Topological Representation of Canonicity for Varieties of Modal Algebras

Thesis (Ph.D.) - Indiana University, Mathematics, 2010The main subject of this dissertation is to approach the question of countable canonicity of varieties of modal algebras from a topological and categorical point of view. The category of coalgebras of the Vietoris functor on the category of Stone spaces provides a class of frames we call sv-frames. We show that the semantic of this frames is equivalent to that of modal algebras so long as we are limited to certain valuations called sv-valuations. We show that the canonical frame of any normal modal logic which is directly constructed based on the logic is an sv-frame. We then define the notion of canonicity of a logic in terms of varieties and their dual classes. We will then prove that any morphism on the category of coalgebras of the Vietoris functor whose codomain is the canonical frame of the minimal normal modal logic are exactly the ones that are invoked by sv-valuations. We will then proceed to reformulate canonicity of a variety of modal algebras determined by a logic in terms of properties of the class of sv-frames that correspond to that logic. We define ultrafilter extension as an operator on the category of sv-frames, prove a coproduct preservation result followed by some equivalent forms of canonicity. Using Stone duality the notion of co-variety of sv-frames is defined. The notion of validity of a logic on a frame is presented in terms of ranges of theory maps whose domain is the given frame. Partial equivalent results on co-varieties of sv-frames are proved. We classify theory maps which are maps invoked by a valuation on a Kripke frame using the classification of sv-theory maps and properties of ultrafilter extension. A negative categorical result concerning the existence of an adjoint functor for ultrafilter extension is also proved

Quantifying future water resources availability and agricultural productivity in agro-urban river basins

Includes bibliographical references.2022 Fall.Climate change can have an adverse effect on agricultural productivity and water availability in semi-arid regions, as decreases in surface water availability can lead to groundwater depletion and resultant losses in crop yield due to reduced water for irrigation. Competition between urban and agricultural areas intensifies groundwater exploitation as surface water rights are sold to growing municipalities. These inter-relationships necessitate an integrated management approach for surface water, groundwater, and crop yield as a holistic system. This dissertation provides a novel integrated hydrologic modeling approach to quantify future water resources and agricultural productivity in agro-urban river basins, particularly in arid and semi-arid regions where surface water and groundwater are managed conjunctively to sustain urban areas and food production capacity. This is accomplished by i) developing an integrated hydrologic modeling code that accounts for groundwater and surface water processes and exchanges in large regional-scale managed river basins, and demonstrating its use and performance in the economically diverse South Platte River Basin (SPRB), a 72,000 km2 river basin located primarily in the state of Colorado, USA; ii) using the model to understand possible future impacts imposed by climate variation on water resources (surface water and groundwater) and agricultural productivity; and iii) quantifying the combination impacts of agriculture-to-urban water trading and climate change on groundwater resources within the basin. This dissertation presents an updated version of SWAT-MODFLOW that allows application to large agro-urban river basins in semi-arid regions. SWAT provides land surface hydrologic and crop yield modeling, whereas MODFLOW provides subsurface hydrologic modeling. Specific code changes include linkage between MODFLOW pumping cells and SWAT HRUs for groundwater irrigation and joint groundwater and surface water irrigation routines. This conjunctive use, basin-scale long-term water resources, and crop yield modeling tool can be used to assess future water and agricultural management for large river basins across the world. The updated modeling code is applied to the South Platte River Basin, with model results tested against streamflow, groundwater head, and crop yield throughout the basin. To assess the climate change impacts on water resources and agricultural productivity, the coupled SWAT-MODFLOW modeling code is forced with five different CMIP5 climate models downscaled by Multivariate Adaptive Constructed Analogs (MACA), each for two climate scenarios, RCP4.5, and RCP8.5, for 1980-2100. In all climate models and emission scenarios, an increase of 3 to 5 °C in annual average temperature is projected by the end of the 21st century, whereas variation in projected precipitation depends on topography and distance from the mountains. Based on the results of this study, the worst-case climate model in the basin is IPSL-CM5A-MR-8.5. Under this climate scenario, for a 1 °C increase in temperature and the 1.3% reduction in annual precipitation, the basin will experience an 8.5% decrease in stream discharge, 2-5% decline in groundwater storage, and 11% reduction in crop yield. In recent decades, there has been a growing realization that developing additional water supplies to address new demands is not feasible. Instead, managing existing water supplies through reallocations is necessary to tackle water scarcity and climate change. However, third-party effects associated to water transfers has limited the growing water market. This study also quantifies the combination impacts of agriculture-to-urban water trading (widely known as 'buy and dry') and climate change on groundwater availability in semi-arid river basins through the end of 21st century, as groundwater pumping increases to satisfy irrigation water lost to the urban sector. For this analysis, we use the hydrological modeling tool SWAT-MODFLOW, forced by projected water trading amounts and two downscaled GCM climate models, each for two emission scenarios, RCP4.5 and 8.5. According to the results of this study, agriculture-to-urban water trading imposes an additional basin-wide 2% reduction in groundwater storage, as compared to changes due to climate. However, groundwater storage changes for local subbasins can be up to 8% and 10% through the mid-century and end of the century, respectively

Formation of Gaseous Proteins via the Ion Evaporation Model (IEM) in Electrospray Mass Spectrometry.

The mechanisms whereby protein ions are released into the gas phase from charged droplets during electrospray ionization (ESI) continue to be controversial. Several pathways have been proposed. For native ESI the charged residue model (CRM) is favored; it entails the liberation of proteins via solvent evaporation to dryness. Unfolded proteins likely follow the chain ejection model (CEM), which involves the gradual expulsion of stretched-out chains from the droplet. According to the ion evaporation model (IEM) ions undergo electrostatically driven desorption from the droplet surface. The IEM is well supported for small precharged species such as Na+. However, it is unclear whether proteins can show IEM behavior as well. We examined this question using molecular dynamics (MD) simulations, mass spectrometry (MS), and ion mobility spectrometry (IMS) in positive ion mode. Ubiquitin was chosen as the model protein because of its structural stability which allows the protein charge in solution to be controlled via pH adjustment without changing the protein conformation. MD simulations on small ESI droplets (3 nm radius) showed CRM behavior regardless of the protein charge in solution. Surprisingly, many MD runs on larger droplets (5.5 nm radius) culminated in IEM ejection of ubiquitin, as long as the protein carried a sufficiently large positive solution charge. MD simulations predicted that nonspecific salt adducts are less prevalent for IEM-generated protein ions than for CRM products. This prediction was confirmed experimentally. Also, collision cross sections of MD structures were in good agreement with IMS data. Overall, this work reveals that the CRM, CEM, and IEM all represent viable pathways for generating gaseous protein ions during ESI. The IEM is favored for proteins that are tightly folded and highly charged in solution and for droplets in a suitable size regime

Atomistic Insights into the Formation of Nonspecific Protein Complexes during Electrospray Ionization.

Native electrospray ionization (ESI)-mass spectrometry (MS) is widely used for the detection and characterization of multi-protein complexes. A well-known problem with this approach is the possible occurrence of nonspecific protein clustering in the ESI plume. This effect can distort the results of binding affinity measurements, and it can even generate gas-phase complexes from proteins that are strictly monomeric in bulk solution. By combining experiments and molecular dynamics (MD) simulations, the current work for the first time provides detailed insights into the ESI clustering of proteins. Using ubiquitin as a model system, we demonstrate how the entrapment of more than one protein molecule in an ESI droplet can generate nonspecific clusters (e.g., dimers or trimers) via solvent evaporation to dryness. These events are in line with earlier proposals, according to which protein clustering is associated with the charged residue model (CRM). MD simulations on cytochrom

Adaptive Model Learning of Neural Networks with UUB Stability for Robot Dynamic Estimation

Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification. This paper suggests stable updating rules to drive neural networks inspiring from model reference adaptive paradigm. Network structure consists of three parallel self-driving neural networks which aim to estimate robot dynamic terms individually. Lyapunov candidate is selected to construct energy surface for a convex optimization framework. Learning rules are driven directly from Lyapunov functions to make the derivative negative. Finally, experimental results on 3-DOF Phantom Omni Haptic device demonstrate efficiency of the proposed method.Comment: 6 pages, 12 figure

A study on the knowledge, attitudes, and behaviors of pregnant women regarding HIV and routine rapid testing : an assessment in a high-risk marginal area

Acquired immunodeficiency syndrome (AIDS) is one of the main obstacles to communities' development. The disease mostly involves active and productive population groups. This study aimed to determine the knowledge, attitudes, and behaviors of pregnant women regarding HIV prevention and rapid HIV tests. Pregnant women who were referred to the local health centers and who were willing to participate in the study were interviewed. To collect data, a standard questionnaire was used among 200 pregnant women in eight local health centers of Kermanshah, Iran. The survey contained 50 questions on demographic characteristics and the knowledge, attitudes, and behaviors regarding HIV/AIDS prevention and rapid tests for pregnant women. Although the majority (82.5%) of the pregnant women knew that mother-to-child HIV transmission during pregnancy was possible, fewer than half (48.2%) of them knew that HIV can be transmitted from mother to child through breastfeeding. Only 22.5% of pregnant women knew that a Cesarean section for HIV-positive mothers is recommended. The mean attitudes of pregnant women toward HIV prevention and HIV rapid testing were 4.5 (SD = 0.4) and 4 (SD = 0.3), respectively. Of the women, 11.5% had participated in an HIV rapid test counseling class, and 25.5% had participated in HIV education and counseling classes. The low knowledge of mothers regarding HIV transmission highlights the need for education and counseling classes and campaigns to improve knowledge and behaviors related to HIV prevention, especially during pregnancy for women in marginal regions

Application of Artificial Neural Networks for Power Load Prediction in Critical Infrastructure: A Comparative Case Study

This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, shallow models with nearly identical numbers of trainable parameters are constructed and examined. The dataset, which includes 120,884 hourly electricity consumption records, is divided into three subsets (25%, 50%, and the entire dataset) to examine the effect of increasing training data. Additionally, the same models are trained and evaluated for univariable and multivariable data to evaluate the impact of including more features. The case study specifically focuses on predicting electricity consumption using load information from Norway. The results of this study confirm that LSTM models emerge as the best-performed model, surpassing other models as data volume and feature increase. Notably, for training datasets ranging from 2000 to 22,000 instances, GRU exhibits superior accuracy, while in the 22,000 to 42,000 range, LSTM and BiLSTM are the best. When the training dataset is within 42,000 to 360,000, LSTM and ConvLSTM prove to be good choices in terms of accuracy. Convolutional-based models exhibit superior performance in terms of computational efficiency. The convolutional 1D univariable model emerges as a standout choice for scenarios where training time is critical, sacrificing only 0.000105 in accuracy while a threefold improvement in training time is gained. For training datasets lower than 22,000, feature inclusion does not enhance any of the ANN model’s performance. In datasets exceeding 22,000 instances, ANN models display no consistent pattern regarding feature inclusion, though LSTM, Conv1D, Conv2D, ConvLSTM, and FCN tend to benefit. BiLSTM, GRU, and Transformer do not benefit from feature inclusion, regardless of the training dataset size. Moreover, Transformers exhibit inefficiency in time series forecasting due to their permutation-invariant self-attention mechanism, neglecting the crucial role of sequence order, as evidenced by their poor performance across all three datasets in this study. These results provide valuable insights into the capabilities of ANN models and their effective usage in the context of CI prediction tasks.publishedVersio

Hazards identification and risk assessment for UAV-assisted bridge inspections

Unmanned Aerial Vehicles (UAV) technology has found its way into several civilian applications in the last 20 years, predominantly due to lower cost and tangible scientific improvements. In its application to structural bridge inspection, UAVs provide two main functions. The first, being the most common, detect damage through visual sensors. The 2 D image data can be used to quickly establish a basic knowledge of the structure’s condition and is usually the first port of call. The second reconstructs 3D models to provide a permanent record of geometry for each bridge asset, which could be used for navigation and control purposes. However, there are various types of hazards and risks associated with the use of UAVs for bridge inspection, in particular, in a cold operating environment. In this study, a systematic methodology, which is an integration of hazard identification, expert judgment, and risk assessment for preliminary hazard analysis (PHA) in the UAV-assisted bridge inspection system is proposed. The proposed methodology is developed and exemplified via UAV-assisted inspection of Grimsøy bridge, a 71.3 m concrete bridge, located in the Viken county in eastern Norway.publishedVersio