Volume-11, Issue-1, January 2025

Abstract

In recent day modern power, system and industrial application witnessed great interest by engineering and scientist. Converters used with these applications also developed leading to improve efficiency, power management, cost, reliability and others. Bidirectional dc-dc converters are the base for all electric systems, which depends on dc power flow through networks or grids. In this work different kind of bidirectional converter are introduce, As well as the operation principle with limitation will propose. This review include several parts covered converter topology, operations, application, system storage units and control strategy.

Keywords: dc-dc converter, Bidirectional converter, renewable energy PV array, electric vehicle.

References

References not available

Abstract

In response to the growing prevalence of multimodal false information on social media platforms, traditional single-modal models and basic feature concatenation approaches in multimodal models exhibit limitations in effectively detecting fake news. Therefore, this paper presents a multimodal approach for detecting fake news, integrating a multimodal attention mechanism known as MAMFND (Multimodal Attention Mechanism for Fake News Detection). Initially, we utilize pretrained BERT (Bidirectional Encoder Representations from Transformers) and Swin Transformer (Swin Transformer: Hierarchical Vision Transformer using Shifted Windows) models to extract features from text and images, respectively. Subsequently, we introduce a fusion strategy based on attention mechanisms to integrate textual and visual features. To better capture the intrinsic relationships between text and images, we also input the textual features into a BiLSTM (Bi-directional Long Short-Term Memory) model for temporal sequence modeling, followed by an additional attention-based fusion with visual features. Finally, we extract information from the two rounds of feature fusion and input it into a fake news detection model for classification. Experimental results demonstrate that, on the Weibo and CCF competition datasets, the MAMFND model achieved average accuracy improvements of approximately 9.4% and 5.6%, respectively, compared to baseline models.

Keywords: Fake News Detection, Multimodal Features Fusion, Multimodal Attention Mechanism, Deep learning

References

References not available

Abstract

The adoption of methods that are referred to as sustainable business practices is something that companies should do in order to maximize profits while simultaneously addressing the social, environmental, and economic aspects of sustainability. There have been a great number of studies that have explored various aspects of supply chain operations. Nevertheless, there is not yet a complete structure that has been provided for the various industries. Specifically, this study tackles this knowledge gap by examining past research that was both empirical and review-based. A comparison is made between the practices and the industries that were researched in order to highlight the current generation of sustainable industries. The classification of the literature that defines future trends is brought up to date in this research, and it provides a new point of view on the sustainable solutions that are being handled by a variety of corporate entities. In the process of working to improve supply chains as a whole, researchers and practitioners alike may be able to gain multiple advantages from this.

Keywords: supply chain management; sustainable supply chains; sustainable practices; industrial classification; factor analysis.

References

1. Zhu, L. (2020). Optimization and simulation for e-commerce supply chain in the internet of things environment. Complexity, 2020, 1-11. https://doi.org/10.1155/2020/8821128
2. Xu, M., Ma, S., & Wang, G. (2022). Differential game model of information sharing among supply chain finance based on blockchain technology. Sustainability, 14(12), 7139. https://doi.org/10.3390/su14127139
3. Sardar, S. and Lee, Y. (2015). Analysis of product complexity considering disruption cost in fast fashion supply chain. Mathematical Problems in Engineering, 2015, 1-15. https://doi.org/10.1155/2015/670831
4. LeMay, S., Helms, M., Kimball, B., & McMahon, D. (2017). Supply chain management: the elusive concept and definition. The International Journal of Logistics Management, 28(4), 1425-1453. https://doi.org/10.1108/ijlm-10-2016-0232
5. Li, F., & Kunze, O. (2023). A comparative review of air drones (UAVs) and delivery bots (SUGVs) for automated last mile home delivery. Logistics, 7(2), 21.
6. Lei, L., DeCandia, L., Oppenheim, R., & Zhao, Y. (2016). Managing supply chain operations. https://doi.org/10.1142/10010
7. Khan, S., Ling, Y., & Shahzad, S. (2014). Adoption of electronic supply chain management and e-commerce by small and medium enterprises and their performance: a survey of SMEs in Pakistan. American Journal of Industrial and Business Management, 04(09), 433-441. https://doi.org/10.4236/ajibm.2014.49051
8. Phan, A., Nguyen, H., Nguyen, H., & Matsui, Y. (2019). Effect of total quality management practices and JIT production practices on flexibility performance: empirical evidence from international manufacturing plants. Sustainability, 11(11), 3093. https://doi.org/10.3390/su11113093
9. Nagranová, M. (2019). Behavioral tendencies of supply chain managers in context of globalization. Proceedings of ERAZ Conference, 39. https://doi.org/10.31410/eraz.s.p.2019.39
10. Mentzer, J., DeWitt, W., Keebler, J., Min, S., Nix, N., Smith, C., & Zacharia, Z. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1-25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x
11. Oyewole, A. (2024). Reviewing predictive analytics in supply chain management: applications and benefits. World Journal of Advanced Research and Reviews, 21(3), 568-574. https://doi.org/10.30574/wjarr.2024.21.3.0673
12. Ni, S., Yan, P., & Liu, Z. (2022). Logistics demand forecast of fresh food e-commerce based on Bi-LSTM model. Journal of Computer and Communications, 10(09), 51-65. https://doi.org/10.4236/jcc.2022.109004
13. De Angelis, R., Howard, M., & Miemczyk, J. (2018). Supply chain management and the circular economy: towards the circular supply chain. Production Planning & Control, 29(6), 7287. https://doi.org/10.1080/09537287.2018.1449244
14. Ivankova, G. V., Mochalina, E. P., & Goncharova, N. L. (2020). IoT in logistics. IOP Conference Series: Materials Science and Engineering, 940(1), 012033. https://doi.org/10.1088/1757-899X/940/1/012033.