Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Review on the graphene based optical fiber chemical and biological sensors

Abstract: Graphene as a novel material has laid a foundation for its applications in optical fiber sensors, due to its unique properties, especially the optical properties. On the other hand, optical fiber sensors have received world-wide attention due to their high sensitivity, small size, good anti-electromagnetism disturbance ability and other potential advantages. In this paper, the developments of graphene in the applications of optical fiber sensors were reviewed from four aspects. Firstly, the common preparation methods of graphene were introduced. Next, the optical properties of graphene have been concluded. And then, some typical optical fiber chemical and biological sensors based on graphene, such as temperature sensors, biological sensors and gas sensors, were reviewed. It was shown that graphene had a great potential in the optical fiber sensing technology. Furthermore, the deficiencies and challenges of the graphene in the applications of optical fiber sensors were analyzed. In a whole, the unique advantages of graphene have present their versatility and importance in the application fields of optical fiber sensors.

Porous spherical NiO@NiMoO4@PPy nanoarchitectures as advanced electrochemical pseudocapacitor materials

Abstract: In this work, a rational design and construction of porous spherical NiO@NiMoO4 wrapped with PPy was reported for the application of high-performance supercapacitor (SC). The results show that the NiMoO4 modification changes the morphology of NiO, and the hollow internal morphology combined with porous outer shell of NiO@NiMoO4 and NiO@NiMoO4@PPy hybrids shows an increased specific surface area (SSA), and then promotes the transfer of ions and electrons. The shell of NiMoO4 and PPy with high electronic conductivity decreases the charge-transfer reaction resistance of NiO, and then improves the electrochemical kinetics of NiO. At 20 A g−1, the initial capacitances of NiO, NiMoO4, NiO@NiMoO4 and NiO@NiMoO4@PPy are 456.0, 803.2, 764.4 and 941.6 F g−1, respectively. After 10,000 cycles, the corresponding capacitances are 346.8, 510.8, 641.2 and 904.8 F g−1, respectively. Especially, the initial capacitance of NiO@NiMoO4@PPy is 850.2 F g−1, and remains 655.2 F g−1 with a high retention of 77.1% at 30 A g−1 even after 30,000 cycles. The calculation result based on density function theory shows that the much stronger Mo-O bonds are crucial for stabilizing the NiO@NiMoO4 composite, resulting in a good cycling stability of these materials.

Differentially private histogram publication

Abstract: Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data, with strong privacy guarantees against adversaries with arbitrary background knowledge. Existing studies on differential privacy mostly focus on simple aggregations such as counts. This paper investigates the publication of DP-compliant histograms, which is an important analytical tool for showing the distribution of a random variable, e.g., hospital bill size for certain patients. Compared to simple aggregations whose results are purely numerical, a histogram query is inherently more complex, since it must also determine its structure, i.e., the ranges of the bins. As we demonstrate in the paper, a DP-compliant histogram with finer bins may actually lead to significantly lower accuracy than a coarser one, since the former requires stronger perturbations in order to satisfy DP. Moreover, the histogram structure itself may reveal sensitive information, which further complicates the problem. Motivated by this, we propose two novel mechanisms, namely NoiseFirst and StructureFirst, for computing DP-compliant histograms. Their main difference lies in the relative order of the noise injection and the histogram structure computation steps. NoiseFirst has the additional benefit that it can improve the accuracy of an already published DP-compliant histogram computed using a naive method. For each of proposed mechanisms, we design algorithms for computing the optimal histogram structure with two different objectives: minimizing the mean square error and the mean absolute error, respectively. Going one step further, we extend both mechanisms to answer arbitrary range queries. Extensive experiments, using several real datasets, confirm that our two proposals output highly accurate query answers and consistently outperform existing competitors.

Sub‐PCA modeling and on‐line monitoring strategy for batch processes

Abstract: Multivariate statistical methods such as principal component analysis (PCA) and partial least square (PLS) have been successfully used in modeling multivariable continuous processes (Kaspar and Ray, 1992; Kourti and MacGregor, 1995; Chen and McAvoy, 1998) Several extensions of the conventional PCA/PLS to batch processes have also been reported, among which multiway PCA (MPCA) model is the most widely used (Wold et al, 1987; Nomikos and MacGregor, 1994, 1995; Wise et al, 1999; Smilde, 2001) The MPCA model is ill-suited for multistage batch processes, as it takes the entire batch data as a single object, and it is difficult to reveal the changes of process correlation from stage to stage Considering that the multiplicity of the operation stage is an inherent nature of many batch processes, each stage has its own underlying characteristics and the process can exhibit significantly different behaviors over different operation stages; it is desirable to develop a stage-based model that can reflect the inherent process stage nature to improve the process understanding and monitoring efficiency Kosanovich et al (1994) and Dong and McAvoy (1995) developed two MPCA/nonlinear MPCA models, utilizing the two-stage nature of a jacketed exothermic batch chemical reactor Their results show that the two-stage models are more powerful than a single model Their stage models, however, inherit the common weakness of the MPCA model that the unavailable future data in an evolving batch should be estimated for on-line monitoring A new stage-based sub-PCA modeling method is proposed in this article for multistage batch processes, based on the recognition of the following: (1) a batch process may be divided into several “operation” stages reflecting its inherent process correlation nature; (2) despite that the process may be time varying, the correlation of its variables will be largely similar within the same “operation” stage Changes in the correlation may be used to indicate changes in the process “operation” stages We have placed a quotation mark around “operation” to indicate that the operation referred to in this article may not, and does not have to, have the exact correspondence to the physical operations of the process Based on the above recognition, a representative model can be built for each stage, using the conventional two-way PCA model This allows two-way PCA to be “directly” applied to a batch process after a proper stage division; a stage division algorithm is also developed in the article A three-tank process, as an experimental verification system, is finally introduced to illustrate the effectiveness of the proposed