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Introduction: Situational awareness is essential during emergent procedures such as endotracheal intubation. Previous studies suggest that time distortion can occur during intubation. However, only in-hospital intubations performed by physicians have been studied. We aimed to determine whether time distortion affected paramedics performing intubation by examining the perceived vs actual total laryngoscopy time, defined as time elapsed from the laryngoscope blade entering the mouth until the endotracheal tube balloon passes the vocal cords.Methods: For this retrospective study we collected prehospital intubation data from a suburban, fire department-based emergency medical services (EMS) system from January 5, 2021–May 21, 2022. The perceived total laryngoscopy time was queried as a part of the electronic health record. Video laryngoscopy recordings were reviewed by a panel of experts to determine the actual time. Patients >18 years old who underwent intubation by paramedics with video laryngoscopy were included for analysis. The primary outcome was the difference between actual and perceived total laryngoscopy time. Secondary analysis examined the relationship between high time distortion, defined as the highest quartile of the primary outcome, and patient age, paramedic years of experience, perceived presence of difficult anatomy, excess secretions, use of rapid sequence intubation, and multiple intubation attempts. We conducted descriptive analysis followed by logistic regression analysis, chi-square tests, and Fisher exact tests when appropriate.Results: A total of 122 intubations were collected for analysis, and 10 were excluded due to lack of video recording. Final analysis included 112 intubations. Mean actual laryngoscopy time was 50.0 seconds (s)(95% confidence interval [CI] 43.7–56.3). Mean perceived laryngoscopy time was 27.8 s (95% CI 24.7–31.0). The median difference between actual and perceived time was 18 s (interquartile range 6–30). We calculated high time distortion as having a difference greater than 30 s between actual and perceived laryngoscopy time. None of the secondary variables had statistically significant associations with high time distortion. Overall, we show that the paramedic’s perception of total laryngoscopy time is significantly underestimated even when accounting for paramedic experience and perceived airway difficulty.Conclusion: This study suggests that time distortion may lead to an unrecognized prolonged procedure time. Limitations include use of a convenience sample, small sample size, and potential uncollected confounding variables.

The article discusses the culture in surgery and the importance of creating a positive work environment to support colleagues. It highlights the different perspectives and priorities of early-career and late-career surgeons and emphasizes the need for better communication and inclusivity within surgical teams. The article also addresses the challenges faced by surgeons in training and the impact of stress and destructive behavior on patient safety. It calls for a zero-tolerance approach to bullying and harassment and encourages the promotion of diversity in surgery. The article concludes by emphasizing the importance of collaboration, trust, and effective teamwork in surgical settings. [Extracted from the article]

Adaptive sampling and situational awareness are key features of modern autonomous underwater vehicles (AUVs) since data quality can be improved while operation time and cost can be reduced. An example for adaptive sampling in the marine environmental context is thermocline detection and tracking. The thermocline as horizontal ocean layer separates warm and cold water and is a key feature in many marine disciplines. For example, it influences the distribution and exchange of nutrients and is a habitat for many organisms. In this paper we use an unscented Kalman Filter (UKF) based extremum seeking control (ESC) to find and follow ocean layers such as the thermocline. Computer simulations and real-world tests show that the method is able to find and track non-trivial real-world ocean layers with sensors subject to hysteresis and delay effects. [ABSTRACT FROM AUTHOR]

AbstractIn complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies. [ABSTRACT FROM AUTHOR]

Introduction The condition of trauma patients and the urgent need for timely resuscitation present unique challenges to trauma teams. These difficulties are exacerbated for military trauma teams in combat environments. Consequently, there is a need for continued improvement of nontechnical skills (NTS) training for trauma teams. However, current approaches to NTS assessment rely on subjective ratings, which can introduce bias. Accordingly, objective methods of NTS evaluation are needed. Eye-tracking (ET) methods have been applied to studying communication, situation awareness, and leadership in many health care settings, and could be applied to studying physicians' NTS during trauma situations. In this study, we aimed to assess the relationship between trauma team leaders' objective gaze patterns and subjective expert NTS ratings during patient care simulations. Materials and Methods After Institutional Review Board approval, 9 trauma teams from first-year post-graduate general surgery and emergency medicine residents were recruited to participate in 1 of 2 trauma simulations (a difficult airway case and a multi-patient trauma). Each scenario lasted approximately 15 minutes. All team leaders wore a mobile ET system to evaluate gaze metrics—time to first fixation (TTFF), average fixation duration (AFD), and total percentage of the scenario (TPS) focused on Areas of Interest (AOI), which included patient, care team, diagnostic equipment, and patient care equipment. Trained faculty raters completed the Non-Technical Skills for Surgeons (NOTSS) assessment tool and the Trauma Non-Technical Skills (T-NOTECHS) scale. One-way analysis of variance, Kruskal-Wallis, and appropriate post-hoc pairwise comparison tests were run to assess differences between ET metrics across AOI groups. Spearman's Rho tests were used to assess correlations between ET and subjective NTS ratings. Results Compared to other NTS domains, trauma teams scored relatively poorly on communication across both T-NOTECHS (3.29 |$ \pm $| 0.61, maximum = 5) and NOTSS (2.87 |$ \pm $| 0.66, maximum = 4). We found significant differences in trauma team leaders' TTFF between teammates and the patient (Team: 1.56 vs Patient: 29.82 seconds, P  < .001). TTFF on the diagnostic equipment was negatively correlated (P  < .05) to multiple measures of subjective NTS assessments. There were no significant differences in AFD between AOIs, and AFD on teammates was positively correlated (P  < .05) to communication and teamwork. There were significant differences in TPS across most AOI pairs (P  < .05), and the average TPS fixated was highest on the patient (32%). Finally, there were several significant correlations between additional ET and NTS metrics. Conclusions This study utilized a mixed methods approach to assess trauma team leaders' NTS in simulated acute care trauma simulations. Our results provide several objective insights into trauma team leaders' NTS behaviors during patient care simulations. Such objective insights provide a more comprehensive understanding of NTS behaviors and can be leveraged to guide NTS training of trauma physicians in the future. More studies are needed to apply these methods to capture NTS from a larger sample of teams in both simulated and real trauma environments. [ABSTRACT FROM AUTHOR]

Introduction Decision-making is a complex process that relies on situational awareness and experience to create a potential list of actions while weighing the risks and benefits of each action. There is a paucity of data evaluating decision-making for individual service members (SM) during the performance of team-based military-relevant activities. Understanding individual performance and decision-making within the context of a team-based activity has potential to aid in the detection and management of mild traumatic brain injuries and assist with safe and timely return-to-duty decision making. The aim of this project was to evaluate cognitive and motor performance in healthy SM during an augmented reality military specific, team-based activity. Materials and Methods Data from 110 SMs from Fort Moore Georgia were analyzed for this project. Service members completed 3 augmented reality room breaching and clearing scenarios (Empty Room, Civilian/Combatant, and Incorrect Position of a unit member) with 3 avatar team members. Participants wore a Microsoft HoloLens 2 (HL2) device and used a replica M4 weapon (Haptech Defense Systems) during scenarios. Three-dimensional position data from the HL2 headset was used to compute temporal measures of room breaching and clearing events while the number and timing of weapon discharge was monitored by the M4. Temporal outcomes included time to enter room, time to fire first shot, time in fatal funnel, and total trial time while motor outcomes were distance traveled and average movement velocity. Results Pairwise comparisons between the Incorrect Position scenario and the Civilian/Combatant scenario demonstrated no difference in time to enter the room (2.36 seconds in both scenarios). Time to fire the first shot in the Civilian/Combatant scenario was longer (0.97 seconds to 0.58 seconds) while time in fatal funnel (2.58 seconds to 3.31 seconds) and time to trial completion (7.46 seconds to 8.41 seconds) were significantly longer in the Incorrect Position scenario. Conclusions Reaction time to fire the first shot, time in the fatal funnel, and total trial time reflect a change in information-processing and decision-making capabilities during military specific, ecological, team-based scenarios when altering the environment inside of the room and modifying avatar movements. Future studies are planned to evaluate the effects of mild traumatic brain injury on specific aspects of military team performance. [ABSTRACT FROM AUTHOR]

As demonstrated in the first part of this study (Part I), wind-shift boundaries routinely form along the west coast of the Pearl River Delta due to the land–sea contrast of a "trumpet" shape coastline in the summer monsoon season. Through multiple numerical simulations, this article (Part II) aims to examine the roles of the trumpet-shaped coastline in the mesovortex genesis during the 1 June 2020 tornadic event. The modeling reproduced two mesovortices that are in close proximity in time and space to the realistic mesovortices. In addition to the modeled mesovortex over the triple point where strong ambient vertical vorticity was located, another mesovortex originated from an enhanced discrete vortex along an airmass boundary via shear instability. On the fine-scale storm morphology, finger-like echoes preceding hook echoes were also reproduced around the triple point. Results from sensitivity experiments suggest that the unique topography plays an essential role in modifying the vorticity budget during the mesovortex formation. While there is a high likelihood of an upcoming storm evolving into a rotating storm over the triple point, the simulation's accuracy is sensitive to the local environmental details and storm dynamics. The strengths of cold pool surges from upstream storms may influence the stretching of low-level vertically oriented vortex and thus the wrap-up of finger-like echoes. These findings suggest that the trumpet-shaped coastline is an important component of mesovortex production during the active monsoon season. It is hoped that this study will increase the situational awareness for forecasters regarding regional non-mesocyclone tornadic environments. [ABSTRACT FROM AUTHOR]

Can a satellite dodge a collision with untracked orbiting debris? Can a satellite dodge collision with a tracked object, making only the avoidance manœuvers actually required to avoid collision, despite the uncertainties of predicted conjunctions? Satellite-borne radar may distinguish actual collision threats from the much greater number of near misses because an object on a collision course has constant bearing, which may be determined by interferometric detection of the radar return. A large constellation of such radars may enable the determination of the ephemerides of all cm-sized debris in LEO. • Space debris on a collision path has a constant bearing from a threatened satellite. • Satellite-borne radar interferometry can identify such debris. • This may enable evading actual collision threats. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic had a profound effect on construction safety, intensifying the challenges faced by workers and exacerbating the industry's preexisting safety issues. Unlike many other sectors that could shift to virtual operations, construction heavily relies on the presence and skills of workers. While previous studies have examined the effects of COVID-19 on the construction industry, little attention has been given to how the pandemic influenced workers' safety situational awareness and safety decision-making. This research aims to study the impact of COVID-19 on construction workers' situational awareness and safety decision-making, specifically focusing on hazard recognition (HR) and safety risk perception (SRP). The results pertaining to 67 recruited construction workers showed that COVID-19 had no impact on their ability to identify hazards. However, there was a notable difference in their safety risk perception, indicating that workers who had experienced COVID-19 had a higher level of safety risk perception. This finding can be explained by the principle of reinforced learning, which suggests that behaviors are shaped by their consequences. In other words, the experience of injuries and infectious diseases, which are less visibly linked to the occupation, acts as powerful feedback that reinforces the significance of proper perception and mitigation of risks. In an ideal situation, the primary focus should be on preventing accidents. However, if accidents do occur, as the findings of this study uncover, it is important to view them as opportunities to improve safety and prevent future incidents. This perspective acknowledges that accidents and injuries, including personal life events like COVID-19, provide opportunities that can potentially offer valuable safety lessons and increase workers' situational awareness. Employers and workers can utilize these experiences to strive for a safer work environment. Sharing experiences and lessons learned among workers can foster a proactive safety culture, leading to continuous improvement in workplace safety. [ABSTRACT FROM AUTHOR]

Wasted effort limits productivity. Successful construction project management entails identifying and mitigating causes of waste. A time-motion study was conducted to collect and classify mechanical, electrical, and plumbing (MEP) work to understand factors contributing to waste. The data were analyzed for disturbances to direct work and sources of waste within such disturbances. Installation work was disturbed very frequently. This led to low shares of direct work. Disturbances can be divided into three groups (short-, medium-, and long-term) based on duration. Short-term disturbances, involving adjusting installation areas and material handling, enabled continued direct work without waste. Medium-term disturbances included most nonvalue-adding activities within the wider work environment. They depended on installers' understanding and required workplace adjustments. Long-term disturbances extended beyond the installation area, necessitating material searches and coordination, leading to significant waste. The study contributes to understanding MEP work and questions traditional management practices that do not provide sufficient preconditions for construction workers. Current methods are not on a sufficient level of granularity to effectively improve productivity. The study suggests integrating precise motion tracking and digital systems to reduce waste related to medium- and long-term disturbances. Providing real-time, task-specific information using digital tools can enhance situational awareness, minimizing nonvalue-adding tasks. Additionally, mobile workstations and logistical services could alleviate movement issues in such disturbances. Required granularity to accurately assess preconditions requires additional research into automating data collection and analysis. [ABSTRACT FROM AUTHOR]

Modern Chemical Biological Radiological Nuclear (CBRN) operations require a significant reduction in the time between decision making and decision execution. This calls for effective decision support to improve situational awareness (SA) at the lowest practical echelon. Applied Research Associates, Inc. conducted a 22-month project to research Avoidance and Protection, the CBRN phase with greatest uncertainty and cognitive work demands, to determine how the Android Tactical Assault Kit (ATAK) could best support operator procedures and decision making. We confirmed U.S. Department of Defense (DoD) CBRN doctrine, created battalion commander, medical officer, CBRN Specialist, and Soldier or Marine use cases, developed a concept video around existing CBRN passive defense workflows and tasking, then reviewed the concept with CBRN Specialists returning from deployment as well as Integrated Early Warning and CBRN Support to Command-and-Control developers. Resulting improvement to decision support can more effectively sustain the operator's observe, orient, decide, and act (OODA) loop while minimizing cognitive load. [ABSTRACT FROM AUTHOR]

Fire services have a responsibility to record information regarding known hazards for sites posing unique and substantial risks. Such information is important, both for informing service planning, and for supporting the development of situational awareness and decision-making during emergency response. However, public inquiries and service inspections have raised concerns regarding the effectiveness of systems in place for recording, monitoring, and using site-specific risk information (SSRI). In contrast, a lack of research focus has been directed toward the use of SSRI, limiting the evidence base available for understanding the causes of these difficulties and how they may be addressed. The following study seeks to contribute to developing knowledge by examines what factors affect the use of SSRI across organisational levels and why. Interviews were conducted with 23 firefighters from operational and tactical roles in one region of the UK. Thematic analysis highlighted six key themes: (i) Differences in the use of SSRI across organisational levels, (ii) System accessibility, (iii) Content, (iv) Structure, (v) Quality, and (vi) Training. Findings indicate that SSRI use is affected by the relevance, structure, and quality of information, which are influenced by workload, training, guidance, and system accessibility. During emergency response, information requirements, time and cognitive capacity differ across organisational levels, influencing the extent to which barriers affect ability to use SSRI. The implications of findings are discussed in relation to informing future areas of applied cognition research, and fire sector decisions regarding the gathering and storage of SSRI. [ABSTRACT FROM AUTHOR]

Situation awareness (SA) is a key indicator of operator task performance and behavioral safety in Human-Machine Interaction (HMI), and task difficulty is an important influencing factors in establishing and maintaining SA, especially in safety-critical scenarios such as flying and nuclear. However, the complexity and diversity of multitasking make it difficult to quantify task difficulty, which to a certain extent limits the research on cognitive research. To solve this problem, we proposed an approach to quantifying task difficulty in multitasking and analyzed the effect of task difficulty on different levels of SA through experiments in this paper. First, a task difficulty measure for a single meta-task was developed based on Shannon's information theory, including both discrete and continuous meta-tasks. Next, a three-dimensional attribute model of meta-tasks was proposed to measure the difficulty value added by concurrent tasks based on multiple resource theory. Finally, an experiment was conducted to measure SA at different levels based on SAGAT, and SA under different levels of task difficulty was analyzed. The results showed a strong negative correlation between SA and task difficulty. Specifically, compared to SA at the comprehension and prediction levels, SA at the perceptual level was more easily influenced by task difficulty. This study can provide some reference for the quantify of task difficulty in human factors experiments, the optimization of tasks in HMI, and the selection of operator attention allocation strategies. [ABSTRACT FROM AUTHOR]

This study investigated the influence of using optical see-through head-mounted displays (OST-HMDs) as a replacement for conventional monitors for an extended duration, as required for laparoscopic surgery. We developed the experiment scenario including two displays (surgical field and patient vitals) in three different combinations of the HMD and conventional monitors. An experiment was conducted to compare the task performance, situation awareness, physical discomfort, and perceived workload in the three different display settings. From this study, it was shown that even though the OST-HMD resulted in higher physical discomfort and lower self-perceived performance rating, the surgical task performance, and situation awareness were not worse, and in some conditions even better than conventional monitors. The main obstacle to using OST-HMDs to replace conventional monitors in laparoscopic surgery would be the weight of the HMDs and the head discomfort caused by wearing them for a long time. [ABSTRACT FROM AUTHOR]

In recent years, increased interest in the Professional judgment and Decision Making (PJDM) of outdoor instructors has focused on experts. This paper builds on earlier work, exploring the components and development of PJDM in mid-career outdoor instructors. The study conducted and thematically analysed semi-structured interviews with nine senior instructors working for Outward Bound Trust (the Trust) in the UK. Findings included two over-arching themes; (1) a Hahnian approach to development, in line with the philosophies that underpin the Trust's work, and (2) practical wisdom: the synergy of contextual comprehension and appropriate options. We conclude that these mid-career outdoor instructors' decision making is dependent on high levels of situational awareness, situational demands, and availability of appropriate options as a course of action. We further identify a philosophical chain between the Trust's ethos, instructor development, and adaptive expertise. These skills develop through a process akin to a cognitive apprenticeship within the Trust's community of practice. [ABSTRACT FROM AUTHOR]

Inflammatory rheumatic diseases (IRDs), encompassing a broad spectrum of chronic disorders, typically necessitate prolonged therapeutic intervention. Nevertheless, these diseases can sometimes manifest as severe emergencies requiring prompt and extensive medical intervention. Urgent intervention is essential for effectively recognizing and managing these situations, as they have the potential to be life-threatening and can result in severe morbidity and mortality. Emergencies in IRDs can occur with different frequencies and manifestations, including nervous system issues, severe infections, thrombosis-emboli, renal crises, gastrointestinal issues, and cardiovascular events. The fact that these events can occur across different IRDs underscores the necessity for heightened awareness and readiness among healthcare professionals. The pathophysiologic mechanisms that cause rheumatic emergencies are complex and involve multiple factors. These emergencies frequently arise due to the interplay between the inflammatory characteristics of rheumatic diseases and different systemic triggers. Early detection and treatment can have a substantial impact on an individual's prognosis in cases of severe and life-threatening disorders that require prompt recognition. Rapid decision-making and urgent care are required to effectively address rheumatic emergencies, as well as the implementation of a diagnostic flowchart. This article provides an overview of the emergencies linked to IRDs, classifying and assessing them individually. This article aims to enhance healthcare professionals' knowledge and awareness of critical situations by examining current recommendations and pathophysiological information. Implementing standardized diagnostic and treatment methods, providing patient education, and conducting continuing research into the underlying mechanisms are essential for enhancing the management of these critical situations and improving patient outcomes. [ABSTRACT FROM AUTHOR]

Cyber situational awareness (CSA) is a prerequisite for justified decision-making and to maintain cyber security. This becomes particularly complex when establishing inter-organizational awareness across sectors. For example, computer security incident response teams (CSIRTs) and national cyber security centers need to establish CSA among countries when coordinating regional cyber incident response. Today's state of the art of information sharing across larger numbers of organizations is often still the least common denominator in the shape of web-based forms and email reports. These are easily applicable by almost everyone who wants to report findings even in stressful situations. However, these do not prove to be efficient for the coordinator that aggregates and merges the data. Therefore, a cyber coordination platform using online surveys is proposed. This approach uses surveys to collect, aggregate and visualize data in a dashboard to support cyber coordination and knowledge management. Furthermore, the online surveys are easy to use and respond to and therefore simplify the participation of stakeholders. We propose an architecture and implement a prototype using popular web application frameworks. The evaluation in a user study revealed promising results with respect to increased efficiency and decreased resource requirements for establishing situational awareness. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have become essential in disaster management due to their ability to provide real-time situational awareness and support decision-making processes. Visual servoing, a technique that uses visual feedback to control the motion of a robotic system, has been used to improve the precision and accuracy of UAVs in disaster scenarios. The study integrates visual servoing to enhance UAV precision while exploring recent advancements in deep learning. This integration enhances the precision and efficiency of disaster response by enabling UAVs to navigate complex environments, identify critical areas for intervention, and provide actionable insights to decision-makers in real time. It discusses disaster management aspects like search and rescue, damage assessment, and situational awareness, while also analyzing the challenges associated with integrating visual servoing and deep learning into UAVs. This review article provides a comprehensive analysis to offer real-time situational awareness and decision support in disaster management. It highlights that deep learning along with visual servoing enhances precision and accuracy in disaster scenarios. The analysis also summarizes the challenges and the need for high computational power, data processing, and communication capabilities. UAVs, especially when combined with visual servoing and deep learning, play a crucial role in disaster management. The review underscores the potential benefits and challenges of integrating these technologies, emphasizing their significance in improving disaster response and recovery, with possible means of enhanced situational awareness and decision-making. [ABSTRACT FROM AUTHOR]

Overconfidence in situation awareness (SA) can lead to various detrimental consequences, including risky behaviors. This study investigates the influence of stress and attention allocation on SA in manufacturing. Specifically, this study aims to (1) examine the effects of stress on objective SA, (2) explore the relationship between SA levels and SA overconfidence, and (3) investigate the potential alleviation of SA overconfidence through attention allocation to more challenging SA levels. These findings demonstrate that stress impairs the comprehension aspect of SA. Moreover, SA overconfidence increases with the SA level. In exploring strategies to mitigate SA overconfidence, allocating attention specifically to the most challenging SA level--SA projection--proved effective in alleviating overconfidence. These findings contribute to future SA research by clarifying the relationship between stress and SA, uncovering the link between SA levels and SA overconfidence, and exploring methods to alleviate overconfidence and improve decision accuracy in manufacturing systems. [ABSTRACT FROM AUTHOR]

With advancements in autonomous driving technology, the variety of activities that can be performed in a vehicle has increased. This improves the possibility of watching virtual reality (VR) content on a head-mounted display (HMD). However, unlike VR used in stationary environments, in-car VR can lead to discomfort and motion sickness due to the vehicle movements. Additionally, the obstruction of the outside view during driving may cause user anxiety. In this study, we investigated, for the first time, the effect of dynamic road environments, such as turns, stops, and speed bumps, on the in-car VR experience. Based on our findings, we included situational awareness (SA) cues in the in-car VR content to help users perceive their surroundings and improve the user experience. We conducted a user study with thirty participants to validate the impact of these cues. Consequently, we discovered that the Dynamics cue, which provides SA information while maintaining the context of the VR content, improves user immersion and trust while easing VR motion sickness. [ABSTRACT FROM AUTHOR]

As AI technologies advance, teams are beginning to see AI transition from a tool to a full-fledged teammate. Introducing an AI teammate brings several challenges, ranging from how human teammates perceive their new AI teammates from an affective standpoint to how AI should engage in the various teaming behaviors that make up effective teamwork. The current study used a mixed factorial survey and structural equation modeling to assess how participants in hypothetical human-AI teams respond to various forms of AI information-sharing, including information related to explainability, back-up behavior, situational awareness, and augmenting team memory. The study's results found that AI design features related to situational awareness and augmenting the teams' memory had the strongest effect on participants' attitudes and perceived team cognition with their teammates. However, much of this effect was mediated by participants' affective attitudes towards the AI as a teammate, with higher ratings leading directly to higher levels of perceived team cognition constructs. These results highlight the importance of fostering positive attitudes towards AI teammates, such as trust and cohesion in human-AI teams, to support the development of effective team cognition and the ability of AI information-sharing to bring about such positive impacts. [ABSTRACT FROM AUTHOR]

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning. [ABSTRACT FROM AUTHOR]

Vehicle Ad-hoc Networks (VANETs) have experienced significant development in recent years, playing a crucial role in enhancing the driving experience by enabling safer and more efficient inter-vehicle interactions through information exchange. Vehicle-to-Vehicle (V2V) communication is particularly vital as it not only helps to prevent collisions and improve traffic efficiency but also provides essential situational awareness to drivers or autonomous driving systems. Communication is typically supported by roadside units (RSUs); however, in practical applications, vehicles may exceed the communication range of RSUs, thus exposing them to various malicious attacks. Additionally, considering the limited computational resources of onboard units (OBUs) in vehicles, there is a high demand for designing lightweight security protocols that support V2V communication. To address this issue, this paper proposes an efficient anonymous V2V identity-authentication protocol tailored for scenarios that lack RSU support. The proposed protocol was formally assessed using the Scyther tool, demonstrating its capability to withstand major typical malicious attacks. Performance evaluations indicate that the proposed protocol is efficient in terms of communication and computational overhead, making it a viable solution for V2V communication. [ABSTRACT FROM AUTHOR]

AbstractIn complex social-technical systems, human teams are required to handle automation errors to maintain system performance. This study developed two path models that connected the team process variables with the automation error management process, based on data collected from a previous experiment. The models show that trust has negative effects on most of the management activities. Team situation awareness could facilitate the whole process by supporting information selecting activity during the analysis and planning stage. Better teamwork could assist the stages of error detection and analysis and planning. Teams with more balanced decision making and workload would find it easier to understand and handle an automation error. The results also reveal that forming an interpretation of the error is helpful but not a necessity for error management. Path models developed in this study provide a technical basis for better team processes in the face of automation error. [ABSTRACT FROM AUTHOR]

AbstractIn-vehicle infotainment systems can cause various distractions, increasing the risk of car accidents. To address this problem, mid-air gesture systems have been introduced. This study investigated the potential of a novel interface that integrates a Head-Up Display (HUD) with auditory displays (spearcons: compressed speech) in a gesture-based menu navigation system to minimize visual distraction and improve driving and secondary task performance. The experiment involved 24 participants who navigated through 12 menu items using mid-air gestures while driving on a simulated road under four conditions: HUD (with, without spearcons) and Head-Down Display (HDD) (with, without spearcons). Results showed that the HUD condition significantly outperformed the HDD condition in participants’ level 1 situation awareness, perceived workload, menu navigation performance, and system usability. However, there were trade-offs on visual fixation duration on the menu, and lane deviation. These findings will guide future research in developing safer and more effective HUD-supported in-vehicle gesture interaction systems. [ABSTRACT FROM AUTHOR]

The operation of the utility grid is being reshaped by the continuous addition of distributed energy resources and advanced metering infrastructure, which challenge existing grid control strategies. Some utilities deploy advanced distribution management systems (ADMS) to assist with the consolidation of various applications and to augment situational awareness in response to the new power delivery dynamics. An ADMS is an integrated software platform that provides utilities with a way to enhance their reliability, control, and optimization with advanced applications, such as volt/VAR optimization (VVO). A VVO application could serve as a vehicle to deliver cost savings by providing the utility with a method to reduce rates by controlling the voltage and decreasing the energy usage in their service territory. Some utilities are reluctant to integrate an ADMS, because it is a significant investment that requires approval from the public regulatory commission and/or their customers. This paper evaluates the impact on VVO performance when using a lower-quality network model supplemented with additional measurements, which could provide an implementation for cost savings. The results show that a better model quality would provide the highest energy savings; however, some level of telemetry is necessary in all scenarios to prevent voltage exceedances. [ABSTRACT FROM AUTHOR]

The recent proliferation of resident space objects (RSOs) in low Earth orbit (LEO) threatens the sustainability of space as a resource and requires persistent monitoring to avoid collisions involving valuable space assets. State-of-the-art ground-based space surveillance techniques, due to their susceptibility to atmosphere, weather, and lighting conditions, tend to focus on RSOs with characteristic length greater than 10 cm or 1 dm. Consequently, millions of smaller LEO RSOs remain untracked by ground-based methods, which reduces overall space situational awareness. Onboard satellite sensors offer a space-based method for tracking RSOs. Prior research has investigated the feasibility of using commercial star trackers (CSTs) — optical sensors prevalent on most active spacecraft — to observe, detect, and estimate the position and velocity of RSOs larger than 10 cm. In a recent effort, we expanded on these feasibility studies by assessing the capabilities of CSTs to detect debris particles smaller than 10 cm in characteristic length. We modeled the particles as Lambertian spheres with zero phase angle and ten percent reflectivity and found that typical CSTs can detect properly illuminated debris of characteristic length between 1 cm and 10 cm even at distances of tens of kilometers. More sensitive CSTs can characterize decimeter-scale RSOs hundreds of kilometers away; alternatively, they can track centimeter-scale and smaller RSOs at closer distances. In this paper, we summarize these key results and extend our previous study by relaxing its zero-phase-angle assumption and characterizing the effect of Sun-RSO-CST phase angle on debris detection range. We identify a number of representative CSTs with publicly available optical characteristics and consider the effects of properties such as pixel pitch, focal length, aperture diameter, and field of view (FOV) on the capability of each CST to detect debris at a given distance and relative velocity (in the form of streak speed). We find that, for debris particles modeled as diffuse Lambertian spheres, Sun-RSO-CST phase angles as high as 57° result in no more than 20% reduction to the useful RSO-CST detection range. In addition, we find that pixel pitch and focal length, rather than aperture diameter, tend to determine the capability of a given CST to resolve two distinct RSOs. Furthermore, streak speed may serve as a stronger limiting factor for detection of smaller debris particles than for larger ones. Despite these limitations, the overall results indicate that CSTs have the potential to substantially enhance space-based debris detection capability. • Prior state-of-the-art in space domain awareness focuses on objects exceeding 10 cm. • Commercial star trackers — CSTs — can detect smaller objects like sub-decimeter debris. • Resident space objects — RSOs — are modeled as diffuse Lambertian spheres. • CST sensitivity and RSO size and reflectivity determine maximum range of detection. • Sun-RSO-CST phase angle, CST resolution, and streak speed limit detection potential. [ABSTRACT FROM AUTHOR]

In order to further reduce the probability of multiple networks being attacked, different types of network security situation prediction models have received widespread attention and in depth research from scholars both domestically and internationally. With the rapid development of situational awareness modeling technology, various novel technical solutions such as neural networks, time series, and support vector machines have been introduced into the prediction model of network security situations, deeply optimizing and improving the means and methods of situational prediction models, thereby further improving the accuracy of situational prediction models. This study reviews and sorts out the research history and development process of network security situation prediction technology, elaborates on the main principles and current development status of situation prediction models, analyzes the shortcomings and deficiencies of current technical solutions, and points out the future research directions of network security situation prediction model technology. [ABSTRACT FROM AUTHOR]

Objective: This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving. Background: Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems. Method: Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions. Results: Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model. Conclusion: The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time. Application: The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving. [ABSTRACT FROM AUTHOR]

Laparoscopic access, a critical yet challenging step in surgical procedures, often leads to complications. Existing systems, such as improved Veress needles and optical trocars, offer limited safety benefits but come with elevated costs. In this study, a prototype of a novel technology for guiding needle interventions based on vibroacoustic signals is evaluated in porcine cadavers. The prototype consistently detected successful abdominal cavity entry in 100% of cases during 193 insertions across eight porcine cadavers. The high signal quality allowed for the precise identification of all Veress needle insertion phases, including peritoneum puncture. The findings suggest that this vibroacoustic-based guidance technology could enhance surgeons' situational awareness and provide valuable support during laparoscopic access. Unlike existing solutions, this technology does not require sensing elements in the instrument's tip and remains compatible with medical instruments from various manufacturers. [ABSTRACT FROM AUTHOR]

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the 'weakest link' and are vital to prevention. This research investigates what cognitive and internal factors influence information security awareness (ISA) among employees, through quantitative empirical research using a survey conducted at a Dutch financial insurance firm. The research question of "How and to what extent do cognitive and internal factors contribute to information security awareness (ISA)?" has been answered, using the theory of situation awareness as the theoretical lens. The constructs of Security Complexity, Information Security Goals (InfoSec Goals), and SETA Programs (security education, training, and awareness) significantly contribute to ISA. The most important research recommendations are to seek novel explaining variables for ISA, further investigate the roots of Security Complexity and what influences InfoSec Goals, and venture into qualitative and experimental research methodologies to seek more depth. The practical recommendations are to minimize the complexity of (1) information security topics (e.g., by contextualizing it more for specific employee groups) and (2) integrate these simplifications in various SETA methods (e.g., gamification and online training). [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) could improve maritime collaboration by facilitating human factors like situation awareness and decision-making. However, it is not clear in which ways AR can be beneficial and for which maritime operations. This paper addresses the need for a systematic literature review of state-of-the-art ways AR can facilitate maritime collaboration. Following the PRISMA statement, we searched for the constructs "AR," "maritime operation," and "collaboration" in the Web of Science and IEEE Xplore databases. Out of 691 search results, we retained 32 publications for analysis. Ten publications from other sources were included. Our results provide a review of AR applications for the maritime operations of ship navigation, personal navigation, maritime construction, maritime maintenance and inspection, and other. We suggest that research focuses on bringing promising AR applications to higher technology readiness levels, learning from other industries, applying robust evaluation methods, and grounding more on human factors like decision-making, situation awareness, and communication. [ABSTRACT FROM AUTHOR]

Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value. [ABSTRACT FROM AUTHOR]

• The level of urgency affects situation awareness(SA), trust, workload, and perceived urgency. • In high-level urgency, level 3 SA significantly improves situation awareness. • The displays with SA cues enhance system transparency, increasing drivers' trust. • Information that adequately describes the situation reduces the drivers' workload. • Level 1 SA is not significantly different from a display without SA factors. Semi-autonomous vehicles still require drivers to take over in an unexpected situation. In this situation, the increased cognitive load on the driver can lead to distraction, which in turn reduces situational awareness (SA) and prevents appropriate responses, increasing the risk of accidents. For this reason, providing interfaces that enhance SA is essential to ensuring safety and optimal performance. However, existing SA-based display research often overlooks the levels of SA and effectiveness of modalities, especially in urgent situations where a driver's SA might be compromised. The present study aims to design SA-based display that considers the urgency of the situation and the level of SA in line with a specific design framework and modality effectiveness. We conducted an experiment using simulated videos to evaluate the effectiveness of the SA-based display. This experiment assessed the effects of three urgent situations and three levels of situational awareness-based displays on drivers' SA, situational trust, mental workload, and perceived urgency. We employed a 3x4 mixed-factorial design for the experiment. The between-subject factors were the SA levels (perception, comprehension, and projection) and a baseline. The within-subject factors were urgency scenarios (low, medium, and high). The results showed that as urgency increased, displays reflecting Level 3 SA, which requires prediction, significantly improved SA compared to other displays. We expect our findings to contribute to the practical design of automotive displays by providing useful considerations for SA-based display design. [ABSTRACT FROM AUTHOR]

• Eye movement and driving behavior were used to evaluate situation awareness (SA). • The variable message sign gives drivers a higher SA level. • The variable message signs (VMS) have a speed-limiting effect on vehicles. • Experienced drivers have better SA than inexperienced drivers. • Eye movement is a sensitive SA indicator. The driving environment in the freeway exit areas is complex, and the installation of relevant signs can enhance the creation of a safe and smooth exit area environment. This study investigated the situation awareness (SA) scores of two types of drivers using different signs in the freeway exit areas and the correlation between drivers' eye movement, driving behavior, and SA scores. The driving tasks were divided into two situations: continuing on the mainline and entering the exit ramp. The independent variables included scene type (static speed limit signs, static speed limit signs plus variable message signs, variable speed limit signs, variable speed limit signs plus variable message signs), and participant type (experienced and inexperienced participants). The dependent variables were SA, eye movement, and driving behavior. Each participant completed a driving simulation experiment with the two driving tasks, totaling eight scenes. The SA scores were measured using the Situation Awareness Global Assessment Technique (SAGAT). The driving behavior and eye movement were collected during the experimental data analysis segment, and the sensitivity indicators for evaluating drivers' SA were determined by calculating the correlation with the SAGAT scores. This paper focuses on applying variable message signs (VMS) to speed limits in freeway exit areas from the driver's perspective to enhance driving safety. The findings showed that drivers have better SA in scenes with VMS, and experienced drivers have better SA than inexperienced drivers in the same scene. The VMS affects speed control, and eye movement is a sensitive SA indicator. [ABSTRACT FROM AUTHOR]

• This paper proposes an objective method to measure drivers' level of emotional instability in conditionally automated driving and the result showed that integrating subjective ratings and EEG and ECG signals can achieve a more objective and reliable measure of the level of emotional instability. • Our findings lend support for a relation between drivers' level of emotional instability and physiological responses. The result showed that drivers' HR, HRV features and features of EEG can reflect two characteristics of the emotional instability system. • Our result showed that emotional instability has the potential to reduce drivers' lateral takeover performance by decreasing situation awareness, increasing load, and decreasing attention. Attention should be paid to the psychological characteristics of the driver to help reduce driving risks and improve driving safety. When the automated driving system reaches its technical limit, drivers will have to take over control from the vehicle within a limited time. The drivers' emotional instability may affect whether they can safely control the vehicle during takeover transitions. Accordingly, this paper explores the association between emotional instability and physiological responses and driver takeover performance. 42 drivers engaged in negative emotion induced by movie clips were involved in a sequence of takeover events in a driving simulator. Personality questionnaires were used to assess the driver's level of emotional instability. Their physiological data before the takeover request (TOR) and operational behavior data fragments for the takeover process were extracted. The results demonstrated that integration of subjective evaluations with electroencephalogram (EEG) and electrocardiogram (ECG) signals allows for more objective measurement of drivers' level of emotional instability. The statistical analyses indicated that emotional instability has a significant effect on drivers' heartbeat interval, heart rate, the frequency bands of θ(4–8 Hz), α(8–12 Hz), β(13–30 Hz), γ (30–50 Hz) and lateral takeover performance. Those findings of differences in driver physiological responses and takeover performance under emotional instability may provide additional support for the design of driver state monitoring and adaptive warning systems. [ABSTRACT FROM AUTHOR]

• The indicators were collected based on three levels of situation awareness (SA). • The drivers' SA assessment system was built using grey correlation analysis. • The constructed backpropagation neural network model can effectively predict SA. Based on combining the relevant studies on situation awareness (SA), this paper integrated multiple indicators, including eye movement, electroencephalogram (EEG), and driving behavior, to evaluate SA. SA is typically divided into three stages: perception, understanding and prediction. This paper used eye movement indicators to represent perception, EEG indicators to represent understanding, and driving behavior indicators to represent prediction. After identifying indicators for evaluating SA, a driving simulation experiment was designed to collect data on the indicators. 41 subjects were recruited to participate in the investigation, and the experimenter collected data from each subject in a total of 9 groups. After removing 4 groups of invalid data, 365 groups of valid data were finally obtained. The grey correlation analysis was used to optimize the SA indicators, and 10 SA evaluation indicators were finally determined. There were the average fixation duration, the nearest neighbor index, pupil area, the percentage power spectral density values of the 3 rhythmic waves (θ, α, β), rhythmic wave energy combination parameters (α / θ), mean speed, SD of speed and acceleration. Taking the optimized 10 indicators as input and the SA scores as output, a backpropagation neural network model with a topological structure of 10-8-1 was constructed. 75% of the data were randomly selected for model training, and the final network training's mean square error was 0.0025. Using the remaining 25% of data for verification, the average absolute error and average relative error of the predicted results are 0.248 and 0.046, respectively. This showed that the model was effective, and it was feasible to evaluate the SA by using the data of eye movement, EEG and driving behavior parameters. [ABSTRACT FROM AUTHOR]

Remote operations have been greatly enhanced by advancements in technology, enabling remote control of machinery in hazardous environments. However, it is still a challenge to design remote control interfaces and provide feedback in a way that would enhance situational awareness without negatively affecting cognitive load. This study investigates how different audio feedback designs can support remote boom operation and, additionally, explores the potential impact of gamification elements on operator performance and motivation. Due to COVID-19 restrictions, this study was conducted remotely with 16 participants using a simulated environment featuring a virtual excavator. Participants performed digging tasks using two audio feedback designs: frequency-modulated beeping and realistic spatialized steam sounds. The findings indicate that both audio designs are beneficial for remote boom operations: the beeping sound was perceived as more comfortable and efficient in determining the proximity of a hidden object and helped in avoiding collisions, whereas spatial sounds enhanced the sense of presence. Therefore, we suggest combining both audio designs for optimal performance and emphasize the importance of customizable feedback in remote operations. This study also revealed that gamification elements could both positively and negatively affect performance and motivation, highlighting the need for careful design tailored to specific task requirements. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) has received the significant attention in the field of security and surveillance due to its high potential for real-time monitoring, identifying the abnormal activities and situational awareness. HAR is able to identify the abnormal activity or behaviour patterns, which may indicate potential security risks. HAR system attempts to automatically provide the information and classification regarding activities performed in the environment by learning the data captured through sensor or video stream. The overview of existing research work in the security and surveillance area, which includes traditional, machine learning (ML) and deep learning (DL) algorithms applicable to field, is presented. The comparative analysis of different HAR techniques based on features, input source, public data sets is presented for quick understanding, and it focuses on the recent trends in HAR field. This review paper provides guidelines for the selection of appropriate algorithm, data set, performance metrics when evaluating HAR systems in the context of security and surveillance. Overall, this review aims to provide a comprehensive understanding of HAR in the field of security and surveillance and to serve as a basis for further research and development. [ABSTRACT FROM AUTHOR]

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NOAA generates a suite of Ocean Heat Content (OHC) products for the North Atlantic and the North and South Pacific basins on a daily basis. Knowledge of the upper Ocean Heat Content is a critical aid in assessing the ocean's impact on Tropical Cyclones (TCs) and weather events. In-situ and satellite measurements of Sea Surface Temperature (SST) and Sea Surface Height Anomaly (SSHA) are used to generate the OHC product suite. The evaluated suite includes the 20°C (D20) and 26°C (D26) isotherm depths, the mixed layer depth (MLD) and mapping errors. Two cases are presented within the context of situational awareness showing the underling oceanic variability during the lifecycles of TC's Irma and Dorian. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper critically analyzes multifaceted challenges such as real-time data processing demands, adaptability in dynamic environments, and complexities introduced by advanced AI and machine learning techniques. Key contributions include a detailed exploration of UAV-centric SA's transformative potential in industries such as precision agriculture, disaster management, and urban infrastructure monitoring, supported by case studies. In addition, the paper delves into algorithmic approaches for path planning and control, as well as strategies for multi-agent cooperative SA, addressing their respective challenges and future directions. Moreover, this paper discusses forthcoming technological advancements, such as energy-efficient AI solutions, aimed at overcoming current limitations. This holistic review provides valuable insights into the UAV-centric SA, establishing a foundation for future research and practical applications in this domain. [ABSTRACT FROM AUTHOR]

In an effort to improve the efficiency and recommendation accuracy of mobile learning resources, the study proposes a hybrid mobile learning strategy based on Collaborative Filtering (CF), context and interest. Analyse from the perspective of situational awareness, construct a personalised recommendation model for text learning resources based on GimbalTM, and obtain a recommendation form. The experimental results show that the RMSE and MAE of Context-Collaborative filtering (C-CF) are lower than those of traditional CF. The Precision and Recall values of C-CF are higher than those of CF at 10 s, the recommendation growth rates of traditional CF and C-CF are 2.09% and 1.67%, respectively. The Gimbal software enables a certain degree of learner location detection and can trigger contextual rules based on time and location contexts to provide users with personalised text-based learning resources. The research results indicate that in specific applications, over time, under the recommendation system, students' grades steadily increase, which is also beneficial for improving their learning efficiency. [ABSTRACT FROM AUTHOR]

With the development of seaborne trade, international maritime crime is becoming increasingly complex. Detecting maritime threats by fusing the physical movement data from traditional physical sensors is not sufficient. Thus, soft data, including intelligence reports and news articles, need to be incorporated into the situational awareness models of maritime threats. In this regard, this study developed an automated construction method of knowledge graphs for pirate events, which lays a foundation for subsequent maritime threat reasoning and situational awareness. First, a knowledge graph ontology model for pirate events was designed. Secondly, the BERT-BiLSTM-CRF model is proposed for named-entity recognition, and an entity linking algorithm based on distant learning and context attention mechanism is proposed to remove the conceptual ambiguity. Thirdly, based on traditional distant supervision relation extraction, which is based on sentence-level attention mechanism, bag-level and group-level attention mechanism methods are additionally proposed to further enhance the performance of distant supervision relation extraction. The proposed model demonstrated high performance in named-entity recognition, entity linking, and relation extraction tasks, with an overall F1-score of over 0.94 for NER and significant improvements in entity linking and relation extraction compared to traditional methods. The constructed knowledge graphs effectively support maritime threat reasoning and situational awareness, offering a substantial contribution to the field of maritime security. Our discussion highlights the model's strengths and potential areas for future work, while the conclusion emphasizes the practical implications and the readiness of our approach for real-world applications. [ABSTRACT FROM AUTHOR]

Most ground faults in distribution network are caused by insulation deterioration of power equipment. It is difficult to find the insulation deterioration of the distribution network in time, and the development trend of the initial insulation fault is unknown, which brings difficulties to the distribution inspection. In order to solve the above problems, a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed. Firstly, the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network, and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system. Secondly, considering all kinds of fault factors of the distribution network and the influence of the power supply region, the evaluation method of the initial insulation fault situation of the distribution network is proposed, and the development situation of the distribution network insulation fault is classified according to the evaluation method. Then, principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data, and the support vector machine (SVM) was trained. The optimal parameter combination of the SVM model was found by the grid search method, and a multi-class SVM model based on 1-v-1 method was constructed. Finally, the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples. The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%, and the perception accuracy of 4 situation levels is above 96%. In addition, the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage, which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation. The correctness and effectiveness of this method are verified. [ABSTRACT FROM AUTHOR]

The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to identify geo-references in the text messages. We used bi-directional long short term memory (LSTM) neural networks to extract location information in the text messages. The results show that applying Bi-LSTM on dataset gives high level accuracy after fine-tuning (up to 10 epochs). The testing results show that accuracy achieved is 0.98 and 0.076 loss value. This proves that the proposed methodology is better than the previous conditional random field (CRF)-based approaches. [ABSTRACT FROM AUTHOR]

Urban flooding poses a significant threat to the functionality of roadway networks, and the frequency and severity of these events are anticipated to increase as a result of climate change. A key challenge in mitigating urban flood impact is the lack of detailed flood impact prediction methods at a large scale. This study addresses this gap by developing an integrated framework that assesses the flood vulnerability of large-scale urban roadway networks. A framework combining a large-scale hydraulic flood simulation with a roadway traffic network analysis was utilized to map the impact of flooding on the mobility and connectivity of the roadway network. Then, a flood-roadway network analysis was conducted to assess the vulnerability of the Houston roadway network under different phases of the flooding event. The efficacy of the proposed framework is validated through a case study focusing on Hurricane Harvey in Houston, successfully identifying areas with pronounced flood vulnerability. By adopting this framework, decision makers can better evaluate the flood vulnerability of the roadway network and identify areas that require attention to enhance resilience to floods. Practical Applications: Urban flooding is always a great threat to our economy and safety. This study explores a new way of predicting, responding to, and planning for major urban flooding events. Our large-scale flood-roadway prediction framework can be used to predict flood impacts rapidly and accurately in advance, providing policymakers precious time to plan accordingly. Urban planners and city officials can leverage our research to identify areas more vulnerable to floods and guide urban development to increase flood resilience. Moreover, first responders can utilize our large-scale flood-roadway prediction framework to gain critical situation awareness in their disaster rescue efforts. Lastly, our research can play a crucial role in providing public information on flood risks. By providing timely and accurate flood alerts to the public, individuals and communities can take proactive measures to protect themselves and their property. [ABSTRACT FROM AUTHOR]

Robots assist emergency responders by collecting critical information remotely. Deploying multiple cooperative unmanned ground vehicles (UGVs) for a response can reduce the response time, improve situational awareness, and minimize costs. Reliable communication is critical for multiple UGVs for environmental response because multiple robots need to share information for cooperative navigation and data collection. In this work, we investigate a control policy for optimal communication among multiple UGVs and base stations (BSs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to update the control policy for the maximum signal-to-interference ratio. The UGVs communicate with both the fixed BSs and a mobile BS. The proposed control policy can navigate the UGVs and mobile BS to optimize communication and signal strength. Finally, a genetic algorithm (GA) is proposed to optimize the hyperparameters of the MADDPG-based training. Simulation results demonstrate the computational efficiency and robustness of the GA-based MADDPG algorithm for the control of multiple UGVs. [ABSTRACT FROM AUTHOR]

Each year maritime accidents occur at sea causing human casualties. Training facilities serve to reduce the risk of human error by allowing maritime teams to train safety procedures in cooperative real-size immersive simulators. However, they are expensive and only few maritime professionals have access to such simulators. Virtual Reality (VR) can provide a digital all-immersive learning environment at a reduced cost allowing for increased access. However, a key ingredient of what makes all-immersive physical simulators effective is that they allow for multiple participants to engage in cooperative social interaction. Social interaction which allows trainees to develop skills and competencies in navigating situational awareness essential for safety training. Social interaction requires social fidelity. Moving from physical simulators into digital simulators based upon VR technology thus challenges us as HCI researchers to figure out how to design social fidelity into immersive training simulators. We explore social fidelity theoretically and technically by combining core conceptual work from CSCW research to the design experimentation of social fidelity for maritime safety training. We argue that designing for social fidelity in VR simulators requires designers to contextualize the VR experience in location, artifacts, and actors structured through dependencies in work allowing trainees to perform situational awareness, coordination, and communication which are all features of social fidelity. Further, we identify the risk of breaking the social fidelity immersion related to the intent and social state of the participants entering the simulation. Finally, we suggest that future designs of social fidelity should consider not only trainees in the design, but also the social relations created by the instructors’ guidance as part of the social fidelity immersion. [ABSTRACT FROM AUTHOR]