Proposed Monitoring Method Research Articles

Automated monitoring of tooth wear progression using AI on intraoral scans

ObjectivesThis study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol. MethodsEight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown. ResultsThe automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals. ConclusionsThe proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs. Clinical significanceGeneral practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

Experimental study on test of wheel-rail impact based on iron plate strain of fastener system

The wheel-rail impact load can reach up to 3–4 times the normal wheel load, with two peaks including a high-frequency impact force (P1) and a mid-frequency or quasi-static impact force (P2). To effectively monitor the wheel-rail impact, this study utilizes strain gauges adhered to bottom of iron plates of fastener to measure rail-seat force, and conducts indoor static load and wheel-drop tests. A finite element model is established for verification and analysis. By analyzing the transmission law of the wheel-rail impact, a monitoring method through rail-seat force is proposed. The results reveal that the strain change of the iron plate in the static load test remains stable and exhibits a linear relationship with rail-seat force. Through a full-scale wheel-drop test, it demonstrates that rail-seat force can effectively monitor P2 force of wheel-rail impact. Compared to traditional method of directly adhering strain gauges to rail, the method of adhering strain gauges to iron plate enabled wheel-rail impact invalid test sections measurement. The peak value of wheel-rail impact P2 can be deduced from peak value of rail-seat force under different impact location, impact levels, and elastic pad stiffness. The proposed monitoring method of based on fastener iron plate strain offers a more efficient alternative for identifying wheel defects and rail defects.

Cellular network measurements can unravel spatiotemporal properties of bird movement to enhance basic and applied knowledge globally

Abstract A major problem in studying bird movement in many countries is data scarcity, precluding information about the spatial and temporal properties of avian distribution and dynamics as well as their consequences for human lives. We address this problem by proposing an innovative approach based on the relation between counts of signal attenuation of wireless communication to the presence of birds across or near wireless links of cellular backhaul networks. Wireless point-to-point communication links, on either ground level or earth-satellite links, cover the globe. We statistically relate between signal attenuation in terrestrial Commercial Microwave Links (CMLs) and bird migration. Because modern communication systems measure and often log signal levels routinely, we propose using existing signal level measurements of cellular and other wireless communication systems around the world as sensors for monitoring of bird movement. Using actual measurements from operational CMLs, we show that the daily cycle of signal attenuation during bird migration periods matched that of waterbird migration traffic rate recorded by nearby bird radar. This demonstrates the potential of the proposed method for opportunistic bird movement monitoring by CMLs across the globe, with no additional hardware installation, maintenance or communication costs.

Determining the extent and frequency of on-site monitoring: a bayesian risk-based approach

BackgroundOn-site monitoring is a crucial component of quality control in clinical trials. However, many cast doubt on its cost-effectiveness due to various issues, such as a lack of monitoring focus that could assist in prioritizing limited resources during a site visit. Consequently, an increasing number of trial sponsors are implementing a hybrid monitoring strategy that combines on-site monitoring with centralised monitoring. One of the primary objectives of centralised monitoring, as stated in the clinical trial guidelines, is to guide and adjust the extent and frequency of on-site monitoring. Quality tolerance limits (QTLs) introduced in ICH E6(R2) and thresholds proposed by TransCelerate Biopharma are two existing approaches for achieving this objective at the trial- and site-levels, respectively. The funnel plot, as another threshold-based site-level method, overcomes the limitation of TransCelerate’s method by adjusting thresholds flexibly based on site sizes. Nonetheless, both methods do not transparently explain the reason for choosing the thresholds that they used or whether their choices are optimal in any certain sense. Additionally, related Bayesian monitoring methods are also lacking.MethodsWe propose a simple, transparent, and user-friendly Bayesian-based risk boundary for determining the extent and frequency of on-site monitoring both at the trial- and site-levels. We developed a four-step approach, including: 1) establishing risk levels for key risk indicators (KRIs) along with their corresponding monitoring actions and estimates; 2) calculating the optimal risk boundaries; 3) comparing the outcomes of KRIs against the optimal risk boundaries; and 4) providing recommendations based on the comparison results. Our method can be used to identify the optimal risk boundaries within an established risk level range and is applicable to continuous, discrete, and time-to-event endpoints.ResultsWe evaluate the performance of the proposed risk boundaries via simulations that mimic various realistic clinical trial scenarios. The performance of the proposed risk boundaries is compared against the funnel plot using real clinical trial data. The results demonstrate the applicability and flexibility of the proposed method for clinical trial monitoring. Moreover, we identify key factors that affect the optimality and performance of the proposed risk boundaries, respectively.ConclusionGiven the aforementioned advantages of the proposed risk boundaries, we expect that they will benefit the clinical trial community at large, in particular in the realm of risk-based monitoring.

Monitoring of early curing stage of cemented soil using polymer optical fiber sensors and microscopic observation

Deep cement mixing (DCM) piles are widely utilized for reinforcing soft clay foundations, particularly in coastal regions where soil stability is critical. Monitoring the quality and early-stage behavior of cemented soil is essential to ensure the effectiveness of DCM pile projects in meeting design requirements. Innovative methods for on-site monitoring are necessary to enhance the reliability and efficiency of these reinforcement techniques. In this study, a novel approach is proposed utilizing polymer optical fiber (POF) sensors based on physical optical sensing principles to monitor the initial hydration degree and overall quality of cemented soil during the early curing stage. The proposed method relies on the principle of physical optical sensing, where POF sensors are employed to measure changes in reflected light intensity (LI) and temperature in cemented soil. Two crucial variables, namely the initial water content and the amount of cement, are considered in analyzing their impact on the LI and temperature changes over time. Unconfined compressive strength (UCS) tests and scanning electron microscopy (SEM) analysis are conducted on cemented soil samples with varying water-cement ratios to investigate their mechanical properties and microstructure evolution. The results of the UCS tests indicate that higher initial water content prolongs the initial hydration reaction time required for cemented soil with different cement contents. Analysis of LI curves reveals a rapidly rising trend as the hydration reaction progresses, particularly evident in samples with higher initial water content. SEM analysis further demonstrates that a stable POF LI corresponds to the completion of the hydration process, with hydrated gel formation resulting in a more compact microstructure and smaller voids. The findings highlight the significant influence of cement quantity and initial water content on the mechanical strength and microstructure of cemented soil. By analyzing changes in reflected LI and temperature, the proposed monitoring method provides valuable insights into the early-stage behavior and quality of cemented soil during the curing process. This innovative use of POF sensors for on-site monitoring offers a novel approach to evaluating cemented soil in DCM pile projects.

Monitoring Equipment Malfunctions in Composite Material Machining: Acoustic Emission-Based Approach for Abrasive Waterjet Cutting

This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process.

Monitoring high-carbon industry enterprise emission in carbon market: A multi-trusted approach using externally available big data

Carbon markets are widely recognized as effective strategies for regulating carbon emissions from the enterprises of the high-carbon industry. Accurate monitoring of emissions in these enterprises is crucial for ensuring fair and trusted carbon trading. However, traditional monitoring methods have encountered various challenges including poor accuracy, low frequency, significant hysteresis and weak credibility. Addressing these concerns, this paper introduces a novel, multi-trusted, high-frequency monitoring approach for the measurement of both direct and indirect carbon emissions of enterprises, utilizing externally available multi-source big data, vertical federated long short-term memory network with a self-attention mechanism (VF-LSTMSA) and additive homomorphic encryption. The efficacy of this model is thoroughly assessed via case studies on six hourly datasets from the power and steel sectors. Our principal findings include: (1) In the current scenario, the proposed VF-LSTMSA model demonstrates superior federated efficiency, improving with the inclusion of more federated participants. (2) The VF-LSTMSA model surpasses other prevalent AI models, achieving a Mean Absolute Percentage Error (MAPE) of less than 9% across power and steel datasets. (3) The advanced high-frequency carbon emission monitoring method exhibits errors of +0.28%, +1.29%, and −0.41% in the current scenario monitoring task, suggesting that the model effectively leverages multi-source externally available big data to precisely monitor the target enterprise's carbon emissions while safeguarding multi-party data security. These findings suggest that the proposed monitoring method can accurately track corporate hourly carbon emissions, significantly improving the carbon trading and verification processes for enterprises

Transfer learning-based quality monitoring of laser powder bed fusion across materials

Laser powder bed fusion (LPBF) is a promising additive manufacturing (AM) technique that has gained rapid growth. Despite the unique advantages such as producing complex-shaped metal parts, LPBF has been still slowed by the poor process repeatability and part consistency. In-situ monitoring methods combining sensing sensors and machine leaning (ML) tools have attracted much attention for defect detection and quality identification. However, the current ML algorithms usually rely on the availability of massive labeled datasets, and the trained model for monitoring a certain powder material cannot be directly applied to monitoring another material with different properties. Moreover, for a new material, it is time-consuming and expensive to collect the sufficient labeled data for building the quality monitoring model from scratch. To this end, motivated by transfer learning (TL), we propose a TL-based quality monitoring approach across two different materials in the LPBF process. Three different sensing datasets of layer-wise images of the solidified layer, photodiode signals, and acoustic emission signals are separately transferred from the 316L stainless steel to the TC4 titanium alloy. A convolutional neural network (CNN)-based quality monitoring model is first built by the sufficient source datasets from 316L stainless steel material. Then, the pre-trained CNN model is fine-tuned with a small amount of the target datasets for quality monitoring on TC4 titanium alloy material. Experimental results demonstrate the effectiveness of the proposed method for quality monitoring from one material to another with limited target datasets, with a classification accuracy above 92 % just with 5 % of target datasets on all three transferred datasets.

L-PBF AM process failures causal chain: an FMEA-based monitoring approach for process control

ABSTRACT The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring.

Milling Tool Wear Monitoring via the Multichannel Cutting Force Coefficients

Tool wear monitoring (TWM) is of great importance for improving the machining quality and the efficiency of the milling process. Extracting a discriminative tool wear feature is the key to TWM. Cutting force coefficients, which reflect the tool–chip and tool–material contact form, are good indicators of tool wear condition. However, in the existing studies, only the tangential and radial cutting force coefficients are adopted to monitor tool wear. The axial coefficients extracted from the axial cutting force are neglected. Preliminary experiments have shown that, although the axial cutting force has a small amplitude, the axial cutting force coefficients are very discriminative regarding the tool wear condition. Fusing the axial coefficients and the traditional tangential and radial coefficients can improve the monitoring accuracy. Based on such a consideration, this study proposes a milling tool wear monitoring method in which the multichannel cutting force coefficients, viz., the tangential, radial, and axial cutting force coefficients, are fused to indicate the tool wear. A long short-term memory (LSTM) network is adopted to sequentially estimate the progressive tool wear value from the multichannel cutting force coefficients. The effectiveness of the proposed monitoring method is examined using the PHM 2010 data. The results show that the proposed method outperforms the traditional method. With the fusion of the multichannel coefficients, the monitoring accuracy improves by 2.74–6.35%.

Mechanical behavior monitoring of composite hybrid bonded‐riveted joints using high‐stability MXene sensors

AbstractComposite adhesive rivet hybrid connection structure has been more and more recognized and applied in aerospace, automobile, and other industries due to its high strength, corrosion resistance, and good stability. However, due to the particularity of the composite structure and the complex nonlinear coupling factors existing in the bearing process of the adhesive rivet hybrid connection structure, it is difficult to carry out structural health monitoring of the composite adhesive rivet hybrid connection structure. In this paper, a new fabrication technique is proposed to fabricate MXene thin film sensors with excellent performance. The MXene sensor can be tailored to the required size and arrangement to monitor the hybrid connection structure. The multi‐stage loading‐unloading test verifies the feasibility of using the MXene sensor to monitor the hybrid connection structure. The monitoring curves of the MXene sensor for composite adhesive rivet mixed connection structure specimens under low‐speed impact test and vibration fatigue test were obtained, and the sensing mechanism of the MXene sensor was explained. The proposed monitoring method based on the MXene sensor is suitable for the health monitoring of adhesive rivet hybrid connection structures under tensile‐compression, impact, and vibration conditions.Highlights MXene sensors can be integrated into composite hybrid bonded‐riveted joints without defects. MXene sensors combined with flexible circuits can form a high‐stability monitoring array. Monitor the mechanical behavior of bonded and riveted areas by the resistance change rate of MXene sensors. Real‐time monitor the micro damage and acceleration changes under the vibration environments.

Magnetic Resonance Imaging Monitoring of Thermal Lesions Produced by Focused Ultrasound

Abstract Background: The main goal of the study was to find the magnetic resonance imaging (MRI) parameters that optimize contrast between tissue and thermal lesions produced by focused ultrasound (FUS) using T1-weighted (T1-W) and T2-weighted (T2-W) fast spin echo (FSE) sequences. Methods: FUS sonications were performed in ex vivo porcine tissue using a single-element FUS transducer of 2.6 MHz in 1.5 and 3 T MRI scanners. The difference in relaxation times as well as the impact of critical MRI parameters on the resultant contrast-to-noise ratio (CNR) between coagulated and normal tissues were assessed. Discrete and overlapping lesions were inflicted in tissue with simultaneous acquisition of T2-W FSE images. Results: FUS lesions are characterized by lower relaxation times than intact porcine tissue. CNR values above 80 were sufficient for proper lesion visualization. For T1-W imaging, repetition time values close to 1500 ms were considered optimum for obtaining sufficiently high CNR at the minimum time cost. Echo time values close to 50 ms offered the maximum lesion contrast in T2-W FSE imaging. Monitoring of acute FUS lesions during grid sonications was performed successfully. Lesions appeared as hypointense spots with excellent contrast from surrounding tissue. Conclusion: MRI monitoring of signal intensity changes during FUS sonication in grid patterns using optimized sequence parameters can provide useful information about lesion progression and the success of ablation. This preliminary study demonstrated the feasibility of the proposed monitoring method in ex vivo porcine tissue and should be supported by in vivo studies to assess its clinical potential.

Power-cycling degradation monitoring of an IGBT module with VCE(sat) measurement in continuous operation of a chopper circuit

This paper presents a power-cycling degradation monitoring method of an IGBT module with a VCE(sat) sensing circuit and junction temperature prediction by a three-dimensional structure model. A chopper circuit was introduced to provide a continuous-current-conducting operation of the IGBT module. The VCE(sat) sensing circuit with a low-cost IoT platform “Leafony” was utilized to monitor the junction temperature of an IGBT chip, which transferred the measured signal as digital data, and thus obtained a higher noise immunity than an analog-based circuit. The junction temperature of IGBT chip in the power module was analyzed from the dissipated power of IGBT and the transient thermal impedance between the chips and the ambient. This analysis is effective not only to observe the degradation but also to estimate the thermal resistance. Comparing the temperature profile between experiment and prediction provides health condition of the IGBT model.Predicted thermal profiles agreed with measured ones with 10 % increase of thermal resistance, which was degraded by a power cycle tester. From these results, the proposed monitoring method is effective to detect the progress of power cycle degradation.

Wind speed monitoring using entropy theory and a copula-based approach

In this study, wind speeds in the Lut desert (Iran) was monitored at the Bam, Tabas and Birjand stations during the period 1973–2020 using entropy theory. The conventional entropy method was improved by considering the interaction between stations by a copula-based approach. Two different methods were examined for this purpose: 1) A trivariate vine copula was used to evaluate the interaction among the stations. For this purpose, a d-vine copula was considered to simulate the wind speed at each station as a function of the wind speed at the other stations. 2) A bivariate copula was used to model the simulated and observed wind speed at each station. In particular, a Frank copula was used to perform the joint probability analysis. The results indicate that implementing the proposed method for wind speed monitoring increases the Information Transfer Index (ITI) by approximately 24 %, 10 %, and 33 % at the Bam, Birjand, and Tabas stations, respectively, compared to using the conventional entropy method.

Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis.

This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis.

Research on percussion-based bolt looseness monitoring under noise interference and insufficient samples

Loose bolts are the key cause of tower collapse. The maintenance of bolts in transmission towers remains at the stage of individual inspection, and it is urgent to develop practical bolt looseness monitoring methods for engineering applications. The percussion-based method has shown its full potential due to not relying on fixed signal generators and sensors. However, its performance can severely degrade under noise interference and data scarcity. To address this problem, the new voiceprint feature that resist noise interference and classifier that alleviate sample shortages are proposed in this study, namely all-pole group delay function (APGDF) and prototypical networks. The proposed new bolt torque monitoring method is composed of the two. Under the condition that each bolt torque has only 50 percussion sound samples and −6dB signal-to-noise ratio (SNR), the accuracy of the proposed method for single bolt looseness monitoring is 77.87 %, and the accuracy of multi-bolt looseness monitoring is 80.60 %, which is significantly better than existing feature-based methods. In addition, the monitoring of bolt looseness with highly few samples and the data dimensionality reduction analysis are carried out to demonstrate the superiority of this method. The percussion point needs to be close to the loose bolt to achieve the best monitoring effect, and the percussion intensity does not affect the monitoring effect. The proposed method is expected to become the fundamental mode of bolt looseness monitoring in engineering practice.

Modeling and monitoring multilayer attributed weighted directed networks via a generative model

As data with network structures are widely seen in diverse applications, the modeling and monitoring of network data have drawn considerable attention in recent years. When individuals in a network have multiple types of interactions, a multilayer network model should be considered to better characterize its behavior. Most existing network models have concentrated on characterizing the topological structure among individuals, and important attributes of individuals are largely disregarded in existing works. In this article, first, we propose a unified static Network Generative Model (static-NGM), which incorporates individual attributes in network topology modeling. The proposed model can be utilized for a general multilayer network with weighted and directed edges. A variational expectation maximization algorithm is developed to estimate model parameters. Second, to characterize the time-dependent property of a network sequence and perform network monitoring, we extend the static-NGM model to a sequential version, namely, the sequential-NGM model, with the Markov assumption. Last, a sequential-NGM chart is developed to detect shifts and identify root causes of shifts in a network sequence. Extensive simulation experiments show that considering attributes improves the parameter estimation accuracy and that the proposed monitoring method also outperforms the three competitive approaches, static-NGM chart, score test-based chart (ST chart) and Bayes factor-based chart (BF chart), in both shift detection and root cause diagnosis. We also perform a case study with Enron E-mail data; the results further validate the proposed method.

Monitoring method for detecting and localizing overheat, smoke and fire faults in wind turbine nacelle

This study presents a monitoring method that utilizes 3D object classification to accurately detect mechanical and electrical components of a wind turbine by combining a geometric and statistic feature extractor (GSFE) with a multiview approach. The proposed monitoring method also detect outlier after executing object detection to localize overheat faults in these components with fused Optical or Infrared/LiDAR measurements. The proposed method hasthree key characteristics. First, the proposed outlier detection allocates two extremes of normal and faulty clusters by using 2D object classification/detection model or measuring thestandard deviation of temperature with sensor fusing measurements. Specifically, the outlier detection with sensor fusing measurements extracts the position coordinates andtemperature data to localize overheat faults, effectively detecting an overheat component. Second, the GSFE utilizes a group sampling approach to extract the local geometric feature information from neighboring point clouds, aggregating normal vectors and standard deviation. This method ensures the high accuracy of object classification. Third, a multi-view approach focuses on updating local geometric and statistic features through a graph convolution network, improving the accuracy and robustness of object classification. The proposed outlier detection is verified through overheat/fire field tests. The effectiveness of the proposed 3D object classification method is also validated by using a virtual wind turbine nacelle CAD dataset and a public CAD dataset named ModelNet40. Consequently, the proposed method is practical and effective for monitoring a fire and overheat component because it can accurately detect critical components with only a few virtual datasets because gathering bigdata for training a neural network is extremely difficult.

Wayside wheelset lateral motion detection and vehicle hunting instability evaluation

Wheel profile wear, or bogie suspension failure will result in hunting instability of the vehicle system which will lead to dramatic changes in the lateral motion of the wheelset. The occurrence of hunting instability will worsen the ride performance of the vehicle or even cause the danger of wheelset derailment. Thus, the in-time and effective detection of wheelset lateral motion is very important for the vehicle hunting stability evaluation. By installing a certain amount of displacement sensors on the rail side along the track and detecting the distance between the wheel and rail when the wheelset passing by, the lateral displacements of the wheelset in measuring points can be obtained. The detection method of wheelset lateral motion by the wayside is studied, which includes the measuring principle, structure design, sensor installation, data acquisition and data analysis, and the application of the method to on-site detection for typical instability cases are demonstrated. At the same time, innovate on the existing on-board hunting instability monitoring method, proposed an evaluation method of hunting instability for wayside lateral movement monitoring system. The results show that: installing 20 eddy current sensors on both sides of the straight track to measure the lateral displacement of the wheelset can meet the requirement of detecting the vehicle hunting instability. At the same time, analyzing the measurement data and fitting the maximum value of the signal collected by each sensor can obtain the lateral trajectory of the wheelset. The wheelset lateral displacement can be used to monitor the occurrence of vehicle hunting instability if periodic motion is detected. The amplitude of the motion can determine the hunting instability level, and the frequency can be used to describe the wheel wear level and calculate the wheel/rail contact equivalent conicity. This method is able to provide a reliable data support for the vehicle operation safety. The proposed monitoring method and monitoring index are also of reference value to the development of other wayside hunting instability monitoring systems.

Circulating tumor DNA based minimal residual disease detection and adjuvant treatment decision-making for muscle-invasive bladder cancer guided by modern clinical trials

Up to 430,000 cases of bladder cancer are diagnosed each year worldwide. A proposed method for non-invasive monitoring has been to utilize a “liquid biopsy.” Liquid biopsy has been proposed as a non-invasive method of testing biomarkers in bodily fluids in order to detect and survey cancer. The liquid biopsy could be utilized to obtain information regarding circulating tumor cells, circulating cell-free tumor DNA, circulating cell-free tumor RNA, and more. It is currently being investigated to help guide adjuvant therapy and improve oncological outcomes. We highlight an array of exciting past and ongoing clinical trials regarding ctDNA and adjuvant therapy in regard to urothelial carcinoma which we believe to be amongst the leaders in the field.