The influence of brand marketing on consumers’ emotion in mobile social media environment

With the development of urban economy and the enhancement of competition among cities, urban marketing has attracted more and more attention. Emotional marketing is a people-oriented marketing strategy, which cannot be ignored under the current economic development and urban development level. Today, with abundant commodities and diversified shopping channels, how to attract new customers, maintain old customers and enhance customer loyalty through emotional marketing has become the focus of enterprises’ work. This paper studies from the perspective of clothing. Facing the fierce market competition, in the marketing era of domestic and foreign big enterprises seeking development by brands, if small and medium-sized enterprises want to survive and develop, they must set up the lofty goal of becoming big enterprises, implement brand marketing, and constantly grow and grow healthily in the process of building strong brands. It can be seen from the research in this paper that the recommendation success of this algorithm is 19% better than that of the traditional algorithm in the case of a certain number of partitions, and it is suitable for being put into extensive practice

Influence of static magnetic field on rapid solidified structure and nanocrystallization behavior of Fe–Si–B–Cu soft magnetic alloys with pre-existing α-Fe nanocrystals

The effects of static magnetic field strength on the rapid solidified structure, thermal stability, crystallization structure and soft magnetic properties of melt-spun Fe81.6Si4B13Cu1.4 alloys were investigated and the corresponding related mechanism was discussed in terms of formation of pre-existing α-Fe crystals and nanocrystallization behaviors. The rise of magnetic field strength promotes the formation of the α-Fe crystals in as-spun alloy, enhances the competitive growth between the crystals during annealing, and then refines the crystals in the annealed alloy and improves their magnetic softness. The Fe81.6Si4B13Cu1.4 alloy prepared under 200 mT contains α-Fe crystals with a high number density and average size (D) of 8.3 × 1022 m−3 and 5.9 nm, respectively, in as-spun state, and possesses fine crystals with a D of 18.2 nm and excellent soft magnetic properties with a high saturation magnetization of 1.75 T, low coercivity of 9.2 A/m and high effective permeability of 10,160 at 1 kHz after annealing at 633 K for 60 min

Effects of Si content on structure and soft magnetic properties of Fe(81.3)Si(x)B(17-x)Cu(1.7)nanocrystalline alloys with pre-existing alpha-Fe nanocrystals

The as-spun structure, thermal stability, crystallization structure and soft magnetic properties of Fe81.3SixB17-xCu1.7(x = 0-8) alloys were investigated. The Fe-Si-B-Cu amorphous alloys contain alpha-Fe nanocrystals with a high number density (N-d) in as-spun state and show uniform nanocrystalline structure and typical soft magnetic characteristics after annealing. The rise of Si content from 0 to 4 at% increases theN(d), while the further rise to 8 at% shows an adverse effect. The increasedN(d)enhances competitive growth between the crystals during crystallization process, then refines structure and improves soft magnetic properties of the nanocrystalline alloys. Contrarily, the decreasedN(d)results in coarsened nanostructure and deteriorated magnetic softness. The alloy with Si content of 4 at% contains alpha-Fe crystals with a highN(d)of 2.2 x 10(23) m(-3)in as-spun state and possesses fine alpha-Fe grains with an average size (D) of 14 nm, low coercivity (H-c) of 7.1 A/m, high effective permeability (at 1 kHz) of 16,500 and saturation magnetic flux density of 1.77 T after annealing at 668 K for 60 min. In addition, theH(c)of present Fe-Si-B-Cu nanocrystalline alloys is almost proportional toD(3)due to the high ratio of uniaxial anisotropy to average random anisotropy

Estimation of Fusarium Head Blight Severity Based on Transfer Learning

The recognition accuracy of traditional image recognition methods is heavily dependent on the design of complicated and tedious hand-crafted features. In view of the problems of poor accuracy and complicated feature extraction, this study presents a methodology for the estimation of the severity of wheat Fusarium head blight (FHB) with a small sample dataset based on transfer learning technology and convolutional neural networks (CNNs). Firstly, we utilized the potent feature learning and feature expression capabilities of CNNs to realize the automatic learning of FHB characteristics. Using transfer learning technology, VGG16, ResNet50, and MobileNetV1 models were pre-trained on the ImageNet. The knowledge was transferred to the estimation of FHB severity, and the fully connected (FC) layer of the models was modified. Secondly, acquiring the wheat images at the peak of the outbreak of FHB as the research object, after preprocessing for size filling on the wheat images, the image dataset was expanded with operations such as mirror flip, rotation transformation, and superimposed noise to improve the performance of the model and reduce the overfitting of models. Finally, under the Tensorflow deep learning framework, the VGG16, ResNet50, and MobileNetV1 models were subjected to transfer learning. The results showed that in the case of transfer learning and data augmentation, the ResNet50 model in Accuracy, Precision, Recall, and F1 score was better than the other two models, giving the highest accuracy of 98.42% and F1 score of 97.86%. The ResNet50 model had the highest recognition accuracy, providing technical support and reference for the accurate recognition of FHB

Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery

Crop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring of wheat FHB is important. This study proposed a method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and multispectral imaging technology combined with spectral and textural analysis to monitor FHB. First, the multispectral imagery of the wheat population was collected by UAV. Second, 10 vegetation indices (VIs)were extracted from multispectral imagery. In addition, three types of textural indices (TIs), including the normalized difference texture index (NDTI), difference texture index (DTI), and ratio texture index (RTI) were extracted for subsequent analysis and modeling. Finally, VIs, TIs, and VIs and TIs integrated as the input features, combined with k-nearest neighbor (KNN), the particle swarm optimization support vector machine (PSO-SVM), and XGBoost were used to construct wheat FHB monitoring models. The results showed that the XGBoost algorithm with the fusion of VIs and TIs as the input features has the highest performance with the accuracy and F1 score of the test set being 93.63% and 92.93%, respectively. This study provides a new approach and technology for the rapid and nondestructive monitoring of wheat FHB

Refined assessment of flash flood vulnerability in Linzhi based on spatialization and GIS

Flash floods are one of the most serious mountain disasters in Linzhi, China, pose a grave threat to the lives and property of the residents. However, the current regional vulnerability research cannot meet the needs of refined disaster prevention and mitigation in mountainous areas. Therefore, to address this knowledge gap, this study presented a vulnerability assessment framework base on spatialization technique along with the triangular fuzzy number-based analytic hierarchy process (TFN-AHP) and random forest (RF) model. To achieve this goal, an assessment system containing 12 indicators was constructed from the three perspectives of exposure, susceptibility and coping capacity. Firstly, factor spatialization is introduced in data preprocessing. Then TFN-AHP and the RF model were used to calculate the exposure, susceptibility, and coping capacity. Finally, the spatial distribution of the vulnerability throughout Linzhi was obtained. According to the results, the exposure, susceptibility and coping capacity in most areas are extremely low and low, accounting for 74.1%, 99% and 99%, respectively. The vulnerability is similar to the exposure that most of the regions have extremely low vulnerability and low vulnerability, accounting for 83.5%. The moderate vulnerability regions account for 8.5%, and the high vulnerability regions account for only 1%. The assessment results show that high-exposure areas mainly exhibit a linear distribution, and mainly include river valleys, cultivated land, and residential areas. Obviously, these areas are mainly distributed in the population and economic agglomeration areas and on low-altitude terrain. The distribution of high and comparatively high vulnerability zones, where economic and human activities are considerably high, are closely to the topography of the catchment. Thus, the results of this study provide scientific and technological evidence for resettlement, population distribution adjustment, and prevention and mitigation of flash floods in Linzhi

Reversible Mechanical Regulation and Splicing Ability of Alginate-Based Gel Based on Photo-Responsiveness of Molecular-Level Conformation

In this study, benefiting from the sensitive molecular conformation transversion in azobenzene, a new strategy for fabricating alginate gels with the abilities of splicing and photo-responsive mechanical adjustment is reported. Firstly, a 4,4’-azobis(benzoylhydrazide) (Azo-hydrazide) linker was used to crosslink alginate physically via the electrostatic interaction between hydrazide groups and carboxyl groups. It was then shaped and transferred in situ to a chemically crosslinked gel via 450 nm light irradiation. Under the irradiation, the molecular conformation change of azobenzene in the linker was able to form covalent bonds at the crosslinking points of the gels. Furthermore, the reversible conformation transformation of azobenzene was able to induce the increase and decrease of the storage modulus under irradiation with 365 nm light and 450 nm light, respectively, while also providing gel-like mechanical properties, depending upon the irradiation time and given wavelength. Meanwhile, the results also indicated that active groups could contribute to the splicing ability of the gel and construct a hollow cavity structure. It is believed that this work could provide a versatile strategy for preparing photo-responsive gels with reversibly tunable mechanical properties