长白山森林生态系统腐木生苔藓植物生态分布的DCA排序研究

应用除趋势对应分析（ＤＣＡ）对长白山不同森林类型腐木生苔藓植物分布研究表明，长白山红松阔叶混交林、暗针叶林以及两者之间的过渡林林下腐木生苔藓植物组成明显不同．红松阔叶混交林以尖叶匍灯藓、毛尖羽藓、细枝青藓等为主；过渡林以尖叶匍灯藓、鞭枝疣灯藓、羽藓、腐木藓、塔藓等为主；暗针叶林的以塔藓、钩枝镰刀藓、拟垂枝藓、曲背藓、毛梳藓等为主．暗针叶林林下腐木生苔藓植物发生量最大，物种的多样性也最高．海拔高度和林冠郁闭度是影响长白山森林林下腐木生苔藓植物生态分布的主要因素．

DCA、CCA和DCCA三种排序方法在中国草地植被群落中的应用现状

排序是用来分析植被与环境之间生态关系的重要手段。该文主要是对除趋势对应分析(DCA)、典范对应分析(CCA)和除趋势典范对应分析(DCCA)这三种排序方法进行总结,讨论它们在中国草地植被群落研究中的应用现状,并得出除趋势对应分析与聚类分析结合使用效果比较好,主要是用来揭示群落之间的关系,并且在实际的应用中也比较多,而典范对应分析和除趋势典范对应分析在揭示种与环境关系方面具有明显的优势,但是由于某些条件的限制,在实际研究中应用的比较少。可见这几种排序方法在实际应用中具有一定的优势,所以应该加强这方面的应用,更加深入的研究草地植被群落,以期对草地生态学及草地植被群落生态关系的研究工作发挥重要的借鉴作用。

Synthesis, crystal structure, magnetic behavior and thermal property of three polynuclear complexes: [M(dca)(2)(H2O)2](n)center dot(hmt)(n) [M = Mn(II), Co(II)] and [Co(dca)(2)(bpds)](n) [dca, dicyanamide; hint, hexamethylenetetramine; bpds, 4,4 '-bipyridyl disulfide]

Three mu(1.5)-dicyanamide bridged Mn(II) and Co(II) complexes having molecular formula [Mn(dca)(2)(H2O)(2)](n)center dot(hmt)(n) (1), [Co(dca)(2) (H2O)(2)](n)center dot(hmt)(n) (2) and [Co(dca)(2)(bpds)](n) (3) [dca = dicyanamide; hmt = hexamethylenetetramine; bpds = 4,4'-bipyridyl disulfide] have been synthesized and characterized by single crystal X-ray diffraction study, low temperature (300-2 K) magnetic measurement and thermal behavior. The X-ray diffraction analysis of 1 and 2 reveals that they are isostructural, comprising of 1D coordination polymers [M(dca)(2)(H2O)(2)](n) [M = Mn(II), Co(II) for 1 and 2. respectively] with uncoordinated hmt molecules located among the chains. The [M(dca)(2)(H2O)(2)](n) chains and the lattice hint molecules are connected through H-bonds resulting in a 3D supramolecular architecture. The octahedral N4O2 chromophore surrounding the metal ion forms via two trans located water oxygens and four nitrogens from four nitrile dca. Complex 3 is a 1D chain formed by two mu(1.5)-dca and one bridging bpds. The octahedral N-6 coordination sphere surrounding the cobalt ions comprises four nitrogens from dca and two from bpds. Low temperature magnetic study indicates small antiferromagnetic coupling for all the complexes. Best fit parameters for 1: J = -0.17 cm(-1), g = -2.03 with R = 6.1 x 10(-4), for 2, J = -0.50 cm(-1), and for 3, J = -0.95 cm(-1). (c) 2005 Elsevier B.V. All rights reserved.

Agreement between HbA1c measured by DCA 2000 and by HPLC: effects of fetal hemoglobin concentrations

In subjects with type 1 diabetes, persisting elevations of fetal hemoglobin (HbF) have been demonstrated. This study evaluated whether HbF levels typically seen in type 1 diabetes (up to 3%) interfere with glycohemoglobin determinations using a common immunologic method (DCA 2000).

Regional vegetation changes in the tropical Andes expressed as arboreal pollen (AP%), standardised ordination scores (DCA z-scores), and aquatic vegetation ratios (D/SS); files used for plots

We compare eight pollen records reflecting climatic and environmental change from the tropical Andes. Our analysis focuses on the last 50 ka, with particular emphasis on the Pleistocene to Holocene transition. We explore ecological grouping and downcore ordination results as two approaches for extracting environmental variability from pollen records. We also use the records of aquatic and shoreline vegetation as markers for lake level fluctuations, and precipitation change. Our analysis focuses on the signature of millennial-scale variability in the tropical Andes, in particular, Heinrich stadials and Greenland interstadials. We identify rapid responses of the tropical vegetation to this climate variability, and relate differences between sites to moisture sources and site sensitivity.

DCA for bot detection

Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a dasiahotpsila - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the dasiabotmaster of a botnetpsila. In this work, we use the biologically inspired dendritic cell algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.

The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms

The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.

PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.

Performance evaluation of DCA and SRC on a single bot detection

Malicious users try to compromise systems using new techniques. One of the recent techniques used by the attacker is to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These compromised machines are said to be infected with malicious software termed a “bot”. In this paper, we investigate the correlation of behavioural attributes such as keylogging and packet flooding behaviour to detect the existence of a single bot on a compromised machine by applying (1) Spearman’s rank correlation (SRC) algorithm and (2) the Dendritic Cell Algorithm (DCA). We also compare the output results generated from these two methods to the detection of a single bot. The results show that the DCA has a better performance in detecting malicious activities.

Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics

The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data. In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.

Digital complementary assets

Google, Facebook, Twitter, LinkedIn, etc. are some of the prominent large-scale digital service providers that are having tremendous impact on societies, corporations and individuals. However, despite the rapid uptake and their obvious influence on the behavior of individuals and the business models and networks of organizations, we still lack a deeper, theory-guided understanding of the related phenomenon. We use Teece’s notion of complementary assets and extend it towards ‘digital complementary assets’ (DCA) in an attempt to provide such a theory-guided understanding of these digital services. Building on Teece’s theory, we make three contributions. First, we offer a new conceptualization of digital complementary assets in the form of digital public goods and digital public assets. Second, we differentiate three models for how organizations can engage with such digital complementary assets. Third, user-base is found to be a critical factor when considering appropriability.

The 2013 creative industries statistical analysis for Western Australia

The Department of Culture and the Arts undertook the first mapping of Perth’s creative industries in 2007 in partnership with the City of Perth and the Departments of Industry and Resources and the Premier and Cabinet. The 2013 Creative Industries Statistical Analysis for Western Australia report has updated the mapping with the 2011 Census employment data to provide invaluable information for the State’s creative industries, their peak associations and potential investors. The report maps sector employment numbers and growth between the 2006 and 2011 Census in the areas of music, visual and performing arts, film, TV and radio, advertising and marketing, software and digital content, publishing, and architecture and design, which includes designer fashion.

The environmental fate of diuron under a conventional production regime in a sugarcane farm during the plant cane phase

BACKGROUND: Field studies of diuron and its metabolites 3-(3,4-dichlorophenyl)-1-methylurea (DCPMU), 3,4-dichlorophenylurea (DCPU) and 3,4-dichloroaniline (DCA) were conducted in a farm soil and in stream sediments in coastal Queensland, Australia. RESULTS: During a 38 week period after a 1.6 kg ha^-1 diuron application, 70-100% of detected compounds were within 0-15 cm of the farm soil, and 3-10% reached the 30-45 cm depth. First-order t1/2 degradation averaged 49 ± 0.9 days for the 0-15, 0-30 and 0-45 cm soil depths. Farm runoff was collected in the first 13-50 min of episodes lasting 55-90 min. Average concentrations of diuron, DCPU and DCPMU in runoff were 93, 30 and 83-825 µg L^-1 respectively. Their total loading in all runoff was >0.6% of applied diuron. Diuron and DCPMU concentrations in stream sediments were between 3-22 and 4-31 µg kg^-1 soil respectively. The DCPMU/diuron sediment ratio was >1. CONCLUSION: Retention of diuron and its metabolites in farm topsoil indicated their negligible potential for groundwater contamination. Minimal amounts of diuron and DCMPU escaped in farm runoff. This may entail a significant loading into the wider environment at annual amounts of application. The concentrations and ratio of diuron and DCPMU in stream sediments indicated that they had prolonged residence times and potential for accumulation in sediments. The higher ecotoxicity of DCPMU compared with diuron and the combined presence of both compounds in stream sediments suggest that together they would have a greater impact on sensitive aquatic species than as currently apportioned by assessments that are based upon diuron alone.

Helix-coil stability constants for amino acids in nonaqueous solvents. Studies of random poly(-benzyl-L-glutamate-co-L-alanine) and poly(-benzyl-L-glutamate-co-L-leucine) in a mixed solvent

The host-guest technique has been applied to the determination of the helix-coil stability constants of two naturally occurring amino acids, L-alanine and L-leucine, in a nonaqueous solvent system. Random copolymers containing L-alanine and L-leucine, respectively, as guest residues and -benzyl-L-glutamate as the host residue were synthesized. The polymers were fractionated and characterized for their amino acid content, molecular weight, and helix-coil transition behavior in a dichloroacetic acid (DCA)-1,2-dichloroethane (DCE) mixture. Two types of helix-coil transitions were carried out on the copolymers: solvent-induced transitions in DCA-DCE mixtures at 25°C and thermally induced transitions in a 82:18 (wt %) DCA-DCE mixture. The thermally induced transitions were analyzed by statistical mechanical methods to determine the Zimm-Bragg parameters, and s, of the guest residues. The experimental data indicate that, in the nonaqueous solvent, the L-alanine residue stabilizes the -helical conformation more than the L-leucine residue does. This is in contrast to their behavior in aqueous solution, where the reverse is true. The implications of this finding for the analysis of helical structures in globular proteins are discussed.

Dynamical cluster approximation: Nonlocal dynamics of correlated electron systems

We recently introduced the dynamical cluster approximation (DCA), a technique that includes short-ranged dynamical correlations in addition to the local dynamics of the dynamical mean-field approximation while preserving causality. The technique is based on an iterative self-consistency scheme on a finite-size periodic cluster. The dynamical mean-field approximation (exact result) is obtained by taking the cluster to a single site (the thermodynamic limit). Here, we provide details of our method, explicitly show that it is causal, systematic, Phi derivable, and that it becomes conserving as the cluster size increases. We demonstrate the DCA by applying it to a quantum Monte Carlo and exact enumeration study of the two-dimensional Falicov-Kimball model. The resulting spectral functions preserve causality, and the spectra and the charge-density-wave transition temperature converge quickly and systematically to the thermodynamic limit as the cluster size increases.