantly exceed previously published estimates.

Neuromuscular Electrical Stimulation for Dysphagia Treatment: Adoption, Perceived Barriers, and Clinical Practices.

This study aims to learn about the global adoption of neuromuscular electrical stimulation (NMES) in managing dysphagia, identify the barriers to its adoption, and describe the existing clinical practices. A total of 171 professionals from various international backgrounds initiated a specially developed online survey, and 122 responses were included in the final analysis. The survey consisted of 44 items related to NMES usage, perceived adoption barriers of NMES in dysphagia management, and clinical practices. The data were subjected to descriptive and correlational statistical analysis. NMES is adopted by 50% of the participants' workplaces when considering responses globally and 42.7% in the United States. Most respondents reported both high self-assessed knowledge level and high interest in learning about NMES. Among the 21 posed barriers to NMES adoption, 19 were agreed upon by over 50% of participants, with the most reported being insufficient academic coverage, lack of expert endorsement, and heterogeneity in stimulation parameters. There was considerable diversity in the clinical application of NMES, particularly regarding the duration, frequency, and total number of sessions typically completed when using NMES. NMES is adopted by half of the facilities and utilized by one third of dysphagia practitioners among the international respondents to this survey. The high interest expressed in NMES parallels the increasing volume of related research. Nonetheless, the widespread recognition of barriers and the substantial variability in clinical application underscore the need for international efforts to establish standardized protocols or guidelines, ensuring its effective and consistent use in clinical settings.

Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning.

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score-based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model-based partitioning algorithm to identify sources of differences in the strength of trait-based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score-based partitioning in the analysis of latent response processes with real data.

The impact of fibre and prebiotic interventions on outcomes in cancer and haematopoietic stem cell transplantation: a systematic review

Background & aimsCancer therapy is associated with a range of toxicities that severely impact patient well-being and a range of clinical outcomes. Dietary fibre/prebiotics characteristically improve the gastrointestinal microenvironment, which consequently elicits beneficial downstream effects that could be relevant to the prevention and management of treatment-related toxicities. Despite the compelling theoretical scientific rationale there has been limited effort to synthesise the available evidence to conclude such scientific underpinning to the clinical use of fibre/prebiotics in cancer patients. Therefore, this systematic review aimed to evaluate the clinical impact of fibre/prebiotic-based interventions on gastrointestinal-side effects; gastrointestinal-microbiome; clinical outcomes; nutrition status and body composition; and quality-of-life in children and adults being treated for cancer or undergoing a haematopoietic stem cell transplant (HSCT). MethodsThis study was conducted in adherence to PRISMA guidelines, and the protocol was published prospectively with PROSPERO (CRD42022299428). Three databases (MEDLINE (Ovid), CINHAL, EMBASE) were searched from inception to December 2023. All articles were assessed for bias using the Cochrane risk-of-bias tool RoB 2.0 (for RCTs) and ROBINS-I (for non-RCTs). ResultsA total of 9989 de-duplicated records were identified, of these, 14 (paediatrics [n= 1], adults [n=13]) met the inclusion criteria (randomised controlled trials (RCT) [n = 11], observational or non-RCTs [n = 3]). The risk-of-bias was graded to be serious/high (n=6); moderate/some concerns (n=7); low (n=1). Interventions included prebiotic supplement (n=8), nutrition supplement/formula with added fibre/prebiotic (n=3) and dietary modification (n=3). The dose of fibre intervention ranged from 2.4g-30g per day. Substantial heterogeneity of target parameters was identified across a range all outcome categories, precluding definitive conclusions. ConclusionThe scientific rationale for fibre/prebiotics-based interventions for the prevention or management of cancer treatment-related toxicities is compelling. However, it is clear that the scientific and clinical field remains disconnected in how to effectively translate this approach to improve cancer outcomes. High-quality intervention studies translatable to clinical practice are now evidently crucial to determine if and how fibre/prebiotics should be used to support people undergoing cancer or HSCT therapy.

MARS CHRONOLOGY DERIVED FROM CRATERS HETEROGENEITY AT GALE CRATER

Craters sample into diachronic surfaces and different depths on Mars and other planetary surfaces. These surfaces are affected by cosmonuclide radiation that offers one of the most reliable age anchors to date. Craters also accumulate, evolve and display an universal characteristic that can be measured at any scale, regardless of the geologic unit they sample, in the form of the heterogeneity parameter (Capitan, 2021). Here we use an age equation, which is based on the measurements of craters diameter, depths and area they occupy, to derive the ages of deposits that are sampled by medium-scale craters (meters to few hundred meters in diameter). We show that units sampled by the deepest craters near MSL exploration area are formed during the early stages of Gale crater formation before 2870 Ma. In contrast, units sampled by shallower craters were formed during the stages that correspond to the time of sediment recycling and lithification periods, near 2129 Ma to present. Given the heterogeneity of initial formation conditions of craters of diverse diameters and their different depths of sampling, our proposed synchronous ages with ground-truth ages has the potential to redefine the paradigm of using the impact crater morphometry as a tool to date the planetary surfaces.

Phase velocity of love waves as function of heterogeneity and void parameter

The present study looks at the Love wave propagating through an elastic layer containing empty pores situated above a heterogeneous elastic semi-infinite space. We have constructed separate formulations of equations of motion for both media under congruous boundary conditions. The separation of variables approach is used to build the phase velocity frequency relation in compact form using the Whittaker function. The resulting closed-form dispersion equation matches the conventional Love wave equation when heterogeneity has been removed. The propagation of Love waves is strongly influenced by a porous layer of limited thickness across an elastic semi-infinite space. Three wave fronts are demonstrated to have the potential to propagate. The equilibrated inertia and the variation in the void volume fraction are related to two wave fronts that are connected to the characteristics of the void pores. Numerical treatments are applied and graphically illustrated to implement these effects associated to Love waves’ phase velocity.

Sensitivity of mass flux reduction and mass removal of perfluoroalkyl substances to groundwater flow and transport parameter variability and heterogeneity

Heterogeneity of soil hydraulic (e.g., hydraulic conductivity (KS), porosity (θS)) and chemical (e.g., solid-phase adsorption (Kd)) properties complicates contaminant transport by creating spatial variability in sources of contaminant leaching. There is a knowledge gap on the effect of the interplay between these properties on the retardation and transport of per- and polyfluoroalkyl substances (PFAS) with different properties including carbon–fluorine chain-length and functional groups even in water-saturated conditions. Breakthrough curves have been used to evaluate PFAS transport behavior through heterogeneous media, including arrival time, maximum concentration, and tailing behavior. Contaminant mass flux reduction and mass removal correlations are also compared using numerical modeling to characterize PFAS transport through different source zones within a two-domain, heterogeneous system with comparison to homogeneous scenarios under water-saturated conditions. With heterogeneous properties, model sensitivity to KS was the highest among the other parameters and was controlled by the KS ratio between the different soils. The PFAS models in the homogeneous and heterogeneous scenarios were both sensitive to θS, depending on PFAS chain length. However, long-chain PFAS were less sensitive to θS variability compared to short-chain PFAS due to their higher Kd. The homogeneous and heterogeneous scenarios were equally sensitive to Kd variability, which was dependent on PFAS chain length.

Comparison of Prior Distributions for the Heterogeneity Parameter in a Rare Events Meta-Analysis of a Few Studies.

Bayesian meta-analysis is a promising approach for rare events meta-analysis. However, the inference of the overall effect in rare events meta-analysis is sensitive to the choice of prior distribution for the heterogeneity parameter. Therefore, it is crucial to assign a convincing prior specification and ensure that it is both plausible and transparent. Various priors for the heterogeneity parameter have been proposed; however, the comparative performance of alternative prior specifications in rare events meta-analysis is poorly understood. Based on a binomial-normal hierarchical model, we conducted a comprehensive simulation study to compare seven heterogeneity prior specifications for binary outcomes, using the odds ratio as the metric. We compared their performance in terms of coverage, median percentage bias, width of the 95% credible interval, and root mean square error (RMSE). We illustrate the results with two recently published rare events meta-analyses of a few studies. The results show that the half-normal prior (with a scale of 0.5), the prior proposed by Turner etal. for the general healthcare setting (without restriction to a specific type of outcome) and for the adverse event setting perform well when the degree of heterogeneity is not relatively high, yielding smaller bias and shorter interval widths with similar coverage and RMSE in most cases compared to other prior specifications. None of the priors performed better when the heterogeneity between-studies were significantly extreme.

Extracting the Heterogeneous 3D Structure of Molecular Films Using Higher Dimensional SFG Microscopy.

Ultrathin molecular films are widespread in both natural and industrial settings, where details of the molecular structure such as density, out-of-plane tilt angles, and in-plane directionality determine their physicochemical properties. Many of these films possess important molecular-to-macroscopic heterogeneity in these structural parameters, which have traditionally been difficult to characterize. Here, we show how extending sum-frequency generation (SFG) microscopy measurements to higher dimensionality by azimuthal-scanning can extract the spatial variation in the three-dimensional molecular structure at an interface. We extend the commonly applied theoretical assumptions used to analyze SFG signals to the study of systems possessing in-plane anisotropy. This theoretical framework is then applied to a phase-separated mixed lipid monolayer to investigate the variation in molecular density and 3D orientation across the chirally packed lipid domains. The results show little variation in out-of-plane structure but a distinct micron-scale region at the domain boundaries with a reduction in both density and in-plane ordering.

Heterogeneity of metabolic phenotypes in adolescent boys with obesity due to excess calories

The aim of the study was to determine the heterogeneity of metabolic parameters in teenage boys diagnosed with “obesity due to excess calories” using cluster analysis. Materials and methods. One hundred and twenty-three randomly selected patients aged 14 to 17 years diagnosed with “obesity due to excess calories” were examined. The statistical analysis included descriptive and analytical methods, in particular aimed to identify clusters of biochemical indicators, including serum fasting glucose level, insulin level, total cholesterol level, low density lipoproteins (LDLP), very low-density lipoproteins (VLDL), high density lipoproteins (HDLP) and triglycerides. Results. Two clusters of observations have been identified. The first cluster consisted of 72 patients (58.5%) having fasting glucose levels slightly below average, insulin levels above average, total cholesterol, triglycerides, LDLP and VLDL above average, and HDLP levels below average (“metabolically unhealthy” obesity). The second cluster included 51 teenagers (41.5%) who presented with opposite trends in metabolic characteristics (“metabolically healthy” obesity). The adolescents from the first cluster were slightly older, had higher body mass, body mass index (BMI) and z-BMI scores, and higher HOMA-IR scores. Arterial hypertension and non-alcoholic fatty liver disease were diagnosed statistically significantly more frequently in adolescents from this cluster. Conclusion. Teenage boys with obesity are a heterogeneous group in terms of the values of major metabolic indicators.

Opinion dynamics beyond social influence

Abstract We present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighboring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter’s belief into the ego’s but by adjusting the ego’s decision-making criteria. We provide an instance from the framework in which social influence between agents generalizes majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarization and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus.