Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Manganese oxide-based catalysts for low-temperature selective catalytic reduction of NOx with NH3: A review

Abstract: Selective catalytic reduction (SCR) technology has been widely used for the removal of NOx from flue gas. However, it is still a challenge to develop novel low-temperature catalysts for SCR of NOx, especially at temperatures below 200 °C. This paper reviewed the recent progress on the Mn-based catalysts for low-temperature SCR de-NOx with NH3. Catalysts were divided into four categories, single MnOx, Mn-based multi-metal oxide, Mn-based multi-metal oxide with support, and Mn-based monolith catalyst. In the section of single MnOx, the effects of several factors, such as Mn oxidation state, crystallization state, specific surface area and morphology on catalytic activity were systematically reviewed. In the section of multi-metal oxide catalysts, the various roles played by the components of catalysts were intentionally summarized from four aspects, improving de-NOx efficiency, enhancing N2 selectivity, improving resistance to SO2 and H2O, extending operation temperature window, respectively. Moreover, the newly emerging morphology-dependent nanocatalysts were highlighted at the end of this section. In the introduction of supported metal oxide catalysts, the effects of supports were systematically analyzed according to their types, such as Al2O3, TiO2, carbon materials, etc. Considering the actual operation, Mn-based monolith catalysts were also introduced with regard to monolith supports, such as ceramics, metal wire mesh, etc. Subsequently, NH3-SCR mechanisms at low temperature, including E-R and L-H mechanisms, were discussed. At last, the perspective and the future direction of low-temperature SCR of NOx were proposed.

Reaction-Based Fluorescent Probes for the Detection and Imaging of Reactive Oxygen, Nitrogen, and Sulfur Species.

Abstract: This Account describes a range of strategies for the development of fluorescent probes for detecting reactive oxygen species (ROS), reactive nitrogen species (RNS), and reactive (redox-active) sulfur species (RSS). Many ROS/RNS have been implicated in pathological processes such as Alzheimer's disease, cancer, diabetes mellitus, cardiovascular disease, and aging, while many RSS play important roles in maintaining redox homeostasis, serving as antioxidants and acting as free radical scavengers. Fluorescence-based systems have emerged as one of the best ways to monitor the concentrations and locations of these often very short lived species. Because of the high levels of sensitivity and in particular their ability to be used for temporal and spatial sampling for in vivo imaging applications. As a direct result, there has been a huge surge in the development of fluorescent probes for sensitive and selective detection of ROS, RNS, and RSS within cellular environments. However, cellular environments are extremely complex, often with more than one species involved in a given biochemical process. As a result, there has been a rise in the development of dual-responsive fluorescent probes (AND-logic probes) that can monitor the presence of more than one species in a biological environment. Our aim with this Account is to introduce the fluorescent probes that we have developed for in vitro and in vivo measurement of ROS, RNS, and RSS. Fluorescence-based sensing mechanisms used in the construction of the probes include photoinduced electron transfer, intramolecular charge transfer, excited-state intramolecular proton transfer (ESIPT), and fluorescence resonance energy transfer. In particular, probes for hydrogen peroxide, hypochlorous acid, superoxide, peroxynitrite, glutathione, cysteine, homocysteine, and hydrogen sulfide are discussed. In addition, we describe the development of AND-logic-based systems capable of detecting two species, such as peroxynitrite and glutathione. One of the most interesting advances contained in this Account is our extension of indicator displacement assays (IDAs) to reaction-based indicator displacement assays (RIAs). In an IDA system, an indicator is allowed to bind reversibly to a receptor. Then a competitive analyte is introduced into the system, resulting in displacement of the indicator from the host, which in turn modulates the optical signal. With an RIA-based system, the indicator is cleaved from a preformed receptor-indicator complex rather than being displaced by the analyte. Nevertheless, without a doubt the most significant result contained in this Account is the use of an ESIPT-based probe for the simultaneous sensing of fibrous proteins/peptides AND environmental ROS/RNS.

Risk Prediction with Electronic Health Records: A Deep Learning Approach.

Abstract: The recent years have witnessed a surge of interests in data analytics with patient Electronic Health Records (EHR). Data-driven healthcare, which aims at effective utilization of big medical data, representing the collective learning in treating hundreds of millions of patients, to provide the best and most personalized care, is believed to be one of the most promising directions for transforming healthcare. EHR is one of the major carriers for make this data-driven healthcare revolution successful. There are many challenges on working directly with EHR, such as temporality, sparsity, noisiness, bias, etc. Thus effective feature extraction, or phenotyping from patient EHRs is a key step before any further applications. In this paper, we propose a deep learning approach for phenotyping from patient EHRs. We first represent the EHRs for every patient as a temporal matrix with time on one dimension and event on the other dimension. Then we build a fourlayer convolutional neural network model for extracting phenotypes and perform prediction. The first layer is composed of those EHR matrices. The second layer is a one-side convolution layer that can extract phenotypes from the first layer. The third layer is a max pooling layer introducing sparsity on the detected phenotypes, so that only those significant phenotypes will remain. The fourth layer is a fully connected softmax prediction layer. In order to incorporate the temporal smoothness of the patient EHR, we also investigated three different temporal fusion mechanisms in the model: early fusion, late fusion and slow fusion. Finally the proposed model is validated on a real world EHR data warehouse under the specific scenario of predictive modeling of chronic diseases.

On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures

Abstract: It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard bus systems, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data-injection attacks, we propose a protection-based defense and a detection-based defense, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.

Canopy closure estimates with GreenOrbs: sustainable sensing in the forest

Abstract: Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.