Control Model Research Articles

Optimal vibration control with considering non-probabilistic time-dependent reliability for hypersonic vehicles

Hypersonic vehicles have complex structures with multiple sources of uncertainties, and the presence of uncertainties may lead to the vehicles’ structural failure. Aiming to ensure the flight safety and structural reliability of a hypersonic vehicle, this study proposes a vibration optimization control method based on non-probabilistic time-dependent reliability. Due to the complex structure of the vehicle, its forebody and aftbody are modeled as two cantilever beams with the center of mass of the vehicle as the fixed fulcrum, respectively, and a vibration control model of cantilever is derived. Considering that the uncertainties in practical engineering are not easy to be counted, the uncertain parameters are quantified by using interval variables, and the dynamic response and time-dependent reliability of the uncertain system are analyzed based on the subinterval method and the non-probabilistic time-dependent reliability analysis method (NTRAM) designed in this paper. Further, to ensure the high reliability of the vehicle, an optimal robust linear quadratic regulator (ORLQR) method with the constraint of time-dependent reliability is proposed. The analysis of the numerical examples shows that the parameter uncertainty has a certain effect on the reliability of the vehicle, and verifies that the proposed ORLQR method can effectively control the vibration and slow down the reliability decline.

Design and Control of Radial Electromagnetic Bearing for Flywheel Battery

Abstract High rotational speed is the key to increasing energy storage capacity of flywheel battery. Non-contact bearing ensures reliable operation of high-speed flywheel battery. To design an electromagnetic bearing that meets the requirement of high-speed flywheel battery, firstly, a multi-parameters and multi-objectives optimization model for radial electromagnetic bearing is established. The optimization aims at the smallest spatial volume while ensuring electromagnetic force. A 16.58% reduction of the spatial volume is obtained. Furthermore, the influence of bias current and control current on the electromagnetic force and rotor displacement of radial magnetic bearing are analyzed. The bias current and control current that meet the requirements of current stiffness and displacement stiffness are determined. Based on this, a differential control model for radial magnetic bearing is constructed. Then, a controller based on adaptive discrete sliding mode control is designed. Finally, performance test and data analysis are completed. Results indicate that the designed radial electromagnetic bearing is able to achieve low response error, low system jitter and high robust under different test conditions. Compared with traditional sliding mode control method, under stochastic excitation, the maximum and average tracking errors of bearing displacement are reduced by 62.31% and 42.74%. The energy consumption is decreased by 49.32%.

ANTMAC: Addressing Novel Congestion Technique Hybrid Model for Collision Control in IoT-based Environments using Contention-based MAC Protocol

In the communication model of the OSI layer, the Media access control (MAC) layer has been given higher priority than other layers. It is a sub-layer of the data link layer, mainly controlling the physical equipment and interacting with the channels over the Internet of Things (IoT) sensor nodes. Mac layers have used two protocol types: contention-based and contention-free during transmission. These two protocols have controlled the physical equipment and data transmission for the last decade. Yet in the MAC layers transmission, some challenging issues are complicated to resolve. Data collisions are the significant changing issues at the MAC layer. As per the survey of researchers, the contention-based protocol is more affected by collision due to allowing the sharing of channels to all nodes over networks. As a result, it has got message delay, demanding more energy, data loss, and retransmission. The researcher always focuses on reducing collision during transmission to overcome these issues. They mainly evaluate the priority-based collision control using the contention-based protocol. In this ANTMAC model, we have considered the lower energy nodes’ priority to enhance the likelihood that a node will gain access to the transmission channel before its power and batteries run out. Our recommended method ANTMAC outperforms ECM-MAC in terms of content retrieval time (CRT), total no of retransmission (TNR), total energy consumption (TEcm), throughput and network lifetime (NLT).

Real-Time Co-Editing of Geographic Features

Real-time GIS enables multiple geographically dislocated users to collaboratively edit geospatial data. However, being based on the strong consistency model, traditional real-time GIS implementations cannot provide fully automatic conflict resolution. In highly dynamic situations with increased probability for conflicts, this will hinder user experience. Conflict-free replicated data types (CRDTs), a technology based on a more relaxed concurrency control model called strong eventual consistency, can resolve all conflicts in real time, letting the users work on their local copies of the data without any restrictions. The application of CRDTs to real-time geospatial geometry co-editing has, to the best of our knowledge, not been investigated. Within this research, we therefore developed a simple web-based real-time geospatial geometry co-editing system using an existing CRDT implementation in Javascript coupled with OpenLayers. When applied to the co-editing of geospatial geometry in its native form, standard CRDT conflict resolution mechanics exhibit some issues. As an attempt to address these issues, we developed an advanced operation generation technique named “tentative operations”. This technique allows for the operations to be generated over the most recent session-wide state of the data, which in effect highly reduces concurrency and provides “geometry aware” conflict resolution. The tests we conducted using the developed system showed that in low-latency network conditions, the negative effects of standard CRDT conflict resolution mechanics do get minimized even under increased system loads.

The analysis of generative adversarial network in sports education based on deep learning

The importance of mental health is increasingly emphasized in modern society. The assessment of mental health qualities among college and university students as the future workforce holds significant significance. Therefore, this study, aiming to streamline the process of writing quality evaluations and enhance the fairness of assessment comments, explores the use of Generative Adversarial Network (GAN) technology in deep learning to evaluate the mental health qualities of college and university students through the unique avenue of sports. Firstly, GAN and Sequence Generative Adversarial Network (SeqGAN) models are introduced. Secondly, GAN is employed to construct a model for generating evaluation texts, encompassing the construction of a generator and discriminator, along with the introduction of a reward function. Finally, the constructed model is utilized to train on evaluation texts related to the mental health qualities of college and university students engaged in sports, validating the effectiveness of the model. The results indicate: (1) The pre-training of the generator in the constructed text generation model stabilizes after the 10th epoch. In contrast, the pre-training of the discriminator gradually stabilizes after the 35th epoch, demonstrating overall good training effectiveness. (2) When the generator’s update speed surpasses that of the discriminator, the model’s loss does not converge. However, with a reduction in the ratio of rounds between the two, there is a noticeable improvement in the convergence of the model. (3) The mean score of adaptability quality is the highest among the four indicators, suggesting a strong correlation between comment generation and adaptability quality. The results validate the effectiveness of the proposed text generation model in semantic control. This study aims to advance the level of mental health education among college and university students in the sports domain, providing theoretical references for enhancing the effectiveness of quality education assessments in other subjects as well.

Study on Watershed Ecological Compensation for Water Pollution Considering Population Flow: A Case Study in the Lower Yellow River Basin

Watershed ecological compensation (WEC) mechanisms can help coordinate the distribution of revenue among different regions and realize the collaborative treatment of water pollution. However, limited research has examined the influence of population flow on the design of ecological compensation mechanisms. In this paper, the differential game method is used to construct a model of water pollution control in upstream and downstream regions with the consideration of population flow. The Lower Yellow River Basin (LYRB), which includes Henan and Shandong Provinces, is taken as a case study, and relevant data are used for simulation analysis. The constraints and population flow factors that influence the establishment of a WEC mechanism between upstream and downstream governments are explored. The results show that (1) the implementation of WEC can stimulate the upstream government’s efforts to treat pollutants, and the amount of pollutants eliminated and the revenue of the upstream and downstream governments increase; (2) with the continuous flow of population from the upstream region to the downstream region, the amount of pollutants eliminated and the revenue of the downstream government decrease; and (3) in the absence of external incentive measures, when the population flow exceeds a certain threshold, the WEC mechanism of the upstream and downstream governments cannot be spontaneously carried out. The conclusions of this study can provide scientific guidance for improving the WEC mechanism between the upstream and downstream governments within a basin.

Nutritional Studies Evaluating Ketogenic Diets as a Treatment for Obesity and Obesity-Associated Morbidities: Underlying Mechanisms and Potential for Clinical Implementation

Background: The ketogenic diet (KD), characterized by high-fat content, virtually no carbohydrates, and adequate protein intake, induces a metabolic state resembling fasting, as the absence of carbohydrates forces the body to rely on the energetic supply from hepatically produced ketone bodies using free fatty acids as substrate. While the KD is clinically used in pharmacologically refractory epilepsy and specific genetic conditions such as GLUT1 deficiency, recent research suggests that, due to its “fasting mimicking” properties, the KD may also beneficially affect obesity and obesity-associated metabolic diseases. Results: Here, we present a narrative review discussing completed and ongoing nutritional studies in human volunteers specifically addressing the potential of the ketogenic diet as an anti-obesity approach and, from a larger perspective, as an intervention to ameliorate the metabolic state in conditions such as type 1 and 2 diabetes and polycystic ovary syndrome (PCOS). Published studies as well as ongoing clinical trials will be discussed. Efficacy and safety considerations will be discussed, as well as the potential physiological mechanisms mediating the effects of the KD in humans in the context of the (i) energy balance model (EBM) and (ii) carbohydrate–insulin model (CIM) of body weight control. Conclusion: Ketogenic diets may be beneficial to attenuate obesity and improve obesity-related metabolic disease, and here, we try, based on current evidence, to define the boundaries of the KD’s nutritional and clinical usefulness.

Control Strategy for Power Fluctuation Smoothing at Distribution Network Substations Considering Multiple Types of Adjustment Resources

With the proposal of the dual carbon target, the distributed photovoltaic (PV) industry has rapidly developed in recent years. However, the randomness and volatility of photovoltaic energy can be transmitted to the main grid through distribution network substations, posing challenges to the stable operation of the power system. Therefore, this paper considers tapping into the regulation potential of feeder loads on the distribution network side, as well as distributed energy storage and distributed PV resources, to enhance the grid’s control methods. A power fluctuation smoothing control strategy for substations in distribution networks, accounting for multiple types of regulation resources, is proposed. In the day-ahead stage, traditional voltage regulation devices such as the OLTC (on-load tap changer) and CB (circuit breaker) are pre-dispatched based on source–load forecasts, optimizing the fluctuation range of substation power and the number of device operations. This provides optimal substation power values for day-to-day optimization. During the intraday phase, fast regulation devices such as PV (photovoltaic), SVC (static var compensator), and energy storage systems are coordinated, and an optimization model is established with the goal of reducing power curtailment while closely tracking substation trends. This model quickly calculates the active power regulation and device operations of various adjustable resources, improving the economic efficiency of the distribution network system while achieving power fluctuation smoothing at the substation level. Finally, the feasibility and effectiveness of the power fluctuation smoothing control model are verified through simulations on an improved standard distribution system.

Synergistic Impact of Active Case Detection and Early Hospitalization for Controlling the Spread of Yellow Fever Outbreak in Nigeria: An Epidemiological Modeling and Optimal Control Analysis

Capturing the factors influencing yellow fever (YF) outbreaks is essential for effective public health interventions, especially in regions like Nigeria, where the disease poses significant health risks. This study explores the synergistic effects of active case detection (ACD) and early hospitalization on controlling YF transmission dynamics. We develop a dynamic model that integrates vaccination, active case detection, and hospitalization to enhance our understanding of disease spread and inform prevention strategies. Our methodology encompasses mechanistic dynamic modeling, optimal control analysis, parameter estimation, model fitting, and sensitivity analyses to study YF transmission dynamics, ensuring the robustness of control measures. We employ advanced mathematical techniques, including next-generation matrix methods, to accurately compute the reproduction number and assess outbreak transmissibility. Rigorous qualitative analysis of the model reveals two equilibria: disease-free and endemic, demonstrating global asymptotic stability and its impact on overall YF transmission dynamics, significantly affecting control and prevention mechanisms. Furthermore, through sensitivity analysis, we identify crucial parameters of the model that require urgent attention for more effective YF control. Moreover, our results highlight the critical roles of ACD and early hospitalization in reducing YF transmission. These insights provide a foundation for informed decision making and resource allocation in epidemic control efforts, ultimately contributing to the enhancement of public health strategies aimed at mitigating the impact of YF outbreaks.

Total Alkaloids of Rhizoma Corydalis regulates gut microbiota and restores gut immune barrier to ameliorate cognitive dysfunction in diabetic rats.

Given the widespread dysbiosis of gut microbiota in patients with T2DM, it has been found that the microbiota-gut-brain axis plays an influential regulatory role in diabetic cognitive dysfunction, and improving gut dysbiosis may be a potential strategy for treating diabetic cognitive dysfunction. Total Alkaloids of Rhizoma Corydalis (TAC) is the main active component extracted from Rhizoma Corydalis. Pharmacological studies have demonstrated its significant pharmacological effects on the cardiovascular and cerebrovascular systems, and berberine, the main component of TAC, has a certain regulatory effect on gut microbiota. Rats were randomly divided into Control group, Model group, TAC-low group, TAC-mid group and TAC-high group. Cognitive function of diabetic rats was evaluated through behavioral testing using the Morris water maze experiment. The relative abundance of gut bacteria in rat feces was determined via 16S rRNA analysis. IHC and Western blot techniques were employed to assess IL-22, IL-23, Reg3g, ZO-1, occludin 1 expression in the colon tissue; GPX4, xCT, NLRP3, Caspase-1 p20, GSDMD-N were detected in the hippocampus. The cognitive function of diabetic rats decreased significantly. TAC demonstrated a significant reduction in inflammatory factors in serum, hippocampus, and colon, thus alleviating inflammation. Additionally, it effectively decreased ferroptosis induced by NLRP3 and reduced pathological damage in the hippocampus of diabetic rats. After treatment, the differential microbiota such as Lachnoclotridium and Bacteroides. TAC improved gut barrier permeability and integrity in rats while remodeling gut mucosal homeostasis. Moreover, pyroptosis and ferroptosis caused by the inflammatory cascade in the rat hippocampus were also significantly inhibited. The combination of high lipid and high glucose with STZ can result in gut microbiota disturbance, damage gut immune barrier, decreased gut mucosal permeability and integrity, aggravated gut inflammation, further spread inflammatory factors to brain tissue, cause inflammatory cascade reaction of encephalopathy, and ultimately resulting in neuronal ferroptosis and cognitive dysfunction in diabetes mellitus. Our study suggests that TAC may regulate gut microbiota, restore gut immune homeostasis, improve gut barrier permeability and integrity, inhibit brain tissue inflammatory cascade, reduce neuronal ferroptosis, and thus improve diabetes. This provides new targets for its treatment strategy.

Multi-spectral Remote Sensing Image Fusion Method Based on Gradient Moment Matching

Image fusion is a popular research direction in the field of computer vision. Traditional image fusion algorithms can achieve good results in fusing grayscale images, but it is difficult to achieve ideal results in processing multi-spectral images. To address the shortcomings of multi-spectral image fusion, this study proposes a low computational complexity and low latency multi-spectral image fusion model by utilizing a multi-step degree moment matching algorithm and a generative adversarial network for fusion. Through experiments, it was found that the F1 score of the GAN-MMN model on the TinyPerson dataset was 89.79%, with an average recall rate of 89.76%. The GAN-MMN performance was higher than that of the control model. Meanwhile, the GAN-MMN model also exhibited superior performance in high-frequency feature extraction and time delay compared to the control model. According to the experimental results, the proposed multi-spectral remote sensing image fusion model had a high feature extraction effect, and its recall rate and F1 score were better than the control model, so the research model had a certain progressiveness. The proposal of this model gives a new approach for the processing of multi-spectral remote sensing images, effectively promoting the development of the computer vision industry.

Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing

In the era of Industry 4.0, Zero Defect Manufacturing (ZDM) has emerged as a prominent strategy for quality improvement, emphasizing data-driven approaches for defect prediction, prevention, and mitigation. The success of ZDM heavily depends on the availability and quality of data typically collected from diverse and heterogeneous sources during production and quality control, presenting challenges in data interoperability. Addressing this, we introduce a novel approach leveraging Asset Administration Shell (AAS) and Large Language Models (LLMs) for creating interoperable information models that incorporate semantic contextual information to enhance the interoperability of data integration in the quality control process. AAS, initiated by German industry stakeholders, shows a significant advancement in information modeling, blending ontology and digital twin concepts for the virtual representation of assets. In this work, we develop a systematic, use-case-driven methodology for AAS-based information modeling. This methodology guides the design and implementation of AAS models, ensuring model properties are presented in a unified structure and reference external standardized vocabularies to maintain consistency across different systems. To automate this referencing process, we propose a novel LLM-based algorithm to semantically search model properties within a standardized vocabulary repository. This algorithm significantly reduces manual intervention in model development. A case study in the injection molding domain demonstrates the practical application of our approach, showcasing the integration and linking of product quality and machine process data with the help of the developed AAS models. Statistical evaluation of our LLM-based semantic search algorithm confirms its efficacy in enhancing data interoperability. This methodology offers a scalable and adaptable solution for various industrial use cases, promoting widespread data interoperability in the context of Industry 4.0.

Comparative analysis of two static var compensator models in voltage control of transmission network

The static var compensator (SVC) is a member of the family of flexible alternating current transmission systems controllers used in power system engineering to manage specific transmission network characteristics to enhance the performance of the transmission networks and thus increase the networks’ reliability. Power system engineers typically find it difficult to choose which SVC model to implement for simulations. This research aims to address this issue by conducting a comparative examination of two key SVC models on a transmission network. The two models of SVC variable shunt susceptance and firing angle were mathematically modeled and methodically included into the Newton-Raphson power flow algorithm for the network power flow solution. The IEEE 30-bus network was adopted as the test case, and the method was implemented in the MATLAB/Simulink environment. The network performance metric utilized was the voltage profile of the network. The two SVC models were successful in enhancing the network's performance; however, the variable shunt susceptance model was computationally faster than the firing angle model, as revealed by the simulation results. Therefore, among the two SVC models, the variable shunt susceptance model may be taken into account for simulation to enhance the performance of the transmission networks.

Long-term deep reinforcement learning for real-time economic generation control of cloud energy storage systems with varying structures

Energy storage systems play a crucial role in modern power systems. Consequently, a mixed cloud energy storage (CES) system is proposed. The mixed CES system comprises consumers and prosumers. The consumers can only consume energy. The prosumers can either produce or consume energy at different time intervals. The proposed mixed CES system is designed for investigating the generation control challenges in mixed interconnected power systems. To optimize the active power balance and economic efficiency of the mixed CES system, a long-term deep reinforcement learning (LDRL) artificial intelligence approach is proposed as the real-time economic generation controller to control the mixed CES system. The LDRL consists of a reinforcement mechanism and two models: a long short-term memory model for economic dispatch and a deep neural networks model for smart generation control. The reinforcement framework updates policies for the prosumers. The efficacy of proposed method is validated across three mixed systems, i.e., the improved IEEE 300-bus, Polish 2383-bus, and mixed systems with varying structures. The numerical simulations verify that the LDRL method can efficiently and economically control the mixed CES systems with diverse structures.

Leaching behavior and kinetics of arsenic’s leaching from arsenic-antimony dust in H2SO4-H3PO4-H2O system

In response to the challenges of low rates of leaching of arsenic and poor separation of arsenic and antimony in arsenic-antimony dust using acidic leaching, the present study proposed a selective leaching approach for arsenic from arsenic-antimony dust using a mixed acid of H2SO4 and H3PO4. The leaching mechanism of arsenic-antimony dust in the H2SO4-H3PO4-H2O system was elucidated through the characterization of the solution and the leaching residue, identifying the effects of various parameters such as the concentration of phosphoric acid and temperature on the separation efficiency of arsenic and antimony, and clarifying the leaching kinetics of arsenic-antimony dust. The results indicated that the introduction of PO43- into the reaction system promoted the formation of SbPO4 precipitate, effectively inhibiting the leaching of antimony and preventing the formation of AsSbO3 and (Sb,As)2O3, as well as enabling efficient leaching of arsenic. Under the H3PO4 concentration of 0.97 mol/L, reaction temperature of 90 ℃, liquid-to-solid ratio of 30:1, and leaching duration of 3 h, the rate of leaching of arsenic reached 99.53 %, with the corresponding value of only 5.13 % for antimony. The arsenic content in the leaching residue was reduced to 0.98 %, while the antimony content was as high as 65.34 %, indicating a preliminary separation of arsenic and antimony. The results for the leaching kinetics indicated that the leaching process of arsenic was governed by the diffusion control model within the Avrami model, with an apparent activation energy (Ea) of 74.863 kJ/mol. Last but not the least, the results showed that reducing the particle size or disrupting the product layer coating resulted in enhanced leaching of arsenic.

Federated Risk-Based Access Control Model for P2P Lending Platforms: A Multi-Agent Systems (MAS) Approach

This study addresses the inherent risk management challenges in decentralized finance, particularly for peer-to-peer (P2P) lending platforms. We propose a novel framework that leverages a Multi-Agent System (MAS) to establish a collaborative network encompassing loan originators, investors, regulators, and service providers. This distributed approach facilitates federated risk management, where risk assessment and mitigation responsibilities are shared across these entities. The MAS employs a comprehensive nine-factor assessment (detailed in Table 5) to evaluate industry risk profiles, considering industry environment, competition, and internal capabilities. This data is further visualized using a color matrix (Tables 5 & 6) and utilized alongside state diagrams (Figure 2) to depict the workflow and manage tasks within the P2P lending process. Additionally, the MAS informs a novel Federated Risk-Based Access Control (FRkBAC) system that tailors access permissions (lending origination, disbursement, etc.) based on dynamic risk assessments of industry trends and individual borrower profiles. This data-driven approach fosters trust within the P2P ecosystem and represents a significant advancement in decentralized finance risk management compared to traditional methods. Doi: 10.28991/ESJ-2024-08-06-05 Full Text: PDF

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment

To accurately predict traffic flow and optimize the operations of freeway bottleneck areas in a mixed-vehicle driving environment, this paper proposes a traffic prediction model and a variable speed limit (VSL) cooperative control strategy. Firstly, a lane-level short-term traffic prediction model, physics informed Transformer and cell transmission model (PIT-CTM), is constructed by combining the Transformer neural network and lane-level cell transmission model (CTM) based on the physics-informed deep learning framework. On this basis, the accuracy and transferability of PIT-CTM are analysed. Secondly, a lane assignment decision model is presented, which enables the dynamic planning of the optimal traffic distribution across lanes. Furthermore, a lane-level VSL control model is constructed based on the model predictive control (MPC) framework. The model induces vehicles to change lanes earlier by setting the speed limit difference between lanes. By regulating the input flow in the bottleneck area of the freeway, it reduces conflicts between mainline vehicles and ramp vehicles. Finally, the feedback regulation between the lane assignment decision model and the lane-level VSL control model promotes the cooperative optimisation of the lateral and longitudinal flows and adapts the control strategy to the dynamic traffic characteristics. A three-lane freeway merging zone is selected, the numerical experiment is conducted and compared with differential lane-level VSL. The results show that the strategy can effectively optimise the mixed-vehicle traffic state and maintain better control performance under any connected and autonomous vehicle (CAV) penetration rates.

Influence of Axial Flow Field Parameter Fluctuations on Performance of Scramjet Combustor

Abstract When air-breathing aircraft is flying over a wide area, the upflow of the combustor central axis will change greatly, and the flow field parameters will fluctuate along the axis. Therefore, it is necessary to carry out a study on the influence of axis flow field parameter fluctuation on combustion chamber performance, so as to provide relevant theoretical support for the combustion chamber of air-breathing aircraft in wide-area flight design. Based on the N-S gas-phase control model, combined with combustion model, turbulence model, burning rate model and mass transfer model, a numerical simulation model of flow combustion in solid fuel scramjet combustor is established. Through this model, the influence of the upflow field parameter fluctuation of the central axis on the combustor performance is carried out. The results show that for a optimized combustor configuration, different inlet air flow rates will cause the Mach number of the combustor to oscillate along the flow direction. The greater the oscillation amplitude, the greater the total pressure loss. Too high or too low inlet air flow rate will both result in Mach number oscillation in the combustor. However, selecting a suitable inlet air flow rate can significantly reduce the Mach number oscillation of combustor flow. Therefore, for different combustor configurations, appropriate inlet air flow should be designed to reduce the Mach number oscillation of the flow field in the combustor, reducing the flow loss and improving the working performance of the combustor.

Influence of Betrayal on Emotional Modulation of Executive Control: Evidence From ERPs.

Previous studies have found that betrayal increases negative attentional bias and hinders executive control. However, it remains unclear how betrayal influences emotional modulation of executive control. What's more, according to the dual mechanisms of control model, executive control can be divided into reactive and proactive control. It also requires clarification whether both aspects of executive control are affected equally by betrayal in emotional contexts. Thus, the present study aims to provide insight into how betrayal influences the emotional modulation of executive control. Betrayal was induced using a repeated trust game in two experiments. Eighty-two participants (40 for Experiment 1 and 42 for Experiment 2) completed emotional executive control tasks while event-related potentials were recorded. In Experiment 1, an emotional Go/No-go task was used to explore the impact of betrayal on the emotional modulation of executive control. The results indicated that betrayal resulted in inefficient top-down attention processing towards negative stimuli and impaired executive control over these stimuli. This was evidenced by higher N2a and N2b amplitudes in the angry Go condition, and smaller angry No-go P3 amplitudes in individuals who experienced betrayal compared to the control group. In Experiment 2, a modified emotional Stroop task was employed to measure proactive and reactive control in emotional contexts. The results indicated that betrayal impaired only reactive control towards negative stimuli and did not affect proactive control. This was evidenced by the betrayed group exhibiting smaller SP amplitudes under the happy incongruent condition in the most congruent context, with no significant difference observed in the most incongruent context. In summary, betrayal decreases the efficiency of top-down attentional processing directed towards negative stimuli and hampers executive control over negative stimuli. Moreover, this impairment appears to be confined to reactive control strategy.

An integrated system theoretic accident model and process (STAMP)-Bayesian network (BN) for safety analysis of water mist system on tanker ships

A water mist on tanker ships is a specialized fire suppression system intended to mitigate fire hazards on board. This innovative safety measure disperses microscopic water droplets, creating a mist that rapidly absorbs heat and suppresses fires by reducing oxygen levels and cooling the surrounding area autonomously. This paper attempts to perform a comprehensive safety analysis of the water mist system on a tanker ship under the system theoretic accident model and process (STAMP) and Bayesian network (BN) robust modeling, which is capable of presenting a dynamic structure comprising complex elements. In the paper, whilst the STAMP is utilized to determine failure scenarios by establishing a hierarchical control and feedback model structure, the BN quantifies the probabilities of potential failures based on the outcomes of STAMP. A water mist system in tanker ships is analyzed under STAMP-BN modeling since it is an essential part of the ship, including an advanced system comprising elements of human, software, and hardware. The research results indicate that the failure probability of the water mist system has a failure probability of 3.93E-02. Besides its robust theoretical background, the research findings will provide a helpful reference for maritime safety managers, safety inspectors, technical inspectors, HSEQ managers, and ship crew on improving fire-fighting safety in tanker ships.