Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

Envelope Method and More General New Global Structures of Solutions for Multi-dimensional Conservation Law

For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.

Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Forty Five Years of Working Hand in Hand: A Review and Outlook on Friendship Between China and Djibouti

Since the formal establishment of diplomatic relations in 1979,China and Djibouti have made significant progress in developing bilateral relations,with the two countries supporting and cooperating with each other and the two peoples sharing a strong bond.Especially since the 18th National Congress of the Communist Party of China,with the joint efforts of China and Djibouti,the bilateral relations have enjoyed comprehensive and rapid development.

2024 Outlook:Unlocking Cooperation Potential

The year 2023 marked the 14th consecutive year China has been the largest trading partner of ASEAN and the third consecutive year both sides have been each other’s largest trading partner.With the full entry into force of the Regional Comprehensive Economic Partnership(RCEP)and the launch of negotiations on China-ASEAN FTA 3.0,economic and trade exchanges between the two sides have been moving ahead at an accelerated pace with bilateral cooperation enjoying promising prospects and huge potential.

Opportunities And Outlooks Embracing new quality productive forces

New quality productive forces refer to advanced productivity driven by revolutionary technological breakthroughs and empowered by digital technology.They are distinguished from conventional productive capacity which is supported by workshop flfloors,assembly lines,and modular technology.

Review of the Global Upstream Oil and Gas Market in 2023 and 2024 Outlook

Review of the global upstream oil and gas market in 2023,International oil prices fluctuated significantly throughout theyear,In 2023,the international oil prices overall exhibited "box fluctuation"within the range of USD 65-95/barrel,presenting a short-term significant high-level volatility trend of"two steady phases,two increases,and three decreases",which is rare in recent years.This also fully reflects the evident "tug of war"between bulls and bears in the global crude oil market.

Positive Outlook for 2024

The end of the year 2023 and the beginning of 2024 mark a remarkable phase in China-Africa relations.This relationship has evolved significantly and Africa continues to register key milestones in its economic development,particularly in trade,investment and infrastructure.The remarkable growth can be attributed to initiatives by the Chinese government such as the Belt and Road Initiative(BRI).

The Study on College English Writing Based on Multi-dimensional Feed-back Mode

The study constructed a multi-dimensional feedback mode integrating teacher feedback, peer feedback and network feedback, and applied it in the teaching of College English Writing. After 16 weeks of teaching, the students in the multi-dimen-sional feedback class had significantly better overall writing scores than those in the teacher-feedback class. In terms of individual scores, multi-dimensional feedback played a better role in improving vocabulary and grammar than the class using teacher feed-back. However, there were no significant differences in the responses of writing tasks, coherence and cohesion. The study showed that most students were satisfied with the mode, believing that it was helpful to relieve writing anxiety, stimulate writing interest and improve their writing level.

Seismic fragility analysis of highway bridges considering multi-dimensional performance limit state

Fragility analysis for highway bridges has become increasingly important in the risk assessment of highway transportation networks exposed to seismic hazards. This study introduces a methodology to calculate fragility that considers multi-dimensional performance limit state parameters and makes a first attempt to develop fragility curves for a multi-span continuous (MSC) concrete girder bridge considering two performance limit state parameters: column ductility and transverse deformation in the abutments. The main purpose of this paper is to show that the performance limit states, which are compared with the seismic response parameters in the calculation of fragility, should be properly modeled as randomly interdependent variables instead of deterministic quantities. The sensitivity of fragility curves is also investigated when the dependency between the limit states is different. The results indicate that the proposed method can be used to describe the vulnerable behavior of bridges which are sensitive to multiple response parameters and that the fragility information generated by this method will be more reliable and likely to be implemented into transportation network loss estimation.

Structural Engineering of Hierarchical Aerogels Comprised of Multi-dimensional Gradient Carbon Nanoarchitectures for Highly Efficient Microwave Absorption

Recently,multilevel structural carbon aerogels are deemed as attractive candidates for microwave absorbing materials.Nevertheless,excessive stack and agglomeration for low-dimension carbon nanomaterials inducing impedance mismatch are significant challenges.Herein,the delicate“3D helix-2D sheet-1D fiber-0D dot”hierarchical aerogels have been successfully synthesized,for the first time,by sequential processes of hydrothermal self-assembly and in-situ chemical vapor deposition method.Particularly,the graphene sheets are uniformly intercalated by 3D helical carbon nanocoils,which give a feasible solution to the mentioned problem and endows the as-obtained aerogel with abundant porous structures and better dielectric properties.Moreover,by adjusting the content of 0D core-shell structured particles and the parameters for growth of the 1D carbon nanofibers,tunable electromagnetic properties and excellent impedance matching are achieved,which plays a vital role in the microwave absorption performance.As expected,the optimized aerogels harvest excellent performance,including broad effective bandwidth and strong reflection loss at low filling ratio and thin thickness.This work gives valuable guidance and inspiration for the design of hierarchical materials comprised of dimensional gradient structures,which holds great application potential for electromagnetic wave attenuation.

Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

Visualization analysis of the capability of weapon system of systems for multi-dimensional indicators

In the field of weapon system of systems(WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis,reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.

Low-Temperature Growing Anatase TiO2/SnO2 Multi-dimensional Heterojunctions at MXene Conductive Network for High-Efficient Perovskite Solar Cells

A multi-dimensional conductive heterojunction structure,composited by TiO2,SnO2,and Ti3C2TX MXene,is facilely designed and applied as electron transport layer in efficient and stable planar perovskite solar cells.Based on an oxygen vacancy scramble effect,the zero-dimensional anatase TiO2 quantum dots,surrounding on two-dimensional conductive Ti3C2TX sheets,are in situ rooted on three-dimensional SnO2 nanoparticles,constructing nanoscale TiO2/SnO2 heterojunctions.The fabrication is implemented in a controlled lowtemperature anneal method in air and then in N2 atmospheres.With the optimal MXene content,the optical property,the crystallinity of perovskite layer,and internal interfaces are all facilitated,contributing more amount of carrier with effective and rapid transferring in device.The champion power conversion efficiency of resultant perovskite solar cells achieves 19.14%,yet that of counterpart is just 16.83%.In addition,it can also maintain almost 85%of its initial performance for more than 45 days in 30–40%humidity air;comparatively,the counterpart declines to just below 75%of its initial performance.

Performance-based global reliability assessment of a high-rise frame-core tube structure subjected to multi-dimensional stochastic earthquakes

When evaluating the seismic safety and reliability of complex engineering structures,it is a critical problem to reasonably consider the randomness and multi-dimensional nature of ground motions.To this end,a proposed modeling strategy of multi-dimensional stochastic earthquakes is addressed in this study.This improved seismic model has several merits that enable it to better provide seismic analyses of structures.Specifically,at first,the ground motion model is compatible with the design response spectrum.Secondly,the evolutionary power spectrum involved in the model and the design response spectrum are constructed accordingly with sufficient consideration of the correlation between different seismic components.Thirdly,the random function-based dimension-reduction representation is applied,by which seismic modeling is established,with three elementary random variables.Numerical simulations of multi-dimensional stochastic ground motions in a specific design scenario indicate the effectiveness of the proposed modeling strategy.Moreover,the multi-dimensional seismic response and the global reliability of a high-rise frame-core tube structure is discussed in detail to further illustrate the engineering applicability of the proposed method.The analytical investigations demonstrate that the suggested stochastic model of multi-dimensional ground motion is available for accurate seismic response analysis and dynamic reliability assessment of complex engineering structures for performance-based seismic resistance design.

Measurement and Decomposition of Multi-Dimensional Poverty in China

Based on China Family Panel Studies(CFPS) data and global MPI standard,this paper measures and analyzes multi-dimensional poverty in China. The study finds that the level of multi-dimensional poverty in China is not high and tends to decrease over time.Uneven regional development significantly affects multi-dimensional poverty. The poor are deprived in health, education and other aspects, but indicator contributions vary among specific groups of people. Overlap between economic poverty and multi-dimensional poverty has a trend of inter-temporal reduction. China's development-centered poverty reduction policy has achieved great results and significantly improved the development capabilities of the poor. Development-oriented approach is China's important experience in poverty reduction, and forebodes China's bright prospect of achieving its goal to complete building a moderately prosperous society by 2020.

Multi-dimensional force sensor for haptic interaction:a review

Haptic interaction plays an important role in the virtual reality technology,which let a person not only view the 3D virtual environment but also realistically touch the virtual environment.As a key part of haptic interaction,force feedback has become an essential function for the haptic interaction.Therefore,multi-dimensional force sensors are widely used in the fields of virtual reality and augmented reality.In this paper,some conventional multi-dimensional force sensors based on different measurement principles,such as resistive,capacitive,piezoelectric,are briefly introduced.Then the mechanical structures of the elastic body of multi-dimensional force sensors are reviewed.It is obvious that the performance of the multi-dimensional force sensor is mainly dependent upon the mechanical structure of elastic body.Furthermore,the calibration process of the force sensor is analyzed,and problems in calibration are discussed.Interdimensional coupling error is one of the main factors affecting the measurement precision of the multi-dimensional force sensors.Therefore,reducing or even eliminating dimensional coupling error becomes a fundamental requirement in the design of multi-dimensional force sensors,and the decoupling state-of-art of the multi-dimensional force sensors are introduced in this paper.At last,the trends and current challenges of multi-dimensional force sensing technology are proposed.