Personal Tacit Knowledge and Global Learning Professional Competencies—Multi-Dimensional Relationships

Global learning professional competencies (GLPCs) are essential for college students to be able to address the impact of globalization in the 21st century. Organizations and society-at-large look to higher education to prepare college students with GLPCs. In addition, there is a body of literature that suggest personal tacit knowledge enhance GLPCs. However, researchers have done little from an empirical perspective to determine the relationship between the use of P-T K and enhancement of GLPCs, hence the purpose of this study. The statistical results revealed significant correlations, p st century knowledge society through use of P-T K.

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.

What diameter?What height?Influence of measures of average tree size on area-based allometric volume relationships

Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume equation.When density was included in the models(Volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height measure.When density was not included(Volume=f(Diameter,Height)),the opposite was true.For models with density included,moment-based estimators of stand diameter and height performed better than all other measures.For models without density,largest tree estimators of stand diameter and height performed better than other measures.The overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.

Three anti-inflammatory polysaccharides from Lonicera japonica Thunb.:insights into the structure-function relationships

This study demonstrates the feasibility of producing three polysaccharides(neutral LJP-1,acidic LJP-2 and acidic LJP-3)with significant in vitro and in vivo anti-inflammatory activities from the flowers of Lonicera japonica.The three polysaccharides differed in chemical composition,molecular weight(Mw)distribution,glycosidic linkage pattern,functional groups and morphology.They exhibited excellent protective effects(in a dose-dependent manner)in lipopolysaccharide-injured RAW264.7 macrophages and Cu SO4-damaged zebrafish via reducing NO production and inhibiting the overexpressions of inflammation-related transcription factors,inflammatory proteins and cytokines in the NF-κB/MAPK signaling pathways.Their antiinflammatory effects varied owing to their different molecular characteristics and chemical compositions.Overall,LJP-2 at 400μg/m L was the most effective.LJP-2 consisted mainly of→5)-α-L-Araf(1→,→4)-α-LGalp A(1→and→2)-α-L-Rhap(1→residues with terminal T-β-D-Glcp.Thus,honeysuckle flowers are good sources of anti-inflammatory polysaccharides,and precise fractionation enables the production of potent antiinflammatory agents for the development of functional foods and healthcare products.

Floristic composition and edaphic relationships in ferruginous campo rupestre reference ecosystems

Land use change and occupation have led to modifications in the environment causing loss of biodiversity and ecosystem services throughout the planet.Some environments with high economic relevance,such as the ferruginous campo rupestre(rupestrian grassland known as Canga in Brazil),are even more susceptible to severe impacts due to their extreme habitat conditions and low resilience.The determination of reference ecosystems based on the intrinsic characteristics of the ecosystem is essential for conservation as well as to the implementation of ecological restoration.We proposed the reference ecosystem of the three main types of habitats of the ferruginous campo rupestre based on their floristic composition.We described the floristic composition of each habitat and evaluated the physicochemical properties of the soils and the relationship between plants and soils.All three habitats showed high diversity of plant species and many endemic species,such as Chamaecrista choriophylla,Cuphea pseudovaccinium,Lychnophora pinaster,and Vellozia subalata.The distribution of vegetation was strongly related with the edaphic characteristics,with a set of species more adapted to high concentration of base saturation,fine sand,organic carbon,and iron,while another set of species succeeded in more acidic soils with higher S and silt concentration.We provide support for the contention that the ferruginous campo rupestre is a mosaic of different habitats shaped by intrinsic local conditions.Failure to recognize the floristic composition of each particular habitat can lead to inappropriate restoration,increased habitat homogenization and increased loss of biodiversity and ecosystem services.This study also advances the knowledge base for building the reference ecosystem for the different types of ferruginous campo rupestre habitats,as well as a key database for highlighting those species contribute most to community assembly in this diverse and threatened tropical mountain ecosystem.

Different mechanisms underlie similar species-area relationships in two tropical archipelagoes

Despite much research in the field of island biogeography,mechanisms regulating insular diversity remain elusive.Here,we aim to explore mechanisms underlying plant species-area relationships in two tropical archipelagoes in the South China Sea.We found positive plant species-area relationships for both coral and continental archipelagoes.However,our results showed that different mechanisms contributed to similar plant species-area relationships between the two archipelagoes.For coral islands,soil nutrients and spatial distance among communities played major roles in shaping plant community structure and species diversity.By contrast,the direct effect of island area,and to a lesser extent,soil nutrients determined plant species richness on continental islands.Intriguingly,increasing soil nutrients availability(N,P,K)had opposite effects on plant diversity between the two archipelagoes.In summary,the habitat quality effect and dispersal limitation are important for regulating plant diversity on coral islands,whereas the passive sampling effect,and to a lesser extent,the habitat quality effect are important for regulating plant diversity on continental islands.More generally,our findings indicate that island plant species-area relationships are outcomes of the interplay of both niche and neutral processes,but the driving mechanisms behind these relationships depends on the type of islands.

Modern pollen-vegetation relationships along altitudinal vegetation belts in the Leigong Mountain of southwestern China

Understanding the distribution,dispersal,and correlation of modern pollen with vegetation in mountainous regions is essential for establishing accurate modern analogs for fossil pollen records.This study,conducted in Leigong Mountain on the YunnanGuizhou Plateau of southwestern China,involved the collection of 35 surface soil samples from diverse vegetation communities along an elevational gradient ranging from 1210 to 1875 meters.The results reveal a close correspondence between modern pollen assemblages and vegetation zones.Principal Component Analysis(PCA)results indicate that pollen assemblages can effectively distinguish between subtropical montane evergreen broad-leaved forest(SEBF)and subtropical montane deciduous broadleaved forest(SDBF).However,both SEBF and SDBF show significant overlap with subtropical montane evergreen-deciduous broad-leaved mixed forest(SEMF).Detrended Correspondence Analysis(DCA)results clearly distinguish the three vegetation zones,and the first axis of DCA shows a significant positive correlation with elevation(p<0.01,R=0.48).Discriminant Analysis(DA)successfully assigns 94.4%of the modern pollen samples to their respective vegetation zones.Pollen taxa such as Impatiens,Astertype,and Rosaceae exhibit significant indicative capabilities for the SEBF zone,effectively distinguishing this vegetation zone from others.Pinus and Alnus display overrepresentation in the Leigong Mountain region,while Quercus(D,deciduous-type)and Poaceae exhibit high representation in the SEBF zone.In the SEBF zone,both pollen diversity and richness are the lowest.Our study reveals the complex relationship between the richness and diversity of pollen and vegetation.The diversity and richness of tree and shrub pollen are found to be lower than those of the corresponding plants.The pollen-vegetation relationship elucidated in this study serves as a critical reference for reconstructing ancient environments from fossil pollen retrieved in this region.

Research on the Evaluation System of Community Daycare Center Planning and Implementation from the Perspective of Subject-Object Relationships

Through literature review,we found that evaluations of urban planning implementation often focus narrowly on either the subject(stakeholders)or the object(projects),while micro-level aspects such as site selection and construction are primarily considered within the context of planning implementation.There is insufficient research on the evaluation of implementation.Community daycare centers play a crucial role in the community home care model,yet there is relatively little research on their usage efficiency,satisfaction levels,and spatial evaluation of planning implementation.Based on the theoretical understanding of the human environment,including the subject,object,and their interactions,an evaluation system for the planning and implementation of community day care centers was constructed,incorporating subjective evaluation,objective evaluation,and comparative analysis.

Mediating role of physical activity in the relationship between psychological distress and intimate relationships among stroke patients

BACKGROUND Stroke patients often experience psychological distress,which can impact their intimate relationships with spouses,subsequently influencing their treatment and recovery.Physical activity is considered a key rehabilitation method for stroke patients.This paper aims to analyze whether psychological distress affects intimate relationship between spouses through physical activity.AIM To explore the mediating effect of physical activity between psychological distress and intimate relationship in stroke patients.METHODS A total of 256 stroke patients who underwent treatment at the First People’s Hospital of Shangqiu between July 2021 and July 2022 were enrolled in this study.The participants completed questionnaires,including the Kessler Psychological Distress Scale(K10),the Quality of Relationship Index(QRI),International Physical Activity Questionnaire,and a general information questionnaire.Data analysis were performed using SPSS 23.0.The Harman test was employed to detect common method deviations,and Spearman correlation was used for correlation analysis.The mediating effect was assessed using Process 3.4.1,with significance testing of the regression coefficients conducted using the bias-corrected percentile Bootstrap method(5000 iterations,95%confidence interval).Statistical significance was set at P<0.05.RESULTS The results showed that patients scored an average of 21.61±6.44 points on the K10,32.40±6.19 points on the QRI;the median physical activity level according to the International Physical Activity Questionnaire was 1861(566,2846)MET·min/w.The level of physical activity(the physical activity intensity of the patients reflected by the Interna-tional Physical Activity Questionnaire-Long Form scale)negatively correlated with psychological distress and intimacy(P<0.05),and positively correlated with each other(P<0.05),with the correlation stronger at lower physical activity levels compared to higher ones.The mediating effect of physical activity between psychological distress and intimate relationship was calculated to be 40.23%.Bootstrap analysis further validated the results.The mediating effect of psychological distress on intimate relationships through physical activity level was-0.284,with a confidence interval of-0.409 to-0.163,excluding 0,confirming a significant mediating effect of psychological distress on intimate relationships.CONCLUSION Physical activity significantly affects relationship between psychological distress and intimate relationships among stroke patients.Addressing the role of physical activity may have implications for improving patient outcomes and rehabilitation strategies.

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.

Four new orientation relationships of Mg_(17)Al_(12) phase with magnesium matrix in Mg-8Al-0.5Zn alloys

Although the non-basal precipitates, those not parallel to the basal plane, are more effective to block basal slip in Mg-Al alloys, the crystallographic orientation relationship(OR) between these precipitates and the α-Mg matrix has not been well established. In this work, the crystallography of the non-basal Mg_(17)Al_(12) precipitates in AZ80 alloy was systematically investigated by transmission electron microscopy(TEM). By tilting to a suitable electron beam direction, different kinds of non-basal precipitates were recognized in TEM, and the following four new ORs between the non-basal Mg_(17)Al_(12) precipitates and the matrix were revealed: ■, and ■.Furthermore, these ORs and their habit planes were explained using the edge-to-edge matching model. The findings in this work can provide some guidelines for designing the microstructure of Mg-Al alloys to enhance their precipitation hardening potential.

Structure-guided Capacitance Relationships in Oxidized Graphene Porous Materials Based Supercapacitors

Supercapacitors formed from porous carbon and graphene-oxide(GO)materials are usually dominated by either electric double-layer capacitance,pseudo-capacitance,or both.Due to these combined features,reduced GO materials have been shown to offer superior capacitance over typical nanoporous carbon materials;however,there is a significant variation in reported values,ranging between 25 and 350 F g^(−1).This undermines the structure(e.g.,oxygen functionality and/or surface area)-performance relationships for optimization of cost and scalable factors.This work demonstrates important structure-controlled charge storage relationships.For this,a series of exfoliated graphene(EG)derivatives are produced via thermal-shock exfoliation of GO precursors and following controlled graphitization of EG(GEG)generates materials with varied amounts of porosity,redox-active oxygen groups and graphitic components.Experimental results show significantly varied capacitance values between 30 and 250 F g^(−1)at 1.0 A g^(−1)in GEG structures;this suggests that for a given specific surface area the redox-active and hydrophilic oxygen content can boost the capacitance to 250–300%higher compared to typical mesoporous carbon materials.GEGs with identical oxygen functionality show a surface area governed capacitance.This allows to establish direct structure-performance relationships between 1)redox-active oxygen functional concentration and capacitance and 2)surface area and capacitance.

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.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has matured.Existing neural networks disregard the spatial relationship between two targets in remote sensing images.Semantic segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature boundaries.In this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and GCNs.In the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature information.Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.

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.

Relationship between Kawasaki disease and abdominal pain

This editorial presents an analysis of an article recently published in the World Journal of Clinical Cases.Kawasaki disease(KD)is a well-known pediatric vasculitis characterized by fever,rash,conjunctivitis,oral mucosal changes,and swelling of the extremities.This editorial aims to delve into the intricate relationship between KD and abdominal pain,drawing insights from recent research findings to provide a comprehensive understanding and potential avenues for future investigation.

Elucidating the structure-activity relationship of Cu-Ag bimetallic catalysts for electrochemical CO_(2) reduction

Developing bimetallic catalysts is an effective strategy for enhancing the activity and selectivity of electrochemical CO_(2) reduction reactions,where understanding the structure-activity relationship is essential for catalyst design.Herein,we prepared two Cu-Ag bimetallic catalysts with Ag nanoparticles attached to the top or the bottom of Cu nanowires.When tested in a flow cell,the Cu-Ag catalyst with Ag nanoparticles on the bottom achieved a faradaic efficiency of 54%for ethylene production,much higher than the catalyst with Ag nanoparticles on the top.The catalysts were further studied in the H-cell and zero-gap MEA cell.It was found that placing the two metals in the intensified reaction zone is crucial to triggering the tandem reaction of bimetallic catalysts.Our work elucidates the structure-activity relationship of bimetallic catalysts for CO_(2) reduction and demonstrates the importance of considering both catalyst structures and cell characteristics to achieve high activity and selectivity.