Diffusive search with spatially dependent resetting

We consider a stochastic search model with resetting for an unknown stationary target $a\in \mathbb{R}$ with known distribution $\mu$. The searcher begins at the origin and performs Brownian motion with diffusion constant $D$. The searcher is also armed with an exponential clock with spatially dependent rate $r=r\left(\cdot \right)$, so that if it has failed to locate the target by the time the clock rings, then its position is reset to the origin and it continues its search anew from there. Denote the position of the searcher at time $t$ by $X\left(t\right)$. Let $E_0^{\left(r\right)}$ denote expectations for the process $X\left(\cdot \right)$. The search ends at time $T_a=inf\{t\ge 0:X\left(t\right)=a\}$. The expected time of the search is then ${\int }_{\mathbb{R}}\left(E_0^{\left(r\right)}{T}_a\right)\mu \left(da\right)$. Ideally, one would like to minimize this over all resetting rates $r$. We obtain quantitative growth rates for $E_0^{\left(r\right)}{T}_a$ as a function of $a$ in terms of the asymptotic behavior of the rate function $r$, and also a rather precise dichotomy on the asymptotic behavior of the resetting function $r$ to determine whether $E_0^{\left(r\right)}{T}_a$ is finite or infinite. We show generically that if $r\left(x\right)$ is of the order ${\left|x\right|}^{2l}$, with $l>-1$, then $log{E}_0^{\left(r\right)}{T}_a$ is of the order ${\left|a\right|}^{l+1}$; in particular, the smaller the asymptotic size of $r$, the smaller the asymptotic growth rate of $E_0^{\left(r\right)}{T}_a$. The asymptotic growth rate of $E_0^{\left(r\right)}{T}_a$ continues to decrease when $r\left(x\right)\sim \frac{D\lambda }{x^2}$ with $\lambda >1$; now the growth rate of $E_0^{\left(r\right)}{T}_a$ is more or less of the order ${\left|a\right|}^{\frac{1+\sqrt{1+8\lambda }}{2}}$. Note that this exponent increases to $\infty$ when $\lambda$ increases to $\infty$ and decreases to 2 when $\lambda$ decreases to 1. However, if $\lambda =1$, then $E_0^{\left(r\right)}{T}_a=\infty$, for $a\ne 0$. Our results suggest that for many distributions $\mu$ supported on all of $\mathbb{R}$, a near optimal (or optimal) choice of resetting function $r$ in order to minimize ${\int }_{{\mathbb{R}}^d}\left(E_0^{\left(r\right)}{T}_a\right)\mu \left(da\right)$ will be one which decays quadratically as $\frac{D\lambda }{x^2}$ for some $\lambda >1$. We also give explicit, albeit rather complicated, variational formulas for ${inf}_{r\gneqq 0}{\int }_{{\mathbb{R}}^d}\left(E_0^{\left(r\right)}{T}_a\right)\mu \left(da\right)$. For distributions $\mu$ with compact support, one should set $r=\infty$ off of the support. We also discuss this case.     

空中目标多波段红外辐射特性描述与仿真分析

针对空中目标在复杂背景下的探测需求,根据实际目标的运动特性,分析目标在飞行高度、飞行姿态角改变时的辐射特点,基于MODTRAN计算得到大气辐射和衰减数据,建立目标的三维模型、热辐射和反射模型,搭建空中目标的红外成像仿真系统.分析和仿真结果表明:在中波波段,目标尾焰的红外辐射比蒙皮强很多,在长波波段,蒙皮的红外辐射比较强,仿真图像的细节比较多,尾焰的红外辐射虽然有所减弱,红外成像效果依旧很好;相同探测条件下,由于位置越高大气越稀薄,探测器的可探测距离会变得比较远.目标红外辐射特性的分析和红外仿真系统的搭建对缩短红外探测器的研制周期和进一步确定探测器波段和系统分辨率等指标提供了参考依据.     

基于气象卫星云图的红外吸收带火山特征分析

飞行目标在2.7和4.3 μm谱段附近具有较强的红外辐射，因此这两个波段是探测飞行目标的最佳波段, 但是由于这两个波段并非大气窗口，不被大多数遥感器包含。对这两个谱段的典型地物特性开展研究具有重要的价值，但由于缺乏必要的数据获取能力，经常面临数据缺乏的问题。世界范围内频频有各种程度的火山爆发，火山爆发时温度较高的火山口，是否对于天基红外探测系统典型目标探测有影响，其影响程度如何一直缺乏相关的分析和研究。基于大气辐射传输理论，利用多元统计分析得到波段转换模型，使用气象卫星已有波段获得红外吸收谱段数据。将火点像元视为明火和背景的混合像元，采用目标与背景分离的方式描述高温目标像元的热辐射。对高温目标辐射量，在气溶胶模式固定的情况下，选取观测天顶角、大气可降水量、大气廓线为自变量影响因子。对于背景辐射亮度关系，选取观测天顶角、大气可降水量、大气廓线、背景温度为自变量影响因子, 利用多元统计，建立相关模型。利用对地面热状态非常敏感的风云三号可见光红外扫描辐射计第3通道数据的统计特征探测火山口，获取高温目标在特定波段的表观多维特征并定量分析。火山的多维特征分析，主要从时间和空间两个维度展开。时间维度是对同一火山在不同时间的数据进行分析，空间维度上，主要统计火山口的辐射亮度和亮度温度的空间分布特征。一般气象卫星分辨率较低，单纯利用像元个数表示火山面积, 明显夸大了火山的实际面积，所以基于亚像元特性对火山进行分析，将混合像元火点视为明火和背景的组合，运用线性光谱混合模型，通过混合像元的辐射率精确计算火山高温点的面积和温度，提高定量分析精度。分析结果表明： 通过仿真手段结合多元统计分析方法建立高温目标的波段转换模型是一种可行的预研手段。在2.7~2.95 μm谱段，火山口在弱背景环境下可能会对高温目标造成干扰，而在4.2~4.45 μm谱段，火山口能量远高于一般地表类型，是不可忽视的干扰。     

Target cell extraction coupled with LC‐MS/MS analysis for screening potential bioactive components in Ginkgo biloba extract with preventive effect against diabetic nephropathy

A rapid and useful approach for screening potential bioactive components in Ginkgo biloba extract (GBE) with preventive effect against diabetic nephropathy (DN) was developed using mesangial cells extraction coupled with high‐performance liquid chromatography tandem mass spectrometry (LC‐MS/MS) analysis. Mesangial cells were first divided into two groups according to their treatments with high glucose or high glucose plus GBE. After incubation for 4, 8, 12, 16, 24 and 48 h, the cells were harvested and extracted with 40% acetic acid in water before LC‐MS/MS analysis. Then, 19 compounds and five metabolites were found to selectively combine with mesangial cells. Notably, compounds including quercetin and rutin were identified or tentatively characterized according to the results of retention time and MS spectra, which is highly consistent with our previous reports that quercetin and rutin are potent protective agents against glomerulosclerosis in DN. Therefore, all these results indicate that target cell extraction coupled with LC‐MS/MS analysis can be successfully applied for predicting the bioactive components in GBE with preventive effect against DN. Copyright © 2014 John Wiley & Sons, Ltd.     

考虑塑性变形发展程度系数的RC梁开裂弯矩改进计算公式

针对RC梁开裂荷载计算方法尚未统一的现状，首先，结合18根RC梁试验数据对比了已有的6种RC梁开裂弯矩计算公式，发现开裂弯矩理论计算值与试验值的偏差大小和混凝土强度有关；然后，通过提出塑性变形发展程度系数k，推导新的RC梁开裂弯矩计算公式，并进一步基于k值和塑性影响系数计算值γ_k进行改进；最后，选取12根RC试验梁验证改进公式的准确性，证明改进公式的计算值与试验值吻合更好且偏于安全。     

超声动载荷下三维随机骨料混凝土的损伤

混凝土是一种由粗骨料与水泥砂浆组成的非均质复合材料。本研究利用APDL语言程序编写三维水泥混凝土骨料随机投放程序并导入ABAQUS中,同时赋予各相材料塑性损伤本构关系来研究混凝土动态加载下的破坏规律,运用超声波在混凝土破碎中的作用机理对混凝土动态损伤破坏过程进行模拟研究。结果表明:随着超声动态载荷的增大,粗骨料体积分数为40%的混凝土始终能够承受最大应力载荷;随着超声应力波幅值增大,混凝土在动载荷下的损伤值逐渐增大,且粗骨料体积分数为40%时,其抗损伤能力最优;当粗骨料最大粒径逐渐增大,或者当粗骨料最小粒径增大,混凝土级配不合理导致性能不稳定,更易损伤破坏。     

基于粒子滤波的运动目标光电定位仿真研究

运动目标的光电定位不能像静止目标那样简单做均值滤波，鉴于此，引入粒子滤波算法，它不仅可以应用于线性系统，而且还适用于非线性系统。结合光电定位需求，详细推导了计算公式及初值和参数选取公式，对只含测量噪声以及含有测量和运动噪声等的海面运动目标光电无源定位算法进行了仿真计算，验证了算法的有效性，讨论了噪声强度对滤波效果的影响，滤波参数选择对滤波效果的影响，目标运动方式对滤波跟随性的影响，重采样算法对滤波效果的影响等。所得结论为:粒子滤波可用于运动目标光电定位过程，可有效降低定位误差；粒子滤波算法具有较强鲁棒性，适用于噪声较大、目标运动形态变化大等情况。     

Mechanically strong and stiff supramolecular polymers enabled by fiber reinforced long-chain alkane matrix

Fiber-reinforced-concrete (FRC) mechanism refers short discrete fibers that are uniformly distributed and randomly oriented, which offers an effective way to improve the mechanical performance of concrete. In the design of supramolecular polymers, an analogous concept of FRC appears to have been considered very rarely-although fibrous structure has been frequently observed/generated during the supramolecular polymerization. In this work, we apply the alkane thermosets, octadecane (C₁₈H₃₈) and tetracosane (C₂₄H₅₀), taking the role of “concrete”, and the low-molecular-weight monomer with long alkyl chains as the essential “fiber” component, to fabricate the “fiber reinforced supramolecular polymer”. Very much like FRC mechanism in material science, the resulting fiber reinforced supramolecular polymer thus exhibit unusually high mechanical strength and stiffness, which is unprecedented in the conventional supramolecular strategy.     

Recent Advances in Nanotechnology for the Treatment of Melanoma

Melanoma is one of the most aggressive forms of skin cancer, with few possibilities for therapeutic approaches, due to its multi-drug resistance and, consequently, low survival rate for patients. Conventional therapies for treatment melanoma include radiotherapy, chemotherapy, targeted therapy, and immunotherapy, which have various side effects. For this reason, in recent years, pharmaceutical and biomedical research has focused on new sito-specific alternative therapeutic strategies. In this regard, nanotechnology offers numerous benefits which could improve the life expectancy of melanoma patients with very low adverse effects. This review aims to examine the latest advances in nanotechnology as an innovative strategy for treating melanoma. In particular, the use of different types of nanoparticles, such as vesicles, polymers, metal-based, carbon nanotubes, dendrimers, solid lipid, microneedles, and their combination with immunotherapies and vaccines will be discussed.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.