Real-time data operations and causal security analysis for edge-cloud-based Smart Grid infrastructure

The electric power grids are one of the fundamental infrastructures of modern society and are among the most complex networks ever made. Recent development in communications, sensing and measurement techniques has completely changed the traditional electric power grid and has brought us the intelligent electric power grid known as Smart Grid. As a critical cyber-physical system (CPS), Smart Grid is an integration of physical components, sensors, actuators, control centers, and communication networks. The key to orchestrate large scale Smart Grid is to provide situational awareness of the system. And situational awareness is based on large-scale, real-time, accurate collection and analysis of the monitoring and measurement data of the system. However, it is challenging to guarantee situational awareness of Smart Grid. On the one hand, connecting a growing number of heterogeneous programmable devices together introduces new security risks and increases the attack surface of the system. On the other hand, the tremendous amount of measurements from sensors spanning a large geographical area can result in a reduction of available bandwidth and increasing network latency. Both the lack of security protection and the delayed sensor data impede the situational awareness of the system and thus limit the ability to efficiently control and protect large scale Smart Grids in time-critical scenarios. To target the aforementioned challenge, in this thesis, I propose a series of frameworks to provide and guarantee situational awareness in Smart Grid. Taking an integrated approach of edge-cloud design, real-time data operations, and causal security analysis, the proposed frameworks enhance security protection by anomaly detection and managing as well as causal reasoning of alerts, and reduce traffic volume by online data compression. Extensive experiments by real or synthetic traffic demonstrate that the proposed frameworks achieve satisfactory performance and bear great potential practical value

Identification of a mutated BHK-21 cell line that became less susceptible to Japanese encephalitis virus infection

The pathogenesis of Japanese encephalitis virus (JEV) is not definitely elucidated as the initial interaction between virus and host cell receptors required for JEV infection is not clearly defined yet. Here, in order to discover those membrane proteins that may be involved in JEV attachment to or entry into virus permissive BHK-21 cells, a chemically mutated cell line (designated 3A10-3F) that became less susceptible to JEV infection was preliminarily established and selected by repeated low moi JEV challenges and RT-PCR detection for viral RNA E gene fragment. The susceptibility to JEV of 3A10-3F cells was significantly weakened compared with parental BHK-21 cells, verified by indirect immunofluorescence assay, virus plague formation assay, and flow cytometry. Finally, two-dimensional electrophoresis (2-DE) coupled with LC-MS/MS was utilized to recognize the most differentially expressed proteins from membrane protein extracts of 3A10-3F and BHK-21 cells respectively. The noted discrepancy of membrane proteins included calcium binding proteins (annexin A1, annexin A2), and voltage-dependent anion channels proteins (VDAC 1, VDAC 2), suggesting that these molecules may affect JEV attachment to and/or entry into BHK-21 cells and worthy of further investigation

Secure and Scalable Data Collection With Time Minimization in the Smart Grid

Deployment of data generation devices such as sensors and smart meters have been accelerating toward the vision of smart grid. The volume of data to be collected increases tremendously. Secure, efficient, and scalable data collection becomes a challenging task. In this paper, we present a secure and scalable data communications protocol for smart grid data collection. Under a hierarchical architecture, relay nodes [also known as data collectors (DCs)] collect and convey the data securely from measurement devices to the power operator. While the DCs can verify the integrity, they are not given access to the content, which may pave the way for third party providers to deliver value-added services or even the data collection itself. We further present optimization solutions for minimizing the total data collection time.This work was supported by the Department of Energy under Award DE-OE0000097Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111642/3/Uludag2015Secure_ToSG.pd

Does Japanese encephalitis virus share the same cellular receptor with other mosquito-borne flaviviruses on the C6/36 mosquito cells?

Japanese encephalitis virus (JEV) is a member of mosquito-borne Flaviviridae. To date, the mechanisms of the early events of JEV infection remain poorly understood, and the cellular receptors are unidentified. There are evidences that the structure of the virus attachment proteins (VAP), envelope glycoprotein of mosquito-borne flaviviruses is very similar, and the vector-virus interaction of mosquito-borne flaviviruses is also very similar. Based on the studies previously demonstrated that the similar molecules present on the mosquito cells involved in the uptake process of JEV, West Nile virus (WNV) and Dengue virus (DV), it is proposed that the same receptor molecules for mosquito-borne flaviviruses (JEV, WNV and DV) may present on the surface of C6/36 mosquito cells. By co-immunoprecipitation assay, we investigated a 74-KDa protein on the C6/36 cells binds JEV, and the mass spectrometry results indicated it may be heat shock cognate protein 70(HSC70) from Aedes aegypti. Based upon some other viruses use of heat shock protein 70 (HSP70) family proteins as cell receptors, its possible HSC70's involvement in the fusion of the JEV E protein with the C6/36 cells membrane, and known form of cation channels in the interaction of HSC70 with the lipid bilayer, it will further be proposed that HSC70 as a penetration receptor mediates JEV entry into C6/36 cells

Causal reasoning about attacks in SCADA networks

The Supervisory Control and Data Acquisition (SCADA) system is the most commonly used industrial control system but is subject to a wide range of serious threats. Intrusion detection systems are deployed to promote the security of SCADA systems, but they continuously generate tremendous number of alerts without further comprehending them. There is a need for an efficient system to correlate alerts and discover attack strategies to provide explainable situational awareness to SCADA operators. In this paper, we present a causal-polytree-based anomaly reasoning framework for SCADA networks, named CAPTAR. CAPTAR takes the meta-alerts from our previous anomaly detection framework EDMAND, correlates the them using a naive Bayes classifier, and matches them to predefined causal polytrees. Utilizing Bayesian inference on the causal polytrees, CAPTAR can produces a high-level view of the security state of the protected SCADA network. Experiments on a prototype of CAPTAR proves its anomaly reasoning ability and its capabilities of satisfying the real-time reasoning requirement.DE-OE0000780Ope

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions. The existing methods either have difficulties in balancing the tasks of image enhancement and object detection, or often ignore the latent information beneficial for detection. To alleviate this problem, we propose a novel Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively enhanced for better detection performance. Specifically, a differentiable image processing (DIP) module is presented to take into account the adverse weather conditions for YOLO detector, whose parameters are predicted by a small convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP to enhance the image for detection in a weakly supervised manner. Our proposed IA-YOLO approach can adaptively process images in both normal and adverse weather conditions. The experimental results are very encouraging, demonstrating the effectiveness of our proposed IA-YOLO method in both foggy and low-light scenarios.Comment: AAAI 2022, Preprint version with Appendi

PO-209 Effects of aerobic exercise on oxidant/anti-oxidant indexes and gut microbiota in young obese volunteers

  Objective The mean body mass index (BMI) and the prevalence of obese and overweight individuals increasing substantially worldwide during the previous three decades. Variation in gut microorganisms might play an important role in the pathogenesis of obesity, but the mechanisms by which gut microbiota promote metabolic disturbances are not well understood. Exercise is associated with altered gut microbial composition, but few studies have investigated whether the gut microbiota and associated metabolites are modulated by exercise training in humans. We explored the impact of 8 weeks aerobic exercise on serum oxidant and anti-oxidant indexes and gut microbiota. Methods All 40 young male volunteers are enrolled in the study, the lean ones (n=11), which BMI≤22 are as control group. And the obese ones (n=29), which BMI > 28 participated in the 8 weeks aerobic exercise. The body weight and BMI of each volunteers were recorded. The serum malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GPx) and total antioxidant capacity (TAOC) were measured by ELISA. The composition and diversity of gut microbiota were analyzed with 16S rDNA sequencing. Results ① Compared with the control, the level of serum GPx, SOD and TAOC decreased significantly (P < 0.001), and the MDA increased significantly (P < 0.001) in the obese group. After the 8 weeks aerobic exercise intervention, the MDA level decreased significantly (P＜0.01), the TAOC level increased significantly (P < 0.01), and there were no significant changes in the level of GPX and SOD. ② Compared with the control, the ratio of Bifidobacteriaceae, Alcaligenaceae, Erysipelotrichaceae, and Verrucomicrobiaceae decreased significantly(p＜0.05) in the obese group, and the ratio of Ruminococcaceae, Helicobacteraceae increased significantly (P＜0.05). After the 8 weeks aerobic exercise intervention, the ratio of Bifidobacteriaceae, Alcaligenaceae increased significantly (P＜0.05)，and the ratio of Ruminococcaceae reduced remarkably (p＜0.05). The ACE index was significantly increased after the intervention (P < 0.05). ③ Through the correlation analysis of the data above, there was a certain correlation between the serum oxidant/anti-oxidant indexes and gut microbiota composition. After the aerobic exercise, there was a positive correlation between MDA and Ruminococcaceae、TAOC and Bifdobacteriace (P＜0.05)；a negative correlation between MDA and Bifdobacteriace、TAOC and Ruminococcaceae (P＜0.05). After 8 weeks of aerobic exercise, MDA level was negatively correlated with ACE index (r=-0.466，P＜0.05). Conclusions ① Compared with the control, there was a significant increase of serum oxidation index and the serum anti-oxidation index dropped significantly among the obese subjects, which indicated that the accumulation of serum free radicals might be one of the causes of obesity. The 8 weeks aerobic exercise intervention can enhance the antioxidant capacity effectively. ② Compared with the control, the proportion of Bifidobacteriaceae in the obese group was significantly decreased, and the proportion of the bacteria with direct correlation to obesity, such as Ruminococcaceae increased significantly. 8 weeks aerobic exercise could inverse these changes among the obese subjects, suggested that the improvement of body phenotype of obese subjects was closely related to the effective regulation of their gut microbiota structure.The change of ACE index indicated that aerobic exercise could increase the diversity of gut microbiota. ③ Combined the correlation analysis of gut microbiota diversity index showed that there were tight correlation between the serum oxidant/anti-oxidant indexes and the gut microbiota composition and structure. It might caused metabolic disorders and eventually increased fat accumulation and changed the host body phenotype.     &nbsp

Transformation of animal utilization strategies from the late Neolithic to the Han Dynasty in the Hexi Corridor, northwest China: Zooarchaeological and stable isotopic evidence

The trajectory and influencing factors for changes to ancient human livelihoods in the Hexi Corridor of northwest China have been intensively discussed. The Hexi Corridor is a key crossroads for trans-Eurasian exchange in both the prehistoric and historical periods. Although most studies have focused on the reconstruction of human paleodiet and plant subsistence, the diachronic change of animal utilization strategies spanning the prehistoric and historical periods remains unclear, due to the absence of zooarchaeological and isotopic studies, especially in Han Dynasty (202 BCE–220 CE). Here we report new zooarchaeological, stable isotope, and radiocarbon dating data from the Heishuiguo Cemetery of the Han Dynasty in the Hexi Corridor, indicating that humans mainly used domestic chickens, pigs and sheep as funerary objects, with other buried livestock including cattle, horses and dogs. Stable carbon and nitrogen isotope data suggest humans might have fed chickens, pigs and dogs more C4 foods (likely millets or their byproducts) than herbivorous livestock in the Heishuiguo during the Han Dynasty. Compared to other prehistoric zooarchaeological and isotopic studies in the Hexi Corridor, we detected an increasing significance of herbivorous livestock in animal utilization strategies compared with omnivorous livestock, and a basic declining weight of C4 foods in fodders from ∼2,300 to 200 BCE, which was probably induced by long-distance exchange and climate fluctuation. However, the trend was reversed during the Han Dynasty in the Hexi Corridor, primarily due to the control of the area by the Han Empire and the subsequent massive immigration from the Yellow River valley of north China

A geostationary orbit microwave multi-channel radiometer

The geostationary orbit microwave multi-channel radiometer has the advantages of high real-time performance and large coverage, which plays an important role in typhoon, strong precipitation detection, and medium-to-short-term meteorological/oceanic forecasting. However, due to the difficulty in engineering development of the payload, its application on-orbit has not yet been achieved at present. To satisfy the requirements of fine and quantitative application of satellite observation data, a geostationary orbit microwave multi-channel radiometer with a 10-m-caliber is developed, in which the spatial resolution at horizontal polarization is better than 24 km at 54 GHz. In geostationary orbit microwave multi-channel radiometer, a quasi-optical feed network covering nearly 28 frequency octave bands and ranging from 23.8 to 664 GHz is proposed to solve the technical problem of multi-frequency sharing in the system. Meanwhile, a high-precision reflector preparation method and a high-precision unfolding scheme are proposed, which are considered as a solution for the large-diameter reflector with a high maintaining surface accuracy. A high-precision antenna prototype with 0.54-m is developed, and the tests are performed to verify the key technologies, such as the preparation of high-precision grating reflectors at the micron level, high surface accuracy detection, and sub-millimeter wave antenna electrical performance testing. The results indicate that measured main beam efficiency of the 664 GHz antenna is better than 95.5%. In addition, the system sensitivity is greater than 1.5 K, and the calibration accuracy is better than 1.8 K, according to the results of an analysis of the multi-channel radiometer’s essential parameters and calibration errors