Currently, there is a significant gap between the training objectives and the actual situation of electromechanical talents in higher vocational colleges. Many teachers in electromechanical departments do not meet the required qualifications and are unable to adapt to the developments of the new era. The talent training mode is insufficiently comprehensive, and the criteria for talent assessment are not unified. In response to these issues, it is necessary to promptly change the mindset, innovate educational ideas, focus on the present while planning for the future, clarify training objectives, adopt a dual education model that integrates production and education, strengthen the faculty, utilize their potential, and improve the overall educational quality to provide guarantees for talent development.

This article focuses on studying how transportation connectivity affects Vietnam’s trade with Association of Southeast Asian Nations (ASEAN) countries. By using a gravity model, the article applies fixed effects (FE) and random effects (RE) to analyze panel data on trade, GDP, tariffs, border effects, and indicators. The number represents Vietnam’s transport connectivity with ASEAN countries from 2004 to 2021. Research results show that transport connectivity hurts Vietnam’s trade with other countries. ASEAN. The article proposes solutions for the Government and Vietnamese export enterprises to promote intra-ASEAN trade in the direction of increasing the added value of Vietnam’s imported and exported goods within ASEAN countries and balancing between Developing intra-ASEAN and foreign trade.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

We studied Zeta potentials of nanoparticles titanium dioxides （nTiO₂） in different concentration of NaNO₃ and phosphate (P) solutions. In addition, the effect of flow rate on the transport of nTiO₂ in P was investigated at pH＝6.5. Experimental results show that the Zeta potential of nTiO₂ is compressed with the increasing ion concentration (IC) of NaNO₃ at pH＝6.5. The negative charge increases with the augment of P. Therefore, the high P and low NaNO3 induce the stabilization of nTiO₂ aggregates. The transport experiments suggest that the rapid flow rate is favorable for the transportability of nTiO₂ and soluble phosphate. The breakthrough transport curves (BTCs) of nTiO₂ in sand columns can be fitted well with two-site kinetic attachment model. The modeling results suggest that the values of first-order attachment rate coefficients (k₂) and detachment rate coefficients (k_2d) on site 2 and first-order attachment rate coefficients (k₁) on site 1 are responsible to the attaching efficiency of nTiO₂ on sands and their transportability.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.