Development of physical and mathematical models for the Porous Ceramic Tube Plant Nutrification System (PCTPNS)

A physical model of the Porous Ceramic Tube Plant Nutrification System (PCTPNS) was developed through microscopic observations of the tube surface under various operational conditions. In addition, a mathematical model of this system was developed which incorporated the effects of the applied suction pressure, surface tension, and gravitational forces as well as the porosity and physical dimensions of the tubes. The flow of liquid through the PCTPNS was thus characterized for non-biological situations. One of the key factors in the verification of these models is the accurate and rapid measurement of the 'wetness' or holding capacity of the ceramic tubes. This study evaluated a thermistor based moisture sensor device and recommendations for future research on alternative sensing devices are proposed. In addition, extensions of the physical and mathematical models to include the effects of plant physiology and growth are also discussed for future research

NOTATIONAL ANALYSIS OF INTERNATIONAL BADMINTON COMPETITIONS

Notational analysis of competitions is a widely used method for observing the playing patterns of many sports. An experiment was carried out to determine the playing tactics adopted by athletes in international badminton competitions. A total of 10 major competitions were recorded and the data was processed using a generic notational analysis software. The types of techniques used, the success and failure rates of the various shots were computed and compared using statistical methods of analysis. The results showed that the top three most popular shots used were Lift, Net and Clear. It was also found' that there were significant differences among the percentage of shots used in the three court areas as well, as the type of shots used by males and females

Fuzzy Students’ Knowledge Modelling System through Revised Bloom’s Taxonomy

The conveniences of web-based educational systems have attracted a large heterogeneous group of learners with various knowledge levels, learning goals, and others learning characteristics, to study online. To enhance the effectiveness of the web-based educational system in delivery knowledge, a system should be capable to identify the learners’ learning characteristics, and adapt the instructional process accordingly. Hence, this paper presented a students’ knowledge modelling system that is capable of infer and updating the students’ knowledge level in accordance to the cognitive processes dimension in the Revised Bloom’s Taxonomy. However, the students’ knowledge modeling process consists of tasks and factors that are vague and unmeasured, thus Fuzzy Logic is integrated into the students’ knowledge modeling system to deal with such uncertainties. The proposed fuzzy students’ knowledge modeling system uses fuzzy sets to represent students’ knowledge level and other influencing factors, and uses Mamdani type inference technique to determine and update knowledge levels

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

From Heisenberg matrix mechanics to EBK quantization: theory and first applications

Despite the seminal connection between classical multiply-periodic motion and Heisenberg matrix mechanics and the massive amount of work done on the associated problem of semiclassical (EBK) quantization of bound states, we show that there are, nevertheless, a number of previously unexploited aspects of this relationship that bear on the quantum-classical correspondence. In particular, we emphasize a quantum variational principle that implies the classical variational principle for invariant tori. We also expose the more indirect connection between commutation relations and quantization of action variables. With the help of several standard models with one or two degrees of freedom, we then illustrate how the methods of Heisenberg matrix mechanics described in this paper may be used to obtain quantum solutions with a modest increase in effort compared to semiclassical calculations. We also describe and apply a method for obtaining leading quantum corrections to EBK results. Finally, we suggest several new or modified applications of EBK quantization.Comment: 37 pages including 3 poscript figures, submitted to Phys. Rev.

Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework to model such dependencies. We develop the LDDP in the context of mixture modeling, and develop a mean field variational inference algorithm for this mixture model. The effectiveness of the proposed modeling framework is shown on an image segmentation task

Targeting 1.5 degrees with the global carbon footprint of the Australian Capital Territory

In 2019 the Australian Capital Territory (ACT) government stated an ambition to prioritise reduction of Scope 3 greenhouse gas emissions, the size of which had not been fully quantified previously. This study calculated the total carbon footprint of the ACT in 2018, including Scope 1, 2 and 3 emissions and modelled scenarios to reduce all emissions in line with a 1.5 °C target approach. This is the first time a multi-scale analysis of local, sub-national and international supply chains has been undertaken for a city, using a nested and trade-adjusted global multi-region input-output model. This allowed for the quantification of global origins and destinations of emissions, which showed that the 2018 carbon footprint for the ACT was approximately 34.7 t CO2-eq/cap, with 83% attributed to Scope 3. Main contributions came from transport, electricity, manufacturing and public administration and safety, with emissions generated primarily in Australian States and Territories. Modelling in accordance with a 1.5 °C warming scenario showed a plausible reduction to 5.2 t CO2-eq/cap by 2045 (excluding offsets or carbon dioxide removal technologies), with remaining emissions predominantly embodied in international supply chains. This study demonstrates the radical changes required by a wealthy Australian city to achieve 1.5 °C compliance and identifies sectors and supply chains for prioritising policies to best achieve this outcome