Initial guess by improved population-based intelligent algorithms for large inter-frame deformation measurement using digital image correlation

Digital image correlation (DIC) has received a widespread research and application in experimental mechanics. In DIC, the performance of subpixel registration algorithm (e.g., Newton-Raphson method, quasi-Newton method) relies heavily on the initial guess of deformation. In the case of small inter-frame deformation, the initial guess could be found by simple search scheme, the coarse-fine search for instance. While for large inter-frame deformation, it is difficult for simple search scheme to robustly estimate displacement parameters and deformation parameters simultaneously with low computational cost. In this paper, we proposed three improving strategies, i.e. Q-stage evolutionary strategy (T), parameter control strategy (C) and space expanding strategy (E), and then combined them into three population-based intelligent algorithms (PIAs), i.e. genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO), and finally derived eighteen different algorithms to calculate the initial guess for qN. The eighteen algorithms were compared in three sets of experiments including large rigid body translation, finite uniaxial strain and large rigid body rotation, and the results showed the effectiveness of proposed improving strategies. Among all compared algorithms, DE-TCE is the best which is robust, convenient and efficient for large inter-frame deformation measurement.     

Optimal hedging strategies for multi-period guarantees in the presence of transaction costs: A stochastic programming approach

Multi-period guarantees are often embedded in life insurance contracts. In this paper we consider the problem of hedging these multi-period guarantees in the presence of transaction costs. We derive the hedging strategies for the cheapest hedge portfolio for a multi-period guarantee that with certainty makes the insurance company able to meet the obligations from the insurance policies it has issued. We find that by imposing transaction costs, the insurance company reduces the rebalancing of the hedge portfolio. The cost of establishing the hedge portfolio also increases as the transaction cost increases. For the multi-period guarantee there is a rather large rebalancing of the hedge portfolio as we go from one period to the next. By introducing transaction costs we find the size of this rebalancing to be reduced. Transaction costs may therefore be one possible explanation for why we do not see the insurance companies performing a large rebalancing of their investment portfolio at the end of each year.     

电子商务网站竞争模型分析及其策略

研究电子商务网站竞争模型,分析其平衡点的稳定性,给出数值模拟图,建立一系列电子商务网站获胜的新策略.     

Analyzing the sustainability of harvesting behavior and the relationship to personality traits in a simulated Lotka–Volterra biotope

For the analysis presented in this paper we use experiments to study human behavior in a simulation environment based on a simple Lotka–Volterra predator–prey ecology. The aim is to study the influence of different harvesting strategies and of certain personality traits derived from the Hamburg Personality Inventory (HPI) [Andresen, B., 2002. HPI – Hamburger Persönlichkeitsinventar. Hogrefe, Göttingen] on the outcome in terms of sustainability and economic performance.     

半马尔可夫对策

本文考虑半马尔可夫随机对策.在一定条件下,我们证明随机对策有值函数,两个局中人相对于折扣报酬都有最优策略.     

An Algorithm Based on Active Sets and Smoothing for Discretized Semi-Infinite Minimax Problems

We present a new active-set strategy which can be used in conjunction with exponential (entropic) smoothing for solving large-scale minimax problems arising from the discretization of semi-infinite minimax problems. The main effect of the active-set strategy is to dramatically reduce the number of gradient calculations needed in the optimization. Discretization of multidimensional domains gives rise to minimax problems with thousands of component functions. We present an application to minimizing the sum of squares of the Lagrange polynomials to find good points for polynomial interpolation on the unit sphere in ℝ³. Our numerical results show that the active-set strategy results in a modified Armijo gradient or Gauss-Newton like methods requiring less than a quarter of the gradients, as compared to the use of these methods without our active-set strategy. Finally, we show how this strategy can be incorporated in an algorithm for solving semi-infinite minimax problems.     

High Loading Efficiency and Controlled Release of Bioactive Immunotherapeutic Proteins Using Vaterite Nanoparticles

Nanoparticles may limit off-tumor/on-target ubiquitous activation of signaling by protein-based drugs. However, many challenges still exist in the design of a nanoparticle for protein delivery. In this study, conditions to establish vaterite nanoparticles as a pH-sensitive drug delivery system (DDS) for encapsulated protein drugs are comprehensively evaluated. Low coprecipitation pH of vaterite and protein prevents protein denaturation and yields high loading efficiency. Unprotected vaterite recrystallizes in aqueous solutions within 3 h to calcite and releases the loaded protein completely, but surface-modified particles with carboxyl groups containing polymers prove stable for more than 5 months. Notably, modification of vaterite with sulfonated polymers increases the loading of cationic proteins by a multiple. A system is developed for vaterite exposure to (pH) conditions under body-like-flow rates, with the dissolution of vaterite and simultaneous release of active proteins at tumor microenvironmental pH reaching up to 80% and only 20% at physiological pH within 2 h. Importantly, the immunomodulatory protein tumor necrosis factor preserves its native structure and fully retains functional activity in vitro after release from the particles. In conclusion, the studies described here provide a framework for the development of vaterite-based DDS as a carrier for bioactive protein-based therapeutics.     

Sample cleanup-free determination of mycophenolic acid and its glucuronide in serum and plasma using the novel technology of ultra-performance liquid chromatography-electrospray ionization tandem mass spectrometry

Mycophenolic acid (MPA) is an immunosuppressant drug which powerfully inhibits lymphocyte proliferation. Since the early 1990s it has been used to prevent rejection in organ transplantation. The requirement of therapeutic drug monitoring shown in previous studies raises the necessity of acquiring accurate and sensitive methods to measure MPA and its major metabolite mycophenolic acid glucuronide (MPAG).The authors developed a sample cleanup-free, rapid, and highly specific method for simultaneous measurement of MPA and MPAG in human plasma and serum using the novel technology of ultra-performance liquid chromatography-electrospray ionization tandem mass spectrometry. MPA- and MPAG-determinations were performed during a 2.0-min run time. Multiple calibration curves for the analysis of MPA and MPAG exhibited consistent linearity and reproducibility in the range of 0.05-100 (r > 0.999) mg L⁻¹ and 4-4000 mg L⁻¹ (r > 0.999), respectively. Limits of Detection were 0.014 mg L⁻¹ for MPA and 1.85 mg L⁻¹ for MPAG. Lower Limits of Quantification were 0.05 mg L⁻¹ for MPA and 2.30 mg L⁻¹ for MPAG. Interassay imprecision was <10% for both substances. Mean recovery was 103.6% (range 78.1-129.7%) for MPA and 111.1% (range 73.0-139.6%) for MPAG. Agreement was good for MPA and MPAG between the presented method and a validated HPLC-MS/MS method. The Passing-Bablok regression line for MPA and MPAG was HPLC-MS/MS = 1.14 UPLC-MS/MS—0.14 [mg L⁻¹], r = 0.96, and HPLC-MS/MS = 0.77 UPLC-MS/MS + 0.50 [mg L⁻¹], r = 0.97, respectively. This sample cleanup-free and robust LC-MS/MS assay facilitates the rapid, accurate and simultaneous determination of MPA and MPAG in human body fluids.     

Emergent cooperation amongst competing agents in minority games

We study a variation of the minority game. There are N agents. Each has to choose between one of two alternatives every day, and there is a reward to each member of the smaller group. The agents cannot communicate with each other, but try to guess the choice others will make, based only on the past history of the number of people choosing the two alternatives. We describe a simple probabilistic strategy using which the agents, acting independently, and trying to maximize their individual expected payoff, still achieve a very efficient overall utilization of resources, and the average deviation of the number of happy agents per day from the maximum possible can be made O(N^?), for any ?>0. We also show that a single agent does not expect to gain by not following the strategy.