Impact Of Heterogeneity Research Articles

Regional Heterogeneous Impact of Cross-Border E-Commerce on Economic Recovery in China

Regional Heterogeneous Impact of Cross-Border E-Commerce on Economic Recovery in China

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation

Objectives:This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. Materials and methods:A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. Results:For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. Conclusion:Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.

The causal relationship between immune cells mediating FIT3L, CCL4, OSM, and skin-derived deteriorated tumors.

Observational studies have identified a dual effect of circulating inflammatory proteins and immune cells on cancer progression. However, the specific mechanisms of action have not been clarified in the exacerbation of cutaneous-origin tumors. Therefore, this study aims to investigate whether the causal relationship between circulating inflammatory factors and basal cell carcinoma (BCC), cutaneous malignant melanoma (SKCM), and cutaneous squamous cell carcinoma (cSCC) is regulated by immune cells. This study employed the Two-Sample Mendelian Randomization (TSMR) approach to investigate the causal relationships between 91 circulating inflammatory factors and three prevalent types of skin cancer from a genetic perspective. Bayesian Weighted Mendelian Randomization (BWMR) was also used to validate correlation and reverse MR to assess inverse relationships. Subsequent sensitivity analyses were conducted to limit the impact of heterogeneity and pleiotropy. Finally, the two-step Mendelian Randomization (two-step MR) method was utilized to ascertain the mediating effects of specific immune cell traits in the causal pathways linking circulating inflammatory factors with BCC, SKCM, and cSCC. The Inverse Variance Weighted (IVW) method and the Bayesian Weighted Algorithm collectively identified nine inflammatory factors causally associated with BCC, SKCM, and cSCC. The results from Cochran's Q test, mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and MR-Egger intercept were not statistically significant (p<0.05). Additionally, the proportions mediated by CD4+ CD8dim T cell %leukocyte, CD4-CD8-Natural Killer T %T cell, and CD20 on IgD-CD38-B cell for FIt3L, CCL4, and OSM were 9.26%, 8.96%, and 10.16%, respectively. Immune cell levels potentially play a role in the modulation process between circulating inflammatory proteins and cutaneous-origin exacerbated tumors. This finding offers a new perspective for the in-depth exploration of cutaneous malignancies.

Assessing the Spatial Equity of Urban Park Green Space Layout from the Perspective of Resident Heterogeneity

Urban park green spaces (UPGS) are essential resources for improving the urban ecological environment and meeting residents’ recreational needs. However, during rapid urbanization, the layout of UPGS often exhibits spatial inequity, with significant differences in the resources enjoyed by resident groups with different socioeconomic attributes. Accurately assessing the spatial equity of the UPGS layout (the equal accessibility of UPGS) is crucial for optimizing resource allocation and promoting social equity. This study takes the main urban area of Nanjing as an example and utilizes location-based service (LBS) data and multi-source geographic data to conduct an in-depth characterization of residents’ socioeconomic attributes, recreational behaviors, and park green space layout at the street scale. By constructing indicators of resident heterogeneity and UPGS supply–demand matching degree, it reveals the differences in park green space accessibility among different social groups and locations and explores the correlation between resident heterogeneity and UPGS spatial equity. The study finds that the layout of UPGS in the main urban area of Nanjing exhibits significant spatial inequity, with generally poor accessibility to park green spaces in the central urban area and low-income communities. The higher degree of diversification of residents’ socioeconomic attributes leads to a lower level of UPGS spatial equity in their streets. The results of the big data analysis verify the significant impact of resident heterogeneity on the equity of park green space layout. This study reveals the spatial equity issues of UPGS layout from the perspective of resident heterogeneity, providing new ideas and evidence for optimizing the allocation of park green space resources. Future UPGS planning should pay more attention to the diversity of residents’ recreational needs, focus on improving the accessibility of park green spaces in central urban areas and low-income communities, and balance the interests and demands of different stakeholders through public participation mechanisms.

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers.

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

Accelerating CO2 Storage Site Characterization through a New Understanding of Favorable Formation Properties and the Impact of Core-Scale Heterogeneities

CO2 sequestration in deep geologic formations can permanently reduce atmospheric CO2 emissions and help to abate climate change. Target formations must undergo a time- and resource-intensive site evaluation process, assessing storage capacity, environmental safety, and suitability for CO2 trapping via reactive transport models based on data from a limited number of core samples. As such, simulations are often simplified and omit heterogeneities in formation properties that may be significant but are not well understood. To facilitate more rapid site assessment, this work first defines the aquifer properties of favorable storage formations through the analysis of promising and active storage sites. Data show quartz is the most prevalent formation mineral with carbonate minerals, highly reactive with injected CO2, present in over 75% of formations. Porosity and permeability data are highly clustered at 10–30% and 10–1000 mD. Field-scale reactive transport simulations are then constructed and used to analyze CO2 trapping efficiency. The models consider porosity and carbonate mineral heterogeneity as well as the impacts of typical temperature gradients. Simulated sequestration efficiencies are compared to results from a comparable homogenous model to understand the implications of aquifer non-uniformities. The results show a lower sequestration efficiency in the homogeneous model during the injection phase. During the post-injection phase, the homogenization of porosity and carbonate mineralogy results in a higher sequestration efficiency. Incorporating the temperature gradient also increases the sequestration efficiency. Importantly, the maximum deviation between the homogeneous and heterogeneous simulations at the end of the 50-year study period is only ~10%. Larger impacts may be incurred for properties outside the defined, promising ranges suggested here.

Real-time bus arrival delays analysis using seemingly unrelated regression model

AbstractTo effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

The way to improve the quality of new smart city public service under the demand heterogeneity

Due to the diversity, difference and dynamics of residents' needs, the improvement of public service quality is faced with many challenges. Therefore, it is of great practical significance to improve the path of public service quality of new smart city under the heterogeneity of needs. This paper first introduces the background and basic concept of the new smart city, as well as the development process of the new smart city in China. This paper discusses the heterogeneity of residents' needs in the new smart city, and explains the impact of these heterogeneous demands on the public service quality of the new smart city. This paper also discusses how to accurately locate the service demand by strengthening demand research, optimize the resource allocation and realize the balanced development; promote service innovation to solve the impact of demand heterogeneity on new smart city, public service supply, public service resource allocation and public service innovation, as the reference direction for the improvement of public service quality of new smart city. Finally, this paper analyzes and systematically looks into the future of the new smart city to meet the direct needs of the public's survival, life, development and other social needs, as well as promoting the modernization of the urban governance system information and governance capacity as the main goal, and commits itself to the path design of improving the quality of public services.

Performance shortfalls and innovative search: Evidence from the biopharmaceutical firms in China

ABSTRACT Performance feedback literature acknowledges that firms’ innovative behaviour in response to performance shortfalls. However, it overlooks the impact of multidimensional heterogeneity in innovative activities on the direction of search following performance shortfalls. Based on the framework of gain and loss, we propose that underperforming firms are inclined to minimise breadth search and are more likely to engage in depth search, influenced by varying perceptions of risk associated with different innovative search strategies. This hypothesis is tested using a panel dataset of 234-listed biopharmaceutical firms in China from 2008 to 2022. Our findings indicate a negative relationship between performance shortfalls and innovative search breadth, and a positive relationship between performance shortfalls and innovative search depth. Furthermore, we found that both knowledge diversity and knowledge distance negatively moderate the relationship between performance shortfalls and innovative search breadth. Conversely, knowledge distance positively moderates the relationship between performance shortfalls and innovative search depth. This study enriches the understanding of performance feedback mechanisms and their influence on innovative search strategies.

On bi-objective combinatorial optimization with heterogeneous objectives

The heterogeneity among objectives in multi-objective optimization can be viewed from several perspectives. In this paper, we are interested in the heterogeneity arising in the underlying landscape of the objective functions, in terms of multi-modality and search difficulty. Building on recent efforts leveraging the so-called single-objective NK-landscapes to model such a setting, we conduct a three-fold empirical analysis on the impact of objective heterogeneity on the landscape properties and search difficulty of bi-objective optimization problems. Firstly, for small problems, we propose two techniques based on studying the distribution of the solutions in the objective space. Secondly, for large problems, we investigate the ability of existing landscape features to capture the degree of heterogeneity among the two objectives. Thirdly, we study the behavior of two state-of-the-art multi-objective evolutionary algorithms, namely MOEA/D and NSGA-II, when faced with a range of problems with different degrees of heterogeneity. Although one algorithm is found to consistently outperform the other, the dynamics of both algorithms vary similarly with respect to objective heterogeneity. Our analysis suggests that novel approaches are needed to understand the fundamental properties of heterogeneous bi-objective optimization problems and to tackle them more effectively.

Accounting for center-level effects in multicenter randomized controlled trials

Investigators often conduct randomized controlled trials (RCTs) at multiple centers/sites when determining the effect of a treatment or an intervention. Diversifying recruitment across multiple institutions allows investigators to make recruitment go faster within a shorter timeframe and allows generalizing the study results across diverse populations. Despite having a common study protocol across multiple centers, the eligible participants may be heterogeneous, site policies and practices may vary, and the investigators’ experience, training, and expertise may also vary across sites. These factors may contribute to the heterogeneity in effect estimates across centers. As a result, we usually observe some degree of heterogeneity in effect estimates across centers, despite all centers following the same study protocol. During the analysis of such a trial, investigators typically ignore center effects, but some have suggested considering centers as fixed or random effects in the model. It is not clear how considering the effects of centers, either as fixed or random effects, impacts the test of the primary hypothesis. In this article, we first review the practice of accounting for center effects in the analyses of published RCTs and illustrate the extent of heterogeneity observed in a few preexisting multicenter RCTs. To determine the impact of heterogeneity on the test of a primary hypothesis of an RCT, we considered continuous and binary outcomes and the corresponding appropriate model, namely, a simple linear regression model for a continuous outcome and a logistic regression model for the binary outcome. For each model type, we considered three methods: (a) ignore the center effect, (b) account for centers as fixed effects, or (c) account for centers as random effects. Based on simulation studies of these models, we then examine whether considering the center as a fixed or random effect in the model helps to preserve or reduce the type I and type II error rates during the analysis phase of an RCT. Finally, we outline the threshold at which center-level effects are negligible and thus negligible and provide recommendations on when it may be necessary to account for center effects during the analyses of multicenter randomized controlled trials.

Inflation, Unemployment, and Inequality: Beyond the Traditional Phillips Curve

ABSTRACT This article undertakes a theoretical post-Keynesian analysis to explore the relationship between inflation, unemployment, and inequality. Firstly, we show the compatibility of the inequality-augmented Phillips curve, which first appeared in Rolim, Carvalho, and Lang (2023), with the post-Keynesian macroeconomic theory. The curve combines the Phillips curve, which relates unemployment and inflation and can be derived from the conflicting-claims inflation model, with the positive relation between unemployment and inequality due to the heterogeneous impact of unemployment on workers. The unemployment rate thus connects the inflation rate to income inequality in a three-dimensional relationship described by the inequality-augmented Phillips curve, which indicates a possible trade-off between low inflation and low inequality. Secondly, we consider the profits inflation case: when inflation is simultaneous with markup increases. Increases in the ex-ante markup rate shift the curve upwards, thus increasing the inflation rate and inequality. In this context, if monetary policy aims to control inflation by increasing unemployment, it will operate along the inequality-augmented Phillips curve and will further increase inequality.

Application of Heterogeneous Data Analysis based on SEA Grid in User Investment Analysis

To solve the problem of yield calculation in complex investment scenarios, a time-cost double-weighted rate of return calculation method based on SEA grid is proposed, and its effectiveness is verified by comparing with traditional methods and researching the quantitative evaluation and analysis method of user investment based on structured data. To solve the above problems, a data-heterogeneous federated learning method based on user investment analysis FedPSG is proposed, which changes the data form transmitted from the client to the server from model parameters to model scores, and only a small number of clients need to upload model parameters to the server in each round of training, thereby reducing communication costs. At the same time, a model retraining strategy is proposed, which uses server data to train the global model for second iteration, and further improves the model performance by alleviating the impact of data heterogeneity on federated learning. The method of event dimension analysis of user investment is designed, and a credibility index is proposed to evaluate the analysis results. Experiments show that by combining event data, it can effectively provide users with event factors in the fluctuation of investment profit and loss, and help users better analyze their own investments.

Anticipating change: The impact of simulated seasonal heterogeneity on heat tolerances along a latitudinal cline.

An understanding of thermal limits and variation across geographic regions is central to predicting how any population may respond to global change. Latitudinal clines, in particular, have been used to demonstrate that populations can be locally adapted to their own thermal environment and, as a result, not all populations will be equally impacted by an increase in temperature. But how robust are these signals of thermal adaptation to the other ecological challenges that animals commonly face in the wild? Seasonal changes in population density, food availability, or photoperiod are common ecological challenges that could disrupt patterns of thermal tolerance along a cline if each population differentially used these signals to anticipate future temperatures and adjust their thermal tolerances accordingly. In this study, we aimed to test the robustness of a cline in thermal tolerance to simulated signals of seasonal heterogeneity. Experimental animals were derived from clones of the Australian water flea, Daphnia carinata, sampled from nine distinct populations along a latitudinal transect in Eastern Australia. We then factorially combined summer (18 h light, 6 h dark) and winter (6 h light, 18 h dark) photoperiods with high (5 million algal cells individual-1 day-1) and low (1 million algal cells individual-1 day-1) food availabilities, before performing static heat shock assays to measure thermal tolerance. We found that the thermal tolerances of the clonal populations were sensitive to both measures of seasonal change. In general, higher food availability led to an increase in thermal tolerances, with the magnitude of the increase varying by clone. In contrast, a switch in photoperiod led to rank-order changes in thermal tolerances, with heat resistance increasing for some clones, and decreasing for others. Heat resistance, however, still declined with increasing latitude, irrespective of the manipulation of seasonal signals, with clones from northern populations always showing greater thermal resistance, most likely driven by adaptation to winter thermal conditions. While photoperiod and food availability can clearly shape thermal tolerances for specific populations, they are unlikely to overwhelm overarching signals of thermal adaptation, and thus, observed clines in heat resistance will likely have remained robust to these forms of seasonal heterogeneity.

Accelerated depreciation of fixed assets and green transformation of enterprises

Facilitating the green transformation of enterprises is a crucial research topic. Tax policy is a key tool for governments to influence enterprise development, with a significant impact on their innovation behavior. We establish a staggered DID model based on the relevant data of Chinese A-share listed companies from 2012 to 2021, finding that the policy can promote green transformation of enterprises though optimized finance, innovation driven, and responsibility implementation. We also explore the policy's heterogeneous impact in three dimensions: time, space, and corporate attributes. Based on the above findings, suggestions are provided for further improving the policy in China.

The examination of the heterogeneous psychological impact of adverse childhood experiences on Chinese children with and without left-behind status

Adverse childhood experiences (ACEs), encompassing situations like abuse, neglect, and family challenges, have a pervasive global presence and profoundly impact children’s mental well-being (MWB). This study aims to comprehensively examine these effects within the context of Chinese children and explores the potential mediating roles of social support and resilience. Additionally, the study examines whether the unique circumstance of being left behind alters these relationships. The research collected data in Yunnan Province, China and the final sample size was 775. Intriguingly, the finding revealed that direct associations between ACEs and children’s MWB were not statistically significant. However, our analysis underscores the pivotal roles played by social support and resilience in mediating the relationship between ACEs and MWB. This implies that the presence of supportive relationships and the capacity to bounce back from challenges have a considerable impact in mitigating the negative effects of ACEs on children’s mental well-being. Furthermore, this study unveils a noteworthy moderating effect of the left-behind status, notably influencing the connections between ACEs and both resilience and social support. These findings contribute to enrich our understanding of the interacting mechanism in relationship between ACEs and MWB, offering valuable insights for policymakers and social workers. The findings could guide the formulation of targeted interventions that effectively promote the mental well-being of children affected by ACEs.

Influence of sedimentary structure and pore-size distribution on upscaling permeability and flow enhancement due to liquid boundary slip: A pore-scale computational study

Low-permeability sedimentary formations, such as tight sandstones, exhibit fluid flow and transport phenomena distinct from those in conventional porous systems due to the dominance of micro- to nanometer-sized pores and variable amounts of boundary slip. The widely used traditional no-slip boundary condition often fails to accurately describe fluid behavior in these formations. A knowledge gap exists in understanding how liquid slip influences fluid dynamics in complex, heterogeneous sedimentary structures, as previous studies have primarily focused on simplified, homogeneous pore geometries. In this study, we investigated the impact of boundary slip on low-Reynolds number fluid dynamics within synthetically designed two-dimensional graded and random pore networks with varying pore-size distributions to account for heterogeneity. Our results showed that velocity variance increased with increasing heterogeneity, following a power-law relationship. The power-law exponents decreased with boundary slip, quantifying how boundary slip mitigated the impact of heterogeneity on velocity variance. We developed a theoretical model to predict asymptotic flow enhancement and derived constitutive relations to estimate the coefficient C and maximum flow enhancement (ΔE) based on the pore-to-grain size ratio and porosity. Energy dissipation increased with both heterogeneity and boundary slip, which we identified as the primary mechanism contributing to asymptotic flow enhancement. This relationship was illustrated by a 1:1 linear correlation between maximum energy dissipation and maximum flow enhancement, regardless of heterogeneity, indicating that energy dissipation due to boundary slip entirely controls the emerging fluid dynamics. The presented theoretical model and constitutive equations offer practical applications for optimizing fluid dynamics in heterogeneous formations.

Data heterogeneity's impact on the performance of frequent itemset mining algorithms

Frequent itemset mining (FIM) is a widely used task that extracts frequently occurring itemsets from data. Plenty of deterministic algorithms are available for this daunting task. However, experimental studies have not considered that data heterogeneity significantly impacts the algorithms' performance, giving rise to unfair comparisons and biased conclusions. This paper seeks to advance by comparing cutting-edge algorithms using various frequency thresholds, considering the resulting data heterogeneity. An extensive experimental study is carried out, including the number of itemsets mined per second as the performance quality measure to compare algorithms. The experiments include defining eight metrics to quantify data heterogeneity, and their values vary the algorithms' performance. The results revealed that some techniques (hypercube decomposition and k-items machine) are essential to achieve excellent performance on any dataset, and most algorithms behave similarly well when they include those techniques. As a final important point, different threshold values produce dissimilar data subsets (data heterogeneity is not an immutable data characteristic), so a previous study on the database characteristics with a few minimum support thresholds could be beneficial to select the best-suited FIM algorithm beforehand.

Heterogeneity's impact on the sloshing motion in excited tanks

AbstractThis paper aims to investigate the effect of liquid heterogeneity on sloshing in a two-dimensional rectangular tank. The container is excited horizontally, and the density of the liquid at equilibrium can almost be considered a linear dependence of the depth. The linearized equations representing the sloshing phenomenon are solved numerically using ANSYS 2020 R2 software and analytically using the separation method of variables in conjunction with the Fourier analysis. On the other hand, a comparison study has been carried out between the obtained results and other works related to the same phenomenon. Overall, the results exhibit considerable effects of variation in the Heterogeneity Coefficient β on the free surface's motion and the pressure distribution within the liquid. Furthermore, the free surface of liquid heterogeneity is also affected by the variation of Excitation Frequency Ω and the Filling Rate h of the liquid in the tank.

Deciphering the digital divide: the heterogeneous and nonlinear influence of digital economy on urban-rural income inequality in China

ABSTRACT This study investigates the heterogeneous and nonlinear impact of digital economy on the urban-rural income disparities in 30 Chinese provinces from 2013 to 2020. The results from moderated multiple regressions and panel threshold regressions consistently indicate that digital economy generally widens China’s urban-rural inequality, but this widening effect diminishes as the digital economy progresses. Furthermore, the influence of digital economy varies depending on local socio-economic factors, including economic development, industrial structure, education level, government expenditure, and openness. Digital economy exerts a stronger influence on exacerbating the inequality in economically developed regions with higher education levels and greater openness, but it helps prevent the inequality from expanding through its interactions with secondary industry development and fiscal expenditure. These findings provide a reference for formulating target strategies in line with local development conditions to bridge the inequality engendered by the digital divide.