Reduce Safety Risks Research Articles

An ultralight dual-wavelength and dual-beam chemical sensor on small UAV for in-situ determination of phosphate and nitrite in surface water

In order to reduce safety risks related to the field personnel and address the difficulties for deploying in-situ sensors, a novel ultralight wet chemical sensor was developed for the in-situ simultaneous determination of both phosphate and nitrite. The sensor only weighted 220 g and consumed 0.4 W average power, and achieved limit of detections (LODs) of 0.38 and 0.12 μmol/L for phosphate and nitrite, respectively. Moreover, a small experimental multi-rotor unmanned aerial vehicle (UAV) was also fabricated for employing the sensor for the in-situ detection in surface waters.A dual-wavelength and dual-beam detection method that required fewer sensor parts was developed for the simultaneous detection of phosphate and nitrite. Bubbles cut from bubble wrap were used to store liquid reagents. A tandem sampling-reaction-detection cell that enabled merely by rubber bands was designed for avoiding the use of heavy pumps and valves. With these features, the complexity, size, weight and power consumption of the sensor were impressively reduced, hence rendering the sensor especially adaptive to the precious payload capability and battery power of the UAV.To demonstrate the practicability, the developed sensor-UAV system was successfully applied in two water bodies. The UAV flew to the designed water area, then landed and floated on the water surface. The sensor was immersed into the water for in-situ detection, and previously established calibration curves were used for the quantification of the target analytes. The results were compatible with those obtained by the standard methods.

A Trustworthy Safety Inspection Framework Using Performance-Security Balanced Blockchain

Regular safety inspection is critical to reduce safety risk in industry. Applying consortium blockchain technology to safety inspection can ensure the effectiveness of inspection process and tracing of problems. However, there are two major issues when using conventional consortium blockchain. It is challenging to guarantee the authenticity of the retrieved data source, and meanwhile, achieving a balance between performance and security is not easy. Hence, this paper proposes a blockchain based performance-security balanced safety inspection framework (PSB-SIF), in which a safety inspection box is designed to ensure the authenticity of the inspector’s identity while inspection logic is executed automatically via smart contracts. In addition, this paper also proposes a novel credit scoring based Byzantine fault tolerant (BFT) consensus algorithm, named Safety Inspection Byzantine Fault Tolerance consensus algorithm (SIBFT), which is used to balance the performance and security of consensus network in safety inspection. We evaluate the proposed approach by comparing with the solutions using RAFT, Practical Byzantine Fault Tolerance (PBFT), and SIBFT consensus algorithms in terms of throughput, transaction latency, scalability and security of PSB-SIF. The evaluation results show that PSB-SIF is efficient for all these quality metrics.

0004 Sleep, Team and Social Processes, and Health, Performance, and Safety in Naval Operational Environments

Abstract Introduction Sleep disruption and teamwork are inherent features of 24/7 operational environments, yet little is known about how sleep and team/social processes interact to affect crew readiness and endurance. Methods We analyzed data of 3,434 active duty US sailors (80% male) from the Afloat Safety Climate Assessment Survey. Using structural equation modeling, we specified latent factors of Sleep Health (typical hours of sleep per day and sleep disturbances in shipboard environment); crew team and social factors of Team Transition Processes, Team Action Processes, Team Interpersonal Processes, Unit Cohesion, Psychological Safety, and Social Support; and operational outcome risks of Physical Health (no. days in previous 30 with physical illness or injury), Mental Health (no. days with stress, depression, or emotional problems), Performance (frequency of fatigue-induced functional impairments on duty), and Safety (individual and crew noncompliance, and rate of observed near misses). Results Higher Sleep Health reduced impairments in Physical Health, Mental Health, Performance, and Safety (standardized βs = −0.096 to −0.542, ps < 0.0001, CFI/TLI > 0.980, RMSEA = 0.033). Higher Sleep Health improved Team Transition, Action, and Interpersonal Processes, Unit Cohesion, Psychological Safety, and Social Support (βs = 0.178 to 0.380, ps < 0.0001, CFI/TLI > 0.982, RMSEA = 0.029). Social Support reduced risks to Physical Health, Mental Health, and Performance; Team Interpersonal Processes reduced Metal Health risk; Psychological Safety reduced Performance and Safety risks; Unit Cohesion reduced Safety risk (βs = −0.053 to −0.709, ps < 0.05, CFI/TLI > 0.979, RMSEA = 0.027). Mediation models indicated good Sleep Health enhances Social Support’s beneficial impact on Physical Health, Mental Health, and Performance; Psychological Safety’s impact on Performance and Safety; Team Interpersonal Processes’ impact on Physical Health; and Unit Cohesion’s impact on Safety (indirect effect βs = −0.032 to −0.127, ps < 0.0001, CFI/TLI > 0.967, RMSEAs < 0.051). Conclusion Sleep health improves team/social functioning, which serves an additive protective function and enhancement to crew operational health, performance, and safety. Future work should closely examine these interrelationships to identify mechanisms as targets for policy and procedures to help optimize crew readiness and endurance. Support (If Any) Military Operational Medicine Research Program under work unit N2010.

A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures.

This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen’s useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback–Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen’s work.

“Carrying the black pot”: Food safety and risk in China's reactive regulatory state

AbstractThis paper explores the struggles of China's party‐state to address chronic food safety problems by adopting international best practices of risk‐based regulation. Despite formally adopting risk‐based approaches for targeting inspections and enforcement in 2002, implementation has been halting and uneven, as we show in the first analysis of risk‐based regulation beyond its OECD heartlands. Drawing on policy document analysis and 36 key informant interviews with food business operators and government officials working on food safety regulation at every level of the state, we identify contradictions between official commitments to risk‐based inspection and top‐down demands for zero tolerance and strict accountability, which leave local inspectors preoccupied with avoiding blame more than reducing safety risks. Our analysis advances recent scholarship on regulatory states of the global South by highlighting how risk‐based ideas, instruments, and practices are refracted through the distinctive norms and style of China's reactive regulatory state.

Impact of COVID-19 on physical activity: A rapid review.

BackgroundPhysical activity is a commonly prescribed medicine for people with conditions such as obesity and diabetes who are also at increased risk of being hospitalized or severely ill from COVID-19. However, many people are reporting challenges in engaging in a healthy dose of physical activity amid the pandemic.ObjectiveThis rapid review synthesizes the current empirical evidence about the impacts of COVID-19 on people’s outdoor physical activity and sedentary behavior while highlighting the role of community environments in promoting or hindering physical activity during the pandemic.MethodsLiterature searches were conducted using keywords related to COVID-19: physical activity, mobility, and lifestyle behaviors. Eligibility criteria were peer-reviewed empirical and quantitative studies published in English, addressing COVID-19 and using physical activity and/or sedentary behavior as the study outcomes.ResultsOut of 61 eligible studies, the majority (78.3%) were conducted in Asian and European countries, with only four (6.7%) being US studies. The results showed that COVID-19 was linked with significant decreases in mobility, walking, and physical activity, and increases in sedentary activity. A few studies also reported contradicting results including increased uses of parks/trails and increased recreational activity among certain groups of population.ConclusionsEvidence suggests an overall negative impact of COVID-19 on physical activity, with differential effects across different sub-populations. Significant knowledge gaps are also found in the roles of social and physical attributes that can promote physical activity during pandemics with reduced safety risks.

Recovery of platinum from diesel catalysts by combined use of H2O2/HCl leaching and adsorption

Spent automotive catalysts are industrial non-hazardous wastes of high added values because of the presence of platinum group metals, whose recovery is gaining increasing attention in European countries. This paper proposes a hydrometallurgical process to recover platinum from diesel catalysts, based on a first leaching step with aqueous solutions of H2O2 (up to 0.2 M) and mild HCl concentrations (0.4 M), followed by a refining step in which platinum is deposited over a granular activated carbon. Tests were carried out at different H2O2 content, temperatures, and sizes of granulated catalyst. The use of H2O2 and low HCl content allows higher sustainability than conventional hydrometallurgical processes by reducing safety risks associated with the use of concentrated HCl and the emissions of NOx deriving from the use of nitrogen species in conventional leaching. Besides, the use of an adsorbent avoids the utilization of cyanides or other toxic organic solvents for the refining of the leaching solution. Experiments revealed that the low HCl concentration results in longer leaching times; however, process conditions can be tuned to completely recover platinum with negligible extraction of other metals on the catalyst, minimizing on the subsequent adsorption process. Optimal conditions appear as leaching at 20 °C with a 0.13 M H2O2 0.4 M HCl solution followed by an adsorption step at 20 °C using an activated carbon with high BET surface area and high content of reducing surface groups. These findings suggest that the proposed recovery process can be a suitable candidate for future technology implementations.

A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents.

Artificial Intelligence (AI) systems are increasingly applied to complex tasks that involve interaction with multiple agents. Such interaction-based systems can lead to safety risks. Due to limited perception and prior knowledge, agents acting in the real world may unconsciously hold false beliefs and strategies about their environment, leading to safety risks in their future decisions. For humans, we can usually rely on the high-level theory of mind (ToM) capability to perceive the mental states of others, identify risk-inducing errors, and offer our timely help to keep others away from dangerous situations. Inspired by the biological information processing mechanism of ToM, we propose a brain-inspired theory of mind spiking neural network (ToM-SNN) model to enable agents to perceive such risk-inducing errors inside others' mental states and make decisions to help others when necessary. The ToM-SNN model incorporates the multiple brain areas coordination mechanisms and biologically realistic spiking neural networks (SNNs) trained with Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP). To verify the effectiveness of the ToM-SNN model, we conducted various experiments in the gridworld environments with random agents' starting positions and random blocking walls. Experimental results demonstrate that the agent with the ToM-SNN model selects rescue behavior to help others avoid safety risks based on self-experience and prior knowledge. To the best of our knowledge, this study provides a new perspective to explore how agents help others avoid potential risks based on bio-inspired ToM mechanisms and may contribute more inspiration toward better research on safety risks.

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling.

Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.

Physicochemical and microbiological changes in postmortem crayfish (Procambarus clarkii) stored at 4 °C and 25 °C

SummaryThe physicochemical and microbiological parameters of postmortem crayfish stored at 4 °C and 25 °C were evaluated in order to reduce safety risks of crayfish from temperature abuse during transportation and storage. Results showed that hepatopancreas of postmortem crayfish had significantly higher contents of total volatile basic nitrogen, biogenic amines (BAs) and higher microbial loads than tail meat, regardless of storage temperature. Enterobacteriaceae and Pseudomonas counts reached log 6.41 log10 CFU g−1 and 6.31 log10 CFU g−1 in hepatopancreas of crayfish at 25 ℃ for 6 h. Putrescine and cadaverine were the main BAs in tail meat with levels of 28.18 ± 0.73 mg kg−1 and 187.32 ± 3.57 mg kg−1, respectively, whilst cadaverine, spermidine and spermine were the major BAs in hepatopancreas, reaching 283.45 ± 3.95 mg kg−1, 62.87 ± 9.02 mg kg−1 and 155.31 ± 4.55 mg kg−1, respectively, after postmortem storage at 25 °C for 12 h. With time increasing, Acinetobacter, Flavobacterium, Aeromonas and Chryseobacterium at 25 °C and Acinetobacter, Flavobacterium, Psychrobacter at 4 °C in tail meat as well as Bacteroides and Muribaculaceae at 25 °C, and Acinetobacter, Psychrobacter at 4 °C in hepatopancreas, gradually became the major genus at the end of storage. Based on the results of spoilage microorganisms and biogenic amines, crayfish stored at 4 °C and 25 °C could be edible within 24 h and 6 h, respectively.

An adaptive safety-risk mitigation plan at process-level for sustainable production in chemical industries: An integrated fuzzy-HAZOP-best-worst approach

Safety-risk management in the hydrocarbon processing industry necessitates the availability of appropriate data and decision-making tools. Operational factors may influence chemical and physical risk occurrences associated with high-hazard plant operation in the chemical process sectors. Thus, safety-risk technologies and assessments should be explored considering potential events that result in fatalities, property destruction, economic loss, and environmental degradation. This research established a systematic framework for reducing safety risks to decrease accidents and hazards associated with the inherent production processes, as well as proposing process-level maintenance techniques for the specified hydrocarbon processing industry. First, the hazards of chemical reactivity were studied to determine which equipment performance poses the greatest risk. Physical and chemical risks were obtained to configure out identical nodes of expressing severe hazard via a qualitative assessment using the HAZOP study. The fuzzy best-to-worst technique combined with an analytical network process (fuzzy-BWANP) was utilized to assess the safety-risk criticality due to the operational reaction process, environmental risk, economical safety, and occupational management. Furthermore, the annual loss of expectancy and exposure factors for the earlier categories based on their operational expenses and failure time were estimated. The case study examined how to increase the process feasibility of an acrylonitrile plant. Consideration of economic loss resulted in the extraction of a risk index that helps decision-makers in determining priority tasks for addressing urgent hazards in any hydrocarbon processing industry's safety management.

Smart heat isolator with hollow multishelled structures

Safe, green and efficient industrial production has always been the pursuit of the chemical industry. Since thermal energy is the driving force for most of chemical reactions, an ideal reaction tank would have the capacity to automatically regulate heat conduction rate. In detail, this reaction tank should endow an ability that resists the heat loss when the reaction temperature is lower than the target, while accelerating the heat dissipation when the system is overheated. In this case, this smart reactor can not only minimize energy consumption but also reduce safety risks. Hollow structures are known to reduce heat conductivity. Particularly, the hollow structure with multishells can provide more interfaces and thus further inhibit heat transmission, which would be more favorable for heat isolation. Step forward, by coupling HoMSs with temperature-sensitive polymer, a smart heat isolation material has been fabricated in this work. It performs as a good heat isolator at a relatively lower temperature. A heat insulation effect of 6.5 °C can be achieved for the TSPU/3S–TiO2 HoMSs with a thickness of 1 mm under the temperature field of 50 °C. The thermal conductivity of composite material would be raised under overheating conditions. Furthermore, this composite displays an unusual two-stage phase transformation during heating. Benefiting from the unique multishelled structure, energy is found to be gradually guided into the hollow structure and stored inside. This localized heat accumulation enables the composite to be a potential coating material for intelligent thermal-regulator and site-defined micro-reactor.

Development and Application of a Bayesian Network-Based Model for Systematically Reducing Safety Risks in the Commercial Air Transportation System

Development and Application of a Bayesian Network-Based Model for Systematically Reducing Safety Risks in the Commercial Air Transportation System

Overtaking risk modeling in two-lane two-way highway with heterogeneous traffic environment of a low-income country using naturalistic driving dataset

Introduction: Driver behavior related to overtaking maneuvers, which are considered a major safety risk determinant on two-lane two-way highway in low- and middle-income countries (LMIC), are an important subject of further analysis. This study evaluates safety risk in terms of nature and severity of probable conflicts during overtaking maneuvers on a bi-directional undivided two-lane highway in a heterogeneous traffic environment of a low-income country. Nature and severity of probable conflicts were defined with the application of surrogate safety proximity indicators in real-world naturalistic driving environment. Method: A risk severity model for overtaking maneuver was developed to better understand the significant factors associated with the probability of conflict and its severity during overtaking maneuver using discrete choice modeling approaches. The relevance of three alternate discrete outcome frameworks, namely multinomial logit (ML), ordered probit (OP), and mixed logit (MXL) models are addressed. The best fitted model is identified and estimated. The impact of the significant attributes was also evaluated. The study collected data from a section of two-lane highway in Bangladesh using naturalistic driving from both observational and computer vision techniques. A total of 46 explanatory variables related to overtaking maneuver are assessed. Results: Speed differential between overtaking and overtaken vehicles have a significant impact on the probability of severe conflicts. Moreover, the presence of a bus as an overtaking vehicle was found to contribute significantly to the severity of conflicts. Conclusions: The study makes substantial research contributions related to overtaking behavior and safety risk evaluation during overtaking in mixed traffic environment in low-income countries. The results can be used as a proactive tool for the evaluation of overtaking maneuvers and associated safety risk, and making policy decisions reducing safety risk during overtaking maneuver as well as overall safety, while acknowledging the limited resources and facilities in low-income countries.

Risk Assessment In The Military: A Work/Rest Schedule-Based Tool For The Usn Surface Fleet

Long working hours, 24/7 shift work, restricted sleep opportunities, and circadian misalignment all lead to degraded human performance and impairments in decision making. After several mishaps involving U.S. Navy warships in 2017, renewed attention was devoted to optimizing warfighter performance and reducing safety risks associated with crew endurance and fatigue issues. This paper summarizes the development and initial evaluation of the Scheduling Management Aid for Risk Tracking (SMART) prototype. SMART is a human performance risk assessment tool designed to help the U.S. Navy systematically assess and mitigate risk due to crew fatigue during in port and at-sea operations. The heuristics underlying the prototype are selected to identify and quantify human performance risk based on a Sailor’s work and rest patterns. The results are designed to enable data-driven crew resource management decisions, take targeted fatigue mitigation actions, and rapidly calculate human performance risks.

Efficacy and Safety of Polyphenols for Osteoarthritis Treatment: A Meta-Analysis

Osteoarthritis (OA) is a chronic and degenerative disorder associated with joint pain and loss of joint function. It is reported that polyphenols could yield articular benefits in patients with OA through the inhabitation of key inflammatory pathways. This meta-analysis was conducted to assess the efficacy and safety of polyphenol products for OA treatment. This study included searches of PubMed, EMBASE, and the Cochrane Library databases from inception to November 6, 2019. Randomized controlled trials (RCTs) comparing polyphenols versus NSAIDs or placebo for human OA were included. Standardized mean differences (SMD) or risk ratios (RRs) were calculated for all relevant outcomes. Meta-analyses were conducted by using random effect models, and heterogeneity was assessed by using the I2 statistic. A total of 18 RCTs (N=1724) were eligible for analysis. Polyphenol products showed a significant advantage over placebo on pain relief (SMD, -1.11; 95% CI, -1.35 to -0.87) and functional improvement (SMD, -1.14; 95% CI, -1.38 to -0.90). No differences in safety outcomes were detected between polyphenols and placebo. There were no differences in efficacy outcomes between polyphenols and NSAIDs, although patients receiving polyphenols had a lower but nonsignificant risk of experiencing gastrointestinal dysfunction compared with those treated with NSAIDs. Polyphenols and NSAIDs in combination yielded more significant benefits in efficacy than NSAIDs alone. The results of our study suggest that polyphenols may be a promising alternative for OA by relieving symptoms while reducing safety risks. However, the generalizability of our results may be limited by the quality and sample size of the available research, as well as the heterogeneity between RCTs. High-quality clinical trials are needed to make meaningful clinical practice recommendations.

A hybrid simulation approach for site layout planning in construction projects

Purpose This study aims to propose a hybrid simulation approach for site layout and material laydown planning in construction projects considering both the project’s continuous and discrete state. Design/methodology/approach Efficient site layout planning (SLP) is a critical task at the early stages of the project to enhance constructability and reduce safety risks, construction duration and cost. In this paper, external and internal conditions affecting SLP gets identified. Then dynamic features of project conditions and project operations are analyzed by using a hybrid simulation approach combining continuous simulation (CS) and discrete event simulation (DES). Findings An efficient site layout plan regarding the project conditions results in cost efficiency. Instead of using DES or CS alone, this paper uses a hybrid simulation approach. Such a hybrid method leads to more accurate results that enable construction managers to make better decisions, such as material management variables. The proposed approach is implemented in a real construction project (i.e. earthmoving operation) to evaluate the hybrid simulation approach’s performance. Practical implications The proposed approach is implemented in a real construction project (i.e. earthmoving operation) to evaluate the performance of the hybrid simulation approach. Originality/value Although DES is used widely in construction simulation, it involves some limitations or inefficiencies. On the other hand, modeling resource interactions and capturing the construction project’s holistic nature with CS or system dynamics face some challenges. This study uses a hybrid DES and CS approach to enhance commercial construction projects’ SLP.

Interdisciplinary systematic review: does alignment between system and design shape adoption and use of barcode medication administration technology?

BackgroundIn order to reduce safety risks associated with medication administrations, technologies such as barcode medication administration (BCMA) are increasingly used. Examining how human factors influence adoption and usability of this...

A New Method for Determining Gob Methane Sources Under Extraction Conditions of Longwall Coal Mines

Underground coal mining operations produce large numbers of gobs that contain methane from mining coal seams and their adjacent coal seams. In longwall mining, gob methane is released to coal faces, presenting safety issues. Therefore, if gob methane sources can by identified accurately under extraction conditions, then efficient methane extraction strategies can be adopted to maximize the capture of methane from gobs and to control effectively methane flow to the coal face, thereby reducing safety risks. In this study, a new method to determine quantitatively gob methane sources under extraction conditions was proposed and tested in China’s Liyazhuang Coal Mine. Firstly, the isotope and gas composition of methane desorbed from coal cores of the mining seams and their adjacent seams, as well as methane collected from methane drainage boreholes at different heights in the gobs, were measured. Then, an isotope source model for calculating gob methane sources was established. Finally, based on isotope and gas compositions, the proportions of sources of gob methane that had been extracted from methane drainage boreholes were determined using the afore-mentioned model, and the extraction range that could maximize efficiently the control of methane emissions from each methane source was obtained.

Prototyping and Evaluation of Infrastructure-Assisted Transition of Control for Cooperative Automated Vehicles

Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.