bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of a Portable Venomanometer System for Episcleral Venous Pressure Measurement
Authors: Yap Tze Yeen , Nelson Carl A. , Ghate Deepta , Gulati Vikas , Fan Shan , Kedar Sachin , Gagrani Meghal , Hahn Adam , Minden Blaine , Moorhous Luke , Fowler Zachary , Khazanchi Deepak 
Published: August 2020

Abstract
Traumatic brain injury (TBI) has been considered a precarious health issue especially within the military population. Research has shown that early treatment of TBI could reduce possible neurocognitive injury. However, the nature of military triage has created challenges for early TBI detection. Intracranial pressure (ICP), which is used as a biomarker of outcomes in TBI, is not only expensive to measure but is also invasive and requires specialized surgical and procedural skills. Episcleral venous pressure (EVP) was proven to be a good alternative biomarker to ICP. However, the current technology in measuring EVP is not portable, and requires a skilled operator with a slit-lamp for testing. Moreover, the measurement is highly subjective and depends on the operator’s skill and technique. Therefore, there is a critical need for alternative technology for non-clinical TBI diagnosis. In this paper, we present an improved venomanometer design for measuring EVP in the field.
Cable-Driven 3D Steerable Surgical Needle for Needle-Based Procedures
Authors: Khashei Varnamkhasti Zahra , Konh Bardia 
Published: August 2020

Abstract
Surgical needles have been used in many percutaneous needle-based procedures such as biopsy and brachytherapy in recent years. The needle-based procedures have replaced open surgeries to perform the tasks with minimal invasiveness to the tissue. Precise needle insertion at target position in cancer diagnostic and therapeutic procedures governs the success of such procedures. This work presents a cable-driven 3D steerable needle for improved guidance inside the tissue towards the target. The needle is manipulated by pulling cable tendons via programmed stepper motors. Feasibility tests in air and in a tissue-mimicking phantom showed an average 3D needle deflection of 11.06 and 10.49 degrees, respectively. The controlled (on demand) 3D deflection of this needle is expected to assist surgeons in trajectory tracking and targeting accuracies. This amount of deflection, when combined with needle steering systems with path planning, can potentially realize higher deflections with small radius of curvature.
Introducing a Cost-Effective Radiopaque Scale Design for Intra-Operative Use
Authors: Dey Roopam , Beukes Giancarlo , Nair Gokul , Sivarasu Sudesh 
Published: August 2020

Abstract
Radiopaque scales have numerous uses in the field of surgery, especially orthopaedic surgery. Scales of this nature can be used to guide surgeons by taking intra-operative measurements, pinpoint insertion points on bones and detect locations of deformations and tumours inside the body. Despite this, these scales are not used widely enough because of its high cost and that there are no widely acceptable ways of developing them from off the shelf materials. This paper details the method of inventing a novel low-cost radiopaque scale using off the shelf materials such as Barium Sulfate and Iodinated Contrast Agent (ICA). The radiopaque scale was manufactured using Perspex® and was filled with the contrast agents. The scales were then scanned using low-dose X-ray machines. The scale filled with Barium was found to be provide a better contrast image suggesting that the Barium to be a better high-contrast agent when compared to iodine and is recommended for use.
Numerical Study on the Delay Effect of Air Brake Mass Flow on Train Coupler Forces
Authors: Aboubakr Ahmed K. , Shabana Ahmed A. 
Published: August 2020

Abstract
In existing freight-train braking systems, braking is applied on individual cars sequentially at the speed of the air pressure. In the case of long trains that consist of hundreds of rail cars, there is a time delay that results in large and impulsive compressive and tensile coupler forces. These coupler forces compromise the train safe operation and stability, make train handling difficult, cause track damage, raise significantly the maintenance cost, and increase the stopping distances which can lead to serious accidents. The objective of this investigation is to investigate the effect of the brake-delay time on the train longitudinal dynamics (LTD) coupler forces by integrating for the first time detailed three-dimensional coupler and air-brake force models. A spatial non-linear coupler model that takes into account the geometric nonlinearities is employed in order to allow for capturing arbitrary three-dimensional rail-car motion and coupler kinematic degrees of freedom that cannot be captured using existing simpler models. This coupler model is integrated with a detailed air brake model that consists of the locomotive automatic brake valve, air brake pipe, and car control unit (CCU) in order to evaluate conventional air brake force models that account for the air-flow effect in long train pipes as well as the effect of leakage and branch pipe flows. The coupling between the air brake, locomotive automatic brake valve, car control units, and train equations is established and used in the nonlinear LTD simulations, which are performed using the computer software ATTIF (Analysis of Train/Track Interaction Forces). Different LTD braking scenarios are considered and the effect of brake signal time delay on the coupler forces is examined. The results obtained in this study demonstrate the importance of applying all brake forces simultaneously in order to ensure train stability and safety.
Creation of 25kV AC Neutral Section in Bored/Round Tunnel of Delhi MRTS
Authors: Pande Om Hari , Tewari Ashok , Kumar Sanjeev , Singh Rajesh Kumar 
Published: August 2020

Abstract
Delhi metro uses 25kV single phase AC traction electric supply to power trains which is usually supplied through a continuous conductor running along the track such as overhead lines. Traction power is supplied from Traction sub stations which can cater a smaller portion of the line. Hence, AC traction system for one line comprises of different sections of suitable length being fed by different sub-stations. The electric power is generated in three phases with each having a phase shift of 120°, there is a possibility of two different traction sections being fed by different phases of power supply. In such condition, to avoid the risk of supply mixing of different phases, there is need to keep these sections isolated from each other. Therefore, a specific arrangement of conductor is done and a neutral section is created, which is preferably an earthed section.
A significant portion of metro lines runs underground therefore the neutral section in underground tunnels is provided in a box tunnel constructed through cut and cover method to maintain the required clearances for separation of different phase power supply. The cut and cover method involves temporary acquisition of land, massive excavation followed by a durable covering to support the overhead construction and subsequent restoration of excavated area which takes around 6–8 months in construction. Moreover, neutral section is to be located at a minimum distance of 250 meters from platform having tangent track, zero gradient, no stop signals.
Accordingly, there is a need of developing improved and cost-effective technology for providing neutral sections in the AC traction system in underground to reduce the time taken for construction and abolish the present practice of constructing a cut & cover tunnel between two stations.
The present invention provides a time and space saving method of incorporating neutral sections in the 25kV AC traction system within the round/bored tunnel by creating a niche in the soffit of tunnel which also ensures all the safety clearances required for making effective neutral sections.
The tunnel with circular cross section is bored using tunnel boring machines. The bored tunnel is approximately 5.6m to 5.8m in diameter to house the track, metro train, overhead equipments for electrification. A neutral section is to be housed in the niche created in the ceiling of the round/bored tunnel during the boring process itself. The time spent in boring tunnel utilizing boring machines is considerably less than the time spent in cut and cover method of construction.
This Paper present in detail the design concept prepared by DMRC for Creation of 25 KV AC neutral section in Bored/Round Tunnel of Propose Phase-IV of Delhi MRTS. DMRC has also filed a patent for the innovation with Indian Patent Authority.
Rapid Charging Energy Storage System for a Hybrid Freight Locomotive
Authors: Spiryagin Maksym , Wolfs Peter , Wu Qing , Bosomworth Chris , Cole Colin , McSweeney Tim 
Published: August 2020

Abstract
This paper examines whether rapid charging technology could enable implementation in a hybrid freight locomotive design and how it might benefit the existing freight train operation scenarios.
In the study, a hybrid locomotive design with rapid charging technology has been proposed and implemented with the application of advanced simulation techniques used in recent locomotive/train/track damage studies. The detailed hybrid locomotive model includes multibody subsystems for the locomotive mechanical system and a traction power system including a full battery pack model with an ESS (Energy Storage System) control algorithm implemented in the Matlab/Simulink software package.
For the train performance study, the traction effort and dynamic braking characteristics obtained from the hybrid locomotive model have been incorporated into an in-house train dynamics package for the analysis of locomotive performance, infrastructure requirements and economics under a realistic operational study.
Simulation of Heavy Haul Train Energy Consumption With Locomotive Adhesion Model
Authors: Wu Qing , Spiryagin Maksym , Cole Colin 
Published: August 2020

Abstract
Conventional train energy calculation models cannot consider locomotive wheel-rail adhesions, traction adhesion control and locomotive dynamics. This paper developed an advanced model to fill this research gap. The model uses a 3D locomotive model with a fully detailed wheel-rail contact model. The locomotive model was integrated into a Longitudinal Train Dynamics model with the consideration of locomotive adhesion control. Energy consumption simulations using a conventional model (1D model) and the new model were conducted and compared. The 3D model reports less energy consumption than the 1D model. Maximum difference in energy consumption rate between the 3D model and the 1D model was 12.5%.
Variable Control Setting to Enhance Rail Vehicle Braking Safety
Authors: Shrestha Sundar , Spiryagin Maksym , Wu Qing 
Published: August 2020

Abstract
Train braking technology needs to be advanced corresponding to the vehicle advancement to fully utilize the benefits of the potential capacity, efficiency and to ensure safety. The effectiveness of the braking changes as the friction condition at the wheel-rail interface and speed of rail vehicle change. The conventional brake control systems do not differentiate these changes in conditions and consider a constant slip reference. To overcome this issue, a new control algorithm for a wheel slide protection device incorporated in the electronically controlled pneumatic brake system has been proposed. Unlike conventional controllers, the proposed controller is responsive to the change of operational and environmental parameters between wheel and rail. It is designed based on multiple modes shifting during operations as the friction conditions change which allows to utilize the maximum adhesion available and to prevent the occurrence of sliding. The control algorithm is developed in a modular approach, where the first module identifies the adhesion condition at the wheel-rail interface. The result from the first module is further implemented in the second module to search for the optimum slip range for that adhesion condition and vehicle speed. For numerical simulation, a wagon model considering in-train forces is developed. The adhesion force is modeled by a proper definition of an adhesion-creep characteristics curve achieved from measured data. The comparison between the proposed control algorithm and the conventional algorithm suggests that the proposed control algorithm can optimally utilize available adhesion between wheel and rail to ensure shorter braking distance while maintaining vehicle stability.
Toward Railway Automated Defect Detection From Onboard Data Using Deep Learning
Authors: Afzalan Milad , Jazizadeh Farrokh , Ahmadian Mehdi 
Published: August 2020

Abstract
Regular monitoring of railway systems is imperative for improving safety and ride quality. To this end, data collection is carried out regularly in the rail industry to document performance and maintenance. The use of machine learning methods in the past recent years has provided opportunities for improved data processing and defect detection and monitoring. Such methods rely on installing instrumentation wayside or collecting data from onboard rolling stock. Using the former approach, only specific locations can be monitored, which could hinder covering a large territory. The latter, however, enables monitoring large sections of track, hence proving far more spatial efficiency. In this paper, we have investigated the feasibility of rail defect detection using deep learning from onboard data. The source of data is acceleration and track geometry collected from onboard railcars. Such an approach allows collecting a large set of data on a regular basis. A long short-term memory (LSTM) architecture is proposed to examine the measured time-series to flag potential track defects. The proposed architecture investigates the characteristics of time-series signatures during a short time (∼ls) and classifies the associated track segment to normal/defect states. Furthermore, a novel automated labeling method is proposed to parse the exception report data (recorded by the maintenance team) and label defects for associated time-series signatures during the training phase. In a pilot study, field data from a revenue service Class I railroad has been used to evaluate the proposed deep learning method. The results show that it is possible to efficiently analyze the data (collected onboard a railcar operated in revenue service) for automated defect detection, with relatively higher accuracy for FRA type I defects.
Optimizing Power Consumption of Freight Railroad Bearings Using Laboratory Experimental Data
Authors: Lopez Carlos E. , Tarawneh Constantine , Fuentes Arturo , Siegal Harry 
Published: August 2020

Abstract
Based on projected freight truck fuel efficiency, freight railroad and equipment suppliers need to identify, evaluate and implement technologies and/or operating practices to maintain traditional railroad economic competitiveness. The railway industry uses systems that record the total energy efficiency of a train but not energy efficiency or consumption by components. Lowering the energy consumption of certain train components will result in an increase in its overall energy efficiency, which will yield cost benefits for all the stakeholders. One component of interest is the railroad bearing whose power consumption varies depending on several factors that include railcar load, train speed, condition of bearing whether it is healthy or defective, and type of defect. Being able to quantify the bearing power consumption, as a function of the variables mentioned earlier, would make it possible to obtain optimal operating condition ranges that minimize energy consumption and maximize train energy efficiency.
Several theoretical studies were performed to estimate the power consumption within railroad bearings, but those studies lacked experimental validation. For almost a decade now, the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV) has been collecting power consumption data for railroad bearings under various loads, speeds, ambient temperatures, and bearing condition. The objective of this ongoing study is to use the experimentally acquired power consumption to come up with a correlation that can be used to quantify the bearing power consumption as a function of load, speed, ambient temperature, and bearing condition. Once obtained, the model can then be used to determine optimal operating practices that maximize the railroad bearing energy efficiency. In addition, the developed model will provide insight into possible areas of improvement for the next generation of energy efficient railroad bearings. This paper will discuss ongoing work including experimental setup and findings of energy consumption of bearings as function of railcar load, train speed, condition of bearing whether it is healthy or defective, and type of defect. Findings of energy consumption are converted into approximations of diesel gallons to quantify the effect of nominal energy consumption of the bearings and show economic value and environmental impact.
Evaluation of Machine Learning-Driven Automatic Modulation Classifiers Under Various Signal Models
Authors: Ghasemzadeh Pejman , Banerjee Subharthi , Hempel Michael , Sharif Hamid , Omar Tarek 
Published: August 2020

Abstract
Automatic Modulation Classification (AMC) is becoming an essential component in receiver designs for next-generation communication systems, such as Cognitive Radios (CR). AMC enables receivers to classify an intercepted signal’s modulation scheme without any prior information about the signal. This is becoming increasingly vital due to the combination of congested frequency bands and geographically disparate frequency licensing for the railroad industry across North America. Thus, a radio technology is needed that allows train systems to adapt automatically and intelligently to changing locations and corresponding RF environment fluctuations. Three AMC approaches have been proposed in the scientific literature. The performance of these approaches depends especially on the particular environment where the classifiers are employed. In this work, the authors present a performance evaluation of the Feature-based AMC approach, as this is the most promising approach for railroads in real-time AMC operations under various different wireless channel environments. This study is done as the first one for railroads application where it considers different environments models including Non-Gaussian Class A noise, Multipath fast fading, and their combination. The evaluation is conducted for signals using a series of QAM modulation schemes. The authors selected the signal’s Cumulant statistical features for the feature extraction stage in this study, coupled with three different machine learning classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN) and Recurrent Neural Network (RNN) utilizing long-short term memory (LSTM), in order to maintain control over the classifiers’ accuracy and computational complexity, especially for the non-linear cases. Our results indicate that when the signal model noise shows higher non-linear behavior, the RNN classifier on average achieves higher classification accuracy than the other classifiers.
Wireless Sensor Nodes for Freight Trains Condition Monitoring
Authors: Cii Stefano , Tomasini Gisella , Tarsitano Davide , Bacci Maria Laura 
Published: August 2020

Abstract
Objective of the present work is to present the design and the laboratory tests of Wireless Sensor Nodes (WSNs) equipped with three different on-board energy production systems, to be installed on freight trains for condition monitoring and structural diagnosis of the railway vehicle. The three sensor nodes are designed to be completely energetically autonomous, exploiting three different energy sources easily available on a moving train: Solar, Wind and Mechanical vibration power. a PCB board hosts different ICs, able to convert the uncontrolled and unregulated power sources in order to properly feed all the on-board components and possibly to charge the battery. The electronics for the acquisition, elaboration and wireless transmission of data, is presented and analyzed in terms of performances and consumptions. The three WSNs, based on the different energy sources, are designed and compared in terms of fatigue behavior and performances by means of laboratory tests.
A recombinant oncolytic Newcastle virus expressing MIP-3α promotes systemic antitumor immunity
Authors: Huang Feng-Ying , Wang Jin-Yan , Dai Shu-Zhen , Lin Ying-Ying , Sun Yan , Zhang Liming , Lu Zhuoxuan , Cao Rong , Tan Guang-Hong 
Published: August 2020

BackgroundThe oncolytic Newcastle disease virus (NDV) is inherently able to trigger the lysis of tumor cells and induce the immunogenic cell death (ICD) of tumor cells and is also an excellent gene-engineering vector. The macrophage inflammatory protein-3α (MIP-3α) is a specific chemokine for dendritic cells (DCs). Thus, we constructed a recombinant NDV expressing MIP-3α (NDV-MIP3α) as an in vivo DC vaccine for amplifying antitumor immunities.MethodsThe recombinant NDV-MIP3α was constructed by the insertion of MIP-3α cDNA between the P and M genes. Western blotting assay and ELISA were used to detect MIP-3α, HMGB1, IgG, and ATP in the supernatant and sera. The chemotaxis of DCs was examined by Transwell chambers. The phenotypes of the immune cells (eg, DCs) were analyzed by flow cytometry. The antitumor efficiency of NDV-MIP3α was observed in B16 and CT26 tumor-bearing mice. Immunofluorescence and immunohistochemistry were applied to observe the ecto-calreticulin (CRT) and intratumoral attraction of DCs. Adoptive transfer of splenocytes and antibodies and depletion of T-cell subsets were used to evaluate the relationship between antitumor immunities and the role of the T-cell subtype.ResultsThe findings show that NDV-MIP3α has almost the same capabilities of tumor lysis and induction of ICD as the wild-type NDV (NDV-WT). MIP-3α secreted by NDV-MIP3α could successfully attract DCs in vitro and in vivo. Both B16 and CT26 cells infected with NDV-MIP3α could strongly promote DC maturation and activation. Compared with NDV-WT, intratumoral injection of NDV-MIP3α and the adoptive transfer of T lymphocytes from mice injected with NDV-MIP3α resulted in a significant suppression of B16 and CT26 tumor growth. The NDV-MIP3α-induced production of tumor-specific cellular and humoral immune responses was dependent on CD8+T cells and partially on CD4+T cells. A significant reversion of tumor microenvironments was found in the mice injected with NDV-MIP3α.ConclusionsCompared with NDV-WT, the recombinant NDV-MIP3α as an in vivo DC vaccine demonstrates enhanced antitumor activities through the induction of stronger system immunities and modulation of the tumor microenvironment. This strategy may be a potential approach for the generation of an in vivo DC vaccine.