A Comparative Study of Physicochemical, Dielectric and Thermal Properties of Pressboard Insulation Impregnated with Natural Ester and Mineral Oil

Natural ester is considered to be a substitute of mineral oil in the future. To apply natural ester in large transformers safely, natural ester impregnated solid insulation should be proved to have comparable dielectric strength and thermal stability to mineral oil impregnated solid insulation. This paper mainly focuses on a comparative study of physicochemical, ac breakdown strength and thermal stability behavior of BIOTEMP natural ester/pressboard insulation and Karamay 25# naphthenic mineral oil/pressboard insulation after long term thermal ageing. The physicochemical and dielectric parameters including moisture, acids and the ac breakdown strength of these two oil/pressboard insulation systems at different ageing status were compared. The permittivity and ac breakdown strength of these two oil/pressboard insulation systems at different temperatures were also investigated. And a comparative result of the thermal stability behavior of these two oil/pressboard insulation systems with different ageing status was provided at last. Results show that though natural ester has higher absolute humidity and acidity during the long ageing period, the lower relative humidity of natural ester helps to keep its ac breakdown strength higher than mineral oil. The pressboard aged in natural ester also has higher ac breakdown strength than that aged in mineral oil. The lower relative permittivity ratio of natural ester impregnated paper to natural ester is beneficial to its dielectric strength. Using natural ester in transformer, the resistance to thermal decomposition of the oil/pressboard insulation system could be also effectively improved

Multi-body dynamics modelling on a self-propelled pufferfish with its application in AUV

We developed a Computational Fluid Dynamics (CFD) based tool coupled with a Multi-Body Dynamics (MBD) technique to investigate a self-propelled pufferfish motion within a still water environment. The 3D pufferfish model consists of body, caudal, dorsal and anal fins. The locomotion of fish is entirely determined by the computation and fully induced by the oscillation motion of fish fins. The influence of the phase angle difference on the fish swimming behaviour is examined by varying the angle difference between the caudal, dorsal, and anal fins. The swimming displacement, hydrodynamic force and the wake pattern are analysed

Global Nonlinear Kernel Prediction for Large Dataset with a Particle Swarm Optimized Interval Support Vector Regression

A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data

Faster Projected GAN: Towards Faster Few-Shot Image Generation

In order to solve the problems of long training time, large consumption of computing resources and huge parameter amount of GAN network in image generation, this paper proposes an improved GAN network model, which is named Faster Projected GAN, based on Projected GAN. The proposed network is mainly focuses on the improvement of generator of Projected GAN. By introducing depth separable convolution (DSC), the number of parameters of the Projected GAN is reduced, the training speed is accelerated, and memory is saved. Experimental results show that on ffhq-1k, art-painting, Landscape and other few-shot image datasets, a 20% speed increase and a 15% memory saving are achieved. At the same time, FID loss is less or no loss, and the amount of model parameters is better controlled. At the same time, significant training speed improvement has been achieved in the small sample image generation task of special scenes such as earthquake scenes with few public datasets.Comment: 9 pages,7 figures,4 table

Evolutionary game and simulation analysis on management synergy in China’s coal emergency coordination

Once coal mine accidents occur, a series of chain reactions will bring radiation effects that are difficult to solve in the short term to the normal operation of the economy and society. Therefore, the post-disaster management of coal mine accident is particularly important. Coal mine emergency response involves many stakeholders, and it needs various regions, departments to achieve multi-agent, multi-level effective collaboration to ensure that the coal mine accidents are controlled as soon as possible. Local governments and coal mine enterprises are the main forces in the post-accident emergency management of coal mines, but the differences in their interest motives, preferences and cognitive structures make it difficult for the relevant emergency managers to make correct decisions in the complex accident management environment, therefore, the game relationship between conflict and cooperation among related subjects is explored based on the perspective of game theory. This study establishes a game model of coal mine accident response behavior between coal mining enterprises and local governments, and quantitatively adopts the method of numerical simulation analysis to conduct in-depth analysis of the influencing factors of their decision-making behavior. The results reveal that: 1) the establishment of an information sharing mechanism is an important condition for local governments to efficiently and quickly start the incident response process for coal mine accidents; 2) Under the proper supervision of local government, the impact of the reduction of emergency response cost on the active response of coal mining enterprises is more significant and direct, that is, The cost of emergency response is the decisive factor affecting the incident response work of coal mining company; 3) the establishment of emergency cost compensation mechanism and incentive mechanism should also be the focus of local governments in formulating emergency coordination policies in the future. This study provides scientifc and reasonable management suggestions in line with the actual situation of China and provides a useful reference for local government to formulate the optimal strategy for emergency coordination in coal mine emergencies, to improve the motivation of each coordinating subjects and to improve the current situation of emergency coordination in China’s coal mines

Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization

In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve $m\gg 1$ lower level problems and have important applications in machine learning. Designing a stochastic gradient and controlling its variance is more intricate due to the hierarchical sampling of blocks and data and the unique challenge of estimating hyper-gradient. We aim to achieve three nice properties for our algorithm: (a) matching the state-of-the-art complexity of standard BO problems with a single block; (b) achieving parallel speedup by sampling $I$ blocks and sampling $B$ samples for each sampled block per-iteration; (c) avoiding the computation of the inverse of a high-dimensional Hessian matrix estimator. However, it is non-trivial to achieve all of these by observing that existing works only achieve one or two of these properties. To address the involved challenges for achieving (a, b, c), we propose two stochastic algorithms by using advanced blockwise variance-reduction techniques for tracking the Hessian matrices (for low-dimensional problems) or the Hessian-vector products (for high-dimensional problems), and prove an iteration complexity of $O(\frac{m\epsilon^{-3}\mathbb{I}(I<m)}{I\sqrt{I}} + \frac{m\epsilon^{-3}}{I\sqrt{B}})$ for finding an $\epsilon$-stationary point under appropriate conditions. We also conduct experiments to verify the effectiveness of the proposed algorithms comparing with existing MBBO algorithms

Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering

Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, including multiple trips within each passenger and multi-dimensional information about each trip. Furthermore, existing approaches rely on an accurate specification of the clustering number to start. Finally, existing methods do not consider spatial semantic graphs such as geographical proximity and functional similarity between the locations. In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically. The spatial graphs are utilized in community detection to link the semantic neighbors. We further propose a tensor version of Collapsed Gibbs Sampling method with a minimum cluster size requirement. A case study based on Hong Kong metro passenger data is conducted to demonstrate the automatic process of cluster amount evolution and better cluster quality measured by within-cluster compactness and cross-cluster separateness. The code is available at https://github.com/bonaldli/TensorDPMM-G.Comment: Accepted in ACM SIGSPATIAL 2023. arXiv admin note: substantial text overlap with arXiv:2306.1379

Polarization-insensitive dual-wavelength dispersion tunable metalens achieved by global modulation method

SF$_6$ is widely used as a gas-insulator in high-voltage power electrical system. Detecting SF$_6$ leaks using unmanned aerial vehicle (UAV)-based thermal cameras allows efficient large-scale inspections during routine maintenance. The emergence of lightweight metalenses can increase the endurance of UAVs. Simultaneously controlling dispersion and polarization properties in metalens is significant for thermal camera applications. However, via a propagation phase modulation method in which the phase is tuned locally, it is difficult and time-consuming to obtain enough different nanostructures to control multiwavelength independently while maintain the polarization-insensitive property. To this end, by using a global modulation method, a polarization-insensitive dual-wavelength achromatic and super-chromatic metalens are designed respectively. The working wavelength is set at 10.6 and 12 $\rm\mu$m to match the absorption peaks of SF$_6$ and one of its decompositions (SO$_2$F$_2$), respectively. According to the operating wavelengths, only the geometric parameters of two nanofins are required to be optimized (through genetic algorithm). Then they are superimposed on each other to form cross-shaped meta-atoms. In order to control the influence between the two crossed nanofins, an additional term $\Delta f$ is introduced into the phase equation to modify the shape of the wavefront, whereby the phase dispersion can be easily engineered. Compared with local modulation, the number of unique nanostructures that need to be optimized can be reduced to two (operating at dual wavelengths) by the Pancharatnam-Berry (PB) phase based global modulation method. Therefore, the proposed design strategy is expected to circumvent difficulties in the local design approaches and can find widespread applications in multiwavelength imaging and spectroscopy

A bidirectional causal relationship study between mental disorders and male and female infertility

BackgroundThe relation between mental disorders (MDs) and infertility can be reciprocal. But exactly which MD affects infertility remains controversial. Our aim was to use Mendelian randomization (MR) to explore bidirectional causality between 15 MDs and male infertility and female infertility.MethodsThe data of MDs, male infertility, and female infertility were derived from published genome-wide association studies (GWAS). The inverse variance weighted method was considered to be the main analytical approach. Sensitivity analysis was performed using MR-Egger, Cochran’s Q, radial MR, and MR-PRESSO tests.ResultsOur results found that mood disorders (OR, 1.4497; 95% CI, 1.0093 – 2.0823; P = 0.0444) and attention deficit hyperactivity disorder (OR, 1.3921; 95% CI, 1.0943 – 1.7709; P = 0.0071) were positively correlated with male infertility, but obsessive-compulsive disorder (OR, 0.8208; 95% CI, 0.7146 – 0.9429; P = 0.0052) was negatively associated with male infertility. For females, anorexia nervosa (OR, 1.0898; 95% CI, 1.0070 – 1.1794; P = 0.0329), attention deficit hyperactivity disorder (OR, 1.1013; 95% CI, 1.0041 – 1.2079; P = 0.0406), and major depressive disorder (OR, 1.1423; 95% CI, 1.0213 – 1.2778; P = 0.0199) increased risk of infertility. In reverse relationship, female infertility increased the incidence of bipolar disorder (OR, 1.0009; 95% CI, 1.0001 – 1.0017; P = 0.0281).ConclusionWe demonstrated the association between five MDs and male or female infertility. Female infertility was also found to be associated with an increased risk of one MD. We look forward to better designed epidemiological studies to support our results