A Statistical Approach to Estimating Adsorption-Isotherm Parameters in Gradient-Elution Preparative Liquid Chromatography

Determining the adsorption isotherms is an issue of significant importance in preparative chromatography. A modern technique for estimating adsorption isotherms is to solve an inverse problem so that the simulated batch separation coincides with actual experimental results. However, due to the ill-posedness, the high non-linearity, and the uncertainty quantification of the corresponding physical model, the existing deterministic inversion methods are usually inefficient in real-world applications. To overcome these difficulties and study the uncertainties of the adsorption-isotherm parameters, in this work, based on the Bayesian sampling framework, we propose a statistical approach for estimating the adsorption isotherms in various chromatography systems. Two modified Markov chain Monte Carlo algorithms are developed for a numerical realization of our statistical approach. Numerical experiments with both synthetic and real data are conducted and described to show the efficiency of the proposed new method.Comment: 28 pages, 11 figure

Manifold Fitting

While classical data analysis has addressed observations that are real numbers or elements of a real vector space, at present many statistical problems of high interest in the sciences address the analysis of data that consist of more complex objects, taking values in spaces that are naturally not (Euclidean) vector spaces but which still feature some geometric structure. Manifold fitting is a long-standing problem, and has finally been addressed in recent years by Fefferman et al. (2020, 2021a). We develop a method with a theory guarantee that fits a $d$-dimensional underlying manifold from noisy observations sampled in the ambient space $\mathbb{R}^D$. The new approach uses geometric structures to obtain the manifold estimator in the form of image sets via a two-step mapping approach. We prove that, under certain mild assumptions and with a sample size $N=\mathcal{O}(\sigma^{(-d+3)})$, these estimators are true $d$-dimensional smooth manifolds whose estimation error, as measured by the Hausdorff distance, is bounded by $\mathcal{O}(\sigma^2\log(1/\sigma))$ with high probability. Compared with the existing approaches proposed in Fefferman et al. (2018, 2021b); Genovese et al. (2014); Yao and Xia (2019), our method exhibits superior efficiency while attaining very low error rates with a significantly reduced sample size, which scales polynomially in $\sigma^{-1}$ and exponentially in $d$. Extensive simulations are performed to validate our theoretical results. Our findings are relevant to various fields involving high-dimensional data in machine learning. Furthermore, our method opens up new avenues for existing non-Euclidean statistical methods in the sense that it has the potential to unify them to analyze data on manifolds in the ambience space domain.Comment: 60 page

Rational design of dibenzo[a,c]phenazine-derived isomeric thermally activated delayed fluorescence luminophores for efficient orange-red organic light-emitting diodes

It is an immense challenge to develop efficient long-wavelength (orange-to-red) thermally activated delayed fluorescence (TADF) materials due to the increasing nonradiative decay rates following the energy-gap law. Herein, two pairs of asymmetric isomers; DPyPzTPA and TPAPzDPy, and PyPzDTPA and DTPAPzPy based on electron-deficient moieties dibenzo[a,c]phenazine (Pz) and pyridine (Py) combined with electron-donor units of triphenylamine (TPA) were designed and synthesized. Their photophysical properties could be finely modulated by changing the position and number of Py groups as well as TPA fragments onto Pz cores. DPyPzTPA and DTPAPzPy possess much more rigidity and thus less geometry relaxation and non-radiative decay between ground states and excited states than those of PyPzDTPA and TPAPzDPy. Intriguingly, DPyPzTPA exhibits the highest relative photoluminescence quantum yield (ΦPL) and the fastest reverse intersystem crossing (rISC) rate among them owing to relatively stronger rigidity and spin-orbit coupling (SOC) interactions between the lowest singlet (S1) and energetically close-lying excited triplet state and therefore, the device showed the highest maximum external quantum efficiency (EQEmax) of 16.6% (60.9 lm/W, 53.3 cd/A) with Commission Internationale de I'Eclairage (CIE) coordinates of (0.43, 0.55), peak wavelength 556 nm. In stark contrast, due to its lower rigidity and extremely weak delayed fluorescence (DF) characteristic and thus the much lower ΦPL, TPAPzDPy-based devices are only half as efficient (30.8 lm/W, 27.5 cd/A, 8.3% EQE) despite the isomers possessing equal singlet-triplet energy gaps (ΔEST) of 0.43 eV. On the other hand, the device based on DTPAPzPy also demonstrated a strongly enhanced performance (59.1 lm/W, 52.7 cd/A, 16.1% EQE) than its isomer PyPzDTPA-based device (39.5 lm/W, 35.2 cd/A, 10.3% EQE). This work explicitly implicates that the asymmetric and isomeric molecular design is a potential strategy for promoting the development of highly efficient long-wavelength TADF materials

MANIFOLD FITTING AND GENERATIVE NEURAL NETWORKS

Ph.DDOCTOR OF PHILOSOPHY (FOS

A Deep Learning Method Based on the Attention Mechanism for Hardware Trojan Detection

The chip manufacturing of integrated circuits requires the participation of multiple parties, which greatly increases the possibility of hardware Trojan insertion and poses a significant threat to the entire hardware device landing; however, traditional hardware Trojan detection methods require gold chips, so the detection cost is relatively high. The attention mechanism can extract data with more adequate features, which can enhance the expressiveness of the network. This paper combines an attention module with a multilayer perceptron and convolutional neural network for hardware Trojan detection based on side-channel information, and evaluates the detection results by implementing specific experiments. The results show that the proposed method significantly outperforms machine learning classification methods and network-related methods, such as SVM and KNN, in terms of accuracy, precision, recall, and F1 value. In addition, the proposed method is effective in detecting data containing one or multiple hardware Trojans, and shows high sensitivity to the size of datasets

A Deep Learning Method Based on the Attention Mechanism for Hardware Trojan Detection

The chip manufacturing of integrated circuits requires the participation of multiple parties, which greatly increases the possibility of hardware Trojan insertion and poses a significant threat to the entire hardware device landing; however, traditional hardware Trojan detection methods require gold chips, so the detection cost is relatively high. The attention mechanism can extract data with more adequate features, which can enhance the expressiveness of the network. This paper combines an attention module with a multilayer perceptron and convolutional neural network for hardware Trojan detection based on side-channel information, and evaluates the detection results by implementing specific experiments. The results show that the proposed method significantly outperforms machine learning classification methods and network-related methods, such as SVM and KNN, in terms of accuracy, precision, recall, and F1 value. In addition, the proposed method is effective in detecting data containing one or multiple hardware Trojans, and shows high sensitivity to the size of datasets

Nomogram for predicting the risk of postoperative myasthenic crisis in patients with thymectomy

Abstract Objective This study aimed to develop and validate internally a clinical predictive model, for predicting myasthenic crisis within 30 days after thymectomy in patients with myasthenia gravis. Methods Eligible patients were enrolled between January 2015 and May 2019. The primary outcome measure was postoperative myasthenic crisis (POMC). A predictive model was constructed using logistic regression and presented in a nomogram. The area under the receiver operating characteristic curve (AUC) was calculated to examine the performance. The study population was divided into high‐ and low‐risk groups according to Youden index. Calibration curves with 1000 replications bootstrap resampling were plotted to visualize the calibration of the nomogram. Decision curve analyses (DCA) with 1000 replications bootstrap resampling were performed to evaluate the clinical usefulness of the model. Results A total of 445 patients were enrolled. Five variables were screened including thymus imaging, onset age, MGFA classification, preoperative treatment regimen, and surgical approach. The model exhibited moderate discriminative ability with AUC value 0.771. The threshold probability was 0.113, which was used to differentiate between high‐ and low‐risk groups. The sensitivity and specificity were 72.1% and 77.1%, respectively. The high‐risk group had an 8.70‐fold higher risk of POMC. The calibration plot showed that when the probability was between 0 and 0.5, the deviation calibration curve of the model was consistent with the ideal curve. Interpretation This nomogram could assist in identifying patients at higher risk of POMC and determining the optimal surgical time for these patients