DETERMINATION OF PORE SIZE DISTRIBUTION IN CAPILLARY-CHANNELED POLYMER (C-CP) FIBER STATIONARY PHASES BY INVERSE SIZE-EXCLUSION CHROMATOGRAPHY (ISEC) AND THE STUDY OF THE ROLE OF INTERSTITIAL FRACTION ON C-CP FIBERS ON PROTEIN BINDING CAPACITY

ABSTRACT High performance liquid chromatography (HPLC), first used in the 1960\u27s, is a rapidly evolving analytical technique, widely employed for identification, separation, and purification in biotechnology and pharmaceutical industries. The development of the stationary phases has played an important role in improving this technique. Each stationary phase will have its own disadvantages. Polysaccharide-based stationary phases such as cross-linked dextran cannot tolerate high pressures and linear velocities; silica stationary phases are rigid enough but slow mass transfer in the pores on the surface causes another problem; with the introduction of non-porous and small bead packing materials, the low surface area and high backpressure still handicapped people from achieving better separations. Therefore, fiber based polymer stationary phases came into view. Capillary-channeled polymer (C-CP) fibers have been investigated in the Marcus laboratory for several years as a stationary phases for ion-exchange (IEC), reversed phase (RP), and hydrophobic interaction (HIC) chromatography. When packed into a column, the unique eight-channeled shape makes them interdigitate to form parallel channels with high surface area-to-volume ratio and low backpressure. Additionally, C-CP fibers are virtually non-porous toward large molecules, which decreases the mass transfer to achieve fast protein separations. In the first study, polypropylene (PP) C-CP fibers and inverse size exclusion chromatography (iSEC) were employed to determine the pore size distribution (PSD) on the surface of the fibers. With the findings of mean pore size radius and standard deviation, the fibers\u27 geometric structure and adsorption behavior is better understood. In the second study, with the evaluation of the effects of different factors such as interstitial fraction and flow rate on the loading capacity of nylon-6 fibers, the kinetic and thermodynamic properties of the fibers have been further revealed. All results presented the potential of C-CP fibers as an innovative stationary phase for fast macromolecule separations

Modified cam-clay model with dynamic shear modulus under cyclic loads

In order to study the dynamic characteristics of clay under metro loads, a dynamic triaxial test for clay was conducted. The function formula between the dynamic shear modulus and the number of oscillation periods was presented to calculate and analyze the dynamic characteristics of clay, then the function formula applicability was verified for different regional clays. In addition, the relationship between dynamic shear modulus and the parameters of cam-clay was established. The function formula for calculating dynamic shear modulus can be generalized to apply to the cam-clay model. The results show that the dynamic shear modulus function formula can be well applied. This modified cam-clay model can not only describe hysteresis loops, but also consider the effects of loading frequency on the dynamic characteristics of clay. Therefore, it is convenient to study the dynamic characteristics of clay under metro loads for theoretical analysis and verification

Efficient Fully Bayesian Approach to Brain Activity Mapping with Complex-Valued fMRI Data

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. We support these claims with both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment

Quality at the Tail

Practical applications employing deep learning must guarantee inference quality. However, we found that the inference quality of state-of-the-art and state-of-the-practice in practical applications has a long tail distribution. In the real world, many tasks have strict requirements for the quality of deep learning inference, such as safety-critical and mission-critical tasks. The fluctuation of inference quality seriously affects its practical applications, and the quality at the tail may lead to severe consequences. State-of-the-art and state-of-the-practice with outstanding inference quality designed and trained under loose constraints still have poor inference quality under constraints with practical application significance. On the one hand, the neural network models must be deployed on complex systems with limited resources. On the other hand, safety-critical and mission-critical tasks need to meet more metric constraints while ensuring high inference quality. We coin a new term, ``tail quality,'' to characterize this essential requirement and challenge. We also propose a new metric, ``X-Critical-Quality,'' to measure the inference quality under certain constraints. This article reveals factors contributing to the failure of using state-of-the-art and state-of-the-practice algorithms and systems in real scenarios. Therefore, we call for establishing innovative methodologies and tools to tackle this enormous challenge.Comment: 9 pages, 4 figure

Using Non-Additive Measure for Optimization-Based Nonlinear Classification

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2 – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are a relatively small number of training cases available (). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered

Epidemic Spreading with Heterogeneous Awareness on Human Networks

The spontaneous awareness behavioral responses of individuals have a significant impact on epidemic spreading. In this paper, a modified Susceptible-Alert-Infected-Susceptible (SAIS) epidemic model with heterogeneous awareness is presented to study epidemic spreading in human networks and the impact of heterogeneous awareness on epidemic dynamics. In this model, when susceptible individuals receive awareness information about the presence of epidemic from their infected neighbor nodes, they will become alert individuals with heterogeneous awareness rate. Theoretical analysis and numerical simulations show that heterogeneous awareness can enhance the epidemic threshold with certain conditions and reduce the scale of virus outbreaks compared with no awareness. What is more, for the same awareness parameter, it also shows that heterogeneous awareness can slow effectively the spreading size and does not delay the arrival time of epidemic spreading peak compared with homogeneous awareness

SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6\% can be achieved by using only 12 images per class

Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network

Multimodal sentiment analysis has currently identified its significance in a variety of domains. For the purpose of sentiment analysis, different aspects of distinguishing modalities, which correspond to one target, are processed and analyzed. In this work, we propose the targeted aspect-based multimodal sentiment analysis (TABMSA) for the first time. Furthermore, an attention capsule extraction and multi-head fusion network (EF-Net) on the task of TABMSA is devised. The multi-head attention (MHA) based network and the ResNet-152 are employed to deal with texts and images, respectively. The integration of MHA and capsule network aims to capture the interaction among the multimodal inputs. In addition to the targeted aspect, the information from the context and the image is also incorporated for sentiment delivered. We evaluate the proposed model on two manually annotated datasets. the experimental results demonstrate the effectiveness of our proposed model for this new task