Metformin Preserves β-Cell Compensation in Insulin Secretion and Mass Expansion in Prediabetic Nile Rats

Prediabetes is a high-risk condition for type 2 diabetes (T2D). Pancreatic β-cells adapt to impaired glucose regulation in prediabetes by increasing insulin secretion and β-cell mass expansion. In people with prediabetes, metformin has been shown to prevent prediabetes conversion to diabetes. However, emerging evidence indicates that metformin has negative effects on β-cell function and survival. Our previous study established the Nile rat (NR) as a model for prediabetes, recapitulating characteristics of human β-cell compensation in function and mass expansion. In this study, we investigated the action of metformin on β-cells in vivo and in vitro. A 7-week metformin treatment improved glucose tolerance by reducing hepatic glucose output and enhancing insulin secretion. Although high-dose metformin inhibited β-cell glucose-stimulated insulin secretion in vitro, stimulation of β-cell insulin secretion was preserved in metformin-treated NRs via an indirect mechanism. Moreover, β-cells in NRs receiving metformin exhibited increased endoplasmic reticulum (ER) chaperones and alleviated apoptotic unfold protein response (UPR) without changes in the expression of cell identity genes. Additionally, metformin did not suppress β-cell mass compensation or proliferation. Taken together, despite the conflicting role indicated by in vitro studies, administration of metformin does not exert a negative effect on β-cell function or cell mass and, instead, early metformin treatment may help protect β-cells from exhaustion and decompensation.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Photophysical investigation of the formation of defect levels in P doped ZnO thin films

Zinc oxide (ZnO) offers a major disadvantage of asymmetry doping in terms of reliability, stability, and reproducibility of p-type doping, which is the main hindrance in realization of optoelectronic devices. The problem is even more complicated due to formation of various native defects in unintentionally doped n-type ZnO. The realization of p-type conductivity in doped ZnO requires an in-depth understanding of the formation of an effective shallow acceptor, as well as donor-acceptor compensation. Photophysical properties such as photoconductivity along with photoluminescence (PL) studies have unprecedentedly and effectively been utilized in this work to monitor the evolution of various in-gap defects. Phosphorus (P) doped ZnO thin films have been grown by RF magnetron sputtering under various Ar to O₂ gas ratios to investigate the effect of O₂ on the donor-acceptor compensation by comprehensive photoconductivity measurements supported by the PL studies. Initial elemental analyses indicate presence of abundant zinc vacancies (V_Zn) in O-rich ambience. The results predict that P sits in the zinc (Zn) site rather than the oxygen (O) site causing the formation of P_Zn–2V_Zn acceptor-like defects, which compensates the donor defects in P doped ZnO films. Photocurrent spectra uniquely reveal presence of more oxygen vacancies (V_O) defects states in lower O₂ flow, which gets compensated with an increase in the O₂ flow. Successive photocurrent transients indicate probable presence of more V_O in the films grown with lower O₂ flow and more V_Zn in higher O₂ flow. Overall the photosensitivity measurements clearly present that O-rich ambience expedites the formation of acceptor defects which are compensated, thereby lowering the dark current and enhancing the ultraviolet photosensitivity.     

《安徽省农村供水工程运行管理指南（试行）》解析

“十四五”期间，通过强化供水工程运行管理，巩固维护好已建农村供水工程成果，是水利部门实施乡村振兴的基本任务。2021年4月，《安徽省农村供水工程运行管理指南（试行）》正式印发实施。《指南》是在安徽省农村供水工作重点由工程建设转入运行管理的背景下，指导推进农村供水工程规范化管理的重要文件。通过介绍《指南》编制的有关背景、编制目标和特点，解析《指南》的主要内容和条款，可帮助供水管理人员快速掌握《指南》要点，助力《指南》在安徽省内贯彻和实施。     

南水北调中线总干渠藻类的生态调度

南水北调中线总干渠无在线调蓄水库,对藻类生态调度过程中出现的问题开展生态调度实现策略和实施方式研究。主要实现策略包括:划定自身的调蓄区,隔离生态调度对下游的影响;采用高效的渠池运行方式,减少生态调度时蓄量的反复调整;综合考虑安全、快速、平稳等需求,设定生态调度实施进程和方式。具体实施方式包括:将总干渠划分为流速调控区、调蓄区和正常运行区,分别实施等体积、控制蓄量和闸前常水位方式运行;将生态调度过程划分为充水阶段和泄水阶段,基于流速调控目标值、持续时长和水位降幅约束条件,确定各阶段时长和各分区的闸门群调控方案等。基于2018年3月输水工况,采用明渠一维非恒定流模型,仿真总干渠上游15个渠池的藻类生态调度过程。结果表明,生态调度可在3.5 d内完成,各渠池的平均流速由0.48 m/s增至0.93 m/s,持续时间超过2 h。在整个生态调度过程中,水位变化平稳,水位变幅符合安全阈值要求,下游渠道的正常运行未受生态调度明显影响。     

基于平均飞行航迹的CCO减噪效用研究

近年来，通过优化飞行程序降低机场飞机噪声影响成为机场环境保护的重要研究方向。文章首先建立了基于飞机“噪声-功率-距离”数据的噪声计算模型，介绍了平均飞行航迹以及连续爬升运行(Continuous Climb Opera-tion, CCO)离场程序的相关理论，最后以大型国际机场为实例，使用飞机平均飞行航迹进行噪声预测，运用综合噪声模型计算出噪声影响面积并绘制噪声影响等值线图，比较了CCO离场相对常规的标准仪表离场(Standard Instru-ment Departure, SID)的降噪效果。结果表明，CCO离场程序可有效降低机场噪声影响，在高噪声级影响区域的降噪效果更佳。     

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

Steering feel improvement by mathematical modeling of the Electric Power Steering system

Currently, the Electric Power Steering (EPS) system is an essential component of the vehicle because it provides assistive steering torque to the driver. To ensure a faster steering response, the position of the EPS in some vehicles is moved closer to the tire rather than the steering wheel. The steering torque, which is provided by the EPS in the steering system, mainly affects the driver’s feel while steering. Therefore, the driver often feels uncomfortable owing to such positioning of the EPS in the steering system. In particular, the nonlinearity of the Universal Joint (UJ), which is one of parts of the steering system, can be felt at the steering wheel side.In this paper, we proposed an algorithm based on the mathematical model of the steering torque in the steering system to improve the steering feel. The mathematical model is structured using parameters that can be obtained from the information of the steering system. Moreover, the formulation of the steering torque consists of the two parts, namely the deformation part, which describes the propagation inside the steering system, and the friction part that describes the inherent friction in the UJ.Simulation and experiments were conducted to verify the proposed mathematical model with similar conditions to the real tire load during the steering motion. Furthermore, to improve the driver’s feel during steering, a torque compensation algorithm is proposed and verified through experiments.     

Adaptive compensation of persistent actuator failures of nonlinear systems

In this article, adaptive compensation designs are developed for nonlinear systems with uncertainties from the system functions and persistent actuator failures of characterizations that (i) some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants and (ii) the failure pattern always switches from one to another and the switching does not stop. Such a controlled plant is described by an uncertain time-varying nonlinear system, and some robust adaptive feedback linearization based failure compensation results are studied for closed-loop system stabilization and bounded output tracking for some specific conditions. To improve the tracking performance in the presence of persistent actuator failures, a new adaptive control scheme is developed, using the failure indicator function which contains the failure pattern and failure time in the formulation. Detailed stability and tracking performance are shown. Simulation results are shown to verify the effectiveness of the proposed adaptive actuator failure compensation method.