Static and dynamic TSEPs of SiC and GaN transistors

This paper investigates the static and dynamic temperature sensitive electrical parameters (TSEPs) for both SiC and GaN transistors. It is shown that both the qualitative and quantitative temperature characteristics of these parameters are various when different type of transistors are concerned. This finding can be used to select the most appropriate temperature sensitive parameter for the device under specific situation. In this paper, two types of transistors, SiC SCT2080KE MOSFET and GaN PGA26E19BA HEMT are evaluated and compared in terms of six TSEPs, including source-drain reverse bias voltage (VSD), static on-resistance (RDS,ON), gate threshold voltage (VGS(TH)), transconductance (gm), dIDS/dt switching transients and gate current (IG). Then, these TSEPs are compared using four criteria: temperature sensitivity, linearity, material and the capability of on-line temperature monitorin

Understanding the Problem Structure of Optimisation Problems in Water Resources

Optimisation algorithms are widely used in water resources to identify the optimal solutions for problems with multiple possible solutions. Many studies in this field focus on the development and application of advanced optimisation algorithms, making significant contributions in improving optimisation performance. On the other hand, the performance of optimisation algorithms is also related to the features of the problems being solved, therefore, selecting appropriate algorithms for corresponding problems is also a key to the success of optimisation. Although a number of metrics have been developed to assess these features, they have not been applied to problems in the water resources field. The primary reason for this is that the computational cost associated with the calculation of many of these metrics increases significantly with problem size, making them unsuitable for problems in water resources. Consequently, there is a lack of knowledge about the features of problems in the water resources field. This PhD thesis aims to understand the features of problems in water resources, and the process can be split into two stages. The first stage is to identify metrics that can be applied within an affordable computational cost. This is addressed in the first content chapter (Paper 1). The second stage is to apply metrics identified in the first stage to understand the features of problems in the water resources field, including the calibration of artificial neural network models (Paper 2) and conceptual rainfall runoff models (Paper 3). This includes the understanding of optimisation difficulty of these problems according to their features, and how their features change through the change of their problem structure and the types of problems to which they are applied. In the first paper, the computational cost of fitness landscape metrics (explanatory landscape analysis (ELA) metrics) used in computer science is tested and metrics that are suitable for application to water resources problems are identified. Each metric used to understand the features of problems requires a given number of samples, which usually increases with an increase in problem size (dimensionality). Consequently, metrics which require a big increase in sample size through the increase of problem size are not suitable for real-world water resources problems. To identify ELA metrics that have low dependence on problem size, 110 metrics in total are tested on a range of benchmark functions and a number of environmental modelling problems, and 28 are identified to be able to be applied to complex problems without significant increase in computational cost. This finding provides us a new approach to better understand the problem structure of optimisation problems in water resources and has the potential to provide guidance in optimisation algorithm selection for problems in the water resources field. In the second paper, metrics identified to have low dependence on problem size in the first paper are applied to Artificial Neural Network (ANN) model calibration problems. ANN models for different environmental problems with different number of inputs and hidden nodes are used in the test. The environmental problems considered include Kentucky River Catchment Rainfall‐Runoff Data (USA), Murray River Salinity Data (Australia), Myponga Water Distribution System Chlorine Data (Australia), and South Australian Surface Water Turbidity Data (Australia). It is demonstrated that ELA metrics can be used successfully to characterize the features of the error surfaces of ANN models, thereby helping to explain the reasons for an increase or decrease in calibration difficulty, and in doing so, shedding new light on findings in existing literature. Results show that the error surfaces of ANNs with relatively simple structures have a more well-defined overall shape and have fewer local optima, while the error surfaces of ANNs with more complex structures are flatter and have many distributed, deep local optima. Consequently, ANNs with simpler structures can be calibrated successfully using gradient-based methods, such as the back-propagation algorithm, whereas ANNs with more complex structures are best calibrated using a hybrid approach combining metaheuristics, such as genetic algorithms, with gradient-based methods. In the third paper, the ELA metrics identified to have low dependence on problem size in the first paper are applied to Conceptual Rainfall Runoff (CRR) model calibration problems. Different CRRs with different model types, error functions, catchment conditions and data lengths are tested to identify how they affect the features of problem structure, which are related to their model calibration and parameter identification difficulty. It is suggested that ELA metrics can be used to quantify key features of the error surfaces of CRR models, including their roughness and flatness, as well as their degree of optima dispersion. This enables key error surface features to be compared for CRR models with different combinations of attributes (e.g. model structure, catchment climate conditions, error metrics and calibration data lengths and composition) in a consistent, efficient and easily communicable fashion. Results from the application of these metrics to the error surfaces of 420 CRR models with different combinations of the above attributes indicate that model structure differences result in the differences in surface roughness and relative optima dispersion. Additionally, increasing catchment wetness increases the relative roughness of error surfaces, it also decreases optima dispersion. This suggests that model structure and catchment climate conditions can be key issues in affecting the calibration difficulty, efficiency and parameter uniqueness. The experiments conducted in this study also encourage further tests on further CRR models and catchments to identify general patterns between calibration performance, model structure and catchment characteristics.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 202

Late Fusion Multi-view Clustering via Global and Local Alignment Maximization

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in various applications, most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering, which could cause over-complicated optimization and intensive computational cost. In this paper, we propose late fusion MVC via alignment maximization to address these issues. To do so, we first reveal the theoretical connection of existing k-means clustering and the alignment between base partitions and the consensus one. Based on this observation, we propose a simple but effective multi-view algorithm termed LF-MVC-GAM. It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones. Such an alignment is beneficial to integrate partition level information and significantly reduce the computational complexity by sufficiently simplifying the optimization procedure. We then design another variant, LF-MVC-LAM to further improve the clustering performance by preserving the local intrinsic structure among multiple partition spaces. After that, we develop two three-step iterative algorithms to solve the resultant optimization problems with theoretically guaranteed convergence. Further, we provide the generalization error bound analysis of the proposed algorithms. Extensive experiments on eighteen multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The codes of the proposed algorithms are publicly available at https://github.com/wangsiwei2010/latefusionalignment

Static and dynamic TSEPs of SiC and GaN transistors

This paper investigates the static and dynamic temperature sensitive electrical parameters (TSEPs) for both SiC and GaN transistors. It is shown that both the qualitative and quantitative temperature characteristics of these parameters are various when different type of transistors are concerned. This finding can be used to select the most appropriate temperature sensitive parameter for the device under specific situation. In this paper, two types of transistors, SiC SCT2080KE MOSFET and GaN PGA26E19BA HEMT are evaluated and compared in terms of six TSEPs, including source-drain reverse bias voltage (VSD), static on-resistance (RDS,ON), gate threshold voltage (VGS(TH)), transconductance (gm), dIDS/dt switching transients and gate current (IG). Then, these TSEPs are compared using four criteria: temperature sensitivity, linearity, material and the capability of on-line temperature monitorin

Fibroblast growth factor 3 promotes spontaneous mammary tumorigenesis in Tientsin albino 2 mice via the FGF3/FGFR1/STAT3 pathway

IntroductionTientsin albino 2 (TA2) mice can develop spontaneous breast cancer (SBC), which is associated with multiple pregnancies and infection with the mouse mammary tumor virus (MMTV). In this study, we sought to elucidate the molecular mechanisms underlying the development of SBC in TA2 mice induced by MMTV.MethodsThe integration site of MMTV in TA2 SBC was identified using whole-genome sequencing. The expression of fibroblast growth factor 3 (FGF3) in SBCs and normal breast tissues was compared. The primary cell line, TA-1106, derived from SBC, was cultured. The proliferation, cell cycle, migration, invasion, and tumorigenicity abilities, as well as the expression of epithelial-mesenchymal transition-related proteins, phosphorylated STAT3, and phosphorylated Akt, were assessed in MA-891cell line from TA2 and TA-1106 cells after FGF3 knockdown. The binding of FGF3 to FGF receptor 1 (FGFR1) was determined by co-immunoprecipitation. Additionally, the relationship between STAT3 and Akt phosphorylation was investigated using a small molecule inhibitor and STAT3 knockdown.ResultsMMTV integrated upstream of the FGF3 gene, and the FGF3 protein was highly expressed in TA2 SBCs. FGF3 knockdown in MA-891 and TA-1106 decreased their proliferation, migration, and invasion abilities, affected the cell cycle and expression of epithelial-mesenchymal transition-related proteins, and inhibited the growth of animal xenografts. FGF3 binds to FGFR1, and either FGF3 or FGFR1 knockdown decreases STAT3 and Akt phosphorylation levels. Inhibition of phosphorylation or expression of STAT3 resulted in decreased Akt phosphorylation levels. Inhibition of Akt phosphorylation also resulted in decreased STAT3 phosphorylation levels. Furthermore, treatment of MA-891 and TA-1106 cells with Wortmannin or Stattic caused FGFR1 upregulation in addition to inhibiting Akt or STAT3 phosphorylation.ConclusionThe results of this study demonstrate that FGF3 plays a significant role in the development of SBC through the FGF3/FGFR1/STAT3 signaling pathway. There is a reciprocal activation between STAT3 and Akt. Inhibition of STAT3 or Akt phosphorylation promoted the expression of FGFR1. Validating the conclusions obtained in this study in human breast cancer (HBC) may contribute to targeted therapy and it is worth exploring whether the homologous sequences of MMTV in HBC have a similar oncogenic effect

RDGSL: Dynamic Graph Representation Learning with Structure Learning

Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline