Multitasking and interruptions in control rooms of complex systems such as nuclear power plants (NPPs) are often identified as contributing to human errors. This paper aims to provide a nuanced understanding of how and why multitasking/interruptions occur in such environments and how NPP operators respond to them. Through a mixed-method approach combining field observation, simulator observation, and in-depth interviews, we found that the average frequency of multitasking/interruption was 3.19 times/h and 17.5 times/h in routine and emergent scenarios. Sixteen reasons for engaging in multitasking/interruptions were identified and classified according to whether they are internally-driven or externally-driven, and whether they occur purposefully in service of specific system goals or not. To handle them, NPP operators developed a range of organisational, procedural, training, and technological measures that operate across different stages of multitasking/interruptions. These findings suggest that multitasking/interruptions in complex systems should be systemically managed rather than simply eliminated.

Abstract This paper reports on the generic prediction capability of full electromagnetic plasma initiation modelling with DYON, which was carried out for the first time in fusion research by the joint modelling of the International Tokamak Physics Activity (ITPA) - Integrating Operation Scenario (IOS) group. The following devices were included in the experiment database: VEST (spherical torus, copper coils, Stainless steel wall, R/a=0.3m/0.2m, Vv=3.7m 3 ), MAST-U (spherical torus, copper coils, C wall, R/a=0.7m/0.5m, Vv=55m 3 ), EAST (conventional tokamak, superconducting coils, metallic wall, R/a=1.85m/0.5m, Vv=38m 3 ), DIII-D (conventional tokamak, copper coils, C wall, R/a=1.67m/0.65m, Vv=35m 3 ), and KSTAR (conventional tokamak, superconducting coils, C wall, R/a=1.8m/0.5m, Vv=55m 3 ). Despite the different hardware features of the devices, the required operating spaces of the loop voltage induction and prefill gas pressure for inductive plasma initiation in each device were successfully reproduced by the predictive simulations with DYON using only the individual hardware design and the control room input data for each discharge. This successful validation across multiple machines demonstrates that the full electromagnetic DYON modelling can capture the essential physics of inductive plasma initiation. The simulation settings commonly employed for all modelling and the modifications necessary to account for the discrepancies between individual devices are reported. Predictions for ITER based on the multi-machine validation indicate that a wide range of prefill gas pressures exists for the Townsend breakdown and the plasma burn-through (0.01 - 1.5mPa).

Production is always a challenging task in underground coal mines as the environment is dynamic, full of hazards, and beyond prediction. The occurrence of any natural disaster or explosion is a common phenomenon. The losses of wealth or lives are more in many cases due to the absence of proper visibility, ventilation, communication with the control room, access to the escapes, etc. So, the implementation of a proper and effective communication system, along with illumination, can enrich the communication and visibility inside the underground for production and rescue operations. But, wireless communication does not work efficiently there due to the high attenuation rate. On the other hand, the low conductivity of the surrounding earth and stone limits the communication through the earth. Similarly, existing conventional lights with tungsten filament bulbs are not sufficient for such purposes. The proposed low-voltage DC (LVDC) distribution system of 24 V, together with illumination provided by LEDs, additionally equips the wired personal communication system to be aptly suited for the task at hand. The implementation of a voice frequency (VF) communication system with the same LVDC distribution as a backbone is the most significant contribution of the work, as it provides not only illumination but also the much needed a means of communication underground. To explore the scope of application of this voice communication through LVDC distribution lines, propagation characteristics are discussed in this paper. The characteristic impedance and attenuation are analysed in the case of the cable with its physical dimensions and the data acquired in the experiments. The analysis firmly establishes the feasibility of this model.

The environment for human-computer interactions in the control room of a marine floating nuclear power plant (MFNPP) is more complex than that of a land-based nuclear power plant, as the reliability of the human-computer interaction directly affects the occurrence of human error accidents. Currently, some methods for evaluating human reliability in human-computer interactions are traditional; however, they fail to adequately reflect dynamic and intelligent characteristics. To address this issue, this paper constructs a model for evaluating the human reliability in human-computer interactions in the control room of MFNPPs based on the backpropagation neural network theory, which provides a dynamic method for assessing the human reliability in human-computer interactions and resolves the limitations of static methods. This study first establishes the influencing factors by reviewing the literature, conducting questionnaires, calculating relative importance, and consulting experts, then determines the network structure and calculation methods. Later, the expected values are obtained through the linear weighting method. Two different weights of the same influencing factors were initially determined using the analytic hierarchy process and the decision experiment and evaluation experiment method, respectively. Then, the game theory combination weighting method is used to synthesize two different weights to determine the final weight, with the weight results reflecting the importance and correlation among the influencing factors. Finally, the model is trained. From the perspective of model performance, the constructed neural network evaluation model has good accuracy and can be used to evaluate human reliability in human-computer interactions.

Soybean-derived trypsin inhibitor proteins (TIU) impair amino acid bioavailability and increase exogenous and endogenous nitrogen flow to the hindgut, thereby attenuating pig growth performance. High protein diets potentiate proteolytic fermentation induced alterations to the gut microbiome, which may increase opportunistic enterotoxigenic Escherichia coli (ETEC) proliferation and exacerbate disease. We hypothesized that feeding high TIU diets to nursery pigs would reduce growth rates and exacerbate F18 ETEC disease. Two hundred and eighty-eight (5.42 ± 0.93 kg BW; Camborough 1050 × 337, [PIC, Hendersonville, TN]) newly weaned pigs were evenly allotted across two control rooms (CON) and two challenge rooms (ETEC). Pigs were allotted based on sex and α-(1,2) fucosyltransferase (FUT1) genotype, with both factors evenly distributed across all pens. Pens were randomly assigned to corn-soy diets consisting of 1.1, 2.4, or 4.2 TIU/mg, creating six treatments: CON1.1, CON2.4, CON4.2, ETEC1.1, ETEC2.4, and ETEC4.2 (8 pens/treatment). On day 10 (day post-infection [dpi] 0), pigs in the ETEC rooms were orally inoculated with 5 ml of 3.8 × 109 cfu/ml of an F18 ETEC culture. Pooled pen feces were assessed for dry matter (DM) on dpi 0, 3, 5, 7, 9, 11, 14, and 21. Pen growth performance, medical treatments, and mortality were assessed prior to infection (dpi -10 to 0) and post infection (dpi 0 to 28). On dpi 5 and 12, one pig/pen was sacrificed to assess ileal mucosal attachment of F18 ETEC via in situ hybridization. Pen was the experimental unit and data were analyzed for the interactive and main effects of diet and challenge. Increasing dietary TIU to 4.2 TIU/mg led to a 13-16% decrease in ADG compared to the 1.1 TIU/mg diets, regardless of the challenge (P = 0.014). A diet × challenge interaction was observed regarding mortality, with ETEC2.4 showing a 20.8% mortality rate compared to 0% in CON2.4 (P = 0.001). Similarly, 22.9% of ETEC2.4 pigs required antibiotic treatment compared with 0% of CON2.4 pigs (P = 0.001). Fecal DM from dpi 0-21 showed no interaction, but ETEC pens had lower fecal DM compared to CON pens from dpi 3-11 (P < 0.0001). On dpi 5, ileal F18 attachment was increased in ETEC versus CON pigs (3.7 × 10-3 vs 0.1 × 10-3 F18 copies/μm2, P < 0.0001). By dpi 12, ileal F18 attachment did not differ between challenge groups (P > 0.05), suggesting disease resolution. In conclusion, diets at or greater than 2.4 TIU/mg decreased pig growth and reduced livability in ETEC-infected nursery pigs.

Operating large-scale scientific facilities requires coordinating diverse subsystems, translating operator intent into precise hardware actions, and maintaining strict safety oversight. Language model-driven agents offer a natural interface for these tasks, but most existing approaches are not yet reliable or safe enough for production use. In this paper, we introduce Osprey, a framework for using agentic AI in large, safety-critical facility operations. Osprey is built around the needs of control rooms and addresses these challenges in four ways. First, it uses a plan-first orchestrator that generates complete execution plans, including all dependencies, for human review before any hardware is touched. Second, a coordination layer manages complex data flows, keeps data types consistent, and automatically downsamples large datasets when needed. Third, a classifier dynamically selects only the tools required for a given task, keeping prompts compact as facilities add capabilities. Fourth, connector abstractions and deployment patterns work across different control systems and are ready for day-to-day use. We demonstrate the framework through two case studies: a control-assistant tutorial showing semantic channel mapping and historical data integration and a production deployment at the Advanced Light Source, where Osprey manages real-time operations across hundreds of thousands of control channels. These results establish Osprey as a production-ready framework for deploying agentic AI in complex, safety-critical environments.

The ‘push model’ describes an approach where 999 calls are routed by clinical navigators in the control room directly to other services based on a clinical review rather than a full clinical assessment. It is an active model, based on pre-determined agreements with NHS providers, which then contact the patient rapidly and determine the right response for their needs. This approach aims to provide patients with access to an appropriately qualified clinician, while releasing clinical capacity in the ambulance control room for other patients. The approach is delivered through training and collaborative working, requiring minimal financial investment. This article describes the implementation of the push model into Yorkshire Ambulance Service. By investing in this initiative, the team aimed to lay the groundwork for a sustainable business model that will bring long-term benefits to the public. This approach allows the ambulance service to respond to life-threatening emergencies more promptly and facilitates the redirection of urgent care to community services, reducing obstacles to accessing community-based healthcare.

Pathogen reduction in an endoscopy unit using AI-enabled autonomous UV-C disinfection.

Nitrogen dioxide (NO2) is an environmental air pollutant and a potent oxidant. Epidemiological studies have revealed a close association between chronic cough and long-term inhalation exposure to NO2. ATP can be released from airway/lung epithelial cells upon inhalation exposure to oxidants, and convincing evidence established in recent clinical studies indicated that extracellular ATP in the respiratory tract plays an important role in the pathogenesis of refractory chronic cough. However, whether inhalation exposure to NO2 elevates the ATP release in the lung is not yet known. Results of this study showed: 1) in awake rats, acute and chronic inhalation exposures to NO2 (5-10 ppm) evoked an increase in the ATP release in the bronchoalveolar lavage fluid (BALF), reaching >225% of that in the control (room air) group. 2) The NO2-induced increase in ATP release in the lung generated by chronic exposure was substantially greater than that by acute exposure to the same concentration of NO2. 3) Chronic but not acute exposure to NO2 induced a mild and transient airway inflammation. 4) The elevated ATP release in the lung returned to control within 1-2 days after the acute exposure to NO2; in sharp contrast, the increase in ATP in BALF persisted for >11-15 days following the chronic exposure when the airway inflammation had already completely recovered. These results suggest that the sustained elevation of ATP release in the lung may act as a contributing factor to the pathogenesis of chronic cough associated with long-term inhalation exposure to NO2 in humans.NEW & NOTEWORTHY This study demonstrates that chronic inhalation exposure to NO2, an air pollutant, induced a long-sustaining elevation of the extracellular ATP (eATP) concentration in rat lungs. This finding suggests that the elevated eATP concentration in the lungs may be an important contributing factor to the close association between chronic cough and long-term inhalation of NO2 observed in humans. Furthermore, it presents a promising experimental animal model for future studies of the pathogenic mechanisms of chronic cough.

Dependence assessment of human operators in advanced main control rooms considering uncertainty and dynamic situations

Severe accidents require accurate and fast prediction tools to support real-time decision making. This study presents a machine learning-based framework for forecasting thermal-hydraulic (TH) variables during a loss-of-component-cooling-water (LOCCW) accident. Using datasets generated by the Modular Accident Analysis Program (MAAP), surrogate models were developed to predict key TH variables observable in the main control room. Two approaches, Multi-Input Multi-Output (MIMO) and Multi-Input Single-Output (MISO), were evaluated. The MISO approach outperformed MIMO, reducing regression errors by an average of 43% and improving full-scenario prediction accuracy in over 90% of test cases. This improvement is attributed to the MISO ability to eliminate gradient conflicts and specialize in individual variable predictions. However, challenges persist in accurately forecasting reactor vessel water level (RV WL) and maximum core exit temperature (MAX CET), which exhibit higher errors. Despite requiring more computational resources, MISO models significantly reduce computation time compared to traditional system codes, offering predictions within seconds. This study demonstrates the potential of MISO-based models for real-time accident forecasting and lays a foundation for further refinement and broader application to other accident scenarios.

Research on the Performance of a Passive Cooling System for Main Control Room Emergency Habitability

In India, like most countries, the COVID-19 pandemic in successive waves severely hampered the emergency medical services (EMS) and the government made prompt interventions, including substantial investments in both EMS and maternal health care immediately after the first wave. The study assessed variations in EMS efficiency and critical perinatal outcomes between the pre-pandemic era and the post pandemic-resilient phase in 2023 and 2024. The study analysed the key EMS metrics based on the calls related to pregnancy, including call volume, response and transfer time, hospital handoff time and ambulance travel distance, and important maternal-newborn health outcomes such as maternal and neonatal mortalities, home deliveries, institutional childbirths, C-section deliveries, miscarriages and complicated vaginal births. The data relied upon encompasses the period from January 2017 to December 2024, including eight pandemic phases in 2020-22 and the resilient period of 2023 and 2024, obtained from the Tamil Nadu 108 Ambulance Control Room. A time series analysis method evaluated the EMS metrics in various pandemic phases; a statistical comparison was made with the pre-pandemic period for maternal-newborn outcomes. The appropriate effect size metric quantified the change in both analyses. In the pandemic phases, despite an increase in pregnancy related call volume, the EMS metrics such as response times, transfer times and hospital handoff times witnessed notable improvement. The maternal and childbirth outcomes, especially in the post-pandemic and resilient phases during 2023-24, were markedly superior when compared with the corresponding period in the pre-pandemic era. In particular, the maternal mortality rate reduced by 19%, with 37 deaths per 100,000 live births. This is far lower than the national average of 97 deaths per 100,000 live births. Also, the rates of infant death, neonatal death, miscarriage, difficult vaginal births, and home births went down by 19.35%, 17.03%, 28.02%, 19.23%, and 36.05%, respectively. Government investments during the pandemic, along with the sustained focus on maternal health programmes, appear to have provided substantial support to pregnant women and newborns. The reproductive health of women in Tamil Nadu does not seem to have been undermined by the pandemic.

Coal Mill serves as a critical component in Steam Power Plants (PLTU) for pulverizing coal into fine powder ensuring efficient boiler combustion. This study aims to analyze the performance of Coal Mill Unit 4 at PT. PLN Indonesia Power UBP Pangkalan Susu, type MPS160HP-II. Quantitative descriptive research was conducted through field observation in June 2024. Population comprised 5 Coal Mill units (4 active, 1 standby), with sampling focused on one unit comparing design vs. actual data. Instruments included observation sheets and Central Control Room (CCR) data, analyzed using energy efficiency formula and production efficiency . Results revealed production efficiency of 89.5% and energy efficiency of 84.5%, below design standards due to 35% coal moisture, HGI 50, air-coal ratio 2.44, and mechanical wear. Conclusions recommend real-time coal quality monitoring, predictive maintenance, and air ratio optimization to achieve efficiency above 90%.

Assessment of the clinical feasibility of robot-assisted endovascular visceral interventions to reduce physical strain caused by prolonged standing and enabling remote interventions. Between 05/2024 and 09/2024, 45 patients were included in this prospective, single-center study. Patients scheduled for elective endovascular abdominal and pelvic interventions with superselective catheterization were assigned to manual (27 patients) or robotic-assisted treatment (18 patients). Radiation dose of the interventionalist, examination time (including preparation and follow-up), procedure duration and fluoroscopy time were compared between procedures using the CorPath GRX platform (Corindus, Waltham, MA) and conventional procedures. Technical success of robotic interventions was defined as achieving stable microcatheter positioning at the predefined target treatment point in the target vessel under robotic navigation, allowing execution of the planned therapy without conversion to manual navigation. 18 patients underwent robotic-assisted interventions (mean age 68 ± 12years; 15 male), transarterial chemoembolization (TACE) (n = 9), 99mTc-MAA simulation (MAA)/transarterial radioembolization (TARE) (n = 2) and prostatic artery embolization (PAE) (n = 7). 27 comparable procedures were performed manually (mean age 68 ± 10years; 21 male): TACE (n = 13); MAA/TARE (n = 7); PAE (n = 7). 16/18 (88.9%; 95%-CI (Wilson) 67.3-96.7%) robotic-assisted procedures were technically successful, with manual conversion occurring in 2 patients (11.1%; 95%-CI (Wilson) 3.1-32.8%). Neither median fluoroscopy time nor procedural dose, procedure duration or examination time differed between the robotic and conventional interventions [19min (IQR 19.55) vs. 31min (IQR 19.85); p = .053; 107.85Gycm2 (IQR 164.03) vs. 128.00Gycm2 (IQR 186.20); p = .286; 65min (IQR 35.50) vs. 59min (IQR 49.00); p = .711; 100min (IQR 37.50) vs. 100min (IQR 40.00); p = .853]. In comparison with conventional procedures, the operator's dose was lower in robotic interventions [0.000mSv (IQR 0.000) vs. 0.005mSv (IQR 0.005); p < .001]. Findings from this pilot case series indicate that robotic-assisted endovascular visceral interventions are feasible and demonstrate a high technical success rate, while simultaneously providing the interventionalist zero radiation exposure through remote operation from the control room.

Global climate change is a critical issue affecting the sustainability of projects, especially in the mineral and coal mining industry. At PT Putra Perkasa Abadi, climate change was identified as the highest risk in the company's risk management profile, impacting mining operations. One significant effect is the inconsistency between planned and actual rain hours, leading to high rainfall, which hampers production and productivity targets. Dewatering management, which deals with rainwater filling the sump, is particularly affected. In extreme weather, workers must operate pumps, a high-risk activity near water. The increased frequency of pump operations during 2024’s high rainfall heightens accident risks. To mitigate this, the company developed SIC-MUFS, a tool that allows remote pump operation. This system enables workers to avoid crossing the sump in dangerous weather conditions and can also monitor the water level automatically. If the water reaches critical levels, SIC-MUFS sends notifications to the Central Control Room (CCR) for further action.

Public hospitals in South Africa are regularly faced with safety and security incidents that expose personnel, patients, and visitors to serious and violent crimes such as assault, rape and murder. In addition, theft of valuable assets, is a huge problem in public hospitals. This article identifies the ineffectiveness of security measures implemented in public hospitals and formulation of an integrated security management model. A quantitative study was conducted at five public hospitals in the province of Gauteng. Quantitative data were collected through a survey questionnaire. Additionally, safety and security documents were analysed, followed by observations of security personnel in the control rooms. The findings revealed that safety and security measures implemented by these hospitals do not fully comply with the Control of Access to Public Premises and Vehicles Act, No. 53 of 1985. The research participants identified gaps and shortcomings such as poor implementation of access?control measures that included the searching of personnel and visitors. This results in the loss of assets and the disappearance of patients from hospital premises. The research confirmed that inadequate security systems regarding perimeter fences, closed?circuit television and trained security personnel at the research sites made it difficult to deter, detect and detain perpetrators. This research contributes by highlighting safety and security procedures that need to be followed to keep personnel, patients and visitors in public hospitals safe. Furthermore, the use of an Integrated Security Risk Management Model developed specifically for public hospitals during this research is recommended.

Abstract We introduce a machine learning framework, HIPED (HeIght and width Predictor for Edge Dynamics), for predicting and optimizing pedestal and core performance in spherical tokamak plasmas. Trained on pedestal and core datasets from the third MAST-U campaign, HIPED provides accurate estimates of pedestal height vs. width. The results reveal notable differences compared with conventional aspect-ratio studies; for instance, a simple power-law relation between pedestal width and height has very low accuracy. Instead, additional parameters such as normalized plasma pressure, elongation, and Greenwald fraction significantly improve accuracy. HIPED can also be trained only on ‘control room parameters’ to inform experimentalists of which controllable parameters to adjust for improving core-integrated performance. The framework further includes a multi-objective optimization scheme that helps guide experimental planning and optimization. We find Pareto-optimal discharges with respect to various features, including distance from edge-localized modes and normalized plasma pressure, track their parameter trajectories over time, and identify the control room parameters required for these Pareto-optimal discharges. This provides a framework for systematically optimizing core and edge performance according to different experimental priorities.

Introduction: Surgical site infections (SSIs) are a substantial contributor to morbidity and mortality rates. 405 nm visible light disinfection (VLD) has been shown to reduce bacterial burden in operating rooms (ORs); however, its effect on SSIs is poorly described. Therefore, we investigated whether the usage of VLD correlated with a decrease in SSI rates. Methods: A prospective cohort study was conducted by comparing three operating rooms equipped with VLD and three operating rooms equipped with standard lights. Bacterial loads within the six ORs of interest were obtained before and after initiation of VLD. Patients of various surgical subspecialties were randomly assigned to rooms. SSI rates were tracked from July 2019 to June 2022. The primary outcome of interest was the SSI rate. Results: A total of 11,381 patients were included in the study, comprising 5,036 in VLD rooms and 6,345 in control rooms. Although age, gender, and body mass index were similar between the groups, there were differences in American Society of Anesthesiologists class, operation length, and wound class. There was a 66% reduction in bacterial load in the VLD rooms. No substantial difference was observed in SSI rates between the control and VLD rooms (0.8% vs. 0.8%, p = 0.71). There was no substantial difference in SSI rates by specialty. After adjusting for covariates, VLD was not independently associated with SSI rate. Conclusion: VLD effectively reduced overall operating room bacterial burden; however, this did not result in a substantial reduction in SSI rates. The cause of SSIs is multi-factorial and may be influenced more by patient and technical factors than overall operating room bacterial burden.

Abstract This study investigated the diffusion of leaked liquefied petroleum gas (LPG) and analyzed the load distribution characteristics of a petrochemical control room under a vapor cloud explosion (VCE) flow field based on the ANSYS/Fluent software. The results showed that the leaked LPG tended to accumulate near the ground. High wind speed accelerated the propagation of the leaked LPG cloud along the leaked direction and accelerated the dilution of the leaked LPG in the side direction. Besides, the existence of obstacles can significantly increase the gas cloud concentration near the obstacles. The nonuniform concentration field generated by leakage accidents can initiate detonation with relatively low ignition energy. In sparsely obstructed environments, the explosive behavior followed the classic open‐space gas cloud explosion pattern, where peak overpressure decreased with distance. The peak overpressure during explosions increased markedly with more obstacles, demonstrating that complex obstructions substantially elevated the risk of explosion accidents. The load distribution across control room walls exhibits significant nonuniformity in both temporal and spatial dimensions. The statistical analysis on historical maximum load distributions at different wall positions was conducted, employing bivariate quadratic polynomial fitting to derive a simplified calculation formula, which can be applied to analyze load distribution patterns across control room walls.