MEMS RIGHT (China)

Agricultural products have time constraints and logistics transportation cost constraints, which reduce the elasticity of the supply chain. In order to combine market trends, reasonably control supply volume, and improve supply efficiency, an application method of NLP and deep learning is proposed to enhance the elasticity of the cross-border e-commerce supply chain for agricultural products. Using deep learning algorithm BP neural network to predict market demand for agricultural cross-border e-commerce; Using NLP technology to monitor and analyze textual information in the cross-border e-commerce supply chain of agricultural products, and utilizing deep learning models to identify potential risk factors that may affect the supply chain; Apply the word frequency inverse document frequency algorithm and Word2Vec algorithm from NLP technology to multi feature extraction of logistics transportation text. A logistics transportation optimization model for agricultural cross-border e-commerce supply chain considering potential risk factors was constructed by combining deep learning. Adopting enhanced particle swarm optimization algorithm to solve the model, matching multiple features, and achieving optimization of agricultural cross-border e-commerce supply chain logistics transportation. The experimental results show that this method has a lower frequency of supply chain interruptions and a lower error rate in supply chain demand forecasting after application. By integrating NLP and deep learning methods, logistics transportation costs can be effectively reduced, supply chain resilience can be enhanced, and it has good practical application effects.

A sequentially coupled multi-phase thermo-mechanical model for laser powder bed fusion (LPBF) of Al-based composites reinforced by 1 wt.% random TiB2 micron particles was established. Due to the remarkable difference in thermophysical properties, the maximum thermal stress was predicted at the TiB2/Al matrix interface and formed at the liquid–solid transition stage. Meanwhile, complicated evolution curves of temperature, strain, and strain rate were predicted with the laser moving time during the solidification stage. To evaluate the interface cracking susceptibilities of micron-TiB2/AlSi10Mg composites, the flow stress of the matrix was calculated, instead of ultimate tensile strength, based on the physical constitutive relationship. From the comparison between the calculated flow stress and the simulated Von Mise equivalent stress, it was found that an increase in TiB2 particle size was inclined to induce a larger interfacial stress than the calculated flow stress, therefore increasing the interfacial crack tendency, which was also effectively verified by the experimental results.

BackgroundThe Personality Inventory for DSM-5 Brief Form (PID-5-BF) is a 25-item measuring tool evaluating maladaptive personality traits for the diagnosis of personality disorders(PDs). As a promising scale, its impressive psychometric properties have been verified in some countries, however, there have been no studies about the utility of the PID-5-BF in Chinese settings. The current study aimed to explore the maladaptive personality factor model which was culturally adapted to China and to examine psychometric properties of the PID-5-BF among Chinese undergraduate students and clinical patients.MethodsSeven thousand one hundred fifty-five undergraduate students and 451 clinical patients completed the Chinese version of the PID-5-BF. Two hundered twenty-eight students were chosen randomly for test-retest reliability at a 4-week interval. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to discover the most suitable factor structure in China, measurement invariance(MI), internal consistency, and external validity were also calculated.ResultsThe theoretical five-factor model was acceptable, but the exploratory six-factor model was more applicable in both samples (Undergraduate sample: CFI = 0.905, TLI = 0.888, RMSEA = 0.044, SRMR = 0.039; Clinical sample: CFI = 0.904, TLI = 0.886, RMSEA = 0.047, SRMR = 0.060). In the Chinese six-factor model, the Negative Affect domain was divided into two factors and the new factor was named “Interpersonal Relationships”, which was in line with the Big-Six Personality model in Chinese. Measurement invariance across non-clinical and clinical sample was established (configural, weak, strong MI, and partial strict MI). Aside from acceptable internal consistency (Undergraduate sample: alpha = 0.84, MIC = 0.21; Clinical sample: alpha = 0.86, MIC = 0.19) and test-retest reliability(0.73), the correlation between the 25-item PID-5-BF and the 220-item PID-5 was significant(p < 0.01). The six PDs measured by Personality diagnostic questionnaire-4+ (PDQ-4+) were associated with and predicted by expected domains of PID-5-BF.ConclusionsBoth the theoretical five-factor model and the exploratory six-factor model of the PID-5-BF were acceptable to the Chinese population. The five-factor model could allow for comparison and integration with other work on the original theoretical model. However, the Chinese six-factor structure may be more culturally informed in East Asian settings. In sum, the PID-5-BF is a convenient and useful screening tool for personality disorders.

Styrax japonicus (Styraceae) is a deciduous tree that distributed in East Asia, and grown at altitudes between 300 and 1800 meter in China. In this study, the complete chloroplast genome of S. japonicus is reported. The whole genome with size is 157,929 bp contains a pair of inverted repeats (IRs) of 26,054 bp, separated by a large single copy region (LSC) of 87,540 bp and a small single copy region (SSC) of 18,281 bp. The genome contains 131 genes, including 85 protein-coding genes, 38 transfer RNA genes, and 8 ribosomal RNA genes. Phylogenetic analysis supported that the S. japonicus closely clustered with S. grandifloras, S. confuses and S. calvescens.