Prospects for Chinese-Russian cooperation in the dairy sector

Relevance. The COVID-19 pandemic is a new challenge facing humanity, which has already caused serious damage to the global economy. In particular, international cooperation has faced somewhat unprecedented challenges. In the post-pandemic era, it is extremely important to study the state of the dairy sector in China in order to find a way to develop cooperation between the People’s Republic of China and the Russian Federation in this field.Research objective. To analyse the state of China’s dairy sector in the post-pandemic period, taking into account the trend of recovery, as well as to identify the priority areas and interaction directions in the dairy sector between China and the Russian Federation.Data and Methods. The research was conducted using the methods of comparative analysis, focusing mainly on quantitative and qualitative indicators. The conditions for the interaction of the dairy sector between China and the Russian Federation were analysed.Results. It is shown that, at present, the Chinese-Russian interaction in the dairy sector includes four aspects: trade in dairy products, experience exchange, investment cooperation and interaction at the governmental level. Trade in dairy products and the exchange of experience and technologies in the dairy sector are developing steadily, supported by increased interaction at the state level.Conclusions. The Chinese-Russian cooperation in the dairy sector is experiencing some difficulties; however, the common interest of both countries in dairy production and the strong support of both governments ensure excellent prospects for achieving mutual benefits and high performance

Chinese and Russian transport corridors and the belt and road initiative: prospects of Sino-Russian cooperation

Relevance. The article discusses mutually beneficial cooperation between Russia and China within the framework of the Belt and Road Initiative and the role of Russia as a key link between China and the Eurasian Economic Union. The relevance of the study is determined by the need for a comprehensive analysis of the current state of transport cooperation between the countries with shared borders (Russia and China) and on a more global level. It is especially important to identify the priority areas of intergovernmental cooperation in the transportation sphere. Research objective. The study is aimed at evaluating the prospects of Sino-Russian transport cooperation in connection with the Belt and Road Initiative.Data and methods. For comparative analysis, we use qualitative and quantitative indicators to consider the current state of Sino-Russian cooperation. Our research draws from the official statistical data of Russia and China and from the findings of the previous studies. Results. The research has shown that there is a steady trend for integration of Russian and Chinese crossborder infrastructure. In particular, the Economic Corridor China-Mongolia-Russia relies on the expansion and modernization of the railway and highway infrastructure. Conclusions. The connection of the Belt and Road Initiative with the Eurasian Economic Union will contribute to transport cooperation between China and Russia. Sino-Russian transport cooperation will develop not only on the state level but also on regional and local levels. The Belt and Road Initiative will enable Russia and China unite their transport infrastructure into a single network. Apart from the transport infrastructure, Sino-Russian cooperation also encompasses other aspects, such as training of specialists in logistics and transportation technologies

Sino-Russian environmental cooperation: past, present, and future

In order to address vital environmental issues, China and Russia have established a set of cooperation mechanisms, such as the Sub-Committee on Environmental Protection of the Regular Meeting of the Prime Ministers of China and Russia. There is currently a multi-level environmental cooperation system between the two countries. In recent years, China and Russia have strengthened their ecological cooperation and have achieved certain results in the conservation of cross-border water resources and establishment of transboundary nature reserves. There are still, however, many problems to handle such as the discrepancies in legislation and the limited character of investment each of the countries is willing to make into environmental protection. Therefore, as the article shows, it is necessary to formulate a unified regulatory framework; to establish a resource protection zone; to enhance joint monitoring of the water quality in transboundary rivers as well as soil and air quality in adjacent areas; and, finally, to raise public awareness in both countries of environmental security and nature conservation. In 2017, Russia hosted the Year of Ecology, which was a good opportunity for both countries to promote information exchange and cooperation in the sphere of joint monitoring and governance, environmental legislation, and ecological education

Global Earth’s gravity field solution with GRACE orbit and range measurements using modified short arc approach

Traditionally, the Earth’s gravity field model is computed from GRACE orbit and range rate measurements, e.g., in a short arc approach where both the position and the velocity vectors are integrated from a force model. In this contribution, we use the GRACE orbit and range measurements to recover the Earth’s gravity field model, thus we only need to integrate the position vectors. We use the range differences between two adjacent epochs to eliminate the range ambiguities. Using GRACE Level-1B RL02 data released by Jet Propulsion laboratory, the gravity field model TJGRACE02O complete to degree and order 90 is developed from 7 years of reduced dynamic orbits covering the period 2004–2010, and the gravity field model TJGRACE02K complete to degree and order 120 is computed from 1 month of kinematic orbits and K-band range data of January. Comparing the degree geoid errors of our new models with recent gravity field models such as the CHAMP-only models EIGEN-CHAMP05S, AIUB-CHAMP03S, ULUX-CHAMP2013S and the GRACE-only models GGM05S, Tongji-GRACE01 as well as a monthly model from the ITG-GRACE2010 time series, and validating these models with GPS-leveling data sets in the USA, we can conclude that the TJGRACE02O model is more accurate than all the CHAMP-only models and TJGRACE02K is comparable in quality to the corresponding GRACE monthly model from ITG-GRACE2010.Department of Land Surveying and Geo-Informatic

Mesh-MLP: An all-MLP Architecture for Mesh Classification and Semantic Segmentation

With the rapid development of geometric deep learning techniques, many mesh-based convolutional operators have been proposed to bridge irregular mesh structures and popular backbone networks. In this paper, we show that while convolutions are helpful, a simple architecture based exclusively on multi-layer perceptrons (MLPs) is competent enough to deal with mesh classification and semantic segmentation. Our new network architecture, named Mesh-MLP, takes mesh vertices equipped with the heat kernel signature (HKS) and dihedral angles as the input, replaces the convolution module of a ResNet with Multi-layer Perceptron (MLP), and utilizes layer normalization (LN) to perform the normalization of the layers. The all-MLP architecture operates in an end-to-end fashion and does not include a pooling module. Extensive experimental results on the mesh classification/segmentation tasks validate the effectiveness of the all-MLP architecture.Comment: 8 pages, 6 figure

An improved accelerometer calibration model for gravity field estimates

During gravity field modelling, accelerometer measurements must be calibrated via scale and bias parameters. Klinger and Mayer-Gürr (2016) found that behaviors of both scales and biases are related to the thermal control service for the accelerometers. This finding indicates that the scales and biases may change significantly after April 2011 as the thermal control service has been switched off since then. To improve gravity field estimates, the time-related variations in either scales or biases should be better modelled. For the purpose of considering the time-dependent changes of scales and biases, we propose an improved accelerometer calibration model in this study, where the scales and biases are modelled by polynomials besides estimating the errors of attitude and accelerometer data. Detailed discussions on the selection of the optimal orders of polynomials for scales and biases, their time-dependent changes and the benefits from the improved accelerometer calibration model are given in this investigation. Compared to other accelerometer calibration models, the improved model has the comparable ability to calibrate the accelerometer measurements, while it achieves better conditioned normal equation and noticeable improvement in gravity field determination

Lightweight equivariant interaction graph neural network for accurate and efficient interatomic potential and force predictions

In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. A century ago, Felix Bloch demonstrated how leveraging the equivariance of the translation operation on a crystal lattice (with geometric symmetry) could significantly reduce the computational cost of determining wavefunctions and accurately calculate material properties. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions in crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By extracting both local and global structures from vector representations and learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Finally, to further validate the predicted interatomic potentials from our model, we conduct classical molecular dynamics (MD) and ab initio MD simulation across various systems, including solid, liquid, and gas. It is found that LEIGNN can achieve the accuracy of ab initio MD and retain the computational efficiency of classical MD across all examined systems, demonstrating its accuracy, efficiency, and universality

Amlexanox Enhances Premature Termination Codon Read-Through in COL7A1 and Expression of Full Length Type VII Collagen: Potential Therapy for Recessive Dystrophic Epidermolysis Bullosa.

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare monogenic blistering disorder caused by the lack of functional type VII collagen, leading to skin fragility and subsequent trauma-induced separation of the epidermis from the underlying dermis. A total of 46% of patients with RDEB harbor at least one premature termination codon (PTC) mutation in COL7A1, and previous studies have shown that aminoglycosides are able to overcome RDEB PTC mutations by inducing read-through and incorporation of an amino acid at the PTC site. However, aminoglycoside toxicity will likely prevent widespread clinical application. Here the FDA-approved drug amlexanox was tested for its ability to read-through PTC mutations in cells derived from patients with RDEB. Eight of 12 different PTC alleles responded to treatment and produced full length protein, in some cases more than 50% relative to normal controls. Read-through type VII collagen was readily detectable in cell culture media and also localized to the dermal-epidermal junction in organotypic skin culture. Amlexanox increased COL7A1 transcript and the phosphorylation of UPF-1, an RNA helicase associated with nonsense-mediated mRNA decay, suggesting that amlexanox inhibits nonsense-mediated mRNA decay in cells from patients with RDEB that respond to read-through treatment. This preclinical study demonstrates the potential of repurposing amlexanox for the treatment of patients with RDEB harboring PTC mutation in COL7A1

Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction

Surface reconstruction is very challenging when the input point clouds, particularly real scans, are noisy and lack normals. Observing that the Multilayer Perceptron (MLP) and the implicit moving least-square function (IMLS) provide a dual representation of the underlying surface, we introduce Neural-IMLS, a novel approach that directly learns the noise-resistant signed distance function (SDF) from unoriented raw point clouds in a self-supervised fashion. We use the IMLS to regularize the distance values reported by the MLP while using the MLP to regularize the normals of the data points for running the IMLS. We also prove that at the convergence, our neural network, benefiting from the mutual learning mechanism between the MLP and the IMLS, produces a faithful SDF whose zero-level set approximates the underlying surface. We conducted extensive experiments on various benchmarks, including synthetic scans and real scans. The experimental results show that {\em Neural-IMLS} can reconstruct faithful shapes on various benchmarks with noise and missing parts. The source code can be found at~\url{https://github.com/bearprin/Neural-IMLS}

An optimized short-arc approach: methodology and application to develop refined time series of Tongji-Grace2018 GRACE monthly solutions

Abstract Considering the unstable inversion of ill-conditioned intermediate matrix required in each integral arc in the short-arc approach presented in Chen et al. (2015), an optimized short-arc method via stabilizing the inversion is proposed. To account for frequency-dependent noise in observations, a noise whitening technique is implemented in the optimized short-arc approach. Our study shows the optimized short-arc method is able to stabilize the inversion and eventually prolong the arc length to 6 hours. In addition, the noise whitening method is able to mitigate the impacts of low-frequency noise in observations. Using the optimized short-arc approach, a refined time series of GRACE monthly models called Tongji-Grace2018 has been developed. The analyses allow us to derive the following conclusions: (a) during the analyses over the river basins (i.e. Amazon, Mississippi, Irrawaddy and Taz) and Greenland, the correlation coefficients of mass changes between Tongji-Grace2018 and others (i.e. CSR RL06, GFZ RL06 and JPL RL06 Mascon) are all over 92 and the corresponding amplitudes are comparable; (b) the signals of Tongji-Grace2018 agree well with those of CSR RL06, GFZ RL06, ITSG-Grace2018 and JPL RL06 Mascon, while Tongji-Grace2018 and ITSG-Grace2018 are less noisy than CSR RL06 and GFZ RL06; (c) clearer global mass change trend and less striping noise over oceans can be observed in Tongji-Grace2018 even only using decorrelation filtering; and (d) for the tests over Sahara, over 36 and 19 of noise reductions are achieved by Tongji-Grace2018 relative to CSR RL06 in the cases of using decorrelation filtering and combined filtering, respectively