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BackgroundCognitive impairment and brain atrophy are common in chronic kidney disease patients. It remains unclear whether differences in renal function, even within normal levels, influence hippocampal volume (HCV) and cognition. We aimed to investigate the association between estimated glomerular filtration rate (eGFR), HCV and cognition in outpatients.MethodsThis single-center retrospective study enrolled 544 nonrenal outpatients from our hospital. All participants underwent renal function assessment and 3.0 T magnetic resonance imaging (MRI) in the same year. HCV was also measured, and cognitive assessments were obtained. The correlations between eGFR, HCV, and cognitive function were analyzed. Logistic regression analysis was performed to identify the risk factors for hippocampal atrophy and cognitive impairment. Receiver-operator curves (ROCs) were performed to find the cut-off value of HCV that predicts cognitive impairment.ResultsThe mean age of all participants was 66.5 ± 10.9 years. The mean eGFR of all participants was 88.5 ± 15.1 mL/min/1.73 m2. eGFR was positively correlated with HCV and with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Univariate and multivariate logistic regression analysis showed Age ≥ 65 years, eGFR < 75 mL/min/1.73 m2, Glucose ≥6.1 mmol/L and combined cerebral microvascular diseases were independent risk factors for hippocampal atrophy and Age ≥ 65 years, left hippocampal volume (LHCV)

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.

DC voltage stability and power balance are the key conditions for stable operation of DC transmission systems. The droop control does not depend on inter-station communication and has the advantage of multi-station cooperative unbalanced power dissipation, which has been widely used in the voltage source converter based multi-terminal direct current (VSC-MTDC) system. However, its control will cause DC voltage deviation, and there are a series of problems such as unstable system control and overload operation of the VSC-station due to improper setting of droop coefficient. In this paper, the infeasibility of DC voltage error-free correction under droop control mode is theoretically analyzed, and a VSC-MTDC cooperative optimization droop control strategy with DC voltage "quasi-error-free" adjustment ability is proposed. The strategy adjusts the droop coefficient in real-time by monitoring the DC voltage deviation and the power margin of the VSC-station to solve the problem of voltage deviation caused by unbalanced power consumption. At the same time, an additional DC voltage stabilizer is designed to automatically adjust the power reference value and restore the DC voltage. Finally, a five-terminal VSC-MTDC system is built under PSCAD/EMTDC to verify the feasibility of the proposed strategy.

This research aims to optimize the comprehensive performance of wood-based electromagnetic shielding interference (EMI) materials and master the effect of heat treatment on its coating density, interfacial morphology, conductivity, and hydrophobic and electromagnetic shielding. The results showed that the surface roughness of composite coatings was 11.0 μm when the wood was conducted via electroless two deposition Cu and one Ni and the heat treatment temperature was 150 °C. The composite coating’s surface gradually became more uniform with increasing temperature. The coating’s thickness via 120 °C heat treatment was 97.5 μm. Energy Dispersive Spectroscopy (EDS) spectra verified the existence of Cu and Ni particles. The heat treatment had an obvious influence on conductivity of composite materials and the pore network structure. The contact angle of composite materials reached 119°. The average electromagnetic shielding efficiency via 180 °C heat treatment was up to 91.4 dB in the frequency ranging from 300 to 3.0 GHz, which verified that the composite materials can shield 99.99% of the incident electromagnetic wave energy. The conductivity gradient structure can realize multi-dielectric interface loss and multiple reflection loss.

Abstract Background Genetic variants associated with acute side effects of radiotherapy in nasopharyngeal carcinoma (NPC) remain largely unknown. Methods We performed a two-stage genome-wide association analysis including a total of 1084 patients, where 319 individuals in the discovery stage were genotyped for 688,783 SNPs using whole genome-wide screening microarray. Significant variants were then validated in an independent cohort of 765 patients using the MassARRAY system. Gene mapping, linkage disequilibrium, genome-wide association analysis, and polygenic risk score were conducted or calculated using FUMA, LDBlockShow, PLINK, and PRSice software programs, respectively. Results Five SNPs (rs6711678, rs4848597, rs4848598, rs2091255, and rs584547) showed statistical significance after validation. Radiotherapy toxicity was more serious in mutant minor allele carriers of all five SNPs. Stratified analysis further indicated that rs6711678, rs4848597, rs4848598, and rs2091255 correlated with skin toxicity in patients of EBV positive, late stage (III and IV), receiving both concurrent chemoradiotherapy and induction/adjuvant chemotherapy, and with OR values ranging from 1.92 to 2.66. For rs584547, high occurrence of dysphagia was found in A allele carriers in both the discovery (P = 1.27 × 10− 6, OR = 1.55) and validation (P = 0.002, OR = 4.20) cohorts. Furthermore, prediction models integrating both genetic and clinical factors for skin reaction and dysphagia were established. The area under curve (AUC) value of receiver operating characteristic (ROC) curves were 0.657 (skin reaction) and 0.788 (dysphagia). Conclusions Rs6711678, rs4848597, rs4848598, and rs2091255 on chromosome 2q14.2 and rs584547 were found to be novel risk loci for skin toxicity and dysphagia in NPC patients receiving radiotherapy. Trial registration Chinese Clinical Trial Register (registration number: ChiCTR-OPC-14005257 and CTXY-140007-2).