Chronic obstructive pulmonary disease (COPD) has been recognized as a leading cause of mortality in older adults involving acute exacerbations as life-threatening events that lead to frequent hospitalization for care. Informal caregivers have been essential to helping older adults with COPD during an acute exacerbation of chronic obstructive pulmonary disease (AECOPD). A lack of empirical knowledge exists regarding the experience of informal caregivers of older adults with AECOPD in situation awareness for recognizing, understanding, and responding to an AECOPD in an emergent situation. This qualitative descriptive study explored situation awareness and its components of perception, comprehension and projection of next steps, including the caregiver’s confidence level during the AECOPD event. Fifteen informal caregivers, ages 31-77 years (mean age 48), who provided care for older adults with COPD were interviewed from an underserved community health center. The overarching theme derived from this study was something was wrong and something needed to be done. Subthemes emerged as a heightened sense of awareness, caregiver tipping point, planning next steps, caregiver confidence, and caregiver commitment. This study utilized situation awareness theory as a relevant guiding framework in exploring the experience of lay informal caregivers caring for older adults with AECOPD events. Study findings provided a description of the complex processes involved, including confidence level, for informal caregiver’s in situation awareness to recognize and respond to an AECOPD event in the older adult. Future targeted interventions need to address strategies to enhance individualized care for older adults with AECOPD events for managing care at home.

Syftet med detta examensarbete var att undersöka, utifrån en etnografisk och kvalitativ ståndpunkt, möjligheten till att utöka kunskapen om Situation Awareness (SA). Forskning inom området har länge präglats av ett kvantitativt förhållningssätt vilket anses ha missgynnat och till en viss del hindrat alternativa kvalitativa forsningsansatser för att studera detta intressanta fenomen. Detta arbete försöker, i form av två studier (en litteraturstudie och en fallstudie), bryta trenden av kvalitativ forskning och presentera en annan infallsvinkel för hur SA kan vara organiserat och ett annat förhållningssätt för hur forskning kring SA kan bedrivas. Utgångspunkten för detta försök är teorierna bakom Distributed Cognition (DC), vilka föreslår studien av mänsklig kognition i ett större sammanhang där hjärna, kropp och omgivning bildar ett kognitivt system. Resultatet från litteraturstudien visar på att det finns stöd för användningen av DC då utifrån dessa teorier uppstår möjligheten till att studera andra aspekter och förhållanden kring SA. Resultatet från fallstudien i sin tur ger indikationer på att inom forskningsområdet finns ett dominerat kvantitativt förhållningssätt samt går det att identifiera ett behov av kvalitativa ansatser som ett sätt att komplettera den nuvarande forskningen. Sammanfattningsvis uppvisar detta arbete indicier som tyder på att DC och kvalitativa ansatser erbjuder andra användbara utgångspunkter och forskningsmöjligheter för studier av SA.

The current study aimed at investigating the concepts of team situation awareness, mental models, and team processes in relation to performance in the complex, dynamic environment of breathing apparatus rescues. Data was collected during exercises at Ågesta training center through questionnaires and after action reviews. 28 firemen and 5 instructors participated in the exercises. Also, a stimulated recall interview was conducted with 2 of the firemen that had participated in one of the exercises. The different data collection methods all indicated that well-developed mental models or a high degree of pre-task knowledge affected performance in a positive way. Moreover, a multiple regression analysis showed that both pre-task knowledge and team processes significantly can predict performance. The results of the analysis of team situation awareness in relation to performance were fairly ambiguous. Therefore, further research is needed to establish the relation between these concepts in the domain at matter.

In military battlefield domain, decision making plays a very important part because safety and protection depends upon the accurate decisions made by the commanders in complex situations. In military and defense applications, there is a need of such technology that helps leaders to take good decisions in the critical situations with information overload. With the help of multi-sensor information fusion, the amount of information can be reduced as well as uncertainties in the information in the decision making of identifying and tracking targets in the military area.   Information fusion refers to the process of getting information from different sources and fusing this information, to supply an enhanced decision support. Decision making is the very core and a vital part in the field of information fusion and better decisions can be obtained by understanding how situation awareness can be enhanced. Situation awareness is about understanding the elements of the situation i.e. circumstances of the surrounding environment, their relations and their future impacts, for better decision making. Efficient situation awareness can be achieved with the effective use of the sensors. Sensors play a very useful role in the multi-sensor fusion technology to collect the data about, for instance, the enemy regarding their movements across the border and finding relationships between different objects in the battlefield that helps the decision makers to enhance situation awareness.   The purpose of this thesis is to understand and analyze the critical issue of uncertainties that results information in overload in military battlefield domain and benefits of using multi-sensor information fusion technology to reduce uncertainties by comparing uncertainty management methods of Bayesian and Dempster Shafer theories to enhance decision making and situation awareness for identifying the targets in battlefield domain.

The aviation industry evolves all the time in every possible sphere with new the technology that is advancing and the increasing amount of traffic on the airports. The air traffic control services has come a long way with automated systems and more advanced technology and the work of the air traffic controller (ATCO) has become more efficient to meet the higher demands for more traffic. But along with highly automated systems and work efficiency there can be a lack of safety instead if there is too much to do for the controller. A new concept in the aviation industry is developing; multiple remote towers, which means that one ATCO can be several miles away from the airports and handle two or more airports at the same time from the same work station. The air traffic controllers has to monitor the aircrafts, direct the traffic, make decisions and make sure that the pilots follows the instructions. At the same time the ATCO need to monitor the systems as well, making sure that everything is working as it is supposed to. To be able to maintain a safe environment for the controllers and the aircrafts, on and surrounding the runways, the controllers need to have situation awareness and the system they are working within need to be resilient to be able to cope with the different kind of situations that might occur. This study has focused on the role of the ATCO in air traffic control towers and by eye-tracking mapped what the controllers are looking at while handling arrivals in two different air traffic control towers. An episode analysis was made on several episodes that took place during different kinds of conditions in the two different air traffic control tower simulators, one single tower simulator and one multiple remote tower simulator. Patterns in the controllers’ way of handling arrivals were identified and the results from the controllers’ eye-movements shows that the ATCOs have a habitual behaviour pattern and that for almost every arrival they will act in the same way. The ATCO in the single tower simulator used the air radar several times during the episodes while in the multiple remote tower simulator the ATCOs almost never looked at the air radar. The radio was used more by the controllers in the multiple remote tower than in the single tower and it is discussed if this is something that can take too much time from an ATCO in a multiple remote tower. The results also highlights the importance of the strip-table, a tool used by the controllers during every step of the arrival process. The conclusions are that the system (controllers and non-human agents) has situation awareness and that the air traffic controllers have a clear frame of the situation. The results from this study can be seen as a guideline and a start for further research in this field and for the development of multiple remote towers. Further research should investigate in the controllers’ ability of reframing in situations of runway incursions and other unexpected events and the usage of the radio in multiple remote towers.

Recent major electrical disturbances highlight the extent to which modern societies depend on a reliable power infrastructure and the impact of these undesirable events on the economy and society. Numerous blackout models have been developed in the last decades that capture effectively the cascade mechanism leading to a partial or complete blackout. These models usually consider only the state of the electrical part of the system and investigate how failures or limitations in this system affect the probability and severity of a blackout.However, an analysis of the major disturbances that occurred during the last decade, such as the North America blackout of 2003 and the UCTE system disturbance of 2006, shows that failures or inadequacies in the Information and Communication Technology (ICT) infrastructure and also human errors had a significant impact on most of these blackouts.The aim of this thesis is to evaluate the contribution of these non-electrical events to the risk of power system blackouts. As the nature of these events is probabilistic and not deterministic, different probabilistic techniques have been developed to evaluate their impact on power systems reliability and operation.In particular, a method based on Monte Carlo simulation is proposed to assess the impact of an ICT failure on the operators’ situation awareness and consequently on their performance during an emergency. This thesis also describes a generic framework using Markov modeling for quantifying the impact of insufficient situation awareness on the probability of cascading electrical outages leading to a blackout. A procedure based on Markov modeling and fault tree analysis is also proposed for assessing the impact of ICT failures and human errors on the reliable operation of fast automatic protection actions, which are used to provide protection against fast-spreading electrical incidents. The impact of undesirable interactions and the uncoordinated operation of these protection schemes on power system reliability is also assessed in this thesis.The simulation results of these probabilistic methods show that a deterioration in the state of the ICT infrastructure and human errors affect significantly the probability and severity of power system blackouts. The conclusion of the work undertaken in this research is that failures in all the components of the power system, and not just the “heavy electrical” ones, must be considered when assessing the reliability of the electrical supply.

<div>Cyber-based infrastructures and systems serve as the operational backbone of many industries and resilience of such systems against cyber-attacks is of paramount importance. As the complexity and scale of the Cyber-based Systems (CBSs) has increased many folds over the years, the attack surface has also been widened, making CBSs more vulnerable to cyber-attacks. This dissertation addresses the challenges in post intrusion security management operations of threat detection and threat response in the networks connecting CBSs. In threat detection, the increase in scale of cyber networks and the rise in sophistication of cyber-attacks has introduced several challenges. The primary challenge is the requirement to detect complex multi-stage cyber-attacks in realtime by processing the immense amount of traffic produced by present-day networks. In threat response, the issue of delay in responding to cyber-attacks and the functional interdependencies among different systems of CBS has been observed to have catastrophic effects, as a cyber attack that compromises one constituent system of a CBS can quickly disseminate to others. This can result in a cascade effect that can impair the operability of the entire CBS. To address the challenges in threat detection, this dissertation proposes PRISM, a hierarchical threat detection architecture that uses a novel attacker behavior model-based sampling technique to minimize the realtime traffic processing overhead. PRISM has a unique multi-layered architecture that monitors network traffic distributedly to provide efficiency in processing and modularity in design. PRISM employs a Hidden Markov Model-based prediction mechanism to identify multi-stage attacks and ascertain the attack progression for a proactive response. Furthermore, PRISM introduces a stream management procedure that rectifies the issue of alert reordering when collected from distributed alert reporting systems. To address the challenges in threat response, this dissertation presents TRAP, a novel threat response and recovery architecture that localizes the cyber-attack in a timely manner, and simultaneously recovers the affected system functionality. The dissertation presents comprehensive performance evaluation of PRISM and TRAP through extensive experimentation, and shows their effectiveness in identifying threats and responding to them while achieving all of their design objectives.</div>

<div>From prehistoric man who needs to be aware of the surrounding situations and hunt for food, to modern industry where machines and robots are programmed to explore the environment and accomplish assignments, situation awareness has always been an essential topic to everyone.</div><div><br></div><div>Advanced Driver-Assistance Systems (ADAS) is one of the modern technologies seeking effective solutions for driving safety. It also utilizes situation awareness model to interpret the driver's state in the environment and provide safe driving advice, with the potential to significantly reduce the traffic accident fatalities.</div><div><br></div><div>To enable situation awareness, an intelligent driving system needs to fulfill the following: (1) perceives the traffic elements in the environment, (2) comprehends the spatial-temporal interactions between a driver and other objects, and (3) projects the states of traffic elements to forecast future actions.</div><div><br></div><div>However, each level of situation awareness encounters its unique challenges in driving scenarios, for example, how to perceive vehicles in low-illuminated conditions? How to represent the complicated interactive relations in complicated driving situations? And how to anticipate the temporal dynamics of traffic elements and identify the where the potential risk comes from? To answer these questions, we explore situation awareness model for Advanced Driver-Assistance Systems at 3 levels: Perception, Comprehension and Projection. We discuss how to realize situation awareness based on three different computer vision tasks. We demonstrate that our proposed system is able to forecast the driver's operational intentions and identify risk objects to avoid hazards.</div>

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction.

Purpose: The purpose of this study was to explore the experiences of distracted practice across the healthcare team. Definition: Distracted practice is the diversion of a portion of available cognitive resources that may be needed to effectively perform/carry out the current activity. Background: Distracted practice is the result of individuals interacting with the healthcare team, the environment and technology in the performance of their jobs. The resultant behaviors can lead to error and affect patient safety. Methods: A qualitative descriptive (QD) approach was used that integrated observations with semi-structured interviews. The conceptual framework was based on the distracted driving model and a completed concept analysis. Results: There were 22 observation sessions and 32 interviews (12 RNs, 11 MDs, and 9 Pharmacists) completed between December, 2014 and July 2015. Results suggested that distracted practice is based on the main theme of cognitive resources which varies by the subthemes of individual differences; environmental disruptions; team awareness; and “rush mode”/time pressure. Conclusions and Implications: Distracted practice is an individual human experience that occurs when there are not enough cognitive resources available to effectively complete the task at hand. In that moment an individual shifts from thinking critically, being able to complete their current task without error, to not thinking critically and working in an automatic mode. This is when errors occur. Additional research is needed to evaluate intervention strategies to reduce and prevent distracted practice.