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Background: First responders have unique and important roles. The duties performed can be dangerous, physically demanding and stressful, leading to high risks of injury and illness. Because of their unique job demands, it is important to identify aspects of the disability management process and predictors of return to work that are specific to first responders Thesis Objectives: To analyze first responder disability management claims associated with injuries and mental health issues to determine predictors of return to work and differences in the disability management claims when comparing first responders to high and low demand occupations. Methods: The claim data were obtained from a disability management company. In the first study, all first responder claims were included in the Cox proportional regression models and the log-rank tests to identify predictors of return to work. For the second study, the claims of high and low demand occupations were randomly age and sex-matched to the first responder claims. Differences in the duration of time off work, the duration of the claim, the injury and mental health diagnoses, and the duties performed when returning to work existed between first responders and the two occupation groups. Results: Musculoskeletal injuries predicted an increased likelihood of returning to work in a shorter duration of time. Medical report lag and claim lag decreased the likelihood of returning to work. First responders returned to work sooner, had shorter disability claim durations, differed in the injuries and mental health issues sustained, and were less likely to return to their pre-injury duties compared to the two occupation groups. Conclusions: Predictors of return to work specific for first responders were identified, yet the results lack generalizability. Although first responders returned to work sooner compared to the other occupations, they were more likely relegated to modified duties. / Thesis / Master of Science (MSc) / First responders perform dangerous and stressful work. They are at risk of injuries and illnesses that require time off work to recover. The goal of this dissertation is to identify features of the return to work process that are specific to first responders. The studies found that first responders with injuries like sprains and strains went back to work sooner than those with mental health issues. The study also found that sooner the injury claim was started and the medical information was received, the sooner first responders could return to work. When compared to other injured workers, first responders returned to work quicker, but were more likely to only be able to do modified work rather than their typical jobs. It is important that first responders fully recovery from injuries before they can get return to responding to emergency calls.

After fire is discovered in an underground coal mine, a decision must be made to mitigate fire consequences. The decision should be made based on existing conditions, with the goal of increasing the probability of fire extinguishing without compromising the health and safety of the firefighting personnel. However, the determination of fire conditions can be difficult due to coarse in-situ measurements, fire hazards, and the large domains of interest. Additionally, CFD and network models used for predicting fire conditions are computationally expensive with long simulation processing times for informing real-time decision making. A new generalized procedure to design artificial neural networks (ANNs) capable of making predictions of fire conditions, performing hazard/risk assessment, and providing useful information to the firefighters is presented and applied to different underground coal mine fire scenarios. The feed-forward ANNs were developed to classify fires so as to provide the best firefighting decision and determine useful information in real time, such as response time and fire size. The networks were trained to make predictions on different mine locations and to use only available and measurable information in underground coal mines as inputs. The data used for training and testing the networks was generated using high-fidelity CFD and network fire simulations. Additionally, this research presents the applicability of optical fiber sensing technology for continuous, distributed, and real-time sensing. This new technology could be used for collection of input parameters during ongoing fires, leading to improvement of the prediction performance of the ANNs developed. Finally, a new approach to simulate firefighting foam flow through gob areas is proposed and tested using experimental results obtained from a scaled down experimental setup. / Doctor of Philosophy / Mine fires still represent a serious hazard in underground coal mines. The MSHA incident database shows that around 600 mine fire incidents and 33 fatalities were reported in the U.S. during the last two decades. Most fatalities and injuries that occurred in the aforementioned incidents can be attributed to lack of knowledge on existing fire conditions, leading to poor subjective decisions during fire response. Unfortunately, the in-situ determination or prediction of fire conditions are not easy tasks due to fire hazards, mine entries extensions, and simulation processing times. For this reason, this work presents new data-driven models capable of predicting and evaluating fire conditions. Its goal is to recommend the most suitable firefighting decision, as well as determine fire characteristics and response time to increase the probability of fire extinguishing without compromising mine personnel health and safety. These data-driven models are composed of artificial neural networks (ANNs), allowing for performing predictions in real time and using only available information in underground coal mines. The data used for training and testing these ANNs was generated from fire simulations. Additionally, this research proposes a new technology, such as optical fiber sensing for continuous, distributed, and real time sensing. Optical fiber sensing could contribute with more precise ANNs inputs collection, leading to a better performance prediction. Finally, an alternative way to simulate firefighting foam through gob areas for fire mitigation was proposed and tested using results obtained from experiments. This work represents a significant advancement in underground coal mine fire characterization and response.

Title: Development and current status of the Rescue Unit of the Czech Republic Fire Rescue Service within the scope of its missions and further activities analysis. Objective: The objective of this work is the brief description of the development, organisation structure, force and resources and targets of military rescue units and of Rescue Unit of the Czech Republic Fire Rescue Service as their ancestor, all from their origin up to the present day. Furthermore, missions and further activities analysis of Rescue Unit of the Czech Republic Fire Rescue Service with outline of future possible development. Methods: Diploma work in a theoretical style is focused on the search of available study sources, its background research, collection of publications, articles, conceptual materials, statistic analytical works and other written and electronic records connected to the history of army rescue units and information regarding the present day activity of Rescue Unit of the Czech Republic Fire Rescue Service within the scope of its missions and further activities analysis. Results: Result of the diploma work is a complete overview of the development and activities of the military rescue units and of the Rescue Unit of the Czech Republic Fire Rescue Service since their origin up to present day....