215 research outputs found
Thermal ageing and its impact on charge trap density and breakdown strength in ldpe LDPE
Low-density polyethylene (LDPE) has been widely used as power cable insulation, because of its good electrical performance and stable chemical characteristics. However, in recent years, with the rise of large-capacity and long-distance HVDC transmission systems, the effect of space charge has a significant impact on the insulation selection and design. Furthermore, the change in the electrical performance of insulation after ageing is also required to be understood. It has been reported that ageing leads to an increase in charge trap density. The increase of trap density in LDPE makes the transport of charge carriers between traps easier. Consequently, the electrical breakdown strength will also be affected. This paper focuses on the LDPE films with different degrees of thermal ageing and studies its impact on charge trap density and change in electrical breakdown strength. The ageing degrees of sample were characterized using Fourier-Transform Infrared (FTIR). Space charge dynamics were measured using the pulsed electroacoustic (PEA) technique. In addition, electrical breakdown strength of the aged samples was measured and breakdown data were processed using the Weibull distribution. The change in characteristic breakdown strength is related to the change in charge trap density. The results suggest that the change in charge trap density of an insulating material can be used to characterize electrical performance of the material, therefore, the ageing status
Biomechanical analysis of Combi-hole locking compression plate during fracture healing:a numerical study of screw configuration
Mechanical Behavior of Shale Rock under Uniaxial Cyclic Loading and Unloading Condition
In order to investigate the mechanical behavior of shale rock under cyclic loading and unloading condition, two kinds of incremental cyclic loading tests were conducted. Based on the result of the short-term uniaxial incremental cyclic loading test, the permanent residual strain, modulus, and damage evolution were analyzed firstly. Results showed that the relationship between the residual strains and the cycle number can be expressed by an exponential function. The deformation modulus E50 and elastic modulus ES first increased and then decreased with the peak stress under the loading condition, and both of them increased approximately linearly with the peak stress under the unloading condition. On the basis of the energy dissipation, the damage variables showed an exponential increasing with the strain at peak stress. The creep behavior of the shale rock was also analyzed. Results showed that there are obvious instantaneous strain, decay creep, and steady creep under each stress level and the specimen appears the accelerated creep stage under the 4th stress of 51.16 MPa. Based on the characteristics of the Burgers creep model, a viscoelastic-plastic creep model was proposed through viscoplastic mechanics, which agrees very well with the experimental results and can better describe the creep behavior of shale rock better than the Burgers creep model. Results can provide some mechanics reference evidence for shale gas development
Combination and compression of multiple pulses with same or different wavelengths
Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61675008 and in part by Shenzhen Science and Technology Innovation Commission under Grant GJHZ20180411185015272. K. Nakkeeran wishes to thank The Royal Society Kan Tong Po International Fellowship 2018 for the financial support to visit The Hong Kong Polytechnic University.Peer reviewedPostprin
Biomechanical analysis of Combi-hole locking compression plate during fracture healing:a numerical study of screw configuration
Locking compression plates (LCPs) have become a widely used option for treating femur bone fractures. However, the optimal screw configuration with combi-holes remains a subject of debate. The study aims to create a time-dependent finite element (FE) model to assess the impacts of different screw configurations on LCP fixation stiffness and healing efficiency across four healing stages during a complete fracture healing process. To simulate the healing process, we integrated a time-dependent callus formation mechanism into a FE model of the LCP with combi-holes. Three screw configuration parameters, namely working length, screw number, and screw position, were investigated. Increasing the working length negatively affected axial stiffness and healing efficiency (p < 0.001), while screw number or position had no significant impact (p > 0.01). The time-dependent model displayed a moderate correlation with the conventional time-independent model for axial stiffness and healing efficiency (ρ ≥ 0.733, p ≤ 0.025). The highest healing efficiency (95.2%) was observed in screw configuration C125 during the 4–8-week period. The results provide insights into managing fractures using LCPs with combi-holes over an extended duration. Under axial compressive loading conditions, the use of the C125 screw configuration can enhance callus formation during the 4–12-week period for transverse fractures. When employing the C12345 configuration, it becomes crucial to avoid overconstraint during the 4–8-week period
Supervised Knowledge May Hurt Novel Class Discovery Performance
Novel class discovery (NCD) aims to infer novel categories in an unlabeled
dataset by leveraging prior knowledge of a labeled set comprising disjoint but
related classes. Given that most existing literature focuses primarily on
utilizing supervised knowledge from a labeled set at the methodology level,
this paper considers the question: Is supervised knowledge always helpful at
different levels of semantic relevance? To proceed, we first establish a novel
metric, so-called transfer flow, to measure the semantic similarity between
labeled/unlabeled datasets. To show the validity of the proposed metric, we
build up a large-scale benchmark with various degrees of semantic similarities
between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical
class structure. The results based on the proposed benchmark show that the
proposed transfer flow is in line with the hierarchical class structure; and
that NCD performance is consistent with the semantic similarities (measured by
the proposed metric). Next, by using the proposed transfer flow, we conduct
various empirical experiments with different levels of semantic similarity,
yielding that supervised knowledge may hurt NCD performance. Specifically,
using supervised information from a low-similarity labeled set may lead to a
suboptimal result as compared to using pure self-supervised knowledge. These
results reveal the inadequacy of the existing NCD literature which usually
assumes that supervised knowledge is beneficial. Finally, we develop a
pseudo-version of the transfer flow as a practical reference to decide if
supervised knowledge should be used in NCD. Its effectiveness is supported by
our empirical studies, which show that the pseudo transfer flow (with or
without supervised knowledge) is consistent with the corresponding accuracy
based on various datasets. Code is released at
https://github.com/J-L-O/SK-Hurt-NCDComment: TMLR 2023 accepted paper. arXiv admin note: substantial text overlap
with arXiv:2209.0912
Seeing is believing: in-situ visualising dynamic evolution in CO2 electrolysis
CO2 reduction reaction (CO2RR), as a promising carbon-neutral strategy, enables the production of valuable chemicals and fuels from greenhouse gas. Despite tremendous efforts in developing CO2RR catalysts to improve activity, selectivity, and stability, mechanisms behind the catalytic performance, however, are still under-explored due largely to limited characterisation capability. In this review, advances of in-situ imaging technologies for studying CO2RR have been overviewed. These technologies emerge as powerful tools to track the transformation of catalyst materials over real-time and space, under CO2RR operating conditions. The review discusses emerging opportunities in the direction of combined in-situ characterisation techniques as well as machine learning to aid further discovery of structure-function relationships in CO2RR
Combination and Compression of Multiple Optical Pulses in Nonlinear Fibers with the Exponentially Decreasing Dispersion
ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (No. Project 61675008).Peer reviewedPostprin
Cubic phase nanoparticles for sustained release of ibuprofen formulation characterization and enhanced bioavailability study
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