Safety Requirements Research Articles

Combining switching mechanism with re-initialization and anomaly detection for resiliency of cyber–physical systems

Cyber–physical systems (CPS) play a pivotal role in numerous critical real-world applications that have stringent requirements for safety. To enhance the CPS resiliency against attacks, redundancy can be integrated in real-time controller implementations by designing strategies that switch among multiple controllers. However, existing switching strategies typically overlook remediation measures for compromised controllers, opting instead to simply exclude them. Such a solution reduces the CPS redundancy since only a subset of controllers are used. To address this gap, this work proposes a multi-controller switching strategy with periodic re-initialization to remove attacks. Controllers that finish re-initialization can be reused by the switching strategy, preserving the CPS redundancy and resiliency. The proposed switching strategy is designed to ensure that at each switching moment, a controller that has just completed re-initialization is available, minimizing the likelihood of compromise. Additionally, the controller’s working period decreases with the number of involved controllers, reducing the controller’s exposure time to attacks. An anomaly detector is used to detect CPS attacks during the controller’s working period. Upon alarm activation, the current control signal is set to a predefined value, and a switch to an alternative controller occurs at the earliest switching moment. Our switching strategy is shown to be still effective even if the anomaly detector fails to detect (stealthy) attacks. The efficacy of our strategy is analyzed through three derived conditions under a proposed integrated attack-defense model for mean-square boundedness of the CPS states. Simulation results on a third-order system and a single-machine infinite-bus (SMIB) system confirm that our approach significantly bolsters CPS resiliency by leveraging the advantages of re-initialization, anomaly detection, and switching mechanisms.

Strategic Approach for the Elaboration of a Safety Design Basis for Nonreactor Nuclear Facilities in Brazil

A nonreactor nuclear facility (NRNF) for maritime activities is being designed as part of the Brazilian naval nuclear propulsion program. The absence of national regulations for the design and licensing of naval NRNFs presents a challenge that must be addressed. This study aims to present an initial proposal for developing a safety design basis for NRNFs in Brazil. This proposal is based on the collection and analysis of national and international standards (documentary research), as well as articles, dissertations, and theses (bibliographic research) that discuss the criteria and safety requirements relevant to the licensing and design of NRNFs. This research develops a strategy for applying U.S. Department of Energy (DOE) regulations derived from the U.S. Code of Federal Regulations, Title 10, “Energy,” Part 830, “Nuclear Safety Management,” in conjunction with CNEN (National Nuclear Energy Commission, Brazil) regulation NE 1.04, “Licensing of Nuclear Installations,” aligned with International Atomic Energy Agency requirements to develop a safety design basis for naval NRNFs. The first step of the proposed strategy involves categorizing facility hazards conducted in accordance with DOE-STD-1027. The process continues for facilities categorized as hazard categories 1, 2, and 3. The directive DOE O 420.1C, “Facility Safety,” and the invoked standards serve as foundational elements for developing the safety design basis. The results of this research demonstrate that the DOE regulatory framework for NRNFs is a suitable alternative to address the national regulatory gap and improve the national licensing process by incorporating the safety-in-design concept.

Hydraulic characteristics and energy dissipation of a new baffle-drop shaft with two wet sides

To satisfy the operational capacity and structural safety requirements of larger flow rates under urban flood risk, a new baffle-drop shaft with two wet sides has been proposed. A turbulence mathematical model is established to simulate the hydraulic characteristics of traditional and new shafts, when the dimensionless flow rate(Q*) ranges from 0.1 to 0.25. Compared to that of the traditional shaft, the operational capacity of the new shaft is larger. The drainage of the new shaft is still smoother, but water blockage occurs in the traditional shaft. The pressure in the new shaft walls and baffles is smaller than that of the traditional shaft. The energy dissipation rate is higher than that of the traditional shaft. Consequently, this research could provide more reasonable design for dropshafts in deep tunnel drainage systems.

A current review on animal models of anti-asthmatic drugs screening

Asthma is a chronic inflammatory respiratory condition characterised by airway constriction, smooth muscle spasm, and severe morbidity. It affects around 300 million people globally, with children being especially vulnerable. Despite its worldwide effect, the invention of innovative asthma medicines has been slow over the last 5 decades, leaving significant unmet requirements in asthma care. Although intriguing medicines have demonstrated efficacy in animal models, many fail to fulfil safety and effectiveness requirements in human trials, highlighting the critical need for more predictive models that better transfer to human results. This comprehensive review investigates the mechanisms and efficacy of anti-asthmatic drugs using both genetic and conventional animal models. Both genetic and traditional models of anti-asthmatic agents, their characteristics, and their significance are summarized as: In-Vitro Animal Models: Histamine receptor assay, Cell Culture Method, WST Assay, Spasmolytic Activity of the Lungs of Guinea Pigs, Airway and Vascular Responses to an Isolated Lung, The Isolated Perfused Guinea Pig Trachea’s Reactivity. In-Vivo Models: In vivo small animal models, Broncho Spasmolytic Activity in anaesthetized Guinea Pigs, Guinea Pigs Respiratory and Vascular Dysfunction Caused by Arachidonic Acid or platelet-activated factor (PAF), Guinea Pig Asphyxia Induced by Serotonin Aerosol and Anaphylactic Microshock, Guinea Pigs Under Anaesthesia: Histamine-Induced Bronchoconstriction, Microshock in Rabbits and Pneumotachography in Guinea Pigs, Guinea Pig Bronchial Hyperactivity, Guinea Pig Airway Microvascular Leakage, Mice With Inflammatory Airways. Conclusion: This review focusses on the benefits and limitations of current animal models in asthma research, emphasising the need for more sophisticated, predictive models to decrease translational failures. By critically evaluating these models, the review emphasises their importance in directing anti-asthmatic drug development and highlights the urgent need for innovation to bridge the gap between preclinical success and clinical efficacy.

Multi-level risk classification of distributed embedded software failures for autonomous systems

With increasing autonomy in systems, the role of software becomes more prominent as it overtakes human operator functions. The software in autonomy differs from automation with respect to functionality, implementation, and complexity, and software failures contribute to system and operational risk. Such failures, however, are often not sufficiently catered for in current risk assessments and mitigation processes, as they are challenging to identify and quantify, in particular, in the early conceptual design phase. Software reliability is not the same as software safety, as the latter encompasses the context and use of the software, as well as interactions and potential cascading failures to hardware, humans, and the environment. It is also difficult to investigate cascading effects on the system that may follow from software failures. The objective of this paper is to propose a novel classification taxonomy to support a more thorough identification of software failures for systems with different degrees of autonomy, as well as for software implementation techniques. The risk from software is interwoven into the design, development, validation, and verification processes, impacting safe operation. The proposed taxonomy can be used iteratively from the early design phase as the detailed design concepts evolve. The level of abstraction for system and software functions decreases with the design and development process. The validation and verification processes must ensure the software’s safety and reliability on different system abstraction levels. The software taxonomy in this paper includes relevant causes, consequences, and process relationships, and has been created based on existing industry classifications, research, and system models. A case study applying the taxonomy to navigation and collision avoidance functions on the subsystem level of a Maritime Autonomous Surface Ship (MASS) is performed. Software properties extracted from existing systems and knowledge are transformed into a functional model. Each software failure is then described in the context of the system level valid for the design, development, validation, and verification processes for MASS. The overall outcome of the paper may contribute to the safer design of systems through enhanced identification of potential hazards and software failures, leading to improved risk assessments and, as such, a better basis for defining more efficient safety requirements for autonomous systems from the early system development. Even though the paper exemplifies the taxonomy and classification by focusing on MASS, the work has relevance to other types of software-intensive systems.

Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs

Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to testing control systems is falsification, which aims to find an input signal that causes the control system to violate a formal safety specification using optimization algorithms. However, applying falsification to AI-enabled control systems poses two significant challenges: (1) it requires the system to execute numerous candidate test inputs, which can be time-consuming, particularly for systems with AI models that have many parameters, and (2) multiple safety requirements are typically defined as a conjunctive specification, which is difficult for existing falsification approaches to comprehensively cover. This paper introduces Synthify , a falsification framework tailored for AI-enabled control systems, i.e., control systems equipped with AI controllers. Our approach performs falsification in a two-phase process. At the start, Synthify synthesizes a program that implements one or a few linear controllers to serve as a proxy for the AI controller. This proxy program mimics the AI controller's functionality but is computationally more efficient. Then, Synthify employs the \(\epsilon\) -greedy strategy to sample a promising sub-specification from the conjunctive safety specification. It then uses a Simulated Annealing-based falsification algorithm to find violations of the sampled sub-specification for the control system. To evaluate Synthify , we compare it to PSY-TaLiRo , a state-of-the-art and industrial-strength falsification tool, on 8 publicly available control systems. On average, Synthify achieves a 83.5% higher success rate in falsification compared to PSY-TaLiRo with the same budget of falsification trials. Additionally, our method is 12.8 \(\times\) faster in finding a single safety violation than the baseline. The safety violations found by Synthify are also more diverse than those found by PSY-TaLiRo , covering 137.7% more sub-specifications.

A Digital Twin Framework for Nuclear Power Equipment Maintenance: Design, Prototyping, and Preliminary Validation

Throughout the lifecycle of Nuclear Power Equipment (NPE), maintaining high-safety maintenance services is essential for optimal operation. Traditional time-based maintenance strategies are limited in NPE contexts due to stringent safety requirements and high costs of complex systems. Specifically, maintenance processes lack transparency, state monitoring relies heavily on manual inspections, and decisions depend passively on individual expertise. Digital Twin (DT) effectively breaks down "information silos," leverages data value, and enables proactive maintenance decision-making for NPE. However, DT application in the nuclear industry is still exploratory, with limited systematic and practical research, especially for critical equipment maintenance. This paper introduces a DT-based intelligent maintenance decision system featuring three key technologies: DT modeling, state monitoring with dynamic early warning, and systematic intelligent decision-making and verification. A DT-based prototype using a cooling water pump case study preliminarily validates the state monitoring model's accuracy. Results indicate that the proposed framework and methods are feasible and hold significant application potential.

Optimization of Inbound and Outbound Vessel Scheduling in One-Way Channel Based on Reinforcement Learning

As the size and number of ships continue to grow, effective management of vessel scheduling has become more and more important for the efficient one-way channel port operation, whose characteristics significantly affect the safety and efficiency of ports. This paper presents a reinforcement-learning-based approach to optimize the scheduling of vessels in a one-way channel, aiming to quickly identify a scheduling solution that enhances port operational efficiency. This method models the vessel scheduling problem in a one-way channel by incorporating navigational constraints, safety requirements, and vessel-specific characteristics. Using the Q-learning algorithm to minimize vessel wait times, it identifies an optimal scheduling solution. Experiments were conducted using real data from the Dayao Bay Pier of Dalian Port to validate the rationality and effectiveness of the proposed model and algorithm. The results show that the reinforcement learning approach achieved approximately a 16% improvement in solution quality compared to the genetic algorithm (GA) while requiring only half the computation time. Additionally, it reduced delay times by over 40% relative to the traditional FCFS strategy, indicating superior overall performance. This research presents an efficient, intelligent approach to vessel scheduling, providing a theoretical foundation for further advancements in this field and enhancing decision support for vessel scheduling in one-way channels with practical implications.

Analysis of The Application of Value Engineering In The Construction of Soil Retaining Walls on The Bendung-Bantengan Road Section In Mojokerto District, East Java

Mojokerto, as a key economic center in East Java, faces increasing demands for reliable and efficient road infrastructure to support its economic growth and regional connectivity. The lack of adequate retaining walls along the Bendung-Bantengan road section has resulted in significant road damages, including cracks and subsidence, which pose risks to road users and increase maintenance costs. This study investigates the application of Value Engineering (VE) as a strategic method to enhance the efficiency and cost-effectiveness of retaining wall construction in the area. Using secondary data from topographic surveys and triaxial soil tests, the study evaluates various design alternatives to identify cost savings, stability improvements, and impacts on project duration. The results demonstrate that adopting VE (Alternative 1) reduces construction costs by IDR 284,244,698 through optimized structural dimensions, while maintaining quality and stability standards. Additionally, the project completion period is shortened to 121 days, offering a faster turnaround compared to the initially planned six-month schedule. The findings emphasize VE's potential to optimize material use, minimize waste, and achieve environmental sustainability by reducing carbon emissions during construction. Moreover, VE enables enhanced collaboration among stakeholders, including consultants and contractors, to develop effective and efficient solutions for infrastructure challenges. This research underscores the importance of integrating VE during the planning stages of infrastructure projects, particularly in areas with high erosion risks. By applying VE principles, the Bendung-Bantengan retaining wall project not only meets safety and quality requirements but also contributes to long-term infrastructure resilience and economic efficiency in Mojokerto.

Sicurpest: A Prototype of a User-Friendly Tool for Preventive Risk Assessment of Pesticide Use in Agriculture

In recent years, there has been a significant increase in attention towards the use of pesticides, as evidenced by the introduction of regulations aimed at ensuring their safe and environmentally responsible application. Although this stricter legislative framework offers potential benefits, the challenges faced by farmers, particularly those in small-scale operations, in complying with occupational health and safety (OHS) requirements are considerable. To address this issue, a research project was promoted by the Italian Workers’ Compensation Authority (INAIL) aimed at developing a user-friendly software tool to support farmers in planning pesticide applications in safe conditions. This study summarizes the results of the research, which consisted of three main phases: the definition of the main parameters that characterize the farmers’ exposure based on the analysis of the literature; the development of a risk assessment model that integrates these determinants with data provided by producers for each authorized pesticide; and the development of software tool (called SICURPEST) for a preliminary risk assessment when using pesticides. This tool serves as a preliminary risk assessment method, offering a simplified model for practical use by farmers. Its initial verification was conducted through a case study and the results represent a step towards promoting safer pesticide practices, providing a solid basis for further validation.

Detection Method for Safety Helmet Wearing on Construction Sites Based on UAV Images and YOLOv8

With the increasing demand for safety management on construction sites, traditional manual inspection methods for detecting helmet usage face challenges such as low efficiency, limited coverage, and strong subjectivity, making them inadequate for modern construction site safety requirements. To address these issues, this study proposes a helmet detection method based on unmanned aerial vehicles (UAVs) and the YOLOv8 object detection algorithm. The method utilizes UAVs to flexibly capture construction site images, combined with the optimized YOLOv8s model, and employs transfer learning to annotate and train labels for “person” and “helmet”. Additionally, to improve detection accuracy, the study introduces matching criteria and a time-window strategy to further reduce false positives and negatives. Experimental results demonstrate that the proposed method can achieve efficient and accurate helmet usage detection in diverse construction site scenarios, significantly enhancing the automation and reliability of site safety management. Despite its excellent performance, future research should focus on optimizing real-time adaptability and improving performance in complex environments. This study provides an innovative and efficient technical solution for construction site safety management, contributing to the creation of safer and more efficient construction environments.

Improving the Quality of Mountain Facilities: A Novel Project in the Dolomites Area

In its broadest sense, the mountain environment represents a setting useful for promoting health. This Italian project, conceived and applied in the Dolomites, aims to encourage multi-sector preventive public health policies; improve refuge hygiene; strengthen safety requirements; and implement projects aimed at promoting health. With the support of healthcare workers (HCWs) of the Prevention Department and other professionals in this field, 27 establishments in the Alpine territory have joined the project, benefiting from being recognized as a “Healthy and Safe Refuge” at the end of the quality improvement process. This process consisted of a documentary assessment, an on-site evaluation, and an assessment of analytical evidence relating to parameters investigated by both HCWs and refuge managers according to specific protocols. Data were collected from a specific database via an application to join the project and from sampling reports of parameters investigated for evaluating the quality of water intended for human consumption. The outdoor Alpine environment has recently witnessed extraordinary development and must continue to meet all health and safety requirements, especially in view of a global event—the Olympic Games—that will be hosted in the Dolomites area in 2026.

Safety Requirement of Electric Facilities in BESS Fire Accidents Viewpoint

Safety Requirement of Electric Facilities in BESS Fire Accidents Viewpoint

SENSING WITH SOUND: IMPROVING GASES AND SOLID ANALYSIS BY PHOTOACOUSTIC SPECTROSCOPY

The development of photoacoustic spectroscopy is being driven by the growing demand for precise, efficient, and reliable detection methods that can be used for in situ measurements and real-time monitoring. Along with rapid technological progress, photoacoustic spectroscopy became an ultra-sensitive, selective, cost-effective technique that can meet the demanding requirements for environmental monitoring, industrial safety, and medical diagnostics. This paper highlights how continuous improvements in photoacoustic technologies, including the use of appropriate laser sources as well as sensing elements, and machine learning methods, are pushing the limits of gases and solid analysis and providing critical tools for addressing modern scientific and industrial challenges.

Understanding and Streamlining Dose Finding: From Dose Simulation to Dose Estimation.

Understanding drug dosing to fulfill safety and efficacy requirements in a patient population is an essential part of dose finding in clinical practice and drug development. The majority of current dose finding methods are simulation-based, which can be time consuming and resource intensive. Model-based simulations also do not guarantee that the dose, that will optimally fulfill the safety and efficacy endpoints, will be found. In this work, an advanced dose estimation approach based on the OptiDose concept is utilized to understand therapeutic doses in a patient population and to streamline dose finding. To demonstrate the potential of this concept, two illustrative case studies are presented, each representing specific challenges for dose finding, including different safety and efficacy requirements, complex dose-response relationships, and changes of dynamics in special populations. For both applications, the distributions of therapeutic doses in the population were estimated, such that therapeutic population doses can be determined for any proportion of patients to fulfill the safety and efficacy endpoints. In addition, the therapeutic population dose with which 50% and 95% of the population will fulfill the safety and efficacy requirements was estimated. Dose estimation for both drug applications was implemented in Monolix. The presented OptiDose approach has the potential to identify the "optimal" dose for any pharmacometric and clinical pharmacology scenario, allowing to guide clinical practice and facilitate dose selection in drug development, particularly in special populations such as pediatric or cancer patients. As such, we suggest moving from current "dose simulation" approaches toward a more efficient "dose estimation" paradigm.

A Metamodeling Approach for Planning Critical IoT Systems

This paper presents a metamodeling approach to address the lack of methodological support in canvas model development, focusing on planning critical IoT systems. We introduce MM4Canvas - a metamodel that provides a solid foundation for developing structured and standardized canvas models, allowing for consistent reuse and extension across diverse project types. A proof of concept was conducted by instantiating a general-purpose canvas model based on MM4Canvas for project planning, aiming to establish a connection between this activity and the Requirements Engineering process. We extended this model to incorporate safety and security requirements for critical IoT systems. Our contribution demonstrates the metamodel’s capacity to support standardization, reuse, and extensibility in canvas-based project planning.

Evaluating the interaction between human and paediatric robotic lower-limb exoskeleton: a model-based method

Abstract Lower-limb exoskeletons (LLEs) have shown potential in improving motor function in patients for both clinical rehabilitation and daily life. Despite this, the development and control of pediatric exoskeletons remain notably underserved. This study focuses on a unique pediatric robotic lower limb exoskeleton (PRLLE), tailored particularly for children aged 8–12. Each leg of the robot has 5 Degrees of Freedom (DOFs)—three at the hip and one each at the knee and ankle. The interaction between the child user and the PRLLE is intricate, necessitating adherence to essential requirements of comfort, safety, and adaptability. Testing numerous prototype variations against diverse user profiles, particularly for children with neurological disorders where each child differs, is impractical. Model-based methods offer a virtual testbed that is useful in the design stage. This study uses MATLAB® to simulate and evaluate the interaction between users and PRLLE after deriving the nonlinear dynamic model of the PRLLE, which is simplified through multiple layers. To verify the accuracy of the derived dynamic model, a Computed Torque Control method is employed. The study provides detailed outcomes for children aged 8, 10, and 12 years, for passive and active users along with variations in PRLLE assistance levels. The study shows significant reductions in human joint torques, up to 56%, alongside substantial actuator powers, reaching up to 98W, for a 10-year-old child user. Furthermore, examining 8 and 12-year-old child users revealed variations in interaction forces, with changes up to 29.5%. Consequently, meticulous consideration of the human user’s limitations is crucial during the PRLLE’s design and conceptualization phases, particularly for PRLLEs.

System Safety Monitoring of Learned Components Using Temporal Metric Forecasting

In learning-enabled autonomous systems, safety monitoring of learned components is crucial to ensure their outputs do not lead to system safety violations, given the operational context of the system. However, developing a safety monitor for practical deployment in real-world applications is challenging. This is due to limited access to internal workings and training data of the learned component. Furthermore, safety monitors should predict safety violations with low latency, while consuming a reasonable computation resource amount. To address the challenges, we propose a safety monitoring method based on probabilistic time series forecasting. Given the learned component outputs and an operational context, we empirically investigate different Deep Learning (DL)-based probabilistic forecasting to predict the objective measure capturing the satisfaction or violation of a safety requirement ( safety metric ). We empirically evaluate safety metric and violation prediction accuracy, and inference latency and resource usage of four state-of-the-art models, with varying horizons, using autonomous aviation and autonomous driving case studies. Our results suggest that probabilistic forecasting of safety metrics, given learned component outputs and scenarios, is effective for safety monitoring. Furthermore, for both case studies, the Temporal Fusion Transformer (TFT) was the most accurate model for predicting imminent safety violations, with acceptable latency and resource consumption.

Designing an effective educational toy: incorporating design-thinking in the design classroom

“Designing an effective educational toy” is a research project that emerged from a teaching and learning course designed to facilitate creative engagement both inside and outside the classroom at Wenzhou-Kean University in China. This research study examines the incorporation of design thinking concepts in the development of an educational toy by graphic design students. Utilising the ‘Double Diamond Model of Design’, the research methodically progresses through the stages of observation, generation, prototyping, and testing to create a 3D letter-building set with the goal of improving children's educational experiences. The approach prioritises the integration of ideas through iterative design, taking safety into account, involving users, and focusing on instructional value. The key findings emphasise the effective implementation of design principles in developing a product that not only fulfils safety and educational requirements but also provides novel learning experiences. The essay continues by recommending the wider implementation of these concepts in the creation of educational toys to encourage improved learning outcomes and the development of skills in children.

Factors Influencing the Bond Strength between FRP Bars and Concrete and the Calculation Methods for Anchorage Length

Since the 21st century, developed countries such as Japan, the United States, and Europe have designated Fiber Reinforced Polymer (FRP) as a key industry for development. Currently, FRP sheets, represented by carbon fiber fabric, have become an important material for structural reinforcement and are widely used in the renovation and reinforcement of various civil and industrial buildings. For example, in the early 1960s, the American company Marshall-Vega produced Glass Fiber Reinforced Polymer (GFRP) bars to address the issue of salt corrosion in reinforced concrete structures in coastal and cold regions. In the design of the ArtScience Museum in Singapore, to ensure the integrity and smoothness of the architectural form, facilitate prefabrication and assembly of components, and meet structural, fire safety, and security requirements, the designers evaluated various materials and ultimately selected polyethylene resin-based GFRP panels. This paper employs a literature review methodology, first analyzing the factors that affect the bond strength between FRP bars and concrete, then reviewing standards to understand the calculation methods for anchorage length in different countries, collecting relevant experimental data, and finally analyzing the advantages and disadvantages of various anchorage length calculation methods.