Safety Systems Research Articles

Kind, Firm, and Fair Medical Regulation: A Systems Approach and Six Ethical Principles

ABSTRACT Background: Regulatory processes can be stressful, anxiety provoking, and complicated for the health professional involved, the institutions they work for, and for the regulators. Methods: This paper uses a case study to describe a systems approach to comprehensively identify the agents (ie: the complainant, the health professional concerned, the regulator dedicated to the case, the institution, the healthcare profession, the public, and the regulatory authority) and 6 ethical issues that emerge during the regulatory process: potential to cause harm, not in the best interest of a person, affects autonomy, unjust, not truthful, and loss of trust. Results: A systems approach lends to a mental model in which there are dynamic interactions across these elements accompanied by positive and negative feedback loops that maintain overall stability of the whole system. Conclusions: The process can be further refined and kinder to those concerned by developing a sensitive approach to improve assessment of risks, using feedback to improve the regulatory process, adapting transparent procedures, not causing undue delays to the process, embedding regulatory activities in a patient safety system, minimizing individual blame, using non-legalistic, non-threatening and respectful communications, and directing the different actors to a support network for guidance and advice.

Local two-group bubble size model for adiabatic air–water flow in a large diameter pipe using CFD code

Accurate prediction of bubble behavior in large diameter pipes is crucial for evaluating the performance of safety systems, steam generators, and heat exchangers in nuclear systems. Bubble behavior in large diameter pipes under two-phase flow significantly differs from that in small pipes. With the increasing use of computational fluid dynamics (CFD) codes, predicting interfacial area concentration (IAC) is critical for understanding multi-dimensional bubble behavior. This study developed a two-group local bubble size model for bubbly, slug, and churn flows under adiabatic conditions. The model includes correlations for void fraction and bubble sizes of two groups, which were implemented into CFD codes and validated against experimental data from large diameter pipes with low-pressure air–water flow. Results show the model's prediction accuracy surpasses existing correlations. The developed correlations are applicable across a range of flow conditions covering pipe diameters in the range 0.05–0.152 m, pressures from atmospheric to 300 kPa, superficial liquid velocities from 0.25 m/s to 2.85 m/s, and superficial gas velocities from 0.04 m/s to 5.48 m/s. The model is expected to enhance the prediction capabilities of CFD codes for the adiabatic two-group two-phase flows in the large diameter pipes.

Test Methodology for Short-Circuit Assessment and Safe Operation Identification for Power SiC MOSFETs

The short-circuit (SC) immunity of power silicon carbide (SiC) MOSFETs is critical for high-reliability applications, where robust monitoring and protection strategies are essential to ensure system safety. Despite their superior voltage blocking capabilities and high energy efficiency, SiC MOSFETs exhibit greater sensitivity to SC-induced degradation compared to their silicon counterparts. This increased vulnerability necessitates the precise assessment of the key SC performance metrics, such as short-circuit withstand time (TSCWT), as well as a deeper understanding of the failure mechanisms. In this study, a comprehensive experimental methodology for evaluating the SC behavior of SiC MOSFETs is presented and validated using industrial references. The investigation further explores the concept of a Safe Operating Area (SOA) under SC conditions, highlighting the significant impact of quasi-simultaneous SC events on device lifetime. Additionally, an application case study demonstrates how these events can drastically reduce the device’s lifespan.

Thermal Stratification Prediction in Reactor System Based on CFD Simulations Accelerated by A Data-driven Coarse-grid Turbulence model

Thermal stratification in large enclosures is an integral phenomenon to nuclear reactor system safety. Currently, the effective model for thermal stratification utilizes a multi-scale method that integrates 1-D system-level and 3-D CFD code, which offers thermal stratification details while supplying system-level data across various domains. Nonetheless, harmonizing two codes that operate on different spatial and temporal scales presents a significant challenge, with high-resolution CFD simulations requiring substantial computational resources. This study introduced a data-driven coarse-grid turbulence model based on local flow characteristics at a significantly coarser scale targeting improved efficiency and accuracy in reactor safety analysis concerning thermal stratification. A machine learning framework has been introduced to expedite the RANS-solving process by coupling of OpenFOAM and TensorFlow, which entails training a deep neural network with fine-grid CFD-generated data to predict turbulent eddy viscosity. The feasibility of the developed data-driven turbulence model was proven through the SUPERCAVNA experimental facility problem validation.

Use of Automated Insulin Delivery in Pregnancies Complicated by Type 1 Diabetes.

Background: Diabetes during pregnancy is associated with significant maternal and foetal health risks. Insulin requirements also change during pregnancy. This necessitates careful and effective management of diabetes. Although commonly used in clinical practice, the US Food and Drug Administration (FDA)-approved algorithms for automated insulin delivery (AID) systems do not have pregnancy-specific glycaemic targets. This review aims to evaluate the safety and efficacy of AID systems in reaching glycaemic targets in pregnant women with type 1 diabetes (T1D). Methods: In this retrospective case review, six pregnant women with T1D used three types of AID systems. Two patients used Omnipod 5, two patients used Control-I Q and two patients used Do-I t-Yourself (DIY) Loop. Results: Across trimesters, the two patients using Omnipod 5 had an average time in range (TIR) of 68 and 82%. Patients using Control-I Q had an average TIR of 77 and 69%. Both the patients using DIY Loop had an average TIR of 85%. Hypoglycaemia occurrence was minimal. Additionally, four of the six patients had uncomplicated vaginal deliveries in their third trimester, and four of the six patients achieved guideline-r ecommended TIR targets. Birth complications for the other two patients were resolved shortly after birth. Throughout the pregnancies, insulin needs approximately doubled. Conclusions: AID systems can achieve near-desired glycaemic targets with minimal hypoglycaemia in pregnant women with T1D. Randomized controlled trials are needed to confirm these findings and to win FDA indications in pregnancy.

Towards Sustainable Transportation: Adaptive Trajectory Tracking Control Strategies of a Four-Wheel-Steering Autonomous Vehicle for Improved Stability and Efficacy

The objective of continuous increase in the evolution of autonomous and intelligent vehicles is to attain a trustworthy, economical, and safe transportation system. Four-wheel steering (4WS) vehicles are favored over traditional front-wheel steering (FWS) vehicles because they have excellent dynamic characteristics. This paper exhibits the trajectory tracking task of a two degree of freedom (2DOF) underactuated 4WS Autonomous Vehicle (AV). Because the system is underactuated, MIMO, and has a nontriangular form, the traditional adaptive backstepping control scheme cannot be utilized to control it. For the purpose of rectifying this issue, two-state feedback-based methods grounded on the hierarchical steps of the block backstepping controller are proposed and compared in this paper. In the first strategy, a modified block backstepping is applied for the entire dynamic system. Global stability of the overall system is manifested by Lyapunov theory and Barbalat’s Lemma. In the second strategy, a block backstepping controller has been applied after a reduction of the high-order model into various first-order subsystems, consisting of Lyapunov-based design and stability warranty. A trajectory tracking controller that can follow a double lane change path with high accuracy is designed, and then simulation experiments of the CarSim/Simulink connection are carried out against various vehicle longitudinal speeds and road surface roughness to demonstrate the effectiveness of the presented controllers. Furthermore, a PID driver model is introduced for comparison with the two proposed controllers. Simulation outcomes show that the proposed controllers can attain good response implementation and enhance the 4WS AV performance and stability. Indeed, enhancement of the stability and efficacy of 4WS autonomous vehicles would afford a sustainable transportation system by lessening fuel consumption and gas emissions.

Prospects of electricity generation through the use of alternative sources such as waste tires processing products

The object of this study is a hybrid energy system that integrates the processes of recycling used tires with the use of alternative energy sources to design a sustainable and environmentally safe power generation system. The problem addressed in this study is to find effective methods for recycling waste tires for electricity generation, as traditional recycling methods are energy-intensive and lead to the loss of valuable resources. Three methods of tire processing have been considered: pyrolysis, thermal degradation, and mechanical grinding. The method of electricity generation using pyrolysis has demonstrated high environmental performance due to a significant reduction in greenhouse gas emissions and a high rate of renewable energy (80 %). According to the results of the study, the use of 5 MW pyrolysis furnaces in combination with 500 kW solar panels provides a reduction in dependence on fossil sources, reducing CO2 emissions to 50.0 kg/year. The method has high capital costs (NPC USD 4.2 million), but the cost of energy production (COE USD 0.18/kWh) makes this method competitive in the long run. Thermal degradation provides a balanced approach in terms of energy efficiency (75 %) and environmental performance. Although its CO2 emissions are higher than in pyrolysis, the method makes it possible to obtain additional products that could be used in other industries, thereby increasing economic efficiency. Mechanical shredding has the lowest average energy costs (USD 0.12/kWh) and the lowest CO2 emissions (30.0 kg/year), making it ideal for cost-conscious businesses. The study confirms the possibility of effective integration of renewable sources with tire recycling methods. The practical use of the research results is possible during the implementation of grant projects for the recycling of used tires, and in the work of relevant government structures for devising a policy for the disposal of used tires

An empirical evaluation of a formal approach versus ad hoc implementations in robot behavior planning

As autonomous robotic systems integrate into various domains, ensuring their safe operation becomes increasingly crucial. A key challenge is guaranteeing safe decision making for cyber-physical systems, given the inherent complexity and uncertainty of real-world environments.Tools like Gwendolen, vGOAL, and Tumato enable the use of formal methods to provide guarantees for correct and safe decision making. This paper concerns Tumato, a formal planning framework that generates complete behavior from a declarative specification. Tumato ensures safety by avoiding unsafe actions and states while achieving robustness by considering nondeterministic outcomes of actions. While formal methods claim to manage complexity, provide safety guarantees, and ensure robustness, empirical evaluation is necessary to validate these claims.This work presents an empirical study comparing the characteristics of various ad hoc behavior planning implementations (developed by participants with diverse levels of experience in computer science), with implementations using Tumato. We investigate the usability of the different approaches and evaluate i) their effectiveness, ii) the achieved safety (guarantees), iii) their robustness in handling uncertainties, and iv) their adaptability, extensibility, and scalability. To our knowledge, this is the first participant-based empirical study of a formal approach for (safe and robust) autonomous behavior.Our analysis confirms that while ad hoc methods offer some development flexibility, they lack the rigorous safety guarantees provided by formal methods. The study supports the hypothesis that formal methods, as implemented in Tumato, are effective tools for developing safe autonomous systems, particularly in managing complexity and ensuring robust decision making and planning.

A Study on the Derivation of Risk Factors in Railway Site for the Improvement of Artificial Intelligence(AI)-Railway Safety System

A Study on the Derivation of Risk Factors in Railway Site for the Improvement of Artificial Intelligence(AI)-Railway Safety System

Under Voltage Relay System for Battery and DC Load Safety Using Arduino Nano

The quality of the electric power supply in an electrical system is very necessary. One of the important things that need to be considered is the quality of the voltage supply. The disturbance in the electrical system is under voltage. Undervoltage is an electrical system disturbance that will affect the performance of connected electrical equipment and reduce the life of the equipment, including battery components in DC voltage source electrical systems such as solar power plants. To anticipate this occurrence, Under Voltage Relay equipment is needed which can be used to protect batteries and DC equipment from under voltage conditions by using a potentiometer as a voltage divider and IC 7809 to reduce the voltage from the battery input, Relays to disconnect the load, LCD for monitoring and using Arduino Nano microcontroller control. The method used is a development method or Research and Development. This method is used as a basis for designing and manufacturing Low Voltage Relay devices to protect batteries and DC equipment. From the results of the tests carried out, it can disconnect the DC load when detecting low voltage conditions with an accuracy of ± 99.795%. for the normalized execution test for under voltage relay at voltages of 12 V and 24 V, there is a difference value of ± 0.096V and an error of ± 0.554%.

Evaluation and Improvement of Construction Safety for Prefabricated Buildings Under the Concept of Resilience

In the construction of prefabricated buildings, safety issues occur frequently, posing challenges to project progress and personnel safety. As a new trend in the construction industry, the complexity of the environment in prefabricated construction demands an update to traditional safety management concepts. This study introduces the concept of resilience to analyze safety issues in prefabricated construction and develops a WSR-4Rs framework for a systematic evaluation of construction safety. The study first combines the WSR (Wuli-Shili-Renli) systematic methodology with the 4R resilience theory to construct an evaluation index system for construction safety. Then, it uses the Analytic Hierarchy Process (AHP) and the entropy weight method to determine the combined weights of each index, establishing a balanced and objective weighting scheme. A fuzzy comprehensive evaluation model is then applied to assess actual project cases. Finally, an obstacle degree model is introduced to identify key indicator factors that significantly impact construction safety, and specific improvement measures are proposed based on these findings. The aim is to provide practical references and guidance for enhancing the safety management level in prefabricated construction.

Rancang Bangun Grounding Box Soil Treatment Sebagai Media Pengukuran Resistansi Pentanahan untuk Proteksi Pembangkit Hybrid PV – Angin Politeknik Negeri Malang

This study addresses the issue of high resistance in the grounding system of the Savonius-Solar Hybrid Power Plant at Politeknik Negeri Malang, caused by rocky and sandy soil conditions, with an initial resistance value of 35 Ω. The purpose of this research was to test the effectiveness of soil treatment using bentonite, charcoal, and salt in reducing resistance to meet the PUIL 2011 standard (<5 Ω). This study employed an experimental method with a quantitative approach, measuring resistance values before and after soil treatment application using a three-point earth tester.The results showed that soil treatment successfully reduced the soil resistance from 35 Ω to 2.85 Ω, meeting the maximum standard. This finding indicates that soil treatment is effective in improving the safety and reliability of grounding systems, making it beneficial for application in similar electrical systems located in areas with challenging soil characteristics.

Adequacy of measures to threats as one of the fundamental ­principles of safety riskology

Purpose. To justify the need to apply the “principle of adequacy of probabilities” as a measure to improve the risk management methodology, to prevent (counteract) existing and emerging risks (threats) to the security of complex systems. Methodology. The well-known methodological principle called “Ockam’s Razor” was used. In the course of the research, a complex method was used, which included formal and logical analysis of normative and scientific and technical literature, analysis of statistical data, synthesis, abstraction, experiment, observation, generalization of data, description. Findings. In the course of the research, one of the threats (danger, risk) was identified, which simultaneously affects the existence of such a system due to traffic safety of rolling stock of rail transport enterprises, and the full implementation of the system’s capabilities due to axle loads, mass and speed movement of rolling stock, level of operating costs, passenger flow, regularity of transportation, etc. Experimental application of the lubricant “Mariol NT” in SPP 12-5, GS-3 lubrication systems showed a decrease in wear intensity by 240 %. Due to the use of a new lubricating material applied to the friction surface of wheel rims as part of the paint coating, the wear intensity of flanges of twin-wheel rims is reduced by 2.2 times, and that of locomotives by 2.4 times. Originality. It was determined that the “principle of adequacy of probabilities” as a measure of improvement of the risk management methodology, prevention (counteraction) of existing and prospective risks (threats, dangers) during the safety assessment of complex systems is currently not applied, but its consideration helps to prevent (countermeasures) to existing and emerging risks (threats) to the safety of complex systems. Practical value. The use of the “principle of adequacy of probabilities” allows for the theoretical and experimental determination of the numerical value of the ratio between the probability of an event occurring, caused by the action of an identified threat that negatively affects the state (level) of safety and operability of system elements at all stages of its existence, and the probability of a positively compensatory effect of the developed countermeasure to the identified threat.

An Enhanced Multiscale Retinex, Oriented FAST and Rotated BRIEF (ORB), and Scale-Invariant Feature Transform (SIFT) Pipeline for Robust Key Point Matching in 3D Monitoring of Power Transmission Line Icing with Binocular Vision

Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower collapses, and widespread power outages, resulting in economic losses and infrastructure damage. This study proposes an enhanced image processing pipeline to accurately detect and match key points in PTLI images for 3D monitoring of ice thickness using binocular vision. The pipeline integrates established techniques such as multiscale retinex (MSR), oriented FAST and rotated BRIEF (ORB) and scale-invariant feature transform (SIFT) algorithms, further refined with m-estimator sample consensus (MAGSAC)-based random sampling consensus (RANSAC) optimization. The image processing steps include automatic cropping, image enhancement, feature detection, and robust key point matching, all designed to operate in challenging environments with poor lighting and noise. Experiments demonstrate that the proposed method significantly improves key point matching accuracy and computational efficiency, reducing processing time to make it suitable for real-time applications. The effectiveness of the pipeline is validated through 3D ice thickness measurements, with results showing high precision and low error rates, making it a valuable tool for monitoring power transmission lines in harsh conditions.

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.

Shielded Reinforcement Learning for Safe and Optimal Cyber Physical Systems

Shielded Reinforcement Learning for Safe and Optimal Cyber Physical Systems

Brain Tumor Detection and Size Estimation using Microwave Imaging

This project focuses on developing an antenna that utilising microwave imaging technology for visualising, detecting, and estimating the size of human tumors using simulation approach. A rectangular microstrip patch antenna is chosen for its advantages of low cost, convenience, efficiency, and compactness, offering a non-ionised alternative. To meet the antenna specifications, rectangular slots are incorporated into the design. The antenna performs effectively at 7.5 GHz, exhibiting a return loss of -24.20383 dB, well below the -10 dB threshold. Placing the antenna 15 mm away from the human brain model results in a specific absorption rate (SAR) value of 0.2 W/kg for 10g, indicating its safety for brain imaging. By scanning a human head phantom with and without a tumor, the antenna captures reflected signals from different locations, enabling the generation of tumour images. A 10-mm-radius tumor is introduced to the phantom, and the unique reflected signal serves as an indicator for tumor detection, using the signal without a tumor as a reference. MATLAB software is employed for image processing, allowing the generation of tumour images and the estimation of tumor size. The simulation results demonstrate 63% accuracy in tumor size estimation. In conclusion, the antenna proves to be a safe and effective brain imaging system for tumor detection.

Phosphorus-nitrogen based flame retardant polyurethane composite phase change materials for battery thermal safety system

Thermal energy storage with phase change materials (PCMs) plays an important role in thermal utilization and energy management. However, the low thermal stability and easy flammable of CPCMs are great challenges for accelerating PCM-based battery thermal management system. Herein, an innovative high flame-retardant and form stable Polyethylene glycol (PEG)/ Expanded graphite (EG) and Diphenylmethane diisocyanate (MDI) and Melamine (MA) with 9,10-dihydro-9-oxa-10-phospha-phenanthrene-10-oxide (DOPO) (PMDM) has been successfully prepared and utilized in square battery module. The PMDM2containing DOPO and MA with the ratio of 1:2can maintain superior stable shape without leakage after heating 5 h at 80 °C. Besides, the total heat release (THR) and peak heat release rate (PHRR) of PMDM2 are decreased to 322.8 kW/m2 and 94.3 MJ/m2, respectively. Comparing with the PM-Module and PEA-Module, at 2C discharge rate after ten cycles, the maximum temperature and temperature differences of the battery module with PMDM2can be maintained below 55 °C and 5.5 °C, respectively. This research reveals that flame retardant CPCM with polyurethane structured can improve the fireproof and anti-leakage properties, which will offer an effective thermal safety solution for energy storage and other fields. The battery module with this designed CPCM can provide an effective approach of passive heat management system with these notable performances.

Fire safety performance of functional vegetated green building systems: A comprehensive review

The rapid growth of the urban population and the increasing energy consumption of buildings due to global urbanization have led to a significant environmental impact. Therefore, energy conservation, emission reduction, and the adoption of green and low-carbon practices have become the focus of the transformation of construction industry in response to energy shortages and greenhouse gas emissions. This paper focuses on the use of functional green building technologies, specifically the integration of plant microbial fuel cell (PMFC) systems into green building systems, to regulate the indoor and outdoor thermal environment, mitigate the negative effects of urbanization, and enhance building energy efficiency. However, the incorporation of vegetation structures in green building systems significantly increases the risk of fire, necessitating a thorough evaluation of the fire safety system. This paper introduces the working mechanism of functional green building systems with energy harvesting capabilities by PMFC technique, the benefits they bring to the residential environment, and their impact in regulating indoor and outdoor thermal environments while improving building energy performances. Moreover, this paper discusses potential fire hazards caused by vegetation, electrical wire, curtain wall glass, and system layout in PMFC systems. It proposes methods and future research directions to improve safety levels. The paper presents ideas for developing resource-saving and environmentally friendly functional green building systems based on the principles of sustainable development and energy harvesting technology.

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.