Short-term train arrival delay prediction:a data-driven approach

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.

Overseas Short-term Training Program for Chinese University Teacher Educators:Theoretical Basis and Course Design

Overseas three-month intra-university training program is rear in Yunnan,China.Using adult learning and social con-structivism as theoretical basis,the author,introduces the background of the program and the course design,highlighting the inten-tion for the program and similar programs.

Short-term effect of rehabilitation training on children with neuropathic acontractile sphincter incontinence

Objective To evaluate the short-term effect of pelvic floor muscle rehabilitation training under the guidance of doctors on children with neuropathic acontractile sphincter incontinence ( NASI) . Methods Sixty-eighty children ( aged 4 - 12 mean,7) years with NASI

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Building the Capacity of Health Professionals in Monitoring and Evaluation in a Public Health Institution: Experience of the National Institute of Public Health (NIPH) of Côte d’Ivoire

Background: In the context of the fight against HIV, a lack of skills in monitoring and evaluating the personnel in charge of activities has been identified at the national level. It was the subject of a priority axis of the national plan for monitoring and evaluating the fight against HIV (2006-2010) that was aimed at strengthening the capacities of actors in this area. To increase the critical mass of competent human resources in the short term, the National Institute of Public Health (NIPH) of Côte d’Ivoire organized monitoring and evaluation training sessions for healthcare professionals from 2011 to 2016. Methods: A single case study with multiple levels of analysis was carried out, combining a qualitative survey and a literature review. An evaluation was carried out six months after each training session. In addition, the results of the pre- and post-tests and of the daily and final evaluations that accompanied the various training sessions were used to provide further information. The qualitative data collected were analyzed using INVIVO 15 software. Results: Some 89 health professionals (69% men and 31% women) working at the national level (51% at the central level, including 58% in health programs) and in development partner agencies (37%) participated in this capacity building program. Most participants were senior health managers (56%), data managers (23%), and statisticians and computer scientists (10%). Almost all the trainings were financed by 16 technical and financial partners (85%), mainly the MEASURE Evaluation project (27%). Conclusion: M&E training, despite all its imperfections, has made it possible to identify M&E training needs at the national level and to increase the critical mass of national skills and to have some culture in M&E.

Analysis of Characteristics of a Heavy Rainstorm Process in Nanchang City on July 7,2020

Based on the conventional observation data,dual polarization radar data and NCEP reanalysis data,the large-scale circulation background field,mesoscale conditions and formation causes of a heavy rainstorm in Nanchang on July 7,2020 were studied.It was found that this heavy rainstorm occurred under the weather background of the confrontation between the northward air flow behind the trough and the strong southwest warm and humid air flow to the northwest of the subtropical high.The divergence at the upper level,the shear in the middle and low levels,the southward movement of cold air at the low level,unusually abundant water vapor and high unstable energy caused the heavy rainstorm weather.In this process,under the influence of continuous eastward movement of several strong echo cells,an obvious"train effect"was formed in Nanchang,so that the local rainfall was continuous and intense.Moreover,the average of VIL was about 17 kg/m 2,and its variation characteristics were consistent with the variation trend of 5-min rainfall intensity,which had a certain indicator effect on short-term heavy precipitation.The topography of the Meiling Mountain in the west of Nanchang had a great influence on the formation and precipitation distribution of the heavy rain process.There was a strong rainstorm center near the mountain,and the precipitation was obviously larger than that in the plain area.

Analysis of Short-term Heavy Precipitations in a Regional Heavy Rainstorm in Shannxi Province

[Objective] This study aimed to analyze the cause of the generation of short-term heavy precipitations in a regional heavy rainstorm in Shannxi Province. [Method] Taking a heavy rainstorm covering most parts of Shaanxi Province in late July 2010 as an example, data of five Doppler weather radars in Shaanxi Province were employed for a detailed analysis of the evolution of the heavy rainstorm pro- cess. [Result] Besides the good large-scale weather background conditions, the de- velopment and evolution of some mesoscale and small-scale weather systems direct- ly led to short-term heavy precipitations during the heavy rainstorm process, involv- ing the intrusion of moderate IS-scale weak cold air and presence of small-scale wind shear, convergence and adverse wind area. In addition, small-scale convection echoes were arranged in lines and formed a ＂train effect＂, which would also con- tribute to the generation of short-term heavy precipitation. [Conclusion] This study provided basic information for more clear and in-depth analysis of the formation mechanism of short-term heavy precipitations.

Joint On-Demand Pruning and Online Distillation in Automatic Speech Recognition Language Model Optimization

Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.

Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization

Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.

Endoscopic submucosal dissection for colorectal neoplasms:A review

The introduction of colorectal endoscopic submucosal dissection(ESD)has expanded the application of endoscopic treatment,which can be used for lesions with a low metastatic potential regardless of their size.ESD has the advantage of achieving en bloc resection with a lower local recurrence rate compared with that of piecemeal endoscopic mucosal resection.Moreover,in the past,surgery was indicated in patients with large lesions spreading to almost the entire circumference of the rectum,regardless of the depth of invasion,as endoscopic resection of these lesions was technically difficult.Therefore,a prime benefit of ESD is significant improvement in the quality of life for patients who have large rectal lesions.On the other hand,ESD is not as widely applied in the treatment of colorectal neoplasms as it is in gastric cancers owing to the associated technical difficulty,longer procedural duration,and increased risk of perforation.To diversify the available endoscopic treatment strategies for superficial colorectal neoplasms,endoscopists performing ESD need torecognize its indications,the technical issues involved in its application,and the associated complications.This review outlines the methods and type of devices used for colorectal ESD,and the training required by endoscopists to perform this procedure.

Recent Progresses in Deep Learning Based Acoustic Models

In this paper,we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.We first discuss models such as recurrent neural networks(RNNs) and convolutional neural networks(CNNs) that can effectively exploit variablelength contextual information,and their various combination with other models.We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system,the connectionist temporal classification(CTC) criterion,and the attention-based sequenceto-sequence translation model.We further illustrate robustness issues in speech recognition systems,and discuss acoustic model adaptation,speech enhancement and separation,and robust training strategies.We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.

THE CORRELATION STUDY BETWEEN PLASMA VISCOSITY AND FIBRINOGEN COAGULABILITY

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Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration

It is of great significance to guarantee the efficient statistics of high-speed railway on-board equipment fault information,which also improves the efficiency of fault analysis. Considering this background, this paper presents an empirical exploration of named entity recognition(NER) of on-board equipment fault information. Based on the historical fault records of on-board equipment, a fault information recognition model based on multi-neural network collaboration is proposed. First, considering Chinese recorded data characteristics, a method of constructing semantic features and additional features based on character granularity is proposed. Then, the two feature representations are concatenated and passed into the gated convolutional layer to extract the dependencies from multiple different subspaces and adjacent characters in parallel. Next, the local features are transmitted to the bidirectional long short-term memory(BiLSTM) to learn long-term dependency information. On top of BiLSTM, the sequential conditional random field(CRF) is used to jointly decode the optimized tag sequence of the whole sentence. The model is tested and compared with other representative baseline models. The results show that the proposed model not only considers the language characteristics of on-board fault records, but also has obvious advantages on the performance of fault information recognition.