纳米成型技术中的锚栓效应

纳米成型技术(NMT)是一种将聚合物直接注射于预先经纳米化处理的金属表面,使2种材料牢固结合的新技术。使用NMT制备的聚合物/金属一体化制品质量轻、界面强度高、结构稳定性好,近年来被广泛应用于智能手机、便携式电子器件等领域。文中系统介绍了NMT中金属表面纳米化处理方法、可用于NMT的金属和聚合物材料的种类及特点,以及注射成型加工中各项工艺参数的选择。结合受限态聚合物的研究成果,重点阐述了造成NMT制品高界面结合强度的锚栓效应的形成机理及失效机制。从NMT原理出发,在总结现有纳米成型技术相关成果的基础上,预测了NMT技术的未来发展趋势。

Risk factors of hyperprolactinemia induced by risperidone and olanzapine and their correlations with plasma glucose and lipids

Background Hyperprolactinemia is a common adverse reaction in patients with schizophrenia who take antipsychotic drugs;it often leads to treatment noncompliance in patients and has an adverse effect on their prognosis.Aims This study aimed to explore the risk factors of elevated prolactin(PRL)caused by risperidone(RIS)and olanzapine(OLZ)and the relationship between PRL and fasting plasma glucose and lipids.Methods Patients with schizophrenia were divided into two groups:264 patients who were taking RIS and 175 patients who were taking OLZ.These two groups were further divided according to serum PRL levels:an elevated PRL group(>30ng/mL)and a normal PRL group(PRL<30ng/mL).The demographics,medication dosage,fasting plasma glucose,total cholesterol and triglycerides were compared in the two groups.Logistic regression analysis was performed to explore the risk factors of elevated PRL levels.Results Compared with the OLZ group,the RIS group had a greater number of patients with elevated PRL(155/264 vs 58/175).Either the RIS or the OLZ group,the proportion of elevated PRL was greater in female patients(RIS:x^2=6.76,p=0.009;OLZ:x^2=12.98,p<0.001)and with higher doses of the related drugs(RIS:U=-3.73,p<0.001;OLZ:U=-2.31,p=0.021).In patients taking RIS,the elevated PRL subgroup took the drug for a longer period(U=-2.76,p=0.006)and had lower triglyceride levels(U=2.76,p=0.006).In patients taking OLZ,the elevated PRL subgroup had lower fasting plasma glucose levels(U=2.29,p=0.022).Logistic regression analysis showed that gender,dose and fasting glucose levels were significantly associated with elevated PRL levels(RIS:p=0.001,OLZ:p<0.001;RIS:p<0.001;OLZ:p=0.003;RIS:p=0.020,OLZ:p=0.001,respectively).Conclusion Compared with OLZ,RIS had a greater effect on PRL in patients with schizophrenia,and in patients with schizophrenia taking RIS or OLZ,gender and dose were significantly correlated with the PRL value.Moreover,the plasma glucose level of the group with elevated PRL was lower than that of the group with normal PRL.The results also showed that high serum PRL may be associated with a favourable glucose metabolic profile in patients with schizophrenia taking RIS or OLZ.Further studies are warranted to confirm this association.

Seepage-stress coupled modeling for rainfall induced loess landslide

Although rainfall is rare on the Loess Plateau of western China, landslides occur frequently there in rainy season. Surveys report that landslide hazards always follow heavy rains. In this study, a seepage-stress coupling model for rainfall induced landslide is used to examine an actual disastrous event in Yulin by the end of July, 2017. The effects of rainfall duration, rainfall intensity and soil weakening on slope stability are studied in detail. The results illustrate that the safety factor drops sharply at first and then is gradually declining to below 1.05 during additional two days of heavy rain. With soil strength softening considered, the slope would be more unstable, in which the weakening in soil cohesion is found to be a more sensitive factor.

An Early Warning System for Curved Road Based on OV7670 Image Acquisition and STM32

Nowadays,the number of vehicles in China has increased significantly.The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need.However,the existing early warning devices such as geomagnetic,ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance.In addition,geomagnetic detection will damage the road surface,while ultrasonic and infrared detection will be greatly affected by the environment.Considering the shortcomings of the existing solutions,this paper puts forward a solution of early warning for vehicle turning meeting based on image acquisition and microcontrollers.This solution combines image acquisition and processing technology,which uses image sensor to perceive traffic condition and image data analysis algorithm to process perceived image,and then utilize LED display screen to issue an early warning.

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have emerged.Compared to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single image.In addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual performance.In this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics.Then,we review the improved network structures and loss functions of GAN-based perceptual SISR.Subsequently,the advantages and disadvantages of different networks are analyzed by multiple comparative experiments.Finally,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.

Improving Language Translation Using the Hidden Markov Model

Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.

A Fair Blind Signature Scheme to Revoke Malicious Vehicles in VANETs

With the rapid development of IoT(Internet of Things),VANETs(Vehicular Ad-Hoc Networks)have become an attractive ad-hoc network that brings convenience into people’s lives.Vehicles can be informed of the position,direction,speed and other real-time information of nearby vehicles to avoid traffic jams and accidents.However,VANET environments could be dangerous in the absence of security protection.Because of the openness and self-organization of VANETs,there are plenty of malicious pathways.To guarantee vehicle security,the research aims to provide an effective VANET security mechanism that can track malicious vehicles as necessary.Therefore,this work focuses on malicious vehicles and proposes an anonymous authentication scheme in VANETs based on the fair blind signature to protect vehicle security.

Study on the targets and mechanisms of traditional Chinese medicine in inhibiting platelet aggregation

Platelets aggregation and thrombosis formation are major reasons of cardiovascular and cerebral vascular diseases.To develop novel,effective and safe agents for inhibiting platelet aggregation and preventing above diseases is urgently needed.Some traditional Chinese medicines for“Houxue Huayu”have been shown to inhibit platelet aggregation potently.They are used frequently in China and also in Asian countries.Recently,the major effective components of Pueraria lobata,Salvia miltiorrhiza Bunge,Apium graveolens L.and so on were studied.The mechanisms and the molecular targets of puerarin,salvianolic acid B and the analogue of 3-n-butylphthalide,dl-PHPB were investigated.Four platelet aggregation inducers,ADP,arachidonic acid(AA),collagen(Col)and thrombin,were used in the study.

On the degradation of granular materials due to internal erosion

A new state-based elasto-plastic constitutive relationship along with the discrete element model is established to estimate the degradation of granular materials due to internal erosion.Four essential effects of internal erosion such as the force network damage and relaxation are proposed and then incorporated into the constitutive relationship to formulate internal erosion impacts on the mechanical behavior of granular materials.Most manifestations in the degradation of granular materials,such as reduction of peak strength and dilatancy are predicted by the modified constitutive relationship in good agreement with the discrete element method（DEM）simulation.In particular,the sudden reduction of stress for conspicuous mass erosion in a high stress state is captured by force network damage and the relaxation mechanism.It is concluded that the new modified constitutive relationship is a potential theory to describe the degradation of granular materials due to internal erosion and would be very useful,for instance,in the prediction and assessment of piping disaster risk during the flood season.

Sensitive Information Protection Model Based on Bayesian Game

A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of service.We quantified the sensitive level of information according to the user’s personalized sensitive information protection needs.Based on the probability distribution of sensitive level and attacker’s knowledge background type,the strategy combination of service provider and attacker was analyzed,and a game-based sensitive information protection model was constructed.Through the combination of strategies under Bayesian equilibrium,the information entropy was used to measure the leakage of sensitive information.Furthermore,in the paper the influence of the sensitive level of information and the attacker’s knowledge background on the strategy of both sides of the game was considered comprehensively.Further on,the leakage of the user’s sensitive information was measured.Finally,the feasibility of the model was described by experiments.

Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes,and help people live well in mosquito-infested areas.In this study,we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things.In our method,decision-making is controlled by a deep learning model,and the proposed method uses infrared sensors and an array of pressure sensors to collect data.Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model,determining automatically the intention of the user to open or close the mosquito net.We used optical flow to extract pressure map features,and they were fed to a 3-dimensional convolutional neural network(3D-CNN)classification model subsequently.We achieved the expected results using a nested cross-validation method to evaluate our model.Deep learning has better adaptability than the traditional methods and also has better anti-interference by the different bodies of users.This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

Effect of the post-gate annealing on the gate reliability of AlGaN/GaN HEMTs

In this paper,we investigated the effect of post-gate annealing(PGA)on reverse gate leakage and the reverse bias reliability of Al_(0.23)Ga_(0.77)N/GaN high electron mobility transistors(HEMTs).We found that the Poole-Frenkel(PF)emission is dominant in the reverse gate leakage current at the low reverse bias region(V_(th)<V_(G)<0 V)for the unannealed and annealed HEMTs.The emission barrier height of HEMT is increased from 0.139 to 0.256 eV after the PGA process,which results in a reduction of the reverse leakage current by more than one order.Besides,the reverse step stress was conducted to study the gate reliability of both HEMTs.After the stress,the unannealed HEMT shows a higher reverse leakage current due to the permanent damage of the Schottky gate.In contrast,the annealed HEMT shows a little change in reverse leakage current.This indicates that the PGA can reduce the reverse gate leakage and improve the gate reliability.

Fabrication and characterization of AlGaN/GaN HEMTs with high power gain and efficiency at 8 GHz

State-of-the-art AlGaN/GaN high electron mobility structures were grown on semi-insulating 4H-SiC substrates by MOCVD and X-band microwave power high electron mobility transistors were fabricated and characterized.Hall mobility of 2291.1 cm^(2)/(V·s)and two-dimensional electron gas density of 9.954×10^(12)cm^(-2)were achieved at 300 K.The HEMT devices with a 0.45-μm gate length exhibited maximum drain current density as high as 1039.6 mA/mm and peak extrinsic transconductance of 229.7 mS/mm.The f_(T)of 30.89 GHz and f_(max)of 38.71 GHz were measured on the device.Load-pull measurements were performed and analyzed under(-3.5,28)V,(-3.5,34)V and(-3.5,40)V gate/drain direct current bias in class-AB,respectively.The uncooled device showed high linear power gain of 17.04 dB and high power-added efficiency of 50.56%at 8 GHz when drain biased at(-3.5,28)V.In addition,when drain biased at(-3.5,40)V,the device exhibited a saturation output power dens-ity up to 6.21 W/mm at 8 GHz,with a power gain of 11.94 dB and a power-added efficiency of 39.56%.Furthermore,the low f_(max)/f_(T)ratio and the variation of the power sweep of the device at 8 GHz with drain bias voltage were analyzed.

A Novel Anonymous Authentication Scheme Based on Edge Computing in Internet of Vehicles

The vehicular cloud computing is an emerging technology that changes vehicle communication and underlying trafc management applications.However,cloud computing has disadvantages such as high delay,low privacy and high communication cost,which can not meet the needs of realtime interactive information of Internet of vehicles.Ensuring security and privacy in Internet of Vehicles is also regarded as one of its most important challenges.Therefore,in order to ensure the user information security and improve the real-time of vehicle information interaction,this paper proposes an anonymous authentication scheme based on edge computing.In this scheme,the concept of edge computing is introduced into the Internet of vehicles,which makes full use of the redundant computing power and storage capacity of idle edge equipment.The edge vehicle nodes are determined by simple algorithm of dening distance and resources,and the improved RSA encryption algorithm is used to encrypt the user information.The improved RSA algorithm encrypts the user information by reencrypting the encryption parameters.Compared with the traditional RSA algorithm,it can resist more attacks,so it is used to ensure the security of user information.It can not only protect the privacy of vehicles,but also avoid anonymous abuse.Simulation results show that the proposed scheme has lower computational complexity and communication overhead than the traditional anonymous scheme.

A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System

Internet of Things(IoT)is an emerging paradigm involving intelligent sensor networks that incorporates embedded technology for collecting data,communicating with external environments.Recently,cloud computing together with fog computing has become an important research area of the Internet of Things because of big data processing capabilities.It is a promising technology that utilizes cloud or fog computing/architecture to improve sensor computing,storage,and communication capabilities.However,recently it has been shown that this computing/architecture may be vulnerable to various attacks because of the openness nature of the wireless network.Therefore,it becomes more and more important to ensure the security and privacy in these scenes.Encryption security and information hiding technology can provide authentication,confidentiality,integrity,anti-eavesdropping,availability and so on for these computing models or architectures.The purpose of this review is to look for original articles with novel ideas and solutions to address encryption security and information hiding technologies in cloud or fog-based Internet of Things systems.We hope this review will provide an opportunity for scientists,researchers and industry engineers to study original manuscripts and know developments in all aspects of security,privacy,trust,and covert communication issues in cloud or fog computing/architecture Internet of Things systems.

Security Analysis for a VANET Privacy Protection Scheme

Vehicular ad hoc network(VANET)is a self-organizing wireless sensor network model,which is extensively used in the existing traffic.Due to the openness of wireless channel and the sensitivity of traffic information,data transmission process in VANET is vulnerable to leakage and attack.Authentication of vehicle identitywhile protecting vehicle privacy information is an advantageous way to improve the security of VANET.We propose a scheme based on fair blind signature and secret sharing algorithm.In this paper,we prove that the scheme is feasible through security analysis.

An Overview of the Miniaturization and Endurance for Wearable Devices

The miniaturization and endurance of wearable devices have been the research direction for a long time.With the development of nanotechnology and the emergence of microelectronics products,people have explored many new strategies that may be applied to wearable devices.In this overview,we will summarize the recent research of wearable devices in these two directions,and summarize some available related technologies.

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.

Differential regulation of TNF receptor 1 and receptor 2 in adiponectin expression following myocardial ischemia

2013年10月12—13日中国生理学会张锡钧基金会第十二届全国青年优秀生理学学术论文交流会在湖南长沙顺利召开。由各省生理学会推荐的37名参赛选手的论文参与评选，会议展示了选手们近3年来在生理学研究方面所取得的最新研究成果。经过专家对参评者论文和现场报告的综合评判，评出一等奖、二等奖、三等奖、特别奖、最佳表达奖、最佳答辩奖和最佳图标奖共11名。从2013年第6期开始，《生理通讯》将陆续转载获奖者的参评论文各一篇，以飨读者。

基于折射率匹配实验图像指标的含液颗粒物质系统内部固体体积分数重构

折射率匹配技术被广泛用于研究颗粒-液体混合流动,但是目前针对其固体体积分数的确定为经验性的且缺乏理论或实验支撑.为了合理确定内部体积分数,本文开展了系统的标定实验来研究固体体积分数和图像指标的关系.结果表明基于面积分数的模型由于颗粒表观尺寸的不稳定性,会产生较大的误差,传统使用的正比例模型仅在靠近标定点附近的体积分数成立.本文提出了基于Voronoi多边形等效直径的预测模型,结果表明该模型更为准确且稳定,该模型被进一步应用于颗粒-液体斜槽流动中以研究内部体积分数剖面和底部应力.