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Title: Regional climate response to land use and land cover change in contiguous United States
Creator: Nikolić, Jovanka
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Future land use and land cover (LULC) pattern in the Contiguous United States (CONUS) is expected to be significantly different from that of the present, and as an important surface forcing for earth's climate system, the potential changes in LULC will contribute to climate change at all scales (local, regional to global). While numerous studies have examined how the earth's climate will respond to the anthropogenic increase of greenhouse gas concentrations in the earth's atmosphere, this...
Show moreFuture land use and land cover (LULC) pattern in the Contiguous United States (CONUS) is expected to be significantly different from that of the present, and as an important surface forcing for earth's climate system, the potential changes in LULC will contribute to climate change at all scales (local, regional to global). While numerous studies have examined how the earth's climate will respond to the anthropogenic increase of greenhouse gas concentrations in the earth's atmosphere, this research aims to quantify the response of several climate variables to the expected LULC change in the CONUS using simulations from a regional climate model. The research is composed of three individual studies. The first study assesses the sensitivity of simulated low-level jet (LLJ) characteristics on changes in LULC pattern. As a prominent weather and climate process responsible for transport of moisture from the Gulf of Mexico northward into central CONUS, LLJ plays an important role in the hydrological cycle and wind energy generation over the Great Plains. Therefore, it is important to quantify the potential changes in jet characteristics, such as jet speed, height and frequency, under the influence of LULC change. The second study investigates the impact of LULC change on frost indices - the dates of last spring frost and first fall frost and the length of frost free seasons. Frost is one of the major factors affecting the growth and development of plants and crop production. Future changes in LULC could make some regions more beneficial, while others more harmful to agricultural practice. Finally, the third study examines the potential impact of the changes in LULC pattern on future wind energy resources. As a zero carbon energy resource, wind energy helps limit greenhouse gasses emissions and mitigate climate change. Knowledge gained on where in the CONUS wind power class would likely to change from unsuitable or marginal to suitable, and vice versa, as a result of LULC change can be useful for future wind farm sitting and for making better informed energy policies.
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Title: Network-wide traffic state analysis : estimation, characterization, and evaluation
Creator: Saedi Germi, Ramin
Date: 2020
Collection: Electronic Theses & Dissertations
Description: The Network Fundamental Diagram (NFD) represents dynamics of traffic flow at the network level. It is exploited to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. This study presents a framework to estimate NFD and incorporates it for three specific applications in large-scale urban networks. Primarily, a resource allocation problem is formulated to find the optimal location of fixed measurement points and optimal sampling of...
Show moreThe Network Fundamental Diagram (NFD) represents dynamics of traffic flow at the network level. It is exploited to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. This study presents a framework to estimate NFD and incorporates it for three specific applications in large-scale urban networks. Primarily, a resource allocation problem is formulated to find the optimal location of fixed measurement points and optimal sampling of probe trajectories to estimate NFD accounting for limited resources for data collection, network traffic heterogeneity and asymmetry in OD demand in a real-world network. Using a calibrated simulation-based dynamic traffic assignment model of Chicago downtown network, a successful application of the proposed model and solution algorithm to estimate NFD is presented. The proposed model, then, is extended to take into account the stochasticity of day-to-day fluctuations of OD demand in NFD estimation.Three main applications of NFD are also shown in this research: network-wide travel time reliability estimation, network-wide emission estimation, and real-time traffic state estimation for heterogenous networks experiencing inclement weather impact. The main objective of the travel time reliability estimation application is to improve estimation of this network-wide measure of effectiveness using network partitioning. To this end, a heterogeneous large-scale network is partitioned into homogeneous regions (clusters) with well-defined NFDs using directional and non-directional partitioning approaches. To estimate the network travel time reliability, a linear relationship is estimated that relates the mean travel time with the standard deviation of travel time per unit of distance at the network level. Partitioning and travel time reliability estimation are conducted for both morning and afternoon peak periods to demonstrate the impacts of travel demand pattern variations.This study also proposes a network-level emission modeling framework via integrating NFD properties with an existing microscopic emission model. The NFDs and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogenous vehicle types with different emission generation rates in the network-level model. Non-linear and support vector regression models are developed using simulated trajectory data of thirteen simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from underlying microscopic emission model, regardless of the simulation tool that is used to calibrate the network-level emission model.Finally, the NFD application for real-time traffic state estimation in a network experiencing inclement weather conditions is explored. To this end, the impacts of weather conditions on the NFD and travel time reliability relation are illustrated through a scenario-based analysis using traffic simulation. Then, the real-time traffic state prediction framework in the literature is adjusted to capture weather conditions as a key parameter. The extended Kalman filter algorithm is employed as an estimation engine to predict the real-time traffic state. The results highlight the importance of considering weather conditions in the traffic state prediction model.
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Title: Still learning : introducing the learning transfer model, a formal model of transfer
Creator: Olenick, Jeffrey David
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Although training has been a key topic of study in organizational psychology for over a century, a century which has seen great progress in our understanding of what a quality training program entails, a substantial gap persists between what is trained and what is transferred to the job. Reduction of the training-transfer gap has driven research on transfer-focused interventions which have proven effective. However, although we know a lot regarding how individuals learn new material, and...
Show moreAlthough training has been a key topic of study in organizational psychology for over a century, a century which has seen great progress in our understanding of what a quality training program entails, a substantial gap persists between what is trained and what is transferred to the job. Reduction of the training-transfer gap has driven research on transfer-focused interventions which have proven effective. However, although we know a lot regarding how individuals learn new material, and correlates of whether they transfer that material back to their work environment, we know very little about how individuals go about choosing whether to apply their new knowledge to, typically, previously-encountered situations in their work environment and how those decisions unfold over time. Improving our knowledge regarding how individuals transfer learned material will lead to new insights on how to support the transfer of organizationally directed training, or any learning event, back to the work environment. Thus, the present paper introduces a formal model of the transfer process, the Learning Transfer Model (LTM), which proposes a process for how transfer unfolds over time and gives rise to many of the findings we have accumulated in the transfer literature. This is accomplished by reconceptualizing transfer as its own learning process which is affected by the dual nature of human cognitive systems, the learner's social group, and their self-regulatory processes. The LTM was then instantiated in a series of computational models for virtual experimentation. Findings and implications for research and practice are discussed throughout.
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Title: Adaptation to visual perturbations while learning a novel virtual reaching task
Creator: Narayanan, Sachin Devnathan
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn...
Show more"Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn to adapt to perturbations. Methods: 13 college-age participants (females = 7, Mean = 21.74 +/- 2.55) performed upper body movements to control a computer cursor. Visual rotation of the cursor position was introduced as a perturbation for one-half of the practice trials. Movement time and normalized path length were calculated. One way repeated measures ANOVA was performed to analyze significance between the performance at different times of the task. Results: Significant learning seen while learning the initial baseline task (p<0.0001) and a significant drop in performance upon immediate exposure to the perturbation (p =0.005). No significant adaptation over practice with the perturbation (p = 0.103) or significant after-effects on removal of the perturbation (p = 0.383). Conclusions: Results suggests differences in adaptation when the task is novel when compared to other adaptation studies and such novel tasks trigger a different type of learning mechanism when compared to adaptation."--Page ii.
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Title: Atomic simulation on chemical-mechanical coupled deformations in complex nano structures
Creator: Liu, Jialin (Graduate of Michigan State University)
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Nano-structured materials often exhibit very different mechanical properties comparing with their bulk counterpart and are more sensitive and active to chemical interactions with the environments due to the large surface-to-volume ratio. In this thesis, predictive modeling techniques including density functional theory (DFT) and reactive molecular dynamics method (MD) are designed and applied to understand the deformation mechanisms of complex nano-structured material and describe chemical...
Show more"Nano-structured materials often exhibit very different mechanical properties comparing with their bulk counterpart and are more sensitive and active to chemical interactions with the environments due to the large surface-to-volume ratio. In this thesis, predictive modeling techniques including density functional theory (DFT) and reactive molecular dynamics method (MD) are designed and applied to understand the deformation mechanisms of complex nano-structured material and describe chemical-mechanical coupled interactions. Three technologically important materials are investigated, to understanding the high strain rate toughening mechanism in nacre, predicting the formation and fracture of aluminum oxide bifilms in aluminum castings, and revealing the lithium growth morphology as a function of oxygen partial pressure. For nacre, its hierarchical structure and toughening mechanisms have inspired many materials developments. Recently, a new toughening mechanism, deformation twins was observed in nacre after dynamic loading (103 s--1). The deformation twinning tendency and the competition between fracture and deformation twinning were revealed by DFT calculations. We discovered that the ratio of the unstable and the stable stacking fault energy in aragonite is hitherto the highest in a broad range of metallic and oxide materials and the bonding nature for this high ratio is explained. Both aluminum and lithium have high oxygen affinity. Their interaction with the oxygen environment affects the mechanical properties and vice versa. During casting of aluminum, it has long been proposed that the entrapped alumina "bifilms" are detrimental to the fatigue properties of the cast product. However, its properties have never been measured due to experimental limitations. Therefore, a ReaxFF based MD protocol was designed to simulate aging, folding, and fracture of oxide bifilms. The predicted fracture energy, fracture location, and differences between old and young oxides are explained a series of experimental observations. To illustrate the Li-growth mechanism in a solid-state-battery testing platform, we modeled the morphology of Li nano-structure growth in oxygen environment via ReaxFF-based MD. The simulation revealed that the competition of the Li growth rate and oxidation rate leads to the sphere-nanowire-sphere morphology transition with increasing oxygen partial pressure. Understanding the impact of chemical reaction on Li dendrite growth mechanisms and morphology evolution provided insights on the formation of the solid electrolyte interface (SEI) layer in a Li-ion battery. Finally, a shortcoming of the current charge transfer scheme (qEq) used in the ReaxFF MD simulation is discussed. It is demonstrated that qEq method will lead to overductile ionic materials in the MD simulation. A new Force field method and new parameters are proposed to mitigate this problem."--Pages ii-iii.
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Title: Agronomic management of corn using seasonal climate predictions, remote sensing and crop simulation models
Creator: Jha, Prakash Kumar
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Management decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always...
Show moreManagement decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always accurate. Remote sensing observations and climate predictions can improve the accuracy in managing time bound climate-sensitive decisions at larger spatiotemporal scale. However, there is also a disconnect between climate forecasts and crop models. The unavailability of downscaling tool that can downscale rainfall and temperature forecasts simultaneously make this task more challenging. To address these knowledge gaps, this dissertation consists of three studies focused on interdisciplinary approaches to agronomic management of corn.In the first study, we calibrated and validated genetic coefficients of CERES-Maize using field data from the Michigan corn performance trials. Multiple methods of estimating genetic coefficients GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood Uncertainty Estimate), and NMCGA (Noisy Monte Carlo Genetic Algorithm) were evaluated and ensembled to estimate more reliable genetic coefficients. The calibrations were done under irrigated conditions and validation under rainfed conditions. The results suggested that ensembled genetic coefficients performed best among all, with d-index of 0.94 and 0.96 in calibration and validation for anthesis and maturity dates, and yield.In the second study, simulated growth stages from the calibrated crop model were used to develop site-specific crop coefficients (kc) using ensembled ET and reference ET from the nearest weather station. ET from multiple models were ensembled and validated with the measured ET from eddy-covariance flux towers for 2010 - 2017. Results suggest that the ensembled ET performed best among all ET models used, with highest d-index of 0.94. Likewise, the performance of the newly derived kc-curve was compared with FAO-kc curve using a soil water balance model. Then, the derived region-specific Kc-curve was used to design irrigation scheduling and results suggest that it performed better than FAO Kc-curve in minimizing the amount irrigation while maintaining a prescribed allowable water stress.The third study used the calibrated crop model to simulate anthesis using downscaled seasonal climate forecasts. The predicted anthesis and downscaled seasonal climate forecasts were used to develop risk analysis model for ear rot disease management in corn. In this study an innovative downscaling tool, called FResamplerPT, was introduced to downscale rainfall and temperature simultaneously. The results suggest that temperature and relative humidity are better predictors (combined) as compared to temperature and rainfall (combined). With this risk analysis model, growers can evaluate and assess the future climatic conditions in the season before planting the crops. The seasonal climate information with the lead-time of 3 months can help growers to prepare integrated management strategies for ear rot disease management in maize.
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Title: Experiments and model development of a dual mode, turbulent jet ignition engine
Creator: Tolou, Sedigheh
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "The number of vehicles powered by a source of energy other than traditional petroleum fuels will increase as time passes. However, based on current predictions, vehicles run on liquid fuels will be the major source of transportation for decades to come. Advanced combustion technologies can improve fuel economy of internal combustion (IC) engines and reduce exhaust emissions. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is an advanced, distributed combustion technology which can...
Show more"The number of vehicles powered by a source of energy other than traditional petroleum fuels will increase as time passes. However, based on current predictions, vehicles run on liquid fuels will be the major source of transportation for decades to come. Advanced combustion technologies can improve fuel economy of internal combustion (IC) engines and reduce exhaust emissions. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is an advanced, distributed combustion technology which can achieve high diesel-like thermal efficiencies at medium to high loads and potentially exceed diesel efficiencies at low-load operating conditions. The DM-TJI strategy extends the mixture flammability limits by igniting lean and/or highly dilute mixtures, leading to low-temperature combustion (LTC) modes in spark ignition (SI) engines. A novel, reduced order, and physics-based model was developed to predict the behavior of a DM-TJI engine with a pre-chamber air valve assembly. The engine model developed was calibrated based on experimental data from a Prototype II DM-TJI engine. This engine was designed, built, and tested at the MSU Energy and Automotive Research Laboratory (EARL). A predictive, generalized model was introduced to obtain a complete engine fuel map for the DM-TJI engine. The engine fuel map was generated in a four-cylinder boosted configuration under highly dilute conditions, up to 40% external exhaust gas recirculation (EGR). A vehicle simulation was then performed to further explore fuel economy gains using the fuel map generated for the DM-TJI engine. The DM-TJI engine was embodied in an industry-based vehicle to examine the behavior of the engine over the U.S. Environmental Protection Agency (EPA) driving schedules. The results obtained from the drive cycle analysis of the DM-TJI engine in an industry-based vehicle were compared to the results of the same vehicle with its original engine. The vehicle equipped with the DM-TJI system was observed to benefit from 103033% improvement in fuel economy and 103031% reduction in CO2 emission over the EPA combined city/high driving schedules. Potential improvements were discussed, as these results of the drive cycle analysis are the first-ever reported results for a DM-TJI engine embodied in an industry-based vehicle. The resulting fuel economy and CO2 emission were used to conduct a cost-benefit analysis of a DM-TJI engine. The cost-benefit analysis followed the economic and key inputs used by the U.S. EPA in a Proposed Determination prepared by that agency. The outcomes of the cost-benefit analysis for the vehicle equipped with the DM-TJI system were reported in comparison with the same vehicle with its base engine. The extra costs of a DM-TJI engine were observed to be compensated over the first three years of the vehicle's life time. The results projected maximum savings of approximately 2400 in 2019 dollars. This includes the lifetime-discounted present value of the net benefits of the DM-TJI technology, compared to the base engine examined. In this dollar saving estimate, the societal effects of CO2 emission were calculated based on values by the interagency working group (IWG) at 3% discount rate."--Pages ii-iii.
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Title: Monte-Carlo simulations of the (d,²He) reaction in inverse kinematics
Creator: Carls, Alexander B.
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Charge-exchange reactions offer an indirect method for the testing of theoretical models for Gamow-Teller strengths that are used to calculate electron-capture rates on medium-heavy nuclei, which play important roles in astrophysical phenomena. Many of the relevant nuclei are unstable. However, a good general probe for performing charge-exchange reactions in inverse kinematics in the (n,p) reaction has not yet been established. The (d,2He) reaction in inverse kinematics is being developed as...
Show moreCharge-exchange reactions offer an indirect method for the testing of theoretical models for Gamow-Teller strengths that are used to calculate electron-capture rates on medium-heavy nuclei, which play important roles in astrophysical phenomena. Many of the relevant nuclei are unstable. However, a good general probe for performing charge-exchange reactions in inverse kinematics in the (n,p) reaction has not yet been established. The (d,2He) reaction in inverse kinematics is being developed as a potential candidate for this probe. This method uses the Active-Target Time Projection Chamber (AT-TPC) to detect the two protons from the unbound 2He system, and the S800 spectrograph to detect the heavy recoil. The feasibility of this method is demonstrated through Monte-Carlo simulations. The ATTPCROOTv2 code is the framework which allows for simulation of reactions within the AT-TPC as well as digitization of the results in the pad planes for realistic simulated data. The analysis performed on this data using the ATTPCROOTv2 code shows the techniques that can be done in experiment to track the scattered protons through the detector using Random Sampling Consensus (RANSAC) algorithms.
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Title: Improving the representation of irrigation and groundwater in global land surface models to advance the understanding of hydrology-human-climate interactions
Creator: Felfelani, Farshid
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Hydrological models and satellite observations have been widely used to study the variations in the Earth's hydrology and climate over multitude of scales, especially in relation to natural and human-induced changes in the terrestrial water cycle. Yet, both satellite products and model results suffer from inherent uncertainties, calling for the need to improve the representation of critical processes in the models and to make a combined use of satellite data and models to examine the...
Show moreHydrological models and satellite observations have been widely used to study the variations in the Earth's hydrology and climate over multitude of scales, especially in relation to natural and human-induced changes in the terrestrial water cycle. Yet, both satellite products and model results suffer from inherent uncertainties, calling for the need to improve the representation of critical processes in the models and to make a combined use of satellite data and models to examine the variations in the terrestrial hydrology. The representation of irrigation and groundwater-two major hydrologic processes with complex reciprocal interplay-in large-scale hydrological models is rather poorly parameterized and heavily simplified, hindering our ability to realistically simulate groundwater-human-climate interactions. This dissertation advances the physical basis for irrigation and groundwater parameterizations in global land surface models, leveraging the potential of emerging satellite data (i.e., data from GRACE and SMAP satellite missions) toward a more realistic quantification of the impacts of human activities on the hydrological cycle. A comprehensive global analysis is developed to examine the historical spatial patterns and long-term temporal response, i.e., the terrestrial water storage (TWS), of two models to natural and human-induced drivers. Human-induced changes in TWS are then quantified in the highly managed global regions to identify the uncertainties arising from a simplistic representation of irrigation and groundwater. The potential of improving irrigation representation in the Community Land Model version 4.5 (CLM4.5) is then investigated by assimilating the soil moisture data from SMAP satellite mission using 1-D Kalman Filter assimilation approach. The new irrigation scheme is then tested over the heavily irrigated central U.S. Next, the existing groundwater module of CLM5 is broadly evaluated over conterminous U.S. and a new prognostic groundwater module is implemented in CLM5 to account for lateral groundwater flow, pumping, and conjunctive water use for irrigation. In particular, an explicit parameterization for the steady-state well equation is introduced for the first time in large-scale hydrological modeling. Finally, the impacts of climate change on global TWS variabilities and the implications on sea level change are examined for the entire 21st century using multi-model hydrological simulations. The key findings and conclusions from the aforementioned multi-scale analysis and model developments are: (1) in terms of TWS, notable differences exist not only between simulations of hydrological models and GRACE but also among different GRACE products, therefore, TWS variations from a single model cannot be reliably used for global analyses; (2) these differences significantly increase in projections of TWS under climate change, however, models agree in sign of change for most global areas; (3) TWS is expected to decline in many regions in southern hemisphere, but increase in northern high latitudes, projected to accelerate sea level rise by the mid- and late-21st century; (4) constraining the target soil moisture in CLM4.5 using SMAP data assimilation with 1-D Kalman Filter reduces the bias in the simulated irrigation water by up to 60% on average, improving irrigation and soil moisture simulations in CLM4.5; (5) the new groundwater model significantly improves the simulation of groundwater level change and promisingly captures most of the hotspots of groundwater depletion across the U.S. overexploited aquifers; and (6) the simulation with the lateral groundwater flow substantially enhances the TWS trends relative to the default CLM5. These results and findings could provide a basis for improved large-scale irrigation and groundwater modeling and improve our understanding of hydrology-human-climate interactions.
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Title: Investigating the impact of manmade reservoirs on large-scale hydrology and water resources using high-resolution modeling
Creator: Shin, Sanghoon
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Manmade reservoirs are important components of the terrestrial hydrologic system. Dam installments fragment river systems, and reservoir operations alter flow regimes. The total storage capacity of existing global reservoirs is large enough to hold one sixth of annual continental discharge to global oceans. Due to growing energy demands, hundreds of large dams are being built and planned around the world, especially in the developing countries. Therefore, there is an urgent need to develop a...
Show moreManmade reservoirs are important components of the terrestrial hydrologic system. Dam installments fragment river systems, and reservoir operations alter flow regimes. The total storage capacity of existing global reservoirs is large enough to hold one sixth of annual continental discharge to global oceans. Due to growing energy demands, hundreds of large dams are being built and planned around the world, especially in the developing countries. Therefore, there is an urgent need to develop a better understanding of the impact of the existing and new dams on hydrological, ecological, agricultural, and socio-economic systems. Owing to increasing computational power and needs to understand and simulate processes in small-scale, hydrological models are advancing towards hyper-resolution global hydrological models. One of benefits of the increased spatial resolution is that the dynamics of surface water inundation over natural river-floodplain systems and manmade reservoirs can be explicitly represented; however, existing global models are not capable of simulating the river-floodplain-reservoir inundation dynamics in an integrated manner. This dissertation addresses this important standing issue by developing a high-resolution, continental-scale model to simulate the spatial and temporal dynamics of reservoir storage and release, thus paving pathways toward hyper-resolution surface water modeling in continental- to global-scale hydrological and climate models. The newly developed model is applied to simulate reservoirs within the contiguous United States (CONUS) and the Mekong River Basin (MRB) in Southeast Asia. With respect to the model development, the following advances are made over the previous global reservoir modeling studies: (1) an existing algorithm for reservoir operation is improved by conducting analytical analysis and numerical experiments and by introducing new calibration features for reservoir operation; (2) the spatial extent and its seasonal dynamics of reservoirs are explicitly simulated and reservoirs are treated as an integral part of river-floodplain routing, thus reservoir storage is no longer isolated from river and floodplain storages; and (3) a novel approach for processing and integrating high-resolution digital elevation models (DEMs) in river-floodplain-reservoir routing is introduced. The newly developed reservoir scheme is integrated within the river-floodplain routing scheme of a continental hydrological model, LEAF-Hydro-Flood, which is set for the CONUS, where abundant data are available for model validation. Then, the reservoir scheme is integrated into a global hydrodynamics model, CaMa-Flood, to investigate the historical impact of manmade reservoirs in the MRB that is experiencing an unprecedented boom in hydropower dam construction. Using the new scheme, the role of flood dynamics in modulating the hydrology of the MRB and the potential impact of flow regulation by the dams on the inundation dynamics are investigated. The significance of hydrologic effect of increasing dams is compared with that of climate variability. The fully coupled river-reservoir-floodplain storage simulation approach presented in this dissertation provides an advancement in hydrological modeling in terms of the representation of surface water dynamics, which is indispensable for better attribution of the observed changes in the water cycle, prediction of changes in water resources, and the understanding of the continually changing environmental and ecological systems.
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Title: Computational modeling of cardiac mechanics : microstructual modeling & pulmonary arterial hypertension
Creator: Xi, Ce
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Heart diseases, which approximately account for 31% of all human mortality every year, are the leading cause of death worldwide. Computational cardiac models have gained increasing popularity and become an indispensable and powerful tool in elucidating the pathological process of different heart diseases. They can be used to estimate important physiological and clinically relevant quantities that are difficult to directly measure in experiments. The broad goals of this thesis were to develop...
Show moreHeart diseases, which approximately account for 31% of all human mortality every year, are the leading cause of death worldwide. Computational cardiac models have gained increasing popularity and become an indispensable and powerful tool in elucidating the pathological process of different heart diseases. They can be used to estimate important physiological and clinically relevant quantities that are difficult to directly measure in experiments. The broad goals of this thesis were to develop 1) a microstructure-based constitutive model of the heart and 2) patient-specific computational models that would ultimately help medical scientists to diagnose and treat heart diseases.Heart diseases such as heart failure with preserved ejection fraction (HFpEF) are characterized by abnormalities of ventricular function that can be attributed to, changes in geometry, impaired myocyte (LV) relaxation, cardiac fibrosis and myocyte passive stiffening. Understanding how LV filling is affected by each of the many microstructural pathological features in heart diseases is very important and may help in the development of appropriate treatments. To address this need, we have developed and validated a microstructure-based computational model of the myocardium to investigate the role of tissue constituents and their ultrastructure in affecting the heart function. The model predicted that the LV filling function is sensitive to the collagen ultrastructure and the load taken up by the tissue constituents varies depending on the LV transmural location. This finding may have implications in the development of new pharmaceutical treatments targeting individual cardiac tissue constituents to normalize LV filling function in HFpEF.Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by elevated pulmonary artery pressure (PAP) and pulmonary artery vascular resistance, with limited survival rate and can affect patients of all ages. The increased pressure or afterload in the right ventricle (RV) can result in pathological changes in RV mechanics, which are currently not well-understood. To FB01ll this void, we have developed patient-specific computational models to investigate effects of PAH on ventricular mechanics. SpeciFB01cally, we have quantified regional ventricular myoFB01ber stress, myoFB01ber strain, contractility, and passive tissue stiffness in PAH patients, and compare them to those found in age- and gender-matched normal controls. Our results showed that RV longitudinal, circumferential and radial strain were depressed in PAH patients compared with controls; RV passive stiffness increased progressively with the degree of remodeling as indexed by the RV and LV end-diastolic volume ratio (RVEDV/LVEDV); Peak contractility of the RV was found to be strongly correlated, and had an inverse relationship with RVEDV/LVEDV. These results provide the mechanical basis of using RVEDV/LVEDV as a clinical index for delineating disease severity and estimating RVFW contractility in PAH patients.
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Title: Integration of topological data analysis and machine learning for small molecule property predictions
Creator: Wu, Kedi
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Accurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH...
Show moreAccurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants and is different from conventional chemical and physical representations. Based on ESPH and its modified version, element-specific topological descriptors (ESTDs) are constructed. The advantage of ESTDs is that they are systematical, comprehensive, and scalable with respect to molecular size and composition variations, and are readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between different small molecule properties, multi-task frameworks are further employed to simultaneously predict related properties. Deep neural networks, along with ensemble methods such as random forest and gradient boosting trees, are used to develop quantitative predictive models. Physical based molecular descriptors and auxiliary descriptors are also used in addition to ESTDs. As a result, we obtain state-of-the-art results for various benchmark data sets of small molecule properties. We have also developed two online servers for predicting properties of small molecules, TopP-S and TopTox. TopP-S is a software for topological learning predictions of partition coefficient and aqueous solubility, and TopTox is a software for computing element-specific tological descriptors (ESTDs) for toxicity endpoint predictions. They are available at http://weilab.math.msu.edu/TopP-S/ and http://weilab.math.msu.edu/TopTox/, respectively.
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Title: Sensitivities of simulated fire-induced flows to fire shape and background wind profile using a cloud-resolving model
Creator: Stageberg, Marshall S.
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Wildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud...
Show moreWildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud Model 1 (CM1) because it is designed to simulate high resolution, cloud scale processes that are comparable in scale to fire-induced flows. A surface sensible heat flux is added to CM1 to simulate the effect of a fire and the resultant fire-induced circulations and complex flows are examined. Using CM1 allows us to produce simulations with fine spatial and temporal resolution with a detailed representation of the evolution of the fire-atmosphere system. For the purpose of this study, we perform a series of simulations to examine the sensitivity of fire-induced flows to the shape of the simulated fire and to background wind profile. We show how fire shape and the background wind profile affect the intensity and extent of fire-induced perturbations to the lower atmosphere. The results from these numerical simulations, when combined with field observations, help improve our understanding of fire-atmosphere interactions. The results from this study can potentially help fire managers with decision-making when fighting wildland fires.
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Title: The quest for active media models : a self-consistent framework for simulating wave propagation in nonlinear systems
Creator: Glosser, Connor Adrian
Date: 2018
Collection: Electronic Theses & Dissertations
Description: This work presents new approaches to simulations of active media at the level of individual particles. Active systems contain internal, nonlinear, processes beyond those of simple scattering systems; thus these new models afford high degrees of fidelity in exploring the underlying physics without recourse to continuum or spatially-averaged approximations.First, I examine the dynamics of microspheres set into motion by ambient acoustic radiation in a fluid described by potential flow in the...
Show moreThis work presents new approaches to simulations of active media at the level of individual particles. Active systems contain internal, nonlinear, processes beyond those of simple scattering systems; thus these new models afford high degrees of fidelity in exploring the underlying physics without recourse to continuum or spatially-averaged approximations.First, I examine the dynamics of microspheres set into motion by ambient acoustic radiation in a fluid described by potential flow in the long-wavelength limit. Variations in the local surface pressure caused by scattering from each microsphere set each microsphere into motion following Newton’s second law. By expanding this pressure in terms of spherical harmonics—natural eigenfunctions of the unretarded radiation kernel—I recover an analytic description of the force on individual microspheres due to an incident waveform. High-order numerical integrations then relate the surface potential on one microsphere to the surface pressure on the others, thereby coupling the microspheres’ trajectories. These simulations predict a dominant translational effect along the direction of propagation of the incident waveform, though they also reveal significant dipolar interactions between microspheres that produce secondary expansions and contractions of the collective microsphere system.Extending my approach from acoustic to electromagnetic systems, I apply it to a collection of quantum dots: “artificial” two-level atoms with a size-dependent energy structure. The optical Maxwell-Bloch equations give the evolution of quantum dots under the influence of electromagnetic fields; this evolution then produces secondary radiation that couples a collection of quantum dots together. In my computational model, I castmy secondary electromagnetic fields in terms of a point-to-point integral operator that accurately recovers both near- and far-field effects. These fields, then, drive a set of implicitly coupled Bloch equations (solved with an exponentially-fitted predictor/corrector scheme) to give the dynamics of the system as a whole. In ensembles of up to 10 000 quantum dots, my model predicts synchronized multiplets of particles that exchange energy, quantum dots that dynamically couple to screen the effect of incident external radiation, localization of the polarization due to randomness and interactions, as well as wavelength-scale regionsof enhanced and suppressed polarization.The remainder of the work uses the same physical quantum dot system while moving towards efficient computer-aided device design. I detail an improved propagation algorithm to reduce the time and space complexity of the simulation dramatically, thereby facilitating rapid analysis of promising device structures. The algorithm makes use of physical and numerical approximations to effect large-scale calculations in reasonable CPU time. A rotating-frame approximation removes high-frequency components in the evolution of the system while simultaneously preserving accurate interference phenomena in space,thereby affording far larger simulation timesteps. Additionally, projecting the source current distribution onto a regular spatial grid makes use of a low-rank approximation to the field propagator to communicate radiation information between distant groups of particles via fast Fourier transforms in a manner reminiscent of fast multipole methods.
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Title: Balancing exploration and exploitation in bottom-up organizational learning contexts
Creator: Walker, Ross Ian
Date: 2018
Collection: Electronic Theses & Dissertations
Description: In order to keep pace with a rapidly changing environment, organizations must navigate a fundamental tension between exploration and exploitation. Over time, organizations often drift toward exploitation of known strengths and established resources, but this tendency can be harmful in a dynamic and competitive landscape. A classic simulation by James March (1991) demonstrated the importance of maintaining some degree of belief heterogeneity in an organization for the sake of long-term...
Show moreIn order to keep pace with a rapidly changing environment, organizations must navigate a fundamental tension between exploration and exploitation. Over time, organizations often drift toward exploitation of known strengths and established resources, but this tendency can be harmful in a dynamic and competitive landscape. A classic simulation by James March (1991) demonstrated the importance of maintaining some degree of belief heterogeneity in an organization for the sake of long-term learning. In March’s lineage, this thesis examines the effects of various exploratory strategies (i.e., individual experimentation, codification frequency, structural modularity, and employee turnover) on organizational learning in a bottom-up, networked, interpersonal learning context. Results demonstrate the complex interdependency of these variables in the exploration/exploitation tradeoff. Exploratory analyses suggest that a small degree of random individual experimentation has a favorable reward-to-risk ratio and that it is preferable to turnover as an exploratory strategy.
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Title: Modeling and control of pre-chamber initiated turbulent jet ignition combustion systems
Creator: Song, Ruitao
Date: 2018
Collection: Electronic Theses & Dissertations
Description: "Turbulent jet ignition (TJI) combustion is a promising concept for achieving high thermal efficiency and low NOx (nitrogen oxides) emissions. A control-oriented TJI combustion model with satisfactory accuracy and low computational effort is usually a necessity for optimizing the TJI combustion system and developing the associated model-based TJI control strategies. A control-oriented TJI combustion model was first developed for a rapid compression machine (RCM) configured for TJI combustion....
Show more"Turbulent jet ignition (TJI) combustion is a promising concept for achieving high thermal efficiency and low NOx (nitrogen oxides) emissions. A control-oriented TJI combustion model with satisfactory accuracy and low computational effort is usually a necessity for optimizing the TJI combustion system and developing the associated model-based TJI control strategies. A control-oriented TJI combustion model was first developed for a rapid compression machine (RCM) configured for TJI combustion. A one-zone gas exchange model is developed to simulate the gas exchange process in both pre- and main-combustion chambers. The combustion process is modeled by a two-zone combustion model, where the ratio of the burned and unburned gases flowing between the two combustion chambers is variable. To simulate the influence of the turbulent jets to the rate of combustion in the main-combustion chamber, a new parameter-varying Wiebe function is proposed and used for mass fraction burned (MFB) calculation in the main-combustion chamber. The developed model is calibrated using the Least-Squares fitting and optimization procedure. The RCM model was then extended to a TJI engine model. The combustion process is modeled by a similar two-zone combustion model based on the newly proposed parameter-varying Wiebe function. The gas exchange process is simulated by one-zone model considering piston movement and intake and exhaust processes. Since the engine uses liquid fuel, a pre-chamber air-fuel mixing and vaporization model is developed. And correspondingly, the pre-chamber uses a chemical kinetics based model for combustion rate calculation. The model was validated using the experimental data from a single cylinder TJI engine under different operational conditions, and the simulation results show a good agreement with the experimental data. For control design, a nonlinear state-space engine model with cycle-to-cycle dynamics is developed based on the previous crank-angle-resolved (CAR) TJI engine model. The state-space model successfully linked the combustion processes in the two chambers using the parameter-varying Wiebe function. The validated CAR model is used to calibrate and validate the state-space engine model. The simulation results of the two engine models show a good agreement with each other. Thereafter, a linear-quadratic tracking controller is developed for combustion phasing control. Simulation results are presented and a baseline controller has been implemented on the research engine. Combustion phasing control is very important for internal combustion engines to achieve high thermal efficiency with low engine-out emissions. Traditional open-loop map-based control becomes less favorable in terms of calibration effort, robustness to engine aging, and especially control accuracy for TJI engines due to the increased number of control variables over conventional spark-ignition engines. In this research, a model-based feedforward controller is developed for the TJI engine, and a feedback controller is also designed based on the linear quadratic tracking control with output covariance constraint. Since the TJI main-chamber combustion is influenced by the pre-chamber one, the proposed controller optimizes the control variables in both combustion chambers. The proposed feedforward and feedback controllers show significant performance improvement over a group of baseline controllers through a series of dynamometer engine tests."--Pages ii-iii.
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Title: A global modeling framework for plasma kinetics : development and applications
Creator: Parsey, Guy Morland
Date: 2017
Collection: Electronic Theses & Dissertations
Description: The modern study of plasmas, and applications thereof, has developed synchronously with com-puter capabilities since the mid-1950s. Complexities inherent to these charged-particle, many-body, systems have resulted in the development of multiple simulation methods (particle-in-cell,fluid, global modeling, etc.) in order to both explain observed phenomena and predict outcomesof plasma applications. Recognizing that different algorithms are chosen to best address specifictopics of interest, this...
Show moreThe modern study of plasmas, and applications thereof, has developed synchronously with com-puter capabilities since the mid-1950s. Complexities inherent to these charged-particle, many-body, systems have resulted in the development of multiple simulation methods (particle-in-cell,fluid, global modeling, etc.) in order to both explain observed phenomena and predict outcomesof plasma applications. Recognizing that different algorithms are chosen to best address specifictopics of interest, this thesis centers around the development of an open-source global model frame-work for the focused study of non-equilibrium plasma kinetics. After verification and validationof the framework, it was used to study two physical phenomena: plasma-assisted combustion andthe recently proposed optically-pumped rare gas metastable laser.Global models permeate chemistry and plasma science, relying on spatial averaging to focusattention on the dynamics of reaction networks. Defined by a set of species continuity and energyconservation equations, the required data and constructed systems are conceptually similar acrossmost applications, providing a light platform for exploratory and result-search parameter scan-ning. Unfortunately, it is common practice for custom code to be developed for each application-an enormous duplication of effort which negatively affects the quality of the software produced.Presented herein, the Python-based Kinetic Global Modeling framework (KGMf) was designed tosupport all modeling phases: collection and analysis of reaction data, construction of an exportablesystem of model ODEs, and a platform for interactive evaluation and post-processing analysis. Asymbolic ODE system is constructed for interactive manipulation and generation of a Jacobian,both of which are compiled as operation-optimized C-code.Plasma-assisted combustion and ignition (PAC/PAI) embody the modernization of burning fuelby opening up new avenues of control and optimization. With applications ranging from engineefficiency and pollution control to stabilized operation of scramjet technology in hypersonic flows,developing an understanding of the underlying plasma chemistry is of the utmost importance.While the use of equilibrium (thermal) plasmas in the combustion process extends back to the ad-vent of the spark-ignition engine, works from the last few decades have demonstrated fundamentaldifferences between PAC and classical combustion theory. The KGMf is applied to nanosecond-discharge systems in order to analyze the effects of electron energy distribution assumptions onreaction kinetics and highlight the usefulness of 0D modeling in systems defined by coupled andcomplex physics.With fundamentally different principles involved, the concept of optically-pumped rare gasmetastable lasing (RGL) presents a novel opportunity for scalable high-powered lasers by takingadvantage of similarities in the electronic structure of elements while traversing the periodic ta-ble. Building from the proven concept of diode-pumped alkali vapor lasers (DPAL), RGL systemsdemonstrate remarkably similar spectral characteristics without problems associated with heatedcaustic vapors. First introduced in 2012, numerical studies on the latent kinetics remain immature.This work couples an analytic model developed for DPAL with KGMf plasma chemistry to bet-ter understand the interaction of a non-equilibrium plasma with the induced laser processes anddetermine if optical pumping could be avoided through careful discharge selection.
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Title: Computational chemistry : investigations of protein-protein interactions and post-translational modifications to peptides
Creator: Jones, Michael R. (Graduate of Michigan State University)
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Computational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit...
Show moreComputational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit β (IKKβ) or NF-κB Inducing Kinase (NIK), initiates this cascade of events. Understanding the structure-property relationships associated with IKKβ and NIK is essential for the development of prevention strategies. Although the signaling pathways are known, how the molecular mechanisms respond to changes in the intracellular microenvironment (i.e., pH, ionic strength, temperature) remains elusive. In this dissertation, computer simulation and modeling techniques were used investigate two protein kinases complexed with either small molecule activators or inhibitors in the active, inactive, and mutant states to correlate structure-property and structure-function relationships as a function of intracellular ionic strength. Additionally, radical-induced protein fragmentation pathways, as a result of reactions with reactive oxygen species, were investigated to yield insight into the thermodynamic preference of the fragmentation mechanisms. Analyses of the relationship between structure-activity and conformational-activity indicate that the protein-protein interactions and the binding of small molecules are sensitive to changes in the ionic strength and that there are several factors that influence the selectivity of peptide backbone cleavage. As there are many computational approaches for predicting physical and chemical properties, several methods were considered for the predictions of protein-protein dissociation, protein backbone fragmentation, and partition coefficients of drug-like molecules.
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Title: Suicide, signals, and symbionts : evolving cooperation in agent-based systems
Creator: Vostinar, Anya E.
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Cooperation is ubiquitous in nature despite the constant pressure for organisms to cheat by receiving a benefit from cooperators, while not cooperating themselves. The continued evolution and persistence of countless forms of cooperation is a central topic in evolutionary theory. Extensive research has been done on the theoretical dynamics of cooperation through game theory and the natural examples of cooperation. However, it remains difficult to understand thoroughly the evolution of many...
Show moreCooperation is ubiquitous in nature despite the constant pressure for organisms to cheat by receiving a benefit from cooperators, while not cooperating themselves. The continued evolution and persistence of countless forms of cooperation is a central topic in evolutionary theory. Extensive research has been done on the theoretical dynamics of cooperation through game theory and the natural examples of cooperation. However, it remains difficult to understand thoroughly the evolution of many cooperative systems, due in part to the ancient origins of these systems and the long time scales required to see cooperation evolve in any natural populations. I have systematically analyzed the evolution of three broad types of cooperation: programmed cell death, quorum sensing, and mutualisms (cooperation across species). I have provided evidence that programmed cell death can originate due to kin selection. I have also created two new systems to enable the extensive exploration of factors that affect the evolution of public goods cooperation and mutualism. Using these systems, I determine the effects of environmental factors on the evolution of public goods cooperation and mutualism. By uniting the expansive theoretical work on these forms of cooperation with a fully-controlled experimental system, I contributed to our understanding of how these forms of cooperation can emerge and be maintained in industrial and medical applications that rely on bacterial cooperation.
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Title: Data-driven and task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment
Creator: Ashtawy, Hossam Mohamed Farg
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Molecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation ...
Show moreMolecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation (docking task), binary activity label (screening task), and binding affinity (scoring task) of ligands against a critical protein in the disease's pathway. In most molecular docking software packages available today, a generic binding affinity-based (BA-based) SF is invoked for the three tasks to solve three different, but related, prediction problems. The vast majority of these predictive models are knowledge-based, empirical, or force-field scoring functions. The fourth family of SFs that has gained popularity recently and showed potential of improved accuracy is based on machine-learning (ML) approaches. Despite intense efforts in developing conventional and current ML SFs, their limited predictive accuracies in these three tasks have been a major roadblock toward cost-effective drug discovery. Therefore, in this work we present (i) novel task- specific and multi-task SFs employing large ensembles of deep neural networks (NN) and other state-of-the-art ML algorithms in conjunction with (ii) data-driven multi-perspective descriptors (features) for accurate characterization of protein-ligand complexes (PLCs) extracted using our Descriptor Data Bank (DDB) platform.We assess the docking, screening, scoring, and ranking accuracies of the proposed task-specific SFs with DDB descriptors as well as several conventional approaches in the context of the 2007 and 2014 PDBbind benchmark that encompasses a diverse set of high-quality PLCs. Our approaches substantially outperform conventional SFs based on BA and single-perspective descriptors in all tests. In terms of scoring accuracy, we find that the ensemble NN SFs, BsN-Score and BgN-Score, have more than 34% better correlation (0.844 and 0.840 vs. 0.627) between predicted and measured BAs compared to that achieved by X-Score, a top performing conventional SF. We further find that ensemble NN models surpass SFs based on other state-of-the-art ML algorithms. Similar results have been obtained for the ranking task. Within clusters of PLCs with different ligands bound to the same target protein, we find that the best ensemble NN SF is able to rank the ligands correctly 64.6% of the time compared to 57.8% obtained by X-Score. A substantial improvement in the docking task has also been achieved by our proposed docking-specific SFs. We find that the docking NN SF, BsN-Dock, has a success rate of 95% in identifying poses that are within 2 Å RMSD from the native poses of 65 different protein families. This is in comparison to a success rate of only 82% achieved by the best conventional SF, ChemPLP, employed in the commercial docking software GOLD. As for the ability to distinguish active molecules from inactives, our screening-specific SFs showed excellent improvements over the conventional approaches. The proposed SF BsN-Screen achieved a screening enrichment factor of 33.90 as opposed to 19.54 obtained from the best conventional SF, GlideScore, employed in the docking software Glide. For all tasks, we observed that the proposed task-specific SFs benefit more than their conventional counterparts from increases in the number of descriptors and training PLCs. They also perform better on novel proteins that they were never trained on before. In addition to the three task-specific SFs, we propose a novel multi-task deep neural network (MT-Net) that is trained on data from three tasks to simultaneously predict binding poses, affinities, and activity labels. MT-Net is composed of shared hidden layers for the three tasks to learn common features, task-specific hidden layers for higher feature representation, and three outputs for the three tasks. We show that the performance of MT-Net is superior to conventional SFs and competitive with other ML approaches. Based on current results and potential improvements, we believe our proposed ideas will have a transformative impact on the accuracy and outcomes of molecular docking and virtual screening.
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