Heterogeneous integration of InP HEMTs on quartz wafer using BCB bonding technology

Heterogeneous integrated InP high electron mobility transistors(HEMTs)on quartz wafers are fabricated successfully by using a reverse-grown InP epitaxial structure and benzocyclobutene(BCB)bonding technology.The channel of the new device is In_(0.7)Ga_(0.3)As,and the gate length is 100 nm.A maximum extrinsic transconductance gm,max of 855.5 mS/mm and a maximum drain current of 536.5 mA/mm are obtained.The current gain cutoff frequency is as high as 262 GHz and the maximum oscillation frequency reaches 288 GHz.In addition,a small signal equivalent circuit model of heterogeneous integration of InP HEMTs on quartz wafer is built to characterize device performance.

First-principles studies of electronic,optical,and mechanical properties of γ-Bi2Sn2O7

The detailed theoretical studies of electronic,optical,and mechanical properties of γ-Bi2Sn2O7 are carried out by using first-principle density functional theory calculations.Our calculated results indicate that γ-Bi2Sn2O7 is the p-type semiconductor with an indirect band gap of about 2.72 e V.The flat electronic bands close to the valence band maximum are mainly composed of Bi-6s and O-2p states and play a key role in determining the electrical properties of γ-Bi2Sn2O7.The calculated complex dielectric function and macroscopic optical constants including refractive index,extinction coefficient,absorption coefficients,reflectivity,and electron energy-loss function show that γ-Bi2Sn2O7 is an excellent light absorbing material.The analysis on mechanical properties shows that γ-Bi2Sn2O7 is mechanically stable and highly isotropic.

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion,the increasing attention on power electronic equipment is fault detection and diagnosis.A power electronic circuit is an essential part of a power electronic system.The state of its internal components affects the performance of the system.The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits.Therefore,an algorithm based on adaptive simulated annealing particle swarm optimization(ASAPSO)was used in the present study to optimize a backpropagation(BP)neural network employed for the online fault diagnosis of a power electronic circuit.We built a circuit simulation model in MATLAB to obtain its DC output voltage.Using Fourier analysis,we extracted fault features.These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization(PSO)and the ASAPSO algorithm.The accuracy of fault diagnosis was compared for the three networks.The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy,better reliability,and adaptability and can more effectively diagnose and locate faults in power electronic circuits.

Structural evolutions and electronic properties of Au_nGd(n=6–15)small clusters:A first principles study

Structural, electronic, and magnetic properties of AunGd （n = 6-15） small clusters are investigated by using first principles spin polarized calculations and combining with the ab-initio evolutionary structure simulations. The calculated binding energies indicate that after doping a Gd atom AunGd cluster is obviously more stable than a pure AUn＋l cluster. Au6Gd with the quasiplanar structure has a largest magnetic moment of 7.421 gB. The Gd-4f electrons play an important role in determining the high magnetic moments of AunGd clusters, but in Au6Gd and Aul2Gd clusters the unignorable spin polarized effects from the Au-6s and Au-5d electrons further enhance their magnetism. The HOMO-LUMO （here, HOMO and LUMO stand for the highest occupied molecular orbital, and the lowest unoccupied molecular orbital, respectively） energy gaps of munGd clusters are smaller than those of pure AUn＋l clusters, indicating that AunGd clusters have potential as new catalysts with enhanced reactivity.

Community structure and species diversity dynamics of a subtropical evergreen broad-leaved forest in China:2005 to 2020

Here,we characterize the temporal and spatial dynamics of forest community structure and species diversity in a subtropical evergreen broad-leaved forest in China.We found that community structure in this forest changed over a 15-year period.Specifically,renewal and death of common species was large,with the renewal of individuals mainly concentrated within a few populations,especially those of Aidia canthioides and Cryptocarya concinna.The numbers of individual deaths for common species were concentrated in the small and mid-diameter level.The spatial distribution of community species diversity fluctuated in each monitoring period,showing a more dispersed diversity after the 15-year study period,and the coefficient of variation on quadrats increased.In 2010,the death and renewal of the community and the spatial variation of species diversity were different compared to other survey years.Extreme weather may have affected species regeneration and community stability in our subtropical monsoon evergreen broad-leaved forests.Our findings suggest that strengthening the monitoring and management of the forest community will help better understand the long-and short-term causes of dynamic fluctuations of community structure and species diversity,and reveal the factors that drive changes in community structure.

Technology Importation and Domestic Technology Acquisition in China from the Perspective of Innovation

Foreign and domestic technologies play different roles in a country's innovation. In recent years, Chinese firms have spent less on importing foreign technology and more on acquiring domestic technology. Based on the panel data model, state space model(SSM) and the Bayesian vector autoregressive model, this paper finds that the purchase of domestic technology plays a critical role in the innovation of high-tech firms with an elasticity coefficient higher than that of technology importation before 2010. China's high-tech firms can effectively absorb purchased domestic technology, which also brings about an increase in their independent R&D input and contributes more to innovation output than imported technology. Performance of technology importation is lackluster but shows an improving trend. Technology importation is not correlated with innovation output and has a substitutive effect with independent R&D input. China is yet to enhance its absorption of imported technology. Elasticity coefficient of technology importation turned positive in 2007 and increased year by year. China should promote synergy between technology importation and domestic technology acquisition.

Generation of subwavelength inverted pin beam via fiber end integrated plasma structure

A pin-like beam is a kind of structured light with a special intensity distribution that can be against diffraction,which can be seen as a kind of quasi-nondiffracting beam(Q-NDB).Due to its wide applications,recently,numerous researchers have used optical lenses or on-chip integrated optical diffractive elements to generate this kind of beam.We theoretically verify and experimentally demonstrate an all-fiber solution to generate a subwavelength inverted pin beam by integrating a simple plasma structure on the fiber end surface.The output beams generated by two kinds of plasma structures,i.e.,nanoring slot and nanopetal structure,are investigated and measured experimentally.The results show that both the structures are capable of generating subwavelength beams,and the beam generated using the nanopetal structure has the sidelobe suppression ability along the x-axis direction.Our all-fiber device can be flexibly inserted into liquid environments such as cell cultures,blood,and biological tissue fluids to illuminate or stimulate biological cells and molecules in them.It provides a promising fiber-integrated solution for exploring light–matter interaction with subwavelength resolution in the field of biological research.

A Blockchain-Based Efficient Cross-Domain Authentication Scheme for Internet of Vehicles

The Internet of Vehicles(IoV)is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the network.However,due to the open and variable nature of its network topology,vehicles frequently engage in cross-domain interactions.During such processes,directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers,thus compromising the security of the cross-domain authentication process.Additionally,IoV imposes high real-time requirements,and existing cross-domain authentication schemes for IoV often encounter efficiency issues.To mitigate these challenges,we propose CAIoV,a blockchain-based efficient cross-domain authentication scheme for IoV.This scheme comprehensively integrates technologies such as zero-knowledge proofs,smart contracts,and Merkle hash tree structures.It divides the cross-domain process into anonymous cross-domain authentication and safe cross-domain authentication phases to ensure efficiency while maintaining a balance between efficiency and security.Finally,we evaluate the performance of CAIoV.Experimental results demonstrate that our proposed scheme reduces computational overhead by approximately 20%,communication overhead by around 10%,and storage overhead by nearly 30%.

Modified multiple-component scattering power decomposition for PolSAR data based on eigenspace of coherency matrix

A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.

Regulating Effect of Substrate Temperature on Sputteringgrown Ge/Si QDs under Low Ge Deposition

The effect of deposition temperature on the morphology and optoelectronic performance of Ge/Si QDs grown by magnetron sputtering under low Ge deposition(~4 nm)was investigated by atomic force microscopy,Raman spectroscopy,and photoluminescence(PL)tests.The experimental results indicate that temperatures higher than 750℃effectively increase the crystallization rate and surface smoothness of the Si buffer layer,and temperatures higher than 600℃significantly enhance the migration ability of Ge atoms,thus increasing the probability of Ge atoms meeting and nucleating to form QDs on Si buffer layer,but an excessively high temperature will cause the QDs to undergo an Ostwald ripening process and thus develop into super large islands.In addition,some PL peaks were observed in samples containing small-sized,high-density Ge QDs,the photoelectric properties reflected by these peaks were in good agreement with the corresponding structural characteristics of the grown QDs.Our results demonstrate the viability of preparing high-quality QDs by magnetron sputtering at high deposition rate,and the temperature effect is expected to work in conjunction with other controllable factors to further regulate QD growth,which paves an effective way for the industrial production of QDs that can be used in future devices.

Experiment on inducing apoptosis of melanoma cells by micro-plasma jet

As a promising cancer treatment method,cold atmospheric plasma has received widespread attention in recent years.However,previous research has focused more on how to realize and expand the anti-cancer scope of plasma jet.There are also studies on the killing of small-scale cancer cells,but the effects of plasma jet on normal cells and normal cell clusters have been ignored.Therefore,we proposed a 50μm sized micro-plasma jet device,and used the device to treat melanoma cells(A-375)and human glial cells(HA1800)to evaluate their anti-cancer effects and effects on normal cells.The experimental results show that this kind of micro-plasma jet device can effectively inactivate cancer cells in a short period of time,while having little effect on normal cells.This work provides a certain experimental basis for the application offine plasma jet to clinically inactivate cancer cells.

Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Understanding the dehydrogenation properties of Mg(0001)/MgH_(2)(110)interface from first principles

Magnesium hydride is one of the most promising solid-state hydrogen storage materials for on-board application.Hydrogen desorption from MgH_(2) is accompanied by the formation of the Mg/MgH_(2) interfaces,which may play a key role in the further dehydrogenation process.In this work,first-principles calculations have been used to understand the dehydrogenation properties of the Mg(0001)/MgH_(2)(110) interface.It is found that the Mg(0001)/MgH_(2)(110) interface can weaken the Mg-H bond.The removal energies for hydrogen atoms in the interface zone are significantly lower compared to those of bulk MgH_(2).In terms of H mobility,hydrogen diffusion within the interface as well as into the Mg matrix is considered.The calculated energy barriers reveal that the migration of hydrogen atoms in the interface zone is easier than that in the bulk MgH_(2).Based on the hydrogen removal energies and diffusion barriers,we conclude that the formation of the Mg(0001)/MgH_(2)(110) interface facilitates the dehydrogenation process of magnesium hydride.

Scheduling on tractor and trailer transportation considering the influence of disrupted events based on the contract net and simulated annealing algorithm

To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupted events.Three states of tractors including towing loaded trailers,towing empty trailers,and idle driving are taken into account.Based on the disruption management theory,a scheduling model is constructed to minimize the total deviation cost including transportation time,transportation path,and number of used vehicles under the three states of tractors.A heuristics based on the contract net and simulated annealing algorithm is designed to solve the proposed model.Through comparative analysis of examples with different numbers of newly added transportation tasks and different types of road networks,the performance of the contract net algorithm in terms of deviations in idle driving paths,empty trailer paths,loaded trailer paths,time,number of used vehicles,and total deviation cost are analyzed.The results demonstrate the effectiveness of the model and algorithm,highlighting the superiority of the disruption management model and the contract net annealing algorithm.The study provides a reference for handling unexpected events in the tractor and trailer transportation industry.

Research Status,Practical Dilemmas,and Optimization Strategies of Blended Teaching in Universities

Blended teaching has emerged as a prominent subject in the recent reform and innovation of higher education.It has become imperative and guiding for colleges and universities to embrace a mixed teaching approach that aligns with the evolving needs of education and teaching in the new era.This paper aims to provide a comprehensive overview of the research status surrounding blended teaching,encompassing fundamental issues,teaching design,practical guidance,teaching effectiveness,and evaluation.By critically examining the current challenges associated with blended teaching,this study proposes optimization strategies including enhancing student participation and interaction,promoting deep learning,improving teachers’preparedness,teaching technologies,and curriculum design capabilities,strengthening top-level design,and perfecting evaluation and incentive mechanisms.These strategies provide new directions for the reform of blended teaching.

Exploration and Practice of Big Data Introductory Courses for Big Data Management and Application Majors

As an introductory course for the emerging major of big data management and application,“Introduction to Big Data”has not yet formed a curriculum standard and implementation plan that is widely accepted and used by everyone.To this end,we discuss some of our explorations and attempts in the construction and teaching process of big data courses for the major of big data management and application from the perspective of course planning,course implementation,and course summary.After interviews with students and feedback from questionnaires,students are highly satisfied with some of the teaching measures and programs currently adopted.

Soil Acidification in Response to Acid Deposition in Three Subtropical Forests of Subtropical China

Long-term changes in soil pH, the current status of soil acidification, and the response of bulk soil and soil water pH to experimental nitrogen addition under three subtropical forests were investigated in Dinghushan Biosphere Reserve of subtropical China. The results showed that the mineral soil pH at 0-20 cm depth declined significantly from 4.60-4.75 in 1980s to 3.84-4.02 in 2005. Nitrogen addition resulted in the decrease of pH in both bulk soil and soil water collected at 20-cm depth. The rapid decline of soil pH was attributed to long-term high atmospheric acid deposition (nitrogen and sulphur) therein. The forest at earlier succession stage with originally higher soil pH appeared to be more vulnerable to acid deposition than that at later succession stage with originally low soil pH.

Modelling of ultrasonic motor with dead-zone based on Hammerstein model structure

The ultrasonic motor (USM) possesses heavy nonlinearities which vary with driving conditions and load-dependent characteristics such as the dead-zone. In this paper, an identification method for the rotary travelling-wave type ultrasonic motor (RTWUSM) with dead-zone is proposed based on a modified Hammerstein model structure. The driving voltage contributing effect on the nonlinearities of the RTWUSM was transformed to the change of dynamic parameters against the driving voltage. The dead-zone of the RTWUSM is identified based upon the above transformation. Experiment results showed good agreement be- tween the output of the proposed model and actual measured output.

Deformation behaviors of magnesium alloy AZ31 sheet in cold deep drawing

To investigate how the popular magnesium alloy AZ31 sheet(aluminum 3%,zinc 1%)behaves in cold working,deep drawing experiments at room temperature,along with finite element(FE)simulation,were performed on the cold forming sheet of the AZ31 alloy after being annealed under various conditions.The activities were focused on the fracture pattern,limit drawing ratio(LDR),deformation load,thickness distribution,anisotropic effect,as well as the influences of the annealing conditions and tool configuration on them.The results display that punch shoulder radius instead of die clearance,has much influence on the thickness distribution.The anisotropy is remarkable in cold working,which adversely impacts the LDR.The fracture often happens on the side wall at an angle to axis of the deformed specimen.The results also imply that the LDR for the material under present experimental conditions is 1.72,and annealing the material at 450 ℃ for 1 h may be preferable for the cold deep drawing.

Effect of Sintering Time on Structure and Properties in CuO-doping KNN-LS-BF Piezoelectric Ceramics

The 0.6 mol% CuO-doping 0.996(0.95 Na_(0.5)K_(0.5)NbO_3-0.05 LiSbO_3)-0.004 FeBiO_3(KNN-LSBF-CuO) piezoelectric ceramics were synthesized by a solid-state reaction technique, and the structure and piezoelectric properties dependence of sintering time in KNN-LS-BF-CuO ceramics were studied. It is found that all the samples sintered for various time are perovskite structure mixed with orthorhombic symmetry phase and tetragonal phase, but the sintering time has significant influences on the crystalline and properties. When the sintering time increases from 2 hours to 6 hours, the grain of KNN-LS-BF-CuO ceramics becomes more homogeneous and more tight-arrangement. The experimental results reveal that the longer sintering time than 4 hours is beneficial for improving partial properties, such as d_(33), tgδ, and Q_m, but is adverse to ε_r and k_p, the KNNLS-BF-CuO ceramics with optimum properties can be synthesized for 6 hours at 1 060 ℃.