Computational Modeling Systems for the Development of Remote Monitoring Medical Devices

Cardiovascular disease and Diabetes Mellitus are chronic diseases that affect the quality of life of tens of millions of Americans and result in hundreds of billions of dollars in economic impact within the United States. Remote monitoring medical devices can provide real time updates of physiological parameters that have the potential to improve disease management, thereby enhancing life quality and reducing the financial burden. However, developing these medical devices to be cost effective and robust to patient-specific factors has proven difficult to this point. Computational modeling and simulation is a valuable tool which can aid in the process of designing medical devices and overcoming the barriers experienced to this point. Specifically in this work, computational modeling and simulation frameworks are developed to (1) enable design and evaluation of an insertable glucose biosensor and (2) to estimate the impact of patient and device specific factors on extracting photoplethysmographic waveforms, ultimately for use in remote blood pressure monitor. A computational framework for a multi-modal optical and fully-insertable glucose biosensor was developed via Monte Carlo modeling and Finite Element Method. The optical output of such a biosensor when implanted 2 mm in the volar wrist was determined by first validating the representation of a phosphorescence lifetime decay assay and a Förster Resonance Energy Transfer against known assays. It was found that using near infrared components yields sufficient signal to be detectable by a simple photodiode across skin tones. This framework was then used to determine biosensor geometries that would yield stronger luminescent output, and it was found that a stacked cylinder design 0.43 cm in length with 0.036 cm repeating units would yield maximum luminescent output and have limited chemical crosstalk. A combined Monte Carlo and gaussian combination framework was developed to estimate the impact of patient specific factors such as skin tone and age and device specific factors such as device wavelength on extracting Photoplethysmograph (PPG) waveforms for blood pressure (BP) prediction. In this work, Monte Carlo was used to estimate the signal strength of photoplethysmography under certain conditions, and gaussian combination was used to generate synthetic waveforms impacted by combinations of parameters. This framework was used to analyze the impact these factors have on signal strength, feature extraction, and the predictive precision of in-house neural network, bagged trees, and support vector machine algorithms. It was shown that patient specific factors such as age have a large effect on feature extraction, whereas patient specific factors such as skin tone do not. Additionally, differences in signal processing methods generated a large impact in extracting PPG for BP prediction. In this work, we create computational models that aid in the development of medical devices for remote monitoring of diabetes and cardiovascular disease. These models are validated, used to determine the feasibility of in-development medical devices, and are lastly used to demonstrate the impact of patient specific and device factors on device performance

Mobilizing Devotion: Joint Church-Military Efforts to Establish the Modern US Army Chaplain Corps

For most of American military history, military chaplains, or institutional support for them within the professional military establishment, was often an after-thought. Politicians and senior Army officials often only provided for chaplains on an ad hoc basis. Few military chaplains enjoyed permanent billets within the Army, and when war broke out, individual units were often left scrambling to recruit the local town priest, if they thought to take on a chaplain at all. However, the Spanish American War and Philippine War drew Protestant congregations’ attention to the lack of, and dire need for, Army chaplains to provide spiritual care for American soldiers. This paper explores the relationship between the War Department, the United States Army, and the American Protestant Churches’ Federal Council. It argues that the ultimate establishment of the modern U.S. Army Chaplain Corps came only through civil-military cooperation between these key stakeholders. This paper shows that interest in providing spiritual care for troops grew tremendously during the Preparedness Movement due to part-time military chaplains with firsthand experience of institutional shortcomings who petitioned home churches and the War Department for more support. Building on these petitions, the Federal Council of the Churches of Christ in America then unilaterally worked to provide the American Expeditionary Force with sufficient numbers of high-quality chaplains, whilst simultaneously lobbying the U.S. government to ensure they received proper training to minister troops across the battlefields of France. This massive undertaking on the home front, this paper argues, resulted in the creation of the modern-day U.S. Army Chaplain Corps, a permanent administrative unit within the U.S military, signaling the end of the ad hoc provision of chaplains only in times of war

Contrasting Stratospheric Aerosol Injection Geoengineering with Greenhouse Gas Emission Cuts in Mitigating Human Thermal Stress Globally

Greenhouse gas-induced climate change is ongoing, increasingly causing heat-related illness, suffering, and death. While carbon emission reduction is recognized as the main approach of mitigation, geoengineering as an emergency response has gained stronger interests in recent years. Stratospheric Aerosol Injection (SAI) is one proposed method of geoengineering to mitigate rising temperatures in the future decades. Previous studies on SAI have primarily focused on the impacts on surface temperature and precipitation but have lacked a direct and rigorous assessment of human thermal stress indicators, arguably more relevant to the health outcome of vulnerable populations globally. Using CESM2-WACCM6 model simulation over the period of 2020 – 2100 from CMIP6’s ScenarioMIP and GeoMIP projects, this paper investigates the spatial and temporal evolution of temperatures and a suite of 5 heat indices under a decarbonization emission pathway (SSP245) and an SAI scenario (G6sulfur), relative to the baseline a climate scenario of continued fossil fuel economic development (SSP585). These indices are carefully chosen to embody the meteorological influence via temperature, humidity, solar radiation, and wind speed. We find that the SAI scenario does a comparably good job of cooling global average temperatures and heat indices, as SSP245. For example, in 2100, the temperature would be cooler than SSP585 by 3.1ºC in the SAI scenario, and 3.3°C in the decarbonization pathway. However, the cooling is not projected to be uniform across global land regions. For example, the North American region would have stronger cooling benefits under SAI, while regions such as Australia would suffer more under SAI than decarbonization. Therefore, the global disparity of climate impacts are widened rather than reduced. More importantly, the heat relief when quantified using ESI and AT indices shows even larger disparity enhancement, possibly due to an enhancement of the polar vortex in the Northern Hemisphere, which has been observed during previous large volcanic eruptions stemming from ozone depletion, stratospheric heating, and a decrease in temperature gradient. This impact has not been seen in the Southern Hemisphere, which can lead to disparities in the ability of SAI to evenly distribute cooling effects

Demonstrations of Geo-Fencing Occupancy-Based Control in Smart and Connected Homes

This paper analyzes the benefits of a geofencing and geolocational method for occupancy-based control in smart buildings. The research was conducted using the Google Maps Geolocation API that utilizes Wi-Fi points and cellular towers to pinpoint a phone or devices location. The location of the occupant was then compared to the testing location with a geofence radius of 5 miles to determine occupancy status of the homes. This study compared the proposed OBC method to both a non-OBC and a traditional presence sensed OBC method. In the first comparison, scenario one, the energy saving ratio was 14.75 % with an impact to zone temperature of 7.2 ºF-hr unmet degree hours during occupied times over twenty-nine days of testing. The second comparison, scenario two, had an energy saving loss ratio of 30.4 % but a zone temperature improvement of 0.76 ºF-hr unmet degree hours per day over the ten-day testing period

Application of Molecular Interactions for Bio-separations and Drug Discovery

Focus on microalgae continues to grow for the production of biomass and various valueadded products including proteins, lipids, and pigments. The bioproducts from microalgae have found their applications not only in the field of petroleum industry but also in food and nutrition and pharmaceutical industries. However, due to several technical challenges associated with the inability to effectively dewater micro-algal biomass and extracting valuable compounds, microalgae-based bio-refinery is not yet economically feasible. To make the microalgae-based bio-refinery platform sustainable, less energy and resource intensive dewatering and harvesting techniques needs to be deployed. Part 1 of this dissertation focuses on a novel dewatering technique of microalgal biomass by using an amphiphilic polyelectrolyte, which upon adsorbing on the biomass, leads to formation of a net hydrophobic ensemble that consequently migrates into a hydrophobic organic solvent i.e., hexane. The technique also involves simultaneous separation of algal proteins by retaining them in the aqueous phase while migrating algal cellular debris to a hexane phase at the right system pH and polyelectrolyte concentration. Separation and recovery of microalgae from the aqueous medium that they reside in is difficult as a result of the nature of the algal cells, i.e., small cell size, density close to water, low concentration, and ability to stay suspended in water due to surface potential. This study has been divided into four sections: 1) separation studies on model algal particles; 2) separation of model proteins (egg albumin); 3) Simultaneous separation of model algal particles and models proteins in hexane and aqueous phase; and 4) separating algal proteins and cellular debris in aqueous and hexane phase respectively. The technique involves the addition of a positively charged electrolyte, Mono/Poly-(diallyl dimethyl ammonium chloride, DADMAC) which interacts with negatively charged particles to form hydrophobic ensembles. The resulting hydrophobic ensembles, upon addition of a hydrophobic organic solvent, migrate from aqueous phase to the hydrophobic organic solvent phase. From the studies conducted onChlorella sorokiniana,the ability of polyDADMACtodewater and extract cellular debris, lipids, and pigments to the hexane phase while retaining protein fraction in the aqueous phase was investigated. It was observed that different components could be migrated from one phase to the other by modulating the system pH. Close to the isoelectric point, proteins can be retained in the aqueous phase while selectively migrating algal debris to the hexane phase via targeted binding of the polyelectrolyte. Approximately 80% of total proteins were retained in the aqueous phase at pH 4, and 90% of cellular debris were migrated to the hexane phase at pH 4.5. Results indicate the possibility of separating multiple components from microalgae in an aqueous-organic solvent two-phase system using polyDADMAC. Part 2 of this dissertation focuses on screening of highly specific RNA dependent RNA polymerase (RdRp) inhibitors for Tick-Borne encephalitis virus. Tick-Borne encephalitis virus (TBEV) in humans can be caused by direct tick bites or by consumption of nonpasteurized milk or milk products from TBEV- infected sheep, goats and cows. The TBEV genome encodes a single polyprotein, which is co/post-translationally cleaved into seven nonstructural proteins. Of the non-structural proteins, NS5 contains the RdRp domain and a methyltransferase (MTase) domain that are responsible for the replication of the viral genome. The focuses of this section was on screening for potential antivirals using a hybrid receptor and ligand-based pharmacophore search. For identification of pharmacophores, a mixture of small probe molecules and nucleotide triphosphates (NTPs) were used. The ligand/receptor interaction screenings of structures using ZINCPharmer search engine in ZINC database resulted in five compounds that bound to the RdRp domain with high affinity. Compounds Zinc 9662, and Zinc 9041hadsignificantlylower binding energies than native NTPs at the RdRp binding site. Experimental studies indicated that Zinc 7151 substantially inhibited viral growth at 30 µMconcentration while both Zinc 3677 and Zinc 7151 had antiviral activity at 100 µM

Bringing Social Behavior to Light: Essays on the Heterogeneous Effects from Social Status and Preferences

“Rational,” individualistic agents populate theories in neoclassical economics, which are intended to represent how humans respond to incentives and stimuli. However, humans do not always behave in the way the agents in theories behave, as revealed through the study of social preferences. In fact, humans consider social environments when faced with decisions that have direct impacts on payoffs and outcomes. Social environments include the social status of where we stand relative to others or simply the surroundings under which we make decisions with others in mind. Understanding some of the processes behind these social decisions can help us better understand motivations as to why social environments matter in economic contexts. In the first essay of this dissertation, we conduct a field experiment to better understand the role of social status and monetary incentives as motivation to increase physical activity. We find that social status alone does not induce a change in physical activity. When social status is combined with monetary incentives, however, we find an effect in the number of daily steps. This effect is heterogeneous. Individuals with low physical activity increase their number of steps by 12%, while those with high physical activity decrease the number of steps by 25%. Our results call for a cautionary approach for analyzing the role of social status, in many cases unobserved, for physical activity intervention programs. In the second essay of this dissertation, we explore social signaling and how it influences economic behavior. We conduct a laboratory experiment to explore how benefit eligibility stigma drives decisions to competitiveness. We induce a stigma associated with a benefit for the low status group, and then introduce a treatment in which the stigma is reduced by expanding the eligibility to a middle-status group in a plausible deniability treatment. While we do not observe evidence of a stigma affecting benefit take-up, we do observe a difference in preferences for competitiveness in a subsequent and unrelated task; namely, when individuals in the middle group qualify for the benefit their rate of competition is reduced by 33% compared to the treatment in which they do not qualify. A potential interpretation of our results would suggest expansion of eligibility of certain government assistance programs may produce unintended consequences for the newly eligible. In the last essay of this dissertation, we explore the effect of light on social preferences. Based on previous literature, we know that light affects serotonin levels. We explore social behaviors first through a pilot study, followed by a laboratory-controlled setting where we alter the lighting of the room and incentivize several games that measure social preferences: ultimatum, trust, public goods, and gift exchange. We do not replicate previous results from the ultimatum game as seen in previous literature regarding serotonin and fairness, nor do we observe any differential behavior in the public goods game due to lighting condition. However, we find heterogeneous effects due to lighting in our subjects’ trust responses as well as their responses in the gift exchange, yet further analysis and measurement is needed to address whether we can isolate serotonin as a driver for these results

Sensing and Infrastructure Design for Robots: A Plan-Based Perspective

Currently there do not exist general-purpose robots, and the procedures by which robots are designed are often ad hoc. Additionally, designers must deal with considerations including budget, energy requirements, and the availability of parts, all of which complicate the problem. Abstract formal theories have, among other benefits, the potential to assist designers in developing and understanding the capabilities of novel robotic systems. Of particular interest is the concept of action-based sensors, which focus on the idea that a robot need only know enough to know what action to perform next. As a mathematical abstraction, action-based sensors prescribe actions to the agent; details of the sensor itself are irrelevant. From this information-oriented perspective, this concept also links planning directly to the design problem: the definition of what action “should” be taken depends upon the plan a robot is executing, serving to specify its desired behavior. While the theoretical abstractions of sensors are technology-neutral, we present ways to connect action-based sensors to the considerations and constraints faced by real robot designers. Action-based sensors have been formalized in terms of specific plans (informally those that take the fewest actions to achieve a goal), but there exist cases in which it is useful to consider other plans. In extending this formalization to include all plans, we find that certain plans have obstructions that prevent their expression as action-based sensors. We have developed an algorithm to remove these obstructions, which result from the interactions between a robot and its environment. After this, we move from the question of what a robot must sense about the environment to the question of how an environment should provide information. The use of infrastructure for spaces shared by multiple agents is another way in which designers can simplify tasks for agents. The complexity of this design problem arises from infrastructure’s ability to modify both what an agent observes and the outcome of actions. We present a method for modeling the impact of infrastructure to determine its utility to a given agent, and also consider how the utility of the infrastructure can vary depending on the differing needs of agents and how they make use of the environment. The present work, in addition to extending Erdmann’s original theory, focuses on the way in which information that must be retained by the agent can be contained within a plan’s structure. Use of a graph-based framework allows for us to identify if that structure is necessary for successful execution of the plan. This dissertation then shifts to a complementary design problem, examining the ability of infrastructure to externalize information and actuation requirements. It also presents a model for predicting the impact of introducing new infrastructure. Finally, it will explore the ways in which information can be used to estimate sensor failures in robots and bound the space of possible configurations. Transitioning from the design of robots and their environments to their operation, this dissertation also presents a method for estimating sensor failures. Through knowledge of the world structure and expected observations, inconsistencies can be tracked to form hypotheses on potential sensor failures. We introduce a lattice-based method of expressing these failures, as well as an algorithm for tracking inconsistencies. The algorithm allows for an often concise representation of a potentially exponential set of hypotheses, enabling use during a robot’s execution. This basis also allows for the robot to determine if a failure interferes with its ability to complete a task. We also present a method through which the sensors that are required for task completion can be determined at any point. The primary means to validate the theoretical results in this dissertation are a range of case studies. For action-based sensors, we consider several varieties of design problems including sensor selection and navigation problems. Moving beyond the sets of action-based sensors considered in these design problems, we also examine concise combinatorial representations for sets of sensors more generally, and apply these to settings involving robot self-diagnosis. For infrastructure, we provide a taxonomy as a guide by which to examine several different cases in which infrastructure is introduced to an environment. These case studies focus both on changes in agent behavior after being introduced, as well as ways in which the value of the introduced infrastructure can be deter-mined. For the identification of sensor failures, an example is also presented that demonstrates the concise nature of the model, particularly when compared to naïve methods

Optimization of a Combined Cooling, Heat, and Power Plant Design for Existing Central Utility Plants

Cost and emission savings with load-following capability are essential factors in the design optimization of Combined Cooling, Heating, and Power (CCHP) systems. This thesis presents a methodology for evaluating CCHP system design options for large central utility plants to minimize operating costs and emissions. Central utility plants can be considered the backbone of large campus systems providing critical supplies such as cooling and heating energy. In this strategy, multiple Power Generation Units (PGUs) are considered to meet different load profiles annually to improve system utilization and resilience. The systematic process was applied to a case-study plant and identified the most economically viable CCHP design to minimize the annual total cost, including operation, maintenance, and utility costs, while also addressing redundancy issues. A Pareto frontier of design solutions containing wider total operational capacity is recognized using a mixed integer linear program algorithm. The final results of the case study showed that the CCHP system could achieve 23.8% operational cost savings and 30.2% and 60.7% in CO2 and NOx reductions, respectively. In addition, multiple PGU systems provide two percent additional cost savings compared to a single PGU system while avoiding downtime and increasing energy resilience. A sensitivity analysis indicated that utility cost fluctuation could drastically change the optimal operating cost. An increase in natural gas cost and a decrease in electrical grid cost can make the optimal design infeasible

Engineering the Microstructure of Carbon Fiber-Reinforced Polymer Composites by Cellulose Nanocrystal–Carbon Nanomaterials

Carbon fiber reinforced polymer (CFRP) composites suffer from weak interfacial and interlayer bonding, and lack of control on the microstructure formation that has resulted in properties lower than theoretical predictions. Despite the promises, integrating carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) into CFRPs is challenging because of the need for complicated lab-scale processes and toxic chemical grafting or dispersants that makes conventional means of processing less compatible with existing industrial procedures for large-scale applications. Engineering the CNTs/GNPs nanostructured carbon fiber (CF) fabric through conventionally adopted coating approaches can effectively integrate the nanostructures in CFRPs that allow them to boost their functionality and tailor the microstructure of composite components. Hence, the preparation of homogeneous and stable coating suspensions of CNTs/GNPs without damaging their intrinsic properties and efficiently transferring the nanomaterials on the CF surface is essential to enhance the structural performance of CFRPs. This dissertation explores the scalable fabrication of CNT/GNP integrated CFRPs by coating approach and tests their structural and multifunctional contribution to CFRPs’. Cellulose nanocrystals (CNCs) are used to create hybrid nanostructures with CNTs (CNC bonded CNT) and GNPs that enable stabilization of carbon nanomaterials in nontoxic media, e.g., water, and promote the scalability of the process. This work is composed of three main divisions: First, the atomic level interaction of CNC and CNT/GNP is investigated using both experimental (transmission/scanning electron microscopy (TEM/SEM), atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS)) accompanied with quantum-level calculations (density functional theory (DFT)). Second, CNC bonded CNT/GNPs hybrids are integrated into CFRPs by immersion coating, and their effect on mechanical properties e.g., interfacial and interlaminar performance are articulated. Third, the multifunctionality of the manufactured composites is controlled through engineered sub-micron droplets of CNCCNT/GNP to achieve the desired properties such as electrical and/or thermal properties along with mechanical strength. This dissertation presents new possibilities to precisely control the material microstructure and enables the engineering of the bottom-up manufacturing of hybrid nanostructured composites

Development of Phage Display Techniques with Genetic Code Expansion for Peptide Drug Discovery

Phage display is one of the most widely-used techniques to develop peptide therapeutics. Its unique design links the displayed peptides to their encoding DNAs, providing a means for amplifying the peptide library and an easy way to identify selected ligands through sequencing. Although powerful, the conventional phage display technique replies on host cell’s translation machinery, therefore its chemical diversity is confined to twenty canonical amino acids. Further, phage-displayed peptides are also generally linear and unstructured leading to entropy penalty when binding to targets and proneness to proteolytic degradation. To resolve these drawbacks, we expand the functional and structural diversity of phage display using orthogonal aminoacyl-tRNA synthetase/tRNA pairs to incorporate non-canonical amino acids with diverse chemical functionalities into displayed peptide libraries. First, we develop a system to genetically incorporate an N^ε-acryloyl-L-lysine (AcrK) at the C-terminus of an 8-mer library; the non-canonical amino acid undergoes a proximity-driven Michael addition with the cysteine at the N-terminus to generate a phage-displayed cyclic-peptide library. The cyclic peptide library is applied to the affinity selection against histone deacetylase 8 (HDAC8), leading to the discovery of a potent cyclic peptide inhibitor that binds to target protein with a single digit micromolar affinity and displays better potency than its linear counterparts. Then, the same approach is applied to screen against the spike protein of the novel coronavirus SARS-CoV-2 to evolve peptide inhibitors. A 12-mer library is constructed and used in a displacement-based selection which gives two peptide ligands; both are shown to disrupt Spike-RBD/ACE2 binding. Lastly, a previously developed amber-obligate library is used to display an N^ε-butyryl-lysine (BuK) on 7-mer peptides. This lysine derivative is a naturally occurring lysine posttranslational modification that has target-ligand interactions with eleven-nineteen leukemia protein (ENL). During the selection, BuK serves as a warhead to guide displayed peptides towards the active site of ENL, thereby, increasing the selectivity and productivity of biopanning. We validate the selected peptides as ENL inhibitors, and further optimization and investigation have led to the discovery of a potent, cellular active peptide inhibitor that exhibits on-target effects in inhibiting ENL target gene expression and leukemia growth