Researching Dynamic Brand Competitiveness Based on Consumer Clicking Behavior

Analyzing brand dynamic competition relationship by using consumer sequential online click data, which was collected from JD.com. It is found that the competition intensity of the products across categories is quite different. Owing to the purchasing time of durable-like goods is more flexible, that is, the purchasing probability of such products changes more obviously over time. Therefore, we use the Local Polynomial Regression Model to analyze the relationship between the brand competition of durable-like goods and the purchasing probability of the specific brand. Finding that when brands increase at a half of the total market share for consumers cognition preference, the brands’ competitiveness is peak and makes no significant different from one hundred percent for consumer to complete a transaction. The findings contribute to brand competitiveness for setting up marketing strategy from the dynamic and online consumer behavior’s perspective

An Optimal Method For Product Selection By Using Online Ratings And Considering Search Costs

With the collecting and publishing data about consumers purchasing and browsing products at the platform of online, this data prodives new ways to better understand the consumers search behavior before purchase. How to base on consumers online search behavior and simutaneously consider offline experience costs is worth studying. An optimal method based on the utility of the attribute of product is proposed. The proposed method follows steps below. Firstly, based on the multi-attribute utility theory, the overall utility of product is calculated by using ratings data. Secondly, the overall utility is combined into the original sequential search model to find the optimal selection strategy. Thirdly, the candidate product sets arranged in descending order of the reservation utilities are finally obtained. Finally, taking the online ratings data provided by a comprehensive automobile website as an example, lastly the proposed method is simulated and compared with other method. The result shows that the proposed method is feasible and effective

Revealing the cleavage mechanism of the crack propagation process in martensitic steels

A phase field model is presented to investigate the influence of cleavage planes in martensite and austenite phases and the loading rate on the crack propagation process with considering the Kurdjumov-Sachs (K-S) orientation relationship in martensitic steels by the finite element method. The transgranular (through the martensite and austenite phases) and intergranular (along the martensite/austenite interface) fracture processes in martensitic steels can be simulated by this model. This model can simulate the crack propagation in martensitic steels with displaying the crack propagation path in austenite and martensite phases and stress distribution directly. Moreover, this model reveals the energy difference required by transgranular and intergranular fracture in martensitic steels and the hindering effect of martensite on the crack propagation process. The simulation results further indicate that the preferential cleavage planes of crack propagation in martensite and austenite phases are (100)α’ and (111)γ planes, respectively, which is consistent with the experimental results in martensitic steels. An interesting finding is that the influence of the loading rate on the maximum loading for material failure only acts on the crack propagation stage rather than the crack initiation stage. Therefore, this model can be a powerful tool to investigate the cleavage mechanism of the crack propagation process during the water quenching process and to avoid water quenching cracking in martensitic steels

An optimized machine-learning model for mechanical properties prediction and domain knowledge clarification in quenched and tempered steels

Clarifying the relationship between compositions, heat treatment processes, and mechanical properties of carbon steel, as the basis of material design, is challengeable, while machine learning (ML) makes this complex correlation explicit. In this work, three different mechanical properties (ultimate tensile strength, yield strength, and total elongation) were predicted based on the collected quenched and tempered (Q&T) steel dataset by six ML algorithms, in which the optimal Gaussian process regression (GPR) combined with the key descriptors by feature engineering to train an optimized ML model. Such a simplified ML model shows even better prediction accuracy. In the above training process, Bayesian optimization (BO) searches the hyperparameters efficiently. The newly collected data also achieve small prediction errors, showing good generalization capacity. To maximize the application value of the current ML model, the grid prediction of composition and process, and local interpretable model-agnostic explanations (LIME) were utilized to reveal some new insights about the quenched and tempered steels, which could shed light on the ongoing new material design. Besides, the overfitting tendency of the ML model was examined to ensure the rationality of prediction, and the influence of data amount on the prediction performance was discussed

Evaluation of finite element simulation of water quenched cracking for medium carbon alloy steels using acoustic emission technique

Quenching is an important technology for enhancing strength of steels. It is the development tendency of heat treatment to replace both polluted oil quenching and polymer aqueous solution quenching with clean water quenching. However, the problem of easy cracking of medium carbon alloy steels quenched in water has long been a concern for many researchers. Although the finite element simulation (FES) can be used to predict tensile transient stress concentration that may lead cracking during quenching, there has been a lack of experiments to confirm the time of cracking during water quenching. And this problem which has long puzzled us has recently been solved, that is, the occurrence time of quenching cracking can be detected by acoustic emission (AE) technology used k-means clustering method to process AE signals. The results indicate that the cracking time of water quenching for a AISI 4140 medium carbon alloy steel cylinder sample is 11–15 s, which is well consistent with 9–13 s predicted by FES. Moreover, FES reveals that the quenching cracking is caused by the tangential tensile stress in the surface layer, and it is also confirmed by the macroscopical observation. Besides, the FES shows that the tensile stress value leading to water-quenching cracking is far lower than yield strength, which reveals that quenching cracking is a brittle failure under low stress state. The study indicates the high accuracy of our FES, which provides a new path for the process design to avoid water quenching cracking

Effects of Mg contents on microstructures and second phases of as-cast Al–Zn–Mg–Cu alloys

The effects of Mg content on the microstructures and second phases of the as-cast Al–Zn–Mg–Cu alloys were investigated. The results show that the nonequilibrium eutectic structure consists of α(Al), Al7Cu2Fe, η(MgZn2) and T(AlCuMgZn) intermetallic compounds. The alloys with the highest Mg content generate nonequilibrium eutectic structures. The maximum value of nonequilibrium eutectic structures is 10.13 ± 0.62%. As the Mg content increases, the number of tertiary dendrites in the α(Al) matrix increases significantly, and more Mg can be dissolved into the Al- matrix. In addition, as the Mg content increases, the crystallization temperature range decreases from 169.8 K to 157.3 K. When the Mg content is higher than 2.6 wt%, the microstructure evolution of the Al–Zn–Mg–Cu aluminum alloy is as follows: Liq. → Liq. + α(Al) → Liq. + α(Al) + Al7Cu2Fe → α(Al) + Al7Cu2Fe + η(MgZn2) + T(AlCuMgZn). These results play a certain role in promoting the basic research of Al–Zn–Mg–Cu aluminum alloys with high Mg content

Molecular Characterization of LKB1 of Triploid Crucian Carp and Its Regulation on Muscle Growth and Quality

Liver Kinase B1 (LKB1) is a serine/threonine kinase that can regulate energy metabolism and skeletal muscle growth. In the present study, LKB1 cDNA of triploid crucian carp (Carassius auratus) was cloned. The cDNA contains a complete open reading frame (ORF), with a length of 1326 bp, encoding 442 amino acids. Phylogenetic tree analysis showed that the LKB1 amino acid sequence of the triploid crucian carp had a high sequence similarity and identity with carp (Cyprinus carpio). Tissue expression analysis revealed that LKB1 was widely expressed in various tissues. LKB1 expressions in the brain were highest, followed by kidney and muscle. In the short-term LKB1 activator and inhibitor injection experiment, when LKB1 was activated for 72 h, expressions of myogenic differentiation (MyoD), muscle regulatory factor (MRF4), myogenic factor (MyoG) and myostatin 1 (MSTN1) were markedly elevated and the content of inosine monophosphate (IMP) in muscle was significantly increased. When LKB1 was inhibited for 72 h, expressions of MyoD, MyoG, MRF4 and MSTN1 were markedly decreased. The long-term injection experiment of the LKB1 activator revealed that, when LKB1 was activated for 15 days, its muscle fibers were significantly larger and tighter than the control group. In texture profile analysis, it showed smaller hardness and adhesion, greater elasticity and chewiness. Contrastingly, when LKB1 was inhibited for 9 days, its muscle fibers were significantly smaller, while the gap between muscle fibers was significantly larger. Texture profile analysis showed that adhesion was significantly higher than the control group. A feeding trial on triploid crucian carp showed that with dietary lysine-glutamate dipeptide concentration increasing, the expression of the LKB1 gene gradually increased and was highest when dipeptide concentration was 1.6%. These findings may provide new insights into the effects of LKB1 on fish skeletal muscle growth and muscle quality, and will provide a potential application value in improvement of aquaculture feed formula