Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

Case Study of Accidental Confined Natural Gas Detonations and Associated Damage

Nearly half a million miles of pipeline transport hazardous fluids around the United States. The potential for the release of flammable fluids poses a momentous hazard for the surrounding areas. Of particular concern to our built infrastructure is the accidental leakage and detonation of natural gas pipelines. Two Natural Gas (NG) leakage accidents are examined in detail. This includes the 2011 explosion in Allentown, PA and the 2014 explosion in Harlem, New York. The study consisted of prediction of the incident pressure generated by the NG explosion which can be used to create appropriate safety guidelines and suitable structural design. Two widely used methods: (1) the TNO method and (2) the Baker-Strehlow-Tang method are utilized to predict the overpressure generated by the NG vapor cloud explosion (VCE). The observed damage to surrounding buildings is correlated with known window breakage strengths and is used to verify the accuracy of the computed overpressure from these methods for the two case studies. The resulting overpressure values are further verified using the distances that debris was thrown from the explosion site. The Modified Bernoulli Equation (MBE) is utilized for this calculation. The methods are shown to provide an accurate estimate of the initial detonation energy. Utilizing this approach, the effect of doubling the explosion energy of VCE on the surrounding buildings in Allentown and Harlem is examined. This increase results in a 67% and 64% increase to severe damage to brick buildings and a 66% and 44% increase in the region over which window damage is likely to occur, respectively

Negative pressure pulmonary oedema after total parotidectomy

Introduction: Negative pressure pulmonary edema (NPPO) is an uncommon complication of extubation of the trachea mostly caused by laryngospasm. In literature few cases have been reported.Case report: A 27-year-old male, weighing 75 kgs, ASA status class 1, underwent total parotidectomy for pleomorphic adenoma. Postextubation, the patient became restless, tachypnoeic and started desaturating. Soon after reintubation, pink, frothy fluid came out of the endotracheal tube, and a tentative diagnosis of NPPO was made. The condition improved with diuretics, oxygenation and steroids.Result: Portable chest radiograph was taken, which revealed bilateral fluffy shadows with normal cardiothoracic ratio. ECG and arterial blood gas analysis were normal.Conclusion: To conclude, acute pulmonary oedema associated with obstruction of the upper airways can aggravate low morbidity surgeries, affecting mainly young patients. The knowledge of this complication and, most importantly, its prevention are crucial

Risk management prediction for overcrowding in railway stations utilising Adaptive Nero Fuzzy Inference System (ANFIS)

In this research, an intelligent system for managing risks is developed with a framework to aid in managing the risks in the railway stations. A method to advance risk management in the railway stations is needed in order to minimize risk through an automated process taking into consideration all the factors in the system and how they work together to provide an acceptable level of safety and security. Thus, the Adaptive Nero Fuzzy Inference System (ANFIS) is proposed to improve risk management as an intelligently selected model which is powerful in dealing with uncertainties in risk variables. The methods of artificial neural network (ANN) and Fuzzy interface system (FIS) have been proven as tools for measuring risks in many fields. In this case study, the railway is selected as a place for managing the risks of overcrowding in the railway stations taking two parameters as input for risk value output using a hybrid model, which has the potency to deal with risk uncertainties and to learn by ANN training processes. The results show that the ANFIS method is more promising in the management of station risks. The framework can be applied for other risks in the station and more for a wide range of other systems. Also, ANFIS has the ability to learn from past risk records for future prediction. Clearly, the risk indexes are essential to reflect the actual condition of the station and they can indicate a high level of risks at the early stage, such as with overcrowding. The dynamic model of risk management can define risk levels and aid the decision makers by convenient and reliable results based on recorded data. Finally, the model can be generalised for other risks

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Uterine rupture: A review of 15 Cases at Bandier maternity hospital in Somalia

Background: Uterine rupture is a deadly obstetrical emergency endangering the life of both mother and fetus.Objective: To determine the frequency of ruptured uterus at Bandier Hospital and to elicit possible causes and type of management. Methods: It was cross sectional and hospital based descriptive study implemented during a time period of six months (July – December 2013) in Bandier maternity hospital and a total of 15 women presented with rupture uterus during the period of the study were included.Results: There were 15 cases of ruptured uterus out of a total of 2142 deliveries. Incidence of uterine rupture was found to be 0.7%. The mean age of women was 30.03 ± 4.55 years. Concerning risk factors for rupture uterus, 10 (66.7%) had previous uterine surgery, obstructed labor was found in 33.3%, and oxytocin was used in 46.7% of respondents. Repair was done for 8 (53.3%), 3 (20.6%) of respondents underwent total abdominal hysterectomy and 4 (26.7%) were ended by subtotal hysterectomy. Conclusions: Previous uterine surgery, obstructed labour and improper use of oxytocin increase the risk of uterine rupture in this study. Half of the patients underwent hysterectomy

Production and Quality Evaluation of Vinegar from Tamarind (Tamarindus indica L.) Fruit Pulp

ABSTRACT       Vinegar is a liquid consisting mainly of acetic acid (CH3COOH) and water. The acetic acid is produced by the fermentation of ethanol by acetic acid bacteria. This study aimed to produce vinegar from tamarind fruit pulp and evaluation of its quality. Samples of tamarind fruit were collected from different sites in Sudan: Gedaref (GT), Damazin (DT) and Obeid (OT). The vinegar yields from 1 kg tamarind pulp from (GT), (DT) and (KT) were 300, 200, 260 ml, respectively. The physical characteristics of tamarind fruit pulp and its seeds were determined. The average fruit length, width and weight were 14.28± 0.31mm, 11.06± 1.1mm and 12.33± 0.7g, respectively The production of vinegar was carried out at three stages. The concentration of acetic acid of the produced vinegar from (GT), (DT) and (OT) were equivalent to (16.2%), (19%) and (17.7%), and pH values of these samples were found to be (2.2), (1.9) and (2.0), respectively. The study recommends the efficient industrial use of tamarind fruit in many products such as vinegar.&nbsp

Histologic response after neoadjuvant chemoradiotherapy in locally advanced rectal adenocarcinoma: experience from Sudan.

Background: Locally advanced rectal cancer can be down staged by neoadjuvant therapy and the resultant tumor response can be quantified histologically. This study aimed to assess pathological response of neoadjuvant chemoradiation in patients with locally advanced rectal cancers treated in Wad Medani Teaching Hospital (WMTH) and National Cancer Institute (NCI), Wad Medani, Sudan. Patients and Methods: A total of 36 consecutive patients with locally advanced rectal cancer that were managed in WMTH and NCI during the period from 2006-2011 were reviewed. Preoperative pelvic radiotherapy was delivered. Total of 46 Gray were delivered concurrently with 5- fluorouracil (5-FU) on the first and last week of radiation. Total mesorectal excision of the rectal tumour either by anterior or abdominoperineal resections was planned at 6-8 weeks from completion of preoperative treatment. The pathological response to therapy was assessed by histopathology examination of the surgical specimen. Results: Initial clinical staging of patients revealed 58.3% of them were stage T3/T4N2M0 and 41.7% were stage T3N0M0. Down-staging to stage T1/T2N0M0 was found in 36.1% and stage T3N0M0 in 30.6%. No response was seen in 8.3% of cases with stage T3/T4N2M0 while complete clinical response (no residual) was seen in 25.0%. Complete histological response was observed in 13.8%. Positive lymph-nodes metastasis was confirmed in 8.3% of cases. Conclusion: Neoadjuvant chemoradiation is a reasonable option for cases of rectal cancer and deserves further evaluation