Research Progress
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The need to make more accurate grain demand (GD) forecasting has become a major topic in the current international grain security discussion. Our research aims to improve short term GD prediction by establishing a multi-factor model that integrates the key factors: shifts in dietary structures, population size and age structure, urbanization, food waste, and the impact of COVID-19. These factors were not considered simultaneously in previous research. To illustrate the model, we projected China’s annual GDP from 2022 to 2025. We calibrated key parameters such as conversion coef？cients from animal foods to feed grain, standard person consumption ratios, and population size using the latest surveys and statistical data that were either out of date or missing in previous research. Results indicate that if the change in diets continued at the rate as observed during 2013–2019 (scenario 1), China’s GD is projected to be 629.35 million tons in 2022 and 658.16 million tons in 2025. However, if diets shift to align with the recommendations in the Dietary Guideline for Chinese Residents 2022 (scenario 2), GD would be lower by 5.9–11.1% annually compared to scenario 1. A reduction in feed grain accounts for 68% of this change. Furthermore, for every 1 percentage point increase in the population adopting a balanced diet, GD would fall by 0.44–0.73 million tons annually during that period. Overlooking changes in the population age structure could lead to an overprediction of annual GDP by 3.8% from 2022 to 2025. With an aging population, China’s GD would fall slightly, and adopting a balanced diet would not lead to an increase in GD but would have positive impacts on human health and the environment. Our sensitivity analysis indicated that reducing food waste, particularly cereal, livestock, and poultry waste, would have signi？cant effects on reducing GD, offsetting the higher demand due to rising urbanization and higher incomes. These results underscore the signi？cance of simultaneous consideration of multiple factors, particularly the dietary structure and demographic composition, resulting in a more accurate prediction of GD. Our ？ndings should be useful for policymakers concerning grain security, health, and environmental protection.
Publication:
Nutrients 15, 2877 (2023).
https://doi.org/10.3390/nu15132877
Authors:
Xiuli Liu
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Center for Forecasting Science, Chinese Academy of Sciences, Beijing 100190, China
E-mail: xiuli.liu@amss.ac.cn
Mun S. Ho
Harvard China Project on Energy, Economy and Environment, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Geoffrey J. D. Hewings
Regional Economics Applications Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Yuxing Dou
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;
University of Chinese Academy of Sciences, Beijing 100049, China
Shouyang Wang
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Center for Forecasting Science, Chinese Academy of Sciences, Beijing 100190, China
Guangzhou Wang
Institute of Population and Labor Economics, Chinese Academy of Social Sciences, Beijing 100006, China
Dabo Guan
Department of Earth System Science, Tsinghua University, Beijing 100084, China
Shantong Li
Development Research Center of the State Council, Beijing 100010, China -
Water, energy, fertilizer and food are vital for sustainable development, yet their nexuses within various agricultural trade patterns remain ambiguous. Therefore, we analyzed the nexus of these four elements in the global trade of both total and basic agricultural products, focusing on exporting countries from 2000 to 2019. Our approach involved coupling models, complex network analysis and mathematical programming. The results showed that: (1) In the total agricultural trade nexus flows, the export-import flow pairs with the highest average nexus of continents changed from Europe-Europe to Oceania-Asia. Netherlands-Germany maintained the export-import flow pairs with the highest nexus of countries. In the basic agricultural trade nexus flows, Oceania-Asia maintained the highest average nexus export-import flow pairs of continents. Denmark-Germany maintained the export-import flow pairs with the highest nexus of countries. (2) The basic agricultural trade nexus flows network exhibited the higher density and average clustering coefficient. Argentina, Brazil and Canada played pivotal roles in maintaining high closeness centrality. Congo, Dominican Republic and Bosnia and Herzegovina displayed considerable potential on the indirectness of the network with high eigenvector centrality. (3) To improve the stability of the nexus flows network, taking the total agricultural trade nexus of the US, Netherlands and China as examples, the main largest beneficiary countries were Vanuatu, Eswatini and Saint Lucia. Taking the basic agricultural trade nexus of Canada, Brazil and China as examples, the main largest beneficiary countries were Kiribati and Niue. This study utilizes coupling and network models to analyze agricultural trade nexus flows, offering a deeper understanding of the complex nexus relationships within various agricultural trade patterns. Furthermore, the findings serve as a reference point for the formulation of strategies promoting sustainable development.
Publication:
Sustainable Production and Consumption 39, 480-494 (2023).
https://doi.org/10.1016/j.spc.2023.05.034
Authors:
Songhua Huan
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Xiuli Liu
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Center for Forecasting Science, Chinese Academy of Sciences, Beijing 100190, China
Email: xiuli.liu@amss.ac.cn -
This paper studies a Cauchy problem for a scalar multidimensional (multi-d) viscous convex conservation law, in which the initial data is a planar viscous shock with a multi-d periodic perturbation. We show that if the wave-strength and perturbation are both small, then the viscous shock is stable in the \(L^\infty ({\mathbb R}^n)\) space with an exponential decay rate. One contribution of the paper is to establish a new framework to study the stability of viscous shocks in multiple dimensions, where the elementary energy method with the antiderivative technique can be used. The idea is to decompose the multi-d perturbation into a one-dimensional function and a multi-d remainder, where the former can well define its antiderivative and the latter satisfies the Poincaré inequality over a periodic domain.
Publication:
SIAM Journal on Mathematical Analysis, Volume 55, Issue 3 (2023)
http://dx.doi.org/10.1137/21M1462453
Author:
Qian Yuan
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Email: qyuan@amss.ac.cn -
A locally optimal preconditioned Newton-Schur method for symmetric elliptic eigenvalue problems (Xuejun Xu)
09 25, 2023
A locally optimal preconditioned Newton-Schur method is proposed for solving symmetric elliptic eigenvalue problems. Firstly, the Steklov-Poincaré operator is used to project the eigenvalue problem on the domain \Omega onto the nonlinear eigenvalue subproblem on \Gamma, which is the union of subdomain boundaries. Then, the direction of correction is obtained via applying a non-overlapping domain decomposition method on \Gamma. Four different strategies are proposed to build the hierarchical subspace U_{k+1} over the boundaries, which are based on the combination of the coarse-subspace with the directions of correction. Finally, the approximation of eigenpair is updated by solving a local optimization problem on the subspace U_{k+1}. The convergence rate of the locally optimal preconditioned Newton-Schur method is proved to be \Gamma =1-c_{0}T_{h,H}^{-1}, where c_{0} is a constant independent of the fine mesh size h, the coarse mesh size H and jumps of the coefficients; whereas T_{h,H} is the constant depending on stability of the decomposition. Numerical results confirm our theoretical analysis.
Publication:
Mathematics of Computation, 92 (2023), 2655-2684
http://dx.doi.org/10.1090/mcom/3860
Author:
Wenbin Chen
School of Mathematical Sciences and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai 200433, People’s Republic of China
Nian Shao
School of Mathematical Sciences, Fudan University, Shanghai 200433, People’s Republic of China
Xuejun Xu
LSEC, Institute of Computational Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
Email: xxj@lsec.cc.ac.cn -
Identifiability Analysis of Noise Covariances for LTI Stochastic Systems With Unknown Inputs (Biqiang Mu)
09 20, 2023
Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the process and measurement noise covariances (denoted as Q and R, respectively) of systems with unknown inputs. This article considers the identifiability of Q / R using the correlation-based measurement difference approach. More specifically, we establish 1) necessary conditions under which Q and R can be uniquely jointly identified; 2) necessary and sufficient conditions under which Q can be uniquely identified, when R is known; 3) necessary conditions under which R can be uniquely identified, when Q is known. It will also be shown that for achieving the results mentioned above, the measurement difference approach requires some decoupling conditions for constructing a stationary time series, which are proved to be sufficient for the well-known strong detectability requirements established by Hautus.
Publication:
IEEE Transactions on Automatic Control, Volume: 68, Issue: 7, July 2023
http://dx.doi.org/10.1109/TAC.2022.3208338
Author:
He Kong
Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China
Salah Sukkarieh
Sydney Institute for Robotics and Intelligent Systems, The University of Sydney, Sydney, NSW, Australia
Travis J. Arnold
Tianshi Chen
School of Data Science and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China
Biqiang Mu
Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Email: bqmu@amss.ac.cn
Wei Xing Zheng
School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia -
Whole-body regeneration of planarians is a natural wonder but how it occurs remains elusive. It requires coordinated responses from each cell in the remaining tissue with spatial awareness to regenerate new cells and missing body parts. While previous studies identified new genes essential to regeneration, a more efficient screening approach that can identify regeneration-associated genes in the spatial context is needed. Here, we present a comprehensive three-dimensional spatiotemporal transcriptomic landscape of planarian regeneration. We describe a pluripotent neoblast subtype, and show that depletion of its marker gene makes planarians more susceptible to sub-lethal radiation. Furthermore, we identified spatial gene expression modules essential for tissue development. Functional analysis of hub genes in spatial modules, such as plk1, shows their important roles in regeneration. Our three-dimensional transcriptomic atlas provides a powerful tool for deciphering regeneration and identifying homeostasis-related genes, and provides a publicly available online spatiotemporal analysis resource for planarian regeneration research.
Publication:
Nature Communications 14, 3205 (2023).
https://doi.org/10.1038/s41467-023-39016-0
Author:
Guanshen Cui
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
Kangning Dong
NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Jia-Yi Zhou
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
Shang Li
NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Ying Wu
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
Qinghua Han
NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Bofei Yao
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
Qunlun Shen
NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Yong-Liang Zhao
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
University of Chinese Academy of Sciences, Beijing, China
Ying Yang
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China
Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
Jun Cai
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
University of Chinese Academy of Sciences, Beijing, China
Shihua Zhang
NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
Email: zsh@amss.ac.cn
Yun-Gui Yang
CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
China National Center for Bioinformation, Beijing, 100101, China
Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China
Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
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Quantum detector tomography (QDT) is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we utilize regularization to improve the QDT accuracy whenever the probe states are informationally complete or informationally incomplete. In the informationally complete scenario, without regularization, we optimize the resource (probe state) distribution by converting it to a semidefinite programming problem. Then in both the informationally complete and informationally incomplete scenarios, we discuss different regularization forms and prove the mean squared error scales as O(1/N) or tends to a constant with N state copies under the static assumption. We also characterize the ideal best regularization for the identifiable parameters, accounting for both the informationally complete and informationally incomplete scenarios. Numerical examples demonstrate the effectiveness of different regularization forms and a quantum optical experiment test shows that a suitable regularization form can reach a reduced mean squared error.
Publication:
Automatica, Volume 155, September 2023, 111124
http://dx.doi.org/10.1016/j.automatica.2023.111124
Author:
Shuixin Xiao
University of Michigan – Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China
School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia
Yuanlong Wang
Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Centre for Quantum Computation and Communication Technology (Australian Research Council), Centre for Quantum Dynamics, Griffith University, Brisbane, Queensland 4111, Australia
Email: wangyuanlong@amss.ac.cn
Jun Zhang
University of Michigan – Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Daoyi Dong
School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia
Shota Yokoyama
School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia
Centre for Quantum Computation and Communication Technology, Australian Research Council, Canberra, ACT 2600, Australia
Ian R. Petersen
School of Engineering, Australian National University, Canberra, ACT 2601, Australia
Hidehiro Yonezawa
School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia
Centre for Quantum Computation and Communication Technology, Australian Research Council, Canberra, ACT 2600, Australia -
This article investigates a two-timescale opinion dynamics model, named the concatenated Friedkin–Johnsen (FJ) model, which describes the evolution of the opinions of a group of agents over a sequence of discussion events. The topology of the underlying graph changes with the event, in the sense that the agents can participate or less to an event, and the agents are stubborn, with stubbornness that can vary from one event to the other. Concatenation refers to the fact that the final opinions of an event become initial conditions of the next event. We show that a concatenated FJ model can be represented as a time-varying product of stochastic transition matrices having a special form. Conditions are investigated under which a concatenated FJ model can achieve consensus in spite of the stubbornness. Four different sufficient conditions are obtained, mainly based on the special topological structure of our stochastic matrices.
Publication:
IEEE Transactions on Automatic Control, Volume: 68, Issue: 7, July 2023.
http://dx.doi.org/10.1109/TAC.2022.3200888
Author:
Lingfei Wang
Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Carmela Bernardo
Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Benevento, Italy
Division of Automatic Control, Department of Electrical Engineering, Link？ping University, Link？ping, Sweden
Yiguang Hong
Department of Control Science and Engineering, Tongji University, Shanghai, China
Email: yghong@iss.ac.cn
Francesco Vasca
Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Benevento, Italy
Guodong Shi
Australian Center for Field Robotics, School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW, Australia
Claudio Altafini
Division of Automatic Control, Department of Electrical Engineering, Link？ping University, Link？ping, Sweden -
The Modular Inversion Hidden Number Problem (MIHNP), which was proposed at Asiacrypt 2001 by Boneh, Halevi, and Howgrave-Graham, is summarized as follows: Assume that the $\delta $ most significant bits of $z$ are denoted by }_{\delta }(z)$ . The goal is to retrieve the hidden number $\alpha \in \mathbb {Z}_{p}$ given many samples $\left ({t_{i}, {\mathrm {MSB}}_{\delta }((\alpha + t_{i})^{-1} \bmod {p})}\right)$ for random $t_{i} \in \mathbb {Z}_{p}$ . MIHNP is a significant subset of Hidden Number Problems. Eichenauer and Lehn introduced the Inversive Congruential Generator (ICG) in 1986. It is basically characterized as follows: For iterated relations $v_{i+1}=(av^{-1}_{i}+b)\bmod {p}$ with a secret seed $v_{0} \in \mathbb {Z}_{p}$ , each iteration produces $\mathrm {MSB}_{\delta }(v_{i+1})$ where $i \geq 0$ . The ICG family of pseudorandom number generators is a significant subclass of number-theoretic pseudorandom number generators. Sakai-Kasahara scheme is an identity-based encryption (IBE) system proposed by Sakai and Kasahara. It is one of the few commercially implemented identity-based encryption schemes. We explore the Coppersmith approach for solving a class of modular polynomial equations, which is derived from the recovery issue for the hidden number $\alpha $ in MIHNP and the secret seed $v_{0}$ in ICG, respectively. Take a positive integer $n=d^{3+o(1)}$ for some positive integer constant $d$ . We propose a heuristic technique for recovering the hidden number $\alpha $ or secret seed $v_{0}$ with a probability close to 1 when $\delta /\log _{2} p>\frac {1}{d+1}+o\left({\frac {1}{d}}\right)$ . The attack’s total time complexity is polynomial in the order of $\log _{2} p$ , with the complexity of the LLL algorithm increasing as $d^{\mathcal {O}(d)}$ and the complexity of the Gr？bner basis computation increasing as $d^{\mathcal {O}(n)}$ . When $d> 2$ , this asymptotic bound surpasses the asymptotic bound $\delta /\log _{2} p>\frac {1}{3}$ established by Boneh, Halevi, and Howgrave-Graham at Asiacrypt 2001. This is the first time a more precise constraint for solving MIHNP is established, implying that the claim that MIHNP is difficult is violated whenever $\delta /\log _{2} p < \frac {1}{3}$ . Then we study ICG. To our knowledge, we achieve the best performance for attacking ICG to date. Finally, we provide an MIHNP-based lattice approach that recovers the signer’s secret key in the Sakai-Kasahara type signatures when the most (least) significant bits of the signing exponents are exposed. This improves the existing work in this direction.
Publication:
IEEE Transactions on Information Theory, vol. 69, no. 8, pp. 5337-5356, Aug. 2023
http://dx.doi.org/10.1109/TIT.2023.3263485
Author:
Jun Xu
State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
Santanu Sarkar
Indian Institute of Technology Madras, Chennai, India
Lei Hu
State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
Huaxiong Wang
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Jurong West, Singapore
Yanbin Pan
Key Laboratory of Mathematics Mechanization, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Email: panyanbin@amss.ac.cn -
Global Well-Posedness and Regularity of Stochastic 3D Burgers Equation with Multiplicative Noise (Zhao Dong)
09 20, 2023
By utilizing the so-called Doss–Sussman transformation, we link our stochastic 3D Burgers equation with linear multiplicative noise to a random 3D Burger equation. With the help of techniques from partial differential equations (PDEs) and probability, we establish the global well-posedness of stochastic 3D Burgers with the diffusion coefficient being constant. Next, by developing a solution which is orthogonal with the gradient of coefficient of the noise, we extend the global well-posedness to a more general case in which the diffusion coefficient is spatial dependent, i.e., it is a function of the spatial variable. Our results and methodology pave a way to extend some regularity results of stochastic 1D Burgers equation to stochastic 3D Burgers equations.
Publication:
SIAM Journal on Mathematical Analysis, Volume 55, Issue 3 (2023)
https://doi.org/10.1137/21M1413377
Author:
Zhao Dong
RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Email: dzhao@amt.ac.cn
Boling Guo
Institute of Applied Physics and Computational Mathematics, P.O. Box 8009, Beijing 100088, China.
Jiang-Lun Wu
Department of Mathematics, Computational Foundry, Swansea University, Swansea SA1 8EN, UK.
Guoli Zhou
School of Statistics and Mathematics, Chongqing University, Chongqing 400044, China.
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