Two-Layer Predictive Control of a Continuous Biodiesel Transesterification Reactor

A novel two-layer predictive control scheme for a continuous biodiesel transesterification reactor is presented. Based on a validated mechanistic model, the least squares (LS) algorithm is used to identify the finite step response (FSR) process model adapted in the controller. The two-layer predictive control method achieves the steady-state optimal setpoints and resolves the multivariable dynamic control problems synchronously. Simulation results show that the two-layer predictive control strategy leads to a significant improvement of control performance in terms of the optimal set-points tracking and disturbances rejection, as compared to conventional PID controller within a multiloop framework

Controlled growth of atomically thin transition metal dichalcogenides via chemical vapor deposition method

Two-dimensional (2D) transition metal dichalcogenides (TMDC) have attracted great research interest due to their potential application in electronics, optoelectronics, electrocatalysis, and so on. To satisfy expectations, high-quality materials with designed structures are highly desired through the controlled growth of TMDC. Chemical vapor deposition (CVD) offers facile control in synthesizing 2D TMDC as well as a high degree of freedom for tuning their structures and properties. In this review, we elaborate on recent advances in CVD techniques for synthesizing atomically thin TMDC. The novel techniques for achieving continuous uniform 2D films are provided along with insights into the growth mechanisms. Moreover, approaches toward high-quality materials by growing large single crystals and oriented domains are thoroughly summarized. The strategies for controlling the crystal thickness, phase, and doping condition are also discussed. Finally, we address the challenges in the field and prospective research directions

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe, providing basic data for the research of astronomy and space physics. The Soft X-ray Imager (SXI) carried by the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) aims to capture two-dimensional (2-D) images of the Earth’s magnetosheath by using soft X-ray imaging. However, the observed 2-D images are affected by many noise factors, destroying the contained information, which is not conducive to the subsequent reconstruction of the three-dimensional (3-D) structure of the magnetopause. The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models. This makes it difficult to establish the mapping relationship between SXI-simulated observation images and target images by using mathematical models. We propose an image restoration algorithm for SXI-simulated observation images that can recover large-scale structure information on the magnetosphere. The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image, whose mapping relationship with the target image is established by the patch estimator. The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator. Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task, according to the peak signal-to-noise ratio and structural similarity. The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques, significantly improving the reconstruction results. Hence, the proposed technology may be feasible for processing SXI-simulated observation images

Efficient Transduction of Feline Neural Progenitor Cells for Delivery of Glial Cell Line-Derived Neurotrophic Factor Using a Feline Immunodeficiency Virus-Based Lentiviral Construct

Work has shown that stem cell transplantation can rescue or replace neurons in models of retinal degenerative disease. Neural progenitor cells (NPCs) modified to overexpress neurotrophic factors are one means of providing sustained delivery of therapeutic gene products in vivo. To develop a nonrodent animal model of this therapeutic strategy, we previously derived NPCs from the fetal cat brain (cNPCs). Here we use bicistronic feline lentiviral vectors to transduce cNPCs with glial cell-derived neurotrophic factor (GDNF) together with a GFP reporter gene. Transduction efficacy is assessed, together with transgene expression level and stability during induction of cellular differentiation, together with the influence of GDNF transduction on growth and gene expression profile. We show that GDNF overexpressing cNPCs expand in vitro, coexpress GFP, and secrete high levels of GDNF protein—before and after differentiation—all qualities advantageous for use as a cell-based approach in feline models of neural degenerative disease

Correlation analysis of anthropometric indices and type 2 diabetes mellitus in residents aged 60 years and older

Background and purposeIn recent years, the incidence of obesity in people aged 60 and over has increased significantly, and abdominal obesity has been recognized as an independent risk factor for diabetes. Aging causes physiologic decline in multiple body systems, leading to changes in obesity indicators such as BMI. At present, the relationship between abdominal obesity markers and Diabetes mellitus (DM) in people aged 60 years and older remains unclear. Therefore, it is necessary to study the correlation between anthropometric indices and diabetes and explore potential predictors.MethodsThe basic demographic information of participants aged 60 and above in Zhongshan City in 2020 was collected. Physical parameters, blood glucose and other biochemical indices were measured comprehensively. Binary logistic regression analysis was used to explore the relationship between abdominal obesity indicators [Waist circumference, Neck Circumference, Waist-to-hip ratio, Chinese Visceral Obesity Index (CVAI), and visceral obesity index] and diabetes mellitus. ROC characteristic curve was used to analyze the predictive ability of abdominal obesity indicators to DM, and the non-restrictive cubic spline graph was used to visualize the screened obesity indicators and diabetes risk.ResultsAmong 9,519 participants, the prevalence of diabetes was 15.5%. Compared with low CVAI, High CVAI level was significantly associated with increased prevalence of DM in males and females (all p < 0.05), in males (OR, 2.226; 95%CI: 1.128–4.395), females (OR, 1.645; 95%CI: 1.013–2.669). After adjusting for potential confounding factors, there were gender differences between neck circumference and the prevalence of DM, and above-normal neck circumference in males was significantly associated with increased prevalence of DM (OR, 1.381; 95% CI: 1.091–1.747) (p < 0.05).ConclusionAmong these anthropometric indices, CVAI is consistent with the features of fat distribution in older individuals and shows superior discriminative power as a potential predictor of DM, compared to traditional anthropometric parameters