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Speech of speakers from the screen of Russian and regional television has violations of language norms, especially in stress, and does not always correspond to the standard and accentological norms. Analysis of modern television speech allows us to identify the types of violations of the norms of stress and provide a list of words with the correct stress for foreign students., Речь говорящих с экрана российского и областного телевидения имеет нарушения языковых норм, особенно в ударении, и не всегда соответствует эталону и акцентологическим нормам. Анализ современной телевизионной речи позволяет выделить типы нарушений норм ударения и представить список слов с правильным ударением для иностранных студентов.

Recently, the security of Internet of Things (IoT) has been an issue of great concern. Physical layer security methods can help IoT networks achieve information-theoretical secrecy. Nevertheless, utilizing physical security methods, such as artificial noise (AN) may cost extra power, which leads to low secure energy efficiency. In this paper, the hybrid precoding technique is employed to improve the secure energy efficiency of the IoT network. A secure energy efficiency optimization problem is formulated for the IoT network. Due to the non-convexity of the problem and the feasible domain, the problem is firstly transformed into a tractable suboptimal form. Then a secure hybrid precoding energy efficient (SEEHP) algorithm is proposed to tackle the problem. Numerical results indicate that the proposed SEEHP algorithm achieves higher secure energy efficiency compared with three existing physical layer security algorithms, especially when the number of transmit antennas is large.

Computational Sprinting has proven to be an effective way to boost the computing performance for bursty workloads, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to exploit the maximal potential of Computational Sprinting. However, the intermittent nature of renewable energy makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting. We present four sprinting strategies to address the challenge imposed by the intermittent and time-varying nature of renewable energy supply. We build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications. We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.

Modal parameter model acts as an analytical model of tire, because it can represent the entire tire characteristics, when applied to the tire modelling. However, without considering the effect of transfer characteristics of adjacent tread spring elements, the accuracy of modal parameter model may be limited in previous studies. Based on contact mechanics and the modal parameter model, a novel tread model is proposed considering tread transfer characteristics, and then a modified model using the modal parameters was obtained using the novel tread model. The effects of tire external characteristics were analyzed by modifying the modal parameter static vertical model. Finally, the experimental and calculated data were compared, the proposed tread model improved the accuracy of calculation. The results show that the tread transfer characteristics significantly affect contact length, i.e., by improving the predictive accuracy by 50 %, and the effect on improving the squat of tire can be ignored. The developed model may help to optimize tire modelling.

This paper presents a novel approach for improving the estimation accuracy of the road profile for a vehicle suspension system. To meet the requirements of road profile estimation for road management and reproduction of system excitation, previous studies can be divided into data-driven and model based approaches. These studies mainly focused on road profile estimation while seldom considering the uncertainty of parameters. However, uncertainty is unavoidable for various aspects of suspension system, e.g., varying sprung mass, damper and tire nonlinear performance. In this study, to improve the estimation accuracy for a varying sprung mass, a novel algorithm was derived based on the Minimum Model Error (MME) criterion and a Kalman Filter (KF). Since the MME criterion method utilizes the minimum value principle to solve the model error based on a model error function, the MME criterion can effectively deal with the estimation error. Then, the proposed algorithm was applied to a 2 degree-of-freedom (DOF) suspension system model under ISO Level-B, ISO Level-C and ISO Level-D road excitations. Simulation results and experimental data obtained using a quarter-vehicle test rig revealed that the proposed approach achieves higher road estimation accuracy compared to traditional KF methods.

Chunling Du,* Jinchang Lu,* Lei Zhou, Bo Wu, Feng Zhou, Liang Gu, Donghui Xu, Yingxin Sun Department of Respiratory Medicine, Qingpu Branch of Zhongshan Hospital, Fudan University, Shanghai, China *These authors contributed equally to this work Objective: To explore the effect of cigarette smoke (CS) on the development of squamous metaplasia in human airway epithelial cells and the role of MAPK- and FoxA2-signaling pathways in the process.Materials and methods: In an in vitro study, we treated the bronchial epithelial cell line BEAS2B with CS extract, followed by treatment with the ERK inhibitor U0126, the JNK inhibitor SP600125, or the p38 inhibitor SB203580. In vivo, we used a CS-induced rat model. After treatment with CS with or without MAPK inhibitors for 90 days, lung tissues were harvested. p-ERK, p-p38 and p-JNK protein levels in cells and lung tissue were measured using enzyme-linked immunosorbent assays, mRNA- and protein-expression profiles of FoxA2, E-cadherin, CD44, and ZO1 were measured using quantitative real-time polymerase chain reaction and Western blotting, respectively, and morphological changes in bronchial epithelial cells were observed using lung-tissue staining.Results: In both the in vitro and in vivo studies, phosphorylation of the ERK1/2, JNK, and p38 proteins was significantly increased (P<0.05) and mRNA and protein expression of E-cadherin and FoxA2 significantly decreased (P<0.05) compared with the control group. ERK, JNK, and p38 inhibitors reversed the CS-extract-induced changes in E-cadherin, CD44, and ZO1 mRNA and protein expression (P<0.05), decreased p-ERK, p-p38, and p-JNK protein levels in cells and lung tissue, suppressed bronchial epithelial hyperplasia and local squamous metaplasia, and decreased FoxA2 expression.Conclusion: MAPK and FoxA2 mediate CS-induced squamous metaplasia. MAPK inhibitors upregulate FoxA2, resulting in a reduction in the degree of squamous metaplasia. Keywords: MAPK, FoxA2, cigarette