A Review of Carbon Emissions from Electrical Machine Materials

As the world embarks on a global mission to tackle climate change, reducing carbon represents a key challenge given the escalating global warming. The U.K. is among many other nations that are determined to decarbonise all sectors and strive to achieve a net zero carbon target by 2050. While much attention has been paid to improving performance and reducing carbon emissions in electrical machines, the current research landscape focuses mainly on the thermal and electromagnetic facets. Surprisingly, carbon emissions from the production stage, especially those related to raw material consumption, remain a largely unexplored area. This paper wishes to shed light on a neglected dimension by providing a comprehensive review of carbon emissions in the manufacture of electrical machines, thus contributing significantly to the wider discourse on carbon emission reduction by comparing the carbon emission values associated with various materials commonly used for the main components of these machines. A further case study is included to assess and explore the impact of material alterations on a synchronous machine, from a carbon emission perspective. A reliable material guide will provide engineers at the design stage with the critical insight needed to make informed material selection decisions, highlighting the critical role of carbon emission values beyond conventional thermal and electromagnetic considerations, achieving sustainable and environmentally conscious electrical machine design

Virtual Network Function Migration Considering Load Balance and SFC Delay in 6G Mobile Edge Computing Networks

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration

Factors Influencing Ethical Behavior Among Chinese Undergraduate Nursing Students

Learning how to effectively respond to ethical dilemma can affect nurses’ physical and mental health, which is not conducive to developing a nursing career. Nursing students’ ethical behavior warrants attention as professionals about to begin clinical work. We aim to understand the current situation and influencing factors of Chinese nursing students’ ethical behavior. A cross-sectional descriptive study was conducted. Descriptive statistics and multiple linear regression were used to analyze the data. Full-time nursing students were recruited from an undergraduate medical university in Jinan through convenient sampling from November to December 2021. Research ethics approval (No. 2022-0018) was obtained from the Ethics Committee of Affiliated Hospital of Shandong University of Traditional Chinese Medicine. Informed consent was also received from participants. The EBT scores of the nursing students were 95.14 ± 11.37, which was not high compared with the total score. Gender, year level, and professional values had a significant impact on participants’ ethical behavior. A positive correlation was found between nursing professional values and ethical behavior. A gap still exists between the moral development and maturity of undergraduate nursing students. To further cultivate their ethical behavior and improve their confidence and ability to respond to ethical dilemmas, more innovative methods must be employed in teaching ethics courses, and continuity in the ethics education system must be maintained. For male and third- and fourth-year nursing students who showed lower ethical behavior scores, nursing educators can develop their ethical behaviors by helping them establish positive professional values