Web-Based Information Systems in the Stock Market Financial Information Domain

The information domain is a recognised sphere for the influence, ownership, and control of information and it's specifications, format, exploitation and explanation (Thompson, 1967). The article presents a description of the financial information domain issues related to the organisation and operation of a stock market. We review the strategic, institutional and standards dimensions of the stock market information domain in relation to the current semantic web knowledge and how and whether this could be used in modern web based stock market information systems to provide the quality of information that their stakeholders want. The analysis is based on the FINE model (Blanas, 2003). The analysis leads to a number of research questions for future research.

A review of the welfare of dairy cows in continuously housed and pasture-based production systems.

There is increasing interest in the use of continuous housing systems for dairy cows, with various reasons put forward to advocate such systems. However, the welfare of dairy cows is typically perceived to be better within pasture-based systems, although such judgements are often not scientifically based. The aim of this review was to interrogate the existing scientific literature to compare the welfare, including health, of dairy cows in continuously housed and pasture-based systems. While summarising existing work, knowledge gaps and directions for future research are also identified. The scope of the review is broad, examining relevant topics under three main headings; health, behaviour, and physiology. Regarding health, cows on pasture-based systems had lower levels of lameness, hoof pathologies, hock lesions, mastitis, uterine disease, and mortality compared to cows on continuously housed systems. Pasture access also had benefits for dairy cow behaviour, in terms of grazing, improved lying / resting times, and lower levels of aggression. Moreover, when given the choice between pasture and indoor housing, cows showed an overall preference for pasture, particularly at night. However, the review highlighted the need for a deeper understanding of cow preference and behaviour. Potential areas for concern within pasture-based systems included physiological indicators of more severe negative energy balance, and in some situations, the potential for compromised welfare with exposure to unpredictable weather conditions. In summary, the results from this review highlight that there remain considerable animal welfare benefits from incorporating pasture access into dairy production systems.

On precise three-dimensional environment modeling via UAV-based photogrammetric systems

Abstract : Images acquired from unmanned aerial vehicles (UAVs) can provide data with unprecedented spatial and temporal resolution for three-dimensional (3D) modeling. Solutions developed for this purpose are mainly operating based on photogrammetry concepts, namely UAV-Photogrammetry Systems (UAV-PS). Such systems are used in applications where both geospatial and visual information of the environment is required. These applications include, but are not limited to, natural resource management such as precision agriculture, military and police-related services such as traffic-law enforcement, precision engineering such as infrastructure inspection, and health services such as epidemic emergency management. UAV-photogrammetry systems can be differentiated based on their spatial characteristics in terms of accuracy and resolution. That is some applications, such as precision engineering, require high-resolution and high-accuracy information of the environment (e.g. 3D modeling with less than one centimeter accuracy and resolution). In other applications, lower levels of accuracy might be sufficient, (e.g. wildlife management needing few decimeters of resolution). However, even in those applications, the specific characteristics of UAV-PSs should be well considered in the steps of both system development and application in order to yield satisfying results. In this regard, this thesis presents a comprehensive review of the applications of unmanned aerial imagery, where the objective was to determine the challenges that remote-sensing applications of UAV systems currently face. This review also allowed recognizing the specific characteristics and requirements of UAV-PSs, which are mostly ignored or not thoroughly assessed in recent studies. Accordingly, the focus of the first part of this thesis is on exploring the methodological and experimental aspects of implementing a UAV-PS. The developed system was extensively evaluated for precise modeling of an open-pit gravel mine and performing volumetric-change measurements. This application was selected for two main reasons. Firstly, this case study provided a challenging environment for 3D modeling, in terms of scale changes, terrain relief variations as well as structure and texture diversities. Secondly, open-pit-mine monitoring demands high levels of accuracy, which justifies our efforts to improve the developed UAV-PS to its maximum capacities. The hardware of the system consisted of an electric-powered helicopter, a high-resolution digital camera, and an inertial navigation system. The software of the system included the in-house programs specifically designed for camera calibration, platform calibration, system integration, onboard data acquisition, flight planning and ground control point (GCP) detection. The detailed features of the system are discussed in the thesis, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The accuracy of the results was evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy were assessed. The second part of this thesis concentrates on improving the techniques of sparse and dense reconstruction. The proposed solutions are alternatives to traditional aerial photogrammetry techniques, properly adapted to specific characteristics of unmanned, low-altitude imagery. Firstly, a method was developed for robust sparse matching and epipolar-geometry estimation. The main achievement of this method was its capacity to handle a very high percentage of outliers (errors among corresponding points) with remarkable computational efficiency (compared to the state-of-the-art techniques). Secondly, a block bundle adjustment (BBA) strategy was proposed based on the integration of intrinsic camera calibration parameters as pseudo-observations to Gauss-Helmert model. The principal advantage of this strategy was controlling the adverse effect of unstable imaging networks and noisy image observations on the accuracy of self-calibration. The sparse implementation of this strategy was also performed, which allowed its application to data sets containing a lot of tie points. Finally, the concepts of intrinsic curves were revisited for dense stereo matching. The proposed technique could achieve a high level of accuracy and efficiency by searching only through a small fraction of the whole disparity search space as well as internally handling occlusions and matching ambiguities. These photogrammetric solutions were extensively tested using synthetic data, close-range images and the images acquired from the gravel-pit mine. Achieving absolute 3D mapping accuracy of 11±7 mm illustrated the success of this system for high-precision modeling of the environment.

A review of maize-beans and maize-cowpea intercrop systems in the semiarid Northeast Brazil.

Fifty-one experiments on maize -beans and 34 on maize-cowpea intercropping systems conducted mostly in semiarid Northeast Brazil were analysed to get an understanding of the performance of these intercrops in terms of their productivity as well as stability. Both the intercrop systems produced higher yields over their respective sole crops under a wide range of agroclimates; the average advantage with maize-beans was 32%, while that from maize-cowpea was 41%. The optimum row proportion for maize-beans was one maize: three beans, requiring 59% of sole crop maize population and 75% sole bean population. In maize-cowpea, alternate rows or one maize: two cowpea arrangement with about 50% of sole maize density and 100% of sole cowpea population seemed to be optimum. The intercrops failed less frequently compared to sole crops to meet specified incomes or yields. Sorghum seemed to be a good alternative to the traditional cereal because of its improved and consistent performance. Future research needs are discussed for further yield improvement in these two intercrop system.s

Knowledge-based management systems for the police force

Knowledge-Based Management Systems enable new ways to process and analyse knowledge to gain better insights to solve a problem and aid in decision making. In the police force such systems provide a solution for enhancing operations and improving client administration in terms of knowledge management. The main objectives of every police officer is to ensure the security of life and property, promote lawfulness, and avert and distinguish wrongdoing. The administration of knowledge and information is an essential part of policing, and the police ought to be proactive in directing both explicit and implicit knowledge, whilst adding to their abilities in knowledge sharing. In this paper the potential for a knowledge based system for the Mauritius police was analysed, and recommendations were also made, based on requirements captured from interviews with several long standing officers, and surveying of previous works in the area.

Synthesis and catalytic activity of new cu-based catalytic systems for the enhanced electrochemical CO2 reduction

This Ph.D. thesis concerns the synthesis of nanostructured Cu-containing materials to be used as electrode modifiers for the CO2 electroreduction in aqueous phase and the evaluation of their catalytic performances. Inspired by the fascinating concept of the artificial photosynthesis-oriented systems, several catalytic layers were electrochemically loaded on carbonaceous gas diffusion membranes, i.e., 3D structures that allow the design of eco-friendly materials for applications in green carbon recycling processes. In particular, early studies on Cu(I-II)-Cu(0) nanostructured materials were carried out to produce films on 4 cm2 sized supports by means of a fast and low-cost electrochemical procedure. Besides, through a screening of potentials, it was possible to find out a selective value for the CH3COOH production at -0.4 V vs RHE with a maximum productivity (1h reaction), ensured by the presence of the Cu+/Cu0 active redox couple (0.31 mmol gcat-1 h-1). On the basis of these results, further optimisations of the electrocatalyst chemical composition were carried out with the aim of (i) facilitating the interaction with CO2, (ii) increasing the dispersion of the catalytic active phase, and (iii) enhancing the CH3COOH productivity. To this aim, novel electrocatalysts based on layered double hydroxides (LDHs) were optimised, having as a final goal the formation of a new Cu2O-Cu0 based electrocatalyst derived from electrochemically achieved CuMgAl LDHs, subjected to calcination and reduction processes. The as-obtained electrocatalysts were tested for the selective production of CH3COOH and unprecedented results were obtained with the pristine CuMgAl LDH (2.0 mmol gcat-1 h-1). Additional characterisations of such an electrocatalyst have highlighted the possibility to achieve a ternary LDH in intimate contact with Cu2O-Cu0 species starting from the electrochemical deposition. The presence of these species, along with an alkaline environment on the electrode surface, were essential to preserve the selectivity towards the desired product, as confirmed by further operando studies.

A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for (i) discovering the structural commonalities between sub-trees, (ii) identifying sub-tree semantic resemblances, (iii) computing tree-based edit operations costs, and (iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.

Carbon stocks, tree diversity, and the role of organic certification in different cocoa production systems in Alto Beni, Bolivia

This study compares aboveground and belowground carbon stocks and tree diversity in different cocoa cultivation systems in Bolivia: monoculture, simple agroforestry, and successional agroforestry, as well as fallow as a control. Since diversified, agroforestry-based cultivation systems are often considered important for sustainable development, we also evaluated the links between carbon stocks and tree diversity, as well as the role of organic certification in transitioning from monoculture to agroforestry. Biomass, tree diversity, and soil physiochemical parameters were sampled in 15 plots measuring 48 × 48 m. Semi-structured interviews with 52 cocoa farmers were used to evaluate the role of organic certification and farmers’ organizations (e.g., cocoa cooperatives) in promoting tree diversity. Total carbon stocks in simple agroforestry systems (128.4 ± 20 Mg ha−1) were similar to those on fallow plots (125.2 ± 10 Mg ha−1). Successional agroforestry systems had the highest carbon stocks (143.7 ± 5.3 Mg ha−1). Monocultures stored significantly less carbon than all other systems (86.3 ± 4.0 Mg ha−1, posterior probability P(Diff > 0) of 0.000–0.006). Among shade tree species, Schizolobium amazonicum, Centrolobium ochroxylum, and Anadenanthera sp. accumulated the most biomass. High-value timber species (S. amazonicum, C. ochroxylum, Amburana cearensis, and Swietenia macrophylla) accounted for 22.0 % of shade tree biomass. The Shannon index and tree species richness were highest in successional agroforestry systems. Cocoa plots on certified organic farms displayed significantly higher tree species richness than plots on non-certified farms. Thus, expanding the coverage of organic farmers’ organizations may be an effective strategy for fostering transitions from monoculture to agroforestry systems.

Subject identification via ECG fiducial-based systems:influence of the type of QT interval correction

Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV). In particular, HRV causes changes in the QT intervals along the ECG waveforms. This work is aimed at analysing the influence of seven QT interval correction methods (based on population models) on the performance of ECG-fiducial-based identification systems. In addition, we have also considered the influence of training set size, classifier, classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme. The ECG signals used in this study were collected from thirty-nine subjects within the Physionet open access database. Public domain software was used for fiducial points detection. Results suggested that QT correction is indeed required to improve the performance. However, there is no clear choice among the seven explored approaches for QT correction (identification rate between 0.97 and 0.99). MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities, in terms of classification performance, with respect to Decision Tree-based classifiers. No such strong influence of the training-set size and the number of consecutive heartbeats has been observed on the majority voting scheme.

Syntactic Based Machine Translation from English to Malayalam

Due to the emergence of multiple language support on the Internet, machine translation (MT) technologies are indispensable to the communication between speakers using different languages. Recent research works have started to explore tree-based machine translation systems with syntactical and morphological information. This work aims the development of Syntactic Based Machine Translation from English to Malayalam by adding different case information during translation. The system identifies general rules for various sentence patterns in English. These rules are generated using the Parts Of Speech (POS) tag information of the texts. Word Reordering based on the Syntax Tree is used to improve the translation quality of the system. The system used Bilingual English –Malayalam dictionary for translation.

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Potential of legume-based grassland-livestock systems in Europe

European grassland-based livestock production systems face the challenge of producing more meat and milk to meet increasing world demands and to achieve this using fewer resources. Legumes offer great potential for achieving these objectives. They have numerous features that can act together at different stages in the soil–plant–animal–atmosphere system, and these are most effective in mixed swards with a legume proportion of 30–50%. The resulting benefits include reduced dependence on fossil energy and industrial N-fertilizer, lower quantities of harmful emissions to the environment (greenhouse gases and nitrate), lower production costs, higher productivity and increased protein self-sufficiency. Some legume species offer opportunities for improving animal health with less medication, due to the presence of bioactive secondary metabolites. In addition, legumes may offer an adaptation option to rising atmospheric CO2 concentrations and climate change. Legumes generate these benefits at the level of the managed land-area unit and also at the level of the final product unit. However, legumes suffer from some limitations, and suggestions are made for future research to exploit more fully the opportunities that legumes can offer. In conclusion, the development of legume-based grassland–livestock systems undoubtedly constitutes one of the pillars for more sustainable and competitive ruminant production systems, and it can be expected that forage legumes will become more important in the future.