Effectiveness of Train-the-Trainer Workshops in Intelligent Industrial Robotics

In this work we investigate the effectiveness of two train-the-trainer workshops on intelligent industrial robotics. The two workshops, which took place in summer 2021 in Tennessee and Alabama, were the first of a series of six workshops. A total of 32 persons applied to the two summer workshops from 10 states, of whom 15 attended and successfully completed the workshops. Evaluation results show that the participants' knowledge on industrial robotics significantly improved after the workshops, and the vast majority indicated that the training will be used in their home institutions. The major challenge faced during the workshops was the spread of the delta variant of CoVid-19 at the time the workshops were scheduled to take place, and the wide diversity of the educational background of participants. © 2023 IEEE.

Optimization of train formation plan based on technical station under railcar demand fluctuation

Train formation planning (TFP) is essential for rail freight logistics services. The fluctuation of railcar flows dramatically compared with before the outbreak of COVID-19. This paper studies train formation planning, considering three types of train services provided for railcar flow between pairs of technical stations (TS), including direct trains, district trains, and pickup trains. This paper introduces an optimization model with average railcars flow data (OMAD) and an optimization model with dynamic railcars flow data (OMDD) for the train formation planning based on TS under railcar demand fluctuation while minimizing railcar-hour consumption. The OMAD is a deterministic model, and the OMDD is a probability constraint model. To solve the OMDD, an approach for transforming probability constraints into deterministic constraints is presented. Various groups of scenarios are given to verify the effectiveness of the proposed models. [ FROM AUTHOR] Copyright of Journal of Industrial & Production Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Agile Model-Based Systems Engineering of Passenger Train Operational Design

The Railway industry is facing a productivity issue as is often publicised with regular delays in rolling stock projects [1]. Plus, there is a growing need for innovation in remote services and management that have become the new normal during the COVID-19 pandemic. It drives a need for better Systems Engineering (SE) methods which include increased automation and dependence between systems and system performance, increasing number of disparate specialist engineering teams. [2] The aim of this paper is to develop an adaptable model which expresses the operational behavior of a train system in different railway environments, this model will be quickly and accurately configured to a specific environment to define the needs for a specific passenger service mission. Preventing late changes (cost and time-saving) by generating the right system requirements at the very early design phase through agile Model-Based Systems Engineering (MBSE) approach is the key benefit. Another goal includes increased productivity by minimizing unnecessary manual transcription of concepts when coordinating the work of large teams. This Generic* functional model of a Rolling Stock system can be configured to define specific products for an operator or Original Equipment Manufacturer (OEM).

COVID-19 Face Mask Classification Using Deep Learning

The COVID-19 pandemic has triggered a global health disaster because its virus is spread mainly through minute respiratory droplets from coughing, sneezing, or prolonged close contact between individuals. Consequently, World Health Organization (WHO) urged wearing face masks in public places such as schools, train stations, hospitals, etc., as a precaution against COVID-19. However, it takes work to monitor people in these places manually. Therefore, an automated facial mask detection system is essential for such enforcement. Nevertheless, face detection systems confront issues, such as the use of accessories that obscure the face region, for example, face masks. Even existing detection systems that depend on facial features struggle to obtain good accuracy. Recent advancements in object detection, based on deep learning (DL) models, have shown good performance in identifying objects in images. This work proposed a DL-based approach to develop a face mask detector model to categorize masked and unmasked faces in images and real-time streaming video. The model is trained and evaluated on two different datasets, which are synthetic and real masked face datasets. Experiments on these two datasets showed that the performance accuracy rate of this model is 99% and 89%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Evolving partnership: A National Center for Global Health and Medicine Resilient Training Model for clinical research professionals during the COVID-19 pandemic.

The clinical trial industry has encountered challenging circumstances in which the increasing number of trials outpaces the number of trial specialists. For instance, there has been an unprecedented demand for clinical trials following the Covid-19 pandemic, which has worsened the global shortage of qualified personnel. It is therefore imperative to produce more qualified clinical trial professionals. An adaptive and collaborative training model was implemented by the National Center for Global Health and Medicine through the Department of International Trials. This aimed at building capacity among health workers in developing countries and providing them with the skills to be able to conduct all phases of the clinical trial from protocol design to publication of results. It also seeks to foster collaboration and partnership between local health workers and international experts. Since 2016, we have implemented a Japan-led training program, and since 2020, the COVID-19 pandemic has ushered in a shift from a single Train-the-trainer model (ToT) to a mixed model, the Evolving Partnership Training (ePT). In this model, we applied four different methods: train-the-trainer, needs-oriented training, open symposiums, and advanced learning. The total number of training participants increased exponentially from a total of 41 between 2016-2020 to 2,810 in 2021. Our experience has proven that despite the constraint of the pandemic, the ePT is a viable approach compared to a single method for providing quality training and increasing the number of participants.

Retour d'expérience des évacuations par train à grande vitesse de patients en syndrome de détresse respiratoire aiguë sur infection à Covid-19 : les missions Chardon

Lors de la crise Covid-19 en France, il a fallu transférer des patients de zones où les lits de réanimation étaient saturés vers d'autres régions. Tous les moyens ont été utilisés : terrestre, aérien, maritime… Pour la première fois, des trains à grande vitesse (TGV) ont été utilisés. Le transport ferroviaire avait été utilisé largement pendant la Première Guerre mondiale. Ces transferts ont nécessité une collaboration extrêmement importante interservices : ministère, agences régionales de santé, hôpitaux, Samu zonaux, Samu, Smur associations de sécurités civiles, sapeurs-pompiers… L'une des collaborations des plus importantes a été celle avec la SNCF qui a permis une adaptation des rames, sécurisations des itinéraires, adaptation de la conduite… Chaque voiture transporte quatre patients intubés en syndrome de détresse respiratoire aiguë avec un médecin senior, un junior, quatre infirmiers et un logisticien pour la réalisation de la surveillance et des soins. Dans chaque rame, une équipe de régulation médicale est présente pour la coordination. Il y a eu dix évacuations sanitaires, qui ont transporté 197 patients sur 6 600 km (350‒950 km/TGV). Le transport le plus long a été de 7 h 14 min. On n'a pas relevé de complications majeures pendant les transferts. Plusieurs questions restent en suspens comme les critères de sélections des patients, la mise en place d'un train sanitaire aménagé permanent, un stock de matériel. Afin de mieux connaître les conséquences sur les patients, une étude est en cours. Les urgentistes ont une nouvelle corde à leur arc avec la possibilité d'effectuer des évacuations sanitaires en TGV pour des patients médicaux graves sur de longues distances.Alternate : During the COVID-19 crisis in France, despite an incredible increase in the number of intensive care unit (ICU) beds, these were not sufficient in the areas (Great East and Paris areas) most impacted by the disease. The decision was taken to transfer patients to other areas. The medical train was especially used in the First World War. Since then, it had not been used. The SAMU of Paris in collaboration with several partners had organized a bombing exercise in May 2019 with mass casualty evacuation using high speed trains. The ministry of health decided to urgently evacuate COVID-19 patients with acute respiratory syndrome (ARDS). High speed trains (TGV) were equipped accordingly. Sanitary fittings have evolved over time in collaboration with the train company (SNCF). A specific organization was set up: choice of routes, stations, hospitals, etc. This required a multi-service organization. In each wagon, four intubated patients were cared for by a senior and junior doctors, 4 nurses, and a logistician. The operations were coordinated by a medical regulation team posted on the train. In total, 6600 km were traveled (350–950 km per journey), the longest journey being 7 h 14 min, and 197 patients were transferred during these medical train evacuations. There were no major complications during the transfers. Some issues such as patient eligibility need to be further discussed. The possibility of having permanently equipped "hospital trains” with dedicated hardware could also be debated. We are trying in a dedicated study to assess the consequences of these transfers. In any case, sanitary transfers by TGV are definitely an option during major health crises.

Improving Radiology Report Generation via Object Dropout Strategy and MLP-based Captioner

Recently, automatically generating radiology reports has been addressed since it can not only relieve the pressure on doctors but also avoid misdiagnosis. Radiology report generation is a fundamental and critical step of auxiliary diagnosis. Due to the COVID-19 pandemic, a more accurate and robust structure for radiology report generation is urgently needed. Although radiology report generation is achieving remarkable progress, existing methods still face two main shortcomings. On the one hand, the strong noise in medical images usually interferes with the diagnosis process. On the other hand, these methods usually require complex structure while ignoring that efficiency is also an important metric for this task. To solve the two aforementioned problems, we introduce a novel method for medical report generation, the termed attention-guided object dropout MLP(ODM) model. In brief, ODM first incorporates a tailored pre-trained model to pre-align medical regions and corresponding language reports to capture text-related image features. Then, a fine-grained dropout strategy based on the attention matrix is proposed to relieve training pressure by dropping content-irrelevant information. Finally, inspired by the lightweight structure of Multilayer Perceptron(MLP), ODM adopts an MLP-based structure as an encoder to simplify the entire framework. Extensive experiments demonstrate the effectiveness of our ODM. More remarkably, ODM achieves state-of-the-art performance on IU X-Ray, MIMIC-CXR, and ROCO datasets, with the CIDEr-D score being increased from 26.8% to 41.4%, 21.1% to 30.2%, and 9.1% to 19.3%, respectively. © 2022 IEEE.

Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirus-based pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. © 2022 IEEE.

TTRISK: Tensor train decomposition algorithm for risk averse optimization

This article develops a new algorithm named TTRISK to solve high‐dimensional risk‐averse optimization problems governed by differential equations (ODEs and/or partial differential equations [PDEs]) under uncertainty. As an example, we focus on the so‐called Conditional Value at Risk (CVaR), but the approach is equally applicable to other coherent risk measures. Both the full and reduced space formulations are considered. The algorithm is based on low rank tensor approximations of random fields discretized using stochastic collocation. To avoid nonsmoothness of the objective function underpinning the CVaR, we propose an adaptive strategy to select the width parameter of the smoothed CVaR to balance the smoothing and tensor approximation errors. Moreover, unbiased Monte Carlo CVaR estimate can be computed by using the smoothed CVaR as a control variate. To accelerate the computations, we introduce an efficient preconditioner for the Karush–Kuhn–Tucker (KKT) system in the full space formulation.The numerical experiments demonstrate that the proposed method enables accurate CVaR optimization constrained by large‐scale discretized systems. In particular, the first example consists of an elliptic PDE with random coefficients as constraints. The second example is motivated by a realistic application to devise a lockdown plan for United Kingdom under COVID‐19. The results indicate that the risk‐averse framework is feasible with the tensor approximations under tens of random variables.

Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station

In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. © 2023 The Operational Research Society.

Analysis of a Typical COVID-19 Outbreak Spread by Tourist Groups

Background: Novel coronavirus disease (SARS-COV-2 infection or COVID-19) is a respiratory tract infection that has been linked to severe acute respiratory syndrome transmitted particularly through touching and respiration. The purpose of this study is to understand the epidemiological characteristics of COVID-19 cases in a typical tourist-related outbreak and explore the possible route for its transmission. Method(s): All data and epidemiological survey reports of COVID-19 cases in the outbreak were reported by provincial and urban (county) Centers for Disease Control and Prevention and Health Commissions nationwide from October 16th to November 5th, 2021. The epidemiological survey reports included information on gender, age, source of infection (imported from other provinces or locally acquired), daily life track and itinerary, date of symptom onset, and date of diagnosis. The data were analyzed using descriptive statistical methods, one-way analysis of variance, independent t-test, and Chi-square tests. Histograms and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. Result(s): The COVID-19 outbreak associated with the tourist groups has involved 551 COVID-19 cases, with a median age of 44 years (interquartile range: 30-59 years), gradually spreading from the northwestern region to the national level across 15 provinces of China. One-fifth of the cases (16.0%) had traveled to Ejin Banner, resulting in 68 second-generation cases. We estimated an outbreak on 11 flights and 19 trains, accounting for a total of 27 confirmed cases. In addition, 42 clusters of outbreak cases were also reported to occur, 21 (50.0%) in households and 10 (23.81%) in restaurants. About 106 confirmed cases were related to the gatherings in restaurants. The median incubation period for this COVID-19 outbreak was 7 days (inter-quartile range: 5-10 days). Conclusion(s): The survey results indicated that this COVID-19 outbreak originated in Ejin Banner and was spread by tourist groups, which was a typical infection outbreak promoted by travel. Our results further confirmed that travel needs to be more strictly weighed in pandemics like COVID-19, and people need to pay more attention to the prevention against infectious diseases, particularly when traveling in a tourist group.Copyright © 2022 Zheng et al.

"Indian Railway—A Galloping Elephant” (Life in the Times of COVID-19)

Covid-19 pandemic brought the most powerful and intelligent of species on earth, to its knees, forcing countries to lockdown. India, a country of 1.37 billion, was under lockdown initially for 21 days, from 24th Mar'20;extended till 31st May'20. The 2nd wave of COVID-19 infections hit the country from Mar'21 and the situation was far worse than first wave and daily infections were four times more than that in Sep'20. Lockdowns may be a recurring feature till the situation comes under control, though the degree and kind of restrictions, would vary dynamically as our experience and the knowledge improves. Indian Railway (IR), the country's lifeline, used to run 20,000 trains daily before Corona struck, using its infrastructure of 72,038 km of route served by 7,318 stations with over 12 lakh employees. It stopped all passenger operations, first time in history, from 22nd Mar'20. However, transportation of essential goods like food grains, petroleum products, coal for the power plants etc. continued uninterrupted. Special parcel trains were started from 7th Apr'20 for the goods which used to move by parcel vans attached to passenger trains and also to make up for tapering of road transport. On demand from states, Specials Trains were run for migrants from 1st May'20. Special passenger trains connecting major metros were started from 12th May'2020. By 01st June'20, when the lockdown eased, 103 passenger trains were running. Paper attempts to codify the knowledge and experience, charting out the journey of IR during pandemic with focus on developments which were either triggered by the pandemic or where there was a quantum shift or acceleration of pace. When this paper was conceived, the pandemic was still unfolding and even now – a year and a half later, things have not yet settled. Nonetheless, Indian Railways has consolidated in certain areas and is still struggling in others. This paper is not only about what IR did exceptionally well but also of what it could not or did not do. It is about the reflection of reality not selective presentations of reality. For that matter the title is misleading and given the choice we would like to change it to -"Indian Railways—An Elephant on the Move” (Life in the Times of COVID-19) © 2023 DMICS.

Collaborative Determination Method of Metro Train Plan Adjustment and Passenger Flow Control under the Impact of COVID-19

Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.

Analysis of a Typical COVID-19 Outbreak Spread by Tourist Groups

Background: Novel coronavirus disease (SARS-COV-2 infection or COVID-19) is a respiratory tract infection that has been linked to severe acute respiratory syndrome transmitted particularly through touching and respiration. The purpose of this study is to understand the epidemiological characteristics of COVID-19 cases in a typical tourist-related outbreak and explore the possible route for its transmission. Method(s): All data and epidemiological survey reports of COVID-19 cases in the outbreak were reported by provincial and urban (county) Centers for Disease Control and Prevention and Health Commissions nationwide from October 16th to November 5th, 2021. The epidemiological survey reports included information on gender, age, source of infection (imported from other provinces or locally acquired), daily life track and itinerary, date of symptom onset, and date of diagnosis. The data were analyzed using descriptive statistical methods, one-way analysis of variance, independent t-test, and Chi-square tests. Histograms and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. Result(s): The COVID-19 outbreak associated with the tourist groups has involved 551 COVID-19 cases, with a median age of 44 years (interquartile range: 30-59 years), gradually spreading from the northwestern region to the national level across 15 provinces of China. One-fifth of the cases (16.0%) had traveled to Ejin Banner, resulting in 68 second-generation cases. We estimated an outbreak on 11 flights and 19 trains, accounting for a total of 27 confirmed cases. In addition, 42 clusters of outbreak cases were also reported to occur, 21 (50.0%) in households and 10 (23.81%) in restaurants. About 106 confirmed cases were related to the gatherings in restaurants. The median incubation period for this COVID-19 outbreak was 7 days (inter-quartile range: 5-10 days). Conclusion(s): The survey results indicated that this COVID-19 outbreak originated in Ejin Banner and was spread by tourist groups, which was a typical infection outbreak promoted by travel. Our results further confirmed that travel needs to be more strictly weighed in pandemics like COVID-19, and people need to pay more attention to the prevention against infectious diseases, particularly when traveling in a tourist group. Copyright © 2022 Zheng et al.

A Data Mining Approach to Finding Face Mask from Bangladeshi News Channel

Face Mask Detection is currently a hot topic that has piqued the interest of researchers all over the world. Today, the entire world is dealing with the COVID-19 pandemic. To control the spread of the Coronavirus the most important task people need to do is use a mask. There is still a lot of research and study being done on COVID-19. Several studies have also shown that wearing a face mask significantly reduces the problem of viral transmission. In addition, a person wearing a face mask perceives a sense of protection. When we are at home, we take care of everything, but when we are in public places such as offices, malls, and colleges, it becomes more difficult to keep people safe. Machine Learning and Data Mining are a collection of technologies that provide effective solutions to complex problems in a variety of fields. We attempted to develop a face mask recognition system using machine learning in order to prevent the spread of the Coronavirus. This is a good system for detecting a face mask in news channel images and videos. It can recognize both Mask and No Mask faces. With the advancement of this system, it will be possible to detect whether or not a person is wearing a face mask. If the person is not wearing a face mask, it will display a message such as "No Mask,"otherwise it will display "Mask Detected." © 2022 IEEE.

Personal Health Train Architecture with Dynamic Cloud Staging.

Scientific advances, especially in the healthcare domain, can be accelerated by making data available for analysis. However, in traditional data analysis systems, data need to be moved to a central processing unit that performs analyses, which may be undesirable, e.g. due to privacy regulations in case these data contain personal information. This paper discusses the Personal Health Train (PHT) approach in which data processing is brought to the (personal health) data rather than the other way around, allowing (private) data accessed to be controlled, and to observe ethical and legal concerns. This paper introduces the PHT architecture and discusses the data staging solution that allows processing to be delegated to components spawned in a private cloud environment in case the (health) organisation hosting the data has limited resources to execute the required processing. This paper shows the feasibility and suitability of the solution with a relatively simple, yet representative, case study of data analysis of Covid-19 infections, which is performed by components that are created on demand and run in the Amazon Web Services platform. This paper also shows that the performance of our solution is acceptable, and that our solution is scalable. This paper demonstrates that the PHT approach enables data analysis with controlled access, preserving privacy and complying with regulations such as GDPR, while the solution is deployed in a private cloud environment.

Overview of On-board IC Ticket Equipment System for Rolling Stock

West Japan Railway Company (hereinafter referred to as "JR West") introduced the IC card ticket system "JCOCA" and has expanded the use area of the IC card. For the local lines, the onboard type of ticket gate machine for the IC card ticket is introduced to allow to use the IC card. This type of ticket gate machine is also effective for reducing the burden on the train crew. The introduction of this on-board type of IC ticket gate is our effort to allow passengers to smoothly get on/off and to provide safe and secure transport service under the COVID-19 epidemic. © 2022 Japan Railway Engineers' Association. All rights reserved.

Re-envisioning Maritime Education and Training - Technology facilitated lifelong learning for future ship operators

The increasing introduction of novel and digital technologies on-board ships is changing the landscape of work and the seafaring skill and competencies required to perform the job. The maritime industry currently finds itself in a dynamic evolutionary continuum culminating in the techno-saturated realm of Maritime Autonomous Surface Ships (MASS) of the future. This ongoing transition to future ships foregrounds the continuous and lifelong learning for seafarers to remain relevant as the industry irrevocably progresses and evolves. Meanwhile, the imperative incorporation of new technologies in Maritime Education and Training (MET) during the COVID-19 pandemic disrupted the traditional classroom-based teaching-learning process. Even though this accelerated technology adoption in MET was not free from challenges, it cemented the trend of technologically facilitated maritime blended learning and e-learning. This paper notes the need for lifelong learning in an industry in a flux and the maritime education system undergoing a transition. Seafarer training as we know today cannot serve the needs of future operators who would not be physically present on-board autonomous ships. This paper suggests that the proliferation of on-board technology needs to be complemented by technology in education and training. Furthermore, technology facilitated lifelong learning is imperative for current seafarers to remain relevant. © 2022 IAMUC. All Rights Reserved.

Rail Passenger Transport before and during Pandemic COVID-19 in Slovak Republic

Pandemic Covid-19 has influenced all sectors of the economy. Lockdown and other measures meant bankruptcy for many companies. Rail companies providing rail passenger services have a specific status because most services in rail passenger transport are provided as public service obligation (PSO), only some services are provided on a commercial basis. The paper deals with the comparison of rail passenger transport services in the train kilometers and passenger kilometers PSO and commercial services before and during the pandemic COVID-19. We researched the synergistic effect of the change in rail transport performances in terms of social costs. © 2022 Kaunas University of Technology. All rights reserved.

TTRISK: Tensor train decomposition algorithm for risk averse optimization

This article develops a new algorithm named TTRISK to solve high‐dimensional risk‐averse optimization problems governed by differential equations (ODEs and/or partial differential equations [PDEs]) under uncertainty. As an example, we focus on the so‐called Conditional Value at Risk (CVaR), but the approach is equally applicable to other coherent risk measures. Both the full and reduced space formulations are considered. The algorithm is based on low rank tensor approximations of random fields discretized using stochastic collocation. To avoid nonsmoothness of the objective function underpinning the CVaR, we propose an adaptive strategy to select the width parameter of the smoothed CVaR to balance the smoothing and tensor approximation errors. Moreover, unbiased Monte Carlo CVaR estimate can be computed by using the smoothed CVaR as a control variate. To accelerate the computations, we introduce an efficient preconditioner for the Karush–Kuhn–Tucker (KKT) system in the full space formulation.The numerical experiments demonstrate that the proposed method enables accurate CVaR optimization constrained by large‐scale discretized systems. In particular, the first example consists of an elliptic PDE with random coefficients as constraints. The second example is motivated by a realistic application to devise a lockdown plan for United Kingdom under COVID‐19. The results indicate that the risk‐averse framework is feasible with the tensor approximations under tens of random variables. [ FROM AUTHOR]