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Stealthy False Data Injection Attack Detection in Power Transmission System using Security Analytics
No full textThe electric smart grid, a critical national infrastructure and among the largest and most complex cyber-physical systems, is under constant and multifarious threat of cyber-attacks. State estimation (SE) is at the foundation of a series of critical control processes in a power transmission system. A sophisticated cyber-attacker can intelligently change the values in the measurement matrix used to compute state estimation. These data integrity attacks can potentially disrupt the critical control processes, adversely affecting a power system operationally and economically. Stealthy false data injection (SFDI) attacks against SE cannot be detected by the conventional bad-data detection mechanisms. In this dissertation, a security analytics framework to detect SFDI attacks on static SE measurement data is presented. A threat model that identified three possible attack models was developed, and synthetic datasets corresponding to these attack models were generated for standard IEEE 14-bus and 57-bus systems. After normalizing and reducing the number of features in the datasets, a number of supervised, unsupervised, and stacking ensemble machine learning models were trained and tested for model selection. Through the model selection process, including hyper-parameter tuning and cross-validation, trained models were identified that can detect the SFDI attacks accurately and reliably. Evaluation of the models using standard metrics shows that supervised artificial neural networks with four hidden layers and 1200 hidden units per layer can detect 98.24% of the attacks with a false alarm rate of 1.25%. Among the unsupervised models, elliptic envelope performs the best with 73% detection rate with 3% false alarm rate. It was also found that the detection rate is the same for all the machine learning methods for all the six datasets corresponding to different attack models and bus systems. The core contributions of this dissertation are the demonstration that a machine learning based security analytics framework can successfully detect the SFDIA attacks and the identification of artificial neural network with the right set of hyper-parameter values as the best performing model. Additional contributions include a survey and a taxonomy of false data injection attacks on different parts of the power grid for the first time in the literature, an exhaustive survey of machine learning based approaches for detecting SFDI attacks, implementation of a software for running the machine learning models, and identification of a number of research ideas based on this research.doctoral, Ph.D., Computer Science -- University of Idaho - College of Graduate Studies, 2020-1
A QUANTITATIVE STUDY OF STUDENT PERCEPTIONS OF THE COMMUNITY OF INQUIRY PRESENCES IN COMMUNITY COLLEGE ONLINE COURSES
No full textThis three-article dissertation studied the student perceptions of the Community of Inquiry’s cognitive presence, social presence and teaching presence in education and general education online community college courses. Students were queried over several semesters using the Community of Inquiry (CoI) instrument. Manuscript 1 evaluated CoI’s cognitive presence, social presence and teaching presence as self-reported by community college students in one instructor’s online education courses. The research focused on the associations between the three constructs. The results of a multiple linear regression analysis indicated the teaching presence and social presence explained 68% of the variability of the cognitive presence. Further investigation of the associations of each CoI presence with sub-constructs from the remaining two CoI presences indicated a pair of predictor variables for each presence. The cognitive presence sub-construct exploration and the teaching presence sub-construct design and organization showed a significant, strong positive correlation to the social presence. The teaching presence was significantly, positively associated with two cognitive presence sub-constructs resolution and exploration. The analysis also showed a significant association between the cognitive presence and the teaching presence sub-construct facilitation and the social presence sub-construct group cohesive. Using indicators for each CoI sub-construct from previous research, this study aligned pedagogical benchmarks to the predictor variables. In Manuscript 2, the CoI framework and instrument is used to investigate the commonalities and differences between perceived CoI presences experienced by community college students enrolled in an online education (major-specific) course as compared to students enrolled in an online freshmen orientation (general education) course. A Pearson product-moment correlation coefficient was calculated for each of the paired associations between CoI constructs within each group of students. The data used was an interval scale measure because it was an average of several values. Both groups showed significant correlations between the teaching presence and the cognitive presence, as well as the social presence and the cognitive presence. Multiple regression models indicated the teaching presence and social presence explained 68% of the variability of the cognitive presence in the major-specific group. For the general education group, the teaching and social presences explained 76% of the variability of the cognitive presence. Within each group, multiple linear regression was used to study associations between each CoI presence and sub-constructs from the remaining two CoI presences. The cognitive presence sub-construct exploration and the teaching presence sub-construct design and organization were significant predictors, explaining 61% of the variance of the social presence within the major-specific sample. Sixty-five percent of the variance of the social presence within the general education sample was explained by the cognitive presence sub-construct integration and the teaching presence sub-construct facilitation. The cognitive presence sub-constructs resolution and exploration were significant predictors, explaining 63% of the variance of the teaching presence for the major-specific group. The cognitive presence sub-construct integration and the social presence sub-construct group cohesion were significant predictors explaining 66% of the variance in the teaching presence for the general education group. Both teaching presence sub-construct facilitation and the social presence sub-construct group cohesive were significant predictors, explaining 73% of the variability of the cognitive presence in the major-specific group, and 75% of the variability of the cognitive presence in the general education group. Once each CoI sub-construct was aligned to previously established indicators, this study defined specific pedagogical benchmarks to each predictor variable to provide instructional suggestions specific to a major-specific or general education online course. The satisfaction construct was introduced in Manuscript 3, to better understand the relationship between the students’ perceived CoI presences and course satisfaction. Used in conjunction with the CoI instrument, online community college students enrolled in education (major-specific) or freshmen orientation (general education) online courses were surveyed to provide a stronger understanding of their online learning experience. A Pearson product-moment correlation coefficient was calculated for each of the paired associations between the CoI presences and the satisfaction construct. Analysis showed a significant, strong positive correlation between the teaching presence and the satisfaction construct. The cognitive presence showed a slightly less significant, strong positive correlation to the satisfaction construct. The social presence also displayed a significant, strong positive correlation to the satisfaction construct. A Pearson product-moment correlation coefficient was calculated for each of the paired associations between the CoI sub-presences and the satisfaction construct. For each pairwise comparison, a significant correlation was found. The two strongest associations occurred between the teaching presence’s sub-construct facilitation and satisfaction; and the cognitive presence’s sub-construct resolution and satisfaction. Multiple linear regression was used to further investigate the strengths of the associations between variables. The results indicated the teaching and social presences explained 70% of the variability of the satisfaction construct. When multiple linear regression was conducted using the satisfaction construct and CoI sub-constructs, two predictive variables were identified. In this model, the teaching presence sub-construct facilitation and the cognitive presence sub-construct resolution were significant predictors, explaining 70% of the variance of the satisfaction construct. A one-way ANOVA was calculated for each demographic item and the satisfaction construct. No significant differences were found between the demographic items and the satisfaction construct.doctoral, Ph.D., Curriculum & Instruction -- University of Idaho - College of Graduate Studies, 2020-0
How do rocks move in rivers? The grain-scale mechanisms that control the onset of motion
No full textThe onset of sediment motion in rivers is important for predictions of river stability, and for the design of hydraulic structures and river restoration projects. Considerable uncertainties in calculations and measurements of the onset of motion exist. Most calculations of the onset of sediment motion do not explicitly include turbulence effects but recent studies have suggested that impulse, the product of the duration and magnitude of drag forces that are greater than a critical value, is likely to cause grain movement. We explore if sediment motion can be systematically explained with instantaneous drag forces and impulses. In a series of 26 flume experiments, we measured instantaneous pressures and velocities on a mobile test grain for which the precise timing of motion was known. We used these measurements to calculate drag forces and impulses in a range of possible ways. Impulse and drag forces were concluded to cause particle motion in a given experiment if their highest measured values occurred during grain motion rather than during any time when the test grain was stable. Use of the measured upstream velocity profile instead of a single point velocity provided calculated drag forces and impulses that better corresponded to the onset of particle motion. The correlations of drag forces and impulses with particle motion were also greatly dependent on the selected drag coefficient, implying that field applications of impulse may need to consider the effects of grain shape and orientation instead of simply assuming spherical particles. Out of all the various drag force and impulse parameters we tested, an impulse that incorporated a decreasing resisting force during particle rotation out of its pocket explained the greatest percentage (88%) of observed grain motions. The start of grain rotation could not be explained by impulse for 12% and 17% of particle motions when we used velocity and pressure data, respectively, to calculate impulse. This suggests that either the onset of particle motion may be sometimes driven by another flow parameter, or that typically measured velocity and pressure data used to calculate impulse may not adequately capture the spatial variation in flow structure around a grain. A temporally variable drag coefficient could in theory indirectly account for some of these spatial and temporal variations in grain-scale flow. Use of a temporally variable drag coefficient did not improve the performance of impulse in explaining particle motion, implying that a more complex flow parameter that accounts for spatial flow patterns may sometimes be needed. Understanding how sediment fluxes in rivers are related to applied shear stresses is imperative for improving restoration efforts or minimizing loss of property though urbanized reaches. Bedload equations often predict inaccurate sediment fluxes partly because of uncertainty in the shear stresses that cause the start of sediment motion (critical Shields stresses). Although often assumed to be a constant value, the critical Shields stress can increase with greater channel slope, which has been potentially explained by a wide variety of processes. To fully understand this phenomenon, we conducted a series of flume experiments through a range of slopes in which we measured the critical Shields stress and near-bed flow velocity at the onset of motion of a mobile test grain with a fixed pocket geometry. We used two bed configurations, one with and one without large immobile grains, to explore the effects of large boulders on critical Shields stresses. Contrary to previous studies that have shown a general increase in critical Shields stress with greater channel slope or relative roughness, the critical Shields stress in our experiments only increased when (1) boulders were added, and (2) the boulder tops began to emerge from the flow. Otherwise, critical Shield stresses remained roughly constant with greater slope or relative roughness. We tested many of the previously hypothesized reasons for critical Shields stress increases with slope and found that none could fully explain our experimental observations. We hypothesize that in our data and in natural rives, many of the observed changes in critical Shields stress are caused by decreases in boulder submergence and increases in boulder concentration with higher slope. These changes in boulder properties drive previously unaccounted for complex variations in the flow structure that affect the onset of motion of finer, more mobile particles. The critical Shields stress can also vary between grain sizes, and can be predicted with hiding functions, which describe how grain mobility is affected by the underlying grain size distribution, such that the ratio of the grain size (Di) to the median bed grain size (D50) determines the critical Shields stress for the ith grain size. A patch is defined as an area of the bed that is occupied by grains of distinct size distribution, where its boundary is defined by a clear change in grain size distribution indicating the neighboring patch. The relative mobility of grains throughout a reach have been studied, but the effect of local variation of grain sizes between patches on grain relative mobility is largely unknown. We explore the effects of patch-scale grain size variability on the degree of sediment mobility by developing hiding functions for different patch types within the Erlenbach torrent (Brunni, Switzerland). To determine hiding functions for each patch type, we used: (i) the D84 of the mobile tracer grain size distribution from each patch type for 10 storm events (discharges of 0.17 to 2.1 cms), by monitoring the movement of painted and RFID tagged tracer grains from the most prevalent patch types, and (ii) the median local shear stress on each patch type for each discharge modeled using the quasi-3D FaSTMECH model. We also measured in-situ protrusions (vertical distance a grain extends relative to near-by grains upstream) and calculated friction angles (the angle a grain must rotate through for mobilization) for all grain sizes present on each patch type, which are local grain-scale parameters that can lead to the size-selective entrainment that hiding functions often describe. We observed protrusion to be greater for larger grains, but for the same grain size (Di) protrusion was higher on finer patches. However, all patches have about the same relation between relative grain size (Di/D50) and dimensionless values of protrusion (protrusion/grain height) and friction angle.doctoral, Ph.D., Water Resources -- University of Idaho - College of Graduate Studies, 2020-0
Using Molecular Modeling to Determine Protein Stabilities and Energies
No full textProteins are the molecular machines that perform the functions necessary for life. Interactions between proteins and other biomolecules are at the heart of all biological processes in a cell. This thesis explores how molecular modeling can be used to understand both proteins and their interactions. Examples include antibody-antigen interactions in Ebola, how proteins might behave in the subsurface oceans of Titan, and the ability of different software to accurately predict protein interactions. We predict mutations in Ebola that could lead to antibody escape. We explore aspects of possible life on exoplanets by modeling how Earth-based proteins would behave in the environment thought to exist in subsurface oceans on Titan. We analyze a suite of different software to find those that have better predictive capabilities, depending on the location and type of mutation. In short, we show that molecular modeling can be used to make predictions about protein behavior and interactions.doctoral, Ph.D., Physics -- University of Idaho - College of Graduate Studies, 2020-0
Clustered Autoencoder Imputation
No full textMany datasets have missing entries. Since downstream tasks often require full datasets with little noise, accurately imputing the missing data is quite valuable. Autoencoders have proven themselves as effective data imputers. However, while they exploit high order dependencies between the columns of a dataset, autoencoders typically treat each row independently. This produces two problems. First, imputation accuracy is suboptimal because not all of the data is used effectively. Second, downstream classification tasks suffer since rows belonging to different classes get treated the same. Presented in this thesis is CLAIM (CLustered Autoencoder IMputation), an algorithm that adapts existing autoencoder networks in a way that directly addresses these issues. CLAIM first separates rows into clusters based on similarity. Then, in the encoder, it applies different, loosely connected, learned linear transformations to each cluster. Results show that this method improves accuracy with typical autoencoder imputation strategies on large enough datasets. Also presented is a CLAIM-specific iterative clustering algorithm, which allows CLAIM to improve initial cluster assignments as needed.masters, M.S., Mathematics -- University of Idaho - College of Graduate Studies, 2020-0
ANALYZING THE SPATIALITY OF CHINA’S REGIONAL INEQUALITY IN A GEOGRAPHIC INFORMATION SYSTEM ENVIRONMENT
No full textRegional inequality is an essential topic in academic inquiry and policy making. With the rapid economic development after the reform and opening-up, the rising inequality in China has drawn considerable attention. While the studies on the spatiality of regional inequality have flourished and renewed the debate, the examination of space, scale, and locality over a long time period or at the finest level is relatively insufficient. Drawn upon a multi-scale and multi-mechanism framework, this dissertation aims to fill the gap by investigating regional inequality in China for the last sixty years and the patterns and mechanisms based on the county level data. The first empiric chapter focuses on the long-term pattern of regional inequality and how spatially heterogeneous development processes and policy shocks impact the convergence in China. The findings indicate that the launch of reforms and the entry of WTO have led to the unbalanced redistribution of wealth towards the coastal provinces. While a significant convergence is observed for the eastern and northeastern regions, a divergence trend exists in the central and western regions. The impacts of the transitional processes like globalization, decentralization, and marketization are the most evident in coastal China, and their effects decline or become insignificant in interior China. The second empiric chapter adopts the finest county level data and investigates how each spatial scale contributes to the pattern and affects the mechanisms of regional inequality. The results observe a plateauing pattern of inequality since the mid-2000s, to which the intra-provincial inequality and the inter-regional inequality contribute the most. The convergence trend as a whole could mask the tendencies of divergence in each region at multiple scales. The multilevel modeling of mechanisms at the intra-provincial and intra-prefectural level suggests that the role played by spatial scales could not be neglected. The effects of triple processes, i.e. globalization, decentralization, and marketization, may turn from positive to negative or vice versa when inequality at different scales is investigated. The third empiric chapter conducts an in-depth case study of regional inequality in Zhejiang province, a coastal province leading China’s economic growth and reforms. The analyses reveal the importance of local contexts and bottom-up forces in regional development. On the one hand, economic activities are more concentrated due to the Wenzhou model of development and the emergence of new clusters in southern Zhejiang. On the other hand, with the global financial crisis and economic slow-down, the Wenzhou-Taizhou cluster has been challenged by new economic spaces centered on the Hangzhou-Ningbo cluster. The case study suggests the limited efficacy of inequality-reducing policies and the persuasive effects of self-reinforcing agglomeration on economic polarization and income mobility. In summary, this dissertation comprehensively investigates the spatiality of regional inequality in China. It highlights the role of space, scale, and locality in patterns and mechanisms of regional inequality. Specifically, the spatial heterogeneous development processes, the effects of each spatial scale, and the local context and bottom-up forces are the keys to better understand regional inequality and to further make efficient policies towards balanced regional development.doctoral, Ph.D., Geography -- University of Idaho - College of Graduate Studies, 2020-0
Restoration Strategies for Propagation of Camassia quamash on the Weippe Prairie
No full textCamassia quamash (camas) is a plant that is well-known throughout its native habitat in the Pacific Northwest of the United States, despite the growing decline of its preferred habitat type across the region. This plant requires specific site conditions to ensure a successful growing season. Its habitats, often referred to as camas prairies, were important traditional harvest sites for many indigenous cultures. In the 19th century federal land policies removed many tribes and first nations from their ancestral homelands and transferred ownership of those lands to early Euro-American settlers. Ultimately, these land uses proved particularly destructive to wetland prairies, including camas prairies. The decline of wetland areas across North America has resulted in significant loss of a habitat type that provides valuable ecosystem functions, while also reducing and degrading culturally significant landscapes. Camas’ cultural and ecological significance make it an ideal species to focus on for wetland restoration projects. Weippe Prairie, a well-recognized traditional harvest area used by the Nez Perce people within the Palouse Bioregion, of north-central Idaho, provides an ideal site to both study and restore camas prairie habitat. This study identified site characteristics and evaluated different restoration techniques to aid in creating a restoration protocol that can be used to rehabilitate camas prairies across the Pacific Northwest.masters, M.S., Natural Resources -- University of Idaho - College of Graduate Studies, 2020-0
California’s Sustainable Groundwater Management Act: The Paradox of Local Control of a Precious Public Resource
No full textUnlike most states west of the 100th meridian, California has, until recently, never enacted a comprehensive set of regulations to govern consumptive use of groundwater resources, even though groundwater provides between 40 percent and 60 percent of the water used by residents, farmers, business, and municipalities in the state. That changed in 2014, when the California legislature passed the Sustainable Groundwater Management Act (SGMA) in response to one of the worst droughts in the state’s history. The years between 2012 and 2014 had been so dry that surface water deliveries to the major agricultural areas of the San Joaquin Valley were cut to almost zero, forcing farmers to pump groundwater at unprecedented rates to make up the shortfall. This, in turn, caused groundwater levels to drop and domestic wells to go dry. SGMA was enacted to reverse this trend and bring the state’s groundwater resources into sustainability. This thesis examines whether a key feature of SGMA – its focus on local control of groundwater management decisions – will frustrate the sustainability goals of the statute. By reviewing a representative sample of the Groundwater Sustainability Plans prepared in compliance with SGMA, the thesis analyzes how the local water agencies in the San Joaquin Valley differ in their approach to groundwater management when compared to local water agencies outside the San Joaquin Valley. This analysis indicates that much of the groundwater overdraft problem in California can be traced to a recent phenomenon where large farming interests in the San Joaquin Valley switched from annual row crops to permanent orchard crops, primarily almonds and pistachios. This change in crop mix has fundamentally altered water usage in the Valley, largely because almonds and pistachios require substantially more water than annual row crops. Almonds and pistachios, however, are highly profitable, and the farmers who switched to these crops show no interest in converting back to row crops just to save water or improve conditions within their respective subbasin. For this reason, the Groundwater Sustainability Plans prepared by water agencies in the San Joaquin Valley focus almost exclusively on new water supply projects and include few provisions that would address pumping behavior or crop mix. Outside the San Joaquin Valley, however, the water agencies seem more willing to embrace a wide array of actions to achieve sustainability, including pumping restrictions and land fallowing programs. Thus, SGMA appears to create a two-tiered system, one in which San Joaquin Valley farmers can continue to pump as before, while the rest of the overdrafted basins in the state engage in aggressive cutbacks. Without greater guidance and enforcement from the State Water Resources Board and the Department of Water Resources, this two-tiered system may cause SGMA to fail in its objective, which is to bring all overdrafted subbasins, including those in the San Joaquin Valley, into a sustainable condition.masters, M.S., Water Resources -- University of Idaho - College of Graduate Studies, 2020-0
Communities and Towns in North Latah County
No full textPotlatch Historical Society. Occasional Paper. Communities andTowns in North Latah County. Compiled by Gary E. Strong. The Society. 2020
Mechanical and Cellular Factors Regulating Tendon Development
No full textTendons are collagenous musculoskeletal tissues that transfer forces from muscle to bone to facilitate movement. Tendons are frequently injured, and the incidence of tendon injuries is increasing. They have limited intrinsic healing capacity, which results in poor healing and long-term loss of function. Additionally, there are few effective clinical treatments for restoring full function after a tendon injury. Tissue engineering and regenerative medicine strategies using mesenchymal stem cells (MSCs) have been explored as a new way to treat tendon injuries. However, a major challenge is a limited understanding of the factors that direct tendon formation and the differentiation of stem cells toward the tendon lineage (e.g., tenogenesis). A deeper understanding of the factors influencing stem cell fate and the ability to precisely guide their differentiation are needed before stem cells can be used therapeutically in regenerative treatments for tendon injuries. To address this gap in knowledge, the overall goal of this dissertation is to explore the mechanical and cellular factors involved in tendon formation and tenogenic stem cell differentiation. To address this goal, functional formation of neonatal tendons was explored during the onset of locomotor activity to determine impacts of mechanical loading on tendon development. Unique features that distinguish the functional development of distinct tendon types were identified and related to mechanical stimulation. To explore cellular factors, MSCs were treated with a developmentally inspired biochemical, transforming growth factor (TGF)2, to induce tenogenesis. Tenogenesis in MSCs was found to regulate specific cell-cell junction proteins as well as proceed through novel signaling pathways. Overall, results of the studies contribute to our understanding of tendon development, with the ultimate objective of informing and improving regenerative therapies to treat tendon injury and disease.doctoral, Ph.D., Biological & Agricultural Engineering -- University of Idaho - College of Graduate Studies, 2020-0