Preparation of Amidoxime Polyacrylonitrile Chelating Nanofibers and Their Application for Adsorption of Metal Ions.

Polyacrylonitrile (PAN) nanofibers were prepared by electrospinning and they were modified with hydroxylamine to synthesize amidoxime polyacrylonitrile (AOPAN) chelating nanofibers, which were applied to adsorb copper and iron ions. The conversion of the nitrile group in PAN was calculated by the gravimetric method. The structure and surface morphology of the AOPAN nanofiber were characterized by a Fourier transform infrared spectrometer (FT-IR) and a scanning electron microscope (SEM), respectively. The adsorption abilities of Cu2+ and Fe3+ ions onto the AOPAN nanofiber mats were evaluated. FT-IR spectra showed nitrile groups in the PAN were partly converted into amidoxime groups. SEM examination demonstrated that there were no serious cracks or sign of degradation on the surface of the PAN nanofibers after chemical modification. The adsorption capacities of both copper and iron ions onto the AOPAN nanofiber mats were higher than those into the raw PAN nanofiber mats. The adsorption data of Cu2+ and Fe3+ ions fitted particularly well with the Langmuir isotherm. The maximal adsorption capacities of Cu2+ and Fe3+ ions were 215.18 and 221.37 mg/g, respectively

Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance. However, the hyper-parameters are normally tuned manually, which is often a difficult task. Most recently, effective automatic hyper-parameter tuning was achieved by using an empirical auto-tuner. In this work, we address the issue of hyper-parameter auto-tuning using neural network (NN)-based learning. Inspired by the empirical auto-tuner, we design and learn a NN-based auto-tuner, and show that considerable improvement in convergence rate and recovery performance can be achieved

Distributed secondary control based on dynamic diffusion algorithm for current sharing and average voltage regulation in DC microgrids

This paper introduces a distributed secondary control scheme for achieving current sharing and average voltage regulation objectives in a DC microgrid. The proposed scheme employs a dynamic diffusion algorithm (DDA) instead of the consensus algorithm to enable distributed communication among converters. To help understand DDA, the relation of DDA and other diffusion algorithms is discussed in detail and its superiority is shown by comparison with diffusion and consensus algorithms. Furthermore, considering the discrete nature and different sampling time of the digital controller and communication network, a z-domain model of the entire DC microgrid is established. The influence of communication and secondary control parameters on the system stability is investigated. Based on the established model, the tolerable communication rates are obtained. Real-time simulations conducted on the OPAL-RT platform validate the effectiveness of the proposed scheme, showcasing its advantages in terms of convergence speed and stability

Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to "model" the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods

Effect of liposomal bupivacaine for preoperative erector spinae plane block on postoperative pain following video-assisted thoracoscopic lung surgery: a protocol for a multicenter, randomized, double-blind, clinical trial

BackgroundThere is still a controversy about the superiority of liposomal bupivacaine (LB) over traditional local anesthetics in postoperative analgesia after thoracic surgery. This study aims to determine the effect of LB versus bupivacaine hydrochloride (HCl) for preoperative ultrasound-guided erector spinae plane block (ESPB) on postoperative acute and chronic pain in patients undergoing video-assisted thoracoscopic lung surgery.MethodsThis multicenter, randomized, double-blind, controlled trial will include 272 adult patients scheduled for elective video-assisted thoracoscopic lung surgery. Patients will be randomly assigned, 1:1 and stratified by site, to the liposomal bupivacaine (LB) group or the bupivacaine (BUPI) HCl group. All patients will receive ultrasound-guided ESPB with either LB or bupivacaine HCl before surgery and patient-controlled intravenous analgesia (PCIA) as rescue analgesia after surgery. The numeric rating scale (NRS) score will be assessed after surgery. The primary outcome is the area under the curve of pain scores at rest for 0–72 h postoperatively. The secondary outcomes include the total amount of opioid rescue analgesics through 0–72 h postoperatively, time to the first press on the PCIA device as rescue analgesia, the area under the curve of pain scores on activity for 0–72 h postoperatively, NRS scores at rest and on activity at different time points during the 0–72 h postoperative period, Quality of Recovery 15 scores at 72 h after surgery, and NRS scores on activity on postsurgical day 14 and postsurgical 3 months. Adverse events after the surgery are followed up to the postsurgical day 7, including postoperative nausea and vomiting, fever, constipation, dizziness, headache, insomnia, itching, prolonged chest tube leakage, new-onset atrial fibrillation, severe ventricular arrhythmia, deep venous thrombosis, pulmonary embolism, pulmonary atelectasis, cardiac arrest, ileus, urinary retention, chylothorax, pneumothorax, and organ failure. Analyzes will be performed first according to the intention to treat principle and second with the per-protocol analysis.DiscussionWe hypothesize that LB for preoperative ultrasound-guided ESPB would be more effective than bupivacaine HCl in reducing postoperative pain in video-assisted thoracoscopic lung surgery. Our results will contribute to the optimization of postoperative analgesia regimens for patients undergoing video-assisted thoracoscopic lung surgery.Clinical trial registration:http://www.chictr.org.cn, identifier ChiCTR2300074852

Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies