基于电化学老化衰退模型的锂离子动力电池外特性

当前锂离子动力电池电化学模型存在模型复杂、建模难度大、计算效率低、老化评估效果差的问题,本文提出一种考虑电池衰退老化的机理模型(ADME).本文首先通过有限差分法对伪二维(P2D)电化学模型进行离散降阶处理,得到简化伪二维(SP2D)模型.在SP2D模型的基础上,基于阴阳两极发生的副反应导致的衰退老化现象,提出一种考虑电池衰退老化的机理模型.其次,使用多变量偏差补偿最小二乘法实现模型参数辨识.最后通过动力电池衰退老化性能循环实验,对比分析了恒流、脉冲工况下SP2D模型和ADME模型的终端电压输出.结果表明:ADME模型较为简单、计算效率和估算精度高,可以有效评估电池容量老化衰退,得到理想的锂离子动力电池外特性曲线.     

介观电路中量子纠缠的经典对应

从量子力学诞生日起,它的经典对应(或类比)一直是物理学家关心的话题.本文以介观电路量子化的框架中,带有互感的两个介观电容-电感(LC)电路为例,首次讨论了量子纠缠的经典类比(或对应)问题.先用有序算符内的积分理论证明其互感是产生量子纠缠的源头;再推导出求解特征频率的公式,就发现它与一个经典系统的小振动频率的表达式有相似之处,该经典系统组成如下:两个墙壁各连一个相同的弹簧,两个弹簧之间接着一个滑动小车可以在光滑的桌面上运动,小车挂有一根单摆.用分析力学求此系统的小振动频率,发现与上述介观电路的特征频率形式类似,单摆的摆动会造成小车来回振动,摆、小车和弹簧的互相牵制效应反映了小车和摆的"纠缠".     

Vacuum-gap-based lumped element Josephson parametric amplifier

We propose a lumped element Josephson parametric amplifier with vacuum-gap-based capacitor.The capacitor is made of quasi-floating aluminum pad and on-chip ground.We take a fabrication process compatible with air-bridge technology,which makes our design adaptable for future on-chip integrated quantum computing system.Further engineering the input impedance,we obtain a gain above 20 dB over 162-MHz bandwidth,along with a quasi quantum-limit noise performance.This work should facilitate the development of quantum information processing and integrated superconducting circuit design.     

Metabolite identification of tanshinol borneol ester in rats by high‐performance liquid chromatography combined with electrospray ionization quadrupole time‐of‐flight mass spectrometry

Tanshinol borneol ester (DBZ) is a potential drug candidate composed of danshensu and borneol. It shows anti‐ischemic and anti‐atherosclerosis activity. However, little is known about its metabolism in vivo. This research aimed to elucidate the metabolic profile of DBZ through analyzing its metabolites using high‐performance liquid chromatography combined with electrospray ionization quadrupole time‐of‐flight mass spectrometry. Chromatographic separation was performed on an Agilent TC‐C₁₈ column (150 × 4.6 mm, 5.0 μm) with gradient elution using methanol and water containing 0.2% (v/v) formic acid as the mobile phase. Metabolite identification involved analyzing the retention behaviors, changes in molecular weights and MS/MS fragment patterns of DBZ and its metabolites. As a result, 20 potential metabolites were detected and tentatively identified in rat plasma, urine and feces after administration of DBZ. DBZ could be metabolized to O‐methylated DBZ, DBZ‐O‐glucuronide, O‐methylated DBZ‐O‐glucuronide, hydroxylated DBZ and danshensu. Danshensu, a hydrolysis product of DBZ, could further be transformed into 12 metabolites. The proposed method was confirmed to be a reliable and sensitive alternative for characterizing metabolic pathways of DBZ and providing valuable information on its druggability.     

Contingent derivatives and regularization for noncoercive inverse problems

Abstract

We study the inverse problem of parameter identification in noncoercive variational problems that commonly appear in applied models. We examine the differentiability of the set-valued parameter-to-solution map using the first-order and the second-order contingent derivatives. We explore the inverse problem using the output least-squares and the modified output least-squares objectives. By regularizing the noncoercive variational problem, we obtain a single-valued regularized parameter-to-solution map and investigate its smoothness and boundedness. We also consider optimization problems using the output least-squares and the modified output least-squares objectives for the regularized variational problem. We give a complete convergence analysis showing that for the output least-squares and the modified output least-squares, the regularized minimization problems approximate the original optimization problems suitably. We also provide the first-order and the second-order adjoint method for the computation of the first-order and the second-order derivatives of the output least-squares objective. We provide discrete formulas for the gradient and the Hessian calculation and present numerical results.     

Characterization and tentative identification of new flunitrazepam metabolites in authentic human urine specimens using liquid chromatography‐Q exactive‐HF hybrid quadrupole‐Orbitrap‐mass spectrometry (LC‐QE‐HF‐MS)

Flunitrazepam (FNZ) is a potent hypnotic, sedative, and amnestic drug used to treat severe insomnia. In our recent study, FNZ metabolic profiles were investigated carefully. Six authentic human urine samples were purified using solid phase extraction (SPE) without enzymatic hydrolysis, and urine extracts were then analyzed by liquid chromatography‐Q exactive‐HF hybrid quadrupole‐Orbitrap‐mass spectrometry (LC‐QE‐HF‐MS), using the full scan positive ion mode and targeted MS/MS (ddms2) technique to make accurate mass measurements. There were 25 metabolites, including 13 phase I and 12 phase II metabolites, which were detected and tentatively identified by LC‐QE‐HF‐MS. In addition, nine previously unreported phase II glucuronide conjugates and four phase I metabolites are reported here for the first time. Eight metabolic pathways, including N‐reduction and O‐reduction, N‐glucuronidation, O‐glucuronidation, mono‐hydroxylation and di‐hydroxylation, demethylation, acetylation, and combinations, were implicated in this work, and 2‐O‐reduction together with dihydroxylation were two novel metabolic pathways for FNZ that were identified tentatively. Although 7‐amino FNZ is widely considered to be the primary metabolite, a previously unreported metabolites (M12) can also serve as a potential biomarker for FNZ misuse.     

Assessment of Noise Exposure of Sawmill Workers in Southwest,Nigeria

Economic wood processing employs the use of industrial machines for cutting, shaping, milling, and sawing timber, thereby leading to the generation of high levels of noise. Published data from empirical studies have categorized noise as an environmental hazard of global significance. Furthermore, noise exposure limits for different industries and all the industrial machines available has not been formally established as it presently exists in developed nations around the world. Therefore, this study assessed the daily exposure of sawmills workers to noise in Southwestern Nigeria. Reconnaissance surveys were first carried out in Osun, Oyo, Ondo, Ekiti, Lagos, and Ogun States to select sawmills that were fully operational and fit for the study. Two fully functional sawmills in two cities of each State were eventually selected for data collection, making a total of 24 sawmills, while the Circular Machines (CM), Planer Machines (PM), and Band-saw Machines (BM) were the machines in each sawmill considered. Two machines each of CM, PM, and BM were considered in each sawmill, making a total of forty-eight (48) machines each of CM, PM, and BM. Sound data were collected between 7 am and 7 pm each day for six days (between Monday and Saturday) using Extech 407732 sound level meter and all stabilized measurements were taken three times at different intervals. The data collected were in three different periods: Machine No-work Period (NP_m), Machine Idle Period (IP_m), and Machine Working Period (WP_m). A two–way Analysis of Variance (ANOVA) was carried out at P < 0.05 to determine whether there is a significant difference in the sound level average before and after the break, for both the idle and working periods of the three machines considered. This was also done to determine whether there is a significant difference between the sound level average of the results collected during idle and working periods of the three machines. Noise Pollution Levels (L_np) ranged from 83.20 dB (PM) to 107.65 (BM) and 93.42 (CM and PM) – 116.00 (BM) respectively, while IP_m also gave the least noise pollution level of 95.79 dB and WP_m gave the highest level of 102.88 dB. The results revealed that all the machines’ L_np values in the working period are more than the 90 dB acceptable limit the recommendation value of 90 dB while 89.6% of CMs, 75% of PMs, and 89.6% of BM had their L_np above 90 dB in the idle period respectively. The minimum and the maximum noise dose levels for IP_m, WP_m and overall are 0.09 (BM) and 2.37 (CM), 0.50 (CM), and 4.77 (PM) and 0.69 (BM) and 6.64 (PM) respectively. The study found out that the fundamental contributing factors to the high noise levels in sawmills are poor machine maintenance, use of old and obsolete machines, poor housekeeping strategy, limited space, workers’ negligence, lack of PPE, and lack of occupational safety training. The study recommends that proper workplace practices such as use of personal protective equipment, new and modern machines, training, and occupational safety programmes be implemented in the considered sawmills.     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

NPK 2.0: Introducing tensor decompositions to the kinetic analysis of gas–solid reactions

A method for deriving kinetic models of gas–solid reactions for reactor and process design is presented. It is based on the nonparametric kinetics (NPK) method and resolves many of its shortcomings by applying tensor rank-1 approximation methods. With this method, it is possible to derive kinetic models based on the general kinetic equation from any combination of experiments without additional a priori assumptions. The most notable improvements over the original method are that it is computationally much simpler and that it is not limited to two variables. Two algorithms for computing the rank-1 approximation as well as a tailored initialization method are presented, and their performance is assessed. Formulae for the variance estimation of the solution values are derived to improve the accuracy of the model identification and to provide a tool for diagnosing the quality of the kinetic model. The methods effectiveness and performance are assessed by applying it to a simulated data set. A Matlab implementation is available as Supporting Information.     

尾流激励的叶片气动力降阶模型

气动力降阶模型是研究叶片气动弹性振动快速高效的新方法。现有气动力降阶模型的研究主要集中在叶片颤振方面，没有涉及更为常见的上游尾流激励的叶片振动问题。本文提出基于Volterra级数的尾流激励叶片气动力降阶模型，为尾流激励下叶片振动和动静叶干涉振动研究提供了新的思路。采用行波法简化尾流的参数个数，用阶跃信号法识别降阶模型的核函数。二维叶片的算例结果表明，本文方法可以较准确地描述尾流激励引起的叶片气动力振荡，而且计算效率极高。