The structure of the copper(II) complex with 4,5-dichlorophthalic acid: The 1:1 complex 'adduct' salt trans-tetraaqua(2-carboxy-4,5-dichlorobenzoato)(4,5-dichlorobenzene-1,2-dicarboxylic acid)copper(II) 2-carboxy-4,5-dichlorobenzoate

The unusual (1:1) complex ‘adduct’ salt of copper(II) with 4,5-dichlorophthalic acid (H2DCPA), having formula [Cu(H2O)4(C8H3Cl2O4) (C8H4Cl2O4)] . (C8H3Cl2O4) has been synthesized and characterized using single-crystal X-ray diffraction. Crystals are monoclinic, space group P21/c, with Z = 4 in a cell with dimensions a = 20.1376(7), b =12.8408(4) c = 12.1910(4) Å, β = 105.509(4)o. The complex is based on discrete tetragonally distorted octahedral [CuO6] coordination centres with the four water ligands occupying the square planar sites [Cu-O, 1.962(4)-1.987(4) Å] and the monodentate carboxyl-O donors of two DCPA ligand species in the axial sites. The first of these bonds [Cu-O, 2.341(4) Å] is with an oxygen of a HDCPA monoanion, the second with an oxygen of a H2DCPA acid species [Cu-O, 2.418(4) Å]. The un-coordinated ‘adduct’ molecule is a HDCPA counter anion which is strongly hydrogen-bonded to the coordinated H2DCPA ligand [O… O, 2.503(6) Å] while a number of peripheral intra- and intermolecular hydrogen-bonding interactions give a two-dimensional network structure.

Hydrogen bonding in the water stabilized structure of the modified unsymmetrically substituted viologen chromophore, N-ethyl-N'-(2-phosphonomethyl)-4,4'-bipyridylium dichloride

The crystal structure of the modified unsymmetrically N, N'-substituted viologen chromophore, N-ethyl- N'-(2-phosphonoethyl)-4, 4'-bipyridinium dichloride 0.75 hydrate. (1) has been determined. Crystals are triclinic, space group P-1 with Z = 2 in a cell with a = 7.2550(1), b = 13.2038(5), c = 18.5752(7) Å, α = 86.495(3), β = 83.527(2), γ = 88.921(2)o. The two independent but pseudo-symmetrically related cations in the asymmetric unit form one-dimensional hydrogen-bonded chains through short homomeric phosphonic acid O-H...O links [2.455(4), 2.464(4)A] while two of the chloride anions are similarly strongly linked to phosphonic acid groups [O-H…Cl, 2.889(4), 2.896(4)Å]. The other two chloride anions together with the two water molecules of solvation (one with partial occupancy) form unusual cyclic hydrogen-bonded bis(Cl...water) dianion units which lie between the layers of bipyridylium rings of the cation chain structures with which they are weakly associated.

Using honeypots to analyse anomalous Internet activities

Monitoring Internet traffic is critical in order to acquire a good understanding of threats to computer and network security and in designing efficient computer security systems. Researchers and network administrators have applied several approaches to monitoring traffic for malicious content. These techniques include monitoring network components, aggregating IDS alerts, and monitoring unused IP address spaces. Another method for monitoring and analyzing malicious traffic, which has been widely tried and accepted, is the use of honeypots. Honeypots are very valuable security resources for gathering artefacts associated with a variety of Internet attack activities. As honeypots run no production services, any contact with them is considered potentially malicious or suspicious by definition. This unique characteristic of the honeypot reduces the amount of collected traffic and makes it a more valuable source of information than other existing techniques. Currently, there is insufficient research in the honeypot data analysis field. To date, most of the work on honeypots has been devoted to the design of new honeypots or optimizing the current ones. Approaches for analyzing data collected from honeypots, especially low-interaction honeypots, are presently immature, while analysis techniques are manual and focus mainly on identifying existing attacks. This research addresses the need for developing more advanced techniques for analyzing Internet traffic data collected from low-interaction honeypots. We believe that characterizing honeypot traffic will improve the security of networks and, if the honeypot data is handled in time, give early signs of new vulnerabilities or breakouts of new automated malicious codes, such as worms. The outcomes of this research include: • Identification of repeated use of attack tools and attack processes through grouping activities that exhibit similar packet inter-arrival time distributions using the cliquing algorithm; • Application of principal component analysis to detect the structure of attackers’ activities present in low-interaction honeypots and to visualize attackers’ behaviors; • Detection of new attacks in low-interaction honeypot traffic through the use of the principal component’s residual space and the square prediction error statistic; • Real-time detection of new attacks using recursive principal component analysis; • A proof of concept implementation for honeypot traffic analysis and real time monitoring.

Channel tracking algorithms for highly efficient wireless broadband communications in rural areas

High-speed broadband internet access is widely recognised as a catalyst to social and economic development, having a significant impact on global economy. Rural Australia’s inherent dispersed population over a large geographical area make the delivery of efficient, well-maintained and cost-effective internet a challenging task. The novel and highly-efficient Multi-User-Single-Antenna for MIMO (MUSA-MIMO) broadband wireless communication technology can effectively be used to deliver wireless broadband access to rural areas. This research aims to develop for the first time, an efficient and accurate algorithm for the tracking and prediction of Channel State Information (CSI) at the transmitter, by characterising time variation effects of the wireless communication channel on the performance of a highly-efficient MUSA-MIMO technology particularly suited for rural communities, improving their quality of life and economic prosperity.

Influence of smectite suspension structure on sheet orientation in dry sediments : XRD and AFM applications

The structure-building phenomena within clay aggregates are governed by forces acting between clay particles. Measurements of such forces are important to understand in order to manipulate the aggregate structure for applications such as dewatering of mineral processing tailings. A parallel particle orientation is required when conducting XRD investigation on the oriented samples and conduct force measurements acting between basal planes of clay mineral platelets using at. force microscopy (AFM). To investigate how smectite clay platelets were oriented on silicon wafer substrate when dried from suspension range of methods like SEM, XRD and AFM were employed. From these investigations, we conclude that high clay concns. and larger particle diams. (up to 5 μm) in suspension result in random orientation of platelets in the substrate. The best possible laminar orientation in the clay dry film, represented in the XRD 0 0 1/0 2 0 intensity ratio of 47 was obtained by drying thin layers from 0.02 wt.% clay suspensions of the natural pH. Conducted AFM investigations show that smectite studied in water based electrolytes show very long-range repulsive forces lower in strength than electrostatic forces from double-layer repulsion. It was suggested that these forces may have structural nature. Smectite surface layers rehydrate in water environment forms surface gel with spongy and cellular texture which cushion approaching AFM probe. This structural effect can be measured in distances larger than 1000 nm from substrate surface and when probe penetrate this gel layer, structural linkages are forming between substrate and clay covered probe. These linkages prevent subsequently smooth detachments of AFM probe on way back when retrieval. This effect of tearing new formed structure apart involves larger adhesion-like forces measured in retrieval. It is also suggested that these effect may be enhanced by the nano-clay particles interaction.

Hydrate stabilization in the three-dimensional hydrogen-bonded structure of the brucinium compound, bis(2,3-dimethoxy-10-oxostrychnidinium) biphenyl-4,4'-disulfonate hexahydrate

The crystal structure of the 2:1 proton-transfer compound of brucine with biphenyl-4,4’-disulfonate, bis(2,3-dimethoxy-10-oxostrychnidinium) biphenyl-4,4'-disulfonate hexahydrate (1) has been determined at 173 K. Crystals are monoclinic, space group P21 with Z = 2 in a cell with a = 8.0314(2), b = 29.3062(9), c = 12.2625(3) Å, β = 101.331(2)o. The crystallographic asymmetric unit comprises two brucinium cations, a biphenyl-4,4'-disulfonate dianion and six water molecules of solvation. The brucinium cations form a variant of the common undulating and overlapping head-to-tail sheet sub-structure. The sulfonate dianions are also linked head-to-tail by hydrogen bonds into parallel zig-zag chains through clusters of six water molecules of which five are inter-associated, featuring conjoint cyclic eight-membered hydrogen-bonded rings [graph sets R33(8) and R34(8)], comprising four of the water molecules and closed by sulfonate O-acceptors. These chain structures occupy the cavities between the brucinium cation sheets and are linked to them peripherally through both brucine N+-H...Osulfonate and Ocarbonyl…H-Owater to sulfonate O bridging hydrogen bonds, forming an overall three-dimensional framework structure. This structure determination confirms the importance of water in the stabilization of certain brucine compounds which have inherent crystal instability.

Phenyl-ring disorder in the two-dimensional hydrogen-bonded structure of the 1:1 proton-transfer salt of the diazo-dye precursor 4-(phenyldiazenyl)aniline (aniline yellow) with L-tartaric acid

The structure of the 1:1 proton-transfer compound from the reaction of L-tartaric acid with the azo-dye precursor aniline yellow [4-(phenylazo)aniline], 4-(phenyldiazenyl)anilinium hydrogen 2R,3R-tartrate C12H12N3+ . C4H6O6- has been determined at 200 K. The asymmetric unit of the compound contains two independent phenylazoanilinium cations and two hydrogen L-tartrate anions. The structure is unusual in that all four phenyl rings of both cations have identical 50% rotational disorder. The two hydrogen L-tartrate anions form independent but similar chains through head-to-tail carboxylic O--H...O~carboxyl~ hydrogen bonds [graph set C7] which are then extended into a two-dimensional hydrogen-bonded sheet structure through hydroxyl O--H...O hydrogen-bonding links. The anilinium groups of the phenyldiazenyl cations are incorporated into the sheets and also provide internal hydrogen-bonding extensions while their aromatic tails layer in the structure without significant interaction except for weak \p--\p interactions [minimum ring centroid separation, 3.844(3) \%A]. The hydrogen L-tartrate residues of both anions have the common short intramolecular hydroxyl O--H...O~carboxyl~ hydogen bonds. This work has provided a solution to the unusual disorder problem inherent in the structure of this salt as well as giving another example of the utility of the hydrogen tartrate in the generation of sheet substructures in molecular assembly processes.

Reading the play : adaptation by prediction of agent motion

An adaptive agent improves its performance by learning from experience. This paper describes an approach to adaptation based on modelling dynamic elements of the environment in order to make predictions of likely future state. This approach is akin to an elite sports player being able to “read the play”, allowing for decisions to be made based on predictions of likely future outcomes. Modelling of the agent‟s likely future state is performed using Markov Chains and a technique called “Motion and Occupancy Grids”. The experiments in this paper compare the performance of the planning system with and without the use of this predictive model. The results of the study demonstrate a surprising decrease in performance when using the predictions of agent occupancy. The results are derived from statistical analysis of the agent‟s performance in a high fidelity simulation of a world leading real robot soccer team.

Structure of selected basic copper (II) sulphate minerals based upon spectroscopy : implications for hydrogen bonding

The application of near-infrared and infrared spectroscopy has been used for identification and distinction of basic Cu-sulphates that include devilline, chalcoalumite and caledonite. Near-infrared spectra of copper sulphate minerals confirm copper in divalent state. Jahn-Teller effect is more significant in chalcoalumite where 2B1g ® 2B2g transition band shows a larger splitting (490 cm-1) confirming more distorted octahedral coordination of Cu2+ ion. One symmetrical band at 5145 cm-1 with shoulder band 5715 cm-1 result from the absorbed molecular water in the copper complexes are the combinations of OH vibrations of H2O. One sharp band at around 3400 cm-1 in IR common to the three complexes is evidenced by Cu-OH vibrations. The strong absorptions observed at 1685 and 1620 cm-1 for water bending modes in two species confirm strong hydrogen bonding in devilline and chalcoalumite. The multiple bands in v3 and v4(SO4)2- stretching regions are attributed to the reduction of symmetry to the sulphate ion from Td to C2V. Chalcoalumite, the excellent IR absorber over the range 3800-500 cm-1 is treated as most efficient heat insulator among the Cu-sulphate complexes.

Clustering with random indexing K-tree and XML structure

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.

Transforming agriculture through pervasive wireless sensor networks

A large-scale, outdoor, pervasive computing system based on the Fleck hardware platform applies sensor network technology to farming. Comprising static and animal-borne mobile nodes, the system measures the state of a complex, dynamic system comprising climate, soil, pasture, and animals. This data supports prediction of the land's future state and improved management outcomes through closed-loop control. This article is part of a special issue, Building a Sensor-Rich World.

Experimental evaluation of position control methods for hydraulic systems

Presents a unified and systematic assessment of ten position control strategies for a hydraulic servo system with single-ended cylinder driven by a proportional directional control valve. We aim at identifying those methods that achieve better tracking, have a low sensitivity to system uncertainties, and offer a good balance between development effort and end results. A formal approach for solving this problem relies on several practical metrics, which is introduced herein. Their choice is important, as the comparison results between controllers can vary significantly, depending on the selected criterion. Apart from the quantitative assessment, we also raise aspects which are difficult to quantify, but which must stay in attention when considering the position control problem for this class of hydraulic servo systems.

Polymersomes, smaller than you think : ferrocene as a TEM probe to determine core structure

By incorporating ferrocene into the hydrophobic membrane of PEG-b-PCL polymersome nanoparticles it is possible to selectively visualize their core using Transmission Electron Microscopy (TEM). Two different sizes of ferrocene-loaded polymersomes with mean hydrodynamic diameters of approximately 40 and 90 nm were prepared. Image analysis of TEM pictures of these polymersomes found that the mean diameter of the core was 4–5 times smaller than the mean hydrodynamic diameter. The values obtained also allow the surface diameter and internal volume of the core to be calculated.

A connective model for the practice-led research exegesis : an analysis of content and structure

Since the formal recognition of practice-led research in the 1990s, many higher research degree candidates in art, design and media have submitted creative works along with an accompanying written document or ‘exegesis’ for examination. Various models for the exegesis have been proposed in university guidelines and academic texts during the past decade, and students and supervisors have experimented with its contents and structure. With a substantial number of exegeses submitted and archived, it has now become possible to move beyond proposition to empirical analysis. In this article we present the findings of a content analysis of a large, local sample of submitted exegeses. We identify the emergence of a persistent pattern in the types of content included as well as overall structure. Besides an introduction and conclusion, this pattern includes three main parts, which can be summarized as situating concepts (conceptual definitions and theories); precedents of practice (traditions and exemplars in the field); and researcher’s creative practice (the creative process, the artifacts produced and their value as research). We argue that this model combines earlier approaches to the exegesis, which oscillated between academic objectivity, by providing a contextual framework for the practice, and personal reflexivity, by providing commentary on the creative practice. But this model is more than simply a hybrid: it provides a dual orientation, which allows the researcher to both situate their creative practice within a trajectory of research and do justice to its personally invested poetics. By performing the important function of connecting the practice and creative work to a wider emergent field, the model helps to support claims for a research contribution to the field. We call it a connective model of exegesis.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.