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

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

Covalent Organic Framework Nanosheets Embedding Single Cobalt Sites for Photocatalytic Reduction of Carbon Dioxide

Covalent organic framework nanosheets (CONs), fabricated from twodimensional covalent organic frameworks (COFs), present a promising strategy for incorporating atomically distributed catalytic metal centers into well-defined pore structures with desirable chemical environments. Here, a series of CONs was synthesized by embedding single cobalt sites that were then evaluated for photocatalytic carbon dioxide reduction. A partially fluorinated, cobalt-loaded CON produced 10.1 μmol carbon monoxide with a selectivity of 76%, over 6 hours irradiation under visible light (TON = 28.1), and a high external quantum efficiency (EQE) of 6.6% under 420 nm irradiation in the presence of an iridium dye. The CONs appear to act as a semiconducting support, facilitating charge carrier transfer between the dye and the cobalt centers, and this results in a performance comparable with that of the state-of-the-art heterogeneous catalysts in the literature under similar conditions. The ultrathin CONs outperformed their bulk counterparts in all cases, suggesting a general strategy to enhance the photocatalytic activities of COF materials

Exploring atherosclerosis imaging with contrast-enhanced MRI using PEGylated ultrasmall iron oxide nanoparticles

Plaque rupture is a critical concern due to its potential for severe outcomes such as cerebral infarction and myocardial infarction, underscoring the urgency of noninvasive early diagnosis. Magnetic resonance imaging (MRI) has gained prominence in plaque imaging, leveraging its noninvasiveness, high spatial resolution, and lack of ionizing radiation. Ultrasmall iron oxides, when modified with polyethylene glycol, exhibit prolonged blood circulation and passive targeting toward plaque sites, rendering them conducive for MRI. In this study, we synthesized ultrasmall iron oxide nanoparticles of approximately 3 nm via high-temperature thermal decomposition. Subsequent surface modification facilitated the creation of a dual-modality magnetic resonance/fluorescence probe. Upon intravenous administration of the probes, MRI assessment of atherosclerotic plaques and diagnostic evaluation were conducted. The application of Flash-3D sequence imaging revealed vascular constriction at lesion sites, accompanied by a gradual signal amplification postprobe injection. T1-weighted imaging of the carotid artery unveiled a progressive signal ratio increase between plaques and controls within 72 h post-administration. Fluorescence imaging of isolated carotid arteries exhibited incremental lesion-to-control signal ratios. Additionally, T1 imaging of the aorta demonstrated an evolving signal enhancement over 48 h. Therefore, the ultrasmall iron oxide nanoparticles hold immense promise for early and noninvasive diagnosis of plaques, providing an avenue for dynamic evaluation over an extended time frame

Reconstructed covalent organic frameworks

Covalent organic frameworks (COFs) are distinguished from other organic polymers by their crystallinity1–3, but it remains challenging to obtain robust, highly crystalline COFs because the framework-forming reactions are poorly reversible4,5. More reversible chemistry can improve crystallinity6–9, but this typically yields COFs with poor physicochemical stability and limited application scope5. Here we report a general and scalable protocol to prepare robust, highly crystalline imine COFs, based on an unexpected framework reconstruction. In contrast to standard approaches in which monomers are initially randomly aligned, our method involves the pre-organization of monomers using a reversible and removable covalent tether, followed by confined polymerization. This reconstruction route produces reconstructed COFs with greatly enhanced crystallinity and much higher porosity by means of a simple vacuum-free synthetic procedure. The increased crystallinity in the reconstructed COFs improves charge carrier transport, leading to sacrificial photocatalytic hydrogen evolution rates of up to 27.98 mmol h−1 g−1. This nanoconfinement-assisted reconstruction strategy is a step towards programming function in organic materials through atomistic structural control

Linked read technology for assembling large complex and polyploid genomes

Background: Short read DNA sequencing technologies have revolutionized genome assembly by providing high accuracy and throughput data at low cost. But it remains challenging to assemble short read data, particularly for large, complex and polyploid genomes. The linked read strategy has the potential to enhance the value of short reads for genome assembly because all reads originating from a single long molecule of DNA share a common barcode. However, the majority of studies to date that have employed linked reads were focused on human haplotype phasing and genome assembly. Results: Here we describe a de novo maize B73 genome assembly generated via linked read technology which contains ~ 172,000 scaffolds with an N50 of 89 kb that cover 50% of the genome. Based on comparisons to the B73 reference genome, 91% of linked read contigs are accurately assembled. Because it was possible to identify errors with \u3e 76% accuracy using machine learning, it may be possible to identify and potentially correct systematic errors. Complex polyploids represent one of the last grand challenges in genome assembly. Linked read technology was able to successfully resolve the two subgenomes of the recent allopolyploid, proso millet (Panicum miliaceum). Our assembly covers ~ 83% of the 1 Gb genome and consists of 30,819 scaffolds with an N50 of 912 kb. Conclusions: Our analysis provides a framework for future de novo genome assemblies using linked reads, and we suggest computational strategies that if implemented have the potential to further improve linked read assemblies, particularly for repetitive genomes

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.</p