Understanding the Dynamics of Nutrient Management and Runoff from Plant Farms in the Potomac Watershed: Take Example of Nitrogen Management in Corn Planting of Frederick

In this paper, we focus on nitrogen load from corn planting in Frederick, in order to explore a generalized system dynamics structure and policy indications for nutrient pollution from agricultural planting in Potomac watershed. The structure contains two sections: commodity production structure and nitrogen application structure, which separately focus on two core variables as planting acreage and nitrogen application. We find leverage points from structure analysis, simulation results and literature. Nitrogen application control is most efficient method for nitrogen load reduction while soil quality preservation is most significant and has long-term effect for the whole system. Manure application shows more problems than fertilizer application while manure management and transportation are seen as important for manure application control. We further analyzed related best management practices and compared implementation feasibility for each policy. The system dynamics model has reproduced the reference mode, passed sensitivity test and robustness test. The test of soybean planting in commodity production structure indicates the structure can be generalized to similar agricultural products.Master's Thesis in System DynamicsGEO-SD351INTL-MEDINTL-SVMASV-SYSDYINTL-HFINTL-MNINTL-PSYKINTL-KMDINTL-JU

Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers

While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases.Comment: Published at the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 9 pages, 6 figure

TSFool: Crafting Highly-imperceptible Adversarial Time Series through Multi-objective Black-box Attack to Fool RNN Classifiers

Neural network (NN) classifiers are vulnerable to adversarial attacks. Although the existing gradient-based attacks achieve state-of-the-art performance in feed-forward NNs and image recognition tasks, they do not perform as well on time series classification with recurrent neural network (RNN) models. This is because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity of time series data to perturbations challenges the traditional local optimization objective of the adversarial attack. In this paper, a black-box method called TSFool is proposed to efficiently craft highly-imperceptible adversarial time series for RNN classifiers. We propose a novel global optimization objective named Camouflage Coefficient to consider the imperceptibility of adversarial samples from the perspective of class distribution, and accordingly refine the adversarial attack as a multi-objective optimization problem to enhance the perturbation quality. To get rid of the dependence on gradient information, we also propose a new idea that introduces a representation model for RNN to capture deeply embedded vulnerable samples having otherness between their features and latent manifold, based on which the optimization solution can be heuristically approximated. Experiments on 10 UCR datasets are conducted to confirm that TSFool averagely outperforms existing methods with a 46.3% higher attack success rate, 87.4% smaller perturbation and 25.6% better Camouflage Coefficient at a similar time cost.Comment: 9 pages, 7 figure

A new First-Order mixture integer-valued threshold autoregressive process based on binomial thinning and negative binomial thinning

In this paper, we introduce a new first-order mixture integer-valued threshold autoregressive process, based on the binomial and negative binomial thinning operators. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares (CLS) and conditional maximum likelihood (CML) estimators are derived and the asymptotic properties of the estimators are established. The inference for the threshold parameter is obtained based on the CLS and CML score functions. Moreover, the Wald test is applied to detect the existence of the piecewise structure. Simulation studies are considered, along with an application: the number of criminal mischief incidents in the Pittsburgh dataset.Comment: 34 pages;5 figure

Threshold Dynamics of a Huanglongbing Model with Logistic Growth in Periodic Environments

We analyze the impact of seasonal activity of psyllid on the dynamics of Huanglongbing (HLB) infection. A new model about HLB transmission with Logistic growth in psyllid insect vectors and periodic coefficients has been investigated. It is shown that the global dynamics are determined by the basic reproduction number R0 which is defined through the spectral radius of a linear integral operator. If R0 1, then the disease persists. Numerical values of parameters of the model are evaluated taken from the literatures. Furthermore, numerical simulations support our analytical conclusions and the sensitive analysis on the basic reproduction number to the changes of average and amplitude values of the recruitment function of citrus are shown. Finally, some useful comments on controlling the transmission of HLB are given