Form Gene Clustering Method about Pan-Ethnic-Group Products Based on Emotional Semantic

The use of pan-ethnic-group products form knowledge primarily depends on a designer＇s subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers＇ perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.

STRUCTURE AND GLASS FORMING ABILITY(GFA)OF AMORPHOUS ALLOYS

A new microstructure model is developed for amorphous alloys,so called Cluster medel, in which the amorphous phase is thought of composing of randomly distributed ordered clusters of different sizes.Thermodynamic calculation on this model deduces a parameter describing the glass forming ability of metallic alloys:α_c=(1-2.08/Φ_m)T_g/T_m,where T_g is gass transition temperature,T_m is the melting temperature,and Φ_m is entralpy change of melting.It is believed that easy glass forming alloy systems have larger values of a_c.This new criterion of GFA not only provides the theoretical background for several GFA criteria in the literature cited,but also can predict the GFA of many alloy systems more reasonably and accurately.

From blocks to cities:Morphology structure rooted in 3D patterns and forming clusters at the block level

This article proposes a 35D form index system to quantitatively describe the 3D form of urban blocks.Utilizing the T-distributed stochastic neighbor(TSNE)embedding algorithm for cluster analysis,the visually complex and disordered urban 3D texture is translated into distinct form clusters,enabling the recognition of the overall urban form structure from the block perspective.The research methodology includes experiments conducted in the central area of Nanjing and comparative analysis in three neighboring cities:Shanghai,Hangzhou,and Suzhou.Results demonstrate the efficacy of form parameters and cluster analysis in achieving sound recognition.The four cities differ remarkably in the number and distribution structure of clusters.Shanghai has the fewest types of clusters with a compact distribution,Suzhou has the most types with a dispersed distribution,and Hangzhou and Nanjing exhibit similar characteristics,located between Shanghai and Suzhou.Correlation analysis reveals a negative relationship between the number of cluster types and the level of urban socioeconomic development in similar areas.This research implies that governments and urban planners can exploit neighborhood morphological types to devise customized spatial management and renewal strategies.The overall urban structure can be improved by strategically minimizing the quantity and distribution of neighborhood morphological types,fostering socioeconomic development.

Multiquark cluster form factors in the relativistic harmonic oscillator model

A QCD multiquark cluster system is studied in the relativistic harmonic oscillator potential model （RHOPM）, and the electromagnetic form factors of the pion, proton and deuteron in the RHOPM are predicted. The calculated theoretical results are then compared with existing experimental data, finding very good agreement between the theoretical predictions and experimental data for these three target particles. We claim that this model can be applied to study QCD hadronic properties, particularly neutron properties, and to find six-quark cluster and/or nine-quark cluster probabilities in light nuclei such as helium 3He and tritium 3H. This is a problem of particular importance and interest in quark nuclear physics.

A novel unsupervised deep learning method for the generalization of urban form

Accurate delineation of urban form is essential to understand the impacts that urbanization has on the environment and regional climate.Conventional supervised classification of urban form requires a rigidly defined scheme and high-quality sample data with class labels.Due to the complexity of urban systems,it is challenging to consistently define urban form types and collect metadata to describe them.Therefore,in this study,we propose a novel unsupervised deep learning method for urban form delineation while avoiding the limitations of conventional super-vised urban form classification methods.The novelty of the proposed method is the Multiscale Residual Convolutional Autoencoder(MRCAE),which can learn the latent representation of differ-ent urban form types.These vectors can be further used to generalize urban form types by using Self-Organizing Map(SOM)and the Gaussian Mixture Model(GMM).The proposed method is applied in the metropolitan area of Guangzhou-Foshan,China.The MRCAE model along with SOM and GMM is used to generalize the urban form types from satellite images.The physical and functional properties of each urban form type are also analyzed using several auxiliary datasets,including building footprints,Points-of-Interests(POIs)and Tencent User Density(TUD)data.The results reveal that the urban form map generated based on the MRCAE can explain 55%of the building height distribution and 55%of the building area distribution,which are 2.1%and 3.3%higher than those derived from the conventional convolutional autoencoder.As the information of urban form is essential to urban climate models,the results presented in this study can become a basis to refine the quantification of urban climate parameters,thereby introducing the urban heterogeneity to help understand the climate response of future urbanization.