Unified Analytical Framework Research Articles

Predicting the Severity of COVID-19 Pneumonia from Chest X-Ray Images: A Convolutional Neural Network Approach

This study addresses significant limitations of previous works based on the Brixia and COVIDGR datasets, which primarily provided qualitative lung injury scores and focused mainly on detecting mild and moderate cases. To bridge these critical gaps, we developed a unified and comprehensive analytical framework that accurately assesses COVID-19-induced lung injuries across four levels: Normal, Mild, Moderate, and Severe. This approach’s core is a meticulously curated, balanced dataset comprising 9,294 high-quality chest X-ray images. Notably, this dataset has been made widely available to the research community, fostering collaborative efforts and enhancing the precision of lung injury classification at all severity levels. To validate the framework’s effectiveness, we conducted an in-depth evaluation using advanced deep learning models, including VGG16, RegNet, DenseNet, MobileNet, EfficientNet, and Vision Transformer (ViT), on this dataset. The top-performing model was further enhanced by optimizing additional fully connected layers and adjusting weights, achieving an outstanding sensitivity of 94.38%. These results affirm the accuracy and reliability of the proposed solution and demonstrate its potential for broad application in clinical practice. Our study represents a significant step forward in developing AI-powered diagnostic tools, contributing to the timely and precise diagnosis of COVID-19 cases. Furthermore, our dataset and methodological framework hold the potential to serve as a foundation for future research, paving the way for advancements in the detection and classification of respiratory diseases with higher accuracy and efficiency.

Who takes the lead: Synergistic emission reduction effects of proactive government and efficient market in atmospheric pollution mitigation

In environmental protection, government and market forces must work together. However, existing literature often separates the two and discusses their roles independently. Therefore, this study incorporates both the market and government into a unified analytical framework. It redefines government force as environmental regulation and market force as allocation efficiency in environmental protection. The study empirically examines the synergistic emission reduction effects of active government and efficient market on air pollution, using data from 272 prefectural-level cities in China from 2003 to 2019. The results show: (1) Both government and market interventions can significantly reduce air pollution levels, and their combination achieves a synergistic emission reduction effect.(2) Within this synergy, government intervention remains dominant, while the market plays a supportive role. (3) Analyzing the heterogeneous effects under different scenarios reveals that collaboration and mutual monitoring between the government and the market can effectively counteract the adverse effects of economic policy uncertainty and corruption on the environment. Furthermore, when a single entity undertakes environmental protection, it may encounter issues of diminishing effectiveness over time. However, the synergistic effects of government and market cooperation can mitigate these challenges. (4) The synergy exhibits a threshold effect based on the varying strengths of government and market forces. (5) Public participation enhances the synergistic emission reduction effect of government and market, acting as a vital complement to their environmental protection efforts. This study provides new empirical evidence on the synergistic effect of current environmental protection measures in improving air pollution control.

Molecular modeling of odd viscoelastic fluids.

We consider an active, stochastic microscopic model of particles suspended in a fluid and show that the coarse-grained description of this model renders odd viscoelasticity. The particles are odd dumbbells, each featuring a robotic device as the bead, which exhibits a particular torque response. We show that this model can be macroscopically treated as a viscoelastic fluid, analytically calculate the coefficients of the corresponding viscoelastic model, and corroborate the results using molecular dynamics simulations. This study provides a unified analytical framework for several experimental and numerical setups designed to elucidate odd effects in fluids.

Trade facilitation, market size, and supply chain efficiency of Taiwan semiconductor companies.

In an environment marked by global economic volatility and geopolitical uncertainties, the stability of Taiwan's supply chain takes on heightened importance, particularly given Taiwan's crucial role in the global semiconductor supply chain. In recent years, semiconductor companies in Taiwan have faced increasing inventory pressures, which will reduce their competitiveness and increase operational costs over the long term. Although previous studies have explored the influence of trade facilitation on macroeconomic and trade efficiencies, its specific impacts on the semiconductor industry have been less frequently addressed. This study integrates corporate inventory, trade facilitation, and geopolitical factors within a unified analytical framework to construct a model that explores mediating and moderating effects. This study conducted regression analysis on data from 52 Taiwan-listed integrated circuit companies from 2014 to 2022. Contrary to traditional findings that trade facilitation decreases inventory in other industries, it predominantly fosters inventory accumulation within Taiwan's semiconductor sector by expanding market size, thereby affecting supply chain efficiency. Moreover, geopolitical factors were found to intensify the effects of trade facilitation on corporate inventory. Elevated geopolitical risks lead to greater inventory accumulation, which ultimately threatens long-term competitiveness and diminishes the semiconductor industry's advantage in Taiwan, further influencing supply chain efficiency. Consequently, this study recommends that to more accurately forecast market size, semiconductor companies in Taiwan are encouraged to expand their manufacturing investments in Chinese mainland. Additionally, the prudent handling of cross-strait relations by the Taiwan authorities is an important strategy to mitigate geopolitical risks affecting the semiconductor supply chain.

Unified framework for stochastic dynamic responses and system reliability analysis of long-span cable-stayed bridges under near-fault ground motions

Long-span cable-stayed bridges are complex systems with numerous components, and prone to failures of multiple components under near-fault ground motions with long-period velocity pulses. The evaluation of dynamic system reliability of cable-stayed bridges is an important challenging issue. In this work, a unified framework for simultaneously determining both the stochastic dynamic responses and system reliabilities of long-span cable-stayed bridges under near-fault stochastic ground motions is proposed based on direct probability integral method (DPIM) with adaptive strategy. Firstly, the 3-dimensional finite element model of typical cable-stayed bridge and the stochastic synthesis model of near-fault ground motions are established. Based on probability density integral equation, new formulas in input probability space of time-variant moments of response are derived. Then, DPIM with adaptive strategy is suggested to calculate the mean value and standard deviation of response via new formulas. Dynamic system reliability of cable-stayed bridge under near-fault stochastic ground motions is evaluated by establishing failure criteria of extreme value mappings of multiple performance functions. Finally, numerical results indicate that the long-period velocity pulse of near-fault motions imposes an important effect, resulting in large mean responses and failure of cables. Failure probability of cables is significantly increased with the occurrence of velocity pulses, which causes the decrease of system reliability of cable-stayed bridge. Thus, the randomness and velocity pulse effect of near-fault ground motions should be seriously considered for reliability-based design and safety assessment of cable-stayed bridges.

Research on the Effect of the Healthy Cities Pilot Policy on the Labor Supply Time of Middle-Aged and Elderly Workers in China

Against the backdrop of Healthy China and an aging population, there is an urgent need to explore the effect of the Healthy Cities Pilot Policy (HCPP) on residents’ labor supply. This study integrates the HCPP, residents’ health, and labor supply time into a unified analytical framework. Using data from the China Health and Retirement Longitudinal Study (CHARLS) spanning the period 2011–2020 and leveraging the quasi-natural experiment provided by China’s 2016 HCPP, we empirically investigate its effects on labor supply time among middle-aged and elderly workers. Our findings indicate: (1) HCPP increases average weekly working hours by 2.834 h in pilot cities. (2) HCPP significantly enhances residents’ health status in pilot cities, extending their healthy time. (3) Middle-aged and elderly workers allocate more of their extended healthy time to labor rather than leisure, potentially influenced by traditional Chinese perceptions of “ceaseless toil”. (4) The impact of HCPP on labor supply time exhibits group heterogeneity, with stronger promotion effects observed among vulnerable middle-aged and elderly groups characterized by older age, rural household registration, lower educational attainment, and lower income levels. This research offers insights for policymakers aiming to optimize health policies and promote sustainable development within Chinese society.

Can changes in corporate income tax rate affect corporate innovation?

The relationship between corporate income tax rates and innovation is a critical international issue. Existing studies often overlook the potential mechanisms of market competition and productivity heterogeneity. This paper addresses these gaps by integrating these elements into a unified mathematical analysis framework. Using China's corporate income tax reform as a quasi-natural experiment, this study empirically tests the model. The findings reveal that decreases in tax rates significantly foster innovation, while increases have the opposite effect. The underlying mechanism involves market competition, with the impact of tax changes being more pronounced in firms with higher productivity. This study deepens the understanding of the nexus between corporate income tax and innovation, providing new insights to refine tax policies that promote innovative economic development globally.

Enhancing structural damage evaluation of PC girder bridges through digital twinning Bayesian model updating

Developing a unified analytical framework to assess the structural performance of deteriorated prestressed concrete (PC) girder bridges is important for their maintenance. This study aims to construct a physics-based digital twin framework for PC girder bridges incorporating Bayesian model selection and updating, a high-fidelity three-dimensional (3D) finite element (FE) model, and a method for simulating prestressed tendon damage. In this study, three PC girder specimens were designed and tested to evaluate the effects of tendon rupture near the anchorage region on structural performance. Bayesian model selection was used to determine the most plausible model class based on the estimated model evidence. The most probable values (MPVs) of the model parameters, derived from the estimated posterior probability density functions (PDFs), were then used to calibrate the FE model, which reflects a healthy state of the PC girder bridge. This updated FE model serves as the baseline for conducting what-if scenario simulations. Subsequently, nonlinear segments of the constitutive model, obtained from coupon tests, and a simulation method for tendon rupture were incorporated into the updated FE model. Nonlinear static simulation results obtained through the updated model were found to be consistent with both the observed crack pattern and the load–deflection curve. The reliability of the digital twinning FE model for assessing tendon damage and for predicting the residual load-bearing capacity was verified through comparison with measured data. Therefore, the digital twinning Bayesian model updating approach shows potential applications in continuous structural health monitoring (SHM) of PC bridges.

A unified framework for high-dimensional data stream analysis in fault diagnosis

Modern industrial systems routinely generate data in high volume at high velocity. These high-dimensional data streams (HDDS) provide valuable information at granular levels to quality personnel during root cause investigation in cases of a system fault. The goal of fault analysis using HDDS is twofold: (1) identify abnormal data streams and (2) locate the change point when the processes become out of control. Existing research has largely focused on addressing the two issues separately. In this article, we propose a unified framework by formulating the problem as optimal control of hierarchical missed discovery rates in multiple classifications. Theoretically, we establish that our approach minimizes the number of false discoveries while controlling the missed discovery rates at desired levels. Numerically, we develop a computationally efficient algorithm for solving the optimization and demonstrate its superior performance over the existing methods. A data-driven version of the proposed approach is suggested as well. An application to a real data set in semiconductor manufacturing shows that our approach works well in practice.

Structural covariation between cerebellum and neocortex intrinsic structural covariation links cerebellum subregions to the cerebral cortex.

The human cerebellum is increasingly recognized to be involved in nonmotor and higher-order cognitive functions. Yet, its ties with the entire cerebral cortex have not been holistically studied in a whole brain exploration with a unified analytical framework. Here, we characterized dissociable cortical-cerebellar structural covariation patterns based on regional gray matter volume (GMV) across the brain in n = 38,527 UK Biobank participants. Our results invigorate previous observations in that important shares of cortical-cerebellar structural covariation are described as 1) a dissociation between the higher-level cognitive system and lower-level sensorimotor system and 2) an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel pattern of ipsilateral, rather than contralateral, cerebral-cerebellar associations. Furthermore, phenome-wide association assays revealed key phenotypes, including cognitive phenotypes, lifestyle, physical properties, and blood assays, associated with each decomposed covariation pattern, helping to understand their real-world implications. This systems neuroscience view paves the way for future studies to explore the implications of these structural covariations, potentially illuminating new pathways in our understanding of neurological and cognitive disorders.NEW & NOTEWORTHY Cerebellum's association with the entire cerebral cortex has not been holistically studied in a unified way. Here, we conjointly characterize the population-level cortical-cerebellar structural covariation patterns leveraging ∼40,000 UK Biobank participants whole brain structural scans and ∼1,000 phenotypes. We revitalize the previous hypothesis of an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel ipsilateral cerebral-cerebellar associations. Phenome-wide association (PheWAS) revealed real-world implications of the structural covariation patterns.

Euclid preparation

Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the Euclid era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys with independent and uncorrelated systematics. To assess the robustness of the lensing results to systematic errors, we carried out end-to-end tests across different data sets. We performed a unified analysis at the catalogue level by leveraging the Euclid combined cluster and weak-lensing pipeline (COMB-CL). Notably, COMB-CL will measure weak lensing cluster masses for the Euclid Survey. Heterogeneous data sets from five recent, independent lensing surveys (CHFTLenS, DES SV1, HSC-SSP S16a, KiDS DR4, and RCSLenS), which exploited different shear and photometric redshift estimation algorithms, were analysed with a consistent pipeline under the same model assumptions. We performed a comparison of the amplitude of the reduced excess surface density and of the mass estimates using lenses from the Planck PSZ2 and SDSS redMaPPer cluster samples. Mass estimates agree with the results in the literature collected in the LC2 catalogues. Mass accuracy was further investigated considering the AMICO-detected clusters in the HSC-SSP XXL-North field. The consistency of the data sets was tested using our unified analysis framework. We found agreement between independent surveys at the level of systematic noise in Stage-III surveys or precursors. This indicates successful control over systematics. If this control continues into Stage IV, Euclid will be able to measure the weak lensing masses of around 13 000 (considering shot noise only) or 3000 (noise from shape and large-scale-structure) massive clusters with a signal-to-noise ratio greater than three.

Computational Analysis of Marker Genes in Alzheimer’s Disease across Multiple Brain Regions

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the disease, including the entorhinal cortex, prefrontal cortex, superior frontal gyrus, and superior parietal lobe. Our pipeline combines datasets derived from the aforementioned tissues into a unified analysis framework, facilitating cross-regional comparisons to provide a holistic view of the impact of the disease on the cellular and molecular landscape of the brain. We employed advanced computational techniques such as batch effect correction, normalization, dimensionality reduction, clustering, and visualization to explore cellular heterogeneity and gene expression patterns across these regions. Our findings suggest that enabling the integration of data from multiple batches can significantly enhance our understanding of AD complexity, thereby identifying key molecular targets for potential therapeutic intervention. This study established a precedent for future research by demonstrating how existing data can be reanalysed in a coherent manner to elucidate the systemic nature of the disease and inform the development of more effective diagnostic tools and targeted therapies.

Environmental averaging

Many classical examples of models of self-organized dynamics, including the Cucker–Smale, Motsch–Tadmor, multi-species, and several others, include an alignment force that is based upon density-weighted averaging protocol. Those protocols can be viewed as special cases of “environmental averaging”. In this paper we formalize this concept and introduce a unified framework for systematic analysis of alignment models.A series of studies are presented including the mean-field limit in deterministic and stochastic settings, hydrodynamic limits in the monokinetic and Maxwellian regimes, hypocoercivity and global relaxation for dissipative kinetic models, several general alignment results based on chain connectivity and spectral gap analysis. These studies cover many of the known results and reveal new ones, which include asymptotic alignment criteria based on connectivity conditions, new estimates on the spectral gap of the alignment force that do not rely on the upper bound of the macroscopic density, uniform gain of positivity for solutions of the Fokker–Planck-alignment model based on smooth environmental averaging. As a consequence, we establish unconditional relaxation result for global solutions to the Fokker–Planck-alignment model, which presents a substantial improvement over previously known perturbative results.

A complex network-based transmission mechanism and target control method for reducing carbon emission

To achieve carbon neutrality for China, the transmission mechanism, path, diffusion effect,and allocation of Emission Reduction Targets (ERT) among industries must be investigated. Setting short-term ERT will create a reverse pressure mechanism for the industry's carbon reduction, ensuring the achievement of carbon emission control target. However, few studies have explored how to influence the entire system by controlling key industries from the perspective of network control. Therefore, this study investigates Inter-industry Carbon Emission Transfer (ICET) in China from 1997 to 2017,and found that ICET can achieve fair distribution of ERT. Inspired by the controllability theory of complex networks to integrate the driving factors, the transmission paths, diffusion effects, and allocation of ERT into a unified analytical framework. Then, breaking the traditional top-down hierarchical decomposition mechanism and transforming it into a bottom-up target forcing mechanism, a decomposition algorithm for the total control target was proposed.The results reveal that ICET accounts for the majority of carbon emission. The characteristics of network structure have a significant impact on the ICET.Different industries have varying control capabilities. Compared with industry-wide control, target control on the Minimum Control Industry Set (MCMCS) has a slightly worse emission reduction effect, but the economic loss is smaller, making it an effective policy choice.

FS/DS: A Theoretical Framework for the Dual Analysis of Feature Space and Data Space.

With the surge of data-driven analysis techniques, there is a rising demand for enhancing the exploration of large high-dimensional data by enabling interactions for the joint analysis of features (i.e., dimensions). Such a dual analysis of the feature space and data space is characterized by three components, 1) a view visualizing feature summaries, 2) a view that visualizes the data records, and 3) a bidirectional linking of both plots triggered by human interaction in one of both visualizations, e.g., Linking & Brushing. Dual analysis approaches span many domains, e.g., medicine, crime analysis, and biology. The proposed solutions encapsulate various techniques, such as feature selection or statistical analysis. However, each approach establishes a new definition of dual analysis. To address this gap, we systematically reviewed published dual analysis methods to investigate and formalize the key elements, such as the techniques used to visualize the feature space and data space, as well as the interaction between both spaces. From the information elicited during our review, we propose a unified theoretical framework for dual analysis, encompassing all existing approaches extending the field. We apply our proposed formalization describing the interactions between each component and relate them to the addressed tasks. Additionally, we categorize the existing approaches using our framework and derive future research directions to advance dual analysis by including state-of-the-art visual analysis techniques to improve data exploration.

Does digital economy promote regional green innovation? An empirical study based on the transmission effect and threshold effect of marketization

Exploring the green innovation effect of the digital economy under the condition of socialist market economy with Chinese characteristics is of great contemporary value. In this paper, we incorporate digital economy development, marketization and green innovation into a unified analytical framework. Based on the theoretical analysis of the intrinsic logical relationship among the three, a panel data of 31 provinces in mainland China from 2011 to 2021 is employed to empirically test the effect and mechanism of digital economy development on green innovation under the condition of marketization. The results show that digital economy development is effective in improving the level of regional green innovation; Marketization is an important transmission mechanism for digital economy to release the effectiveness of green innovation. Besides, marketization has a significant double threshold effect on green innovation driven by digital economy. To promote the synergistic development of China's digital economy, market-oriented reform and green innovation in the future, we should focus on promoting the construction of an “enterprise-public-government” community for digital economy and improving the mechanism of market-oriented green innovation.

Risk-Adaptive Local Decision Rules

The decision rules developed in this paper help managers recognize relationships between data and decisions and prescribe courses of actions that are guaranteed near-optimal as well as transparent and interpretable. Transparency and interpretability are prerequisites for any decision support tool that needs to be conveyed, understood, and justified broadly. An understanding of the relationship between data and decisions is important for managers as they identify the critical factors, prioritize their focus, guide the resource allocation, and develop mitigation strategies for handling data uncertainty. The relationship between data and decision can be especially convoluted for combinatorial optimization problems. We consider simple decisions rules that ensure transparency and interpretability, but also allow nearly any continuous decision rule within a unified framework of analysis. Our preferred adaptive minimum decision rule prescribes decisions with 1% relative suboptimality gap, on average, even when facing distributional shifts in out-of-sample testing.

Patient-Level and Physician-level Predictors of Discharge Against Medical Advice: A Multilevel Modeling Approach

Background: Discharge Against Medical Advice (DAMA) is a complex and multifaceted issue in healthcare, often challenging the continuity of care and affecting patient outcomes. Objectives: This study aimed to investigate the predictors of Discharge Against Medical Advice (DAMA) by simultaneously examining patient- and physician-level variables within a unified analytical framework. Methods: This cross-sectional study was conducted in 2023 at one of the largest private hospitals in northwest Iran. The study included all 16,071 patients admitted in 2022 and 137 attending physicians. A multilevel analysis model was employed to examine the influence of variables related to patients and physicians in predicting DAMA. Results: The study involved patients with a mean age of 45.28 ± 19.59 years, with 67.7% being women and 14.8% not having health insurance. Among the physicians studied, the mean age was 56.27 ± 12.29 years, with 67.2% being male and 70.1% being hospital shareholders. Patients with DAMA comprised 6.8% (n = 1094). The null model had a log-likelihood value of -3304.90. When patient-level predictors were added, the value increased to -3041.76, a statistically significant improvement (P < 0.001) based on the chi-square test. Subsequently, incorporating physician-level predictors further increased the log-likelihood value to -2996.16, and this increase was also statistically significant compared to the model with only patient-level predictors (P < 0.001). Physician-level factors, including specialization, sex, and experience, were associated with DAMA. Significant patient-level variables included age, type of insurance, and type of disease (P < 0.05). Conclusions: Utilizing multilevel modeling enables the assessment of the significance of both physician-level and patient-level factors. To avoid conflicting results, it is recommended to evaluate hospital performance based on DAMA by considering both levels.

Density estimation via measure transport: Outlook for applications in the biological sciences

AbstractOne among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.

Interaction Effect of Export Trade, Foreign Direct Investment and Technological Independent Innovation in China

China has made significant progress in the field of clean energy and sustainable development, with its photovoltaic industry technology leading globally. What has been the trajectory of China’s successful technological catch-up over the past two decades? Is China’s experience sustainable? To clarify the interaction logic between export trade (ET), foreign direct investment (FDI), and technological independent innovation (TII) in China, this paper uses panel data from 31 provinces and cities in the Chinese Mainland between 2000 and 2022. A panel vector autoregression (PVAR) model is constructed from a dynamic endogenous perspective to verify the interaction and regional heterogeneity among the three. The results are as follows: (1) The unified analytical framework shows a significant bidirectional “positive feedback effect” between ET and TII. However, FDI inhibits TII to a certain extent. Furthermore, the correlation between ET and FDI is weak. (2) The impact of ET on TII is most pronounced in the western region, while the central region sees the highest contribution rate of FDI to TII. The self-evolution effect of TII is most evident in the eastern region. This study provides suggestions for the government to develop an adaptation policy for local industrial technology conditions and establish a National Sustainable Systems of Innovation (NSSI) with multiple comparative advantages, and serves as a reference for establishing a “Chinese model”.