Healthcare-associated infections (HAI) are a significant issue in healthcare facilities worldwide. Hand hygiene (HH) remains the most effective method for preventing the incidence of HAI in routine patient care. Past and current interventions focused on the overall improvement of HH compliance, but studies found that the amount of time required to achieve full HH compliance with the existing guidelines may not be practical. Improving HH compliance at critical moments during patient care may be more effective than improving HH compliance at all opportunities. However, there are little to no studies on healthcare workers’ (HCWs) behavior regarding HH during the patient care process. Secondary data analysis on a prospective dataset from the STAR-ICU trial was completed to identify HCWs’ behavior patterns regarding HH during the patient care process. Multiple logistic regression for transitions with random effects using repeated measures and transition modeling was used to identify possible associations between HH compliance and patient care tasks, the order of tasks, and workload. The models adjusted for the effects of HCW type, glove use, and isolation precautions. The study identified 28,826 task sequences and 42,349 HH opportunities. HCWs were slightly less likely to do HH before critical tasks compared with other tasks (OR: 0.97, 95% CI: 0.96-0.99), but more likely to do HH after contaminating tasks compared with other tasks (OR: 1.12, 95% CI: 1.10-1.13). HCWs are also more likely to move from task sequences that have a relatively lower risk to patients to task sequences that have a relatively higher risk to patients than vice versa (65.4% versus 34.7%). HCWs are also less likely to do HH after moving from tasks that have a relatively lower risk to patients to tasks that have a relatively higher risk to patients than vice versa (OR: 0.93, 95% CI:0.92-0.95). HCWs’ HH compliance rates decreased as the workload level increased (OR: 0.93, 95% CI: 0.89-0.98). Workload did not appear to affect HH compliance before critical tasks or after contaminating tasks and did not affect the order in which HCWs perform patient care tasks. Increase in workload was associated with an increase in the odds of critical tasks occurring (OR: 1.55, 95% CI: 1.45-1.65). In conclusion, HCWs are more likely to perform HH after contaminating tasks to prevent contaminating themselves and to reduce the risk of transmission in subsequent task sequences. However, they do not perform tasks in an order that minimizes risk to the patient; instead, it appears that they perform tasks as they come up in routine care. Furthermore, HH is not being performed at critical moments during patient care. Lastly, workload did not affect the order in which HCWs perform patient care tasks, suggesting that HCWs behavior patterns contribute significantly to how they care for patients and perform HH. Interventions targeting the order in which HCWs perform patient care tasks and improving HH compliance before critical tasks may be more effective than those designed to improve HH compliance at all HH opportunities for reducing HAI rates.

Crew coordination in the context of aviation is a specifically choreographed set of tasks performed by each pilot, defined for each phase of flight. Based on the constructs of effective Crew Resource Management and SOPs for each phase of flight, a shared understanding of crew workload and task responsibility is considered representative of well-coordinated crews. Nominal behavior is therefore defined by SOPs and CRM theory, detectable through pilot eye-scan. This research investigates the relationship between the eye-scan exhibited by each pilot and the level of coordination between crewmembers. Crew coordination was evaluated based on each pilot's understanding of the other crewmember's workload. By contrasting each pilot's workload-understanding, crew coordination was measured as the summed absolute difference of each pilot's understanding of the other crewmember's reported workload, resulting in a crew coordination index. The crew coordination index rates crew coordination on a scale ranging across Excellent, Good, Fair and Poor. Eye-scan behavior metrics were found to reliably identify a reduction in crew coordination. Additionally, crew coordination was successfully characterized by eye-scan behavior data using machine learning classification methods. Identifying eye-scan behaviors on the flight deck indicative of reduced crew coordination can be used to inform training programs and design enhanced avionics that improve the overall coordination between the crewmembers and the flight deck interface. Additionally, characterization of crew coordination can be used to develop methods to increase shared situation awareness and crew coordination to reduce operational and flight technical errors. Ultimately, the ability to reduce operational and flight technical errors made by pilot crews improves the safety of aviation.

This research compares methods for measuring pilot mental workload (MWL) from the electrocardiogram (ECG) signal. ECG-based metrics have been used extensively in MWL research. Heart rate (HR) and heart-rate variability (HRV) exhibit changes in response to varying levels of task demand. Classical methods for HRV analysis examine the ECG signal in the linear time and frequency domains. More contemporary research has advanced the notion that nonlinear elements contribute to cardiac control and ECG signal generation, spawning development of analytical techniques borrowed from the domain of nonlinear dynamics (NLD). Applications of nonlinear HRV analysis are substantial in clinical diagnosis settings; however, such applications are less frequent in MWL research, especially in the aviation domain. Specifically, the relative utility of linear and non-linear HRV analysis methods has not been fully assessed in pilot MWL research. This thesis contributes to aforementioned research gap by comparing a non-linear HRV method, utilizing transition probability variances (TPV), to classical time and frequency domain methods, focusing the analysis on sensitivity and diagnosticity. ECG data is harvested from a recent study characterizing spatial disorientation (SDO) risk amongst three candidate off-boresight (OBS) helmet-mounted display (HMD) symbologies in a tactically relevant live-flight task. A comparative analysis of methods on this dataset and supplemental workload analysis for the HMD study are presented. Results indicate the TPV method may exhibit higher sensitivity and diagnosticity than classical methods. However, limitations of this analysis warrant further investigation into this question.

Research has shown that teacher workload is intensifying and teachers are increasingly leaving the profession prior to having taught for 35 years. The purpose of this mixed method, sequential, phenomenological study was to determine (a) how workload intensification impacts teacher performance and well-being, (b) whether or not workload intensification was a primary factor in teachers’ choosing to leave the profession early, and (c) a formula for maximizing teacher performance and well-being. Apple’s workload intensification thesis was the theoretical framework for this study. Quantitative data obtained via a survey (N=484), together with qualitative data collected via four focus group sessions and individual interviews with 15 teachers who had left the profession early, were utilized to determine if there is a problem with workload intensification in this east coast Canadian province. Quantitative data were analyzed using the chi-square test to determine the relationship between the independent variable (workload intensification) and each of the two dependent variables (performance and well-being). Qualitative data were analyzed to determine emergent themes with respect to workload intensification. Findings from this study indicated that there is a significant relationship between the independent variable and each of the two dependent variables. Qualitative data substantiated the quantitative findings that indicated (a) the presence of a problem with workload intensification and (b) that workload intensification is a primary factor in teachers’ choosing to leave the profession early. Recommendations include having administrators address identified current teacher workload issues. Positive social change may result if administrators utilize the derived formula for maximizing teacher performance and well-being when assigning teaching and nonteaching duties to teachers.

The present study investigated whether there is a relationship between workload and cognitive overload with self-efficacy, perfectionism, and psychological resilience as possible moderators. Cognitive Load Theory states that individuals have a finite amount of working memory. When the working memory load has reached its maximum, individuals experience cognitive overload. Employees with a higher workload receive higher amounts of information, increasing their cognitive load, thus being more likely to reach cognitive overload. However, self-efficacious individuals, perfectionists, and resilient individuals are more motivated to reach their goals and will persevere despite obstacles. Therefore, I proposed that perceived workload and perceived cognitive overload would be correlated and that self-efficacy, perfectionism, and resilience would moderate that relationship. Using a web-based questionnaire, 278 adults working at least 25 hours per week were given a series of self-report measures about their perceived workload, cognitive overload, self-efficacy, perfectionism, and resilience. Workload was found to be positively correlated with cognitive overload, but self-efficacy, perfectionism, and resilience did not moderate the relationship between workload and cognitive overload. Subsequent analyses provide limited support that level of education moderates the workload-cognitive overload relationship. As personal characteristics do not moderate the relationship between workload and cognitive overload, management in organizations will want to explore different ways to affect the perceived workload of their employees.

Present-day datacenter networks (DCNs) are designed to achieve full bisection bandwidth in order to provide high network throughput and server agility. However, the average utilization of typical DCN infrastructure is below 10% for significant time intervals. As a result, energy is wasted during these periods. In this thesis we analyze traffic behavior of datacenter networks using traces as well as simulated models. Based on the insight developed, we present techniques to reduce energy waste by making energy use scale linearly with load. The solutions developed are analyzed via simulations, formal analysis, and prototyping. The impact of our work is significant because the energy savings we obtain for networking infrastructure of DCNs are near optimal. A key finding of our traffic analysis is that network switch ports within the DCN are grossly under-utilized. Therefore, the first solution we study is to modify the routing within the network to force most traffic to the smallest of switches. This increases the hop count for the traffic but enables the powering off of many switch ports. The exact extent of energy savings is derived and validated using simulations. An alternative strategy we explore in this context is to replace about half the switches with fewer switches that have higher port density. This has the effect of enabling even greater traffic consolidation, thus enabling even more ports to sleep. Finally, we explore a third approach in which we begin with end-to-end traffic models and incrementally build a DCN topology that is optimized for that model. In other words, the network topology is optimized for the potential use of the datacenter. This approach makes sense because, as other researchers have observed, the traffic in a datacenter is heavily dependent on the primary use of the datacenter. A second line of research we undertake is to merge traffic in the analog domain prior to feeding it to switches. This is accomplished by use of a passive device we call a merge network. Using a merge network enables us to attain linear scaling of energy use with load regardless of datacenter traffic models. The challenge in using such a device is that layer 2 and layer 3 protocols require a one-to-one mapping of hardware addresses to IP (Internet Protocol) addresses. We overcome this problem by building a software shim layer that hides the fact that traffic is being merged. In order to validate the idea of a merge network, we build a simple mere network for gigabit optical interfaces and demonstrate correct operation at line speeds of layer 2 and layer 3 protocols. We also conducted measurements to study how traffic gets mixed in the merge network prior to being fed to the switch. We also show that the merge network uses only a fraction of a watt of power, which makes this a very attractive solution for energy efficiency. In this research we have developed solutions that enable linear scaling of energy with load in datacenter networks. The different techniques developed have been analyzed via modeling and simulations as well as prototyping. We believe that these solutions can be easily incorporated into future DCNs with little effort.

[pt] Alexandre Plastino em sua tese de doutorado apresenta um ambiente de desenvolvimento de aplicações paralelas SPMD (Single Program, Multiple Data) denominado SAMBA que permite a geração de diferentes versões de uma aplicação paralela a partir da incorporação de diferentes algoritmos de balanceamento de carga disponíveis numa biblioteca própria. O presente trabalho apresenta uma ferramenta de geração de carga dinâmica integrada a este ambiente que possibilita criar, em tempo de execução, diferentes perfis de carga externa a serem aplicados a uma aplicação paralela em estudo. Dessa forma, pretende-se permitir que o desenvolvedor de uma aplicação paralela possa selecionar o algoritmo de balanceamento de carga mais apropriado frente a condições variáveis de carga externa. Com o objetivo de validar a integração da ferramenta ao ambiente SAMBA, foram obtidos resultados da execução de duas aplicações SPMD distintas. / [en] Alexandre Plastino s tesis presents a framework for the development of SPMD parallel applications, named SAMBA, that enables the generation of different versions of a parallel application by incorporating different load balancing algorithms from an internal library. This dissertation presents a dynamic workload generation s tool, integrated to SAMBA, that affords to create, at execution time, different external workload profiles to be applied over a parallel application in study. The objective is to enable that a parallel application developer selects the most appropriated load balancing algorithm based in its performance under variable conditions of external workload. In order to validate this integration, two SPMD applications were implemented.

Flight decks of the future are being enhanced through improved avionics that adapt to both aircraft and operator state. Eye tracking allows for non-invasive analysis of pilot eye movements, from which a set of metrics can be derived to effectively and reliably characterize workload, this research will generate quantitative algorithms to classify pilot state through eye tracking metrics. Through various metrics within the realm of eye tracking, flight deck operation research is used to determine metric correlations between a pilot's workload and eye tracking metric patterns. The basic metrics within eye tracking, such as saccadic movement, fixations and link analysis provide clear measurable elements that experimenters analyzed to create a quantitative algorithm that reliably classifies operator workload. The study conducted at the University of Iowa's Operator Performance Lab 737-800 simulator was outfit with a Smarteye remote eye-tracking system that yielded gaze vector resolution down to 1 degree across the flight deck. Three levels of automation and 2 levels of outside visual conditions were changed on a KORD ILS approach between CAT II and CAT III visual conditions, and varying from full autopilot controlled by the pre-programmed flight management system, flight director guidance, and full manual approach with localizer and glide slope guidance. Initial subjective results indicated a successful variation in driving pilot workload across all 12 IFR pilots that were run through the 7 run testing sequence.

Pilots are subject to varying levels of stress, workload, and fatigue during long flights. During different phases of a commercial flight, pilots are engaged in multiple tasks which include going through checklists, checking conditions at their destination, communicating with Air Traffic Control and dealing with other flight related tasks. The amount of work varies from the earlier stages until the end of the flight. It is not well understood how changes in the amount of workload can affect a pilot's ability to engage with important tasks that relate to safety of flight. The work shown in this thesis focused on the level of engagement displayed by flight crew as a function of level of workload. The principal hypothesis was that very low levels of workload may lead to crew disengagement and sub-optimal levels of performance. The degree to which pilots remain alert and are fatigued during a commercial flight is also not established in a concrete way.

Mobile sensing applications (MSAs) are an emerging class of applications that process continuous sensor data streams to make time-sensitive inferences. Representative application domains range from environmental monitoring, context-aware services to recognition of physical activities and social interactions. Example applications involve city air quality assessment, indoor localization, pedometer and speaker identification. The common application workflow is to read data streams from the sensors (e.g, accelerometers, microphone, GPS), extract statistical features, and then present the inferred high-level events to the user. MSAs in the healthcare domain especially draw a significant amount of attention in recent years because sensor-based data collection and assessment offer finer-granularity, timeliness, and higher accuracy in greater quantity than traditional, labor-intensive, data gathering mechanisms in use today, e.g., surveys methods. The higher fidelity and accuracy of the collected data expose new research opportunities, improve the reliability and accuracy of medical decisions, and empower users to manage personal health more effectively. Nonetheless, a critical challenge to practical deployment of MSAs in real-world is to effectively manage limited resources of mobile platforms to meet stringent quality of service (QoS) requirements in terms of processing throughput and delay while ensuring long term robustness. To address the challenge, we model MSAs in dataflows as a graph of processing elements that are connected by communication channels. The processing elements may execute in parallel as long as they have sufficient data to process. A key feature of the dataflow model is that it explicitly capture parallelism and data dependencies between processing elements. Based on the graph composition, we first proposed CSense, a stream-processing toolkit for robust and high-rate MSAs. In this work, CSense provide a simple language for developers to describe their sensing flow without the need to deal with system intricacy, such as memory allocation, concurrency control and power management. The results show up to 19X performance difference may be achieved automatically compared with a baseline using the default runtime concurrency and memory management. Following this direction, we saw the opportunities that MSAs can be significantly improved from the perspective of memory performance and energy efficiency in view of the iterative execution. Therefore, we next focus on optimizing the runtime memory management through compile time analysis. The contribution is a stream compiler that captures the whole program memory behavior to generate an efficient memory layout for runtime access. Experiments show that our memory optimizations reduce memory footprint by as much as 96% while matching or improving the performance of the StreamIt compiler with cache optimizations enabled. On the other hand, while there is a significant body of work that has focused on optimizing the throughput or latency of processing sensor streams, little to no attention has been given to energy efficiency. We proposed an accurate offline energy prediction model for MSAs that leverages the pipeline structure and iterative execution nature to search for the most energy saving batching configuration w.r.t. a deadline constraint. The developers are expected to visualize the energy delay trade-off in the parameter space without runtime profiling. The evaluation shows the worst-case prediction errors are about 7% and 15% for energy and latency respectively despite variable application workloads.