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.

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.

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.

Mechanism of high-preload support based on the NPR anchor cable in layered soft rock tunnels

The control of large deformation problems in layered soft rock tunnels needs to solve urgently.The roof problem is particularly severe among the deformation issues in tunnels.This study first analyzes the asymmetric deformation modes in layered soft rock tunnels with large deformations.Subsequently,we construct a mechanical model under ideal conditions for controlling the roof of layered soft rock tunnels through high preload with the support of NPR anchor cables.The prominent roles of long and short NPR anchor cables in the support system are also analyzed.The results indicate the significance of high preload in controlling the roof of layered soft rock tunnels.The short NPR anchor cables effectively improve the integrity of the stratified soft rock layers,while the long NPR anchor cables effectively mobilize the self-bearing capacity of deep-stable rock layers.Finally,the high-preload support method with NPR anchor cables is validated to have a good effect on controlling large deformations in layered soft rock tunnels through field monitoring data.

Analysis of the interaction between bolt-reinforced rock and surface support in tunnels based on convergence-confinement method

To investigate the interaction of the bolt-reinforced rock and the surface support,an analytical model of the convergence-confinement type is proposed,considering the sequential installation of the fully grouted rockbolts and the surface support.The rock mass is assumed to be elastic-brittle-plastic material,obeying the linear Mohr-Coulomb criterion or the non-linear Hoek-Brown criterion.According to the strain states of the tunnel wall at bolt and surface support installation and the relative magnitude between the bolt length and the plastic depth during the whole process,six cases are categorized upon solving the problem.Each case is divided into three stages due to the different effects of the active rockbolts and the passive surface support.The fictitious pressure is introduced to quantify the threedimensional(3D)effect of the tunnel face,and thus,the actual physical location along the tunnel axis of the analytical section can be considered.By using the bolt-rock strain compatibility and the rocksurface support displacement compatibility conditions,the solutions of longitudinal tunnel displacement and the reaction pressure of surface support along the tunnel axis are obtained.The proposed analytical solutions are validated by a series of 3D numerical simulations.Extensive parametric studies are conducted to examine the effect of the typical parameters of rockbolts and surface support on the tunnel displacement and the reaction pressure of the surface support under different rock conditions.The results show that the rockbolts are more effective in controlling the tunnel displacement than the surface support,which should be installed as soon as possible with a suitable length.For tunnels excavated in weak rocks or with restricted displacement control requirements,the surface support should also be installed or closed timely with a certain stiffness.The proposed method provides a convenient alternative approach for the optimization of rockbolts and surface support at the preliminary stage of tunnel design.

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.

Stability analysis of longwall top-coal caving face in extra-thick coal seams based on an innovative numerical hydraulic support model

The relationship between support and surrounding rock is of great significance to the control of surrounding rock in mining process.In view of the fact that most of the existing numerical simulation methods construct virtual elements and stress servo control to approximately replace the hydraulic support problem,this paper establishes a new numerical model of hydraulic support with the same working characteristics as the actual hydraulic support by integrating numerical simulation software Rhino,Griddle and FLAC3D,which can realize the simulation of different working conditions.Based on this model,the influence mechanism of the supporting strength of hydraulic support on surrounding rock stress regulation and coal stability in front of the top coal caving face in extra thick coal seam were researched.Firstly,under different support intensity,the abutment pressure of the bearing coal and the coal in front of it presents the “three-stage”evolution characteristics.The influence range of support intensity is 15%–30%.Secondly,1.5 MPa is the upper limit of impact that the support strength can have on the front coal failure area.Thirdly,within a displacement range of 2.76 m from the coal wall,a support strength of1.5 MPa provides optimal control of the horizontal displacement of the coal.

Translocation of telomerase reverse transcriptase coincided with ATP release in postnatal cochlear supporting cells

The spontaneous bursts of electrical activity in the developing auditory system are derived from the periodic release of adenosine triphosphate(ATP)by supporting cells in the Kölliker’s organ.However,the mechanisms responsible for initiating spontaneous ATP release have not been determined.Our previous study revealed that telomerase reverse transcriptase(TERT)is expressed in the basilar membrane during the first postnatal week.Its role in cochlear development remains unclear.In this study,we investigated the expression and role of TERT in postnatal cochlea supporting cells.Our results revealed that in postnatal cochlear Kölliker’s organ supporting cells,TERT shifts from the nucleus into the cytoplasm over time.We found that the TERT translocation tendency in postnatal cochlear supporting cells in vitro coincided with that observed in vivo.Further analysis showed that TERT in the cytoplasm was mainly located in mitochondria in the absence of oxidative stress or apoptosis,suggesting that TERT in mitochondria plays roles other than antioxidant or anti-apoptotic functions.We observed increased ATP synthesis,release and activation of purine signaling systems in supporting cells during the first 10 postnatal days.The phenomenon that TERT translocation coincided with changes in ATP synthesis,release and activation of the purine signaling system in postnatal cochlear supporting cells suggested that TERT may be involved in regulating ATP release and activation of the purine signaling system.Our study provides a new research direction for exploring the spontaneous electrical activity of the cochlea during the early postnatal period.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Correlation Analysis between Self-Disclosure and Social Support in Patients with Breast Cancer

Objective: Describe the status quo of self-disclosure and social support in breast cancer patients and analyze the correlation between them. Methods: General data questionnaire, distress disclosure Index scale and Chinese version of medical social support scale were used to investigate the correlation between self-disclosure and social support in breast cancer patients by Pearson correlation analysis. Results: 1) The total self-disclosure score was (38.75 ± 9.18);the total score of social support was (70.57 ± 14.04) scores, including emotional information support dimension (28.39 ± 6.06) scores, practical support dimension (15.62 ± 3.31) scores, elastic support dimension (14.85 ± 3.23) scores, and emotional support dimension (11.70 ± 2.56) scores. 2) Self-disclosure was positively correlated with social support (r = 0.433, p Conclusion: Breast cancer patients had a moderate level of self-disclosure, and the higher the level of self-disclosure, the better the social support. It is suggested that improving the self-disclosure level of breast cancer patients can help them obtain more social support.

Aqueous-phase reforming of hydroxyacetone solution to bio-based H_(2)over supported Pt catalysts

Aqueous-phase reforming(APR)is an attractive process to produce bio-based hydrogen from waste biomass streams,during which the catalyst stability is often challenged due to the harsh reaction conditions.In this work,three Pt-based catalysts supported on C,AlO(OH),and ZrO_(2)were investigated for the APR of hydroxyacetone solution in afixed bed reactor at 225℃and 35 bar.Among them,the Pt/C catalyst showed the highest turnover frequency for H_(2)production(TOF of 8.9 molH_(2)molPt^(-1)min^(-1))and the longest catalyst stability.Over the AlO(OH)and ZrO_(2)supported Pt catalysts,the side reactions consuming H_(2),formation of coke,and Pt sintering result in a low H_(2)production and the fast catalyst deactivation.The proposed reaction pathways suggest that a promising APR catalyst should reform all oxygenates in the aqueous phase,minimize the hydrogenation of the oxygenates,maximize the WGS reaction,and inhibit the condensation and coking reactions for maximizing the hydrogen yield and a stable catalytic performance.

Critical role of carbon support in metal nanoaggregate facilitating Fe-N-C catalyst for PEM fuel cell application

Metal nanoaggregates can simultaneously enhance the activity and stability of Fe-N-C catalysts in proton-exchange-membrane fuel cells(PEMFC).Previous studies on the relevant mechanism have focused on the direct interaction between FeN_(4)active sites and metal nanoaggregates.However,the role of carbon support that hosts metal nanoaggregates and active sites has been overlooked.Here,a Fe-N-C catalyst encapsulating inactive gold nanoparticles is prepared as a model catalyst to investigate the electronic tuning of Au nanoparticles(NPs)towards the carbon support.Au NPs donate electrons to carbon support,making it rich inπelectrons,which reduces the work function and regulates the electronic configuration of the FeN_(4)sites for an enhanced ORR activity.Meanwhile,the electron-rich carbon support can mitigate the electron depletion of FeN_(4)sites caused by carbon support oxidation,thereby preserving its high activity.The yield and accumulation of H_(2)O_(2)are thus alleviated,which delays the oxidation of the catalyst and benefits the stability.Due to the electron-rich carbon support,the composite catalyst achieves a top-level peak power density of 0.74 W/cm^(2) in a 1.5 bar H_(2)-air PEMFC,as well as the improved stability.This work elucidates the key role of carbon support in the performance enhancement of the FeN-C/metal nanoaggregate composite catalysts for fuel cell application.

Research on the flow stability and noise reduction characteristics of quasi-periodic elastic support skin

To enhance flow stability and reduce hydrodynamic noise caused by fluctuating pressure,a quasiperiodic elastic support skin composed of flexible walls and elastic support elements is proposed for fluid noise reduction.The arrangement of the elastic support element is determined by the equivalent periodic distance and quasi-periodic coefficient.In this paper,a dynamic model of skin in a fluid environment is established.The influence of equivalent periodic distance and quasi-periodic coefficient on flow stability is investigated.The results suggest that arranging the elastic support elements in accordance with the quasi-periodic law can effectively enhance flow stability.Meanwhile,the hydrodynamic noise calculation results demonstrate that the skin exhibits excellent noise reduction performance,with reductions of 10 dB in the streamwise direction,11 dB in the spanwise direction,and 10 dB in the normal direction.The results also demonstrate that the stability analysis method can serve as a diagnostic tool for flow fields and guide the design of noise reduction structures.

The Support Systems and Coping Strategies of Infertile Women Attending the Out-Patient Consultation Unit of CHRACERH Yaoundé, Cameroon: A Cross-Sectional Study

Background: Infertility is a complex disorder with significant psycho-social and economic consequences. It globally affects 10% - 15% of couples. In Cameroon, little is known about what women do to overcome the psychosocial aspects of the disease. Objectives: This study aimed to identify the support systems and coping strategies of infertile women attending the outpatient consultation unit of the Gynaecological Endoscopic Surgery and Reproductive Teaching Hospital (CHRACERH), Yaoundé, Cameroon. Methods: A hospital-based cross-sectional study was conducted from the 14th of March to the 6th of April 2023 at CHRACERH Yaoundé. A total of 190 participants were recruited using a convenience sampling method. Data regarding socio-demographic characteristics, support systems and coping strategies were collected using a pretested questionnaire. Descriptive and analytic statistics were conducted using SPSS version 25. Results: The mean age of participants was 39.52 ± 7.64 years. The majority 78.9% of participants were workers (public, private sector, or traders) and were Christians 95.8%. The most common source of psychological support was from family 76.8 and husbands 72.63%. Most of the participants 89.5% resorted to prayer and getting busy 48.4% as a coping strategy. There was no statistically significant relationship between coping strategies and psychological disorders p > 0.05. Conclusion: The main support system of participants was family, husband, and friends. Prayer, getting busy and adoption were the most common coping strategies. There is a need for the Ministry of Public Health and other stakeholders to put in place other support systems and coping strategies (FELICIA) used elsewhere and provide adequate health education and infection control to prevent infertility in Cameroon.

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Enhanced stability of nitrogen-doped carbon-supported palladium catalyst for oxidative carbonylation of phenol

Enhancing the stability of supported noble metal catalysts emerges is a major challenge in both science and industry.Herein,a heterogeneous Pd catalyst(Pd/NCF)was prepared by supporting Pd ultrafine metal nanoparticles(NPs)on nitrogen-doped carbon;synthesized by using F127 as a stabilizer,as well as chitosan as a carbon and nitrogen source.The Pd/NCF catalyst was efficient and recyclable for oxidative carbonylation of phenol to diphenyl carbonate,exhibiting higher stability than Pd/NC prepared without F127 addition.The hydrogen bond between chitosan(CTS)and F127 was enhanced by F127,which anchored the N in the free amino group,increasing the N content of the carbon material and ensuring that the support could provide sufficient N sites for the deposition of Pd NPs.This process helped to improve metal dispersion.The increased metal-support interaction,which limits the leaching and coarsening of Pd NPs,improves the stability of the Pd/NCF catalyst.Furthermore,density functional theory calculations indicated that pyridine N stabilized the Pd^(2+)species,significantly inhibiting the loss of Pd^(2+)in Pd/NCF during the reaction process.This work provides a promising avenue towards enhancing the stability of nitrogen-doped carbon-supported metal catalysts.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.