Search

When a high impedance grounding fault occurs in the power cables, the current at the fault point is extremely low and the fault characteristics are too weak to be detected by the reflectometry. Interfered by the multiple reflections, the fault is hard to be located in the field. In order to solve the problem of conventional frequency domain reflectometry, a high impedance grounding fault location method for 10 kV distribution cables is proposed in this paper, which is based on the reflection coefficient spectrum. Firstly, the equivalent model of cable distributed parameters is established, and the reflection coefficient spectrum at the cable head is derived. Then, according to the theorem of integral transformation and generalized orthogonality, the diagnostic function is built in the spatial domain. Besides, the Kaiser window is introduced to improve the location sensitivity. Finally, the feasibility of the method is verified by simulated and experimental results. It is shown that this method can effectively eliminate the impact of multiple reflections, and precisely locate the single- and multi-point grounding faults. The fault location error is within 0.2%.

With the wide utilization of lithium-ion batteries in the fields of electronic devices, electric vehicles, aviation, and aerospace, the prediction of remaining useful life (RUL) for lithium batteries is important. Considering the influence of the environment and manufacturing process, the degradation features differ between the historical batteries and the target ones, and such differences are called individual differences. Currently, lithium battery RUL prediction methods generally use the characteristics of a large group of historical samples to represent the target battery. However, these methods may be vulnerable to individual differences between historical batteries and target ones, which leads to poor accuracy. In order to solve the issue, this paper proposes a prediction method based on transfer learning that fully takes individual differences into consideration. It utilizes an extreme learning machine (ELM) twice. In the first stage, the relationship between the capacity degradation rate and the remaining capacity is constructed by an ELM to obtain the adjusting factor. Then, an ELM-based transfer learning method is used to establish the connection between the remaining capacity and the RUL. Finally, the prediction result is adjusted by the adjusting factor obtained in the first stage. Compared with existing typical data-driven models, the proposed method has better accuracy and efficiency.

Abstract Background The cultivation of bananas encounters substantial obstacles, particularly due to the detrimental effects of cold stress on their growth and productivity. A potential remedy that has gained attention is the utilization of ethyl mesylate (EMS)-induced mutagenesis technology, which enables the creation of a genetically varied group of banana mutants. This complex procedure entails subjecting the mutants to further stress screening utilizing L-Hyp in order to identify those exhibiting improved resistance to cold. This study conducted a comprehensive optimization of the screening conditions for EMS mutagenesis and L-Hyp, resulting in the identification of the mutant cm784, which exhibited remarkable cold resistance. Subsequent investigations further elucidated the physiological and transcriptomic responses of cm784 to low-temperature stress. Results EMS mutagenesis had a substantial effect on banana seedlings, resulting in modifications in shoot and root traits, wherein a majority of seedlings exhibited delayed differentiation and limited elongation. Notably, mutant leaves displayed altered biomass composition, with starch content exhibiting the most pronounced variation. The application of L-Hyp pressure selection aided in the identification of cold-resistant mutants among seedling-lethal phenotypes. The mutant cm784 demonstrated enhanced cold resistance, as evidenced by improved survival rates and reduced symptoms of chilling injury. Physiological analyses demonstrated heightened activities of antioxidant enzymes and increased proline production in cm784 when subjected to cold stress. Transcriptome analysis unveiled 946 genes that were differentially expressed in cm784, with a notable enrichment in categories related to ‘Carbohydrate transport and metabolism’ and ‘Secondary metabolites biosynthesis, transport, and catabolism’. Conclusion The present findings provide insights into the molecular mechanisms that contribute to the heightened cold resistance observed in banana mutants. These mechanisms encompass enhanced carbohydrate metabolism and secondary metabolite biosynthesis, thereby emphasizing the adaptive strategies employed to mitigate the detrimental effects induced by cold stress.

With the development of highly integrated electronic equipment, electromagnetic pollution and heat congregation have become serious problems. Electromagnetic interference (EMI) shielding materials with high thermal conductivity and mechanical properties are highly desirable. Herein, the reduced graphene oxide/Ni nanowire aerogels (GNiA) were firstly prepared via hydrothermal reducing process and freeze-drying. Subsequently, using epoxy (EP) as a polymer matrix, the GNiA/EP composites with high compression strength of 147 MPa were fabricated by vacuum filtration and curing. The GNiA endows the composites with great electromagnetic wave loss, as well as improved phonon propagation efficiency, resulting in the super high EMI SE. With the GNiA content of 12.14 wt%, the EMI SE and thermal conductivity of GNiA/EP composites can reach as high as 85 dB and 1.69 W/(m·K) respectively. The obtained GNiA/EP composites provide a strategy to meet the demand for electronic equipment for new-generation EMI shielding materials.