5G is the fifth generation of cellular networks and has been used in a lot of different areas. 5G often requires sudden rises in power consumption. To stabilize the power supply, a 5G system requires a lithium-ion battery (LIB) or a mechanism called AC main modernization to provide energy support during the power peak periods. The LIB approach is the best option in terms of simplicity and maintainability. Moreover, a 5G system requires not only high-performance energy but also the ability of tracking and prediction. Therefore, the requirement for a smart power supply for lithium-ion batteries with temporal monitoring and estimation is highly desirable. In this paper, we focus on artificial intelligence (AI) improvements to increase the accuracy of LIB state-of-health prediction. By observing the SeqInSeq nature of the battery data, our approach uses self-attention and fixed-point positional encoding. We also take advantage of autoregression to archive the trainable dependency from a non-linear branch and a linear branch in creating the final output. Compared with the current state-of-the-art (SOTA) method, our experimental results show that we provide better accuracy, compared with the baseline output using the NASA and CALCE datasets. From the same setting, we archive a reduction of 20.08% root mean square error (RMSE) and 29.01% mean absolute percentage error (MAPE) on NASA loss, compared to the SOTA approaches. On CALCE, the numbers are a 5.99% RMSE and 12.59% MAPE decrement, which is significant.

This study is conducted to assess the Lean performances of 10 Vietnamese manufacturing companies in terms of 13 factors proposed by Hirano (2009). Managers of 6 large companies and 4 small and medium ones are invited to participate in semi-structured interviews to assess their own company’s Lean performances and raise the ideas for their assessments. The research results show that large companies perform better than the small and medium ones in all 13 factors. As a whole, the studied companies apply Lean at acceptable levels for their operations in terms of awareness revolution, the 5S’s, multi-process operations, labor cost reduction, visual control, quality assurance, standard operations, maintenance and safety. These companies have to effort much more in flow production, Kanban, leveled production, change-over, and human automation. To survive in the context of today Vietnam economy, these companies should focus on long-term strategies to take advantages of Lean philosophy for their future development.

Small and medium sized enterprises (SMEs) cover a high percentage of total operating enterprise number and play an important role in economic development and job creation. They have very limited resources. Knowledge is the only resource that they can exploit to create competitive advantage and organizational performance. The managerial system of SMEs is established and controlled by the owner manager reflexing their cognitition and values. Hence, knowledge management in SMEs is also dependent upon their cognition and values created from their demographic variables such as age, education, profession, managerial experience as well as personality characteristics; namely, need for achievement, locus of control, flexibility to name a few. These owner manager’s characteristics exert influence on the knowledge management and organizational performance. In this study, a model is introduced to explain the relationship between owner manager’s characteristics, knowledge management and organizational performance with market orientation as a mediator for the relationship between knowledge management orientation and organizational performance.

This study evaluates the impact of managerial factors on firm productivity and the relationship between them. SEM (Structural Equation Modeling) results indicate that managerial factors (including top management commitment, human resources training, production management, customer orientation and organisational communication) explain 55% the variation in firm productivity. The results demonstrate a statistically significiant positive relationship between top management commitment and human resources training (1835), production management (.714). In adition, there is a significiant correlation between managerial factors. Implications for managers and directions for future research are also discussed.