Spatiotemporal graphical modeling for cyber-physical systems

Cyber-Physical Systems (CPSs) are combinations of physical processes and network computation. Modern CPSs such as smart buildings, power plants, transportation networks, and power-grids have shown tremendous potential for increased efficiency, robustness, and resilience. However, such modern CPSs encounter a large variety of physical faults and cyber anomalies, and in many cases are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among their sub-systems. To address these issues, this study proposes a graphical modeling framework to monitor and predict the performance of CPSs in a scalable and robust way. This thesis investigates on two critical CPS applications to evaluate the effectiveness of this proposed framework, namely (i) health monitoring of highway traffic sensors and (ii) building energy consumption prediction. In highway traffic sensor networks, accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the physical systems. Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision making is required. With the purpose of efficiently determining the traffic network status and identifying failed sensor(s), this study proposes a cost-effective spatiotemporal graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this work to formulate and analyze the proposed sensor health monitoring system. The historical time-series data from the networked traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, this study demonstrates that the proposed graphical modeling approach can: (i) extract spatiotemporal dependencies among the different sensors which lead to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network. In the building energy consumption prediction case, we consider the fact that energy performance of buildings is primarily affected by the heat exchange with the building outer skin and the surrounding environment. In addition, it is a common practice in building energy simulation (BES) to predict energy usage with a variable degree of accuracy. Therefore, to account for accurate building energy consumption, especially in urban environments with a lot of anthropogenic heat sources, it is necessary to consider the microclimate conditions around the building. These conditions are influenced by the immediate environment, such as surrounding buildings, hard surfaces, and trees. Moreover, deployment of sensors to monitor the microclimate information of a building can be quite challenging and therefore, not scalable. Instead of applying local weather data directly on building energy simulation (BES) tools, this work proposes a spatiotemporal pattern network (STPN) based machine learning framework to predict the microclimate information based on the local weather station, which leads to better energy consumption prediction in buildings

A Data-driven Approach towards Integration of Microclimate Conditions for Predicting Building Energy Performance

Energy consumption in buildings is a major part of the overall energy usage in the United States and across the world. Energy performance of buildings is primarily affected by the heat exchange with the building outer skin and the surrounding environment. Building energy simulation (BES) tools are capable of predicting energy usage with variable degree of accuracy using the building geometry, construction information and weather data. In this regard, it is a common practice in BES tools to include boundary conditions of the building shell based on the local weather station. However, to account for accurate building energy consumption, especially in urban environments with significant amount of anthropogenic heat source, it is necessary to consider the microclimate around the building. These conditions are influenced by the immediate environment such as surrounding buildings, hard surfaces and trees. However, deployment of sensors to monitor microclimate of a building can be quite expensive and hence, not scalable. Therefore, a model to predict the microclimate information based on local weather station is essential to provide a more reasonable outdoor weather information for the BES tools, and hence predicting energy consumption in buildings more accurately. In this work, we propose a scalable, computationally inexpensive data-driven approach for predicting microclimate information (e.g., temperature) under multiple weather conditions. We demonstrate that such a framework can be implemented based on machine learning techniques such as spatiotemporal pattern network (STPN) and neural networks (NNs). We demonstrate the efficacy of our proposed framework by using the predicted microclimate data to predict the building energy consumption with higher accuracy compared to the prediction using local weather station data alone

Integrating genotype and weather variables for soybean yield prediction using deep learning

Realized performance of complex traits is dependent on both genetic and environmental factors, which can be difficult to dissect due to the requirement for multiple replications of many genotypes in diverse environmental conditions. To mediate these problems, we present a machine learning framework in soybean (Glycine max (L.) Merr.) to analyze historical performance records from Uniform Soybean Tests (UST) in North America, with an aim to dissect and predict genotype response in multiple envrionments leveraging pedigree and genomic relatedness measures along with weekly weather parameters. The ML framework of Long Short Term Memory - Recurrent Neural Networks works by isolating key weather events and genetic interactions which affect yield, seed oil, seed protein and maturity enabling prediction of genotypic responses in unseen environments. This approach presents an exciting avenue for genotype x environment studies and enables prediction based systems. Our approaches can be applied in plant breeding programs with multi-environment and multi-genotype data, to identify superior genotypes through selection for commercial release as well as for determining ideal locations for efficient performance testing

Crop yield prediction integrating genotype and weather variables using deep learning

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)—Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.This article is published as Shook J, Gangopadhyay T, Wu L, Ganapathysubramanian B, Sarkar S, Singh AK (2021) Crop yield prediction integrating genotype and weather variables using deep learning. PLoS ONE 16(6): e0252402. https://doi.org/10.1371/journal.pone.0252402.Funding for this project was provided by Iowa Soybean Association (AKS), Monsanto Chair in Soybean Breeding (AKS), RF Baker Center for Plant Breeding (AKS), Plant Sciences Institute (SS, BG and AKS), USDA (SS, BG, AKS), NSF NRT (graduate fellowship to JS) and ISU’s Presidential Interdisciplinary Research Initiative (AKS, BG, 378 SS)

E2CFD\mathrm{E^{2}CFD}: Towards Effective and Efficient Cost Function Design for Safe Reinforcement Learning via Large Language Model

Different classes of safe reinforcement learning algorithms have shown satisfactory performance in various types of safety requirement scenarios. However, the existing methods mainly address one or several classes of specific safety requirement scenario problems and cannot be applied to arbitrary safety requirement scenarios. In addition, the optimization objectives of existing reinforcement learning algorithms are misaligned with the task requirements. Based on the need to address these issues, we propose $\mathrm{E^{2}CFD}$, an effective and efficient cost function design framework. $\mathrm{E^{2}CFD}$ leverages the capabilities of a large language model (LLM) to comprehend various safety scenarios and generate corresponding cost functions. It incorporates the \textit{fast performance evaluation (FPE)} method to facilitate rapid and iterative updates to the generated cost function. Through this iterative process, $\mathrm{E^{2}CFD}$ aims to obtain the most suitable cost function for policy training, tailored to the specific tasks within the safety scenario. Experiments have proven that the performance of policies trained using this framework is superior to traditional safe reinforcement learning algorithms and policies trained with carefully designed cost functions

Sulfone-containing covalent organic frameworks for photocatalytic hydrogen evolution from water

Nature uses organic molecules for light harvesting and photosynthesis, but most man-made water splitting catalysts are inorganic semiconductors. Organic photocatalysts, while attractive because of their synthetic tunability, tend to have low quantum efficiencies for water splitting. Here we present a crystalline covalent organic framework (COF) based on a benzo-bis(benzothiophene sulfone) moiety that shows a much higher activity for photochemical hydrogen evolution than its amorphous or semicrystalline counterparts. The COF is stable under long-term visible irradiation and shows steady photochemical hydrogen evolution with a sacrificial electron donor for at least 50 hours. We attribute the high quantum efficiency of fused-sulfone-COF to its crystallinity, its strong visible light absorption, and its wettable, hydrophilic 3.2 nm mesopores. These pores allow the framework to be dye-sensitized, leading to a further 61% enhancement in the hydrogen evolution rate up to 16.3 mmol g −1 h −1 . The COF also retained its photocatalytic activity when cast as a thin film onto a support

ARMC5 Controls the Degradation of Most Pol II Subunits, and ARMC5 Mutation Increases Neural Tube Defect Risks in Mice and Humans

BACKGROUND: Neural tube defects (NTDs) are caused by genetic and environmental factors. ARMC5 is part of a novel ubiquitin ligase specific for POLR2A, the largest subunit of RNA polymerase II (Pol II). RESULTS: We find that ARMC5 knockout mice have increased incidence of NTDs, such as spina bifida and exencephaly. Surprisingly, the absence of ARMC5 causes the accumulation of not only POLR2A but also most of the other 11 Pol II subunits, indicating that the degradation of the whole Pol II complex is compromised. The enlarged Pol II pool does not lead to generalized Pol II stalling or a generalized decrease in mRNA transcription. In neural progenitor cells, ARMC5 knockout only dysregulates 106 genes, some of which are known to be involved in neural tube development. FOLH1, critical in folate uptake and hence neural tube development, is downregulated in the knockout intestine. We also identify nine deleterious mutations in the ARMC5 gene in 511 patients with myelomeningocele, a severe form of spina bifida. These mutations impair the interaction between ARMC5 and Pol II and reduce Pol II ubiquitination. CONCLUSIONS: Mutations in ARMC5 increase the risk of NTDs in mice and humans. ARMC5 is part of an E3 controlling the degradation of all 12 subunits of Pol II under physiological conditions. The Pol II pool size might have effects on NTD pathogenesis, and some of the effects might be via the downregulation of FOLH1. Additional mechanistic work is needed to establish the causal effect of the findings on NTD pathogenesis

Biodegradable polymeric micelles coencapsulating paclitaxel and honokiol: a strategy for breast cancer therapy in vitro and in vivo

The combination of chemotherapy drugs attracts more attention in clinical cancer trials. However, the poor water solubility of chemotherapeutic drugs restricts their anticancer application. In order to improve antitumor efficiency and reduce side effects of free drugs, we prepared paclitaxel (PTX) and honokiol (HK) combination methoxy poly(ethylene glycol)–poly(caprolactone) micelles (P–H/M) by solid dispersion method against breast cancer. The particle size of P–H/M was 28.7±2.5 nm, and transmission electron microscope image confirmed that P–H/M were spherical in shape with small particle size. After being encapsulated in micelles, the release of PTX or HK showed a sustained behavior in vitro. In addition, both the cytotoxicity and the cellular uptake of P–H/M were increased in 4T1 cells, and P–H/M induced more apoptosis than PTX-loaded micelles or HK-loaded micelles, as analyzed by flow cytometry assay and Western blot. Furthermore, the antitumor effect of P–H/M was significantly improved compared with PTX-loaded micelles or HK-loaded micelles in vivo. P–H/M were more effective in inhibiting tumor proliferation, inducing tumor apoptosis, and decreasing the density of microvasculature. Moreover, bioimaging analysis showed that drug-loaded polymeric micelles could accumulate more in tumor tissues compared with the free drug. Our results suggested that P–H/M may have potential applications in breast cancer therapy