Youla-Kucera parameterized adaptive tracking control for optical data storage systems

In the next generation optical data storage systems, the tolerance of the tracking error will become even smaller under various unknown working situations. However, the unknown external disturbances caused by vibrations make it difficult to maintain the desired tracking precision during normal disk operation. It is proposed in this paper to use an adaptive regulation approach to maintain the tracking error below its desired value despite these unknown disturbances. The design of the regulator is formulated by augmenting a base controller into a Youla-Kucera (Q) parameterized set of stabilizing controllers so that both the deterministic and the random disturbances can be deal with properly. The adaptive algorithm is developed to search the desired Q parameter which satisfies the Internal Model Principle and thus the exact regulation against the unknown deterministic disturbance can be achieved. The performance of the proposed control approach is evaluated with experimental results that illustrate the capability of the proposed adaptive regulator to attenuate the unknown disturbances and achieve the desired tracking precision

Correlation between glycaemic variability and prognosis in diabetic patients with CKD

Introduction: Glycaemic variability (GV), rather than glucose level, has been shown to be an important factor associated with in-hospital mortality. The coefficient of variation of glucose (GLUCV) is one of the methods used to evaluate GV. However, the clinical significance of GLUCV in diabetes mellitus (DM) patients diagnosed with chronic kidney disease (CKD) as a risk factor for long-term adverse changes is unknown. Material and methods: In this retrospective study, we extracted data of adult DM patients diagnosed with CKD from the Medical Information Mart for Intensive Care (MIMIC-IV). We sought to investigate the relationship between GV and in-hospital mortality as well as 30-day mortality. A non-parametric test was used to compare baseline characteristics between groups. Kaplan-Meier analysis and Cox regression model were used to analyse the risk factors associated with in-hospital and 30-day mortality. Results: A total of 1572 DM patients with CKD were included in our data analysis. The quartile of the GLUCV values was used to assign subjects to 4 groups: GLUCV1 (GLUCV < 24), GLUCV2 (24 ≤ GLUCV < 31), GLUCV3 (31 ≤ GLUCV < 39) and GLUCV 4 (GLUCV ≥ 39). COX regression analysis revealed that the GLUCV was an independent risk factor for in-hospital and 30-day mortality [GLUCV2 group (HR = 0.639, 95% CI: 0.454–0.899, p = 0.010), GLUCV3 group (HR = 0.668, 95% CI: 0.476–0.936, p = 0.019), and GLUCV3 group (HR = 0.726, 95% CI: 0.528–0.999, p = 0.049)]. The Kaplan-Meier survival curve was steeper in the GLUCV1 and GLUCV4 groups, and the survival rate decreased in a time-dependent manner. Conclusions: Herein, we validated GV as a mortality risk factor for DM patients with CKD. Therefore, monitoring and adjusting GV in hospitalized patients might have a significant treatment benefit

Genome-wide characterization of L-aspartate oxidase genes in wheat and their potential roles in the responses to wheat disease and abiotic stresses

L-aspartate oxidase (AO) is the first enzyme in NAD+ biosynthesis and is widely distributed in plants, animals, and microorganisms. Recently, AO family members have been reported in several plants, including Arabidopsis thaliana and Zea mays. Research on AO in these plants has revealed that AO plays important roles in plant growth, development, and biotic stresses; however, the nature and functions of AO proteins in wheat are still unclear. In this study, nine AO genes were identified in the wheat genome via sequence alignment and conserved protein domain analysis. These nine wheat AO genes (TaAOs) were distributed on chromosomes 2, 5, and 6 of sub-genomes A, B, and D. Analysis of the phylogenetic relationships, conserved motifs, and gene structure showed that the nine TaAOs were clustered into three groups, and the TaAOs in each group had similar conserved motifs and gene structure. Meanwhile, the subcellular localization analysis of transient expression mediated by Agrobacterium tumetioniens indicated that TaAO3-6D was localized to chloroplasts. Prediction of cis-elements indicated that a large number of cis-elements involved in responses to ABA, SA, and antioxidants/electrophiles, as well as photoregulatory responses, were found in TaAO promoters, which suggests that the expression of TaAOs may be regulated by these factors. Finally, transcriptome and real-time PCR analysis showed that the expression of TaAOs belonging to Group III was strongly induced in wheat infected by F. graminearum during anthesis, while the expression of TaAOs belonging to Group I was heavily suppressed. Additionally, the inducible expression of TaAOs belonging to Group III during anthesis in wheat spikelets infected by F. graminearum was repressed by ABA. Finally, expression of almost all TaAOs was induced by exposure to cold treatment. These results indicate that TaAOs may participate in the response of wheat to F. graminearum infection and cold stress, and ABA may play a negative role in this process. This study lays a foundation for further investigation of TaAO genes and provides novel insights into their biological functions

The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID‐19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran’s Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92–0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels

Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties

Energy storage (ES) is playing an increasingly important role in reducing the spatial and temporal power imbalance of supply and demand caused by the uncertainty and periodicity of renewable energy in the microgrid. The utilization efficiency of distributed ES belonging to different entities can be improved through sharing, and considerable flexibility resources can be provided to the microgrid through the coordination of ES sharing and demand response, but its reliability is affected by multiple uncertainties from different sources. In this study, a two-stage ES sharing mechanism is proposed, in which the idle ES capacity is aggregated on the previous day to provide reliable resources for real-time optimization. Then, a two-layer semi-coupled optimization strategy based on a deep deterministic policy gradient is proposed to solve the asynchronous decision problems of day-ahead sharing and intra-day optimization. To deal with the impact of multiple uncertainties, Monte Carlo sampling is applied to ensure that the shared ES capacity is sufficient in any circumstances. Simulation verifies that the local consumption rate of renewable energy is effectively increased by 12.9%, and both microgrid operator and prosumers can improve their revenue through the joint optimization of ES sharing and demand response

Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties

Energy storage (ES) is playing an increasingly important role in reducing the spatial and temporal power imbalance of supply and demand caused by the uncertainty and periodicity of renewable energy in the microgrid. The utilization efficiency of distributed ES belonging to different entities can be improved through sharing, and considerable flexibility resources can be provided to the microgrid through the coordination of ES sharing and demand response, but its reliability is affected by multiple uncertainties from different sources. In this study, a two-stage ES sharing mechanism is proposed, in which the idle ES capacity is aggregated on the previous day to provide reliable resources for real-time optimization. Then, a two-layer semi-coupled optimization strategy based on a deep deterministic policy gradient is proposed to solve the asynchronous decision problems of day-ahead sharing and intra-day optimization. To deal with the impact of multiple uncertainties, Monte Carlo sampling is applied to ensure that the shared ES capacity is sufficient in any circumstances. Simulation verifies that the local consumption rate of renewable energy is effectively increased by 12.9%, and both microgrid operator and prosumers can improve their revenue through the joint optimization of ES sharing and demand response

Preparation and Electrocatalytic Characteristics of PdW/C Catalyst for Ethanol Oxidation

A series of PdW alloy supported on Vulcan XC-72 Carbon (PdW/C) with total 20 wt. % as electrocatalyst are prepared for ethanol oxidation by an ethylene glycol assisted method. Transmission electron microscopy (TEM) characterization shows that PdW nanoparticles with an average size of 3.6 nm are well dispersed on the surface of Vulcan XC-72 Carbon. It is found that the catalytic activity and stability of the PdW/C catalysts are strongly dependent on Pd/W ratios, an optimal Pd/W composition at 1/1 ratio revealed the highest catalytic activity toward ethanol oxidation, which is much better than commercial Pd/C catalysts

Bond of Ribbed Steel Bar in High-Performance Steel Fiber Reinforced Expanded-Shale Lightweight Concrete

For the structural application of high-performance Steel Fiber Reinforced Expanded-shale Lightweight Concrete (SFRELC), a reliable bond of ribbed steel bar should be ensured. In this paper, an experimental study was carried out on the bond properties of ribbed steel bar embedded in SFRELC by the direct pull-out test. The SFRELC was produced with a strength grade of 35 MPa and a volume fraction of steel fiber as 0%, 0.8%, 1.2%, 1.6% and 2.0%, respectively. Fifteen groups of specimens were made with a central placed steel bar with diameter of 14 mm, 20 mm and 28 mm, respectively. Complete bond stress-slip curves were determined for each group of specimens, and the characteristic values of bond-stress and slip at key points of the curves were ascertained. Results show that the bond strength, peak-slip and residual bond strength increased with the increase of the volume fraction of steel fiber. With the increase of steel bar diameter, bond strength decreased while the peak-slip increased, and the descending curves became sharp with a decreased residual bond strength. Formulas for calculating the bond strength and peak-slip were proposed. The relationships were determined for the splitting bond strength, residual bond strength with the bond strength, the splitting bond slip and residual bond slip with the peak-slip. Combined with rational fitting analyses of bond strength and slip, a constitutive model was selected for predicting the bond stress-slip of ribbed steel bar in SFRELC