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: Control theory & Microstructure. 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).

Thin Flexible Pressure Sensor Array Based on Carbon Black/Silicone Rubber Nanocomposite

Abstract: In this paper, the key technologies for the development of the thin flexible pressure sensor array based on carbon black/silicone rubber nanocomposite are reported. The piezoresistive mechanism of the nanocomposite is explained by analyzing the changes in effective conductive paths. The technical data of the sensor system are given. With the measurement range of 0-1 MPa, the maximum measurement deviation is less than 30 kPa.

A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy.

Abstract: Purpose: We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV EGFR-mutated non–small cell lung cancer (NSCLC) patients. Experimental Design: A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV EGFR-mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV EGFR-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts. Results: The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid and slow progression to EGFR-TKI therapy in three cohorts (HR: 3.61, 3.77, and 3.67, respectively). Rapid progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit (P = 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model (P Conclusions: The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pretherapy personalized management of TKIs. Clin Cancer Res; 24(15); 3583–92. ©2018 AACR.

Secure State Estimation Against Sparse Sensor Attacks With Adaptive Switching Mechanism

Abstract: This paper investigates the problem of secure state estimation for cyber-physical systems modeled by continuous or discrete-time linear systems when some sensors are corrupted by an attacker. A novel state observer is proposed with adaptive switching mechanism. Attack tolerance principle is established based on adaptively truncating the injection channels of attacks. To implement it, a switching function matrix is introduced into the observer design. Driven by a well-defined performance index, the switching function matrix automatically reaches and remains in the desired entry mode and turns off the input channels of attacks. Based on the equivalence between $s$ -strong detectability of the observation error system and $2s$ -sparse detectability of the original system, the observation error system is proven to be asymptotically stable even under the cyber attacks. Compared with the existing complex static batch optimization algorithms, the proposed adaptive observer can be derived only by offline solving a set of simple linear matrix inequalities. Simulation examples are given to illustrate the estimation performance and the computational efficiency of the proposed method.