Multigroup Data Research Articles

The Digitalization Imperative: Unveiling the Impacts of Eco-Industry Integration on Sectoral Growth and Transformation

In the context of the global shift toward sustainable development, ecosystem-based ecological industries have become a critical area of focus for achieving green economic transformation and promoting the harmonious development of both society and the environment. This paper aims to explore the impact of industrial integration on the progression of ecological production within the framework of digitalization, with a particular emphasis on key sectors such as agriculture, forestry, and tourism. Utilizing multi-group data from 284 cities spanning the period 2011 to 2020, the study establishes a relationship between industrial integration and the growth of ecological industries, employing a two-way fixed effects model for analysis. The results demonstrate that industrial integration within ecological sectors significantly fosters sectoral development through two main mechanisms: the extension of industrial value chains and cross-sectoral synergies. Moreover, in the era of the digital economy, digitalization plays a pivotal role in facilitating the integrated development of ecological industries. The study further identifies that digital technology, digital finance, and digital expertise serve as mediating factors in this integration process. It is recommended that future initiatives focus on leveraging digital tools to enhance the integration of ecological industries, thereby advancing industrial growth. Furthermore, China is encouraged to continue promoting the integrated development of ecological industries by supporting the expansion of industrial value chains and fostering intersectoral cooperation. Simultaneously, the role of digitalization should be emphasized, with particular attention to the incorporation of relevant digital components into the industrial integration process moving forward.

Hierarchical Bayesian spectral regression with shape constraints for multi-group data

We propose a hierarchical Bayesian (HB) model for multi-group analysis with group–specific, flexible regression functions. The lower–level (within group) and upper–level (between groups) regression functions have hierarchical Gaussian process priors. HB smoothing priors are developed for the spectral coefficients. The HB priors smooth the estimated functions within and between groups. The HB model is particularly useful when data within groups are sparse because it shares information across groups, and provides more accurate estimates than fitting separate nonparametric models to each group. The proposed model also allows shape constraints, such as monotone, U and S–shaped, and multi-modal constraints. When appropriate, shape constraints improve estimation by recognizing violations of the shape constraints as noise. The model is illustrated by two examples: monotone growth curves for children, and happiness as a convex, U-shaped function of age in multiple countries. Various basis functions could also be used, and the paper also implements versions with B-splines and orthogonal polynomials.

Modeling of effect of fly ash amount on microstructure and chloride diffusivity of blended fly ash-cement systems

Reasonable utilization of industrial fly-ash wastage in concrete engineering has been regarded as an effective way to reduce carbon emissions. By adding the reasonable dosage of fly ash to cement paste, the compactness and microstructure characteristics of the blended cement systems can be significantly improved, thereby effectively enhancing the durability of concrete structures. The vector hydration model of blended fly ash-cement systems with the interference effects of different hydration layers of fly ash and cement particles, the solubility equilibrium of solid phases and the electrical neutrality of the pore solution involved is established. By fully comparing with the experimental data of hydration degree, capillary porosity and CH content, the accuracy of the developed hydration model is verified. Based on the hydration model, the effect of fly ash content on the concentration of different ions and pH in the pore solution with curing time is discussed. Additionally, the evolution characteristic of hydration degree, capillary porosity and CH content with curing time at different fly ash content is also analyzed. Based on the reconstructed microstructure of blended systems, the rolling sweep (RS) technique is used to determine the pore size distribution of capillary pores. The effect of fly ash content on cumulative pore size distribution and differential pore size distribution at different curing time is quantitatively evaluated and discussed. By utilizing the first-passage cube, the dual-probability-Brownian motion method is constructed to predict the chloride diffusivity of blended cement systems. After diffusion model is verified by multi-group experimental data, the optimal dosage of fly ash at different water-binder ratio is calculated, which can provide theoretical guidance for practical applications in concrete engineering.

An optimal normalization method for high sparse compositional microbiome data.

In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.

Construction and application of a risk index of Echinococcus infection based on the classification of echinococcosis lesions

To investigate the feasibility of constructing the risk index of Echinococcus infection based on the classification of echinococcosis lesions, so as to provide insights into the management of echinococcosis. The imaging data of echinococcosis cases were collected from epidemiological surveys of echinococcosis in China from 2012 to 2016, and the detection of incident echinococcosis cases was captured from the annual echinococcosis prevention and control reports across provinces (autonomous regions) and Xinjiang Production and Construction Corps in China from 2017 to 2022. After echinococcosis lesions were classified, a risk index of Echinococcus infection was constructed based on the principle of discrete distribution marginal probability and multi-group classification data tests. The correlation between the risk index of Echinococcus infection and the detection of incident echinococcosis cases was evaluated in the provinces (autonomous regions and corps) from 2017 to 2022, and the correlations between the short and medium-term risk indices and between the medium and long-term risk indices of Echinococcus infection were examined using a univariate linear regression model. A total of 4 014 echinococcosis cases in China from 2012 to 2016 were included in this study. The short-, medium- and long-term risk indices of E. granulosus infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 4.12 to 708.65, all P values < 0.05), with high short- (0.058), medium- (0.137) and long-term risk indices (0.104) in Tibet Autonomous Region, and the short-, medium- and long-term risk indices of E. multilocularis infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 6.74 to 122.60, all P values < 0.05), with a high short-term risk index in Sichuan Province (0.016) and high medium- (0.009) and long-term risk indices in Qinghai Province (0.018). There were no significant correlations between the risk index of E. granulosus infection and the detection of incident cystic echinococcosis cases during the study period (t = -0.518 to 2.265, all P values > 0.05), and strong correlations were found between the risk indices of E. multilocularis infection and the detection of incident alveolar echinococcosis cases (including mixed type) in 2018, 2020, 2021, 2022, during the period from 2017 through 2020, from 2017 through 2021, from 2017 through 2022 (all r values > 0.7, t = 2.521 to 3.692, all P values < 0.05). Linear regression models were established between the risk index of E. multilocular infection and the detection of alveolar echinococcosis cases (including mixed type), and the models were all statistically significant (b = 0.214 to 2.168, t = 2.458 to 3.692, F = 6.044 to 13.629, all P values < 0.05). The regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. granulosus infection were 2.339 and 0.765, and the regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. multilocular infection were 0.280 and 1.842, with statistical significance seen in both the regression coefficients and regression models (t = 16.479 to 197.304, F = 271.570 to 38 928.860, all P values < 0.05). The risk index of Echinococcus infection has been successfully established based on the classification of echinococcosis lesions, which may provide insights into the prevention and control, prediction, diagnosis and treatment, and classified management of echinococcosis.

Study of MOX-fueled VVER-1000 pin cell using [formula omitted]-TRAC with ENDF/B-VIII.0 library

This paper compares the ENDF/B-VIII.0 library for the MOX-fueled VVER-1000 pin cell benchmark with ENDF/B-VII.0 library using the ν-TRAC code and demonstrates the modeling capability of the code. ν-TRAC is a MOC-based code for simulating neutron transport in fuel assembly lattices, with the ability to model cell heterogeneities. The material cross-section significantly impacts neutron transport calculations; therefore, a 172 multigroup cross-section library has been generated using the most recent version of ENDF/B-VIII.0 and the open-source NJOY-2016 code. This nuclear data file includes modifications to the cross-section of major actinides and other light nuclides. Five MOX fuel variants differing in fuel isotopic composition and five states differing in fuel temperature, the moderator, and poison in the moderator are investigated using the most recent library. The impact of new release on the infinite multiplication factor and reactivity change due to the different operating states is studied. Results of ν-TRAC code using multigroup data libraries based on ENDF/B-VIII.0 and ENDF/B-VII.0 are compared with corresponding results from SCALE/SAS2H, HELIOS, and MCU.

Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric.

Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.

Generalized reporter score-based enrichment analysis for omics data.

Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.

An extension of PARAFAC to analyze multi-group three-way data

This paper introduces a novel methodology for analyzing three-way array data with a multi-group structure. Three-way arrays are commonly observed in various domains, including image analysis, chemometrics, and real-world applications. In this paper, we use a practical case study of process modeling in additive manufacturing, where batches are structured according to multiple groups. Vast volumes of data for multiple variables and process stages are recorded by sensors installed on the production line for each batch. For these three-way arrays, the link between the final product and the observations creates a grouping structure in the observations. This grouping may hamper gaining insight into the process if only some of the groups dominate the controlled variability of the products. In this study, we develop an extension of the PARAFAC model that takes into account the grouping structure of three-way data sets. With this extension, it is possible to estimate a model that is representative of all the groups simultaneously by finding their common structure. The proposed model has been applied to three simulation data sets and a real manufacturing case study. The capability to find the common structure of the groups is compared to PARAFAC and the insights into the importance of variables delivered by the models are discussed.

Copula Graphical Models for Heterogeneous Mixed Data

This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of samples obtained under heterogeneous conditions in space and time, potentially resulting in differences in network structure among groups. Therefore, the iid assumption is unrealistic, and fitting a single graphical model on all data results in a network that does not accurately represent the between group differences. In addition, real-world observational data is typically of mixed discrete-and-continuous type, violating the Gaussian assumption that is typical of graphical models, which leads to the model being unable to adequately recover the underlying graph structure. Both these problems are solved by fitting a different graph for each group, applying the fused group penalty to fuse similar graphs together and by treating the observed data as transformed latent Gaussian data, respectively. The proposed model outperforms related models on learning partial correlations in a simulation study. Finally, the proposed model is applied on real on-farm maize yield data, showcasing the added value of the proposed method in generating new production-ecological hypotheses. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=heteromixgm. Supplementary materials for this article are available online.

Single-cell analysis of matrisome-related genes in breast invasive carcinoma: new avenues for molecular subtyping and risk estimation.

The incidence of breast cancer remains high and severely affects human health. However, given the heterogeneity of tumor cells, identifying additional characteristics of breast cancer cells is essential for accurate treatment. This study aimed to analyze the relevant characteristics of matrix genes in breast cancer through the multigroup data of a breast cancer multi-database. The related characteristics of matrix genes in breast cancer were analyzed using multigroup data from the breast cancer multi database in the Cancer Genome Atlas, and the differential genes of breast cancer matrix genes were identified using the elastic net penalty logic regression method. The risk characteristics of matrix genes in breast cancer were determined, and matrix gene expression in different breast cancer cells was evaluated using real-time fluorescent quantitative polymerase chain reaction (PCR). A consensus clustering algorithm was used to identify the biological characteristics of the population based on the matrix molecular subtypes in breast cancer, followed by gene mutation, immune correlation, pathway, and ligand-receptor analyses. This study reveals the genetic characteristics of cell matrix related to breast cancer. It is found that 18.1% of stromal genes are related to the prognosis of breast cancer, and these genes are mostly concentrated in the biological processes related to metabolism and cytokines in protein. Five different matrix-related molecular subtypes were identified by using the algorithm, and it was found that the five molecular subtypes were obviously different in prognosis, immune infiltration, gene mutation and drug-making gene analysis. This study involved analyzing the characteristics of cell-matrix genes in breast cancer, guiding the precise prevention and treatment of the disease.

GALILÉE-1: Verification and processing system for Evaluated Data

This paper gives an overview of the processing methods available for now in GALILÉE-1 [1], the verification and processing system for evaluated data, developed at CEA. This paper shows also results illustrating the main processing steps. GALILÉE-1 consists of various components respectively dedicated to read/write the evaluated data independently of the format, to diagnose inconsistencies in the evaluated data and to provide continuous-energy and multigroup data as well as probability tables for particle transport and depletion codes. All these components are written in C++ language and share the same objects.

The fast neutron fluence and the activation monitor activity calculations using fine multigroup and continuous nuclear data

The fast neutron fluence and the activation monitor activities are routinely calculated with TORT deterministic code and BUGLE-B7 nuclear data library with 47 broad energy groups. The objective of the paper is to analyse options to improve reactor dosimetry transport calculations. There are two paths to improve reactor dosimetry calculations. Increasing geometry, angular and energy mesh size is applicable for TORT code while using newer nuclear data libraries is relevant for both deterministic and Monte Carlo codes. Two new calculation options (improved TORT and Monte Carlo MCNP6) were compared with the standard TORT calculation for VVER-440 Dukovany Unit 3 Cycle 31. The fast neutron fluence with 0.5 MeV threshold as well as activity of Fe. Ni, Ti, Cu. Mn and Nb monitors were evaluated. Standard TORT calculations were improved from S16P3 to S30P3 with three times finer axial mesh size. 120° core symmetry r-ϑ mesh size with 0.5° step and fine multigroup libraries VUAMIN-B7 with 199 neutron energy groups and ENDF/B-VH.1 with 200 neutron energy groups. Both ENDFB and IRDFF activation cross sections were used. The drawback of expanded mesh size is raised calculation runtime since TORT deterministic code is not parallelized and one calculation can require multiple weeks of CPU time. An alternative option of using MCNP6 Monte Carlo code with continuous ENDF/B-VH.1 nuclear data with detailed 3-D geometry and pin-wise effective neutron source prepared by MOBY-DICK diffusion code reactor analysis wras explored. It was found that using finer mesh size affects reactor dosimetry’ tallies less than the choice of nuclear data library. BUGLE-B7 and VTTAMIN-B7 produce results typically within 1% difference. ENDF/B-VH.1 calculations with 200 neutron energy’ groups with TORT code are even in better agreement with MCNP6 calculations with continuous nuclear data libraries. The largest differences of around 2% were observed between VTTAMIN-B7 library based onENDFB-VH.O nuclear data and ENDF/B-VH.1 library. Nuclear data library’ has larger impact on the results with up to 7 % difference between all 0.5 MeV fast neutron fluence calculations. The largest intact of nuclear data was observed for Mn(n.2n) monitor.

Joint and Individual Component Regression

Multi-group data, which include the same set of variables on separate groups of samples, are commonly seen in practice. Such data structure consists of data from multiple groups and can be challenging to analyze due to data heterogeneity. We propose a novel Joint and Individual Component Regression (JICO) model to analyze multi-group data. Our proposed model decomposes the response into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the predictors respectively. The joint structure has the same regression coefficients across multiple groups, whereas individual structures have group-specific regression coefficients. We formulate this framework under the representation of latent components and propose an iterative algorithm to solve for the joint and individual scores. We us the Continuum Regression (CR) to estimate the latent scores, which provides a unified framework that covers the Ordinary Least Squares (OLS), the Partial Least Squares (PLS), and the Principal Component Regression (PCR) as its special cases. We show that JICO attains a good balance between global and group-specific models and remains flexible due to the usage of CR. We conduct simulation studies and analysis of an Alzheimer’s disease dataset to further demonstrate the effectiveness of JICO. R package of JICO is available online at https://CRAN.R-project.org/package=JICO. Supplementary materials for this article are available online.

A deep-learning representation of multi-group cross sections in lattice calculations

To compute few-group nodal cross sections, lattice codes must first generate multi-group cross sections using continuous energy cross-section libraries for each material in each fuel cell. Since the processing cost is significant, we propose representing the multi-group cross sections during lattice calculations using a pre-trained deep-learning-based model. The model utilizes a combination of Principal Component Analysis (PCA) and fully connected Neural Networks (NN). The model is specifically designed to manage extensive multi-group cross-section data sets, which contain data for several dozen nuclides and encompass more than 50 energy groups. Our testing of the trained model on a PWR assembly with a realistic boron letdown curve revealed an average relative error of around 0.1% for both fission and total macroscopic cross sections. The average time required for the model to generate the cross sections was approximately 0.01% of the time needed to execute the cross-section processing module.

Impact of the resonance elastic scattering on reactor-core physics calculation results based on the deterministic method

NECP-Atlas and similar nuclear data processing codes have developed the function of incorporating the resonance elastic scattering effect into the multi-group cross sections and scattering matrices. However, the hyper-fine group method is commonly used in deterministic-based lattice physics codes and whole-core neutronics codes for resonance self-shielding calculations. In this method, the neutron slowing-down equation is established based on the conventional asymptotic scattering model where the neutron up-scattering caused by the resonance elastic scattering is ignored. Consequently, the deterministic codes cannot directly use the multi-group data produced by the nuclear data processing codes. In this paper, a correction factor method is proposed to correct the multi-group cross sections and scattering matrices obtained by the hyper-fine group calculations to consider the resonance elastic scattering. The proposed method has been implemented in the lattice physics code LOCUST. The few-group constants produced by the LOCUST code are provided to reactor-core analysis code SPARK to simulate a CNP-1000 type PWR reactor operated in China. Some core parameters measured during zero-power physics tests at 600 K and operations are analyzed to show the effect of the resonance elastic scattering on the core parameters. The numerical results show that the boron concentration reduces by 10 ∼ 14 ppm at 900 K during operations, which is closer to the measured value and improves the accuracy of reactor-core physics calculation.

Hierarchical and Multi-Group Data Sharing for Cloud-Assisted Industrial Internet of Things

With the development of Industrial Internet of Things (IIoT) and 5G, massive data are easily collected and transmitted in cloud. Therefore, it is critical to guarantee the security of data sharing. In IIoT applications, the users of a group are in hierarchical structure and they intend to access data by external groups, which requires fine-grained access control, data authenticity and data retrieval. However, existing approaches rarely provide such solutions to satisfy these requirements simultaneously. In this paper, we propose an efficient hierarchical and multi-group data sharing framework (HMGDSF) in cloud-assisted IIoT. Apart from fine-grained data access control for hierarchical users with key leakage resilience, HMGDSF achieves user anonymity with traceability and keyword-based data retrieval. Moreover, the approach supports data authenticity and integrity verification for multi-group data sharing by integrating group signature mechanism. We provide proof for the security of framework and demonstrate its efficiency and practicability by extensive evaluations.

Basic Human Values and Preferences for an EU‐Wide Social Benefit Scheme

AbstractPrevious explanations regarding transnational solidarity in the European Union (EU) have mainly focussed on factors including left–right self‐placement, support for European integration and European identity. We expand this model by considering deeper psychological determinants of transnational solidarity: values, operationalised as Schwartz's basic human values of universalism and security. We expect them to exert (1) direct effects on transnational solidarity – measured as support for pan‐European social benefits – and (2) indirect effects via the three aforementioned factors. We test and find evidence to support our theoretical framework using multigroup structural equation modelling and data from the European Social Survey. We further show that the effect size of the value of universalism on preferences for an EU social benefit scheme in each country is positively moderated by that country's net contribution to the EU budget, highlighting the interaction between material interests and psychological value motivations.

Multi-group data versus dual-side theory: On race contrasts and police-caused homicides

Empirical evidence points to a persistent Black-White racial gap in police-caused homicides. Some scholarship treats the gap as denoting criminal justice exposure either in terms of involvement in crime or living in a high-crime context. By contrast, health scholarship typically points to the importance of racism including the attitudes, institutional practices, and overall structures that operate to privilege one group over another. Still, given the demographics of US society, the Black-White racial contrast overlooks the 25% of Americans who are neither Black nor White: Native Americans, Latinos, and Asians. The question of how the groups should be organized vis-a-vis the current Black-White model and theories arises. An answer is not straightforward. There is a rank-ordering to the groups’ mortality rates as well as an exponential increase in the number of possible comparisons. In this paper we systematically review the literature on race and police-caused homicide with a particular focus on studies that attempt to move beyond the Black-White model. We find that studies on race and police-caused homicide either make no comparison between the groups, or, alternatively, use a White-non-White, a Black-non-Black, and/or a Black-Native American-Latino vs. White-Asian comparison. We use data on group-specific mortality rates to examine the strengths and limits of each of these practices. The limits are the selection of counterfactual gaps, the selection of smaller gaps, and/or the omission of larger gaps. To address these limits, we propose that a Black-Native American vs. Latino-White-Asian model best captures the higher and lower mortality rates in police-caused homicide data.

Analysis of the Influence of Energy Group Structure on Iron Shielding Calculation

The energy group structure of a multi-group cross section library matched with a deterministic method has a significant influence on shielding calculation. The complex resonance cross section of Fe-56 has a significant influence on accuracy with different energy group structures when processing multi-group cross section data. In this study, in order to more accurately test the influence of the iron resonance phenomenon on shielding calculations, this group structure is modified by the 199-group with 69 points interpolated into its group boundaries at an energy range from 1.1 keV to 3.1164 MeV. The new 269-group library is then tested with selected SINBAD iron sphere benchmarks and compared with the results of 199-group, 299-group, and 172-group libraries and measurements. Upon analysis, it is shown that the resonance of Fe-56 has a great influence on the accuracy of the calculated leakage. Further, it is noted that the finer the energy group division, the greater the calculated leakage fluctuation, and the deeper the neutron passes through the iron sphere, the larger the calculated leakage fluctuation. This study and its leakage results may provide a good reference for the development of new multi-group structures for present and future shielding design.