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When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe situation is imminent (in the absence of corrective action). To reliably improve safety, these warning systems should have a provable false negative rate; that is, of the situations that are unsafe, fewer than ϵ will occur without an alert. In this work, we present a framework that combines a statistical inference technique known as conformal prediction with a simulator of robot/environment dynamics, in order to tune warning systems to provably achieve an ϵ false negative rate using as few as 1/ ϵ data points. We apply our framework to a driver warning system and a robotic grasping application, and empirically demonstrate the guaranteed false negative rate while also observing a low false detection (positive) rate. [ABSTRACT FROM AUTHOR]

As the demand for sustainable energy solutions grows, there is a critical requirement for continuous innovation to optimize the performance and safety of renewable energy systems (RESs). Closed-loop digital twins (CLDTs)—synchronized virtual replicas embedded with real-time data and control loops to mirror the behavior of physical systems—have emerged as a promising tool for achieving this goal. This paper presents a systematic literature review on the application of digital twin (DT) technology in the context of RESs with an emphasis on the impact of DTs on the efficiency, performance, and safety assurance of RESs. It explores the concept of CLDTs, highlighting their key functionalities and potential benefits for various renewable energy technologies. However, their effective implementation requires a structured approach to integrate observation, orientation, decision, and action (OODA) processes. This study presents a novel OODA framework specifically designed for CLDTs to systematically identify and manage their key components. These components include real-time monitoring, decision-making, and actuation. The comparison is carried out against the capabilities of DT utilizing the OODA framework. By analyzing the current literature, this review explores how DT empowers RESs with enhanced efficiency, reduced risks, and improved safety assurance. [ABSTRACT FROM AUTHOR]

The increasing societal concern regarding the safety of alcoholic beverages necessitates the implementation of effective and safe intervention measures to enhance their safety. This study aims to repurpose the substantial spent grains generated during Baijiu production, which have a significant environmental impact, into sustainable activated carbon (as an adsorbent) by pyrophosphoric acid to alleviate these concerns directly. Precise optimization strategies were employed for simulated and authentic Baijiu samples to determine ideal conditions for carbon synthesis suitable for different alcoholic beverages (with fewer flavor compounds and rich in flavor compounds). Characterization via FT-IR and SEM confirmed the porous structure of the activated carbon. The adsorption experiments indicate that calcination temperature significantly influences adsorption efficiency, favoring selective adsorption of higher alcohols, acetaldehyde, and methanol, while simultaneously reducing acid adsorption. Compared to commercial activated carbon, spent grain carbon exhibited superior acid retention effects. Through sensory evaluation to determine application parameters, activated carbon can reduce acetaldehyde and higher alcohols in Baijiu by 9.67 % and 14.94 %, respectively, without compromising aroma. Thus, this research not only provides a novel recycling strategy for the significant waste generated in Baijiu production but also offers a green and sustainable assurance measure for the quality and safety of alcoholic beverages.

The general objective of this study was to examine the effect of occupational health and safety management on workers' productivity using the University of Ilorin Teaching Hospital (UITH), Kwara State, Nigeria as a case study. The specific objectives of the study were to examine the effect of safety assurance on employee effectiveness and ascertain the influence of OHS training on job performance. The research adopted a descriptive survey research design while the sampling technique adopted is multi-stage sampling and the population of the study comprised of the staff working in the hospital which is 1526. A total number of three hundred and eight (308) copies of questionnaires were distributed which represents the sample size and was arrived at using Krejicie and Morgan (1970) formula but two hundred and eighty-one (281) copies of the questionnaires were correctly filled and returned and used for the analysis of this study. The responses to the questionnaires were analyzed using multilinear regression at 0.05 significance level for the study. The study, therefore, concluded that there is a significant effect of occupational health and safety management on workers' productivity. Based on the findings, the study recommended that hospitals should ensure the safety of their staff to make them much more effective at work. [ABSTRACT FROM AUTHOR]

Automated Driving Systems (ADSs) bring many benefits, however their safety assurance poses challenges. Scenario-based testing has been proposed. To provide strong safety evidence for their safety assurance, the scenario-based testing process needs to consider the Operational Design Domain (ODD) of the systems, ODD combined with the behavioural elements can provide a foundation for the scenario generation workflow due to common domain elements between ODDs and scenarios. Based on such background, this paper introduces a novel framework to generate scenarios specifically target on testing the system’s claimed ODD. It includes the process going from the system’s ODD and behaviour competency, to logical scenarios generation utilising scenario construct rule sets, and to the concretisation of the logical scenarios into concrete scenarios. This paper also draws link towards the part II of the paper series, which illustrates a novel approach for scenario coverage analysis.

As the railway domain progresses towards autonomy, maintaining safety at levels comparable to human-operated systems is a crucial challenge. Autonomous trains require advanced systems capable of real-time risk assessment and decision-making, a task traditionally managed by human situational awareness. This paper introduces a novel risk-based decision-making approach for autonomous trains, using Partially Observable Markov Decision Processes (POMDPs) for continuous monitoring and evaluation of environmental collision risks. By consistently maintaining an acceptable risk level through ongoing risk estimation (in terms of occurrence probability and severity degree), the approach supports the decision-making capabilities of the autonomous driving system in autonomous trains, enabling safe and informed decisions despite the uncertainties in the train’s operational state and environmental conditions. The approach’s relevance and effectiveness are illustrated through its application in an anti-collision function for autonomous trains.

A safety management system may benefit General Aviation by increasing safety and reducing accident rates. This paper aims to systematically review the existing literature in targeted databases for gaps concerning general aviation safety management systems. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology provided the overall structure. Science Direct®, Embry Riddle Aeronautical University's Scholarly Commons, and the Federal Aviation Administration's (FAA) website were the three publicly available sources. Initial search criteria included PDF research articles (2006-2022), including the terms safety management system and general aviation. Additionally, keyword searches for the terms policy, promotion, risk management, and assurance guided the analysis. A final case review was conducted to eliminate faulty connections and false positive word matches. Bias may exist in this project due to the single author and lack of additional reviewers. However, two strategies were employed to maximally reduce unintended bias: (1) software-generated keyword searches and (2) a 1-week delay between reviews. The initial search returned 197 listings. Twenty-nine records remained after multiple rounds of elimination and review. The results suggest that general aviation participants receive minimalistic exposure to the benefits of the safety management system. Seven FAA publications showed little evidence of a safety management system within general aviation. Twenty-two research articles demonstrated a strong understanding of the safety management system's components, especially safety risk management. As a result of this review, it is recommended that the FAA explore the feasibility of incorporating additional safety management system education, awareness, and best practices. Additionally, future research should explore contributing factors related to the benefits of establishing a safety management system in general aviation operations. This project did not require any funding. [ABSTRACT FROM AUTHOR]

As the demand for sustainable energy solutions grows, there is a critical requirement for continuous innovation to optimize the performance and safety of renewable energy systems (RESs). Closed-loop digital twins (CLDTs)—synchronized virtual replicas embedded with real-time data and control loops to mirror the behavior of physical systems—have emerged as a promising tool for achieving this goal. This paper presents a systematic literature review on the application of digital twin (DT) technology in the context of RESs with an emphasis on the impact of DTs on the efficiency, performance, and safety assurance of RESs. It explores the concept of CLDTs, highlighting their key functionalities and potential benefits for various renewable energy technologies. However, their effective implementation requires a structured approach to integrate observation, orientation, decision, and action (OODA) processes. This study presents a novel OODA framework specifically designed for CLDTs to systematically identify and manage their key components. These components include real-time monitoring, decision-making, and actuation. The comparison is carried out against the capabilities of DT utilizing the OODA framework. By analyzing the current literature, this review explores how DT empowers RESs with enhanced efficiency, reduced risks, and improved safety assurance.

Automated Driving Systems (ADSs) in road vehicles, which can undertake the dynamic driving task without requiring a human driver, are on the verge of large-scale deployment. Assurance frameworks for vehicle systems have traditionally been underpinned by standards. Some commonly adopted standards will provide barriers to the market deployment of ADSs, particularly the inherent capability of ADSs to have their driving functionality changed using Machine Learning (ML) during in-service operation. New standards specific to ADSs are being adopted, but some of these also present challenges for ML-enabled system changes. Then there are emerging cross-sector AI and ML standards that will have implications for the automotive industry and regulators.This paper summarises an in-depth assessment of standards that are anticipated to play an important role in the safety assurance of ADSs. Summarised findings are provided, including key themes, covering issues such as the use of ADS safety cases that are strengthened by adopting complementary standards, ensuring safety assurance models cover the whole ADS lifecycle, and the use of a systems engineering approach that treats safety as a dynamic control problem and appropriately integrates software engineering standards. Other themes were more specific to ML, including the need for rigorous change processes, horizonal AI and ML standards to be appropriately considered by automotive manufacturers and regulators, and authorities to be clear on whether online changes that can occur autonomously without human oversight are allowed or not. The findings, themes and recommendations in this paper are intended to inform future safety assurance frameworks for ADSs.

Wildfires are a common problem in many areas of the world with often catastrophic consequences. A number of systems have been created to provide early warnings of wildfires, including those that use satellite data to detect fires. The increased availability of small satellites, such as CubeSats, allows the wildfire detection response time to be reduced by deploying constellations of multiple satellites over regions of interest. By using machine learned components on-board the satellites, constraints which limit the amount of data that can be processed and sent back to ground stations can be overcome. There are hazards associated with wildfire alert systems, such as failing to detect the presence of a wildfire, or detecting a wildfire in the incorrect location. It is therefore necessary to be able to create a safety assurance case for the wildfire alert ML component that demonstrates it is sufficiently safe for use. This paper describes in detail how a safety assurance case for an ML wildfire alert system is created. This represents the first fully developed safety case for an ML component containing explicit argument and evidence as to the safety of the machine learning. [ABSTRACT FROM AUTHOR]

Improving the level of feed and grain security is not only the core of promoting high-quality agricultural development, but also an important part of implementing the national food security strategy. From the perspective of Chinese modernization, the development of Chinese feed industry has a realistic basis for improving policies, upgrading production methods, and improving the level of marketization. Facing the new journey, the government needs to focus on the mission of creating an ecological feed grain production model, promoting the internationalization of feed trade, and strengthening the information construction of feed safety management. From the policy side, the supply and demand side and the technical side, the government should gradually optimize the implementation of food production policies, promote the adjustment of feed structure, accelerate the innovation of feed production technology, so as to build a deep feed safety barrier under the perspective of Chinese modernization. [ABSTRACT FROM AUTHOR]

• Consensus that automated driving systems require a whole-of life approach to safety. • Support for allowing automated driving systems to adapt within approved boundaries. • Mixed views on requiring human oversight of system changes enabled by machine learning. • Regulators will be challenged by the capabilities needed to assure ADS safety. Introduction: Automated Driving Systems (ADSs) present significant unresolved challenges for traditional safety assurance frameworks. These frameworks did not envisage, nor readily support, automated driving without the active involvement of a human driver, or support safety-critical systems using Machine Learning (ML) to modify their driving functionality during in-service operation. Method: An in-depth qualitative interview study was conducted as part of a broader research project on safety assurance of ADSs that can adapt using ML. The objective was to capture and analyze feedback from leading global experts, from both regulatory and industry stakeholders, with the key objectives of identifying themes that could assist with the development of a safety assurance framework for ADSs, and providing a sense of the level of support and feasibility for various safety assurance concepts relevant to ADSs. Results: Ten themes were identified from an analysis of the interview data. Several themes support a whole-of-life safety assurance approach for ADSs, with strong support for ADS developers to be required to produce a Safety Case, and for ADS operators to maintain a Safety Management Plan throughout an ADSs operational life. There was also strong support for in-service ML-enabled changes to be allowed within pre-approved system boundaries, although there were mixed views on whether human oversight of such changes should be required. Across all themes identified, there was support for progressing reform within current regulatory frameworks, without requiring wholesale changes to current frameworks. The feasibility of some themes was identified as presenting challenges, particularly with the ability for regulators to develop and maintain an appropriate level of knowledge, capability and capacity, and with the ability to effectively articulate and pre-approve boundaries within which in-service changes can occur without additional regulatory approval. Conclusions : Further research on the individual themes and findings would be beneficial to support more informed reform decisions. [ABSTRACT FROM AUTHOR]

Safety critical functions of the engineered railway need to perform at levels of integrity that are so high that an acceptable failure rate cannot be demonstrated through testing alone. Where such functions need to be implemented in complex programmable electronic systems certain design, build and test requirements are defined in technical standards and these are deemed to ensure that the correct level of systematic integrity is achieved. These approaches are based on assumptions around how system requirements are managed and delivered which are increasingly challenging to meet in practice. In particular the V&V lifecycle used in functional safety standards and emerging cyber security design standards is idealised. It assumes a top-down cascade of requirements for each delivery project. The approaches have become the de-facto standard internationally and are now mandated to an extent in European railway safety regulations. This paper proposes a different approach: a new lifecycle model that aligns better with the reality of the modern global supply chain and the order in which asset design and project delivery activities are actually undertaken to improve the ability to proactively manage safety.This leads to a fundamental change in the assurance philosophy to bring a simpler and more understandable approach. A framework for applying this approach is set out along with further research objectives to deliver the solution in practice. [ABSTRACT FROM AUTHOR]

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Industry 4.0 provides an ideal platform to support a direct interaction of humans and machines in adapting changes and uncertainties in system operations. However, a physical human-machine interaction must assure security and safety. The safety requirements of collaborative manufacturing are explored and the techniques to assure safety in different collaboration modes are investigated to identify the challenges of safety assurance of human–machine collaborations in shared workspaces. In addition, system-level safety requirements of collaborative manufacturing are discussed to reveal the importance of sustaining similar safety levels for all system components and tools in developing an integrated collaborative system. [ABSTRACT FROM AUTHOR]

Due to a nationwide shortage of Ringer's lactate, normal saline became the intravenous fluid of choice at our institution in May 2018. Recent studies have shown that the administration of normal saline in critically ill patients may have adverse renal effects. Our objective was to evaluate the renal function effects; if any, of normal saline administered to women with preeclampsia receiving magnesium sulfate. This is a prospective observational study of women identified with preeclampsia with severe features, requiring magnesium sulfate prophylaxis. The amount of normal saline administered was calculated and a basic metabolic panel was surveyed 12 h after initiation of magnesium sulfate prophylaxis. Laboratory analytes were examined according to the volume of intravenous normal saline received, including a comparison to those women who did not receive normal saline, to determine the impact of parameters after 12 h of magnesium sulfate prophylaxis. From May 2018 to November 2018, a total of 233 patients receiving magnesium sulfate were identified, 184 of which had received intravenous normal saline. No differences were identified at 12 h in serum chloride or creatinine between those patients who received intravenous normal saline and those who did not. This finding persisted when serum chloride and creatinine were analyzed across quartiles of normal saline received. Interestingly, serum calcium was found to decrease with increasing serum magnesium (p <.001). This association was also dependent upon the amount of intravenous fluids received (p <.001) when controlling for serum magnesium. Renal function indices were preserved despite the introduction of normal saline with oxytocin into routine practice. No dose–response relationship was identified when serum chloride and creatinine were analyzed across quartiles of normal saline received. Interestingly, serum calcium levels were noted to decline with both the amount of intravenous fluid received as well as with increasing serum magnesium levels. [ABSTRACT FROM AUTHOR]

Highly Automated Vehicles (HAVs) must undergo strict safety testing before being released to the public. Mileage-based on-road testing suffers from unaffordable time costs and high safety risks. Simulated scenario-based testing has been found to be a trustworthy alternative for testing HAVs’ built-in algorithms and functionalities. Test automation is typically used to generate target scenarios. This approach facilitates customized testing and avoids wasting time on simple and redundant scenarios. This study aims to review test automation methods and discuss how to accentuate their strengths rather than be trapped in their weaknesses under certain applicable conditions. According to their main purposes, we classify test automation methods into coverage-oriented, unsafe-scenario-oriented, and naturalistic-assessment-oriented categories. To further demonstrate the differences of these methods, we then design numerical experiment to compare the capabilities of seven test automation methods. Finally, we compile our observations to form a comprehensive guide for selecting test automation methods with different test requirements in mind. [ABSTRACT FROM AUTHOR]

Human-robot collaborative systems have emerged as a solution that aims to satisfy the current demand of highly personalized products by combining the skills of humans and robots within a shared workspace. However, the safety of human operators is of major concern when located next to a robotic companion. The objective of this article is to present a general outlook of safety assurance practices in current collaborative systems of the manufacturing industry. In this sense, the integration of safety standards and technologies is analyzed. A favoring tilt to techno-centered practices is spotted, where policies designed for rigid automation are being used for collaborative systems alike. This brings additional risks to the wellbeing of human operators. Consequently, a human-centered approach is highlighted as the ultimate level of safety compliance. [ABSTRACT FROM AUTHOR]

The past decade has seen efforts to develop new forms of autonomous systems with varying applications in different domains, from underwater search and rescue to clinical diagnosis. All of these applications require risk analyses, but such analyses often focus on technical sources of risk without acknowledging its wider systemic and organizational dimensions. In this article, we illustrate this deficit and a way of redressing it by offering a more systematic analysis of the sociotechnical sources of risk in an autonomous system. To this end, the article explores the development, deployment, and operation of an autonomous robot swarm for use in a public cloakroom in light of Macrae's structural, organizational, technological, epistemic, and cultural framework of sociotechnical risk. We argue that this framework provides a useful tool for capturing the complex "nontechnical" dimensions of risk in this domain that might otherwise be overlooked in the more conventional risk analyses that inform regulation and policymaking., (© 2024 The Author(s). Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

There would be high demand for technology that monitors illegal activity and detects risk with respect to vehicles on the road in a future urban traffic system, for accident prevention. However, there may be substantial overheads from the massive amount of data that would be required for the surveillance system as it would be automatically operated and used, as further advancements are made in sensor network technology and IoT (Internet of Things). In the transportation field, systems were mainly studied to measure the driver's drowsiness or mobile phone use. These systems will collect information from several mobile devices at the same time, resulting in overhead of data. Fog computing reduces the overhead of data because it uses distributed proxy servers. However, security aspects were not considered. Encryption can be done to ensure the privacy of the information on the moving vehicles. However, in order to track a specific vehicle the data of the moving vehicles should be provided decrypted or decrypted by the secondary user. In this paper, to solve security aspects, we provide a new DSS model that utilizes ABPRE (Attribute Based Proxy Re-Encryption). The proposed system encrypts data sensed from moving vehicles. The cipher is configured to re-encrypt quickly at the proxy. The proposed method allows only users with access authority via secondary user's properties to access data with attribute-based re-encryption. Therefore, safety is secured.

Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios. [ABSTRACT FROM AUTHOR]