中国科学院数学与系统科学研究院期刊网

15 September 2025, Volume 45 Issue 9
    

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  • HUANG Tian, XIAO Zhihua, QI Zhenzhong
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2701-2714. https://doi.org/10.12341/jssms23857
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    Firstly, the port-Hamiltonian differential-algebraic systems are transformed into the port-Hamiltonian ordinary differential systems with parameter $\varepsilon$. Then, based on the parameteric ordinary differential systems, two structure-preserving model reduction methods are proposed. The first method is parametric moments matching: Constructing the parametric moments based on the frequency parameter $s$ and the embedding parameter $\varepsilon$ of the parametric systems, and then obtaining the reduced-order models of the parametric systems through parametric moments matching. The reduced-order systems match the parametric moments of the original systems. Finally, by taking the embedded parameter $\varepsilon = 0$, the structure preserving reduced-order models of the original port-Hamiltonian differential-algebraic systems are obtained. The second method is low-rank balanced truncation: Using Laguerre functions to construct the low-rank decomposition factors of the controllability and observability Gramians of the parametric ordinary differential systems. The approximate balanced systems are obtained through projection, and finally, the reduced-order models are constructed by truncating the states corresponding to smaller Hankel singular values. This procedure offers adaptability and enables the construction of reduced-order models meeting specified accuracy conditions while maintaining lower computational complexity. Both algorithms use Gram-Schmidt process to construct new projection matrices, thereby preserving the differential structure of the original system. Finally, the effectiveness of the algorithms is demonstrated through a numerical example.
  • CHENG Zizhou, WANG Houneng, LI Zicheng, CHEN Long, XIE Xuhuan
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2715-2725. https://doi.org/10.12341/jssms240169
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    Dissipative analysis and control for a class of average dwell time switched genetic regulatory networks is investigated. The time-varying delay parameters are also taken into account. Firstly, the sufficient conditions for the strict $\left\langle Q,S,R \right\rangle -\gamma$ dissipation of the system are derived through constructing the multiple Lyapunov functions and the dissipative performance index function and combining with the linear matrix inequality technique. By utilizing Schur complement, it is proved that the genetic regulatory network system satisfying $\left\langle Q,S,R \right\rangle -\gamma$ dissipation is exponentially stable. Furthermore, the state feedback controller is designed, and the controller parameters are obtained by solving linear matrix inequalities. Also, it is proven that the designed controller can ensure that the system is $\left\langle Q,S,R \right\rangle -\gamma$ dissipative. Finally, a numerical example is given to verify the correctness and effectiveness of the proposed method.
  • XIA Xingyu, LI Yuan, CHENG Yuanyuan
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2726-2738. https://doi.org/10.12341/jssms240338
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    For discrete affine nonlinear systems, a new optimal control method with discounted attenuation term is proposed by using adaptive dynamic programming. Firstly, attenuation term is introduced to construct the performance index function, which overcomes the influence of discount factor ignoring the subsequent time on the system performance in traditional research, and improves the control performance of the system. Secondly, different from the traditional discount factor algorithm, this paper analyzes the sequence of performance index functions, proves that the discounted attenuation term can improve the flexibility of the system and avoid the local convergence problem in the traditional iterative process, and the neural network is used to implement the algorithm. Then, monotonically increasing or decreasing iterated performance index function sequences are constructed and the asymptotic stability of the system is proved. Finally, a simulation example is given to verify the effectiveness of the algorithm.
  • WU Zebin, SHEN Yanjun, WU Chenguang
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2739-2757. https://doi.org/10.12341/jssms240263
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    This paper develops a robust non-fragile output feedback control scheme for a class of uncertain nonlinear systems with quantized inputs and outputs. An output filter is employed to augment the considered nonlinear system, and an extended non-fragile observer is constructed. It is avoided that the introduction of quantization errors and measurement noise into each state equation of the observer. Disturbance estimators are used to estimate system noise and disturbances. Based on this, a robust non-fragile controller with quantized inputs and outputs is proposed. Moreover, introducing two time-varying matrix inequalities to solve the problem of uncertain perturbations in observer and controller gains. Stability analysis illustrates that all signals in the closed-loop system are ultimately uniformly bounded. Two numerical simulation cases are presented to verify the accuracy and effectiveness of the proposed scheme.
  • SUN Jian, SUN Haipeng, ZHANG Jianxin, SHAN Qihe, LIU Lei, YAN Jing
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2758-2774. https://doi.org/10.12341/jssms23500
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    This paper investigates the problem of adaptive consensus control for second-order delayed nonlinear multi-agent systems under intermittent communication. The main contribution is to propose a novel energy-dependent intermittent communication mechanism, where the work time and the rest time can be flexibly adjusted online according to the energy of the current error state. In this communication scheme, by dividing the non-negative real number region into three subregions, the work time and the rest time are determined by the pre-assigned subregions and the energy function describing the error state. Then by implementing the distributed adaptive control based on neighboring agent state estimation, the overall consensus is achieved under energy-dependent intermittent communication. Compared with existing time-dependent intermittent communication schemes, the proposed energy-dependent scheme can tolerate more rest time. Finally, the effectiveness of the proposed method is validated through a simulation example with two scenarios.
  • LIU Qing, ZHANG Dan
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2775-2790. https://doi.org/10.12341/jssms240235
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    In this paper, the problem of output consensus control for heterogeneous multi-agent systems with denial-of-service (DoS) attacks is studied. First, aiming at the problem that the cyber attack behavior is changeable and its statistical characteristics of attack modeling method based on dual hidden Markov model is proposed, which converts the communication interruption caused by the attack behavior into the communication topologies switching of multi-agent system. Second, a distributed asynchronous dynamic observer is designed to solve the asynchronous problem when the communication topology mode (CTM) and the transition probability mode (TPM) do not match. Third, based on the stochastic Lyapunov theory and linear matrix inequality technique, sufficient conditions for the solvability of the system output consensus problem are obtained. Finally, the feasibility and effectiveness of the results are illustrated through a simulation example.
  • CHENG Ying, LI Yifeng, YANG Zhichun
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2791-2803. https://doi.org/10.12341/jssms240283
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    In this paper, we study the problem of weak disturbance decoupling for Boolean networks. Firstly, we present a necessary and sufficient algebraic condition using the semi-tensor product of matrices for solving the weak disturbance decoupling problem for Boolean networks. Secondly, we use the directed graph partitioning method to propose a graph-theoretic condition for the weak disturbance decoupling problem of Boolean networks based on the vertex-colored state transition graph. These conditions reflect the relationship between the original disturbance decoupling and the weak disturbance decoupling. Moreover, the obtained graph-theoretic condition provides a new perspective for designing controllers for weak disturbance decoupling. Finally, we provide examples to illustrate the validity of the results.
  • WANG Bei, TANG Xijin
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2804-2818. https://doi.org/10.12341/jssms240215
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    The frequent occurrence of societal events in today's society has a profound impact on people's daily lives and societal development. Prediction of future events helps analysts understand social dynamics, make rapid and accurate decisions as well. This paper proposes a temporal graph model and transforms the target entity prediction into a reasoning task in the temporal event graph. The model first constructs a temporal event graph based on the historical events. In order to explore the influence between different types of events, a dual attention mechanism combining nodes and edges is designed for information aggregation. After encoding the time information through gated recurrent unit, the embedding vectors are input to the fully connected layer to predict the target entity. In addition, given the repeated occurrence of societal events along the historical timeline, the model adopts a copy mechanism to modify the prediction function. Experimental results on multiple datasets demonstrate that the model outperforms other baseline models.
  • JIA Xiaojing, YU Changjiang, MOU Shandong
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2819-2841. https://doi.org/10.12341/jssms240902
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    China has introduced a large-scale equipment upgrade policy that can renovate livestock manure collection and processing facilities. However, the impact of this policy on manure management has not yet been explored in existing research. Additionally, there is a gap in the analysis of refined market strategies regarding the collaboration between third-party companies (TPCs) and small to medium-sized livestock farmers (SMS-LFs). To address these issues, this paper constructs an evolutionary game-theoretic model that examines the equipment upgrade strategy of SMS-LFs and the classified pricing strategy of TPCs. The study incorporates prospect theory and mental accounting theory (PT-MA) to explore how farmers decide whether to invest in equipment upgrades, considering their risk preferences. By combining the expected utility function with the value perception function and adhering to the principle of those who invest receive the subsidies, the paper analyzes which party would benefit more from implementing the upgrades in the context of effective policy execution. The study conducts simulation analyses of strategies and summarizes the systemic archetypes for upgrading manure collection and processing facilities. The findings are as follows: 1) Providing large-scale equipment upgrade subsidies to TPCs, allowing them to enhance the manure collection and processing facilities for SMS-LFs, is the most effective strategy for advancing the policy. 2) TPCs should actively implement a classified pricing strategy. 3) The large-scale renewal and upgrading of livestock manure collection and treatment systems exemplify a limits to growth archetype. The solution is removing constraints from balancing loops through a policy mechanism allowing TPCs to obtain equipment renewal subsidies. This subsidy mechanism encourages TPCs to invest in upgrading manure collection and treatment facilities for SMS-LFs. Subsequently, these companies can implement a classified charging strategy to secure higher-quality manure-based raw materials. This creates an incentive mechanism that motivates SMS-LFs to increase their investments in manure treatment. Ultimately, this virtuous cycle enhances the proportion of subsidies received by SMS-LFs through improved environmental performance.
  • CHEN Jianxin, LU Xuantao, ZHOU Yongwu
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2842-2858. https://doi.org/10.12341/jssms240161
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    This study examines a three-level green agricultural product supply chain system consisting of a farmer, an agribusiness, and the bank. The farmer is capital-constrained, who opts for loans from risk-averse banks to mitigate the impact of funding limitations on the overall profitability of the supply chain. To this end, we have developed the expected profit models for the bank, the agribusiness, and the farmer, and derived the equilibrium strategies, including the optimal interest rate for banks, the optimal wholesale purchased price and green technology investment for agribusiness, and the optimal planting quantity for the farmer. Additionally, we have explored the influence of key parameters in the green agricultural product supply chain on these equilibrium strategies. The results show that: 1) Given the bank's loan loss ratio, when the bank's risk tolerance falls below a certain threshold, its downside risk control measures affect farmer's planting quantities, agribusiness' purchase price, green investment level, and the bank's lending rate. 2) If the bank's risk control is effective, the level of green investment by agribusiness increases with the bank's loan loss ratio and risk tolerance. It also consistently rises with consumer preferences but decreases as the effort cost coefficient in agricultural production increases. 3) The optimal interest rate for the bank is influenced by the risk control parameters and the average yield of agricultural products.
  • YU Dongsheng, LI Xiaoping, YU Juanjuan
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2859-2881. https://doi.org/10.12341/jssms240575
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    New quality productivity itself is green productivity, and its key to development lies in improving green total factor productivity. How enterprises can adapt to and mitigate the risks brought by changes in trade policies by improving new quality productivity, especially green total factor productivity, is an urgent problem that needs to be solved. This paper explores the relationship between trade policy uncertainty and new quality productivity from the perspective of enterprise green total factor productivity using the DID model, based on the merged data of Chinese industrial and commercial enterprises, customs, pollution, patents, US import tariffs from China from 1998 to 2014, and the Tariff Download Facility database of the WTO. Research has found that: 1) The growth rate of green total factor productivity of enterprises during the inspection period was 4.50%, mainly driven by technological progress.The regional growth rates are: Eastern > Central > Northeast > Western, and the industry dimensions are: High tech > Resource based > Medium tech > Low tech. 2) The significant reduction in trade policy uncertainty has significantly improved the green total factor productivity of enterprises, which is conducive to cultivating and developing new quality productivity. This conclusion remains robust after a series of robustness tests. This promoting effect is more significant in mixed trade, pure general trade, eastern regions, and high-tech industry enterprises. 3) The scale effect, technology effect, structural effect, and income effect generated by the decrease in trade policy uncertainty all exist. The scale effect is mainly achieved through the “quality” of enterprise exports, while the technology effect is jointly achieved through the “quantity” and “quality” of enterprise patents.
  • JIANG Ke, ZHANG Xiaojuan
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2882-2901. https://doi.org/10.12341/jssms240140
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    Based on the differences in green product attributes, specifically marginal cost-intensive green products (MIGPs) and development-intensive green products (DIGPs), this study establishes four competitive models related to the adoption of blockchain technology: Neither MIGPs or DIGPs manufacturers adopting blockchain, only MIGPs or DIGPs manufacturer adopting, and both adopting blockchain. Hereafter, the impact of diverse blockchain adoption strategies on the greenness of product, pricing strategies, market demand, and profit improvement levels under green product price competition are investigated. The findings reveal several key insights: 1) In a highly competitive market with high consumer trust, manufacturers are inclined to offer high-quality and high-priced green products; Meanwhile, blockchain-adopting manufacturers produce products with superior environmental attributes, particularly DIGPs; 2) Blockchain adoption significantly enhances consumer trust in green products, allowing manufacturers to achieve a pricing advantage; 3) Manufacturers adopting blockchain technology achieve higher profits than their competitors; However, the decision to adopt depends on various factors, including adoption costs, market price competition intensity, and manufacturers' cost investment levels.
  • XU Linming, SUN Yifang, LIN Hongxi
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2902-2918. https://doi.org/10.12341/jssms240607
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    The traditional data envelopment analysis method based on the farthest goal improves non-effective units by maximizing slack variables, which has high improvement difficulty and cost. Meanwhile, the existing data envelopment analysis methods based on the nearest goal cannot well reflect the subjective preferences of decision-makers and the actual decision-making needs. Therefore, the paper improves the preference setting method in the existing model so that it can be better applied to the model of nearest target. Based on this, the RAM-DEA model considering preferences and the RAM-DEA preference model based on nearest objective are proposed to construct a preference DEA efficiency evaluation method based on nearest objective. This method can be better applied to efficiency evaluation under various preference situations, especially for small and medium-sized enterprises with certain practical significance of cost reduction and efficiency improvement, while considering the economic feasibility of improvement schemes. Finally, the effectiveness of the model improvement is verified through the application analysis of case data from specialized and innovative small and medium-sized enterprises.
  • WU Peng, XIE Huaxin, CHU Chengbin
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2919-2938. https://doi.org/10.12341/jssms240069
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    Facing with public health emergencies such as COVID-19, production shortage of protective equipment may greatly increase the risk of epidemic spread. The management of the production and distribution of protective equipment is pivotal for rapidly and effectively responding to unexpected public health emergencies. Public health emergencies can be categorized into four phases: Onset, outbreak, peak, and decline. This study investigates a multi-period protective equipment production facility location and order allocation for unexpected public health emergencies, aiming to optimize factory capacity migration, production planning, and inventory management at distribution centers. The objective is maximize the overall effectiveness of protective equipment production. Firstly, we formalize it as a mixed-integer nonlinear programming model. Secondly, a problem-specific hybrid adaptive large neighborhood search algorithm are developed, where we design a fixed decomposition optimization strategy and four pairs-tailored operators to enhance its performance. Finally, numerous numerical experimental results demonstrate that: 1) The proposed algorithm can obtain approximate solutions within 9 seconds with an average deviation of 3.25% from the optimal solution for small-scale instances and for large-scale instances, it can achieves approximate solutions with an average deviation of 5.44% from the optimal solution while reducing the average solution time by 82.1%; 2) During public health emergencies, capacity migration can provide more effective material support for severely affected areas, alleviating post-epidemic capacity surplus issues in the supply chain.
  • PU Xujin, DING Yuting, JIN Delong
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2939-2955. https://doi.org/10.12341/jssms240238
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    Considering the financial constraints of new agricultural management subjects such as family farms and the uncertainty of agricultural output, a supply chain financing decision-making model consisting of an individual family farm and an individual e-commerce platform is constructed. The two e-commerce financing models of “agricultural loan” and “agricultural insurance loan” are discussed. By analyzing the operation mechanism of different financing models, the preference conditions of farmers and e-commerce platforms for different financing models are proposed. The study reveals that the insurance rate and consumers' sensitivity to price are the key factors influencing the choice of financing model for farms and e-commerce platforms. When the insurance rate is relatively low, farms and e-commerce platforms consistently prefer the “agricultural insurance loan” model. When the insurance rate is relatively high, the preference of farms and e-commerce platforms will be affected by consumers' sensitivity to price. If consumers are relatively insensitive to prices, farms and e-commerce platforms will prefer the “agricultural loan” model; if consumers are very sensitive to prices, farms and e-commerce platforms will prefer the “agricultural insurance loan” model.
  • WANG Yuhong, HUANG Yiyuan, XU Ziming, REN Youyang
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2956-2969. https://doi.org/10.12341/jssms23074
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    Product attribute weights represent consumers' importance on attributes, serving as a basis for consumer product selection and merchant product optimization. However, current methods for determining attribute weights based on online reviews suffer from significant subjectivity and a tendency towards biased results. Therefore, this paper proposes a new method for determining product attribute weights. Initially, product attributes are identified using an improved LDA topic model. Then, attribute sentiment analysis is applied to label satisfaction levels in comments. Subsequently, the satisfaction-labeled samples undergo $N$-fold cross-validation, and then information gain values for each attribute across different samples are calculated, allowing for parameter estimation of attribute importance value distribution. Next, the distance factor between pairs of distributions is calculated. Finally, the AHP method is employed to determine attribute weights. An empirical analysis focusing on meat and fresh produce demonstrates that this method accurately assigns weights to each attribute to express consumers' level of importance towards them.
  • GUO Jingjun, MA Aiqin, CHENG Zhiyong
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2970-2983. https://doi.org/10.12341/jssms23505
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    Comprehensively considering the assumptions of the option pricing model and the change characteristics of the underlying asset price of carbon options, based on the EUA DEC22 carbon futures option market data from January 4, 2021 to September 27, 2021, the genetic algorithm is used to estimate the parameters of the pricing model. The option pricing performance of the B-S model, fractal Brownian motion model and Heston stochastic volatility model are compared and analyzed according to the stabilized parameter estimates, the most suitable pricing model for the carbon option market is selected, and to provide relevant suggestions for the improvement and smooth operation of the carbon market pricing mechanism. The results show that the Heston stochastic volatility model has the best pricing performance in the carbon option market, followed by the fractal Brownian motion model, and the B-S model is relatively poor. Therefore, the pricing of carbon options based on the Heston stochastic volatility model can improve the pricing accuracy of carbon options, help complete the pricing mechanism of the carbon market, avoid the risk of carbon market transactions, ensure the smooth operation of the carbon market, and promote the realization of the “dual carbon” strategic goal.
  • XIE Xiaoliang, XIAO Meng, ZHAO Yi, PAN Linglong, TANG Chang
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2984-2997. https://doi.org/10.12341/jssms240349
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    Food is a strategic resource for the security of the world, the stability of the people and the well-being of the people. Ensuring the security and stability of the food supply chain is an important feature that a major economy must have. In the pursuit of economic efficiency and resource saving, this paper takes the minimum cost and loss as the main optimization goal, builds a food “supplier-processor-distributor” coordination optimization model, solves by introducing the second generation of non-dominant genetic algorithm, and combines AHP-CRITIC combination weighting method to carry out accurate policies. Case studies show that: NSGA-II algorithm can obtain more quantity and better quality Pareto solutions, and find a high-quality solution set that can minimize the cost and loss of the three-level food supply chain, so as to improve the toughness and safety level of the food supply chain, realize the two-level optimization of the three-level supply chain, and provide a scientific decision-making basis for the country to achieve “stable food supply and guarantee”.
  • LI Kai, LINPENG Zhuanghan, HU Zijian, CHENG Wanyou
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 2998-3008. https://doi.org/10.12341/jssms23733
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    The Barzilai-Borwein (BB) algorithm is an effective method that is widely used to solve unconstrained optimization problems. It has the advantages of small storage capacity and simple iteration. Positive set identification technology has the powerful ability to accurately identify zero components near the optimal solution. This technology can distinguish each iteration point into two parts: Zero components and non-zero components. This paper proposes a subspace Barzilai-Borwein (BB) method for solving large-scale $\ell_1$ egularization problems, using active set recognition techniques, combined with non monotonic line search techniques and appropriate BB step sizes, under appropriate conditions, we demonstrate the convergence of the proposed algorithm. Compared with existing algorithms through numerical experiments, it has been proven that the proposed algorithm runs with shorter CPU time, fewer iterations, and better numerical performance.
  • ZHENG Tianqi, ZHOU Jing, LI Qizhai
    Journal of Systems Science and Mathematical Sciences. 2025, 45(9): 3009-3020. https://doi.org/10.12341/jssms250429
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    Genome-wide association studies (GWAS) commonly employ a two-stage analysis strategy, where results from the two stages cross-validate each other to reduce confounding factors and effectively lower the proportion of false associations. Existing literature on two-stage analyses primarily focuses on single diseases, with limited exploration of multi-disease scenarios. To address multiple diseases, this study proposes two two-stage analytical approaches: Independent analysis and joint analysis. For both methods, this paper constructed quadratic test statistics for phenotype-single nucleotide polymorphism (SNP) association tests at different stages. Under the null hypothesis, this paper establish that these statistics share the same asymptotic distribution as a weighted sum of mixed chi-square random variables. The approximate distribution is then utilized to calculate $p$-values. Numerical results demonstrate that both methods exhibit high statistical power across varying sample sizes and significance levels. When the minor allele frequency (MAF) is low, joint analysis outperforms independent analysis; Conversely, independent analysis becomes superior at higher MAF values. Notably, statistical power increases with MAF for both approaches. Empirical results from a genotype-phenotype association study in mice revealed that both analytical methods effectively identified 48 SNPs with significant associations.