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This is an article in the field of metallurgical engineering, which proposed a new idea of mixing molten blast furnace slag with steel slag to achieve self-pulverization of steel slag, and systematically studied the influence mechanism of special components of CaF2 and CeO2 in blast furnace slag of Baotou Iron and Steel Company on the self-pulverization of steel slags. Using analytically pure CaO, SiO2, CaF2 and CeO2 as starting materials, pure C2S specimens were prepared with n ( CaO ) : n ( SiO2 ) = 2 : 1, then different mass fractions of CaF2 and CeO2 were added, respectively. The effect of CaF2 and CeO2 on the self-pulverization behavior of C2S was investigated by calcining the specimen at 1 450 °C for 1 h and cooling with the furnace. The results showed when CaF2 was incorporated into C2S and the content of CaF2 was less than 2.5%, it had little effect on the self-pulverization of C2S. However, when the content of CaF2 was more than 2.5%, the specimen had different degrees of agglomeration, which affected the self-pulverization of C2S. Considering that excessive CaF2 would affect the environment, the content of CaF2 should be controlled below 2.5%. When CeO2 incorporated into C2S and the content was 0.25%, C2S could still be completely self-pulverized, but when the CeO2 content was more than 0.5%, C2S no longer self-pulverized.Therefore, the CeO2 content must be less than 0.25 %, and the self-pulverization effect of C2S was relatively good.

Tumor subtyping based on its immune landscape may guide precision immunotherapy. The aims of this study were to identify immune subtypes of adult diffuse gliomas with RNA sequencing data, and to noninvasively predict this subtype using a biologically interpretable radiomic signature from MRI. A subtype discovery dataset (n = 210) from a public database and two radiogenomic datasets (n = 130 and 55, respectively) from two local hospitals were included. Brain tumor microenvironment‐specific signatures were constructed from RNA sequencing to identify the immune types. A radiomic signature was built from MRI to predict the identified immune subtypes. The pathways underlying the radiomic signature were identified to annotate their biological meanings. The reproducibility of the findings was verified externally in multicenter datasets. Three distinctive immune subtypes were identified, including an inflamed subtype marked by elevated hypoxia‐induced immunosuppression, a “cold” subtype that exhibited scarce immune infiltration with downregulated antigen presentation, and an intermediate subtype that showed medium immune infiltration. A 10‐feature radiomic signature was developed to predict immune subtypes, achieving an AUC of 0.924 in the validation dataset. The radiomic features correlated with biological functions underpinning immune suppression, which substantiated the hypothesis that molecular changes can be reflected by radiomic features. The immune subtypes, predictive radiomic signature, and radiomics‐correlated biological pathways were validated externally. Our data suggest that adult‐type diffuse gliomas harbor three distinctive immune subtypes that can be predicted by MRI radiomic features with clear biological significance. The immune subtypes, radiomic signature, and radiogenomic links can be replicated externally.

Background and Aim: High-grade glioma is the most common malignant primary brain tumor in the central nervous system. Multiple strategies such as surgery, radiotherapy, and chemotherapy have been used, but the prognosis of patients with high-grade glioma remains poor. No standard treatment exists for recurrent gliomas; however, combination therapies of programmed cell death protein 1 blockades with antiangiogenic agents have demonstrated promising effects in different solid tumors. Therefore, since the end of 2020, a clinical trial designed to evaluate the safety and efficiency of neoadjuvant therapy using camrelizumab and apatinib in patients with recurrent high-grade gliomas has been carried out in our institution. Methods/Design: In this prospective, Phase II, single-arm study, patients with recurrent high-grade gliomas will receive single-dose intravenous injection of camrelizumab (200 mg) and daily oral administration of apatinib (250 mg/day for 7 days) 14 days before reoperation for tumor resection. Sequential therapy will begin 2 weeks after surgery with the biweekly injection of camrelizumab and 4 weeks after surgery with the daily administration of apatinib. Treatment of camrelizumab and apatinib will be continued until disease progression or unacceptable toxicity or death. The primary outcome measure will be the median overall survival rate. Secondary outcome measures will include progression-free survival rate at 6 months and at 12 months and other measures. The trial is planned to enroll 30 patients. This study was approved by the Ethics Committee of Sun Yat-sen University Cancer Center (Guangzhou, China; approval No. SL-B2020-149-01) on July 27, 2020. Results and Conclusions: Although an evaluation is still impossible to be conducted yet, 11 patients had been enrolled by the end of January 2022. Some patients have shown a promising outcome. These preliminary data suggest that this study would be worthwhile. We hope that this study will provide scientific evidence to better care of patients with recurrent high-grade glioma. Trial registration: This study was registered with ClinicalTrials.gov under identifier NCT04588987 on October 19, 2020.

Abstract Background Differentiating glioma recurrence from treatment-related changes can be challenging on conventional imaging. We evaluated the efficacy of quantitative parameters measured by dual-energy spectral computed tomographic (CT) for this differentiation. Methods Twenty-eight patients were examined by dual-energy spectral CT. The effective and normalized atomic number (Zeff and Zeff-N, respectively); spectral Hounsfield unit curve (λHU) slope; and iodine and normalized iodine concentration (IC and ICN, respectively) in the post-treatment enhanced areas were calculated. Pathological results or clinicoradiologic follow-up of ≥2 months were used for final diagnosis. Nonparametric and t-tests were used to compare quantitative parameters between glioma recurrence and treatment-related changes. Sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), and accuracy were calculated using receiver operating characteristic (ROC) curves. Predictive probabilities were used to generate ROC curves to determine the diagnostic value. Results Examination of pre-contrast λHU, Zeff, Zeff-N, IC, ICN, and venous phase ICN showed no significant differences in quantitative parameters (P > 0.05). Venous phase λHU, Zeff, Zeff-N, and IC in glioma recurrence were higher than in treatment-related changes (P

Background: The progression and metastasis of cancers are associated with systematic immune inflammation and nutritional dysfunction. The systemic immune-inflammation index and prognostic nutritional index (PNI) have shown a prognostic impact in several malignancies. Therefore, our study aimed to evaluate immune inflammation and nutritional index prognostic significance in patients with medulloblastoma (MB).Methods: We retrospectively analyzed 111 patients with MB between 2001 and 2021 at our institution. The optimal cutoff values for systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte counts ration (MLR), and PNI were evaluated with receiver operating characteristic (ROC) curve analysis. Clinical characteristics and SII, NLR, MLR, and PNI were tested with the Pearson's chi-squared test. The Kaplan–Meier survival curves and the Cox proportional hazards model were used to evaluate the effects of immune inflammation and nutritional index on overall survival (OS).Results: Receiver operating characteristic curve analysis determined the optimal SII, NLR, MLR, and PNI cutoff values of 2,278, 14.83, 0.219, and 56.5 that significantly interacts with OS and divided the patients into two groups. Comparative survival analysis exhibited that the high-SII cohort had significantly shorter OS (p = 0.0048) than the low-SII cohort. For the univariate analysis, the results revealed that preoperative hydrocephalus (p = 0.01), SII (p = 0.006), albumin–bilirubin score (ALBI) (p = 0.04), and coSII–PNI were predictors of OS. In the multivariate analysis, preoperative hydrocephalus (p < 0.001), ALBI (p = 0.010), SII (p < 0.001), and coSII–PNI as independent prognostic factors were significantly correlated with OS.Conclusion: The preoperative SII, ALBI, and coSII–PNI serve as robust prognostic biomarkers for patients with MB undergoing surgical resection.

Background: Anlotinib is a multi-target anti-angiogenic agent. This retrospective study aimed to evaluate the efficacy and safety of anlotinib alone or in combination with temozolomide for the treatment of recurrent high-grade glioma.Materials and Methods: The clinical data of patients with recurrent high-grade glioma treated with anlotinib alone or in combination with temozolomide in our cancer center were collected and analyzed. Treatment response was evaluated according to the response assessment for neuro-oncology criteria. Progression-free survival, progression-free survival at 6 months, overall survival, and overall survival at 12 months were evaluated by Kaplan–Meier method and compared by log-rank test.Results: Between August 2019 and December 2020, 31 patients with recurrent high-grade glioma (21 of grade 4 and 10 of grade 3) were enrolled in this study. Seventeen patients received anlotinib alone and 14 received anlotinib plus temozolomide. All patients were heavily treated, the median lines of previous treatments were 2, and the median Karnofsky score was 60. At the data cutoff date, the median progression-free survival was 4.5 months and the progression-free survival at 6 months was 43.5%. The median overall survival was 7.7 months, and the overall survival at 12 months was 26.7%. The progression-free survival at 6 months and the overall survival at 12 months for 21 patients with grade 4 glioma was 40.2 and 27.9%, respectively. The tumor objective response rate was 41.9% in all patients and 33.3% in patients with grade 4 glioma. No grade 3 or worse treatment-related adverse events were recorded during the treatment.Conclusion: Anlotinib alone or in combination with temozolomide showed encouraging efficacy and favorable tolerability in patients with recurrent high-grade glioma who had been heavily treated.

Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the atmosphere on the quantitative and qualitative analytical performance of the electronic nose. In light of this, this paper presents a quantitative and qualitative analysis strategy for gas mixture monitoring. This strategy adopts baseline manipulation of the raw sensor data to reduce drift interference, and then performs feature extraction on the multidimensional response signals of the MOS gas sensor array using principal component analysis (PCA). In order to improve gas mixture recognition accuracy, the whale optimization algorithm (WOA) is used to optimize the network structure of the long short-term memory (LSTM) model for turbulent gas mixture composition recognition. The least squares support vector machine (LSSVM) algorithm is adopted to implement turbulent gas mixture concentration prediction. This paper focuses on two aspects of hyper-parameter optimization for the development of an LSSVM with particle swarm optimization (PSO) and for improved training sample selection for the LSSVM which should subsequently increase the accuracy of concentration estimation. The effectiveness of the proposed strategy is evaluated with a dataset from a chemical sensor array exposed to turbulent gas mixtures. Experimental results revealed that the proposed strategy for turbulent gas mixtures has satisfactory outcomes for both qualitative gas composition recognition and quantitative gas concentration prediction.

ObjectivesPhosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC).ResultsThe CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).ConclusionsThe combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.

Background: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. Methods: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). Findings: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). Interpretation: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

Abstract Background Ependymal tumors are pathologically defined intrinsic neoplasms originating in the intracranial compartments or the spinal cord that affect both children and adults. The recently integrated classification of ependymomas based on both histological and molecular characteristics is capable of subgrouping patients with various prognoses. However, the application of histological and molecular markers in Chinese patients with ependymomas has rarely been reported. We aimed to demonstrate the significance of histological characteristics, the v-relavian reticuloendotheliosis viral oncogene homolog A (RELA) fusions and other molecular features in ependymal tumors. Methods We reviewed the histological characteristics of ependymal tumors using conventional pathological slides and investigate the RELA fusions and Cylclin D1 (CCND1) amplification by Fluorescence in situ hybridization (FISH) and trimethylation of histone 3 lysine 27 (H3K27me3) expression by immunohistochemistry (IHC) methods. SPSS software was used to analyze the data. Results We demonstrated that hypercellularity, atypia, microvascular proliferation, necrosis, mitosis, and an elevated Ki-67 index, were tightly associated with an advanced tumor grade. Tumor location, necrosis, mitosis and the Ki-67 index were related to the survival of the ependymomas, but Ki67 was the only independent prognostic factor. Additionally, RELA fusions, mostly presented in pediatric grade III intracranial ependymomas, indicated decreased survival times of patients, and closely related to the patients’ age, tumor grade, cellularity, cellular atypia, necrosis and Ki67 index in the intracranial ependymal tumors, whereas reduction of H3K27me3 predicted the worse prognosis in ependymal tumors. Conclusions Histological and molecular features facilitate tumor grading and prognostic predictions for ependymal tumors in Chinese patients.

The flammable and explosive property of hydrogen is the main danger in its safe use, storage and transportation. In this paper, a novel hydrogen monitoring system is designed based on the principle of semiconductor, catalytic combustion and heat-conducting gas sensors. Also, the gas sensor will inevitably fail due to the nature of gas sensitive materials in the long-time monitoring process. To ensure the accuracy and reliability of hydrogen concentration measurement, a novel fault diagnosis and reconfiguration strategy for hydrogen sensor array based on moving window principle component analysis and extreme learning machine (MWPCA-ELM) is proposed. Firstly, online multiple faults detection is carried out by using MWPCA. Once one or multiple faults are detected, the measured values of other fault-free sensors will be used to recover the faulty data in real-time by using ELM predictor according to the relevancy among the hydrogen sensors. Secondly, the hydrogen concentration is reconfigured seamlessly and accurately based on ELM under the condition of small calibration data sample. Finally, fault diagnosis is conducted by MWPCA feature extraction coupled with ELM multi-classifier. In order to illustrate the effectiveness and feasibility of the proposed fault diagnosis and reconfiguration strategy, a hydrogen concentration monitoring experimental system was established. The average relative error (ARE) of hydrogen concentration estimation is declined from 1.18% to 0.82% compared with the traditional regression methods. Particularly, the proposed fault reconfiguration model can recover the fault data even if the concentration is changed, and the accuracy of fault diagnosis is 100% within 250 samples.

Background and Aim: Guidelines recommend adjuvant treatment for patients with high-grade gliomas and low-grade gliomas with high risk of progression. In clinical practice, however, treatments may not conform to these suggested guidelines. In this study, we reviewed the treatments and outcomes in patients with gliomas at Sun Yat-Sen University Cancer Center (SYSUCC), China. Materials and Methods: Medical records and radiologic images of 1215 glioma patients who underwent surgery at the center from 2000 to 2017 were retrospectively reviewed, and their clinicopathological characteristics, treatment method, and overall survival (OS) were analyzed. The study was approved by the Ethics Committee of SYSUCC on February 20, 2019 (approval No. GZR2019-219). Results: A total of 1001 patients diagnosed with glioma (initially World Health Organization 2007 criteria, then 2016 criteria) were enrolled, including 90 patients with Grade I, 307 Grade II, 239 Grade III, and 365 Grade IV gliomas. A total of 331 of 604 patients with high-grade glioma (54.8%) and 159 of 397 with low-grade glioma (40.1%) received postsurgical radiotherapy, and 285 patients with high-grade tumors (47.1%) and 80 with low-grade tumors (20.2%) received adjuvant chemotherapy. The median OS was 17.5 months for Grade IV and 43.1 months for Grade III gliomas. The median OS of patients with low-grade glioma was not reached. The 5-year survival rates of patients with Grades I, II, III, and IV gliomas were 94.7%, 73.7%, 45.7%, and 18.6%, respectively. Multivariate analysis identified onset age, preoperative seizure, tumor location, pathological subtype, resection extent, and postsurgical treatment as independent predictors of OS in patients with high-grade gliomas. Patients with high-grade glioma who received postsurgical treatment had better survival than those without adjuvant therapy (Grade III: 52.6 vs. 20.3 months, P = 0.012; Grade IV: 22.6 vs. 12.1 months, P < 0.001). Among patients with diffuse low-grade gliomas, age, performance status, preoperative seizure, Ki-67 index, tumor subtype, and resection extent were associated with clinical outcomes. Conclusion: Glioma patients are not always treated according to guidelines. Although standard care may lead to favorable prognoses, individualized treatments may be more acceptable and result in better outcomes and should thus be considered in routine clinical practice.

Background and Aim: Approximately 60% of glioblastoma multiforme (GBM) patients possess an unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) gene, which confers a limited response to standard-of-care treatment with temozolomide (TMZ), resulting in a lower survival. Dianhydrogalactitol (VAL-083) is a novel bi-functional DNA-targeting agent that induces interstrand cross-links at N7-guanine, leading to DNA double-strand breaks and ultimately cell death. VAL-083 circumvents MGMT-mediated repair of the O6 guanine alkylator TMZ. A Phase 2 study has been initiated for VAL-083 in newly diagnosed MGMT unmethylated GBM. Subjects and Methods: The study has two parts: part 1 is a dose–escalation and induction format to enroll up to ten patients in which they received VAL-083 at 20, 30, or 40 mg/m2 per day for 3 days every 21 days concurrently with standard radiation treatment and VAL-083 for up to eight additional cycles. Part 2 comprises an expansion phase to enroll up to twenty additional patients. This study was performed with approval by the Institutional Review Board of Sun Yat-sen University Cancer Center (B2016-058-01) on January 13, 2017, and registered with the ClinicalTrials.gov (NCT03050736) on February 13, 2017. Results: After completion of dose escalation, VAL-083, 30 mg/m2 per day, in combination with radiation therapy, was generally safe and well tolerated. At the cutoff date, 23 patients had been enrolled, 14 of whom had been treated in the expansion phase. Consistent with prior studies, myelosuppression was the most common adverse event. Pharmacokinetic assessment indicated that the levels of VAL-083 were as high in the cerebrospinal fluid as in plasma, 2 h postinfusion. Of the 22 patients who had reached their four precycle magnetic resonance imaging assessments, 12 were assessed with disease progression, with a median progression-free survival of 9.9 (95% confidence interval 7.3–12.0) months for all the patients studied. Conclusion: These preliminary data support VAL-083 as a potentially valuable treatment option for newly diagnosed GBM.

BackgroundMedulloblastoma is the common pediatric malignant tumor with poor prognosis in cerebellum. However, MB is always with clinical heterogeneity. To provide patients with more clinically beneficial treatment strategies, there is a pressing need to develop a new prognostic prediction model as a supplement to the prediction outcomes of clinical judgment.Materials and MethodsFour datasets of mRNA expression and clinical data were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and functionally enriched among GSE50161, GSE74195, GSE86574. Then we used STRING and Cytoscape to constructed and analyze protein-protein interaction network (PPI) and hub genes. Univariate cox regression analysis was performed to identify overall survival-related hub genes in an unique dataset from GSE85217 as train cohort. Lasso Cox regression model was used to construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan–Meier curve, univariate and multivariate Cox regression analysis were used to assess the prognostic capacity of the twelve-gene signature. A unique dataset from GSE85217 was downloaded to further validate the results. Finally, we established the nomogram by using the gene signature and validated it with ROC curve. Gene set enrichment analysis (GSEA) was carried out to further investigate its potential molecular mechanism. Besides, the twelve genes expression at the mRNA and protein levels was validated using external database such as Oncomine, cBioportal and HPA, respectively.ResultsA twelve-gene signature comprising FOXM1, NEK2, CCT2, ACTL6A, EIF4A3, CCND2, ABL1, SYNCRIP, ITGB1, NRXN2, ENAH, and UMPS was established to predict overall survival of medulloblastoma. The ROC curve showed good performance in survival prediction in both the train cohort and the validation cohort. The twelve-gene signature could stratify patients into a high risk and low risk group which had significantly different survival. Univariate and multivariate Cox regression revealed that the twelve-gene signature was an independent prognostic factor in medulloblastoma. Nomogram, which included twelve-gene signatures, was established and showed some clinical benefit.ConclusionOur study identified a twelve-gene signature and established a prognostic nomogram that reliably predicts overall survival in medulloblastoma. The above results will help us to better analyze the pathogenesis and treatment of medulloblastoma in the future.

Summary: Glioblastoma multiforme (GBM) is among the most aggressive of human cancers. Although differentiation therapy has been proposed as a potential approach to treat GBM, the mechanisms of induced differentiation remain poorly defined. Here, we established an induced differentiation model of GBM using cAMP activators that specifically directed GBM differentiation into astroglia. Transcriptomic and proteomic analyses revealed that oxidative phosphorylation and mitochondrial biogenesis are involved in induced differentiation of GBM. Dibutyryl cyclic AMP (dbcAMP) reverses the Warburg effect, as evidenced by increased oxygen consumption and reduced lactate production. Mitochondrial biogenesis induced by activation of the CREB-PGC1α pathway triggers metabolic shift and differentiation. Blocking mitochondrial biogenesis using mdivi1 or by silencing PGC1α abrogates differentiation; conversely, overexpression of PGC1α elicits differentiation. In GBM xenograft models and patient-derived GBM samples, cAMP activators also induce tumor growth inhibition and differentiation. Our data show that mitochondrial biogenesis and metabolic switch to oxidative phosphorylation drive the differentiation of tumor cells. : Xing et al. show that the metabolic shift from glycolysis to oxidative phosphorylation drives differentiation of GBM cells into astrocytes by cAMP activation. Mechanistically, the cAMP-CREB-PGC1α signal mediates mitochondrial biogenesis, which leads to metabolic reprogramming, induced differentiation, and tumor growth inhibition. Keywords: glioblastoma, induced differentiation, Warburg effect, metabolic reprogramming, oxidative phosphorylation, glycolysis, mitochondrial biogenesis, cyclic adenosine monophosphate, cAMP, PPARγ coactivator-1α, PGC1α

The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy.

A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity of rolling bearings, a rolling bearing fault diagnosis method based on multiscale amplitude-aware permutation entropy (MAAPE) and random forest is proposed in this paper. The vibration signals of rolling bearings to be analyzed are decomposed into different coarse-grained time series by using the coarse-graining procedure in multiscale entropy, highlighting the fault dynamic characteristics of vibration signals at different scales. The fault features contained in the coarse-grained time series at different time scales are extracted by using amplitude-aware permutation entropy’s sensitive characteristics to signal amplitude and frequency changes to form fault feature vectors. The fault feature vector set is used to establish the random forest multi-classifier, and the fault type identification and fault severity analysis of rolling bearings is realized through random forest. In order to demonstrate the feasibility and effectiveness of the proposed method, experiments were fully conducted in this paper. The experimental results show that multiscale amplitude-aware permutation entropy can effectively extract fault features of rolling bearings from vibration signals, and the extracted feature vectors have high separability. Compared with other rolling bearing fault diagnosis methods, the proposed method not only has higher fault type identification accuracy, but also can analyze the fault severity of rolling bearings to some extent. The identification accuracy of four fault types is up to 96.0% and the fault recognition accuracy under different fault severity reached 92.8%.

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.

The water tree resistance of crosslinked polyethylene (XLPE) initiated by ultraviolet (UV) irradiation technique is investigated through a water blade electrode method, and the effects of the mechanism of UV irradiation crosslinking on inhibiting water tree growth are revealed with dynamic thermomechanical analysis (DMA). The accelerated water tree aging experiment shows that UV irradiation crosslinking inhibits the growth rate of water trees, and the water tree length and width is reduced with the increase of the crosslinking degree of XLPE. The DMA result demonstrates that the molecular activity of the amorphous phase in XLPE as represented by polyethylene β-relaxation is gradually intensified with the increase of the crosslinking reaction. Combined with the fatigue mechanism of water tree growth in semi-crystalline polymers, it is suggested that the UV irradiation crosslinking reaction can significantly improve the anti-water-tree performance of linear low-density polyethylene (LLDPE). The crosslinking bond in the amorphous phase of UV-photoinitiated crosslinking polyethylene can produce a large number of cross-connected polymer chains, by which the length of fiber is obviously increased, leading to an reduced force from the micro-water beads onto the crack tip and thus decreasing the rate of the material being destroyed by micro-water beads.

This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes.

As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.

Metal Oxide Semiconductor (MOS) gas sensor has been widely used in sensor systems for the advantages of fast response, high sensitivity, low cost, and so on. But, limited to the properties of materials, the phenomenon, such as aging, poisoning, and damage of the gas sensitive material will affect the measurement quality of MOS gas sensor array. To ensure the stability of the system, a health management decision strategy for the prognostics and health management (PHM) of a sensor system that is based on health reliability degree (HRD) and grey group decision-making (GGD) is proposed in this paper. The health management decision-making model is presented to choose the best health management strategy. Specially, GGD is utilized to provide health management suggestions for the sensor system. To evaluate the status of the sensor system, a joint HRD-GGD framework is declared as the health management decision-making. In this method, HRD of sensor system is obtained by fusing the output data of each sensor. The optimal decision-making recommendations for health management of the system is proposed by combining historical health reliability degree, maintenance probability, and overhaul rate. Experimental results on four different kinds of health levels demonstrate that the HRD-GGD method outperforms other methods in decision-making accuracy of sensor system. Particularly, the proposed HRD-GGD decision-making method achieves the best decision accuracy of 98.25%.

The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.