Pattern Recognition of Sweeteners in Biological Fluids,Beverages, and Ketchup using Stochastic Sensors

Three stochastic sensors based on nanodiamond (nDP) paste modified with α, β, and γ‐cyclodextrin were designed and characterized for pattern recognition of aspartame, acesulfame K and sodium cyclamate in beverages, ketchup, and biological fluids. The linear concentration ranges obtained for acesulfame K (between 1.00×10^?10 mol L^?1and 1.00×10^?3 mol L^?1), for aspartame (between 1.00×10^?12 mol L^?1 and 1.00×10^?3 mol L^?1) and for sodium cyclamate (between 4.97×10^?12 mol L^?1 and 4.97×10^?3 mol L^?1) allow their assay in biological fluids, beverages and ketchup. The lowest limits of quantification were obtained using the stochastic sensor based on γ‐CD/nDP: for acesulfame K 1.00×10^?10 mol L^?1, for aspartame 1.00×10^?12 mol L^?1 and for sodium cyclamate 4.97×10^?12 mol L^?1. All three stochastic sensors revealed very high values of sensitivities. The proposed method was reliable for qualitative and quantitative assay of aspartame, acesulfame K and sodium cyclamate in beverages, ketchup, and in biological fluids such as urine.     

Bounds on absorption times of directionally biased random sequences

A sequence of random variables X₀,X₁, … with values in {0, 1, …, n} representing a general finite-state stochastic process with absorbing state 0 is said to be directionally biased towards 0, if, for all j > 0, ϵ_j: = inf_k>0 {j − E[X_k | X_k−1 = j]} > 0. For such sequences, let t be the expected value of the time to absorption at 0. For a fixed set of biases, the least upper bound for this time can be computed with an algorithm requiring O(n²) steps. Simple upper bounds are described. In particular, t ≤ E[b_x0], where b_i = Σ_j≤i 1/¯ϵ_j and ¯ϵ_j = min_l≥j {ϵ_l}. If all ϵ_j ≤ ϵ_{j + 1} (so ¯ϵ_j = ϵ_j) and ϵ_n < 1, this bound for t is the best possible. For certain finite stochastic processes which we term conditionally independent of X₀ = i, b(i) bounds the expected time given X₀ = i. Similar results are given for lower bounds. The results of this paper were designed to be a useful tool for determining rates of convergence of stochastic optimization algorithms. © 1996 John Wiley & Sons, Inc.     

动态环境约束下企业的资本积累

本文讨论动态环境约束下企业的动态投资行为,拓广了文献[4]的结果.为了使讨论的问题更符合实际情况,本文假设政府设定的污染排放上限是与企业的规模大小有关,即假设污染排放上限是生产资本的函数,讨论动态环境约束下企业的最佳动态投资行为,并为政府制定污染排放政策提供依据.     

关于无套利市场的二叉树期权定价模型性质的讨论

本文讨论了二叉树期权市场的无套利条件,引入有随机因素存在的二叉树欧式期权定价模型,并推出单阶段、多阶段情况下欧式期权的计算公式,证明了多阶段市场未定权益的重要性质.     

股本稀释效应下的认股权证定价模型与数值方法

本文研究了在股本稀释效应下认股权证的定价问题.假设随机利率服从Vasicek模型,利用Δ-对冲方法建立了权证价格所满足的偏微分方程.然后,通过计价单位变换,将偏微分方程降维,求得了权证价格的显式解,并给出了一种较好的数值计算方法,可运用市场的可观测变量来计算权证价值.     

Stochastic and upscaled analytical modeling of fines migration in porous media induced by low-salinity water injection

Fines migration induced by injection of low-salinity water(LSW) into porous media can lead to severe pore plugging and consequent permeability reduction. The deepbed filtration(DBF) theory is used to model the aforementioned phenomenon, which allows us to predict the effluent concentration history and the distribution profile of entrapped particles. However, the previous models fail to consider the movement of the waterflood front. In this study, we derive a stochastic model for fines migration during LSW flooding, in which the Rankine-Hugoniot condition is used to calculate the concentration of detached particles behind and ahead of the moving water front. A downscaling procedure is developed to determine the evolution of pore-size distribution from the exact solution of a large-scale equation system. To validate the proposed model,the obtained exact solutions are used to treat the laboratory data of LSW flooding in artificial soil-packed columns. The tuning results show that the proposed model yields a considerably higher value of the coefficient of determination, compared with the previous models, indicating that the new model can successfully capture the effect of the moving water front on fines migration and precisely match the effluent history of the detached particles.     

Immunity in the ABM-DSGE Framework for Preventing and Controlling Epidemics—Validation of Results

The COVID-19 pandemic has raised many questions on how to manage an epidemiological and economic crisis around the world. Since the beginning of the COVID-19 pandemic, scientists and policy makers have been asking how effective lockdowns are in preventing and controlling the spread of the virus. In the absence of vaccines, the regulators lacked any plausible alternatives. Nevertheless, after the introduction of vaccinations, to what extent the conclusions of these analyses are still valid should be considered. In this paper, we present a study on the effect of vaccinations within the dynamic stochastic general equilibrium model with an agent-based epidemic component. Thus, we validated the results regarding the need to use lockdowns as an efficient tool for preventing and controlling epidemics that were obtained in November 2020.     

The Existence and Global $p$-Exponential Stability of Periodic Solution for Stochastic BAM Neural Networks with Delays

In this paper, we consider a class of stochastic BAM neural networks with delays. By establishing new integral inequalities and using the properties of spectral radius of nonnegative matrix, some sufficient conditions for the existence and global $p$-exponential stability of periodic solution for stochastic BAM neural networks with delays are given. An example is provided to show the effectiveness of the theoretical results.     

Structural Entropy of the Stochastic Block Models

With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the “structural information” only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erdős–Rényi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the stochastic block models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for stochastic block models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the stochastic block models, and provide a compression scheme that asymptotically achieves this entropy limit.     

FEEDBACK CONTROL OPTIMIZATION FOR SEISMICALLY EXCITED BUILDINGS

A feedback control optimization method of partially observable linear structures via stationary response is proposed and analyzed with linear building structures equipped with control devices and sensors. First, the partially observable control problem of the structure under horizontal ground acceleration excitation is converted into a completely observable control problem. Then the It6 stochastic differential equations of the system are derived based on the stochastic averaging method for quasi-integrable Hamiltonian systems and the stationary solution to the Fokker-Plank-Kolmogorov （FPK） equation associated with the It6 equations is obtained. The performance index in terms of the mean system energy and mean square control force is established and the optimal control force is obtained by minimizing the performance index. Finally, the numerical results for a three-story building structure model under E1 Centro, Hachinohe, Northridge and Kobe earthquake excitations are given to illustrate the application and the effectiveness of the proposed method.