Heterogeneous Model Research Articles

A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy

BackgroundTriple-negative breast cancer (TNBC) is highly heterogeneous, resulting in different responses to neoadjuvant chemotherapy (NAC) and prognoses among patients. This study sought to characterize the heterogeneity of TNBC on MRI and develop a radiogenomic model for predicting both pathological complete response (pCR) and prognosis.Materials and methodsIn this retrospective study, TNBC patients who underwent neoadjuvant chemotherapy at Fudan University Shanghai Cancer Center were enrolled as the radiomic development cohort (n = 315); among these patients, those whose genetic data were available were enrolled as the radiogenomic development cohort (n = 98). The study population of the two cohorts was randomly divided into a training set and a validation set at a ratio of 7:3. The external validation cohort (n = 77) included patients from the DUKE and I-SPY 1 databases. Spatial heterogeneity was characterized using features from the intratumoral subregions and peritumoral region. Hemodynamic heterogeneity was characterized by kinetic features from the tumor body. Three radiomics models were developed by logistic regression after selecting features. Model 1 included subregional and peritumoral features, Model 2 included kinetic features, and Model 3 integrated the features of Model 1 and Model 2. Two fusion models were developed by further integrating pathological and genomic features (PRM: pathology-radiomics model; GPRM: genomics-pathology-radiomics model). Model performance was assessed with the AUC and decision curve analysis. Prognostic implications were assessed with Kaplan‒Meier curves and multivariate Cox regression.ResultsAmong the radiomic models, the multiregional model representing multiscale heterogeneity (Model 3) exhibited better pCR prediction, with AUCs of 0.87, 0.79, and 0.78 in the training, internal validation, and external validation sets, respectively. The GPRM showed the best performance for predicting pCR in the training (AUC = 0.97, P = 0.015) and validation sets (AUC = 0.93, P = 0.019). Model 3, PRM and GPRM could stratify patients by disease-free survival, and a predicted nonpCR was associated with poor prognosis (P = 0.034, 0.001 and 0.019, respectively).ConclusionMultiscale heterogeneity characterized by DCE-MRI could effectively predict the pCR and prognosis of TNBC patients. The radiogenomic model could serve as a valuable biomarker to improve the prediction performance.

Dynamical heterogeneity and universality of power-grids

Electric power systems during transient states are extensively investigated using variations of the Kuramoto model to analyze their dynamic behavior. However, the majority of current models fail to capture the physics of power flows and the heterogeneity of the grids under study. This study addresses this gap by comparing the levels of heterogeneity in continent-sized power grids in Europe and North America to reveal the underlying universality and heterogeneity of grid frequencies, electrical parameters, and topological structures. Empirical data analysis of grid frequencies from the Hungarian grid indicates that q-Gaussian distributions best fit simulations, with spatio-temporally correlated noise evident in the frequency spectrum. Comparing European and North American power grids reveals that employing homogeneous transmission capacities to represent power lines can lead to misleading results on stability, and nodal behavior is heterogeneous. Community structures of the continent-sized grids are detected, demonstrating that Chimera states are more likely to occur when studying only subsystems. A topographical analysis of the grids is presented to assist in selecting such subsystems. Finally, synchronization calculations are provided to illustrate the occurrence of Chimera states. The findings underscore the necessity of heterogeneous grid models for dynamic stability analysis of power systems.

A blockchain platform selection method with heterogeneous multi-criteria Decision-Making based on hybrid distance measures and an AHP-EWM weight method

The blockchain platforms (BPs) have emerged as important tools for improving enterprise data security. However, the selection of a suitable BP for is a challenging issue for enterprises. In this paper, a novel heterogenous multi-criteria decision-making (MCDM) model is proposed to facilitate the BP selection and evaluation process. First, the evaluation criteria for BPs are defined through an analysis of popular BPs. Second, based on the defined evaluation criteria, multiple types of fuzzy numbers are adopted to accurately represent the evaluation criteria. Then, to deal with multiple types of fuzzy numbers, a hybrid distance measure is proposed to determine the negative and positive ideal separation matrices of the evaluation criteria. In addition, using the axiomatic design’s information content metric the as basis, an improved method is proposed to unify the evaluation of multiple types into a single number. An improved weight method is then proposed based on the combination of objective and subjective weighting to obtain the comprehensive weights of the criteria. Finally, a closeness degree metric is introduced to obtain the ranking order of all BP alternatives. An example case is analyzed to illustrate how to evaluate and select a suitable BP. The superiority of the proposed heterogeneous model is verified by comparisons with state-of-the-art methods.

Climate risk and energy futures high frequency volatility prediction

This study explores the impact of climate risk on the volatility of energy futures, including heating oil, crude oil, and natural gas. In particularly, employing the heterogeneous autoregressive model and its extensions, we find that incorporating climate risk information significantly improves predictive accuracy for high-frequency volatility of energy futures both in- and out-of-sample. These results are robust across different forecasting mechanisms and periods. Additionally, we demonstrate that climate risk provides incremental information compared with existing predictors of energy futures volatility, leading to improved prediction accuracy when combining various predictors. Finally, we demonstrate that considering climate risk can lead to increased economic gains for investors in heating oil and crude oil markets. Our findings provide valuable insights for market participants in the realms of energy portfolio selection and risk management.

Research on the Construction Technology of INS Digital Prototype Based on Digital Twin

In response to the continuous advancement of digitalization and intelligence in equipment as mandated by the state, a digital twin-based inertial navigation digital prototype construction scheme has been designed. This scheme supports the realization of intelligent manufacturing, testing, evaluation, and operational assurance throughout the lifecycle of inertial navigation products. This paper decomposes the physical entities of the " three-autonomy " SINS and integrates component-level inertial navigation digital prototypes based on the principles of inertial navigation. The prototypes were integrated using heterogeneous models in simulation software. Pure navigation functionality verification was conducted on the prototypes. The results show that the errors in pure simulation of the inertial navigation digital prototypes constructed in this study are consistent with the physical navigation results of inertial navigation systems, providing a reference for the development of inertial navigation digital twin systems.

Stochastic extinction and persistence of a heterogeneous epidemiological model

Stochastic extinction and persistence of a heterogeneous epidemiological model

The Tyranny of the Ribbon: The Effects of Prioritizing New Infrastructure over Maintenance

In the allocation of resources toward public infrastructure, spending on maintenance is typically neglected. Using a heterogeneous agent model, we find that reallocating funding away from new infrastructure investment and toward maintenance raises the effectiveness of existing public infrastructure and thus yields gains in GDP and in aggregate welfare. Richer households experience larger welfare gains than poorer households. A reallocation that takes funding away from maintenance is found to have adverse effects on GDP and aggregate welfare. Raising new funding with taxation yields larger positive benefits if this additional spending is divided between maintenance expenditures and investment in new infrastructure. Giving in to the “Tyranny of the Ribbon” by policymakers, that is, raising new funding to be spent only on new infrastructure, is shown to be the worse option.

Learning real-world heterogeneous noise models with a benchmark dataset

Noise modeling is an important research field in computer vision; the design of an accurate model for imaging sensor noise depends on not only a comprehensive benchmark dataset of the real world, but also a precise design of the noise modeling algorithm. However, due to the inaccurate estimation method of noise-free images and limited shooting scenes, the current realistic datasets could not describe the diverse noise properties sufficiently. Moreover, popular parametric noise models are not sophisticated enough to characterize the real-world noise exactly. In this work, we first construct a more comprehensive dataset of the real world by capturing more indoor and outdoor scenes under different lighting conditions using diverse smartphones, then we propose a non-parametric noise estimation method capable of modeling the spatial heterogeneity of real-world noise patterns. Specifically, in order to characterize the spatial heterogeneity of real-world noise, we assume a non-i.i.d Gaussian distribution and propose a deep convolutional neural network (DCNN)-based approach for learning pixel-wise noise variance maps. To learn the pixel-wise variance map, we have constructed a variance estimation network mapping from the conditional signals (clean image, ISO, and camera model) to surrogate labels obtained from the nonlocal search of similar patches from the clean-noisy image pair. Finally, we conducted denoising and classification experiments using different kinds of simulated noisy images, compared to the Poisson-Gaussian and Noise Flow noise models, the proposed method achieves denoising performance improvements (PSNR) of 1.13 dB and 2.51 dB respectively on the proposed real-world test dataset, denoising and classification results on the real noisy data captured by mobile phones have verified that our approach is more accurate than current noise modeling methods.

Brain functional gradient and structure features in adolescent and adult autism spectrum disorders.

Understanding how function and structure are organized and their coupling with clinical traits in individuals with autism spectrum disorder (ASD) is a primary goal in network neuroscience research for ASD. Atypical brain functional networks and structures in individuals with ASD have been reported, but whether these associations show heterogeneous hierarchy modeling in adolescents and adults with ASD remains to be clarified. In this study, 176 adolescent and 74 adult participants with ASD without medication or comorbidities and sex, age matched healthy controls (HCs) from 19 research groups from the openly shared Autism Brain Imaging Data Exchange II database were included. To investigate the relationship between the functional gradient, structural changes, and clinical symptoms of brain networks in adolescents and adults with ASD, functional gradient and voxel-based morphometry (VBM) analyses based on 1000 parcels defined by Schaefer mapped to Yeo's seven-network atlas were performed. Pearson's correlation was calculated between the gradient scores, gray volume and density, and clinical traits. The subsystem-level analysis showed that the second gradient scores of the default mode networks and frontoparietal network in patients with ASD were relatively compressed compared to adolescent HCs. Adult patients with ASD showed an overall compression gradient of 1 in the ventral attention networks. In addition, the gray density and volumes of the subnetworks showed no significant differences between the ASD and HC groups at the adolescent stage. However, adults with ASD showed decreased gray density in the limbic network. Moreover, numerous functional gradient parameters, but not VBM parameters, in adolescents with ASD were considerably correlated with clinical traits in contrast to those in adults with ASD. Our findings proved that the atypical changes in adolescent ASD mainly involve the brain functional network, while in adult ASD, the changes are more related to brain structure, including gray density and volume. These changes in functional gradients or structures are markedly correlated with clinical traits in patients with ASD. Our study provides a novel understanding of the pathophysiology of the structure-function hierarchy in ASD.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication

BackgroundBreast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment. ObjectivesThis study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication. MethodsA novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers—beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)—alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation. ResultsThe proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability. ConclusionBy providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

Design and stabilization of direct autothermal methane steam reformation reactor for producing hydrogen via optimizing oxygen distributed feeding

Direct autothermal methane reforming is an energy-efficient way of hydrogen production. However, significant hot spot is always encountered near the reactor inlet, causing catalyst sinter and even reactor damage. Hence, bed temperature regulation by oxygen distributed feeding was numerically investigated using a one-dimensional heterogeneous model. Simulation results show oxygen feeding along full length of reactor leads to low methane and oxygen conversion despite avoiding hot spot. Elevating feeding temperature solves this problem, but requires extra heat. Shortening feeding length to 1/10 of reactor reduces maximum bed temperature by 100 K; Half oxygen fed into reactor inlet and the other along the length further reduces maximum bed temperature and avoids the transient runaway. Optimization results show that oxygen parabolically and totally fed along the front part of the reactor length shows better performance than that feeding half along reactor length and the other into reactor inlet.

Research on the Construction of a Digital Twin System for the Long-Term Service Monitoring of Port Terminals

Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a “particle model” approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations.

Differentially expressed heterogeneous overdispersion genes testing for count data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

An EGF technique to infer the source parameters of a circular crack growing at a variable rupture velocity

Circular crack models with a constant rupture velocity struggle to effectively model both the amplitude and duration of first P-wave pulses generated by small magnitude seismic events. Assuming a constant rupture velocity is unphysical, necessitating a deceleration phase in the rupture velocity to uphold the causality of the healing process. Moreover, a comprehensive failure model might encompass an initial nucleation phase, typically characterized by an increase of the initial rupture velocity. Studies have demonstrated that quasi-dynamic circular crack models featuring variable rupture velocities can accurately model the shape of the observed first P-wave pulse. Based on these principles, an Empirical Green’s function (EGF) approach was previously formulated to estimate the source parameters of small magnitude earthquakes, called MAIN. In addition to determine the source radius and stress drop, this method also enables the inference of the temporal evolution of rupture velocity. However, this method encounters difficulties when the noise-to-signal ratio in the recordings of smaller earthquakes used as EGF exceeds 5%, a common situation when employing regional-scale recordings of small-magnitude earthquakes as EGF. Through synthetic tests, we demonstrated that, in such instances, the problem of this technique is that the alignment between the onset of P waves of EGF and MAIN is not rightly recovered after the initial inversion step. Consequently, a novel inversion method has been developed to address this issue, enabling the identification of the optimal alignment of P-wave arrivals in EGF and MAIN across all stations. A Bayesian statistical approach is proposed to meticulously investigate the solutions of model parameters and their correlations. Using the new technique on a small magnitude earthquake (ML = 3.3) occurred in Central Italy enabled us to identify the most likely rupture models and examine the issue of correlation among model parameters. Application of Occam’s Razor Principle suggests that, for the investigated event, a circular crack model should be favored over a heterogeneous rupture model.

Towards detailed combustor wall kinetics: An experimental and kinetic modeling study of hydrogen oxidation on Inconel

Gas-phase hydrogen combustion is ubiquitous in industrial processes, and the associated surface kinetics on heat-resistant alloys plays a crucial role in designing efficient low-carbon technologies. We conducted new temperature programmed reduction experiments to determine the reducibility of materials following an oxidation cycle. These experiments were modeled using a thermoconsistent multi-site microkinetic model for H2 heterogeneous oxidation on Inconels, which was validated against literature experiments. This competitive adsorption model considers iron bulk content, hydrogen spillover and subsurface oxygen migration on hydrogen surface oxidation kinetics. New X-ray diffraction experiments confirmed the postulated crystallographic structures in the Inconel samples, suggesting their presence on the surface scale. The phenomenological model was coupled with several state-of-the-art gas-phase oxidation mechanisms to assess gas/surface reactions interaction as a function of material and temperature. The results reveal a complex interaction in which surface removes H radicals from the gas-phase, while the Bradford (H2+OH) reaction converts OH into H2O, promoting water adsorption on Inconel. This interaction was found to give rise to gas-phase thermokinetic oscillations. The model predicts a non-monotonous effect of reactor area-to-volume ratio on reactivity and emphasizes the impact of Inconel composition on product selectivity. Overall, the multi-site model provides new insight into the contrasting reactivities among active sites, bridging the gap between material science and heterogeneous combustion modeling.

Effectiveness of the Antiparasitic Combination of Albendazole and Praziquantel As Compared With Albendazole Monotherapy in the Treatment of Neurocysticercosis in Children: A Systematic Review and Meta-Analysis.

Neurocysticercosis, caused by the tapewormTaenia solium, is a neglected tropical illness that affects millions of people worldwide. The disease leads to seizures and epilepsy as the larvae invade the nervous system. Treatment with albendazole and praziquantel is common, but the comparative effectiveness of combination therapyversusmonotherapy is unclear. This study evaluated the effectiveness of albendazole and praziquantel combination therapy versus albendazole monotherapy for lesion resolution in pediatric neurocysticercosis. The study aimed to assess the effectiveness of the antiparasitic combination of albendazole and praziquantel as compared with albendazole monotherapy in the treatment of neurocysticercosis in children. This study is based on a systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Randomized controlled trials on pediatric patients receiving the mentioned therapies were included.The risk-of-bias tool for randomized trials assessed the study quality once data extraction and analysis were completed. This study included randomized research in neurocysticercosispediatric patients diagnosed with neuroimaging outcomes, using albendazole and praziquantel combination therapy or albendazole monotherapy. We searched articles between September 30 and December 1, 2023. All terms followed the Medical Subject Headings (MeSH) browser, and 13 articles were found. The data was quantitatively analyzed using RevMan 5.4.1 (The Nordic Cochrane Center, The Cochrane Collaboration, Copenhagen, Denmark). We applied the relative risk (RR) for the intervention and control groups before and after treatment, obtained from prior studies on lesion result resolution. The statistical method was Mantel-Haenszel. The model analysis we used was a fixed effect model (FEM) for heterogeneity (I2) < 50% and a random effect model (REM) for I2 ≥ 50%. The impact was measured using the risk difference (RD) by study and the overall 95% confidence interval (CI). The meta-analysis indicated that combination therapy was more effective in achieving complete lesion resolution after both three months (pooled RD = 0.18, 95%CI = 0.03-0.33, p= 0.02, I2 =0%) and six months (pooled RD = 0.24, 95% CI = 0.09-0.40, p = 0.002, I2 =0%) of therapy. However, calcification outcomes were also more significant in the combination therapy group. The study demonstrates that the albendazole and praziquantel combination therapy is superior in lesion resolution in pediatric neurocysticercosis.Clinical caution is advised to prevent calcification during treatment.

Implications of heterogeneous SIR models for analyses of COVID-19

Implications of heterogeneous SIR models for analyses of COVID-19

Exchange rate stability and expectation management under heterogeneous expectations

This study develops a heterogeneous agent model comprising fundamentalists, chartists, carry traders, and stabilizers to determine the RMB exchange rate. The stabilizers act as agents of the central bank and relevant government agencies to manage expectations when the exchange rate appreciates or depreciates excessively. Using the unscented Kalman filter method, our study finds that the principal parameters of the heterogeneous agent model are statistically and economically significant, and the heterogeneous agent model can adequately characterize the main features of expectation management. Furthermore, the stabilizers can significantly reduce excess volatility and promote market exchange rates to their fundamental values. Additionally, the timing of the stabilizers' actions is consistent with that of the central bank's implementation of expectation management.

How P‐Wave Scattering Throughout the Entire Mantle Mimics the High‐Frequency Pdiff and Its Coda

AbstractWe document the arrival of seismic energy in the core shadow zone up to large distances beyond 150° more than 100 s prior to the core phases. Numerical simulations of the energy transport in an established heterogeneity model show that scattering throughout the entire mantle explains these observations. Diffraction at the core‐mantle boundary is unlikely in our 1–2 Hz frequency band and is not required indicating misleading terminology with reference to Pdiff for the scattered P∗P‐energy. Records of the largest deep earthquakes at low‐noise stations are key to the observation of the faint precursory signal which changes appearance with increasing distance from a coda‐like decay over a constant amplitude level around 130° to an emergent wave train. According to our simulations, different depth layers in the mantle dominate different time‐distance windows of the scattered wave train, providing the opportunity to improve the depth resolution of mantle heterogeneity models.

Heterogenous biological network multi-task learning model for ncRNA-disease-drug association prediction

Traditional medical research is characterised by lengthy duration, significant financial investment, and substantial risk of failure. In response to these challenges, network medicine, combined with medicine and computer technology, has become an important development direction, and computational methods have been proposed to predict potential associations. However, most of the current computational methods focus on single-potential association prediction tasks, which face issues of association sparsity and weak generalisation ability. To address these challenges, we developed a heterogeneous biological network multi-task learning model (HBNMM). Unlike previous methods based on bipartite graphs, HBNMM constructs a complex heterogeneous biological network, including ncRNA-disease-drug association networks and diverse similarity networks. HBNMM applies graph attention networks to aggregate node neighbourhood information and acquire node feature embeddings, and is then trained with a multi-task learning strategy to simultaneously predict potential ncRNA-disease, ncRNA-drug, and drug-disease associations. As a result, the HBNMM achieves an excellent performance that is higher than that of the state-of-the-art models. Furthermore, five case studies supported by experiments showed powerful predictive ability for drug discovery and disease treatment.