THE OPTIMAL CYCLE TIME FOR DETERIORATING ITEMS WITH LIMITED STORAGE CAPACITY UNDER PERMISSIBLE DELAY IN PAYMENTS

Inventory models with deteriorating items have received considerable attention in recent years. In considering the deteriorating inventory with permissible delay in payments, most researchers pay attention to a single warehouse. Under conditions of permissible delay in payments, this paper develops a model to determine the optimal cycle time for a single deteriorating item that is stored in two different warehouses. A rented warehouse (RW) is used to store the excess units over the fixed capacity W of the owned warehouse (OW). The rented warehouse is assumed to charge higher unit holding cost than the OW. In this paper, we propose a two-warehouse inventory model for deteriorating items under permissible delay in payments. It is assumed that the deterioration rate in RW is the same as in OW, and the holding cost in RW is greater than that in OW. The stocks of RW are transported to OW in continuous release pattern and the transportation cost is ignored. Three theorems are developed to determine the optimal cycle time and numerical examples are given to illustrate these theorems.Cost–benefit analysis, EOQ, inventory, deteriorating items, permissible delay in payments

Music Intelligent Push Play and Data Analysis System Based on 5G Internet of Things

With the rapid development of information science today, multifunctional and intelligent applications have gradually become the focus of attention. In the data management system, the first consideration is the reliability of the data source, followed by the intelligent processing after the data are collected. Due to the upgrade of the Internet to the Internet of Things, the way of network information transmission has also become a problem that people need to think about. The transmission mode of network information services will be converted from the passive transmission of information by traditional servers to the form of actively pushing information. The application of intelligent push technology in the field of the Internet of Things is a prominent and important direction in the development of the Internet of Things. This article mainly introduces the research on the intelligent music push and data analysis system based on the 5G Internet of Things, with the intention of providing some ideas and directions for the research of the music intelligent push and play and data analysis system. This paper proposes a research method for music intelligent push playback and data analysis system based on 5G Internet of Things, including current intelligent push related technologies, music evaluation matrix, user dissimilarity matrix, and music feature similarity calculation. The experimental results in this paper show that with the increase in the number of users, the accuracy of the recommended results of the system under the Hadoop framework gradually stabilizes, eventually reaching 91.2%

Selective ring-opening metathesis polymerization (ROMP) of cyclobutenes. Unsymmetrical ladderphane containing polycyclobutene and polynorbornene strands

At 0 °C in THF in the presence of Grubbs first generation catalyst, cyclobutene derivatives undergo ROMP readily, whereas norbornene derivatives remain intact. When the substrate contains both cyclobutene and norbornene moieties, the conditions using THF as the solvent at 0 °C offer a useful protocol for the selective ROMP of cyclobutene to give norbornene-appended polycyclobutene. Unsymmetrical ladderphane having polycyclobutene and polynorbornene as two strands is obtained by further ROMP of the norbornene appended polycyclobutene in the presence of Grubbs first generation catalyst in DCM at ambient temperature. Methanolysis of this unsymmetrical ladderphane gives polycyclobutene methyl ester and insoluble polynorbornene-amide-alcohol. The latter is converted into the corresponding soluble acetate. Both polymers are well characterized by spectroscopic means. No norbornene moiety is found to be incorporated into polycyclobutene strand at all. The double bonds in the polycyclobutene strand are mainly in cis configuration (ca 70%), whereas the E/Z ratio for polynorbornene strand is 8:1

Machine learning-based e-commerce platform repurchase customer prediction model.

In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The purpose is to avoid the accuracy of the linear model easy to fit and the decision tree model over-fitting. The results show that the model constructed by the article has further improvement than the single model. Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust