Chemical simulation teaching system based on virtual reality and gesture interaction

Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture interaction.Methods The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine.By establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was created.Using code script logic,particle systems,as well as other systems,chemical phenomena were simulated.Furthermore,we created an online teaching platform using streaming media and databases to address the problems of distance teaching.Results The proposed system was evaluated against two mainstream products in the market.In the experiments,the proposed system outperformed the other products in terms of fidelity and practicality.Conclusions The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.

Layered Coded Cache Placement and Cooperative Delivery with Sharing Links in Satellite-Terrestrial Integrated Networks

Cooperative utilization of multidimensional resources including cache, power and spectrum in satellite-terrestrial integrated networks(STINs) can provide a feasible approach for massive streaming media content delivery over the seamless global coverage area. However, the on-board supportable resources of a single satellite are extremely limited and lack of interaction with others. In this paper, we design a network model with two-layered cache deployment, i.e., satellite layer and ground base station layer, and two types of sharing links, i.e., terrestrial-satellite sharing(TSS) links and inter-satellite sharing(ISS) links, to enhance the capability of cooperative delivery over STINs. Thus, we use rateless codes for the content divided-packet transmission, and derive the total energy efficiency(EE) in the whole transmission procedure, which is defined as the ratio of traffic offloading and energy consumption. We formulate two optimization problems about maximizing EE in different sharing scenarios(only TSS and TSS-ISS),and propose two optimized algorithms to obtain the optimal content placement matrixes, respectively.Simulation results demonstrate that, enabling sharing links with optimized cache placement have more than 2 times improvement of EE performance than other traditional placement schemes. Particularly, TSS-ISS schemes have the higher EE performance than only TSS schemes under the conditions of enough number of satellites and smaller inter-satellite distances.

An mmWave Dual-Band Integrated Substrate Gap Waveguide Single Cavity Filter with Frequency Selectivity

A novel dual-band ISGW cavity filter with enhanced frequency selectivity is proposed in this paper by utilizing a multi-mode coupling topology.Its cavity is designed to control the number of modes,and then the ports are determined by analyzing the coupling relationship between these selected modes.By synthesizing the coupling matrix of the filter,a nonresonating node(NRN)structure is introduced to flexibly tune the frequency of modes,which gets a dualband and quad-band filtering response from a tri-band filter no the NRN.Furthermore,a frequency selective surface(FSS)has been newly designed as the upper surface of the cavity,which significantly improves the bad out-of-band suppression and frequency selectivity that often exists in most traditional cavity filter designs and measurements.The results show that its two center frequencies are f01=27.50 GHz and f02=32.92GHz,respectively.Compared with the dual-band filter that there is no the FSS metasurface,the out-of-band suppression level is improved from measured 5 dB to18 dB,and its finite transmission zero(FTZ)numbers is increased from measured 1 to 4 between the two designed bands.Compared with the tri-band and quadband filter,its passband bandwidth is expanded from measured 1.17%,1.14%,and 1.13% or 1.31%,1.50%,0.56%,and 0.57% to 1.71% and 1.87%.In addition,the filter has compact,small,and lightweight characteristics.

Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.

Extrapolation over temporal knowledge graph via hyperbolic embedding

Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.

Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

Specialty optical fibers for advanced sensing applications

Optical fiber technology has changed the world by enabling extraordinary growth in world-wide communications and sensing.The rapid development and wide deployment of optical fiber sensors are driven by their excellent sensing performance with outstanding flexibility,functionality,and versatility.Notably,the research on specialty optical fibers is playing a critical role in enabling and proliferating the optical fiber sensing applications.This paper overviews recent developments in specialty optical fibers and their sensing applications.The specialty optical fibers are reviewed based on their innovations in special structures,special materials,and technologies to realize lab in/on a fiber.An overview of sensing applications in various fields is presented.The prospects and emerging research areas of specialty optical fibers are also discussed.

Adaptive Resources Allocation Algorithm Based on Modified PSO for Cognitive Radio System

Radio spectrum has become a rare resource due to the rapid development of wireless communication technique. Cognitive radio is one of important techniques to deal with this radio spectrum problem. But the resource allocation in cognitive radio also has its own issues, such as the flexibility of the allocation algorithm, the performance of resource allocation, and so on. In order to increase the flexibility of the allocation algorithm for cognitive radio, more and more researches are focusing on the evolutionary algorithms, such as genetic algorithm(GA), particle swarm optimization(PSO). Evolutionary algorithm can greatly improve the flexibility of the allocation algorithm for cognitive radio system in different communication scenarios, but the performances are relatively lower than the original mathematical methods. So in this paper, we proposed an adaptive resource allocation algorithm based on modified PSO for cognitive radio system to solve these problems. Modified particle swarm optimization(Modified PSO) has both genetic algorithm(GA) and particle swarm optimization(PSO)’s updating processes which makes this modified PSO overcame PSO’s own disadvantages and keep advantages. Simulation results showed our proposed algorithm has enough flexibility to meet cognitive radio systems’ requirements, and also has a better performance than original PSO.

Multi-Scenario Millimeter Wave Wireless Channel Measurements and Sparsity Analysis

Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation,and improve system design and performance analysis.Compared with the traditional sub6 GHz channel,millimeter wave(mmWave)channel has been considered to be more sparse in existing researches.However,most research only assume that the mmWave channel is sparse,without providing quantitative analysis and evaluation.Therefore,this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements.A vector network analyzer(VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz,and multi-scenario channel measurements are conducted.The Gini index,Rician𝐾factor and rootmean-square(RMS)delay spread are used to measure channel sparsity.Then,the key factors affecting mmWave channel sparsity are explored.It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel.In addition,the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.

Prediction Model of Refined Gasoline Blending Formula Based on PSO-DBN

To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,and they are applied to the prediction modeling of refined gasoline blending conservative formula.Firstly,based on historical measured data of refined gasoline blending and according to the characteristics of the data set,we use bootstrapping to divide the training data set and the test data set.Secondly,considering that parameter selection for the network is difficult,particle swarm optimization is adopted to improve the related optimal parameters and replace the tedious process of manually selecting parameters,greatly improving optimization efficiency.In addition,the contrastive divergence algorithm is used for unsupervised forward feature learning and supervised reverse fine-tuning of the network,so as to construct a more accurate prediction model for conservative formula.Finally,in order to evaluate the effectiveness of this method,the simulation results are compared with those of traditional modeling methods,which show that the DBNs has better prediction performance than error back propagation and support vector machines,and can provide production guidance for refined gasoline blending formula.

Identifier Management of Industrial Internet Based on Multi-Identifier Network Architecture

The industrial Internet realizes intelligent control and optimized operation of the industrial system through network interconnection.The industrial Internet identifier is the core element to accomplish this task.The traditional industrial Internet identifier resolution technologies depend excessively on IP networks,and cannot meet the requirements of ubiquitous resource-restraint Internet of Things(IoT)devices.An industrial Internet identifier resolution management strategy based on multi-identifier network architecture is proposed in this paper,which supports content names,identities,locations,apart from the traditional IP address.The application of multiple types of identifiers not only solves the problem of IP addresses exhaustion,but also enhances the security,credibility,and availability of the industrial Internet identification resolution system.An inter-translation scheme between multiple identifiers is designed to support multiple identifiers and the standard ones.We present an addressing and routing algorithm for identifier resolution to make it convenient to put our strategy into practice.

A survey on federated learning:a perspective from multi-party computation

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets.To enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model training.This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training efficiency.We also pinpoint future directions to deploy federated learning to a wider range of applications.

Recent progress on structural coloration

Structural coloration generates colors by the interaction between incident light and micro-or nanoscale structures.It has received tremendous interest for decades,due to advantages including robustness against bleaching and environmentally friendly properties(compared with conventional pigments and dyes).As a versatile coloration strategy,the tuning of structural colors based on micro-and nanoscale photonic structures has been extensively explored and can enable a broad range of applications including displays,anti-counterfeiting,and coating.However,scholarly research on structural colors has had limited impact on commercial products because of their disadvantages in cost,scalability,and fabrication.In this review,we analyze the key challenges and opportunities in the development of structural colors.We first summarize the fundamental mechanisms and design strategies for structural colors while reviewing the recent progress in realizing dynamic structural coloration.The promising potential applications including optical information processing and displays are also discussed while elucidating the most prominent challenges that prevent them from translating into technologies on the market.Finally,we address the new opportunities that are underexplored by the structural coloration community but can be achieved through multidisciplinary research within the emerging research areas.

Meta-device:advanced manufacturing

Metasurfaces are one of the most promising devices to break through the limitations of bulky optical components.By offering a new method of light manipulation based on the light-matter interaction in subwavelength nanostructures,metasurfaces enable the efficient manipulation of the amplitude,phase,polarization,and frequency of light and derive a series of possibilities for important applications.However,one key challenge for the realization of applications for meta-devices is how to fabricate large-scale,uniform nanostructures with high resolution.In this review,we review the state-of-the-art nanofabrication techniques compatible with the manufacture of meta-devices.Maskless lithography,masked lithography,and other nanofabrication techniques are highlighted in detail.We also delve into the constraints and limitations of the current fabrication methods while providing some insights on solutions to overcome these challenges for advanced nanophotonic applications.

IQABC-Based Hybrid Deployment Algorithm for Mobile Robotic Agents Providing Network Coverage

Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target area.Herein,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground devices.In this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage range.Then,we propose a hybrid deployment algorithm based on the improved quick artificial bee colony.The algorithm is composed of a centralized deployment algorithm and a distributed one.The proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically distributed.Simulation results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

10 Gb/s classical secure key distribution based on temporal steganography and private chaotic phase scrambling

Secure distribution of high-speed digital encryption/decryption keys over a classical fiber channel is strongly pursued for realizing perfect secrecy communication systems.However,it is still challenging to achieve a secret key rate in the order of tens of gigabits per second to be comparable with the bit rate of commercial fiber-optic systems.In this paper,we propose and experimentally demonstrate a novel solution for high-speed secure key distribution based on temporal steganography and private chaotic phase scrambling in the classical physical layer.The encryption key is temporally concealed into the background noise in the time domain and randomly phase scrambled bit-by-bit by a private chaotic signal,which provides two layers of enhanced security to guarantee the privacy of key distribution while providing a high secret key rate.We experimentally achieved a record classical secret key rate of 10 Gb/s with a bit error rate lower than the hard-decision forward error correction(HD-FEC)over a 40 km standard single mode fiber.The proposed solution holds great promise for achieving high-speed key distribution in the classical fiber channel by combining steganographic transmission and chaotic scrambling.

All-optical nanoscale thermometry with silicon carbide color centers

All-optical thermometry plays a crucial role in precision temperature measurement across diverse fields.Quantum defects in solids are one of the most promising sensors due to their excellent sensitivity,stability,and biocompatibility.Yet,it faces limitations,such as the microwave heating effect and the complexity of spectral analysis.Addressing these challenges,we introduce a novel approach to nanoscale optical thermometry using quantum defects in silicon carbide(SiC),a material compatible with complementary metal-oxide-semiconductor(CMOS)processes.This method leverages the intensity ratio between anti-Stokes and Stokes emissions from SiC color centers,overcoming the drawbacks of traditional techniques such as optically detected magnetic resonance(ODMR)and zero-phonon line(ZPL)analysis.Our technique provides a real-time,highly sensitive(1.06%K^(-1)),and diffraction-limited temperature sensing protocol,which potentially helps enhance thermal management in the future miniaturization of electronic components.

Low-modal-crosstalk doped-fiber amplifiers in few-mode-fiber-based systems

Independent light propagation through one or multiple modes is commonly considered as a basic demand for mode manipulation in few-mode fiber(FMF)-or multimode fiber(MMF)-based optical systems such as transmission links,optical fiber lasers,or distributed optical fiber sensors.However,the insertion of doped-fiber amplifiers always kills the entire effort by inducing significant modal crosstalk.In this paper,we propose the design of doped-fiber amplifiers in FMF-based systems adopting identical multiple-ring-core(MRC)index profiles for both passive and doped fibers to achieve low modal crosstalk.We develop the direct-glass-transition(DGT)modified chemical vapor deposition(MCVD)processing for precise fabrication of few-mode erbium-doped fibers(FM-EDFs)with MRC profiles of both refractive index and erbium-ion doping distribution.Then,a few-mode erbium-doped-fiber amplifier(FM-EDFA)with a maximum gain of 26.08 dB and differential modal gain(DMG)of 2.3 dB is realized based on fabricated FM-EDF matched with a transmission FMF supporting four linearly polarized(LP)modes.With the insertion of the FM-EDFA,60+60 km simultaneous LP_(01)∕LP_(11)∕LP_(21)∕LP_(02)transmission without inter-modal multiple-input multiple-output digital signal processing(MIMO-DSP)is successfully demonstrated.The proposed design of low-modal-crosstalk doped-fiber amplifiers provides,to our knowledge,new insights into mode manipulation methods in various applications.

Advanced manufacturing of dielectric metadevices

Metasurfaces,composed of two-dimensional nanostructures,exhibit remarkable capabilities in shaping wavefronts,encompassing phase,amplitude,and polarization.This unique proficiency heralds a transformative paradigm shift in the domain of next-generation optics and photonics,culminating in the development of flat and ultrathin optical devices.Particularly noteworthy is the all-dielectric-based metasurface,leveraging materials such as titanium dioxide,silicon,gallium arsenide,and silicon nitride,which finds extensive application in the design and implementation of high-performance optical devices,owing to its notable advantages,including a high refractive index,low ohmic loss,and cost-effectiveness.Furthermore,the remarkable growth in nanofabrication technologies allows for the exploration of new methods in metasurface fabrication,especially through wafer-scale nanofabrication technologies,thereby facilitating the realization of commercial applications for metasurfaces.This review provides a comprehensive overview of the latest advancements in state-of-the-art fabrication technologies in dielectric metasurface areas.These technologies,including standard nanolithography[e.g.,electron beam lithography(EBL)and focused ion beam(FIB)lithography],advanced nanolithography(e.g.,grayscale and scanning probe lithography),and large-scale nanolithography[e.g.,nanoimprint and deep ultraviolet(DUV)lithography],are utilized to fabricate highresolution,high-aspect-ratio,flexible,multilayer,slanted,and wafer-scale all-dielectric metasurfaces with intricate nanostructures.Ultimately,we conclude with a perspective on current cutting-edge nanofabrication technologies.

YuNet: A Tiny Millisecond-level Face Detector

Great progress has been made toward accurate face detection in recent years.However,the heavy model and expensive computation costs make it difficult to deploy many detectors on mobile and embedded devices where model size and latency are highly constrained.In this paper,we present a millisecond-level anchor-free face detector,YuNet,which is specifically designed for edge devices.There are several key contributions in improving the efficiency-accuracy trade-off.First,we analyse the influential state-of-theart face detectors in recent years and summarize the rules to reduce the size of models.Then,a lightweight face detector,YuNet,is introduced.Our detector contains a tiny and efficient feature extraction backbone and a simplified pyramid feature fusion neck.To the best of our knowledge,YuNet has the best trade-off between accuracy and speed.It has only 75856 parameters and is less than 1/5 of other small-size detectors.In addition,a training strategy is presented for the tiny face detector,and it can effectively train models with the same distribution of the training set.The proposed YuNet achieves 81.1%mAP(single-scale)on the WIDER FACE validation hard track with a high inference efficiency(Intel i7-12700K:1.6ms per frame at 320×320).Because of its unique advantages,the repository for YuNet and its predecessors has been popular at GitHub and gained more than 11K stars at https://github.com/ShiqiYu/libfacedetection.Keywords:Face detection,object detection,computer version,lightweight,inference efficiency,anchor-free mechanism.