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Abstract Rural preschool education is an integral part of rural society, and improving the efficiency system of evaluation of rural preschool education resource allocation is an important strategy for the implementation of the rural revitalization. This paper uses an input-oriented three-stage DEA model to analyze the efficiency of rural preschool education resource allocation in 30 provinces in China from 2012 to 2020. The results show that external factors such as the level of urbanization, birth rate, and the scale of kindergarten impacts the efficiency of rural preschool education resource allocation significantly. Without regard to the influence of environmental and random factors, the overall trend of the average efficiency of rural preschool education resource allocation in China has improved, showing a regional pattern of “central > eastern > western.” Therefore, based on the relevant policies, this paper puts forward rational suggestions for the improvement of rural preschool resource allocation efficiency in China from the perspectives of human, financial and material resources.

The mammary gland is a unique organ that undergoes dynamic alterations throughout a female’s reproductive life, making it an ideal model for developmental, stem cell and cancer biology research. Mammary gland development begins in utero and proceeds via a quiescent bud stage before the initial outgrowth and subsequent branching morphogenesis. How mammary epithelial cells transit from quiescence to an actively proliferating and branching tissue during embryogenesis and, importantly, how the branch pattern is determined remain largely unknown. Here, we provide evidence indicating that epithelial cell proliferation and onset of branching are independent processes, yet partially coordinated by the Eda signaling pathway. Through heterotypic and heterochronic epithelial-mesenchymal recombination experiments between mouse mammary and salivary gland tissues and ex vivo live imaging, we demonstrate that unlike previously concluded, the mode of branching is an intrinsic property of the mammary epithelium whereas the pace of growth and the density of ductal tree are determined by the mesenchyme. Transcriptomic profiling and ex vivo and in vivo functional studies in mice disclose that mesenchymal Wnt/ß-catenin signaling, and in particular IGF-1 downstream of it critically regulate mammary gland growth. These results underscore the general need to carefully deconstruct the different developmental processes producing branched organs.

As more and more loads start to join the electricity market and the role of loads in power system balancing and regulation becomes more and more important, the study of the response characteristics of polymorphic loads becomes more and more important. This paper firstly reviews the aggregation methods for loads, introduces its parameter aggregation model using temperature-controlled loads as an example, and introduces the use of machine learning methods to first cluster and then aggregate. It then reviews methods for the study of load response characteristics, with an increasing number of neural networks and hybrid neural networks being introduced into the field. Finally, a review of load uncertainty is presented, introducing sources of load uncertainty and methods for coping with load uncertainty.

This paper proposes a novel synergistic balancing control scheme for low-inertia power systems with high photovoltaic (PV) penetration in Tibet power grid (TPG). The mechanism and dispatching principle of choosing the suitable area control error (ACE) control mode for TPG are analyzed., An adaptive control mode switching method is proposed to meet the different control demands for the dynamically changing topologies. Then, we propose a new coordinated control scheme to dispatch hydropower generators and PV power stations. A set of optimum control objectives considering balancing control performance, regulation mileage of conventional generators, and power curtailment of PV are first presented. The control objectives are decomposed and simplified by step-by-step approximation and linearization, which are easily extended to be applied to real-world automatic generation control (AGC) systems. The simulation results show the effectiveness of the proposed solutions for synergistic balancing control by using the equivalent TPG test system. The equivalent system includes 63 generators with total power equal to 1618.5 MW and 51 PV power stations over 1100 MW. In addition, the actual operation cases present that the coordinated control between conventional and renewable generation dispatched by AGC has the basic conditions in the actual operation of the power grids, which will play a more significant role with the rapid increase of renewable generation sources in the future.

Summary: Morphogenesis of ectodermal organs, such as hair, tooth, and mammary gland, starts with the formation of local epithelial thickenings, or placodes, but it remains to be determined how distinct cell types and differentiation programs are established during ontogeny. Here, we use bulk and single-cell transcriptomics and pseudotime modeling to address these questions in developing hair follicles and epidermis and produce a comprehensive transcriptomic profile of cellular populations in the hair placode and interplacodal epithelium. We report previously unknown cell populations and marker genes, including early suprabasal and genuine interfollicular basal markers, and propose the identity of suprabasal progenitors. By uncovering four different hair placode cell populations organized in three spatially distinct areas, with fine gene expression gradients between them, we posit early biases in cell fate establishment. This work is accompanied by a readily accessible online tool to stimulate further research on skin appendages and their progenitors.

Low-carbon economy is not only an important topic for the globe but also a serious challenge for China with its economy entering a new level. Based on the DEA-undesirable model and Malmquist index model, urban agglomeration of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area from 2010 to 2021 were selected as research samples. Based on that, a panel generalized method of moments model was constructed to analyze the effects of the education level, technological development, and their interaction on urban carbon emission efficiency. It found that 1) the carbon emission efficiency of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area urban agglomerations shows a steady growth trend, but the overall level is low and there are regional differences, among which pure technical efficiency mainly limits the improvement of comprehensive efficiency; 2) the education level and technological development have a high positive correlation on urban carbon emission, and their interaction is conducive to the improvement of carbon emission efficiency. The carbon emission efficiency has a significant advantage under the influence of control variables, such as the economic development level, industrial structure upgrading, opening-up degree, and Internet penetration rate. 3) According to the economic dimension and population dimension, the samples of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area were divided into large cities and small cities, and regression results showed no substantial changes. It shows that the research conclusion is scientific. According to the aforementioned conclusion, this paper puts forward corresponding countermeasures and suggestions.

Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly understood interpretability. As a field critical to security research, this problem clearly exists in underwater acoustic target recognition for ocean sensing. To address this issue, this article investigates the reliability of state-of-the-art deep learning models by exploring adversarial attack methods that add small, exquisite perturbations on acoustic Mel-spectrograms to generate adversarial spectrograms. Experimental results based on real-world datasets reveal that these models can be forced to learn unexpected features when subjected to adversarial spectrograms, resulting in significant accuracy drops. Specifically, when employing the iterative attack method, the overall accuracy of all models experiences a significant decrease of approximately 70% for two datasets under stronger perturbations.

As the environmental protection requirements for surrounding sea areas of Tongming Strait are high, the crossing strata are complex and changeable, the underground fresh water and sea water appear alternately, the crossing section is long with large friction, the stratum is loose, and it is difficult to maintain the stability of borehole, which is very easy to cause drilling problems such as drilling fluid over-spilling, variation of drilling fluid performance, bit balling and lost circulation, et al. In view of the above problems, environment-friendly shale inhibitor, salt-resistant filtrate reducer and lubricant were selected by research, and on this basis, an environment-friendly wellbore stabilizing drilling fluid system was formed for the very-shallow strata, which has excellent performances of shale inhibition, lubrication, solid contamination resistance and wellbore stability. Application of the system in directional crossing project of Tongming Strait shows that, the performance of the drilling fluid is stable with good lubrication effect, capable of effectively reducing the pump pressure and friction resistance, and the wellbore stability is strong with no complex drilling accidents occurring, so that the directional drilling cycle is shortened by 87 days, which provides a reliable guarantee for smooth pipe dragging.

Geoacoustic inversion is a challenging task in marine research due to the complex environment and acoustic propagation mechanisms. With the rapid development of deep learning, various designs of neural networks have been proposed to solve this issue with satisfactory results. As a data-driven method, deep learning networks aim to approximate the inverse function of acoustic propagation by extracting knowledge from multiple replicas, outperforming conventional inversion methods. However, existing deep learning networks, mainly incorporating stacked convolution and fully connected neural networks, are simple and may neglect some meaningful information. To extend the network backbone for geoacoustic inversion, this paper proposes a transformer-based geoacoustic inversion model with additional frequency and sensor 2-D positional embedding to perceive more information from the acoustic input. The simulation experimental results indicate that our proposed model achieves comparable inversion results with the existing inversion networks, demonstrating its effectiveness in marine research.

Southwest China Grid (SCG) is one of the hydropower-rich power grids around the world. SCG has recently been into asynchronous operation mode, it is expected to face challenges in low inertia and frequency stability issues caused by the hydraulic turbine governing system (HTGS), which brings profound changes to the validity and stability for the multi-area automatic generation control (AGC) power systems. This article analyzes the frequency control problems of SCG according to the operation data from asynchronous operation tests. To overcome the large overshoot and reverse regulation for external disturbances, frequency-based segment values of frequency bias for areas under TBC mode are developed considering multi-deadband effects, and a switching method of control modes for supporting large-disturbance recovery is proposed. Based on the complex and nonlinear of AGC systems, a fuzzy PD+I controller is proposed to reduce the excessively large overshoot. Furthermore, the rule for tuning D gain is established by the sign of change of area control error (ACE), and flexible I-part is applied to the control process based on the relationship between ACE and frequency. Then, we also provide filter-based control strategies with adaptive parameters to effectively coordinate hydropower and thermal power generators. The filter coefficients are computed considering various delays within AGC systems. Some case studies and actual operating conditions demonstrate the efficiency and accuracy of the proposed ideas compared with current control strategies.

Underwater acoustic target recognition is a hot research area in acoustic signal processing. With the development of deep learning, feature extraction and neural network computation have become two major steps of recognition. Due to the complexity of the marine environment, traditional feature extraction cannot express the characteristics of the targets well. In this paper, we propose an underwater acoustic target recognition approach named VFR. VFR adopts a novel feature extraction method by fusing three-dimensional FBank features, and inputs the extracted features into a residual network, instead of the classical CNN network, plus cross-domain pre-training to perform target recognition. The experimental results show that VFR achieves 98.5% recognition accuracy on the randomly divided ShipsEar dataset and 93.8% on the time-divided dataset, respectively, which are better than state-of-the-art results.

In the gas-assisted extrusion process, the melt inside the die is in a low-viscosity molten state, so the flow field of the gas cushion layer has a great effect on the cross-sectional shape of the micro-tube. Therefore, this study establishes the gas distribution chamber model of the gas-assisted die. Ansys Fluent software was used to simulate the gas flow field of the gas distribution chamber. The effect of the gas chamber structure on the size of the micro-tube was analyzed by the extrusion experiment. The research shows that the velocity unevenness coefficient of the gas outlet of the single gas chamber die is 11.8%, which is higher than that of the double gas chamber die. The use of a double gas chamber die can improve the stability of the gas cushion layer and the wall thickness non-uniformity of the micro-tube, which verifies the simulation results.

With the evolution of machine learning and deep learning, more and more researchers have utilized these methods in the field of underwater acoustic target recognition. In these studies, convolutional neural networks (CNNs) are the main components of recognition models. In recent years, a neural network model Transformer that uses a self-attention mechanism was proposed and achieved good performance in deep learning. In this paper, we propose a Transformer-based underwater acoustic target recognition model STM. To the best of our knowledge, this is the first work to introduce Transformer into the underwater acoustic field. We compared the performance of STM with CNN, ResNet18, and other multi-class algorithm models. Experimental results illustrate that under two commonly used dataset partitioning methods, STM achieves 97.7% and 89.9% recognition accuracy, respectively, which are 13.7% and 50% higher than the CNN Model. STM also outperforms the state-of-the-art model CRNN-9 by 3.1% and ResNet18 by 1.8%.

Evolving the legacy network to SDN/NFV network is the strategy of carrier network re-architecture.Based on large-scale cases,multi-tier data centers architecture,application-driven telecom cloud infrastructure,vBRAS pool and C-U separation solutions were proposed.Session-consumed services and IPTV are the first several services that can be implemented by the new solution in MAN.And in those usecases,the carriers’ operators really combine the IT and CT operation teams into one virtual team,and push the evolution gradually.

The accuracy and efficiency of sound field calculations highly concern issues of hydroacoustics. Recently, one-dimensional spectral methods have shown high-precision characteristics when solving the sound field but can solve only simplified models of underwater acoustic propagation, thus their application range is small. Therefore, it is necessary to directly calculate the two-dimensional Helmholtz equation of ocean acoustic propagation. Here, we use the Chebyshev–Galerkin and Chebyshev collocation methods to solve the two-dimensional Helmholtz model equation. Then, the Chebyshev collocation method is used to model ocean acoustic propagation because, unlike the Galerkin method, the collocation method does not need stringent boundary conditions. Compared with the mature Kraken program, the Chebyshev collocation method exhibits a higher numerical accuracy. However, the shortcoming of the collocation method is that the computational efficiency cannot satisfy the requirements of real-time applications due to the large number of calculations. Then, we implemented the parallel code of the collocation method, which could effectively improve calculation effectiveness.

The accurate calculation of the sound field is one of the most concerning issues in hydroacoustics. The one-dimensional spectral method has been used to correctly solve simplified underwater acoustic propagation models, but it is difficult to solve actual ocean acoustic fields using this model due to its application conditions and approximation error. Therefore, it is necessary to develop a direct solution method for the two-dimensional Helmholtz equation of ocean acoustic propagation without using simplified models. Here, two commonly used spectral methods, Chebyshev–Galerkin and Chebyshev–collocation, are used to correctly solve the two-dimensional Helmholtz model equation. Since Chebyshev–collocation does not require harsh boundary conditions for the equation, it is then used to solve ocean acoustic propagation. The numerical calculation results are compared with analytical solutions to verify the correctness of the method. Compared with the mature Kraken program, the Chebyshev–collocation method exhibits higher numerical calculation accuracy. Therefore, the Chebyshev–collocation method can be used to directly solve the representative two-dimensional ocean acoustic propagation equation. Because there are no model constraints, the Chebyshev–collocation method has a wide range of applications and provides results with high accuracy, which is of great significance in the calculation of realistic ocean sound fields.

The normal mode model is important in computational atmospheric acoustics. It is often used to compute the atmospheric acoustic field under a time-independent single-frequency sound source. Its solution consists of a set of discrete modes radiating into the upper atmosphere, usually related to the continuous spectrum. In this article, we present two spectral methods, the Chebyshev-Tau and Chebyshev-Collocation methods, to solve for the atmospheric acoustic normal modes, and corresponding programs are developed. The two spectral methods successfully transform the problem of searching for the modal wavenumbers in the complex plane into a simple dense matrix eigenvalue problem by projecting the governing equation onto a set of orthogonal bases, which can be easily solved through linear algebra methods. After the eigenvalues and eigenvectors are obtained, the horizontal wavenumbers and their corresponding modes can be obtained with simple processing. Numerical experiments were examined for both downwind and upwind conditions to verify the effectiveness of the methods. The running time data indicated that both spectral methods proposed in this article are faster than the Legendre-Galerkin spectral method proposed previously.

Abstract Mammary gland development begins with the appearance of epithelial placodes that invaginate, sprout, and branch to form small arborized trees by birth. The second phase of ductal growth and branching is driven by the highly invasive structures called terminal end buds (TEBs) that form at ductal tips at the onset of puberty. Ectodysplasin (Eda), a tumor necrosis factor-like ligand, is essential for the development of skin appendages including the breast. In mice, Eda regulates mammary placode formation and branching morphogenesis, but the underlying molecular mechanisms are poorly understood. Fibroblast growth factor (Fgf) receptors have a recognized role in mammary ductal development and stem cell maintenance, but the ligands involved are ill-defined. Here we report that Fgf20 is expressed in embryonic mammary glands and is regulated by the Eda pathway. Fgf20 deficiency does not impede mammary gland induction, but compromises mammary bud growth, as well as TEB formation, ductal outgrowth and branching during puberty. We further show that loss of Fgf20 delays formation of Eda-induced supernumerary mammary buds and normalizes the embryonic and postnatal hyperbranching phenotype of Eda overexpressing mice. These findings identify a hitherto unknown function for Fgf20 in mammary budding and branching morphogenesis.