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.

Patterns and drivers of avian taxonomic and phylogenetic beta diversity in China vary across geographical backgrounds and dispersal abilities

Geographical background and dispersal ability may strongly influence assemblage dissimilarity;however,these aspects have generally been overlooked in previous large-scale beta diversity studies.Here,we examined whether the patterns and drivers of taxonomic beta diversity(TBD)and phylogenetic beta diversity(PBD)of breeding birds in China vary across(1)regions on both sides of the Hu Line,which demarcates China’s topographical,climatic,economic,and social patterns,and(2)species with different dispersal ability.TBD and PBD were calculated and partitioned into turnover and nestedness components using a moving window approach.Variables representing climate,habitat heterogeneity,and habitat quality were employed to evaluate the effects of environmental filtering.Spatial distance was considered to assess the impact of dispersal limitation.Variance partitioning analysis was applied to assess the relative roles of these variables.In general,the values of TBD and PBD were high in mountainous areas and were largely determined by environmental filtering.However,different dominant environmental filters on either side of the Hu Line led to divergent beta diversity patterns.Specifically,climate-driven species turnover and habitat heterogeneity-related species nestedness dominated the regions east and west of the line,respectively.Additionally,bird species with stronger dispersal ability were more susceptible to environmental filtering,resulting in more homogeneous assemblages.Our results indicated that regions with distinctive geographical backgrounds may present different ecological factors that lead to divergent assemblage dissimilarity patterns,and dispersal ability determines the response of assemblages to these ecological factors.Identifying a single universal explanation for the observed pattern without considering these aspects may lead to simplistic or incomplete conclusions.Consequently,a comprehensive understanding of large-scale beta diversity patterns and effective planning of conservation strategies necessitate the consideration of both geographical background and species dispersal ability.

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.

ZnO Additive Boosts Charging Speed and Cycling Stability of Electrolytic Zn–Mn Batteries

Electrolytic aqueous zinc-manganese(Zn–Mn) batteries have the advantage of high discharge voltage and high capacity due to two-electron reactions. However, the pitfall of electrolytic Zn–Mn batteries is the sluggish deposition reaction kinetics of manganese oxide during the charge process and short cycle life. We show that, incorporating ZnO electrolyte additive can form a neutral and highly viscous gel-like electrolyte and render a new form of electrolytic Zn–Mn batteries with significantly improved charging capabilities. Specifically, the ZnO gel-like electrolyte activates the zinc sulfate hydroxide hydrate assisted Mn^(2+) deposition reaction and induces phase and structure change of the deposited manganese oxide(Zn_(2)Mn_(3)O_8·H_(2)O nanorods array), resulting in a significant enhancement of the charge capability and discharge efficiency. The charge capacity increases to 2.5 mAh cm^(-2) after 1 h constant-voltage charging at 2.0 V vs. Zn/Zn^(2+), and the capacity can retain for up to 2000 cycles with negligible attenuation. This research lays the foundation for the advancement of electrolytic Zn–Mn batteries with enhanced charging capability.

Creative Problem-Solving and Music: Analyzing the Correlation between Music and Divergent Thinking Abilities

This research paper delves into the connection, between problem-solving and music. It’s a topic that has piqued the interest of scholars in fields, including science and neuroscience. The study explores how music can influence our ability to think divergently which is an aspect of creative thinking. It builds upon advancements in methods to investigate the relationship between music and divergent thinking aiming to uncover potential correlations. Doing it offers insights into the interplay between artistic expression and cognitive innovation. This research combines an analysis of existing literature with data collected from a group of participants shedding light on how music impacts our capacity for creative thinking. It demonstrates that music plays a role as a catalyst, for stimulating and enhancing thinking abilities.

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.

Whole-process case management effects on mental state and selfcare ability in patients with liver cancer

BACKGROUND Regarding the incidence of malignant tumors in China,the incidence of liver cancer ranks fourth,second only to lung,gastric,and esophageal cancers.The case fatality rate ranks third after lung and cervical cancer.In a previous study,the whole-process management model was applied to patients with breast cancer,which effectively reduced their negative emotions and improved treatment adherence and nursing satisfaction.METHODS In this single-center,randomized,controlled study,60 randomly selected patients with liver cancer who had been admitted to our hospital from January 2021 to January 2022 were randomly divided into an observation group(n=30),who received whole-process case management on the basis of routine nursing mea-sures,and a control group(n=30),who were given routine nursing measures.We compared differences between the two groups in terms of anxiety,depression,the level of hope,self-care ability,symptom distress,sleep quality,and quality of life.RESULTS Post-intervention,Hamilton anxiety scale,Hamilton depression scale,memory symptom assessment scale,and Pittsburgh sleep quality index scores in both groups were lower than those pre-intervention,and the observation group had lower scores than the control group(P<0.05).Herth hope index,self-care ability assessment scale-revision in Chinese,and quality of life measurement scale for patients with liver cancer scores in both groups were higher than those pre-intervention,with higher scores in the observation group compared with the control group(P<0.05).CONCLUSION Whole-process case management can effectively reduce anxiety and depression in patients with liver cancer,alleviate symptoms and problems,and improve the level of hope,self-care ability,sleep quality,and quality of life,as well as provide feasible nursing alternatives for patients with liver cancer.

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.

Broader Horizons for People with Disabilities

Providing better-quality,more comprehensive education,and employment opportunities for people with disabilities serves their interests and also those of communities,the nation,and humanity as a whole.

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.

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.

Influencing factors of self-management ability among dry eye patients in west China

AIM:To investigate the current situation and influencing factors of self-management ability in dry eye patients in west China.METHODS:A total of 265 patients clinically diagnosed with dry eye received a convenience survey questionnaire at West China Hospital of Sichuan University.All participants completed the rating scale of health self-management skill for adults(AHSMSRS),Huaxi Emotional-Distress Index(HEI),e-health literacy scale(e-HEALS)and Brief Illness Perception Questionnaire(Brief-IPQ).A generalized linear model was employed to establish a multivariate linear model with demographic data,psychological state,e-HEALS,and illness perception as independent variables and health selfmanagement skill score as the dependent variable.RESULTS:The mean score for health self-management skill was 165.58±15.79.Multivariate analysis revealed that advanced age,better illness perception and improved psychological state were associated with better health selfmanagement ability among dry eye patients.Furthermore,the health self-management ability of patients with a disease duration less than 1y was found to be higher compared to those with a disease duration exceeding 1y.CONCLUSION:The health self-management ability of dry eye patients in west China is relatively high.Age,duration of disease,illness perception and psychological state are the influencing factors on the health selfmanagement ability of dry eye patients.

Experimental Study on Wire Melting Control Ability of Twin-Body Plasma Arc

The twin-body plasma arc has the decoupling control ability of heat transfer and mass transfer,which is beneficial to shape and property control in wire arc additive manufacturing.In this paper,with the wire feeding speed as a characteristic quantity,the wire melting control ability of twin-body plasma arc was studied by adjusting the current separation ratio(under the condition of a constant total current),the wire current/main current and the position of the wire in the arc axial direction.The results showed that under the premise that the total current remains unchanged(100 A),as the current separation ratio increased,the middle and minimum melting amounts increased approximately synchronously under the effect of anode effect power,the first melting mass range remained constant;the maximum melting amount increased twice as fast as the middle melting amount under the effect of the wire feeding speed,and the second melting mass range was expanded.When the wire current increased,the anode effect power and the plasma arc power were both factors causing the increase in the wire melting amount;however,when the main current increased,the plasma arc power was the only factor causing the increase in the wire melting amount.The average wire melting increment caused by the anode effect power was approximately 2.7 times that caused by the plasma arc power.The minimum melting amount was not affected by the wire-torch distance under any current separation ratio tested.When the current separation ratio increased and reached a threshold,the middle melting amount remained constant with increasing wire-torch distance.When the current separation ratio continued to increase and reached the next threshold,the maximum melting amount remained constant with the increasing wire-torch distance.The effect of the wire-torch distance on the wire melting amount reduced with the increase in the current separation ratio.Through this study,the decoupling mechanism and ability of this innovative arc heat source is more clearly.

Effect of Y element on atomic structure, glass forming ability,and magnetic properties of FeBC alloy

The atomic structure of amorphous alloys plays a crucial role in determining both their glass-forming ability and magnetic properties. In this study, we investigate the influence of adding the Y element on the glass-forming ability and magnetic properties of Fe_(86-x)Y_xB_7C_7(x = 0, 5, 10 at.%) amorphous alloys via both experiments and ab initio molecular dynamics simulations. Furthermore, we explore the correlation between local atomic structures and properties. Our results demonstrate that an increased Y content in the alloys leads to a higher proportion of icosahedral clusters, which can potentially enhance both glass-forming ability and thermal stability. These findings have been experimentally validated. The analysis of the electron energy density and magnetic moment of the alloy reveals that the addition of Y leads to hybridization between Y-4d and Fe-3d orbitals, resulting in a reduction in ferromagnetic coupling between Fe atoms. This subsequently reduces the magnetic moment of Fe atoms as well as the total magnetic moment of the system, which is consistent with experimental results. The results could help understand the relationship between atomic structure and magnetic property,and providing valuable insights for enhancing the performance of metallic glasses in industrial applications.

Cultivation Path of Nursing Undergraduates’Scientific Research and Innovation Ability

Objective: The cultivation of the innovation ability and scientific research is one of the nursing learning objectives for undergraduate students. To explore the method and effect of training system of scientific research innovation ability of nursing undergraduates based on “3332”. Methods: Three course learning modules are constructed: stage-based course learning module, systematic project practice training module and comprehensive practice training module. A practical training platform for scientific research innovation projects is built, and undergraduate scientific research innovation ability training is carried out from both in-class and out-of-class lines. Results: Since 2017, the students have obtained 7 national innovation and entrepreneurship training programs, 52 university-level undergraduate scientific research projects, published more than 10 academic papers, and obtained 2 patent authorization. Conclusions: The training system of scientific research innovation ability of nursing undergraduates based on “3332” is conducive to the development of scientific research innovation ability of nursing students, and to cultivate nursing talents who can adapt to the development of the new era and have better post competence.

Combining ability of Egyptian cotton(Gossypium barbadense L.)reveals genetic potential for improved yield and fiber quality

Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivation of enhanced hybrid plants with desired traits,such as high yield and fine fiber quality.In this study,the general combining ability(GCA)and specific combining ability(SCA)of yield and fiber quality of nine cotton parents(six lines and three testers)and eighteen F1 crosses produced using a line×tester mating design were analyzed.Results The results revealed significant effects of genotypes,parents,crosses,and interactions between parents and crosses for most of the studied traits.Moreover,the effects of both additive and non-additive gene actions played a notably significant role in the inheritance of most of the yield and fiber quality attributes.The F1 hybrids of(Giza 90×Aust)×Giza 86,Uzbekistan 1×Giza 97,and Giza 96×Giza 97 demonstrated superior performance due to their favorable integration of high yield attributes and premium fiber quality characteristics.Path analysis revealed that lint yield has the highest positive direct effect on seed cotton yield,while lint percentage showed the highest negative direct effect on seed cotton yield.Principal component analysis identified specific parents and hybrids associated with higher cotton yield,fiber quality,and other agronomic traits.Conclusion This study provides insights into identifying potential single-and three-way cross hybrids with superior cotton yield and fiber quality characteristics,laying a foundation for future research on improving fiber quality in cotton.

Research on the Effect of Dance Therapy on Improving Social Communication Ability of Children with Autism

Research motivation:Through the 12 weeks dance therapy intervention for children with autism,the purpose is to explore the intervention model of dance therapy for children with autism and the changes in motor ability,social ability,and communication ability of children with autism after dance therapy intervention.The results of the research are expected to expand the intervention mode of dance therapy in my country and provide practical reference for rehabilitation intervention of children with autism.Research methods:24 autistic boys aged 6 to 12 with mild to moderate symptoms were recruited and screened through the Internet as the subjects of this study.We randomly divided them into experimental group(N=12)and control group(N=12).All children with autism have an autism diagnosis certificate issued by Children’s Hospital or a tertiary hospital,excluding other mental diseases(such as epilepsy,major physical disability,mental illness,no history of drugs and other interventions,etc.).We used the paired sample t-test to compare the score difference between the dance treatment group and the control group before and after the two groups,and used the observation method to record the basic communication behavior and the number of active communication behaviors in the experimental group during the intervention process.All data analysis is used in SPSS 20.0.Research results:After 12 weeks of dance therapy intervention,there were statistically significant differences in the gross movement,balance,and coordination abilities of the children in the experimental group compared with those before the intervention(p<0.01).There was no significant difference between the children in the control group(p>0.05).After 12 weeks of dance therapy intervention,there were statistically significant differences in the scores of the social response scale for children with autism in the experimental group(p<0.05).There was no significant change in the scores of each item of the SRS scale before and after intervention in the control group and the dance treatment group(p>0.05).