Jian Kang

In the supplementary material, we provide supplemental information about the Hermite polynomials and basis function construction, the process of using the GPfit package to estimate the smoothing parameter, sensitivity analysis with varying hyperparameter values, and additional figures describing the detailed simulation and application results.

While the sound field characteristics of idealised long enclosures have been demonstrated, this study aimed to reveal the sound field of subway platforms with complex forms. The sound fields of ten actual subway platforms with complex forms were simulated, and the sound pressure level (SPL) attenuation and reverberation time (RT 30) were analysed. The results indicated that the average SPL attenuations of actual platforms with complex forms were 3.0 ∼ 7.7 dB greater than that with a corresponding idealised long enclosure, and the average RT 30 was 0.1 ∼ 0.5 s shorter. Regarding the receivers along the length, with the source-receiver distance increasing, the SPL attenuation showed an approximately logarithmic curve, and RT 30 presented a slightly increasing trend. The distance from the centre of each protruding space to the source was linearly correlated to both the average SPL attenuation and …

Residents and designers typically pursue healthy urban acoustic environments. The soundscape community has established internationally recommended standards for subjective questionnaires to evaluate soundscape quality; however, much work is still needed to standardise physiological assessments. This study measures the physiological responses and subjective evaluations of participants in 20 common sound sources (scenarios) in urban public open spaces, and discusses the potential dimensions when assessing the quality of urban soundscapes through physiological indicators. Categorical principal components analysis are employed to reduce the dimensionality of the physiological data. The results indicate that physiological Dimension 1 represents the Natural–Artificial dimension, and physiological Dimension 2 potentially represents the Regular–Irregular dimension, providing a rough estimate of …

Conducting STPA (System Theoretic Process Analysis) analysis for hydrogen refueling stations can identify potential safety hazards and risks in the refueling station system, and take measures to prevent and mitigate these risks, ensuring safe and reliable operation of the refueling station. This paper proposes an improved STPA method for risk analysis of hydrogen refueling stations. Specifically, this method aims to first determine the purpose of risk analysis for the refueling station system, analyzing the system-level losses, hazards, and constraints of the entire refueling station. Secondly, the control structure of the refueling station system is constructed, the system boundary is determined, and a system control structure diagram is built. Next, unsafe control actions (UCA) are identified based on control actions, and a quantitative analysis of unsafe control actions is conducted using DEMATEL, obtaining the …

The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. A preliminary analysis of dialysis patient postdischarge hospital readmissions and deaths in 2020 revealed that the COVID-19 effect has varied significantly with postdischarge time and time since the pandemic onset. However, the complex dynamics cannot be characterized by existing varying coefficient models. To address this issue, we propose a bivariate varying coefficient model for competing risks, where tensor-product B-splines are used to estimate the surface of the COVID-19 effect. An efficient proximal Newton algorithm is developed to facilitate the fitting of the new model to the massive data for Medicare beneficiaries on dialysis. Difference-based anisotropic penalization is introduced to mitigate model overfitting and effect wiggliness …

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (eg, image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of …

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite …

In this study, graphene oxide (GO) was modified via electrostatic interactions and chemical grafting by silica (SiO2), and two SiO2@GO hybrids (GO-A and GO-B, respectively) with different structures were obtained and carefully characterized. Results confirmed the successful grafting of SiO2 onto the GO surface using both strategies. The distribution of SiO2 particles on the surface of GO-A was denser and more agglomerated, while it was more uniform on the surface of GO-B. Then, epoxy resin (EP)/GO composites were prepared. The curing mechanism of EP/GO composites was studied by differential scanning calorimetry and in situ infrared spectra spectroscopy. Results of tensile tests, hardness tests, dynamic mechanical analysis, and dielectric measurement revealed that EP/GO-B exhibited the highest tensile properties, with a tensile strength of 79 MPa, a 43% increase compared to raw EP. Furthermore, the …

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high-dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, for which we construct a new class of Bayesian nonparametric prior models by thresholding Gaussian processes. We assign the vMF priors to mixing coefficients in the model. Under some regularity conditions, we show that the proposed method has several desirable theoretical properties including the large support for the priors, the …

Shadow detection is a challenging problem in computer vision due to the high variability in lighting conditions, object shapes, and scene layouts. Despite the positive results achieved by some existing technologies, the problem becomes particularly challenging with complex and heterogeneous images where shadow-casting objects coexist and shadows can have different depths, scales, and morphologies. As a result, more advanced and accurate solutions are still needed to deal with this type of complexities. To address these challenges, this paper proposes a novel deep learning model, called the Cross-Attentional Dual Decoder Network (CADDN), to improve shadow detection by using fine-grained image reconstruction features. Unlike other existing methods, the CADDN uses an innovative encoder-decoder architecture with two decoder segments that work together to reconstruct the input images and their …

Appendices A-E with technical derivations and additional expositions.

Investigating the combined effects of compositive human–environmental factors and developing indices to quantitatively measure environmental quality is essential. In this study, combined thermal-acoustic effects on overall comfort were used to develop evaluation models based on the demand for environmental improvement in severe cold cities, with small-to-medium-sized squares as examples. Parameter analysis was conducted to comprehensively evaluate overall comfort in squares with different spatial forms. Results indicate that thermal and acoustic environments significantly affected the overall comfort and varied with season, accounting for 58.7%–68.2% of the variations in overall comfort. Evaluation results indicated that the distribution of overall comfort was more similar to the sound level because of the significant difference in the acoustic environment. Block orientation, surrounding roads and the length …

Brain-effective connectivity analysis quantifies directed influence of one neural element or region over another, and it is of great scientific interest to understand how effective connectivity pattern is affected by variations of subject conditions. Vector autoregression (VAR) is a useful tool for this type of problems. However, there is a paucity of solutions when there is measurement error, when there are multiple subjects, and when the focus is the inference of the transition matrix. In this article, we study the problem of transition matrix inference under the high-dimensional VAR model with measurement error and multiple subjects. We propose a simultaneous testing procedure, with three key components: a modified expectation-maximization (EM) algorithm, a test statistic based on the tensor regression of a bias-corrected estimator of the lagged auto-covariance given the covariates, and a properly thresholded …

The “trade-off” effect of nanofiltration membranes may lead to problems of lower flux and higher energy cost, which limit their applications. In this work, an ultra-thin polyamide selective layer (34 nm in thickness, much lower than the conventional polyamide selective layer) was prepared by constructing a sulfonated polyethersulfone interlayer on the surface of polysulfone ultrafiltration membranes by liquid-phase deposition, taking advantage of the inhibitory effect of sulfonated polyethersulfone nanoparticles on the diffusion of piperazine. The prepared nanofiltration membrane exhibits a water flux per unit pressure of 33.2 L·m−2·h−1·bar−1, which is more than 2.5 times higher than that of the nanofiltration membrane without the introduction of the interlayer (12.2 L·m−2·h−1·bar−1). Additionally, it has a high rejection of 98.8 % for Na2SO4, which surpasses the upper limit of the equilibrium of trade-off. The study also …

It is important to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible heavy tails and outliers in real-world applications such as imaging data analyses. We propose a new robust high-dimensional regression with coefficient thresholding, in which an efficient nonconvex estimation procedure is proposed through a thresholding function and the robust Huber loss. The proposed regularization method accounts for complex dependence structures in predictors and is robust against heavy tails and outliers in outcomes. Theoretically, we rigorously analyze the landscape of the population and empirical risk functions for the proposed method. The fine landscape enables us to establish both statistical consistency and computational convergence under the high-dimensional setting. We also present an extension to incorporate spatial information into the …

Buildings represent pivotal entities in remote sensing imagery for various applications like urban planning and land resource management. Predominantly, methods for building footprint extraction in the literature focus on optical imagery with visual attributes that faithfully mirror the physical world. Nevertheless, the acquisition of high-quality optical images presents formidable challenges due to the susceptibility to illumination conditions and scene visibility. In contrast, synthetic aperture radar (SAR) images can be acquired in all-weather and all-time situations, unburdened by the aforementioned constraints. However, the coherent imaging mechanism engenders intricate complexities for building footprint extraction SAR images. To address this issue, this paper introduces the Boundary Information Distillation Network (BIDNet) to improve the prediction accuracy in SAR images by distilling knowledge from optical …

In the event of a fire or explosion at a hybrid hydrogen-gasoline fueling station, the consequences can be significantly more severe compared to a traditional refueling station or hydrogen refueling station. It is imperative to establish an effective assessment model specific to the hybrid station to prevent and mitigate such accidents. This paper introduces a temporal dynamic assessment model designed to evaluate the emergency response and rescue capabilities of hybrid hydrogen-gasoline fueling stations. The model is grounded in dynamic scenario construction and utilizes information-driven theory. This innovative approach allows for evaluations of the risk evolution process at different stages of an accident. The proposed assessment model combines the Temporal Weighted Average (TOWA) and Temporal Weighted Geometric Average (TOWGA) operators to integrate assessment values across different stages …

In addressing the challenge of analysing the large-scale Adolescent Brain Cognition Development (ABCD) fMRI dataset, involving over 5,000 subjects and extensive neuroimaging data, we propose a scalable Bayesian scalar-on-image regression model for computational feasibility and efficiency. Our model employs a relaxed-thresholded Gaussian process (RTGP), integrating piecewise-smooth, sparse, and continuous functions capable of both hard- and soft-thresholding. This approach introduces additional flexibility in feature selection in scalar-on-image regression and leads to scalable posterior computation by adopting a variational approximation and utilising the Karhunen-Lo\`eve expansion for Gaussian processes. This advancement substantially reduces the computational costs in vertex-wise analysis of cortical surface data in large-scale Bayesian spatial models. The model's parameter estimation and prediction accuracy and feature selection performance are validated through extensive simulation studies and an application to the ABCD study. Here, we perform regression analysis correlating intelligence scores with task-based functional MRI data, taking into account confounding factors including age, sex, and parental education level. This validation highlights our model's capability to handle large-scale neuroimaging data while maintaining computational feasibility and accuracy.

Recently, complex convolutional sparse coding (ComCSC) has demonstrated its effectiveness in interferometric phase restoration, owing to its prominent performance in noise mitigation and detailed phase preservation. By incorporating the estimated coherence into ComCSC as prior knowledge for re-weighting individual complex residues, coherence-guided complex convolutional sparse coding (CoComCSC) further improves the quality of restored phases, especially over heterogeneous land-covers with rapidly varying coherence. However, due to the exploited norm of the data fidelity term, the original CoComCSC is not robust to outliers when relatively low coherence values are sparsely distributed over high ones. We propose CoComCSC-L1 and CoComCSC-Huber to improve the robustness of CoComCSC based on the and Huber norms. Moreover, we propose an efficient solver to decrease the …

The operation risks for deep-sea oil spill recovery are characterized by their multiplicity, fuzziness, and dynamic nature. To more properly and comprehensively assess the operational risks for deep-sea oil spill recovery, an assessment method has been proposed on the basis of combinatorial weighting and multi-element connection number. A full-domain risk assessment index system was built, which consists of four dimensions, namely personnel, equipment, management, and environment, and the combinatorial weighting was performed for the subjective and objective assessment indexes via the rough set improved analytical hierarchy process (AHP) and entropy method, respectively. Furthermore, in accordance with the set pair theory, the risk trend assessment model, built upon the multi-element connection number, was developed to comprehensively illustrate the current state of safety control and the …