Paravalvular leak closure after TAVI with plugs: A literature review with a specific focus on arterio-arterial loop.

More than mild paravalvular leak (PVL) following transcatheter aortic valve implantation (TAVI) is associated with a twofold increase in all-cause mortality, heart failure hospitalizations, and the need for reintervention. Successfully addressing PVL in TAVIs is more challenging than in surgical valves. The arterial-arterial (A-A) rail technique emerges as a valuable strategy for post-TAVI PVL closure, enhancing success rates by enabling the effective use of lower-profile vascular plug devices. When standard approach is ineffective, generating an A-A loop for post-TAVI PVL closure is probably the most recommended strategy to ensure procedural success.

The interaction of occupational stress and job burnout on depressive symptoms in railway workers in Fuzhou city.

BACKGROUND: To explore the relationship between occupational stress, burnout and depressive symptoms among railroad workers in Fuzhou, and to analyze the interaction of burnout and occupational stress on depressive symptoms. METHODS: In this study, 861 railway employees of Fuzhou railway bureau were randomly selected from January to April, 2022. Occupational stress inventory revised edition (OSI-R), China job burnout inventory (CMBI) and Symptom Checklist-90 (SCL-90) were used to investigate the occupational stress, job burnout and depressive symptoms of railway workers. Interactions associated with depressive symptoms were assessed by linear hierarchical regression analysis and SPSS macros (PROCESS). RESULTS: Occupational stress, job burnout and depressive symptoms accounted for 50.58%, 93.47%, and 11.19% of the study population, respectively. There were intergroup differences between age, marriage status, and length of service (P < 0.05). Occupational stress and job burnout are the main risk factors for depressive symptoms (OR: 2.01, 95% CI: 1.17-3.45; 1.94, 1.69-2.23, respectively). More importantly, further analysis of the interaction between occupational stress and job burnout showed that those with high levels of job burnout had a high-risk effect on depressive symptoms at high levels of occupational stress. CONCLUSION: Occupational stress and job burnout are risk factors for depressive symptoms among railroad workers in Fuzhou City. The interaction of job burnout and occupational stress increases the risk of depressive symptoms.

The relationship between freight train length and the risk of derailment.

In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost-saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013-2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway-Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100-car trains is associated with 1.11 (95% confidence interval: 1.10-1.12) times the derailment odds of running 50-car trains (or a 11% increase), even accounting for the fact that only half as many 100-car trains would need to run. For 200-car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20-1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding.

Train Distance Estimation for Virtual Coupling Based on Monocular Vision.

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.

RSDNet: A New Multiscale Rail Surface Defect Detection Model.

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning.

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between -1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.

Rail Flaw Detection via Kolmogorov Entropy of Chaotic Oscillator Based on Ultrasonic Guided Waves.

Ultrasonic guided wave (UGW) inspection is an emerging non-destructive testing(NDT) technique for rail flaw detection, where weak UGW signals under strong noise backgrounds are difficult to detect. In this study, a UGW signal identification model based on a chaotic oscillator is established. The approach integrates the UGW response into the critical state of the Duffing system to serve as a disturbance control variable. By evaluating the system's motion state before and after introducing the UGW response, identification of UGW signals can be realized. Thus, the parameters defining the critical state of Duffing oscillators are determined by Ke. Moreover, an electromagnetic transducer was specifically devised to enable unidirectional excitation for UGWs targeted at both the rail base and rail head. Experimental studies showed that the proposed methodology effectively detected and located a 0.46 mm notch at the rail base and a 1.78 mm notch at the rail head. Furthermore, Ke was directly proportional to the notch size, which could be used as a quantitative index to characterize the rail flaw.

An Optimized Instance Segmentation of Underlying Surface in Low-Altitude TIR Sensing Images for Enhancing the Calculation of LSTs.

The calculation of land surface temperatures (LSTs) via low-altitude thermal infrared remote (TIR) sensing images at a block scale is gaining attention. However, the accurate calculation of LSTs requires a precise determination of the range of various underlying surfaces in the TIR images, and existing approaches face challenges in effectively segmenting the underlying surfaces in the TIR images. To address this challenge, this study proposes a deep learning (DL) methodology to complete the instance segmentation and quantification of underlying surfaces through the low-altitude TIR image dataset. Mask region-based convolutional neural networks were utilized for pixel-level classification and segmentation with an image dataset of 1350 annotated TIR images of an urban rail transit hub with a complex distribution of underlying surfaces. Subsequently, the hyper-parameters and architecture were optimized for the precise classification of the underlying surfaces. The algorithms were validated using 150 new TIR images, and four evaluation indictors demonstrated that the optimized algorithm outperformed the other algorithms. High-quality segmented masks of the underlying surfaces were generated, and the area of each instance was obtained by counting the true-positive pixels with values of 1. This research promotes the accurate calculation of LSTs based on the low-altitude TIR sensing images.

A Vibration Analysis for the Evaluation of Fuel Rail Pressure and Mass Air Flow Sensors on a Diesel Engine: Strategies for Predictive Maintenance.

This research focuses on the analysis of vibration of a compression ignition engine (CIE), specifically examining potential failures in the Fuel Rail Pressure (FRP) and Mass Air Flow (MAF) sensors, which are critical to combustion control. In line with current trends in mechanical system condition monitoring, we are incorporating information from these sensors to monitor engine health. This research proposes a method to validate the correct functioning of these sensors by analysing vibration signals from the engine. The effectiveness of the proposal is confirmed using real data from a Common Rail Direct Injection (CRDi) engine. Simulations using a GT 508 pressure simulator mimic FRP sensor failures and an adjustable potentiometer manipulates the MAF sensor signal. Vibration data from the engine are processed in MATLAB using frequency domain techniques to investigate the vibration response. The results show that the proposal provides a basis for an efficient predictive maintenance strategy for the MEC engine. The early detection of FRP and MAF sensor problems through a vibration analysis improves engine performance and reliability, minimizing downtime and repair costs. This research contributes to the advancement of monitoring and diagnostic techniques in mechanical engines, thereby improving their efficiency and durability.

King of the castle: organisational influences on authority gradients between network controllers and other team members.

The rail system relies on the effective coordination of multiple disciplines and teams situated within an operational hierarchy to meet a single operational objective-the safe and timely movement of rail traffic. Power and status dispersals across these teams and the various roles within them impact interaction and communication. This study drew on the perceptions of network controllers, to identify organisational factors influencing power imbalances that generate authority gradients between network controllers and other team members. Network controllers (N = 55) across eight Australasian organisations engaged in interviews using the Scenario Invention Task Technique to explore perceptions of risk. Thematic analysis revealed relationships between teams were affected by: (1) the accountability mechanisms adopted by organisations; (2) the way power was vested in roles; and (3) the status attached to roles. This insight into organisational power hierarchies and the generation of authority gradients provides opportunities for understanding teamwork error.Practitioner summary: Communication is impaired by authority gradients across teams in rail and is a contributing factor in incidents occurring on the network. This paper explores the organisational influences on power hierarchies across teams from the perspective of the network controller, pointing to an adversarial culture, resulting in tribalism impeding team interactions.

Are train horns improving road safety? Road users' perspective.

Train horns are used as a control at railway level crossing for safety. Despite their wide use, limited information exists on their actual safety benefits. This study used focus groups with road users to understand their perceptions of the usefulness of train horns. This study showed that the message heard by road users is very simple and contrasts with the amount of information train drivers want to convey. The perceived benefits of the train horn were: (i) an additional layer of protection at active crossings; (ii) an important safety feature at rural/passive crossings; (iii) an increased safety benefit for vulnerable road users; and (iv) a greater safety benefit at night. The disadvantages of the use of train horns were perceived as outweighed by the safety benefits. This study highlights that reducing train horns, if safe to do so, could face resistance from road users and the rail industry.

The safety benefits of train horns at railway level crossings are not well-researched, despite their negative effects on health. This focus group study investigated road users' perceptions of the usefulness of train horns. It showed that reducing the use of train horns could face resistance from road users.

Diurnal rail noise measurement, analysis, and evaluation of associated health impacts on residents living in the proximity of rail track area.

In India, railway is the major transportation mode for carrying goods and people. The tracks for the movement of the rail were initially constructed in the city for the pre-eminence and expediency of the vantage of the people. Rapid modernization and increasing population in the city crammed the area around the railway tracks. Moving rail on the tracks passing through the city is not compatible, which is creating problems for the nearby residents. In the urban and suburban regions, the railway noise has become a major problem. This study was conducted to examine the perception of the physiological and psychological effects of railway noise in the nearby areas of railway stations in Delhi, India. For this purpose, 10 sites near the railway station were selected for the study. To assess the impact of railway noise pollution on the health of humans, a questionnaire survey was conducted. The data of 344 individuals were collected through the questionnaire survey and analyzed to get the perception towards railway noise. Noise level was monitored by a Sound Level Meter (SLM) and the equivalent noise level (Leq) in dB(A) was used to compute the noise pollution in three shifts, i.e., morning, noon, and evening time. Results showed that 57.65% of female and 86.11% of male respondents in the survey reported the disturbance due to railway noise. The level of noise pollution was found higher in the evening time as compared to the noon and morning period, which exceeds the limit set by the Central Pollution Control Board (CPCB) at all the monitored locations. Findings of the study show that the primary cause of the health problems is railroad noise, which is negatively impacting the health of the residents, who are living in the proximity of the rail track region. The perception survey reported that headache, sleep disturbance, irritation, and stress are common health issues among the locals residing around the railway track proximity in Delhi.

A Spatiotemporal Probabilistic Graphical Model Based on Adaptive Expectation-Maximization Attention for Individual Trajectory Reconstruction Considering Incomplete Observations.

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).

Evaluation of a rail logistics transmission system for the transportation of blood components within a medical centre.

BACKGROUND AND OBJECTIVES: Rail logistics transmission systems (RLTSs) are commonly used for the transportation of blood samples, pathological specimens and other medical materials in many hospitals, as they are rapid, secure, cost-effective and intelligent. However, few studies have evaluated blood component transportation from blood banks to the patient care areas of hospitals using RLTS. In this study, we evaluate the RLTS used for the transportation of blood components within a medical centre. MATERIALS AND METHODS: The dispatch of blood components, including packed red blood cells (pRBCs), fresh frozen plasma (FFP), cryoprecipitate and platelet units, from a blood bank to critical care areas or general wards was done using RLTS. Parameters such as the delivery time, temperature, physical integrity and blood component quality were evaluated via analytical testing using specimens obtained before and after transportation by RLTS. RESULTS: The turnaround time and temperature of all tested blood units via RLTS transportation were able to meet the clinical demands of blood component delivery (median time: 323 s [118-668 s]; temperature variation: 4.5-8.9°C for pRBCs and FFP and 21.5-23.5°C for cryoprecipitate and platelet units). Furthermore, parameters of pRBC quality, including the haemolysis index and potassium and lactate dehydrogenase levels in plasma, were not significantly different before and after transportation through RLTS. Similarly, RLTS transportation affected neither the basic coagulation test results in FFP and cryoprecipitate specimens nor platelet aggregation and activation markers in apheresis platelet specimens. CONCLUSION: Hospital-wide delivery of blood components via RLTS seems to be safe, reliable and cost-effective and does not have any negative impact on blood quality. Therefore, the establishment of standard criteria, protocols and guidelines based on further studies is needed.

Qualitative value of information provides a transparent and repeatable method for identifying critical uncertainty.

Conservation decisions are often made in the face of uncertainty because the urgency to act can preclude delaying management while uncertainty is resolved. In this context, adaptive management is attractive, allowing simultaneous management and learning. An adaptive program design requires the identification of critical uncertainties that impede the choice of management action. Quantitative evaluation of critical uncertainty, using the expected value of information, may require more resources than are available in the early stages of conservation planning. Here, we demonstrate the use of a qualitative index to the value of information (QVoI) to prioritize which sources of uncertainty to reduce regarding the use of prescribed fire to benefit Eastern Black Rails (Laterallus jamaicensis jamaicensis), Yellow Rails (Coterminous noveboracensis), and Mottled Ducks (Anas fulvigula; hereafter, focal species) in high marshes of the U.S. Gulf of Mexico. Prescribed fire has been used as a management tool in Gulf of Mexico high marshes throughout the last 30+ years; however, effects of periodic burning on the focal species and the optimal conditions for burning marshes to improve habitat remain unknown. We followed a structured decision-making framework to develop conceptual models, which we then used to identify sources of uncertainty and articulate alternative hypotheses about prescribed fire in high marshes. We used QVoI to evaluate the sources of uncertainty based on their Magnitude, Relevance for decision-making, and Reducibility. We found that hypotheses related to the optimal fire return interval and season were the highest priorities for study, whereas hypotheses related to predation rates and interactions among management techniques were lowest. These results suggest that learning about the optimal fire frequency and season to benefit the focal species might produce the greatest management benefit. In this case study, we demonstrate that QVoI can help managers decide where to apply limited resources to learn which specific actions will result in a higher likelihood of achieving the desired management objectives. Further, we summarize the strengths and limitations of QVoI and outline recommendations for its future use for prioritizing research to reduce uncertainty about system dynamics and the effects of management actions.

Machine learning-based rail corrugation recognition: a metro vehicle response and noise perspective.

Rail corrugation is a common problem in metro lines, and its efficient recognition is always an issue worth studying. To recognize the wavelength and amplitude of rail corrugation, a particle probabilistic neural network (PPNN) algorithm is developed. The PPNN is incorporated with the particle swarm optimization algorithm and the probabilistic neural network. On the basis of the above, the in-vehicle noise characteristics measured in the field are used to recognize normal rail wavelengths of 30 and 50 mm. A stepwise moving window search algorithm suitable for selecting features with a fixed order was developed to select in-vehicle noise features. Sound pressure levels at 400, 500, 630 and 800 Hz of in-vehicle noise are fed into the PPNN, and the average accuracy can reach 96.43%. The bogie acceleration characteristics calculated by the multi-body dynamics simulation model are used to recognize normal rail amplitudes of 0.1 and 0.2 mm. The bogie acceleration is decomposed by the complete ensemble empirical mode decomposition with adaptive noise, and a reconstructional signal is obtained. The energy entropy of the reconstructional signal is fed into the PPNN, and the average accuracy can reach 95.40%. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Shift work and nonalcoholic fatty liver disease incidence among Chinese rail workers: a 4-year longitudinal cohort study.

PURPOSE: Occupational harmful factors, such as shift work, are attracting increasing attention as a potential cause of nonalcoholic fatty liver disease (NAFLD). In this study, we aimed to identify the association between shift work and NAFLD incidence in Chinese rail population. METHODS: A cohort study was conducted among 14,112 rail workers for 4-year follow-up. Shift work frequency and other potential variables were recorded by questionnaires, including demographic, lifestyle, and occupation information. Besides, body mass index, blood pressure, fasting blood glucose, total cholesterol, triglyceride, alanine aminotransferase, and aspartate aminotransferase were measured by anthropometric measurement and blood test. Diagnosis of new NAFLD case was based on abdominal ultrasonography. Cox proportional hazards regression model was used to determine whether shift work has effect on occurrence of NAFLD. RESULTS: The incidence of NAFLD was 30.43% in total subjects. After adjustment for possible confounders, the RRs of NAFLD were 1.069 (95% CI 0.998-1.146) and 1.179 (95% CI 1.059-1.312) in occasionally shift work group and frequently shift work group respectively, compared to the seldom shift work group. In stratified analyses, the RRs of NAFLD incidence linked to shift work exposure seems increase among female and elder. The results of three sensitivity analyses were similar with main analysis. CONCLUSIONS: This research provided further evidence of positive harmful effect of shift work on NAFLD incidence in Chinese rail workers, particularly in frequently shift work population. The risk estimate of shift work on NAFLD was higher in female and elder.

A Laser Plane Attitude Evaluation Method for Rail Profile Measurement Sensors.

The non-coplanar lasers on both sides of the rail during full-section rail profile measurement based on line-structured light vision will cause the measured profile to be distorted, resulting in measurement errors. Currently, in the field of rail profile measurement, there are no effective methods for evaluating laser plane attitude, and it is impossible to determine the degree of laser coplanarity quantitatively and accurately. This study proposes an evaluation method based on fitting planes in response to this problem. Real-time fitting of laser planes with three planar targets of different heights provides information about the laser plane attitude on both sides of the rails. On this basis, laser coplanarity evaluation criteria were developed to determine whether the laser planes on both sides of the rails are coplanar. Using the method in this study, the laser plane attitude can be quantified and accurately assessed on both sides, effectively resolving the problem with traditional methods that can only assess the laser plane attitude qualitatively and roughly, thereby providing a solid foundation for calibration and error correction of the measurement system.

Vibration and Noise Analysis and Experimental Study of Rail Conveyor.

The rail conveyor is a new type of energy-saving system for the long-distance transportation of bulk materials. Operating noise is an urgent problem that the current model faces. It will cause noise pollution and affect the health of workers. In this paper, the factors causing vibration and noise are analyzed by modeling the wheel-rail system and the supporting truss structure. Based on the built test platform, the system vibration of the vertical steering wheel, the track support truss, and the track connection were measured, and the vibration characteristics at different positions were analyzed. Based on the established noise and vibration model, the distribution and occurrence rules of system noise under different operating speeds and fastener stiffness conditions were obtained. The experimental results show that the vibration amplitude of the frame near the head of the conveyor is the largest. The amplitude under the condition of 2 m/s running speed at the same position is 4 times that under the condition of 1 m/s. At different welds of the track, the width and depth of the rail gap have a great influence on the vibration impact, which is mainly due to the impact of the uneven impedance at the track gap, and the greater the running speed, the more obvious the vibration impact. The simulation results show the trend of noise generation, the speed of the trolley, and the stiffness of the track fasteners have a positive effect on the generation of noise in the low-frequency region. The research results of this paper will play an important role in the noise and vibration analysis of rail conveyors and help to optimize the structure design of the track transmission system.

An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel-Rail Coupled Fault Diagnosis for Rail Vehicles.

The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel-rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel-rail coupled faults will improve the safety of rail vehicles' operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel-rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time-frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles' variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.