壳聚糖分子链序列分布的Monte Carlo模拟

为了掌握不同脱乙酰度壳聚糖分子链上不同结构单元的序列分布以及不同糖苷键的相对含量,通过Monte Carlo方法, 对壳聚糖分子的序列分布进行了计算机模拟,求出了(GlcNac)i、(GlcN)i的数量分布,并求出了GlcNatc-GlcNac、GlcNac-GlcN、GlcN-GlcN糖苷键的相对含量及其关联式(均为DD≥55%):FA-A9=9.949 49-2.001 61×DD+O.010 02×DD2、FA·D= 0.336 29+1.996 44×DD-0.019 96×DD2、FD-D=-0.168 09+0.002 78×DD+0.009 97×DD2。结果表明,随着脱乙酰度的提高,(ClcNac)i和(GlcN),分布的差别越来越明显,并且(GlcNac)i分布越来越窄,而(GlcN)i的分布则越来越宽;GlcNac-Gl cNac糖苷键的相对含量(FA-A)、GlcNac-GlcN糖苷键的相对含量(FA-D)均随着脱乙酰度(DD)的增大而减小,而GlcN-GlcN糖苷键的相对含量(FD-D)则随着DD的增大而增大,但它们都不呈线性关系。通过与文献值对比,表明模拟具有很高的精度。该算法为壳聚糖降解动力学以及降解产物分子量分布的研究提供了相应的基础。     

壳聚糖酶解产物分子量分布优化控制研究

在壳聚糖酶解产物分子量分布模拟的基础上,运用遗传算法,对壳聚糖酶解过程产物分子量分布的优化控制进行研究。以酶解原料壳聚糖的脱乙酰度、水解度作为优化参数,以特定范围分子量的酶解产物最大得率为目标,研究优化过程的实现技术,得到了适于壳聚糖酶解产物分子量分布优化控制的算法。经实验验证,优化结果可靠。研究结果表明,遗传算法能有效地运用于壳聚糖酶解产物分子量分布的优化控制。     

基于广义反向学习的磷虾群算法及其在数据聚类中的应用

针对磷虾群（KH）算法在寻优过程中因种群多样性降低而过早收敛的问题，提出基于广义反向学习的磷虾群算法GOBL-KH。首先，通过余弦递减策略确定步长因子平衡算法的探索与开发能力；然后，加入广义反向学习策略对每个磷虾进行广义反向搜索，增强磷虾探索其周围邻域空间的能力。将改进的算法在15个经典测试函数上进行测试并与KH算法、步长线性递减的磷虾群（KHLD）算法和余弦递减步长的磷虾群（KHCD）算法比较，实验结果表明:GOBL-KH算法可有效避免早熟且具有较高的求解精度。为体现算法有效性，将GOBL-KH算法与K均值算法结合提出HK-KH算法用于解决数据聚类问题，即在每次迭代后用最优个体或经过K均值迭代一次后的新个体替换最差个体，使用UCI五个真实数据集进行测试并与K均值、遗传算法（GA）、粒子群优化（PSO）算法、蚁群算法（ACO）、KH算法、磷虾群聚类算法（KHCA）、改进磷虾群（IKH）算法进行比较，结果表明:HK-KH算法适用于解决数据聚类问题且具有较强的全局收敛性和较高的稳定性。     

基于动态压力控制算子的磷虾群算法

针对基础磷虾群（KH）算法在求解复杂函数优化问题时局部搜索能力差、求解精度低、收敛速度慢、容易陷入局部最优等问题,提出一种基于动态压力控制算子的磷虾群算法（DPCKH）。该算法将一种新的动态压力控制算子加入了标准磷虾群算法,使其处理复杂函数优化问题更有效。动态压力控制算子通过欧氏距离量化了多个不同优秀个体对目标个体的诱导效应,进而在优秀个体附近加速产生新磷虾个体,提高了磷虾个体的局部探索能力。通过比较蚁群算法（ACO）、差分进化算法（DE）、磷虾群算法（KH）、改进的磷虾群算法（KHLD）和粒子群算法（PSO）,DPCKH算法在7个测试函数上的结果表明,DPCKH算法与ACO算法、DE算法、KH算法、KHLD算法和PSO算法相比有着更强的局部勘测能力,其开采能力更强。     

利用卫星激光测高数据对星载SAR干涉测量DEM进行倾斜改正的研究

InSAR与ICEsat测高技术作为极具潜力的两项对地观测技术,各有其特点,利用卫星激光测高数据对星载SAR干涉测量DEM进行倾斜改正具有十分重要的意义,为提取无控制特殊困难地区大范围DEM提供了一种全新的方法。本文介绍了InSAR与ICEsat激光测高两种技术的特点及互补性;分析了利用ICEsat测高数据改正InSAR数据本身难以消除的误差的可行性;并以南极Grove山大范围DEM的提取为例,证实了其优越性。     

自适应磷虾群优化Elman神经网络的目标威胁评估

提出一种自适应磷虾群算法,在基本磷虾群算法中引入遗传繁殖机制,并加入进化算子和优化算子构成自适应环节,提高了算法的全局搜索能力和预测精度;通过自适应磷虾群算法对Elman神经网络的初始权值和阈值进行寻优,并在此基础上建立目标威胁评估模型。仿真实验表明,自适应磷虾群优化Elman神经网络既保证了一定的收敛速度,又能够使寻优精度得到明显提升,其对测试集的预测结果优于传统Elman神经网络和基本磷虾群优化Elman神经网络,从而验证了算法模型在目标威胁评估中的可行性、有效性。     

不同溶剂改性壳聚糖及其吸附性能的研究

以市售壳聚糖为原料,用乙醇、异丙醇和正丁醇作为溶剂利用溶剂热法改性壳聚糖,用400mg/L的酸性大红染料为目标的污染物测试改性后的壳聚糖的吸附能力,发现用乙醇作为溶剂改性的壳聚糖具有最高的吸附能力,最高可达80%,远远高于未改性的3%和活性炭6%的吸附能力。通过XRD、FT-IR、SEM、BET等表征手段发现,改性后的壳聚糖的孔径更趋向于均一,孔径为11nm,改性壳聚糖以后对规整孔径的优化,有利于吸附废水溶液中特定大小尺寸的染料胶束团分子。     

合成食用色素胭脂红电极的应用

在食品、饮料生产中,为美化制品或使制品近于天然色彩,常常添加适量食用色素。食用色素有天然的和合成的两大类。天然色素提取困难,来源少,价格昴贵、性质不稳定。而合成色素较天然色素色彩鲜艳、坚牢度大、性质稳定、着色力强、成本低廉。因此,食品及饮料厂家均采用合成色素着色。然而合成色素大多是焦油类染料,其本身不仅无营养价值且对人体有害。它的毒性不仅来自于色素本身的化学性质,而且还来自于合成过程中中被砷、铅等有害化合物的污染。合成食用色素也广泛地被应用于制药工业。因此,食品中色素的检测,对保障人民身体健康至关重要。     

雌二醇脂质体离子型鼻用原位凝胶的制备及体外释放考察

目的制备雌二醇脂质体离子型鼻用原位凝胶(ELG),并考察其体外释放行为。方法采用薄膜分散法制备雌二醇脂质体(EL),以去乙酰结冷胶(DGG)为材料制备ELG,旋转粘度计测定脂质体—凝胶相转变特性筛选处方,扩散池法考察其体外释药规律,并考察其稳定性。结果制备的脂质体包封率较高,粒径分布均匀,多为单室脂质体,确定浓度0.5%的去乙酰结冷胶作为ELG基质,其释药符合一级释药模型,具有良好的体外释药特征,在常温及冷藏条件下,其性质较稳定,更适合冷藏贮存。结论去乙酰结冷胶性质稳定,释药良好,适用于制备ELG。     

基于近邻套索算子的磷虾群算法

针对标准磷虾群算法（KH）在求解复杂函数优化问题时局部搜索能力差,开采能力不足的问题,提出了一种基于近邻套索算子的磷虾群算法（NLKH）。该算法将一种新的近邻套索算子加入了标准磷虾群算法,使得处理复杂函数优化问题更加有效。近邻套索算子通过比较磷虾个体之间的欧式距离来选取目标磷虾对,然后通过在优质个体附近加速操作产生新磷虾个体和剔除劣质磷虾个体的方式,提高了磷虾个体局部搜索的能力。通过比较PSO算法、KH算法、KHLD算法、NLKH算法在10个测试函数上的结果表明,NLKH算法相较于PSO算法、KH算法和KHLD算法有着更强全局搜索能力,寻优精度更高,收敛速度更快,稳定性更好。并且NLKH算法相较于KH算法和KHLD算法有着更强的局部勘测能力,开采能力更强。     

An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering

Krill herd algorithm is a stochastic nature-inspired algorithm for solving optimization problems. The performance of krill herd algorithm is degraded by poor exploitation capability. In this study, we propose an improved krill herd algorithm (IKH) by making the krill the global search capability. The enhancement comprises of adding global search operator for exploration around the defined search region and thus the krill individuals move towards the global best solution. The elitism strategy is also applied to maintain the best krill during the krill update steps. The proposed method is tested on a set of twenty six well-known benchmark functions and is compared with thirteen popular optimization algorithms, including original KH algorithm. The experimental results show that the proposed method produced very accurate results than KH and other compared algorithms and is more robust. In addition, the proposed method has high convergence rate. The high performance of the proposed algorithm is then employed for data clustering problems and is tested using six real datasets available from UCI machine learning laboratory. The experimental results thus show that the proposed algorithm is well suited for solving even data clustering problems.     

Snow optical properties for different particle shapes with application to snow grain size retrieval and MODIS/CERES radiance comparison over Antarctica

We investigated the single scattering optical properties of snow for different ice particle shapes and degrees of microscopic scale roughness. These optical properties were implemented and tested in a coupled atmosphere-snow radiative transfer model. The modeled surface spectral albedo and radiance distribution were compared with surface measurements. The results show that the reflected radiance and irradiance over snow are sensitive to the snow grain size and its vertical profile. When inhomogeneity of the particle size distribution in the vertical is taken into account, the measured spectral albedo can be matched, regardless of the particle shapes. But this is not true for the modeled radiance distribution, which depends a lot on the particle shape. The usual “equivalent spheres” assumption significantly overestimates forward reflected radiances, and underestimates backscattering radiances, around the principal plane. On average, the aggregate shape assumption has the best agreement with the measured radiances to a mean bias within 2%.The snow optical properties with the aggregate assumption were applied to the retrieval of snow grain size over the Antarctic plateau. The retrieved grain sizes of the top layer showed similar and large seasonal variation in all years, but only small year to year variation. Using the retrieved snow grain sizes, the modeled spectral and broadband radiances showed good agreements with MODIS and CERES measurements over the Antarctic plateau. Except for the MODIS 2.13 μm channel, the mean relative model-observation differences are within few percent. The modeled MODIS radiances using measured surface reflectance at Dome C also showed good agreement in visible channels, where radiation is not sensitive to snow grain size and the measured surface bidirectional reflectance is applicable over the Antarctic plateau. But modeled radiances using local, surface-measured reflectance in the near infrared yielded large errors because of the high sensitivity to the snow grain size, which varies spatially and temporally. The CERES broadband shortwave radiance is moderately sensitive to the snow grain size, comparable to the MODIS 0.86 μm channel. The variation of broadband snow reflectance due to the seasonal variation in snow grain size is about 5% in a year over the Antarctic plateau. CERES broadband radiances simulated with grain sizes retrieved using MODIS are about 2% larger than those observed.     

Computational study of polymorphic structures of α- and β- chitin and chitosan in aqueous solution

Chitin is a natural biopolymer and the second most abundant after cellulose. This polysaccharide can be found in the biomass in different polymorphic forms. Chitosan is one of the most important derivatives obtained from the deacetylation of chitin. In this work, Molecular Dynamics simulations of chitin and chitosan nanoparticles enabled us to evaluate their different conformation and solubility properties. The Molecular Dynamics simulations show that the arrangement of the chains of chitin and chitosan significantly affects the structural behavior of these biopolymers in aqueous solution.     

洪河湿地植被地上生物量遥感反演研究

在对洪河湿地植被地上生物量实地采样调查的基础上,利用准同步的TM数据建立了洪河湿地地上生物量遥感反演模型。主要研究了洪河湿地植被地上生物量的空间分布情况,并结合研究区的DEM分析生物量空间分布特征和高程的相关关系。并分析了不同生物量范围内,生物量与高程之间相关性存在差异的原因。研究表明:多元回归模型与其他模型相比拟合精度最高,决定系数为0.813,是洪河湿地地上生物量估算的精度最优模型;经估算得到2007年洪河湿地地上生物量主要集中分布于600~1 200 g/m²之间,总生物量为2.4856×10⁸g,平均生物量为934.7105 g/m²。通过生物量与DEM的相关分析得到,在生物量值为0~600 g/m²的低生物量分布区域,生物量与高程之间存在较好的相关性,相关系数为0.79839;在生物量为600~1 200 g/m²和1 200 g/m²以上范围内,生物量与高程值之间相关性较弱。     

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

The immediate and quick spread of the coronavirus has become a life-threatening disease around the globe. The widespread illness has dramatically changed almost all sectors, moving from offline to online, resulting in a new normal lifestyle for people. The impact of coronavirus is tremendous in the healthcare sector, which has experienced a decline in the first quarter of 2020. This pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical results. The current situation motivated me to choose correlation-based development called correlation-based grey wolf optimizer to perform accurate classification. A proposed multistage model helps to identify Covid from Computed Tomography (CT) scan image. The first process uses a convolutional neural network (CNN) for extracting the feature from the CT scans. The Pearson coefficient filter method is applied to remove redundant and irrelevant features. Finally, the Grey wolf optimizer is used to choose optimal features. Experimental analysis proves that this determines the optimal characteristics to detect the deadly disease. The proposed model’s accuracy is 14% higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image (SARS-CoV-2 CT) dataset. The COVID CT image dataset is 22% higher than the existing krill herd and bacterial foraging optimization techniques. The proposed techniques help to increase the classification accuracy of the algorithm in most cases, which marks the stability of the stated result. Comparative analysis reveals that the proposed classification technique to predict COVID-19 with maximum accuracy of 98% outperforms other competitive approaches.     

Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion

In this paper, we explored fusion of structural metrics from the Laser Vegetation Imaging Sensor (LVIS) and spectral characteristics from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) for biomass estimation in the Sierra Nevada. In addition, we combined the two sensors to map species-specific biomass and stress at landscape scale. Multiple endmember spectral mixture analysis (MESMA) was used to classify vegetation from AVIRIS images and obtain sub-pixel fractions of green vegetation, non-photosynthetic vegetation, soil, and shade. LVIS metrics, AVIRIS spectral indices, and MESMA fractions were compared with field measures of biomass using linear and stepwise regressions at stand (1 ha) level. AVIRIS metrics such as water band indices and shade fractions showed strong correlation with LVIS canopy height (r² = 0.69, RMSE = 5.2 m) and explained around 60% variability in biomass. LVIS variables were found to be consistently good predictors of total and species specific biomass (r² = 0.77, RMSE = 70.12 Mg/ha). Prediction by LVIS after species stratification of field data reduced errors by 12% (r² = 0.84, RMSE = 58.78 Mg/ha) over using LVIS metrics alone. Species-specific biomass maps and associated errors created from fusion were different from those produced without fusion, particularly for hardwoods and pines, although mean biomass differences between the two techniques were not statistically significant. A combined analysis of spatial maps from LVIS and AVIRIS showed increased water and chlorophyll stress in several high biomass stands in the study area. This study provides further evidence that lidar is better suited for biomass estimation, per se, while the best use of hyperspectral data may be to refine biomass predictions through a priori species stratification, while also providing information on canopy state, such as stress. Together, the two sensors have many potential applications in carbon dynamics, ecological and habitat studies.     

Hybrid krill herd algorithm with differential evolution for global numerical optimization

In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid differential evolution KH (DEKH) method has been developed for function optimization. The improvement involves adding a new hybrid differential evolution (HDE) operator into the krill, updating process for the purpose of dealing with optimization problems more efficiently. The introduced HDE operator inspires the intensification and lets the krill perform local search within the defined region. DEKH is validated by 26 functions. From the results, the proposed methods are able to find more accurate solution than the KH and other methods. In addition, the robustness of the DEKH algorithm and the influence of the initial population size on convergence and performance are investigated by a series of experiments.