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Abstract Background Postoperative cognitive dysfunction (POCD) is a common complication after anesthesia/surgery, especially among elderly patients, and poses a significant threat to their postoperative quality of life and overall well-being. While it is widely accepted that elderly patients may experience POCD following anesthesia/surgery, the exact mechanism behind this phenomenon remains unclear. Several studies have indicated that the interaction between silent mating type information regulation 2 homologue 1 (SIRT1) and brain-derived neurotrophic factor (BDNF) is crucial in controlling cognitive function and is strongly linked to neurodegenerative disorders. Hence, this research aims to explore how SIRT1/BDNF impacts cognitive decline caused by anesthesia/surgery in aged mice. Methods Open field test (OFT) was used to determine whether anesthesia/surgery affected the motor ability of mice, while the postoperative cognitive function of 18 months old mice was evaluated with Novel object recognition test (NORT), Object location test (OLT) and Fear condition test (FC). The expressions of SIRT1 and other molecules were analyzed by western blot and immunofluorescence staining. The hippocampal synaptic plasticity was detected by Golgi staining and Long-term potentiation (LTP). The effects of SIRT1 and BDNF overexpression as well as chemogenetic activation of glutamatergic neurons in hippocampal CA1 region of 18 months old vesicular glutamate transporter 1 (VGLUT1) mice on POCD were further investigated. Results The research results revealed that older mice exhibited cognitive impairment following intramedullary fixation of tibial fracture. Additionally, a notable decrease in the expression of SIRT1/BDNF and neuronal excitability in hippocampal CA1 glutamatergic neurons was observed. By increasing levels of SIRT1/BDNF or enhancing glutamatergic neuron excitability in the CA1 region, it was possible to effectively mitigate synaptic plasticity impairment and ameliorate postoperative cognitive dysfunction. Conclusions The decline in SIRT1/BDNF levels leading to changes in synaptic plasticity and neuronal excitability in older mice could be a significant factor contributing to cognitive impairment after anesthesia/surgery. Graphical Abstract

Abstract Objective The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. Method A total of 425 patients pathologically diagnosed with gastric GISTs at the authors’ medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. Results In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). Conclusions The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.

Stainless steel welding has been widely used in industrial manufacturing, and friction stir welding has been recognized to reduce production costs significantly. To fully utilize the different properties of 2507 super duplex stainless steel (SDSS) and 317L austenitic stainless steel (ASS), the mechanical and surface properties of 2507 SDSS and 317L ASS dissimilar stainless steel joints were improved using the friction stir welding (FSW) method. The influence of rotation speed on the microstructural characteristics, mechanical properties, and corrosion behavior of the weld joints was investigated by welding at a constant speed of 20 mm/min within the range of 300–600 rpm. Specifically, at 400 rpm, a welded joint with optimal performance was obtained. When the rotation speed is 400 rpm, the yield strength (YS) and ultimate tensile strength (UTS) of the welding joint are 493 and 692 MPa, respectively, and the elongation (EL) is 32 %. The tensile strength is higher than 317L ASS but lower than 2507 SDSS, the EL is lower than those of 317L ASS and 2507 SDSS, and the friction coefficient of the welded joint is 0.93, which is between 317L ASS and 2507 SDSS. Notably, the refinement of grains in the stirred zone significantly improved the corrosion resistance of the welded joint, considerably exceeding that of the 317L ASS but still lower than the 2507 SDSS. This work can provide a process reference for studying the mechanical and corrosion properties of dissimilar stainless steel FSW.

Liver fibrosis, a common characteristic in various chronic liver diseases, is largely influenced by glycolysis. Quercetin (QE), a natural flavonoid known to regulate glycolysis, was studied for its effects on liver fibrosis and its underlying mechanism. Results showed that QE effectively improved liver injury and fibrosis caused by carbon tetrachloride (CCl4). This was supported by evidence of improved pathological features and reduced levels of serum markers such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), total bile acid (TBA), total bilirubin (TBIL), direct bilirubin (DBIL), hyaluronic acid (HA), laminin (LN), and procollagen type III. QE also decreased lactate production and downregulated the expression of glycolysis-related enzymes – pyruvate kinase M2 (PKM2), phosphofructokinase platelet (PFKP), and hexokinase 2 (HK2) – at both the mRNA and protein levels. In liver sinusoidal endothelial cells (LSECs), QE reduced the expression and activity of these enzymes, resulting in reduced glucose consumption, ATP production, and lactate generation. Further analysis revealed that QE inhibited the production of chemokine (C-X-C motif) ligand 1 (CXCL1) and suppressed neutrophil recruitment. Overall, QE showed promising therapeutic potential for liver fibrosis by targeting LSEC glycolysis and reducing neutrophil infiltration.

Peripheral nerve injuries (PNI) represent one of the primary neuropathies leading to lifelong disability. Nerve regeneration and targeted muscle atrophy stand as the two most crucial factors influencing functional rehabilitation post peripheral nerve injury. Over time, traditional Chinese medicine (TCM) rehabilitation approaches such as acupuncture, Tuina, and microneedles serve as pivot means to activate the regeneration of injured nerve Schwann cells. By promoting axon regeneration, these approaches can accomplish nerve repair, reconstruction, and functional rehabilitation. Although TCM rehabilitation approaches have clinically demonstrated effectiveness in promoting the repair and regeneration of PNI, the related molecular mechanisms remain unclear. This significantly hampers the application and promotion of TCM rehabilitation in PNI recovery. Therefore, deeply delving into the cellular and molecular mechanisms of TCM rehabilitation technologies to foster nerve regeneration stands as the most pressing issue. On the other hand, in recent years, novel biomaterials represented by hydrogels, microfluidic platforms, and new chitosan scaffolds have showed their unique roles in treating various degrees of nerve injury. These methods exhibit immense potential in conducting high-throughput cell and organoid culture in vitro and synthesizing diverse tissue engineering scaffolds and drug carriers. We believe that the combination of TCM rehabilitation technology and novel biomaterials can more effectively address precise treatment issues such as identification of treatment target and dosage control. Therefore, this paper not only summarizes the molecular mechanisms of TCM rehabilitation technology and novel biomaterials in treating peripheral nerve injury individually, but also explores the research direction of precise treatment by integrating the two at both macro and micro levels. Such integration may facilitate the exploration of cellular and molecular mechanisms related to neurodegeneration and regeneration, providing a scientific and theoretical foundation for the precise functional rehabilitation of PNI in the future.

AbstractContext Yiqi Liangxue Shengji prescription (YQLXSJ) is a traditional Chinese medicine (TCM) formula that has long been used for treatment after percutaneous coronary intervention (PCI).Objective To investigate the putative pharmacological mechanism of YQLXSJ on restenosis through an integrated approach utilizing metabolomics and network pharmacology.Materials and methods Forty male Sprague–Dawley rats were divided into sham, model, YQLXSJ, and positive groups. YQLXSJ group received the treatment of YQLXSJ (6 g/kg/d, i.g.) and the positive group was treated with atorvastatin (2 mg/kg/d, i.g.). After 4 weeks, the improvement in intimal hyperplasia was evaluated by ultrasound, H&E staining, and immunofluorescence. UPLC–MS/MS technology was utilized to screen the differential metabolites. Network pharmacology was conducted using TCMSP, GeneCards, and Metascape, etc., in combination with metabolomics. Eventually, the core targets were acquired and validated.Results Compared to models, YQLXSJ exhibited decreased intima-media thickness on ultrasound (0.23 ± 0.02 mm vs. 0.20 ± 0.01 mm, p

Overload failure under coupling conditions of elevated temperature and combined tension-bending (CTB) is one of the typical failure modes of aero-engine low-pressure compressor blade materials, which is commonly occurring in fighter engines performing low-altitude high-speed penetration missions. In this study, the elasto-plastic deformation behavior and fracture mechanism of Ti–6Al–4V titanium alloy under coupling conditions of elevated temperature and CTB were investigated by experiments and finite element analysis (FEA). CTB tests were conducted on Ti–6Al–4V alloy in the pre-tension ratio range of 0.60–0.90 and in the temperature range of 298 K–573 K. The results show that the maximum bending load, effective bending modulus, and total energy dissipated for the Ti–6Al–4V alloy at room temperature increase with the increase of the pre-tension ratio. The maximum bending load and the energy dissipated are significantly higher at elevated temperatures compared to that of room temperature, due to the elevated temperature induced deformation mode change from bending-dominated to tensile-dominated. The FEA results are in good agreement with the experimental results. SEM fractography indicated that the Ti–6Al–4V alloy fracture at elevated temperature showed only equiaxed dimples, which exhibited a tensile fracture mode. This study is valuable for the in-depth understanding of the deformation behavior and fracture mechanism of blade materials under elevated temperature and complex loads.

Summary: The dysfunction and clonal constriction of tumor-infiltrating CD8+ T cells are accompanied by alterations in cellular metabolism; however, how the cell-intrinsic metabolic pathway specifies intratumoral CD8+ T cell features remains elusive. Here, we show that cell-autonomous generation of nicotinamide adenine dinucleotide (NAD+) via the kynurenine pathway (KP) contributes to the maintenance of intratumoral CD8+ T cell metabolic and functional fitness. De novo NAD+ synthesis is involved in CD8+ T cell metabolism and antitumor function. KP-derived NAD+ promotes PTEN deacetylation, thereby facilitating PTEN degradation and preventing PTEN-dependent metabolic defects. Importantly, impaired cell-autonomous NAD+ synthesis limits CD8+ T cell responses in human colorectal cancer samples. Our results reveal that KP-derived NAD+ regulates the CD8+ T cell metabolic and functional state by restricting PTEN activity and suggest that modulation of de novo NAD+ synthesis could restore CD8+ T cell metabolic fitness and antitumor function.

BackgroundClonorchis sinensis (C. sinensis) is a epidemiologically significant food-borne parasite, causing several hepatobiliary diseases. Biliary microbiota community structure might be influenced by infection with pathogens. However, the biliary microbiome of biliary obstruction patients infected with C. sinensis is still an unexplored aspect.MethodsA total of 50 biliary obstruction patients were enrolled, including 24 infected with C. sinensis and 26 non-infected subjects. The bile samples were collected by Endoscopic Retrograde Cholangiopancretography. Biliary microbiota alteration was analyzed through high-throughput 16S ribosomal RNA (rRNA) gene sequencing.ResultsOur findings revealed that there was significant increase in both richness and diversity, as well as changes in the taxonomic composition of the biliary microbiota of C. sinensis infected patients. At the phylum level, C. sinensis infection induced Proteobacteria increased and Firmicutes reduced. At the genus level, the relative abundance of Pseudomonas and Staphylococcus increased significantly, while Enterococcus decreased prominently in infected groups (P < 0.05). The PICRUSt analysis further showed remarkably different metabolic pathways between the two groups.ConclusionC. sinensis infection could modify the biliary microbiota, increasing the abundance and changing the phylogenetic composition of bacterial in biliary obstruction patients. This study may help deepen the understanding of the host-biliary microbiota interplay with C. sinensis infection on the background of biliary obstruction and provide new insights into understanding the pathogenesis of clonorchiasis.

Laser powder bed fused (LPBFed) AlSi10Mg is recognised for its superior mechanical properties. However, its thermal stability has never been justified. Herein, we exposed as-built AlSi10Mg to different temperatures (200–500°C) for only 3 min to evaluate its thermal stability. Results showed that LPBFed AlSi10Mg had relatively low thermal stability. Only 3 min of thermal exposure at 200°C would deteriorate its tensile strength dramatically. Microstructural analysis revealed that with increasing thermal input, as-built AlSi10Mg exhibited a microstructural evolution similar to annealing of cold-worked metals, namely recovery, recrystallisation followed by grain-growth. The excessive energy stored in as-built microstructure due to fast cooling during LPBF was deduced as the driving force for this phenomenon. Therefore, such microstructural change was at the expense of dislocations stored in the as-built material, which in turn caused deterioration in tensile strength. The present findings may provide guidance for the application of LPBFed AlSi10Mg.

ABSTRACTSelective laser melted (SLMed) AlSi10Mg alloys usually fail before necking during tensile tests, which results in inadequate ductility. To overcome this problem, here we introduced Al4C3 nanorods into the primary Al cells of SLMed AlSi10Mg via in-situ chemical reaction. Tensile tests revealed that those in-cell Al4C3 nanorods not only increased the strain-hardening ability of SLMed AlSi10Mg but also delayed the fracture beyond necking. Consequently, 15% of tensile strength increase and 78% of ductility increase were achieved simultaneously. Post-tensile TEM analysis revealed that those in-cell Al4C3 nanorods pinned and accumulated dislocations within cells and thus alleviated stress concentration along cell boundaries.

CeO2-ZrO2-La2O3 supported Pt-Pd bimetallic three-way catalysts (0.6Pt-0.4Pd/CZL) were synthesized through the conventional impregnation method and then subjected to severe thermal aging. Reactivating treatments under different temperatures were then applied to the aged catalysts above. Three-way catalytic performance evaluations and dynamic operation window tests along with detailed physio-chemical characterizations were carried out to explore possible structure–activity evolutions during the reactivating process. Results show that the reactivating process conducted at proper temperatures (500~550 °C) could effectively restore the TWC catalytic performance and widen the operation window width. The suitable reactivating temperature ranges are mainly determined by the decomposing temperature of PMOx species, the thermal stability of PM-O-Ce species, and the encapsulation temperature of precious metals by CZL support. Reactivating under appropriate temperature helps to restore the interaction between Pt and CZL support to a certain extent and to re-expose part of the encapsulated precious metals. Therefore, the dynamic oxygen storage/release capacity, redox ability, as well as thermal stability of PtOx species, can be improved, thus benefiting the TWC catalytic performances. However, the excessively high reactivating temperature would cause further embedment of Pd by CZL support, thus leading to a further decrease in both dynamic oxygen storage/release capacity and the TWC catalytic performance after reactivating treatment.

Abstract Background Remimazolam tosilate (RT) is a novel short-acting GABA (A) receptor agonist that has a rapid recovery from procedural sedation and can be fully reversed by flumazenil. To date, there have been relatively few articles comparing RT and propofol for general anesthesia. This study aimed to assess the efficacy and safety of RT with or without flumazenil compared with propofol in general anesthesia for day surgery. Methods 115 patients scheduled for day surgery were randomized into three groups: RT (n = 39), RT + flumazenil (n = 38) and propofol (n = 38). The primary endpoints were anesthesia induction time and time until fully alert. Anesthesia success rate, bispectral index (BIS) values, injection pain, opioid and vasopressor dosages, postoperative recovery profiles and perioperative inflammatory and cognitive changes were assessed. Any adverse events were recorded. Results Induction times were similar among the three groups (P = 0.437), but the median time until fully alert in patients treated with RT was longer than that of the propofol or RT + flumazenil groups (17.6 min vs. 12.3 min vs. 12.3 min, P 0.05). Smaller percentages of patients who received RT (26.3%) and RT + flumazenil (31.6%) developed hypotension during anesthesia maintenance compared with propofol (68.4%), and consequently less ephedrine (P

Abstract Predicting wind power ramp events directly based on the historical ramp event time series has drawn increasing attention recently. But the class imbalance problem of the ramp event time series significantly affects the prediction accuracy of ramp events. In the present study, a layer oversampling (LOS) method is proposed considering the relation characteristics of wind power amplitudes and the occurrence frequency of wind power ramp events. Meanwhile, a hybrid sampling method of error bootstrap‐LOS (EB‐LOS) is proposed by combining LOS with the EB oversampling method. After balancing the samples of the ramp and nonramp events by using different sampling methods, the backpropagation neural network (BPNN), and the long short‐term memory (LSTM) methods are employed to directly predict ramp events based on historical data collected from eight wind farms. Comparison results proved that the proposed EB‐LOS method achieves the best prediction performance with an average recall of 0.8196 when using the BPNN model to directly predict ramp events. The best prediction performance of the EB‐LOS method is also proved by using the LSTM model to directly predict ramp events.

Background: Positive UroVysion™ fluorescence in situ hybridization (FISH) is generally considered as urothelial carcinoma (UC). We clarify if UroVysion™ FISH can be positive in carcinoma of non-urothelial lineages (CNUL), and verify the consistency of urine FISH and histological FISH in CNUL.Methods: All CNUL subjects detected by urine FISH assay due to haematuria from Tongji Hospital were screened. Meanwhile, 2 glandular cystitis and 2 urothelial carcinoma were served as negative or positive control. Paraffin-embedded tissue sections of all subjects were sent to the pathology department for histological FISH detection.Results: A total of 27 patients were included in this study, including 9 with adenocarcinomas, 11 with squamous cell carcinomas, and 7 with other tumour types. The overall positive rate in urine FISH was 64.00% (16/25) in patients with CNUL, 77.78% (7/9) in those with adenocarcinoma and 54.55% (6/11) in those with squamous carcinoma. There was a significant difference in the GLP p16 gene deletion rate between UC and CNUL (100% vs. 8.00%, p = 0.017). Histological FISH results showed that the histological results of 19 patients were consistent with their urine FISH results, and only one patient with stage Ⅲa urachal carcinoma had inconsistent histological FISH results (positive) and urine FISH (negative) results.Conclusion: We demonstrated for the first time the application value of FISH in CNUL on urine samples. Positive urine FISH tests indicate not only UC, but also CNUL. UroVysion™ FISH possibly has a high positive rate in CNUL. CNUL and UC have different genetic changes shown by FISH.

3-D point cloud semantic segmentation is a fundamental task for scene understanding, but this task remains challenging due to the diverse scene classes, data defects, and occlusions. Most existing deep learning-based methods focus on new designs of feature extraction operators but neglect the importance of exploiting multiscale point information in the network, which is crucial for identifying objects under complex scenes. To tackle this limitation, we propose an innovative network called PointNest that efficiently learns multiscale point feature propagation for accurate point segmentation. PointNest employs a deep nested U-shape encoder–decoder architecture, where the encoder learns multiscale point features through nested feature aggregation units at different network depths and propagates local geometric contextual information with skip connections along horizontal and vertical directions. The decoder then receives multiscale nested features from the encoder to progressively recover geometric details of the abstracted decoding point features for pointwise semantic prediction. In addition, we introduce a deep supervision strategy to further promote multiscale information propagation in the network for efficient training and performance improvement. Experiments on three public benchmarks demonstrate that PointNest outperforms existing mainstream methods with the mean intersection over union scores of 68.8%, 74.7%, and 62.7% in S3DIS, Toronto-3D, and WHU-MLS datasets, respectively.

Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness.

Background/purpose: Glide path management (GPM) is a consequential clinical step that serves to influence predictably successful root canal therapy. However, the effect of GPM on the shaping ability of single-file system remains controversial. This study compared the performance of rotary single-file One Curve (OC) and reciprocating single-file Reciproc Blue (RCB) in combination with/without different glide path techniques: no glide path preparation (NG), PathFile (PF), ProGlider (PG) and WaveOne Gold Glider (WOGG), respectively. Materials and methods: 80 simulated curved canals (n = 10 canals/per group) were instrumented to an apical size of 0.25 mm using OC and RCB in combination with/without different glide path techniques, respectively. The amount of resin removal, canal transportation, and the degree of canal straightening were measured in Photoshop CS6 software. Statistical analysis of the data was performed by using Kruskal–Wallis test, followed by Dunn's post hoc test with Bonferroni correction (α = 0.05). Results: Post–glide path analysis found that WOGG and PG produced less canal transportation and curvature straightening than PF (P

Abstract Background This study attempted to verify the potential of KCNJ14 as a biomarker in colorectal cancer (CRC). Methods Data on transcriptomics and DNA methylation and the clinical information of CRC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Biological information analysis methods were conducted to determine the role of KCNJ14 in the prognosis, diagnosis, immune cell infiltration, and regulation mechanism of CRC patients. The effect of KCNJ14 on the proliferation and migration of HCT116 and SW480 CRC cell lines was verified by in vitro experiments (MTT, colony-forming, wound healing, and transwell assays). Western blotting was performed to detect the effect of KCNJ14 on the levels of mTOR signalling pathway-related proteins. Results KCNJ14 expression was remarkably increased in CRC tissues and cell lines, which reduced the overall survival time of patients. KCNJ14 mRNA was negatively regulated by its methylation site cg17660703, which can also endanger the prognosis of patients with CRC. Functional enrichment analysis suggested that KCNJ14 is involved in the mTOR, NOD-like receptor, and VEGF signalling pathways. KCNJ14 expression was positively correlated with the number of CD4 + T cells and negatively correlated with that of CD8 + T cells in the immune microenvironment. KCNJ14 knockdown significantly reduced not only the proliferation and migration of CRC cell lines but also the levels of mTOR signalling pathway-related proteins. Conclusions This study not only increases the molecular understanding of KCNJ14 but also provides a potentially valuable biological target for the treatment of colorectal cancer.

Abstract Anomaly detection of steam turbines is to recognize infrequent instances within sensor data that plays a vital role in stable power supply. Machine learning models have been applied to diagnose the faults of turbine and verified useful for identifying engine problem. To detect anomalies of steam turbines with machine learning methods, here, an approach called hierarchical pre‐warning strategy is proposed that combines clustering methods with classification methods. Three different clustering methods, K‐means, Isolation Forest and Local Outlier Factor, are chosen to separate anomalies from normal data. Since clustering results cannot give unanimous decision, the clustering instances are labelled with three classes, real anomalies, suspected anomalies and normal data, according to their overlapping recognition. Subsequently, five classification algorithms, k‐nearest neighbour, support vector machine, decision tree, random forest and gradient boosting decision tree, have been examined to train the labelled data set. The classification results illustrate that gradient boosting decision tree and random forest are much more precise to detect real anomalies of steam turbines. The real anomalies identified by clustering methods have been classified into suspected anomalies by this approach that is more practicable and consistent with ground truth.

3D point cloud semantic segmentation is crucial for 3D environment perception and scene understanding, where learning of local context in point clouds is a crucial challenge. Existing approaches typically explore local context based on the predefined neighbors of point clouds. However, the widely used K-nearest neighbor algorithm (KNN) is far from optimal in defining local neighbors. In this study, we propose NeiEA-Net, a conceptually simple and effective network for point cloud semantic segmentation. The key to our approach is to optimize the local neighbors in 3D Euclidean space by taking full advantage of high-dimensional feature space as much as possible. In addition, we introduce a neighbor feature aggregation module to adaptively aggregate features with different scales in the local neighbors to further reduce the redundant information, thereby effectively learning the local details of point clouds. Experiments conducted on three large-scale benchmarks, S3DIS, Toronto3D and SensatUrban, demonstrate the superiority of our network.

Activation of STING signaling in DCs promotes antitumor immunity. Aerobic glycolysis is a metabolic hallmark of activated DCs, but how the glycolytic pathway intersects with STING signaling in tumor-infiltrating DCs remains elusive. Here, we show that glycolysis drives STING signaling to facilitate DC-mediated antitumor immune responses. Tumor-infiltrating DCs exhibited elevated glycolysis, and blockade of glycolysis by DC-specific Ldha/Ldhb double deletion resulted in defective antitumor immunity. Mechanistically, glycolysis augmented ATP production to boost STING activation and STING-dependent DC antitumor functions. Moreover, DC-intrinsic STING activation accelerated HIF-1α–mediated glycolysis and established a positive feedback loop. Importantly, glycolysis facilitated STING-dependent DC activity in tissue samples from patients with non–small cell lung cancer. Our results provide mechanistic insight into how the crosstalk of glycolytic metabolism and STING signaling enhances DC antitumor activity and can be harnessed to improve cancer therapies.

ObjectiveThe aim of this study was to evaluate the left and right ventricular segmental and global myocardial function of normal fetuses using velocity vector imaging and explore the correlation between global myocardial function parameters and gestational age.MethodsA total of 127 normal fetuses were selected and divided into five groups according to gestational age for the measurement of their left and right ventricular segmental and global velocity, strain, and strain rate. This study also explored the change trend in the global myocardial function parameters at different gestational ages and analyzed its correlation with gestational age.ResultsThe peak velocities of the biventricular segments of the normal fetuses showed a decreasing trend from the basal to the middle to the apex segment, and the differences were statistically significant (P 0.05). The peak global velocity of both ventricles increased with the gestational age, and it was moderately correlated with gestational age; however, the correlation of strain and peak strain rate with gestational age was not statistically significant (P > 0.05).ConclusionIn normal fetuses, the peak myocardial velocity of the biventricular segments showed a decreasing trend from the basal to the apical segment. The global peak myocardial velocity was linearly correlated with gestational age; however, the global strain and peak strain rate did not change as gestational age increased, indicating that the myocardial deformability of the fetus' ventricles was constant in the middle and late trimesters.

5Cu-USY and Ce-doped 5Cu8Ce-USY zeolite catalysts were prepared by the conventional impregnation method. The obtained catalysts were subjected to the hydrothermal ageing process. The catalytic performance of the selective catalytic reduction of NOx with NH3 (NH3-SCR) was evaluated on both fresh and aged catalysts. Physical/chemical characterizations such as X-ray diffraction (XRD), H2 temperature-programmed reduction (H2-TPR), and X-ray photoelectron spectroscopy (XPS) were performed, along with detailed in situ diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) experiments including CO adsorption, NH3 adsorption, and NO + O2 reactions. Results showed that, for the 5Cu-USY catalyst, hydrothermal ageing treatment could somehow improve the low-temperature SCR activity, but it also led to significant formation of unfavorable byproducts NO2 and N2O. Such an activity change can be attributed to hydrothermal ageing inducing the migration of isolated Cu+ species in the sodalite cavities towards the super cages of the USY zeolites. The increased content of Cu+ species in the super cages was beneficial for the low-temperature activity improvement, but, at the same time, it also facilitated ammonia oxidation at high temperatures. Ce doping after hydrothermal ageing has a “double-edged sword” effect on the catalytic performance. First of all, Ce doping can inhibit Cu species migration by self-occupying the internal cage sites; thus, the catalytic performance of 5Cu8Ce-USY-700H remains stable after ageing. Secondly, Ce doping introduces a CuOx–CeO2 strong interaction, which facilitates lattice oxygen mobility by forming more oxygen vacancies so as to increase the concentration of surface active oxygen. These changes, on the one hand, could help to promote further oxidative decomposition of nitrate/nitrite intermediates and improve the catalytic performance. But, on the other hand, it also causes the byproduct generation to become more severe.

A series of supported MnOx/MgAl-layered double hydroxide (LDH) catalysts were prepared by hydrothermal co-precipitation to investigate their catalytic performances for low-temperature formaldehyde oxidation reactions. Activity tests show that the 10Mn/Mg3Al1-LDH catalyst exhibits higher efficiency for low-temperature formaldehyde oxidation with a high CO2 yield. It also shows remarkable long-term operational stability as well as good adaptability to different velocities and humidities. Various characterizations were carried out to establish the possible structure–activity correlations. The results show that there were a large number of hydroxyl groups in the 10Mn/MgAl-LDH catalysts, and the hydroxyl groups were positively correlated with Mg2+ content. The outstanding catalytic performance of 10Mn/Mg3Al1-LDH can be attributed to abundant surface hydroxyl groups, surface adsorbed oxygen and higher Mn4+/Mn3+ ratios. Through in situ Fourier-transform infrared spectroscopy (in situ FTIR), it was revealed that formaldehyde was gradually converted into CO2 and water with dioxymethylene (DOM), formate and carbonate as the major intermediates under the action of both active oxygen and active hydroxyl groups. The active oxygen and active hydroxyl groups consumed in the process are continuously replenished by the effective reaction between the oxygen molecules in the air and the active site of the catalyst. The low-temperature asynchronous conversion of formaldehyde results in the accumulation of some intermediates on the catalyst surface covering the active center, which induces catalyst deactivation.

Polyimide (PI) films are widely used in electronic devices owing to their excellent mechanical and electrical properties and high thermal and chemical stabilities. In particular, PI films play an important role in flexible printed circuit boards (FPCBs). However, one challenge currently faced with their use is that the adhesives used in FPCBs cause a high dielectric loss in high-frequency applications. Therefore, it is envisioned that PI films with a low dielectric loss and Cu films can be used to prepare two-layer flexible copper-clad laminates (FCCLs) without any adhesive. However, the preparation of ultra-thin FCCLs with no adhesives is difficult owing to the low peel strength between PI films and Cu films. To address this technical challenge, an FCCL with no adhesive was prepared via high-power helicon wave plasma (HWP) treatment. Field-emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) were tested. Also, the surface roughness of the PI film and the peel strength between the PI film and Cu film were measured. The experimental results show that the surface roughness of the PI film increased by 40–65% and the PI film demonstrated improved adhesion (the peel strength was >8.0 N/cm) with the Cu film following plasma treatment and Cu plating.

The Salix viminalis var. gmelinii Turcz 1854 is a variant of the Salix genus, from the Salicaceae family, and possesses an extremely high economic value. In this study the complete chloroplast genome of the woody plant S. viminalis var. gmelinii was characterized for the first time using a high-throughput approach in conjunction with de novo assembly technology. The S.viminalis var. gmelinii chloroplast genome is 155,405 base pairs (bp) in length and contains 36.71% GC content. It incorporates a large single-copy region (LSC, 84,287bp) alongside one small-copy region (SSC, 16,198bp), and two inverted repeat regions (IRA and IRB, 27,460bp). Moreover, this chloroplast genome encodes 128 genes, which comprises 83 protein-coding genes, 37 transfer RNA (tRNA) genes, and eight ribosomal RNA (rRNA) genes. Furthermore, the phylogenetic analysis revealed that S.viminalis var. gmelinii is closely related to S. cupularis and S.gordejevii.

The concept of low-carbon economic development has led to changes in the business environment and financial environment of enterprises, leading to increased financial risks faced by enterprises. How to help enterprises better warn, prevent and control financial risks from the perspective of low-carbon economy has become a hot issue worth studying. Based on this, this paper is based on the perspective of low carbon economy, on the basis of analyzing the financing risk, investment risk, capital operation risk and growth risk faced by enterprises under the requirements of low carbon economy development. A set of financial risk management framework with clear hierarchy and strict vertical logic has been constructed. Ten financial early-warning indicators are constructed from four aspects. The risk prediction model of the indicator system is established using the research method of BPNN (Back Propagation Neural Network). The model is trained and simulated through the MATLAB neural network toolbox. After 10 indicators passed Bartlett’s correlation test, the BPNN financial early warning model was programmed using MATLAB software. The accuracy rate was 84.3%. The neural network training results show that when the layer node is 8, the best correct recognition rate can be obtained. Incorporate “low carbon” into the financial risk early warning indicator system that meets the requirements of low carbon economic development in the design of enterprise financial risk early warning indicators. This paper is expected to provide reference and reference for low-carbon economy enterprises to deal with financial risks under the new situation.

The aim of this in vitro study was to evaluate the effect of three final irrigants, namely QMix, MTAD and EDTA, combined with three irrigation techniques, namely conventional needle irrigation (CNI), passive ultrasonic irrigation (PUI) and photon-induced photoacoustic streaming (PIPS), on smear layer removal, dentin mineral content and microhardness in oval-shaped canals. 130 decoronated premolars with single, oval root canals were equally divided into1 blank control group and 12 treatment groups (n=10) according to the final irrigation protocols. Roots in treatment groups were instrumented with ProTaper Gold to size F4 and subjected to final irrigation. Smear layer removal was assessed by using a four-level scoring system under an environmental scanning electron microscope. Energy dispersive X-ray spectroscopy was performed to measure the dentin mineral content. Dentin microhardness was measured by Knoop microhardness testing. Statistical analysis of the data was performed by using Kruskal-Wallis test, followed by Dunn’s post hoc test with Bonferroni correction. PUI- and PIPS-activated QMix and EDTA removed smear layer more effectively than MTAD groups (p

Salix kochiana Trautvetter 1837 is one of the highest value shrubs present in northern China with important economic and ecological benefits. This study revealed the structural characteristics and phylogenetic relationships of chloroplast genes in S. kochiana Trautv. The results showed that the length of the complete chloroplast genome was 155,657 bp, which was a typical circular double-stranded structure, including an 84,458 bp large single-copy region (LSC), a 16,221 bp small single-copy region (SSC) and a 27,489 bp pair of inverted repeat regions (IRA and IRB). The chloroplast genome contains 48,757 A bases, 28,017 G bases, 49,843 T bases, and 29,040 C bases, with a GC content of 36.66%. Through bioinformatics annotation, a total of 126 genes were found in the chloroplast genome, including 81 protein-coding genes, 37 tRNA genes, and eight rRNA genes. Phylogenetic analysis showed that S. kochiana Trautv. was closely related to S. triandroides.

Background/purpose: Calcium hydroxide [Ca(OH)2], a wildly used intracanal medicament, should be completely removed from the root canal before obturation to avoid negative effects on the treatment. This study evaluated the effectiveness of conventional needle irrigation (CNI), passive ultrasonic activation (PUI), photon-induced photoacoustic streaming (PIPS), XP-endo Finisher (XP) and EDDY for the removal of Ca(OH)2 from S-shaped root canal. Materials and methods: Eighty-four S-shaped root canals in resin blocks were prepared using Protaper Gold up to size #25/0.08 and filled with Ca(OH)2. Five groups were established according to the removal techniques (n = 16): CNI, PUI, PIPS, XP and EDDY group. The positive and negative control group (n = 2) were also established. Sodium hypochlorite 3% was used as the irrigant. Digital radiographs were used to measure the remaining Ca(OH)2. Statistical analysis of the data was performed by using the Kruskal–Wallis test, followed by Dunn's post hoc test with Bonferroni correction (α = 0.05). Results: All the tested techniques completely removed Ca(OH)2 from the straight portion and coronal curve of the S-shaped root canal in 100% of cases. Regarding the apical curve, PUI, EDDY, PIPS and XP removed significantly more Ca(OH)2 than CNI (P 0.05). The complete clearance of Ca(OH)2 from the apical curve was observed in 75%, 62.5%, 56.3%, 43.8% and 0% of cases of PUI, EDDY, PIPS, XP and CNI group, respectively. Conclusion: Irrigant activation enhanced Ca(OH)2 removal from the apical region of the S-shaped root canal. CNI was significantly less effective than all activation techniques.

Background/purpose: Gold metal technology improves flexibility and the resistance to cyclic fatigue of the endodontic mechanical files. This study compared the performance of ProTaper Gold (PTG) multiple file system and WaveOne Gold (WOG) single file system in simulated S-shaped root canals, which represents one of the most challenging root canal morphology. Materials and methods: Forty S-shaped canals (n = 20 canals/per group) in resin blocks were instrumented to an apical size of 0.25 mm using PTG and WOG Primary, respectively. The total amount of resin removal, canal transportation, centering ratio, and the degree of canal straightening were measured in Photoshop CS6 software. Statistical analysis was performed by using Mann–Whitney U-test (α = .05). Results: None of the files fractured during the instrumentation. The WOG group removed significantly less amount of resin at 0, 3, 6, 7 and 9 mm from the apex (P .05). Conclusion: The WOG Primary file has a less aggressive dentin cutting and more centered apical preparation. The PTG system is more advantageous in shaping the coronal curvature of S-shaped canal.

In recent years, TNS has attracted wide attention because of its simplicity in synthesis and high efficiency in ion exchange. The adsorption of cesium ions in aqueous solution by TNS was investigated in this study. Results show that the removal rate of Cs (I) is about 88% when pH = 5.00 ± 0.05, C0 = 10 ppm and CTNS = 0.1 g/L. The adsorption equilibrium is reached in about 20 minutes and best fits the pseudo-second order model, R2 = 0.9998; compared with the Freundlich isotherm adsorption model and Temkin model, the Langmuir model has the best fitting effect, R2 = 0.9903. The fitting results show the maximum adsorption capacity of TNS for Cs (I) is 200.00 mg/g. The main adsorption mechanism of TNS to cesium ion is ion exchange. Therefore, TNS can be used as a potential adsorbent for effectively adsorbing Cs-containing wastewater. HIGHLIGHTS The removal of Cs (I) from aqueous solutions by TNS was firstly studied.; TNS showed a high adsorption capacity for Cs (I).; The solution pH played a significant role in the adsorption process of Cs (I).; TNS exhibited an excellent selectivity for Cs (I) in the interference of common cations.; The adsorption mechanism was the ion exchange between the interlayer Na (I) of TNS and Cs (I).;

Hall thrusters function as power plants on spacecraft, and its development is crucial for the aerospace industry. The wall erosion of the on-orbit Hall thruster cannot be measured by the control center through ground measurement, but can obtain the discharge current low-frequency oscillation data. Therefore, this study proposes an inversion method to obtain the wall erosion information based on low-frequency oscillation signals and neural networks. Firstly, we use an improved one-dimensional quasi-neutral dynamic fluid mathematical model to build a low-frequency oscillation simulation platform which obtains the corresponding data by varying the cross-sectional area. Secondly, a nonlinear neural network model is established based on the obtained low-frequency oscillation data to invert the wall erosion information. The training function, transfer function, number of hidden layer nodes, and other parameters affecting the results are analyzed and the best model parameters are obtained. The Elman neural network is established and compared with the BP neural network and RBF neural network. The training results of the Elman neural network algorithm present small and stable errors, and the results of multiple predictions remain consistent. The root means square error, average absolute error, and average absolute percentage are 0.0084, 0.0637, and 0.045%, respectively.

Sulfonamide antibiotics (SAs) are widely used in medicine, animal husbandry and aquaculture, and excessive intake of SAs may pose potential toxicity to organisms. The toxicological mechanisms of two classical SAs, sulfamerazine (SMR) and sulfamethoxazole (SMT), were investigated by molecular docking, DFT and multi-spectroscopic techniques using HSA and BSA as model proteins. The quenching of HSA/BSA endogenous fluorescence by SMR was higher than that by SMT due to the stronger binding effect of the pyrimidine ring on HSA/BSA compared to the oxazole ring, and that result was consistent with that predicted by DFT calculations. Thermodynamic parameters show that the binding of SAs to HSA/BSA is an exothermic process that proceeds spontaneously (ΔG < 0). Marker competition experiments illustrate that the binding site of SMR/SMT on serum albumin is located in subdomain IIIA. The combination of SAs and HSA/BSA is mainly realized by hydrogen bond and hydrophobic interaction, and the concept is also supported by molecular modeling. The reduced α-helix content of HSA/BSA induced by SMR/SMT indicates a greater stretching of the protein α-helix structure of the SMR/SMT-HSA/BSA. The results could provide useful toxicological information on the hazards of SAs in response to growing concern that SAs may pose a toxic threat to organisms.

Since flexible peak shaving has been implemented in a growing number of high-power turbo-generator sets in the power grid owing to increasing demand, the load control performance of steam turbines directly affects the safety and efficiency of the unit operation. Load-following issues, especially load mutation, weaken the frequency control performance of the unit and cause load fluctuation faults, threatening power grid safety and stability. However, the definition, classification, characterization, generation mechanism, and diagnostic methods for load mutation problems have not been systematically researched. Based on the operational data of various turbo-generator set cases, this study systematically assessed three typical load mutation problems; namely, the common fault of unreasonable parameter settings of the control system as well as new-found faults in the actuator hardware and electrical interference. Subsequently, the fault mechanisms and characterization parameters of the different set capacities were analyzed and extracted. Furthermore, a diagnosis method was designed according to the actual problem, based on which fault type was identified. Case analysis of typical sets demonstrated that this method can quickly test and diagnose faults when in actual real-world scenarios and effectively determine the cause of the fault. This method can also detect the initial fault features, which is convenient for daily maintenance and avoids fault aggravation.

Given the prominence of 3D sensors in recent years, 3D point cloud scene data are worthy to be further investigated. Point cloud scene understanding is a challenging task because of its characteristics of large-scale and discrete. In this study, we propose a network called LEARD-Net, focuses on semantic segmentation for the large-scale point cloud scene data with color information. The proposed network contains three main components: (1) To fully utilize color information of point clouds rather than just as initial input features, we propose a robust local feature extraction module (LFE) to benefit the network focus on both spatial geometric structure, color information and semantic features. (2) We propose a local feature aggregation module (LFA) to benefit the network to focus on the local significant features while also focus on the entire local neighbor. (3) To allow the network to focus on both local and comprehensive features, we use residual and dense connections (ResiDense) to connect different-level LFE and LFA modules. Comparing with state-of-the-art networks on several large-scale benchmark datasets, including S3DIS, Toronto3D and Semantic3D, we demonstrate the effectiveness of our LEARD-Net.

Improving the dynamic regulation ability of thermal power units is effective for realizing flexible scheduling in modern power systems. At present, the unit regulation capacity is usually reflected by the load adjustment of the main steam pressure and flow tracking ability, through the calculation of the given and real-time deviation to complete the load, and by pressure adjustment. However, although the calculation involved in this method is easy and the results are intuitive, overshoot and lag can easily occur. The main reason for this is that the process from boiler combustion to turbine works has strong hysteresis and inertia, and the feedback signal of the pressure and flow rate cannot dynamically reflect the change in boiler combustion and steam energy. According to the heat transfer process of the unit, the main steam temperature can directly reflect the energy transfer in the furnace combustion process and then reflect the changing trend of steam energy. Analyzing the changing characteristics of the temperature, pressure, and flow of superheated steam under rapid load regulations makes it possible to calculate the instantaneous energy storage value of the main steam before the regulating valve, and this value was inserted into the coordinate system as a new feedforward signal. Finally, a simulation model was established by using the actual running data of the unit. A simulation experiment under variable working conditions demonstrated that this method could improve the dynamic adjustment of the unit to load and pressure and help the power grid absorb renewable energy.

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.

Percutaneous coronary intervention (PCI) is one of the most effective reperfusion strategies for acute myocardial infarction (AMI) despite myocardial ischemia/reperfusion (I/R) injury, causing one of the causes of most cardiomyocyte injuries and deaths. The pathological processes of myocardial I/R injury include apoptosis, autophagy, and irreversible cell death caused by calcium overload, oxidative stress, and inflammation. Eventually, myocardial I/R injury causes a spike of further cardiomyocyte injury that contributes to final infarct size (IS) and bound with hospitalization of heart failure as well as all-cause mortality within the following 12 months. Therefore, the addition of adjuvant intervention to improve myocardial salvage and cardiac function calls for further investigation. Phytochemicals are non-nutritive bioactive secondary compounds abundantly found in Chinese herbal medicine. Great effort has been put into phytochemicals because they are often in line with the expectations to improve myocardial I/R injury without compromising the clinical efficacy or to even produce synergy. We summarized the previous efforts, briefly outlined the mechanism of myocardial I/R injury, and focused on exploring the cardioprotective effects and potential mechanisms of all phytochemical types that have been investigated under myocardial I/R injury. Phytochemicals deserve to be utilized as promising therapeutic candidates for further development and research on combating myocardial I/R injury. Nevertheless, more studies are needed to provide a better understanding of the mechanism of myocardial I/R injury treatment using phytochemicals and possible side effects associated with this approach.

PurposeTo compare the cause-specific survival (CSS) and overall survival (OS) of patients with localized T3a renal cell carcinoma (RCC) after partial nephrectomy (PN) or radical nephrectomy (RN).MethodsWe obtained the demographic and clinicopathological data of 7,127 patients with localized T3a RCC and who underwent PN or RN from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were divided into fat invasion cohort and venous invasion cohort for subsequent analysis. Kaplan–Meier analysis (KMA) and univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the effects of PN or RN on OS and CSS. Meanwhile, 65 cases with clinical T1 (cT1) RCC upstaged to pathological T3a (pT3a) who were treated in Tongji Hospital (TJH) from 2011 to 2020 and underwent PN or RN were identified.ResultsIn the study cohort, 2,085 (29.3%) patients died during the 1–172 months’ follow-up, of whom 1,155 (16.2%) died of RCC. In the two cohorts of fat invasion and venous invasion, KMA indicated that the PN group had favorable survival (p < 0.001). However, after propensity score matching (PSM), univariate and multivariate Cox regression analyses showed that the PN and RN groups had comparable CSS in the fat invasion cohort (p = 0.075) and the venous invasion cohort (p = 0.190). During 1–104 months of follow-up, 9 cases in the Tongji cohort had disease recurrence. There was no significant difference in recurrence-free survival between the RN group and the PN group (p = 0.170).ConclusionsOur analysis showed that after balancing these factors, patients with localized pT3a RCC receiving PN or RN can achieve comparable oncologic outcomes. PN is safe for selected T3a patients.

Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

In the operating process of the coal-fired generation during flexible peaking regulation, the primary and secondary water droplets in the steam flowing through the last two stages of the low-pressure cylinder could influence the efficiency and safety of the steam turbine definitely. However, systematic analysis of the movement characteristics of water droplets under low-load conditions is scarcely in the existing research, especially the ultra-low load conditions below 30%. Toward this end, the more novel algebraic slip model and particle transport model mentioned in this paper are used to simulate the primary and secondary water droplets. Taking a 600 MW unit as a research object, the droplets motion characteristics of the last two stages were simulated within four load conditions, including 100, 50, 40, and 30% THA. The results show that the diameter of the primary water droplets is smaller, ranging from 0 to 1 µm, during the flexible peak regulation process of the steam turbine. The deposition is mainly located at the entire moving blades and the trailing edge of the last two stator blades. With the load decreasing, the deposition effect decreases sustainably. And the larger diameters of secondary water droplets range from 10 to 300 µm. The erosion of secondary water droplets in the last stage is more serious than that of the second last stage for different load conditions, and the erosion of the second last stage could be negligible. The pressure face and suction face at 30% blade height of the last stage blade have been eroded most seriously. The lower the load, the worse erosion from the secondary water droplets, which poses a potential threat to the fracture of the last stage blades of the steam turbine. This study provides a certain reference value for the optimal design of steam turbine blades under flexible peak regulation.

Because the generator power-measuring equipment is often accompanied by electrical interference in a complex electromagnetic environment in an actual thermal power plant, the output signal will change or even distort while it passes through the devices of acquisition and conversion. Several practical cases have found that the abnormal generation change phenomenon, impulse or oscillation caused by electrical interference, has different effects on the load regulation of steam turbines. These faults also exist in combined-cycle power plants (CCPPs). However, the insufficient installed capacity and operating life of CCPPs domestically cause similar load mutation failures that are scarcely found. We had to acknowledge that CCPPs and steam turbine regulation characteristics differ. It is of great value to study the influence of differences in load mutation on the load regulation of single-shaft CCPPs. We extracted the fault characteristics of two sudden load change phenomena using the operation data of an actual steam turbine and analyzed them through simulation. Furthermore, a fault detection and isolation method for sudden load changes in a single-shaft CCPP was proposed and the simulation results verified the method’s effectiveness.

Bronchial asthma is one of the most chronic pulmonary diseases and major public health problems. In general, asthma prevails in developed countries than developing countries, and its prevalence is increasing in the latter. For instance, the hygiene hypothesis demonstrated that this phenomenon resulted from higher household hygienic standards that decreased the chances of infections, which would subsequently increase the occurrence of allergy. In this review, we attempted to integrate our knowledge with the hygiene hypothesis into beneficial preventive approaches for allergic asthma. Therefore, we highlighted the studies that investigated the correlation between allergic asthma and the two different types of infections that induce the two major antagonizing arms of T cells. This elucidation reflects the association between various types of natural infections and the immune system, which is predicted to support the main objective of the current research on investigating of the benefits of natural infections, regardless their immune pathways for the prevention of allergic asthma. We demonstrated that natural infection with Mycobacterium tuberculosis (Mtb) prevents the development of allergic asthma, thus Bacille Calmette-Guérin (BCG) vaccine is suggested at early age to mediate the same prevention particularly with increasing its efficiency through genetic engineering-based modifications. Likewise, natural helminth infections might inhabit the allergic asthma development. Therefore, helminth-derived proteins at early age are good candidates for designing vaccines for allergic asthma and it requires further investigation. Finally, we recommend imitation of natural infections as a general strategy for preventing allergic asthma that increased dramatically over the past decades.

Ti-4Al-5Mo-5V-5Cr-1Nb (wt.%) is a new type of high-strength (~1300 MPa) titanium (Ti) alloy developed for aerospace applications. Until now, the research on its welding and subsequent heat treatment is barren. Herein, we employed electron beam welding (EBW) to a solutionized Ti-4Al-5Mo-5V-5Cr-1Nb with a phase constituent of α + β and investigated its microstructure and mechanical properties in both as-welded (AW) and post-weld aging treated (PWAT) conditions. Results showed that due to the thermal input of the welding process, the α phase in the original microstructure of base material (BM) transformed into the β phase in the fusion zone (FZ). Similar microstructural evolution was observed for the heat-affected zone (HAZ) near the FZ (Near-HAZ), whereas the HAZ far away from FZ (Far-HAZ) contained a small amount of round α phase (ghost α) due to its slower cooling rate. Such a microstructural change resulted in poor tensile strength (~780 Mpa) for the as-welded joint. After PWAT, a large number of acicular α precipitated in the FZ and HAZ and its size (S) in different zones followed the order of SFar-HAZ < SFZ ≈ SNear-HAZ < SBM. The presence of αs precipitates remedied the tensile strength of the weld joint almost to the same as that of the BM. The present findings established the foundation of the application of this high-strength Ti alloy.

Point cloud semantic segmentation, a challenging task in 3D data processing, is popular in many realistic applications. Currently, deep learning methods are gradually being applied to point cloud semantic segmentation. However, as it is difficult to manually label point clouds in 3D scenes, it remains difficult to obtain sufficient training samples for the supervised deep learning network. Although an increasing number of excellent methods have been proposed in recent years, few of these have focused on the problem of semantic segmentation with insufficient samples. To address this problem, this paper proposes a random graph based on graph convolution network, referred to as RG-GCN. The proposed network consists of two key components: (1) a random graph module is proposed to perform data augmentation by changing the topology of the built graphs; and (2) a feature extraction module is proposed to obtain local significant features by aggregating point spatial information and multidimensional features. To validate the performance of the RG-GCN, the indoor dataset S3DIS and outdoor dataset Toronto3D are used to validate the proposed network via a series of experiments. The results show that the proposed network achieves excellent performance for point cloud semantic segmentation of the two different datasets.

Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class-imbalance problem in the credit default dataset cannot be ignored, where the numbers of credit default cases are usually much smaller than the number of non-default ones. To address the class-imbalance problem, we proposed a novel CT-XGBoost model, which adds to XGBoost with two algorithm-level methods for class imbalance, including the cost-sensitive strategy and threshold method. Based on the credit default dataset consisting of energy corporates in western China, which suffers from the class-imbalance problem, the CT-XGBoost model achieves better performance than the conventional models. The results indicate that the proposed model can efficiently alleviate the inherent class-imbalance problem in the credit default dataset. Moreover, we analyze how the prediction performance is influenced by different parameter settings in the cost-sensitive strategy and threshold method. This study can help market investors and regulators precisely assess the credit risk in the energy industry and provides theoretical guidance to solving the class-imbalance problem in credit default prediction.

Wetness loss of a steam turbine seriously affects the security of the unit when operating in deep peak regulation. To obtain the distribution characteristics of the high wetness loss area under different working conditions, especially low-load conditions, the last two stages of the low-pressure cylinder (LPC) of a 600 MW steam turbine were simulated using the non-equilibrium condensation model proposed in this study. The nucleation rate distribution, supercooling degree, and steam velocity droplet were analyzed. Consequently, the diameter distribution of coarse water droplets under 100%, 50%, 40%, 30%, and 20% THA conditions and the distribution of the thermodynamic loss and water droplet resistance loss were obtained. Thermodynamic loss mainly occurred at the front end of second-stage stator blades and trailing end of the last-stage stator blades. The water droplet resistance loss mainly occurred at 40% of the blade height and at the tip of the last-stage stator blades. Moreover, with a reduction in the unit load, the thermodynamic loss continued to decrease, but the water droplet resistance loss continued to increase.