On-line diagnosis method of crack behavior abnormality in concrete dams based on fluctuation of sequential parameter estimates

Research on on-line diagnosis method of crack behavior abnormality in concrete dams can provide support for timely grasping abnormality state of the crack itself and achieving real-time monitoring of the dam safety.Considering that samples of crack effects in concrete dams increase actually over monitoring time,a superiority criterion for the on-line diagnosis is determined so as to detect the abnormality moments timely and reliably.By integrating the safety monitoring statistical model of crack effect variable with change point theory,a fluctuation method of regression coefficients is established for the on-line diagnosis.In addition,each abnormality moment is detected by the cumulative sum of regression model residuals.Results indicate that abnormality of crack behavior in concrete dams can be characterized by structural instability of crack monitoring model.And causes of crack behavior abnormality can be analyzed by the established method,which will play an important role in dam safety monitoring.Further,taking the crack in a concrete gravity-arch dam as an example,the scientific rationality and validity of the established on-line diagnosis method are confirmed.

Movement analysis in the diagnosis and management of Parkinson’s disease

Challenges in the diagnosis and treatment of Parkinson’s disease:Parkinson’s disease(PD)is an increasingly prevalent neurodegenerative disease,at first sight primarily characterized by motor symptoms,although non-motor symptoms also constitute a major part of the overall phenotype.Clinically,this disease cannot be diagnosed reliably until a large part of the vulnerable dopaminergic neurons has been irretrievably lost,and the disease progresses inexorably.New biological criteria for PD have been proposed recently and might eventually improve early diagnosis,but they require further validation,and their use will initially be restricted to a research environment(Darweesh et al.,2024).

Visualizing traumatic brain injury:ocular clues for diagnosis and assessment

Traumatic brain injury (TBI) is defined as damage to the brain resulting from an external sudden physical force or shock to the head.It is considered a silent public health epidemic causing significant death and disability globally.There were 64,000 TBI related deaths reported in the USA in 2020,with about US$76 billion in direct and indirect medical costs annually.

Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies

Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.

MicroRNAs as potential biomarkers for diagnosis of post-traumatic stress disorder

Post-traumatic stress disorder is a mental disorder caused by exposure to severe traumatic life events.Currently,there are no validated biomarkers or laboratory tests that can distinguish between trauma survivors with and without post-traumatic stress disorder.In addition,the heterogeneity of clinical presentations of post-traumatic stress disorder and the overlap of symptoms with other conditions can lead to misdiagnosis and inappropriate treatment.Evidence suggests that this condition is a multisystem disorder that affects many biological systems,raising the possibility that peripheral markers of disease may be used to diagnose post-traumatic stress disorder.We performed a PubMed search for microRNAs(miRNAs)in post-traumatic stress disorder(PTSD)that could serve as diagnostic biomarkers and found 18 original research articles on studies performed with human patients and published January 2012 to December 2023.These included four studies with whole blood,seven with peripheral blood mononuclear cells,four with plasma extracellular vesicles/exosomes,and one with serum exosomes.One of these studies had also used whole plasma.Two studies were excluded as they did not involve microRNA biomarkers.Most of the studies had collected samples from adult male Veterans who had returned from deployment and been exposed to combat,and only two were from recently traumatized adult subjects.In measuring miRNA expression levels,many of the studies had used microarray miRNA analysis,miRNA Seq analysis,or NanoString panels.Only six studies had used real time polymerase chain reaction assay to determine/validate miRNA expression in PTSD subjects compared to controls.The miRNAs that were found/validated in these studies may be considered as potential candidate biomarkers for PTSD and include miR-3130-5p in whole blood;miR-193a-5p,-7113-5p,-125a,-181c,and-671-5p in peripheral blood mononuclear cells;miR-10b-5p,-203a-3p,-4488,-502-3p,-874-3p,-5100,and-7641 in plasma extracellular vesicles/exosomes;and miR-18a-3p and-7-1-5p in blood plasma.Several important limitations identified in the studies need to be taken into account in future studies.Further studies are warranted with war veterans and recently traumatized children,adolescents,and adults having PTSD and use of animal models subjected to various stressors and the effects of suppressing or overexpressing specific microRNAs.

Autism spectrum disorder:difficulties in diagnosis and microRNA biomarkers

We performed a PubMed search for microRNAs in autism spectrum disorder that could serve as diagnostic biomarkers in patients and selected 17 articles published from January 2008 to December 2023,of which 4 studies were performed with whole blood,4 with blood plasma,5 with blood serum,1 with serum neural cell adhesion molecule L1-captured extracellular vesicles,1 with blood cells,and 2 with peripheral blood mononuclear cells.Most of the studies involved children and the study cohorts were largely males.Many of the studies had performed microRNA sequencing or quantitative polymerase chain reaction assays to measure microRNA expression.Only five studies had used real-time polymerase chain reaction assay to validate microRNA expression in autism spectrum disorder subjects compared to controls.The microRNAs that were validated in these studies may be considered as potential candidate biomarkers for autism spectrum disorder and include miR-500a-5p,-197-5p,-424-5p,-664a-3p,-365a-3p,-619-5p,-664a-3p,-3135a,-328-3p,and-500a-5p in blood plasma and miR-151a-3p,-181b-5p,-320a,-328,-433,-489,-572,-663a,-101-3p,-106b-5p,-19b-3p,-195-5p,and-130a-3p in blood serum of children,and miR-15b-5p and-6126 in whole blood of adults.Several important limitations were identified in the studies reviewed,and need to be taken into account in future studies.Further studies are warranted with children and adults having different levels of autism spectrum disorder severity and consideration should be given to using animal models of autism spectrum disorder to investigate the effects of suppressing or overexpressing specific microRNAs as a novel therapy.

A new approach for on-line open-circuit fault diagnosis of inverters based on current trajectory

Security and reliability of inverter are an indispensable part in power electronic system. Faults of inverter are usually caused by switch elements’ operating fault. Taking the inverter with hysteresis current control as the research object, a universal open-circuit fault location method which can be applied to multiple control strategies is proposed in the paper. If the switch open-circuit fault happens in inverter, the output phase current will inevitably change, which can be used as a characteristic for diagnosis, combined with the comparison of phase-current direction before and after the fault occurrence, to diagnose and locate the open-circuit fault in a half cycle. Moreover, this method requires neither system control signals nor sensor. The validity, reliability and limitation of the fault location method in the paper are verified and analyzed through dSPACE-based experiment platform.

On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model

An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.

Particle swarm optimization based RVM classifier for non-linear circuit fault diagnosis

A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.

On-Line Broken-Bar Fault Diagnosis System of Induction Motor

Induction motor faults including mechanical and electrical faults are reviewed.The fault diagnosis methods are summarized.To analyze the influence of stator current,torque,speed and rotor current on faulted bars,a time-stepping transient finite element(FE)model of induction motor with bars faulted is created in this paper.With wavelet package analysis method and FFT method, the simulation result of finite element is analyzed.Based on the simulation analysis,the on-line fault diagnosis system of induction motor with bars faulted is developed.With the speed of broken bars motor changed from 1 478 r/min to 1 445 r/min,the FFT power spectra and the wavelet package decoupling factors are given.The comparison result shows that the on-line diagnosis system can detect broken-bar fault efficiently.

On-Line Fault Diagnosis and Fault-Tolerant Operation of Modular Multilevel Converters –A Comprehensive Review

Modular multilevel converters(MMCs)have been one of the most broadly used multilevel converter topologies in industrial applications,particularly in medium-voltage motor drives and high-voltage dc power conversion systems.However,due to the utilization of large amount of semiconductor devices,the reliability of MMCs becomes one of the severe challenges constraining their further development and applications.In this paper,common electrical faults of the MMC have been summarized and analyzed,including open-circuit switching faults,short-circuit switching faults,dc-bus short-circuit faults,and single line-to-ground faults on the ac side.A thorough and comprehensive review of the existing online fault diagnostic methods has been conducted.In addition,fault-tolerant operation strategies for such various fault scenarios in MMCs have been presented.All the fault diagnosis and fault-tolerant operation strategies are comparatively evaluated,which aims to provide a state-of-the-art reference on the MMC reliability for future research and industrial applications.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

On-line temperature monitoring and process diagnosis of plasma sprayed coatings

On-line temperature monitoring of plasma sprayed coating is presented, which is based on IR pyrometery combined to robot trajectories. Temperature fields of the substrate before spraying and the deposited coating when the damage happens are taken to investigate the temperature fluctuation information. Experimental results demonstrate that coating damage always occurs in the temperature transition area of the substrate from the higher to the lower, as well as the higher temperature area. The temperature difference between the peak and the mean of the relevant regions is beyond 30 - 50℃ or even higher. This case provides the omen of coating damage and the focusing scopes for the process control of coating temperature in plasma spraying

On-line Dynamic Model Correction Based Fault Diagnosis in Chemical Processes

<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, and the parameters are updated via on-line correction. As it can predict the trend of process and determine the existence of malfunctions simultaneously, this method does not need to design problem-specific observer to estimate unmeasured state variables. Application of the proposed method is presented on one water tank and one aromatization reactor, and the results are compared with those from the traditional method.

Fault Diagnosis for Non-linear System Based On Adaptive Fuzzy System

Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.

APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

Based on radial basis function (RBF) neural networks, the healthy working model of each sub system of robot in FMS is established. A new approach to fault on line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.

Stage at diagnosis of colorectal cancer through diagnostic route:Who should be screened?

BACKGROUND Colorectal cancer(CRC)is a global health concern,with advanced-stage diagnoses contributing to poor prognoses.The efficacy of CRC screening has been well-established;nevertheless,a significant proportion of patients remain unscreened,with>70%of cases diagnosed outside screening.Although identifying specific subgroups for whom CRC screening should be particularly recommended is crucial owing to limited resources,the association between the diagnostic routes and identification of these subgroups has been less appreciated.In the Japanese cancer registry,the diagnostic routes for groups discovered outside of screening are primarily categorized into those with comorbidities found during hospital visits and those with CRC-related symptoms.AIM To clarify the stage at CRC diagnosis based on diagnostic routes.METHODS We conducted a retrospective observational study using a cancer registry of patients with CRC between January 2016 and December 2019 at two hospitals.The diagnostic routes were primarily classified into three groups:Cancer screening,follow-up,and symptomatic.The early-stage was defined as Stages 0 or I.Multivariate and univariate logistic regressions were exploited to determine the odds of early-stage diagnosis in the symptomatic and cancer screening groups,referencing the follow-up group.The adjusted covariates were age,sex,and tumor location.RESULTS Of the 2083 patients,715(34.4%),1064(51.1%),and 304(14.6%)belonged to the follow-up,symptomatic,and cancer screening groups,respectively.Among the 2083 patients,CRCs diagnosed at an early stage were 57.3%(410 of 715),23.9%(254 of 1064),and 59.5%(181 of 304)in the follow-up,symptomatic,and cancer screening groups,respectively.The symptomatic group exhibited a lower likelihood of early-stage diagnosis than the follow-up group[P<0.001,adjusted odds ratio(aOR),0.23;95%confidence interval(95%CI):0.19-0.29].The likelihood of diagnosis at an early stage was similar between the follow-up and cancer screening groups(P=0.493,aOR for early-stage diagnosis in the cancer screening group vs follow-up group=1.11;95%CI=0.82-1.49).CONCLUSION CRCs detected during hospital visits for comorbidities were diagnosed earlier,similar to cancer screening.CRC screening should be recommended,particularly for patients without periodical hospital visits for comorbidities.

Value of procalcitonin and presepsin in the diagnosis and severity stratification of sepsis and septic shock

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection.[1,2]Septic shock,the most severe form of sepsis,is characterized by circulatory and cellular/metabolic abnormalities,and can increase mortality to>40%.[1-3]Early recognition and risk stratification of septic shock are crucial but challenging because of the heterogeneity of its presentation and progression.

Handheld bedside ultrasound in the diagnosis of myocarditis

Myocarditis is a disease process that every emergency physician fears missing.Its severity can be mild to life-threatening,and many cases are likely undetected because they are subclinical with nonspecifi c signs.[1]Subtle cardiac signs may be overshadowed by systemic symptoms of the underlying infectious process.Fever,myalgias,lethargy,symptoms commonly associated with viral syndrome,can mask the life-threatening myocarditis that may be present.In fact,in the United States Myocarditis Treatment Trial,almost 90%of patients reported symptoms consistent with a viral prodrome.[2]Ammirati et al[3]reported that 27%of patients with myocarditis had either reduced left ventricular ejection fraction,ventricular arrhythmias,or low cardiac output.Here,we present a case report,in which handheld point-of-care ultrasound was utilized at the bedside to aid in the critical diagnosis of myocarditis.With the additional information provided through this imaging modality,this patient was able to be transferred to the appropriate tertiary care facility in an expeditious manner and receive possible defi nitive treatment.

Marker Ki-67 is a potential biomarker for the diagnosis and prognosis of prostate cancer based on two cohorts

BACKGROUND Prostate cancer(PCa)is a widespread malignancy,predominantly affecting elderly males,and current methods for diagnosis and treatment of this disease continue to fall short.The marker Ki-67(MKI67)has been previously demonstrated to correlate with the proliferation and metastasis of various cancer cells,including those of PCa.Hence,verifying the association between MKI67 and the diagnosis and prognosis of PCa,using bioinformatics databases and clinical data analysis,carries significant clinical implications.AIM To explore the diagnostic and prognostic efficacy of antigens identified by MKI67 expression in PCa.METHODS For cohort 1,the efficacy of MKI67 diagnosis was evaluated using data from The Cancer Genome Atlas(TCGA)and Genotype-Tissue Expression(GTEx)databases.For cohort 2,the diagnostic and prognostic power of MKI67 expression was further validated using data from 271 patients with clinical PCa.RESULTS In cohort 1,MKI67 expression was correlated with prostate-specific antigen(PSA),Gleason Score,T stage,and N stage.The receiver operating characteristic(ROC)curve showed a strong diagnostic ability,and the Kaplan-Meier method demonstrated that MKI67 expression was negatively associated with the progression-free interval(PFI).The time-ROC curve displayed a weak prognostic capability for MKI67 expression in PCa.In cohort 2,MKI67 expression was significantly related to the Gleason Score,T stage,and N stage;however,it was negatively associated with the PFI.The time-ROC curve revealed the stronger prognostic capability of MKI67 in patients with PCa.Multivariate COX regression analysis was performed to select risk factors,including PSA level,N stage,and MKI67 expression.A nomogram was established to predict the 3-year PFI.CONCLUSION MKI67 expression was positively associated with the Gleason Score,T stage,and N stage and showed a strong diagnostic and prognostic ability in PCa.