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Small-radius wheels are gradually introduced on special rail vehicles, such as double-decker coaches and piggyback wagons. It allows for a low-floor design and further increases the loading capacity of vehicles. From the perspective of vehicle dynamics, small-radius wheels have lower unsprung mass than the nominal wheels with a radius of 460 mm, and it could mitigate the wheel-rail dynamic interaction. Also, the contact patch size is reduced due to the increased curvature of the rolling contact circle, and they are more sensitive to changes in the track geometry than nominal wheels, especially in turnouts with discontinuous rail profiles. To investigate the dynamic response of small-radius wheels in a turnout, the Manchester Benchmark passenger coach with variable radius wheels and a left-hand turnout with a radius of 245 m are introduced in this work. The influence of wheel radii on the running performance, including the wheel-rail interaction and wheel damage behaviour, is investigated by numerical simulations. In addition, a sensitivity study is executed to evaluate the influence of unsprung mass and wheel flange friction on the performance of a vehicle whose wheel radius is 275 mm, and the potential remedy to mitigate high wheel wear is proposed. [ABSTRACT FROM AUTHOR]

Recently, composite brake blocks have been widely adopted in European railway systems to replace cast iron brake blocks in order to reduce braking noise. However, the poor heat dissipation of composite materials has caused more wheel tread damage, including wheel flats (WFs). The WF, as an undesirable tread defect in railway vehicles, can cause severe wheel/rail impact and increase the damage probability of wheelset components and track structures. Early detection of WFs is vital to ensure operational safety and reduced maintenance costs. In this work, to help understand the dynamic impact caused by WFs, a single WF with a length of 20 mm is artifically produced on a wheel of a tank wagon, and field experiments are performed. The influence of the WF on the axle box accelerations (ABAs), vertical accelerations of the bogie frame and vertical accelerations of the car body is discussed in the time and time-frequency domains. On the other hand, the angular domain synchronous averaging (ADSA) method is used to detect WFs by averaging the ABA signals in the angular domain over a fixed time interval, and it requires ABA data and the axle rotational speed as inputs. This method can greatly reduce the amount of measured data by averaging it in the angle domain and can eliminate the short-time influence of interference signals caused by switches and crossings, rail joints, and so on. The results show that the ADSA of a defective wheel with the WF has a clear peak with an amplitude of 21–104 m/s2 while that of a healthy wheel fluctuates around zero in the polar coordinate. Also, the waveform of defective signals can be well presented in polar coordinates. The effectiveness of this method is compared with the envelope spectrum technology in three scenarios and its application to three journeys is presented. [ABSTRACT FROM AUTHOR]

In addition to the discontinuity of the rail profile of a normal turnout, the unique S-shaped curve of a crossover significantly affects the negotiation performance of a train. For heavy-haul freight trains, the in-train forces generated during the braking process may further deteriorate the running safety. Investigating the dynamic behaviour of the train passing through the crossover under braking forces, therefore, can provide a reasonable basis for ensuring train safety. This paper analyses the dynamic response and wheel wear of a three-vehicle train negotiating a single crossover under variable conditions, including variable braking forces, wheel-rail friction coefficient, and crossover layout forms. The results show that the three factors have considerable influences on train safety. If one of the three factors keeps an ideal level, the safe probability of empty wagons can be significantly improved. Considering that there are still some crossovers with tight curves in the existing railway system, avoiding large braking or lubricating the wheel flange in the crossover is still an effective solution to improve the vehicle's performance. In addition, for a new single crossover, a 600-m-radius curve is a good option for balancing permissible train speeds and construction costs of the crossover. [ABSTRACT FROM AUTHOR]

Aiming at preventing frequently occurred coupler jack-knifing in slave control locomotives of 20,000-tonne heavy-haul trains, a theoretical and experimental research on enhancing the coupler stability has been carried out. Field tests are conducted to ascertain the load characteristics of the slave control locomotive, and the coupler stability mechanism of the locomotive is studied. A dynamic simulation model considering the locomotive, adjacent wagons, and attached coupling devices is established and validated. Based on the model, countermeasures for improving the coupler stability are studied. The field test results indicate that: 1) the slave control locomotives suffer from sustained compressive forces less than 1500kN at the braking stage of cycle braking, while the maximum short-term impact force at the release stage exceeds 2000kN; 2) The coupler jack-knifing mostly occurs at the braking stage, and the yaw angles of the middle couplers are larger than the front and rear couplers. According to the theoretical analysis and dynamic simulation results, the coupler stability can be improved by increasing the stiffness of secondary lateral bump stops and reducing its clearance. Moreover, the proposed countermeasure of setting up lateral bump stop to locomotive couplers is proved to be effective to prevent the coupler jack-knifing. [ABSTRACT FROM AUTHOR]

The friction draft gear with hysteresis characteristics is one of the typical nonlinear elements in longitudinal train dynamics (LTD). Due to the nonlinear hysteresis of friction damping, the discontinuity between loading and unloading envelopes becomes a mathematical integration issue when adopting look-up table methods to establish draft gear models. It might cause integration divergence and wrong results if ignored. To solve this discontinuity, three different transitional strategies (velocity-dependent, intermediate slope, and smoothing approximation) are applied to building draft gear models, and the one-dimensional (1D) longitudinal train model adopting the 'Zhai' integration method is used. To ensure accurate results, the international benchmark cases of LTD are studied, and three draft gear models with different transitional strategies are compared in this paper. The results show that these three models can successfully work with 'Zhai' method in the LTD simulation to achieve the force agreement with benchmark cases. Compared to the other two models, the velocity-dependent model doesn't exhibit good buffer deflection agreement and requires a smaller step size to achieve convergent results. Therefore, it is recommended that adopting the intermediate slope method or smoothing approximation approach avoids discontinuity problems of modelling draft gears in LTD. [ABSTRACT FROM AUTHOR]

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision Language Retrieval (VLR). Among these applications, cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task, e.g., understand, recognize, retrieve, or generate optimally. Researchers have proposed diverse methods to address these tasks. The different variants of transformer-based architectures performed extraordinarily on multiple modalities. This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks. This study summarizes the (i) recent task-specific deep learning methodologies, (ii) the pretraining types and multimodal pretraining objectives, (iii) from state-of-the-art pretrained multimodal approaches to unifying architectures, and (iv)multimodal task categories and possible future improvements that can be devised for better multimodal learning. Moreover, we prepare a dataset section for new researchers that covers most of the benchmarks for pretraining and finetuning. Finally, major challenges, gaps, and potential research topics are explored. [ABSTRACT FROM AUTHOR]

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation, and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions, and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, and machine translation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG. [ABSTRACT FROM AUTHOR]

Nonisolated high-voltage gain soft-switching dc–dc converter is an ideal candidate for distributed generation system converters. This article proposes a high-voltage gain soft switching quasi-Z-source dc–dc converter. The converter integrates a coupled inductor and switched capacitor into a quasi-Z-source network. Soft switching is achieved for all active devices in the converter by controlling the auxiliary switch Sa. By inheriting the advantages of the quasi-Z-source network and coupled-inductor/switched-capacitor, the converter has high voltage gain, high efficiency, low input current ripple, low voltage spike of main switch, and common ground. In addition, the efficiency of the converter is improved by recycling the energy of leakage inductance, resulting from an inherent active clamp of the main switch. The steady-state operation, voltage relationship, current/voltage stress, and power dissipation of each component of the converter have been analyzed and derived in detail. The properties of the proposed converter are verified by designing a 200 W prototype in the laboratory. When the input voltage is 48 V and the output voltage is 380 V, the converter efficiency reaches 96.92%, while the efficiency is 93% in a hard switching. The key waveforms of the converter obtained from the experiment agree well with the theoretical analysis results. [ABSTRACT FROM AUTHOR]

To improve the electrochemical performance of silicon as a negative electrode material for lithium-ion batteries, a Ni@C-MOF material synthesized in an amino acid system by the liquid phase method was proposed to improve the performance of silicon. XRD results show that the Ni@C-MOF material was composed of Ni and a small amount of NiO. SEM and TEM observations reveal that the Ni-based metal framework with a chelating ring structure was successfully prepared. The electrochemical performance of the Si/Ni@C-MOF composite was improved. At 0.1 A g−1, the Si/Ni@C-MOF sample exhibits a high initial charge capacity of 875.7 mAh·g−1 between 0.01 and 2 V, and the capacity retention is above 70% after 100 cycles. Ni@C-MOF not only provides a stable metal framework but also constructs a conductive network for silicon, which is beneficial for limiting the volume effect of silicon during the alloy/de-alloy process and improving its electrical conductivity. EIS results show that Ni@C-MOF material delivers a low charge transfer resistance. [ABSTRACT FROM AUTHOR]