Safety Systems Research Articles

Fully Coupled Analysis of a 10 MW Floating Wind Turbine Integrated with Multiple Wave Energy Converters for Joint Wind and Wave Utilization

The study focuses on a semi-submersible wind-wave integrated power-generation platform, which consists of an OO-Star semi-submersible platform equipped with a DTU 10 MW wind turbine and a set of wave energy converters. A hydrodynamic model was established using ANSYS-AQWA (2023 R1), and by incorporating upper wind loads and utilizing the open-source program F2A, a fully coupled time-domain model of the integrated power-generation platform was constructed. The primary objective is to explore the interaction mechanisms between the upper wind turbine and the lower wave energy devices under the combined effects of irregular waves and turbulent wind through a series of operational conditions. Additionally, the safety of the mooring system was assessed. The results indicate that, compared to the wave period, the power generation of the lower wave energy devices is more significantly affected by wave height. Overall, the integrated power-generation platform demonstrates optimal performance under the third operational condition. In survival conditions, the introduction of oscillating buoys can improve the motion responses of the platform in terms of sway, roll, pitch, and yaw to a certain extent, but it also increases the surge and heave motion responses and the associated mooring loads. The mooring system can ensure the safety of the integrated power-generation platform under extreme sea conditions.

Multilevel identity fine authentication method based on time-frequency domain feature extraction in industrial internet of things system

The cyber physical system is widely applied to industrial Internet of Things system and its security determines the safety of the industrial IoT system. This paper proposes a multilevel fine fingerprint authentication method for the security protection of key operating equipment. Firstly, Because of the acquired signals, the mathematical representation models are established separately, including the self-encoding model on the basis of the multiorder moment features and the multi-scale representation model based on the wavelet. Secondly, the multigranularity features of the time domain and frequency domain are extracted respectively based on the real-time data's self-encoding representation and multiscale representation. Then, the extracted features of different granularities can be built into the image representation based on the time-frequency fusion portrait. Based on the images of different granularities and the legitimate image library, a multiscale matching authentication method is established. Finally, simulation experiments show that the proposed method is effective.

Estimation of Hydraulic and Water Quality Parameters Using Long Short-Term Memory in Water Distribution Systems

Predicting essential water quality parameters, such as discharge, pressure, turbidity, temperature, conductivity, residual chlorine, and pH, is crucial for ensuring the safety and efficiency of water supply systems. This study employs long short-term memory (LSTM) networks to address the challenge of capturing temporal dependencies in these complex processes. Our approach, using a robust LSTM-based model, has demonstrated significant predictive accuracy, as evidenced by substantial R-squared values (e.g., 0.86 for discharge and 0.97 for conductivity). These models have proven particularly effective in handling non-linear patterns and time-series data, which are prevalent in water quality metrics. The results indicate the potential for LSTMs not only to enhance the real-time monitoring of water systems but also to aid in the strategic planning and management of water supply systems. This study’s findings can serve as a basis for further research into the integration of AI in environmental engineering, particularly for predictive tasks in complex, dynamic systems.

Bacterial Cellulose Membrane Experimentally Implanted in the Peritoneum of Wistar Rats—Inflammatory Immunoreactivity and Oxidative Stress

Bacterial cellulose (BC) has been used for various applications; however, studies investigating the immunohistochemical characteristics of the inflammatory and scarring component in BC implanted in the peritoneum in vivo have not yet been fully described. This study aimed to evaluate the systemic and organic safety of BC through oxidative stress, blood, and serum biochemical markers, as well as the late inflammatory response in rats, using histopathology and immunohistochemistry. Forty-three rats (26 males; 17 females) received BC in the peritoneal cavity (implanted group—IG), while twenty-seven rats (12 males; 15 females) served as the control (sham group—SG). Sixty days after surgery, oxidative stress in tissues, blood biochemical markers, and histopathological and immunohistochemical analyses for lymphocytes, macrophages, collagen, and vascular response around the BC were assessed. Only one oxidative stress marker, glutathione peroxidase, was elevated in the liver of IG rats. Creatine kinase MB and lactate dehydrogenase levels were significantly lower in IG animals. Histopathological analysis showed granulomatous inflammation in 93% of IG rats, with 74% of mild intensity. Immunohistochemistry revealed a significant macrophage presence (F4/80), with CD3, CD20, and F4/80 markers indicating differences favoring macrophages. In conclusion, BC implantation in the peritoneum induces a foreign body granulomatous response with prominent macrophage presence (F4/80). Type I and III collagen were observed around the membrane, and vascularization was intense 60 days post-implantation. From a biochemical and oxidative stress perspective, BC seems to be a safe material to be used in the peritoneal cavity.

Introducing a systematic and sustainable patient safety oversight function in a U.K. arms-length healthcare body

Background The healthcare landscape is experiencing a profound period of change with accelerating developments in medical research, new treatments, care delivery and the use of innovative technology to meet the challenges of delivering care. These changes offer opportunities but as with all innovation, require careful management to reduce the likelihood of harm and error. Objective The National Institute for Health and Care Excellence (NICE) has set out to introduce an adaptable, sustainable and systematic patient safety oversight group to ensure that patient safety is embedded at an organisation level supporting the healthcare system to overcome challenges and deliver high quality care, safely implementing the latest innovations in a timely manner. Method The patient safety oversight group was established through a scoping exercise, led by the senior responsible officer and a small project team. The article sets out the methods used, including extensive internal engagement through structured interviews with senior leadership teams, purposive sampling, data synthesis and reporting. An overview of the group is given and considerations for promoting patient safety information flows and optimising the use of safety signal intelligence are made. Conclusion Through reflection on changing needs within and surrounding the organisation, along with patient safety and complexity literature, NICE has developed an adaptable, systematic and sustainable model for managing patient safety. In addition to generating learning within NICE, the process highlights opportunities for improvement within the patient safety system. Result NICE has developed an adaptable, systematic and sustainable model for managing patient safety.

Design and Implementation of Wind Speed-Based Radar Antenna Safety System Prototype

Air defense radar is a system that detects the presence of one or more air objects at a certain distance, altitude, and direction. One type of air defense radar used by the Indonesian National Armed Forces (TNI) is the Thomson TRS 2215D. An essential part of the radar is the rotating part of the radar antenna support called the antenna pivot. The rotation of the radar antenna must constantly be monitored and controlled for rotational stability at a speed of 6 Rotations per Minute (RPM) with a maximum wind speed of 120 km/h to prevent damage to the driving gear. Stormy weather with high wind speeds can cause the rotation speed of the radar antenna to be uncontrollable, which can cause damage. The solution offered in this study is to build a safety system that will lock the radar antenna automatically when the wind speed is detected to exceed tolerances and maintain the security of the radar antenna and its support system. The safety system was designed using an ESP32 Wi-Fi device equipped with an anemometer wind speed sensor, a Liquid Crystal Display (LCD) monitor, a Direct Current (DC) motor, and a Blynk Internet of Things (IoT) application. The test was conducted in a simulation using a multi-meter and oscilloscope measuring instrument. Testing the radar antenna's safety system prototype on a laboratory scale shows that the safety system can work as designed. The system can lock the radar antenna when the airflow is set at a speed of 54 km/h or 15 m/s, communication with the Blynk server works well, and the ESP32 device can transmit data at a maximum distance of 14 meters.

Utility of syndromic surveillance for the surveillance of healthcare-associated infections in resource-limited settings: a narrative review.

Globally, Healthcare-associated infections (HCAIs) pose a significant threat to patient safety and healthcare systems. In low- and middle-income countries (LMICs), the lack of adequate resources to manage HCAIs, as well as the weak healthcare system, further exacerbate the burden of these infections. Traditional surveillance methods that rely on laboratory tests are cost-intensive and impractical in these settings, leading to ineffective monitoring and delayed management of HCAIs. The rates of HCAIs in resource-limited settings have not been well established for most LMICs, despite their negative consequences. This is partly due to costs associated with surveillance systems. Syndromic surveillance, a part of active surveillance, focuses on clinical observations and symptoms rather than laboratory confirmation for HCAI detection. Its cost-effectiveness and efficiency make it a beneficial approach for monitoring HCAIs in LMICs. It provides for early warning capabilities, enabling timely identification and response to potential HCAI outbreaks. Syndromic surveillance is highly sensitive and this helps balance the challenge of low sensitivity of laboratory-based surveillance systems. If syndromic surveillance is used hand-in-hand with laboratory-based surveillance systems, it will greatly contribute to establishing the true burden of HAIs in resource-limited settings. Additionally, its flexibility allows for adaptation to different healthcare settings and integration into existing health information systems, facilitating data-driven decision-making and resource allocation. Such a system would augment the event-based surveillance system that is based on alerts and rumours for early detection of events of outbreak potential. If well streamlined and targeted, to monitor priority HCAIs such as surgical site infections, hospital-acquired pneumonia, diarrheal illnesses, the cost and burden of the effects from these infections could be reduced. This approach would offer early detection capabilities and could be expanded into nationwide HCAI surveillance networks with standardised data collection, healthcare worker training, real-time reporting mechanisms, stakeholder collaboration, and continuous monitoring and evaluation. Syndromic surveillance offers a promising strategy for combating HCAIs in LMICs. It provides early warning capabilities, conserves resources, and enhances patient safety. Effective implementation depends on strategic interventions, stakeholder collaboration, and ongoing monitoring and evaluation to ensure sustained effectiveness in HCAI detection and response.

Implementation of Solar PV Protection System in Indonesia: A Review

Conventional energy sources will run out if used continuously and damage the environment. Therefore, it is necessary to develop other energy sources that are safe, environmentally friendly, and inexhaustible known as renewable energy sources to meet energy needs in Indonesia. This article presents a review that discusses the protection of solar panels and PLTS. Solar panels are vulnerable to lightning strikes and other electrical disturbances, so an effective protection system is needed. This study uses a qualitative method through a literature study, reviewing various protection methods such as conventional and electrostatic lightning rods, good grounding systems, and overcurrent detection devices such as Solar Charge Controller (SCC) and Maximum Power Point Tracking (MPPT). In addition, battery protection with Automatic Transfer Switch (ATS) and Low Voltage Disconnected (LVD) is also discussed. Using Arduino Uno, ESP 32, and PLC, automation approaches offer real-time monitoring and control solutions for solar power plant performance. Innovations in the lightning protection zone concept and minimal quantity measurement schemes enable more cost-effective design of protection systems without reducing the level of protection. The results show that these various protection methods can provide effective and efficient protection for solar power plants, enabling optimization of system function and safety. In conclusion, the protection systems reviewed in this study offer a sustainable solution to Indonesia's energy challenges by utilizing the maximum potential of solar energy. The potential for further research is presented in this article to develop more efficient and effective protection methods.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

The optimization algorithm of intelligent transportation systems based on reinforcement learning

With the acceleration of urbanization and the increase in traffic load, traditional traffic management methods struggle to cope with the complexity of modern traffic conditions. Intelligent Transportation Systems (ITS), utilizing modern information technology and artificial intelligence, have improved the efficiency and safety of transportation systems. Reinforcement Learning (RL), as an adaptive optimization method, can dynamically adjust traffic signals and flow control strategies through trial and error and reward mechanisms, optimizing traffic flow management and signal control. This paper investigates the optimization algorithms of intelligent transportation systems based on reinforcement learning, including their design, application, and performance evaluation. Research indicates that RL optimization algorithms can significantly enhance the organizational performance of transportation systems, increasing the efficiency and flexibility of traffic management. Empirical analysis verifies the effectiveness of the algorithm, and future improvement directions are proposed.

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Structural health monitoring (SHM) is crucial for ensuring the safety and efficiency of engineering systems. Vibration-based signals have been widely employed in numerous studies for damage detection, localization and quantification, thanks to their high sensitivity to structural damages and versatile applicability across various structures. Among these, transmissibility functions (TFs) have emerged as particularly promising because they can be derived from output-only measurements, making them practical for real-world applications. Vibration-based features, including those captured by TFs, provide a wealth of damage-related information, but extracting these features can be challenging with traditional methods and often requires a priori knowledge. To address this challenge, deep learning has gained significant attention for its capacity to handle intricate patterns and extract hidden features. However, deep learning algorithms are often perceived as black-box models due to their complex and heterogeneous layers, which hinder transparency and raise concerns, especially in critical domains. Although numerous studies have highlighted the diagnostic potential of deep learning in SHM with vibration-based data, there has been limited exploration of the rationale behind using deep neural networks (DNNs) for processing TFs. This paper addresses this gap by introducing explainable artificial intelligence (XAI) methods in two distinct TF-based damage diagnostic case studies: a numerical aluminium structural beam and a large-scale experimental steel frame. In the former, damage is simulated as local stiffness reductions, while in the latter, damage is introduced by loosening joint bolts. In both cases, a convolutional neural network (CNN) is used to process raw TF data for damage detection, localization, and quantification in the beam, and for detection and localization in the frame. Subsequently, an XAI method based on the layer-wise relevance propagation (LRP) algorithm is employed to identify the most relevant input features. A pattern analysis is presented to explain recurrent patterns, and a comparison with other XAI methods is proposed for further validation. The results demonstrate that the CNN provides accurate predictions for both case studies, with its focus on damage-sensitive features aligning closely with findings in the existing literature.

Strain distribution and failure modes of steel lattice tower gusset plates as a function of their geometry

Steel gusset plates (SGP) are structural elements that connect and transfer loads among various members, such as beams, columns, and trusses, in steel structures like buildings, bridges, and lattice towers. Their geometry significantly affects structural performance, requiring a thorough analysis of strain distribution and failure modes to ensure the integrity and safety of structural systems. However, the design and evaluation of lattice tower gusset plates (LTGP) in particular, present challenges due to complex geometries and the lack of universally accepted design methods. This paper presents a comprehensive comparative study of the strain distribution and failure modes of LTGP across different geometries. Experimental tests were conducted using Digital Image Correlation (DIC) on specimens with different configurations, each featuring a single row of bolts. The force–displacement curves and strain distributions were analyzed to determine the influence of gusset plate width, end-distance, and the number of bolts on their behavior. The study compares the experimental data with the prescriptions of design standards. Additionally, it presents numerical modeling of LTGP connections using finite element analysis in ANSYS®, with validation against experimental results confirming model accuracy. The research program is further complemented by a parametric study examining the effects of end-distance, plate width, and bolt spacing on the ultimate strength of LTGP.

Integrating reinforcement learning and supervisory control theory for optimal directed control of discrete-event systems

Directed control is crucial for implementing controllers on programmable logic controllers. A directed controller is one that actively selects at most one controllable event (control command) to execute at any instant. This study investigates the numerical optimization problem of directed control for discrete-event systems, such as traffic systems and robotic systems. By integrating supervisory control theory and reinforcement learning, an algorithmic procedure is designed to synthesize an optimal directed controller, which minimizes the total cost associated with event execution while ensuring the safety and liveness of the controlled system. Two improved Q-learning algorithms incorporating dynamic parameter adaptation strategies are developed to enhance the global search ability of basic Q-learning. To demonstrate applicability and effectiveness, we apply the proposed method to control a guideway system and a multi-train traffic system, respectively. The experimental results indicate the superiority of the proposed method over the comparison methods.

Responses of Vehicular Occupants During Emergency Braking and Aggressive Lane-Change Maneuvers.

To validate active human body models for investigating occupant safety in autonomous cars, it is crucial to comprehend the responses of vehicle occupants during evasive maneuvers. This study sought to quantify the behavior of midsize male and small female passenger seat occupants in both upright and reclined postures during three types of vehicle maneuvers. Volunteer tests were conducted using a minivan, where vehicle kinematics were measured with a DGPS sensor and occupant kinematics were captured with a stereo-vision motion capture system. Seatbelt loads, belt pull-out, and footrest reaction forces were also documented. The interior of the vehicle was 3D-scanned for modeling purposes. Results indicated that seatback angles significantly affected occupant kinematics, with small female volunteers displaying reduced head and torso movements, except during emergency braking with a upright posture seatback. Lane-change maneuvers revealed that maximum lateral head excursions varied depending on the maneuver's direction. The study concluded that seatback angles were crucial in determining the extent of occupant movement, with notable variations in head and torso excursions observed. The collected data assist in understanding occupant behavior during evasive maneuvers and contribute to the validation of human body models, offering essential insights for enhancing safety systems in autonomous vehicles.

Energy and Economic Analysis of a New Combination Cascade Waste Heat Recovery System of a Waste-to-Energy Plant

Waste incineration has become the main treatment method for urban household waste, and it can produce a large amount of electricity. The efficiency of waste incineration plants is reduced due to the large amount of waste heat carried away by the flue gas. Recycling and utilizing the waste heat from flue gas are important in improving the economic benefits of waste incineration, which is necessary for energy conservation and emission reduction. Based on the principle of cascade waste heat recovery from waste incineration flue gas whilst considering system safety and efficiency, this study proposed a new combination cascade waste heat recovery system consisting of a Rankine cycle, an organic Rankine cycle and a heat pump cycle. Thermodynamic and economic analyses of the combined system were conducted in detail. The results indicated that the energy efficiency of the combined system could reach up to 73%. The maximum net present value of the system was million USD 1.59 million, and the dynamic investment payback period was about 6.5 years. The isentropic efficiency of the combined system’s pumps and turbines had a significant impact on the system’s performance. A higher isentropic efficiency resulted in better system performance. The exergy analysis showed that the evaporator of the heat pump system had the highest irreversible loss.

Enhancing multi-type fault diagnosis in lithium-ion battery systems: Vision transformer-based transfer learning approach

Ensuring the accurate diagnosis of internal short circuit (ISC) faults and consistency anomalies is crucial for maintaining the high safety and longevity of battery systems. The challenge lies in the high similarity between the features of ISC faults and consistency anomalies, which can complicate accurate fault diagnosis. This study introduces a multi-fault diagnostic model that leverages the Vision Transformer (ViT) and employs simulated data to accurately pinpoint ISC faults and intricate consistency anomalies, such as capacity and state of charge anomalies, achieving an impressive accuracy rate of up to 100 %. The process begins with an in-depth analysis of fault mechanisms to extract and differentiate multi-fault features using a mean difference model. Subsequently, the ViT's exceptional ability to capture temporal information, which aids in the accurate identification of fault features. The integration of transfer learning with experimental data further refines the real-world diagnosis accuracy. The results confirm the robustness of the proposed method, highlighting its potential to substantially mitigate safety hazards in battery systems and enhance operational reliability.

Electrical damage mechanism analysis of traction motor bearings on high-speed trains considering overvoltage impulse

ABSTRACT As the critical rotating knuckle, traction motor bearings play a vital role in transmitting torque from motors to wheels for high-speed trains, while some electrochemical corrosion signs appear in traction motor bearings. To maintain the safety of the traction system, it is essential to explore the electrical damage mechanism of traction motor bearings considering the impact brought from current or overvoltage impulses. Herein, via analysing the electrochemical corrosion evolutionary process of bearings, main influencing factors are determined that both transient overvoltage impulse and long-term grounding current bring dramatic impact on bearings. To assess overvoltage distribution on different carriages, a ‘catenary-train-substation’ traction power supply model is launched based on practically collected impedances. A ‘carriage-bogie-motor’ circuit model is built to evaluate the overvoltage propagation path, meanwhile, the 3D finite element geometry of the bogie with bearings is launched to observe the overvoltage distribution in the bearings for evaluating breakdown risk.

Spatial patterns, source apportionment, and risk assessment of polychlorinated biphenyls (PCBs) in the surface sediments of eastern China lakes along a latitudinal gradient: Insights guided by full-congener analysis

Understanding the occurrence, sources, and ecological risks of polychlorinated biphenyls (PCBs), which are universal persistent organic pollutants, is critical for improving the sustainability and ecological safety of lake systems. Herein, to determine PCB contamination levels and formulate control strategies in lake sediments, 210 sediment samples were collected from 21 lakes along a latitudinal gradient (18–45°N, ∼3000 km) across eastern China and were analyzed for all 209 PCB congeners. The results showed that the total PCB concentration varied greatly from 0.26 to 163.82 ng/g dry weight and exhibited a latitudinal trend of central > north/south. Spatial variations were affected mainly by the organic carbon fraction and local population density. Most lakes had similar PCB profiles, with lower chlorinated PCBs dominating. Notably, non-Aroclor PCB 11 was the most abundant congener. Moreover, unintentionally produced PCBs (UP-PCBs) accounted for ∼31 % of all PCBs. These findings highlight that the significance of UP-PCBs has been overlooked in past studies and that full-congener analysis is necessary for future monitoring. According to the ecological risk assessment of PCBs, zero to moderate risk existed in lake sediments. Therefore, effective strategies are needed to mitigate the impact of PCBs (especially UP-PCBs) from multiple sources on lakes.

Computational Analysis of Stiffness Reduction Effects on the Dynamic Behaviour of Floating Offshore Wind Turbine Blades

This paper describes the study of a floating offshore wind turbine (FOWT) blade in terms of its dynamic response due to structural damage and its repercussions on structural health monitoring (SHM) systems. Using a finite element model, natural frequencies and mode shapes were derived for both an undamaged and a damaged blade configuration. A 35% reduction in stiffness at node 1 was applied in order to simulate significant damage. Concretely, the results are that the intact blade has a fundamental frequency of 0.16 Hz, and this does not change when damaged, while higher modes exhibit frequency changes: mode 2 drops from 2.05 Hz to 2.00 Hz and mode 3 from 6.15 Hz to 6.01 Hz. The shifts show a critical loss in the capability of handling vibrational energy due to the damage; higher modes (4, 5, and 6) show larger frequency deviations going down to as low as 18.06 Hz in mode 6. The mode shape change is considerable for the edge-wise and flap-wise deflection of the 2D contour plots, indicating possible coupling effects between modes. These results indicate that lower modes are sensitive to stiffness reductions, and the continuous monitoring of the lower harmonic modes early is required to detect damages. These studies have helped to improve blade design, maintenance, and operational safety for FOWT systems.

Development of pseudotyped VSV-SARS-CoV-2 spike variants for the assessment of neutralizing antibodies.

Aim: Serological studies with pseudotyped viruses offer a safer alternative to live SARS-CoV-2 in evaluating neutralizing antibodies, enabling research in standard labs.Methods: The SARS-CoV-2 Spike pseudotyped vesicular stomatitis virus (VSV) pseudoviruses were generated using Spike of Wuhan strain and two variants (B.1.1.7, B.1.351) and utilized to evaluate the serum neutralizing activity of human plasma samples of vaccinated (n=13) and healthy people (n=2) compared with a plaque assay with authentic virus.Results: Neutralizing titer of convalescent plasma resulted with a good correlation (R2=0.7).Conclusion: We evaluated a safe and reliable pseudotyped virus system that effectively mimics authentic virus and correlates well with traditional assays. The developed system allows easier testing of variants and has the potential to improve vaccine development.