Non-additive Models Research Articles

Non-additive stochastic model for supercooled liquids: new perspectives for glass science

Non-additive stochastic model for supercooled liquids: new perspectives for glass science

Proof of concept for a nonadditive stochastic model of supercooled liquids.

The recently proposed nonadditive stochastic model (NSM) offers a coherent physical interpretation for diffusive phenomena in glass-forming systems. This model presents nonexponential relationships between viscosity, activation energy, and temperature, characterizing the non-Arrhenius behavior observed in supercooled liquids. In this work, we fit the NSM viscosity equation to experimental temperature-dependent viscosity data corresponding to 25 glass-forming liquids and compare the fit parameters with those obtained using the Vogel-Fulcher-Tammann (VFT), Avramov-Milchev (AM), and Mauro-Yue-Ellison-Gupta-Allan (MYEGA) models. The results demonstrate that the NSM provides an effective fitting equation for modeling viscosity experimental data in comparison with other established models (VFT, AM, and MYEGA), characterizing the activation energy in fragile liquids, presenting a reliable indicator of the degree of fragility of the glass-forming liquids.

A polygenic score method boosted by non-additive models

Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.

Exploring the role of HLA variants in neuroblastoma susceptibility through whole exome sequencing.

Although a number of susceptibility loci for neuroblastoma (NB) have been identified by genome-wide association studies, it is still unclear whether variants in the HLA region contribute to NB susceptibility. In this study, we conducted a comprehensive genetic analysis of variants in the HLA region among 724 NB patients and 2863 matched controls from different cohorts. We exploited whole-exome sequencing data to accurately type HLA alleles with an ensemble approach on the results from three different typing tools, and carried out rigorous sample quality control to ensure a fine-scale ancestry matching. The frequencies of common HLA alleles were compared between cases and controls by logistic regression under additive and non-additive models. Population stratification was taken into account adjusting for ancestry-informative principal components. We detected significant HLA associations with NB. In particular, HLA-DQB1*05:02 (OR = 1.61; padj = 5.4 × 10-3) and HLA-DRB1*16:01 (OR = 1.60; padj = 2.3 × 10-2) alleles were associated to higher risk of developing NB. Conditional analysis highlighted the HLA-DQB1*05:02 allele and its residue Ser57 as key to this association. DQB1*05:02 allele was not associated to clinical features worse outcomes in the NB cohort. Nevertheless, a risk score derived from the allelic combinations of five HLA variants showed a substantial predictive value for patient survival (HR = 1.53; p = 0.032) that was independent from established NB prognostic factors. Our study leveraged powerful computational methods to explore WES data and HLA variants and to reveal complex genetic associations. Further studies are needed to validate the mechanisms of these interactions that contribute to the multifaceted pattern of factors underlying the disease initiation and progression.

A nonadditive rough set model for long-term clinical efficacy evaluation of chronic diseases in real-world settings

The pursuit of clinical effectiveness in real-world settings is at the core of clinical practice progression. In this study, we address a long-term clinical efficacy evaluation decision-making problem with temporal correlation hybrid attribute characteristics. To address this problem, we propose a novel approach that combines a temporal correlation feature rough set model with machine learning techniques and nonadditive measures. Our proposed approach involves several steps. First, over the framework of granular computing, we construct a temporal correlation hybrid information system, the gradient method is employed to characterize the temporal attributes and the similarity between objects is measured using cosine similarity. Second, based on the similarity of gradient and cosine, we construct a composite binary relation of temporal correlation hybrid information, enabling effective classification of this information. Third, we develop a rough set decision model based on the Choquet integral, which describes temporal correlation decision process. We provide the ranking results of decision schemes with temporal correlation features. To demonstrate the practical applications of our approach, we conduct empirical research using an unlabeled dataset consisting of 3094 patients with chronic renal failure (CRF) and 80,139 EHRs from various clinical encounters. These findings offer valuable support for clinical decision-making. Two main innovations are obtained from this study. First, it establishes general theoretical principles and decision-making methods for temporal correlation and hybrid rough sets. Second, it integrates data-driven clinical decision paradigms with traditional medical research paradigms, laying the groundwork for exploring the feasibility of data-driven clinical decision-making in the field.

Large scale sequence-based screen for recessive variants allows for identification and monitoring of rare deleterious variants in pigs.

Most deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1-5% minor allele frequency). This sequence-based analysis revealed a total of 14 additive and 9 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in the CDHR2 gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in the OBSL1 gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in the MC4R and CCND2 genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program.

High Prevalence of Depression Symptoms Among Bisexual Women: The Association of Sexual Orientation and Gender on Depression Symptoms in a Nationally Representative U.S. Sample

Bisexual individuals experience higher rates of depression than heterosexual individuals and women experience higher rates of depression than men; however, few studies have quantified the joint effects of sexual orientation and gender. In the 2013–2014 and 2015-2016 National Health and Nutrition Examination Survey, depression symptoms were assessedusing the Patient Health Questionnaire. We used pooled and gender-stratified Poisson regression with robust variances to determine the independent effects of sexual orientation and gender on depression symptoms and calculated relative excess risk due to interaction to examine the joint effects of bisexual orientation and women’s gender on depression symptoms. In adjusted models, depression symptoms were 1.78 times higher in women than in men (99% confidence interval [CI]: 1.776, 1.782), 1.73 times higher in bisexual individuals than in heterosexual individuals (99% CI: 1.726, 1.735), and 3.15 times higher in bisexual women than in heterosexual men (99% CI: 3.145, 3.163). We found evidence for a non-additive model of the excess prevalence of depression symptoms among bisexual women. Gender-specific services addressing the unique mental health needs of bisexual women are needed.

A preference-approval structure-based non-additive three-way group consensus decision-making approach for medical diagnosis

The clinical diagnosis decision-making process of integrated traditional Chinese medicine and Western medicine is essentially a type of group decision-making (GDM) problem, which is transparently characterized by the multiformity of data types and the diversity of knowledge structures among experts. However, most existing GDM methods are designed based on single data types and cannot address GDM problems involving multiple data types and diversified attributes. Moreover, the preference-approval structure provides a simple framework to simulate the preference information regarding an individual’s ranking and approval. But it only considers two categories: approval and disapproval, without accounting for the indecision of individuals. In reality, three-way decision (TWD) is common. Given the deficiencies above, this paper puts forward a novel preference-approval structure-based three-way group consensus decision-making approach for a class of GDM problems with the characteristics of incompleteness, multi-granularity, diversity, and compound. To represent the information in these complex GDM problems, we firstly present the concept of incomplete multi-granularity diversified compound decision systems (IMGDCDSs). Secondly, according to the data characteristics, we construct a non-additive TWD model over the framework of granular computing. On the one hand, using the reference point of each attribute, an acquisition method of relative loss functions is given by considering three kinds of states. On the other hand, we present a new fuzzy measure to calculate non-additive conditional probabilities. The above work enriches the existing TWD theory. Based on the three-way classification and ranking results obtained, this paper subsequently defines a preference-approval structure and establishes a new group consensus decision-making approach. Thereby, the TWD model is integrated into preference-approval structures, which further enriches the GDM theory. More importantly, some existing group consensus methods are special cases of our study. Whereafter, we demonstrate the feasibility of the approach using an illustrative example. Finally, the stability and effectiveness of the approach are verified through an empirical study in the context of medical diagnosis.

Socio-economic risk factors and wildfire crime in Italy: a quantile panel approach

In this paper, we analyse the socio-economic determinants of environmental crimes such as those focused on wildfire in Italy using panel data at the regional level. We also investigate the effect of economic downturns on wildfire crime. Using the nonadditive fixed effect quantile panel regression model, it was found that socio-economic factors, such as material deprivation, play an important role in driving wildfire crime. Also, risk factors such as unemployment and income inequality were seen to affect the probability of crime in the same direction. On the other hand, a negative relationship between level of education and wildfire crime was found. The results for business cycle support the conjecture that economic downturns have a significant impact on the probability of environmental crime and that the effect is particularly binding in the southern regions where unemployment and income inequality are greater. We also found evidence of a positive correlation between organised crime and wildfire crime. Once again, the grip of organised crime appears to be stronger in the southern regions.

Using GWAS summary data to impute traits for genotyped individuals.

Genome-wide association study (GWAS) summary data have become extremely useful in daily routine data analysis, largely facilitating new methods development and new applications. However, a severe limitation with the current use of GWAS summary data is its exclusive restriction to only linear single nucleotide polymorphism (SNP)-trait association analyses. To further expand the use of GWAS summary data, along with a large sample of individual-level genotypes, we propose a nonparametric method for large-scale imputation of the genetic component of the trait for the given genotypes. The imputed individual-level trait values, along with the individual-level genotypes, make it possible to conduct any analysis as with individual-level GWAS data, including nonlinear SNP-trait associations and predictions. We use the UK Biobank data to highlight the usefulness and effectiveness of the proposed method in three applications that currently cannot be done with only GWAS summary data (for SNP-trait associations): marginal SNP-trait association analysis under non-additive genetic models, detection of SNP-SNP interactions, and genetic prediction of a trait using a nonlinear model of SNPs.

Impact of inclusion non-additive effects on genome-wide association and variance’s components in Scottish black sheep

Context It’s well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection. Aim This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep. Methods This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used. Key results The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model. Conclusions The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16–24 weeks in Scottish Blackface lambs. Implications It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.

Are gold and cryptocurrency a safe haven for stocks and bonds? Conventional vs Islamic markets during the COVID-19 pandemic

PurposeThis study aims to identify the ability of gold and cryptocurrency (Cryptocurrency Uncertainty Index (UCRY) Price) as safe haven assets (SHA) for stocks and bonds in both conventional (i.e. stock indices and government bonds) and Islamic markets (i.e. Islamic stock indices and Islamic bonds (IB)).Design/methodology/approachThe authors employed the nonadditive panel quantile regression model by Powell (2016). It measured the safe haven characteristics of gold and UCRY Price for stock indices, government bonds, Islamic stocks, and IB under gold circumstances and level of cryptocurrency uncertainty, respectively. The period spanned from 11 March 2020 to 31 December 2021.FindingsThis study discovered three findings, including: (1) gold is a strong safe haven for stocks and bonds in conventional and Islamic markets under bearish conditions; (2) UCRY Price is a strong safe haven for conventional stocks and bonds but only a weak safe haven for Islamic stocks under high crypto uncertainty; and (3) gold offers a safe haven in both emerging and developed countries, while UCRY Price provides a better safe haven in developed than in emerging countries.Practical implicationsGold always wins big for safe haven properties during unstable economy. It can also win over investors who consider shariah compliant products. Therefore, it should be included in an investor's portfolio. Meanwhile, cryptocurrencies are more common for developed countries. Thus, the governments and regulators of emerging countries need to provide more guidance around cryptocurrency so that the societies have better literacy. On top of that, the investors can consider crypto to mitigate risks but with limited safe haven functions.Originality/valueThe originality aspects of this study include: (1) four chosen assets from conventional and Islamic markets altogether (i.e. stock indices, government bonds, Islamic stock indices and IB); (2) indicator countries selected based on the most used and owned cryptocurrencies for the SHA study; and (3) the utilization of UCRY Price as a crypto indicator and a further examination of the SHA study toward four financial assets.

The analysis of the factors affecting the stringency index during COVID-19 pandemic

Coronavirus (COVID-19) pandemic, which started in China’s Wuhan providence in the late 2019s, and then affected the entire world in a short time, causing high disease and death rates, was one of the most important unexpected crises of 21st century. In order to manage the risk the pandemic posed on public health and public order, and to control spread of the disease, governments implemented restriction policies, in which precautions such as limitation and closure were taken. This study aims to examine the factors affecting the stringency index, an indicator of the political measures taken by governments against the epidemic in the selected countries (the United Kingdom, Italy, France, Germany, Türkiye, Russia, Brazil, the United States of America, India) during COVID-19 pandemic. In the analysis, non-additive fixed effect panel quantile regression model with the instrumental variable was used. The data set covers the period between March 11, 2020 and June 29, 2021. The findings indicate that although the level of effects varied, an increase in the number of daily deaths has an increasing effect on the stringency index value in all the countries within the study. Meanwhile, it is observed that as the rate of people with age 65 and over increases, the stringency measures also increase in the countries implemented moderate and high-level restrictions.

Timescale standard to discriminate between hyperbolic and exponential discounting and construction of a nonadditive discounting model

Under the presupposition that human time perception is distorted in intertemporal choice, this study constructs a time scale in the framework of axiomatic measurement. First, the conditions (homogeneity of degree one or two) to identify the form of a time scale are proposed so that one can determine whether the hyperbolic or exponential is a more suitable function for modeling people’s discounting. Homogeneity of degree one implies that subjective time delay is measured by a power scale and its discount function becomes a power-exponential type, whereas homogeneity of degree two implies that subjective time delay is measured by a log scale and its discount function becomes a hyperbolic type. Second, a method is shown for constructing a model that can reflect subadditive and superadditive discounting. A non-commutative and non-associative concatenation operation is provided to generate a time string consisting of subdivided durations that incorporates the effect of repeated delay with a subdivided duration. The discounting model is yielded by substituting these strings in an exponential model equipped with a generalized time scale, and the determination of subadditivity or superadditivity depends on whether the discount function value of a product by the non-commutative and non-associative operation is, respectively, smaller or greater than that of a product by an operation of the usual extensive structure.

Non-Additive Quantity Measurement Model

This work considers a model for measuring non-additive quantities, in particular a model for subjective measurement. The purpose of this work was to develop the measurement theory and form of a measurement model that uses the corrected S. Stevens measurement model.A generalized structure was considered that included an empirical system, a mathematical system, and a homomorphism of the empirical system into a numerical system. The main shortcomings of classical measurement theories seem to be: 1) homomorphism does not display operations (in this case, one cannot speak of the meaningfulness of the model); and 2) there is no empirical measurement model that could confirm the existence of a homomorphism. To overcome the shortcomings of existing theories a definition of the measurement equation is given. As a result a measurement model is obtained that is free from the shortcomings of classical measurement theories. The model uses the corrected model of S. Stevens and the reflection principle of J. Barzilai.The measurement model was tested using laws that were obtained empirically. Using the model it is shown that Fechnerʼs empirical law is equivalent to Stevensʼs empirical law. This means that the problem which has attracted attention of many researchers for almost a century, has been solved.A numerical example demonstrates the possibilities of the proposed measurement model. It is shown that the model can be used for extended analysis of expert assessments.

Constructing interval models using neural networks with non-additive combinations of grey prediction models in tourism demand

PurposeIn contrast to point forecasts, interval forecasts provide information on future variability. This research thus aimed to develop interval prediction models by addressing two significant issues: (1) a simple average with an additive property is commonly used to derive combined forecasts, but this unreasonably ignores the interaction among sequences used as sources of information, and (2) the time series often does not conform to any statistical assumptions.Design/methodology/approachTo develop an interval prediction model, the fuzzy integral was applied to nonlinearly combine forecasts generated by a set of grey prediction models, and a sequence including the combined forecasts was then used to construct a neural network. All required parameters relevant to the construction of an interval model were optimally determined by the genetic algorithm.FindingsThe empirical results for tourism demand showed that the proposed non-additive interval model outperformed the other interval prediction models considered.Practical implicationsThe private and public sectors in economies with high tourism dependency can benefit from the proposed model by using the forecasts to help them formulate tourism strategies.Originality/valueIn light of the usefulness of combined point forecasts and interval model forecasting, this research contributed to the development of non-additive interval prediction models on the basis of combined forecasts generated by grey prediction models.

Physico-chemical Characterization of Paint Films with Electromagnetic Properties

Electromagnetic compatibility issues and those generated by these radiations are a major concern for electrical and electronic products, mainly in the fields of communications, information technology, transportation, security and medical services. The paper presents the way to obtain nanostructured paint/plastic/nanopowder paint systems with electromagnetic shielding properties, as well as their characterization by FTIR analysis, DSC and dielectric tests. These systems have Electro-magnetic Interference (EMI)/Electromagnetic Compatibility (EMC) and Electrostatic Discharge (ESD) applications in the manufacture of enclosures for various electronic devices and for the automotive industry. At the laboratory level, 4 non-additive experimental models (EM) coded M1-M4 and 16 additive experimental models coded M5-M20 were obtained. Revolutionary to these materials is the fact that inside they are insulating and, on the outside, they behave like a shield. The results obtained from the dielectric tests performed on the 16 additive systems showed that the samples with a maximum percentage (20 %) of metal nanopowders show the highest values of electrical conductivity. Of the two nanopowders used, that of Fe from samples M11, M12, M19 and M20 which induces the composite higher conductivities than Al nanopowders. The ATR/FTIR spectra of the two paint samples analyzed showed that they were almost identical, suggesting that the paints tested had the same basic chemical structure. DSC analysis showed that pigment paint (V2) has low thermal oxidation stability and lower decomposition temperatures than pigment-free paint (V1), therefore, V2 is less stable under usage conditions, under the influence of normal environmental factors (temperature, humidity, natural or artificial light, etc.) compared to V1.

On Hamilton–Jacobi PDEs and Image Denoising Models with Certain Nonadditive Noise

We consider image denoising problems formulated as variational problems. It is known that Hamilton–Jacobi PDEs govern the solution of such optimization problems when the noise model is additive. In this work, we address certain nonadditive noise models and show that they are also related to Hamilton–Jacobi PDEs. These findings allow us to establish new connections between additive and nonadditive noise imaging models. Specifically, we study how the solutions to these optimization problems depend on the parameters and the observed images. We show that the optimal values are ruled by some Hamilton–Jacobi PDEs, while the optimizers are characterized by the spatial gradient of the solution to the Hamilton–Jacobi PDEs. Moreover, we use these relations to investigate the asymptotic behavior of the variational model as the parameter goes to infinity, that is, when the influence of the noise vanishes. With these connections, some non-convex models for nonadditive noise can be solved by applying convex optimization algorithms to the equivalent convex models for additive noise. Several numerical results are provided for denoising problems with Poisson noise or multiplicative noise.

Multi-Criteria Analysis and Sustainable Accounting. Defining Indices of Sustainability under Choquet’s Integral

The assessment of sustainability—in its three meanings: economic, social, and environmental ones—needs to be supported by specific econometric and statistical methodologies in order to be properly considered in policymaking processes. In the current literature the use of specific indices, capable of summarizing the three fields of sustainability, is a proven operational practice to express judgments on the convenience and the feasibility of investment in cities. It is necessary to specify that most sustainability indices are ordinarily calculated as arithmetic and geometric means of sub-dimensions. However, these two approaches do not allow investigation of the potential interactions between the various dimensions considered and, specifically, the geometric mean fails to smooth out unbalanced links. The research carried out here is aimed at implementing the use of the Choquet integral, as a non-additive and flexible aggregation model, to calculate evaluation indices able to consider the relationships between the different sustainable dimensions to be used in urban transformation projects. The methodology was tested on a case study, concerning an analysis—under economic, social, and environmental points of view—of different European countries. The evaluation frame based on the Choquet integral is referred to a ranking case aimed to establish the most sustainable country under the economic, environmental, and social point of view. The results obtained from the index processing show that the geometric mean scores and arithmetic ones are rather homogeneous, while the variations obtained among the three dimensions are moderately large. In the synoptic picture obtained with Choquet’s integral, countries with balanced results across dimensions are in higher positions. Therefore, the Choquet integral allows positive interactions to be taken into account across sustainable dimensions, and it is able to detect unbalanced achievements.

Estoque de carbono e modelos aditivos na estimativa da biomassa de Ilex paraguariensis

ABSTRACT Ilex paraguariensis is an important non-timber forest product in southern Brazil, where it is cultivated in association with native species, given its demand for shading, which contributes to the conservation and carbon fixation in its biomass. However, determining this biomass is difficult, since the estimates do not guarantee additivity between the compartments and the total aboveground biomass. This study aimed to evaluate additive (seemingly unrelated regression - SUR) and non-additive (ordinary least squares - OLS) biomass models in an Ilex paraguariensis stand and comparing its carbon stock with other land use types, consolidating its potential in the face of climate change. A total of 30 trees were cut, compartmentalized and weighed on a digital scale, and four biomass models were adjusted. The carbon stocks were compared with values found in the literature. The bias in the SUR model was less than 2 %, except for the leaves, while the bias in the OLS model varied between 1 and 14 %. The error ranged between 23 and 49 % for SUR, and between 31 and 50 % for OLS. The models adjusted by SUR ensured the accuracy and additivity of the compartments. The Ilex paraguariensis stand stored more carbon than agriculture and pasture areas, removing more CO2, evidencing the sustainability of this system and favoring the climate stability.