The growth in demand for rail transportation systems within cities, together with high-speed and long-distance transportation running on a rail network, raises the issues of both rail safety and security. If an accident or an attack occurs, its consequences can be extremely severe. To mitigate the impact of these events, the real-time monitoring of a rail system is required. In that case, the improvements in monitoring can be achieved using artificial intelligence algorithms such as neural networks. Neural networks have been used to achieve real-time incident identification in monitoring the track quality in terms of classifying the graphical outputs of an ultrasonic system working with the rails and track bed, to predict incidents on the rail infrastructure due to transmission channels becoming blocked, and also to attempt scheduling preemptive and preventative maintenance. In terms of forecasting incidents and accidents on board the trains, neural networks have been used to model passenger behavior and optimize responses during a train station evacuation. In tackling the incidents and accidents occurring on rail transport, we contribute with two methodologies to detect anomalies in real-time and identify the level of security risk: at the maintenance level with personnel operating along the railways, and onboard passenger trains. These methodologies were evaluated on real-world datasets and shown to be able to achieve a high accuracy in the results. The results generated from these case studies also reveal the potential for network-wide applications, which could enhance security and safety on railway networks by offering the possibility of better managing network disruptions and more rapidly identifying security issues. The speed and coverage of the information generated through the implementation of these methodologies have implications in utilizing prediction for decision support and enhancing safety and security on board the rail network.

A Changing World Demands Optimized Health Surveillance Systems – and How Data Engineering Can Help There is a growing urgency to manage the public health and emergency response practices effectively today, in light of complex and emerging health threats. Fortunately, a host of new tools, including big and streaming data sources, methods such as machine learning, new types of hardware like blockchain or secure enclaves, and means of data storage and retrieval, have emerged. But, with these innovations comes a grand challenge: how to blend with, and adapt them to, the traditional public health practices. The long-in-place infrastructures and protocols to protect and ensure the welfare of communities are in need of change, or at least update, to enhance their marked longevity of impact directly on the health outcomes and community wellbeing they were designed to fortify. It is in this vein that the essay is written and composed. The investigation in this essay is to query what, particularly, might be the aspects and influences of the emerging veritable cornucopia of new data engineering frameworks that are either being developed specifically for health surveillance and wellness, or are available to be co opted from devices and services already thriving in the current market and research milieu. Knowing what these ways may be could well aid in molding their uptake and spread, ensuring their beneficial impacts on those communities who stand to gain the most. The essay is divided into several key segments. After this introduction, section two details the research methods. In the section that follows, the maximum health outcome potentials of these novel frameworks are reviewed. Part four of the essay takes a more critical approach, addressing how the success of these methods may be hindered and future research avenues. Lastly, the concluding information suggests some actions to take to aid best suit the implementation of these ways, and suggests some thoughts for further research after the completion of these inquiriestrand [1].