Diagnostic aids for detection of oral precancerous conditions

Oral cancer has a tendency to be detected at late stage which is detrimental to the patients because of its high mortality and morbidity rates. Early detection of oral cancer is therefore important to reduce the burden of this devastating disease. In this review article, the most common oral precancerous lesions are discussed and the importance of early diagnosis is emphasized. In addition, the most common non-invasive oral cancer devices that can aid the general practitioners in early diagnosis are also discussed.

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same time.In this paper,a non-negative matrix factorization(NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix,aiming to o er a fast multi-tasking solution.The short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration signal.Then,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity indexes.Subsequently,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks.Finally,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic results.The Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.

Gastric epithelial histology and precancerous conditions

The most common histological type of gastric cancer(GC)is gastric adenocarcinoma arising from the gastric epithelium.Less common variants include mesenchymal,lymphoproliferative and neuroendocrine neoplasms.The Lauren scheme classifies GC into intestinal type,diffuse type and mixed type.The WHO classification includes papillary,tubular,mucinous,poorly cohesive and mixed GC.Chronic atrophic gastritis(CAG)and intestinal metaplasia are recommended as common precancerous conditions.No definite precancerous condition of diffuse/poorly/undifferentiated type is recommended.Chronic superficial inflammation and hyperplasia of foveolar cells may be the focus.Presently,the management of early GC and precancerous conditions mainly relies on endoscopy including diagnosis,treatment and surveillance.Management of precancerous conditions promotes the early detection and treatment of early GC,and even prevent the occurrence of GC.In the review,precancerous conditions including CAG,metaplasia,foveolar hyperplasia and gastric hyperplastic polyps derived from the gastric epithelium have been concluded,based on the overview of gastric epithelial histological organization and its renewal.

Genetic predisposition to Helicobacter pylori-induced gastric precancerous conditions

Gastric cancer is the most common malignancy of the gastrointestinal tract in East Asian populations and the second most frequent cause of cancer-related mortality in the world. While previous studies have investigated the genetic factors involved in gastric carcinogenesis, there still exist relatively few studies that have investigated the genetic traits associated with the risk of gastric precancerous conditions. In this paper we will review the biology and genetic polymorphisms involved in the genesis of gastric precancerous conditions reported to date and discuss the future prospects of this field of study. The associations of gastric precancerous conditions with polymorphisms in the cytotoxin-associated gene A-related genes (e.g. PTPN11 G/A at intron 3, rs2301756), those in the genes involved in host immunity against Helico-bacter pylori (H. pylori) infection (e.g.TLR4 +3725G/C, rs11536889) or polymorphisms of the genes essential for the development/ differentiation of the gastric epithelial cells (e.g. RUNX3 T/A polymorphism at intron 3, rs760805) have been reported to date. Genetic epide-miological studies of the associations between H. pylori-induced gastric precancerous conditions and other gene polymorphisms in these pathways as well as polymor-phisms of the genes involved in other pathways like oxidative DNA damage repair pathways would provide useful evidence for the individualized prevention of these H. pylori-induced gastric precancerous conditions.

Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.

Possibility of non-invasive diagnosis of gastric mucosal precancerous changes

AIM: To assess the possibility of non-invasive screening of atrophic chronic gastritis for preventing further development of gastric cancer.METHODS: One hundred and seventy-eight consecutive Helicobacter pylori ( H pylori)-positive dyspeptic patients after detection of serum levels of pepsinogen-1 (PG-1) and gastrin-17 (G-17) by enzyme immunoassay were proposed for endoscopy and histology. The serologic and morphologic results were compared with estimating the sensitivity, specificity and prognostic values of the tests.RESULTS: There was statistically significant reverse dependence between the grade of stomach mucosal antral or corpus atrophy and the proper decreasing of serum G17 or PG1 levels. The serologic method was quite sensitive in the diagnosis of non-atrophic and severe antral and corpus gastritis. Also, it was characterized by the high positive and negative prognostic values.CONCLUSION: Detection of serum G-17 and PG1 levels can be offered as the screening tool for atrophic gastritis. The positive serologic results require further chromoendoscopy with mucosal biopsy, for revealing probable progressing of atrophic process with development of intestinal metaplasia, dysplasia or gastric cancer.

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

Determination of an Appropriate Endoscopic Monitoring Interval for Patients with Gastric Precancerous Conditions in China

Objective Gastric precancerous conditions such as atrophic gastritis(AG)and intestinal metaplasia(IM)are considered independent risk factors for gastric cancer(GC).The suitable endoscopic monitoring interval is unclear when we attempt to prevent GC development.This study investigated the appropriate monitoring interval for AG/IM patients.Methods Totally,957 AG/IM patients who satisfied the criteria for evaluation between 2010 and 2020 were included in the study.Univariate and multivariate analyses were used to determine the risk factors for progression to high-grade intraepithelial neoplasia(HGIN)/GC in AG/IM patients,and to determine an appropriate endoscopic monitoring scheme.Results During follow-up,28 AG/IM patients developed gastric neoplasia lesions including gastric low-grade intraepithelial neoplasia(LGIN)(0.7%),HGIN(0.9%),and GC(1.3%).Multivariate analysis identified H.pylori infection(P=0.022)and extensive AG/IM lesions(P=0.002)as risk factors for HGIN/GC progression(P=0.025).Conclusion In our study,HGIN/GC was present in 2.2%of AG/IM patients.In AG/IM patients with extensive lesions,a 1–2-year surveillance interval is recommended for early detection of HIGN/GC in AG/IM patients with extensive lesions.

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.

The diagnosis for the tropical Pacific Ocean conditions in 1990

In this paper, on the basis of the observational hydrographic data obtained from the eighth cruise of PRC-USA bilateral air-sea interaction program, and combined with the sea surface temperature (SST) charts provided by NOAA, the data obtained from moored thermistor chains supplied by L. J. Mangum and sea level data provided by K. Wyrtki, the ocean conditions since October, 1989 in the western tropical Pacific are exposed, which indicate that 1990 is a year with weak El Nino event similar to the 1980 El Nino event, and the North Equatorial Countercurrent (NECC) has made a good contribution to the propagation of warm water from the Western to the Central and Eastern Pacific, a characteristic similar to that of the 1976 El Nino event. The 1990 weak El Nino event will soon fall into decay.

Role of Plain Abdominal X-Ray in the Differential Diagnosis of Common Acute Abdominal Conditions in Children at Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State, Nigeria

Background: The plain abdominal x-ray is one of the commonly requested investigations in the children emergency room, paediatric surgical ward and neonatal wards. The short interval required to carry out this investigative procedure and obtain results makes it the first imaging modality used to unravel the different causes of acute abdominal conditions in children. The safety of abdominal x-ray in children makes it attractive for use in paediatric surgical practice as part of routine work-up for undifferentiated acute abdominal conditions and also to diagnose specific causes of acute abdomen in children. Setting: Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State. Objectives: Evaluation of the role of plain abdominal x-ray in diagnosing common acute abdominal conditions in children. Materials and method: Patients admitted to the children emergency room, paediatric surgical wards, children’s ward and neonatal ward who had plain abdominal x-ray as part of their diagnostic work-up were included in the study. They were studied prospectively between March 2011 and April 2021. Results: Three Hundred and Ninety-nine patients who had plain abdominal x-rays as part of their diagnostic work-up were studied. Males were 240 while females were 159, a male to female ratio of 1.5:1. The patients were aged between 1 day to 16 years. Differential diagnoses made with plain abdominal x-ray were intestinal obstruction in 298, perforated viscus 69 patients, intra-abdominal masses 13 patients and location of intra-abdominal foreign body 14. Intestinal obstruction cases in which plain abdominal x-ray played a role in their diagnosis and management included the following: intussusception 66, neonatal sepsis 60, malrotation 48, intestinal atresia 42, anorectal malformation 32, hirschsprung’s disease in 30 cases, pyloric stenosis 24, obstructed hernia 22, post-operative adhesions 16 and intestinal helminthiasis 12. Perforated viscus accounted for 69 indications. Out of these indications, perforated gut in intussusception 19, perforated typhoid ileitis was responsible in 13 cases, gut perforation in blunt abdominal trauma 8, perforation in strangulated hernia 11 cases, perforated gut in malrotation 7, ceacal perforation in hirschsprugs disease 6 and colonic perforation in necrotizing enterocolitis 5 cases. Conclusion: Plain abdominal x-ray remains a role to play in the differential diagnosis and management of common paediatric acute abdominal conditions.

Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Classification research of TCM pulse conditions based on multi-label voice analysis

Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results: The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion: Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Condition Monitoring and Fault Diagnosis Based on Rough Set Theory

In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis.

Fuzzy Condition Pattern Recognition and Fault Diagnosis for Spacecraft

This paper introduces concepts of symptom vector and fuzzy symptom vector forspacecraft condition recognition and fault diagnosis,defines an operator and suggests a fuzzy pat-tern recognition method of fault diagnosis for spacecraft.This method is verified by examples andresults are checked from an expert system.

Analysis of Dynamic Condition Diagnosis of One Rare Regional Rainstorm

[Objective] The aim was to discuss dynamic conditions for one rare regional rainstorm. [Method] By using conventional material, ground encryption automatic station materials, wind profiling radar data and Doppler radar data, strong precipitation and regional large rainstorm in Lingxi area on August 3, 2010, were expounded principal of heavy weather analysis. [Result] The precipitation process was the result of different scales and different height systems influenced by the southwest airflow in the edge of subtropical high, weak cold air penetrating southward before westerly trough and the easterly in the higher layer (10 km above); the instability of atmosphere structure was the premise of strong precipitation. Ground convergent, east storm with senior northwestern current interaction triggered the release of unstable energy; the southwest airstream in the edge of subtropical high provided water vapor supply. The duration of the precipitation was short and the intensity was large. Precipitation moved to certain direction, having typical mesoscale strong convection system. Strong precipitation fell in the same place as the convergence area of wind field. The place having next strong precipitation can be predicted based on the wind field convergence position along with the movement of time. Outline radar data and Doppler radar data contour line products can more accurately represent atmospheric vertical wind field structure. [Conclusion] The study provided certain references for the report of rainstorm.

Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM

The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.

An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP （ Digital Signal Processor） and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration, etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.

CHARACTERISTICS AND THEIR CORRECT SELECTION IN THE FAULTS DIAGNOSIS OF HYDRAULIC

The condition characteristics of hydraulic systems reflect running condition for the hydraulic equipment directly. It is the key for condition monitoring and early fault diagnosis to select characteristics reasonably. In this paper, the types, properties of characteristics in hydraulic equipment are analysed, and some considerations in their selection are presented.

Fault Diagnosis of Mechanical Equipments Through Spectrometric oil Analysis for Worn Off Metallic Elements

In the fault prediction of mechanical equipments through spectromectric oil analysis for worn off debris, a method for the determination of the limiting value of wear is proposed and discussed. In order to diagnose the impending failure and to predict the fault modes and locate the fault spots, a comprehensive approach is studied and outlined on the basis of methods of discriminative analysis and fuzzy logic. A fault diagnosis expert system OAFDS developed by the authors for the nonitoring of working conditions of the ND5 locomotive diesel engine Nd5 is briefly introduced.