Data-driven fuzzy rule generation and its application for student academic performance evaluation

K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25(3):305-319, 2006.

FLT3-driven redox signalling in acute myeloid leukaemia

Acute myeloid leukaemia refers to cancer of the blood and bone marrow characterised by the rapid expansion of immature blasts of the myeloid lineage. The aberrant proliferation of these blasts interferes with normal haematopoiesis, resulting in symptoms such as anaemia, poor coagulation and infections. The molecular mechanisms underpinning acute myeloid leukaemia are multi-faceted and complex, with a range of diverse genetic and cytogenetic abnormalities giving rise to the acute myeloid leukaemia phenotype. Amongst the most common causative factors are mutations of the FLT3 gene, which codes for a growth factor receptor tyrosine kinase required by developing haematopoietic cells. Disruptions to this gene can result in constitutively active FLT3, driving the de-regulated proliferation of undifferentiated precursor blasts. FLT3-targeted drugs provide the opportunity to inhibit this oncogenic receptor, but over time can give rise to resistance within the blast population. The identification of targetable components of the FLT3 signalling pathway may allow for combination therapies to be used to impede the emergence of resistance. However, the intracellular signal transduction pathway of FLT3 is relatively obscure. The objective of this study is to further elucidate this pathway, with particular focus on the redox signalling element which is thought to be involved. Signalling via reactive oxygen species is becoming increasingly recognised as a crucial aspect of physiological and pathological processes within the cell. The first part of this study examined the effects of NADPH oxidase-derived reactive oxygen species on the tyrosine phosphorylation levels of acute myeloid leukaemia cell lines. Using two-dimensional phosphotyrosine immunoblotting, a range of proteins were identified as undergoing tyrosine phosphorylation in response to NADPH oxidase activity. Ezrin, a cytoskeletal regulatory protein and substrate of Src kinase, was selected for further study. The next part of this study established that NADPH oxidase is subject to regulation by FLT3. Both wild type and oncogenic FLT3 signalling were shown to affect the expression of a key NADPH oxidase subunit, p22phox, and FLT3 was also demonstrated to drive intracellular reactive oxygen species production. The NADPH oxidase target protein, Ezrin, undergoes phosphorylation on two tyrosine residues downstream of FLT3 signalling, an effect which was shown to be p22phox-dependent and which was attributed to the redox regulation of Src. The cytoskeletal associations of Ezrin and its established role in metastasis prompted the investigation of the effects of FLT3 and NADPH oxidase activity on the migration of acute myeloid leukaemia cell lines. It was found that inhibition of either FLT3 or NADPH oxidase negatively impacted on the motility of acute myeloid leukaemia cells. The final part of this study focused on the relationship between FLT3 signalling and phosphatase activity. It was determined, using phosphatase expression profiling and real-time PCR, that several phosphatases are subject to regulation at the levels of transcription and post-translational modification downstream of oncogenic FLT3 activity. In summary, this study demonstrates that FLT3 signal transduction utilises a NADPH oxidase-dependent redox element, which affects Src kinase, and modulates leukaemic cell migration through Ezrin. Furthermore, the expression and activity of several phosphatases is tightly linked to FLT3 signalling. This work reveals novel components of the FLT3 signalling cascade and indicates a range of potential therapeutic targets.

Systematic delay-driven power optimisation and power-driven delay optimisation of combinational circuits

With the proliferation of mobile wireless communication and embedded systems, the energy efficiency becomes a major design constraint. The dissipated energy is often referred as the product of power dissipation and the input-output delay. Most of electronic design automation techniques focus on optimising only one of these parameters either power or delay. Industry standard design flows integrate systematic methods of optimising either area or timing while for power consumption optimisation one often employs heuristics which are characteristic to a specific design. In this work we answer three questions in our quest to provide a systematic approach to joint power and delay Optimisation. The first question of our research is: How to build a design flow which incorporates academic and industry standard design flows for power optimisation? To address this question, we use a reference design flow provided by Synopsys and integrate in this flow academic tools and methodologies. The proposed design flow is used as a platform for analysing some novel algorithms and methodologies for optimisation in the context of digital circuits. The second question we answer is: Is possible to apply a systematic approach for power optimisation in the context of combinational digital circuits? The starting point is a selection of a suitable data structure which can easily incorporate information about delay, power, area and which then allows optimisation algorithms to be applied. In particular we address the implications of a systematic power optimisation methodologies and the potential degradation of other (often conflicting) parameters such as area or the delay of implementation. Finally, the third question which this thesis attempts to answer is: Is there a systematic approach for multi-objective optimisation of delay and power? A delay-driven power and power-driven delay optimisation is proposed in order to have balanced delay and power values. This implies that each power optimisation step is not only constrained by the decrease in power but also the increase in delay. Similarly, each delay optimisation step is not only governed with the decrease in delay but also the increase in power. The goal is to obtain multi-objective optimisation of digital circuits where the two conflicting objectives are power and delay. The logic synthesis and optimisation methodology is based on AND-Inverter Graphs (AIGs) which represent the functionality of the circuit. The switching activities and arrival times of circuit nodes are annotated onto an AND-Inverter Graph under the zero and a non-zero-delay model. We introduce then several reordering rules which are applied on the AIG nodes to minimise switching power or longest path delay of the circuit at the pre-technology mapping level. The academic Electronic Design Automation (EDA) tool ABC is used for the manipulation of AND-Inverter Graphs. We have implemented various combinatorial optimisation algorithms often used in Electronic Design Automation such as Simulated Annealing and Uniform Cost Search Algorithm. Simulated Annealing (SMA) is a probabilistic meta heuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. We used SMA to probabilistically decide between moving from one optimised solution to another such that the dynamic power is optimised under given delay constraints and the delay is optimised under given power constraints. A good approximation to the global optimum solution of energy constraint is obtained. Uniform Cost Search (UCS) is a tree search algorithm used for traversing or searching a weighted tree, tree structure, or graph. We have used Uniform Cost Search Algorithm to search within the AIG network, a specific AIG node order for the reordering rules application. After the reordering rules application, the AIG network is mapped to an AIG netlist using specific library cells. Our approach combines network re-structuring, AIG nodes reordering, dynamic power and longest path delay estimation and optimisation and finally technology mapping to an AIG netlist. A set of MCNC Benchmark circuits and large combinational circuits up to 100,000 gates have been used to validate our methodology. Comparisons for power and delay optimisation are made with the best synthesis scripts used in ABC. Reduction of 23% in power and 15% in delay with minimal overhead is achieved, compared to the best known ABC results. Also, our approach is also implemented on a number of processors with combinational and sequential components and significant savings are achieved.

A corpus-driven error analysis of the oral and written production of Leaving Certificate Spanish learners

The Leaving Certificate (LC) is the national, standardised state examination in Ireland necessary for entry to third level education – this presents a massive, raw corpus of data with the potential to yield invaluable insight into the phenomena of learner interlanguage. With samples of official LC Spanish examination data, this project has compiled a digitised corpus of learner Spanish comprised of the written and oral production of 100 candidates. This corpus was then analysed using a specific investigative corpus technique, Computer-aided Error Analysis (CEA, Dagneaux et al, 1998). CEA is a powerful apparatus in that it greatly facilitates the quantification and analysis of a large learner corpus in digital format. The corpus was both compiled and analysed with the use of UAM Corpus Tool (O’Donnell 2013). This Tool allows for the recording of candidate-specific variables such as grade, examination level, task type and gender, therefore allowing for critical analysis of the corpus as one unit, as separate written and oral sub corpora and also of performance per task, level and gender. This is an interdisciplinary work combining aspects of Applied Linguistics, Learner Corpus Research and Foreign Language (FL) Learning. Beginning with a review of the context of FL learning in Ireland and Europe, I go on to discuss the disciplinary context and theoretical framework for this work and outline the methodology applied. I then perform detailed quantitative and qualitative analyses before going on to combine all research findings outlining principal conclusions. This investigation does not make a priori assumptions about the data set, the LC Spanish examination, the context of FLs or of any aspect of learner competence. It undertakes to provide the linguistic research community and the domain of Spanish language learning and pedagogy in Ireland with an empirical, descriptive profile of real learner performance, characterising learner difficulty.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Assessing the potential of autonomous submarine gliders for ecosystem monitoring across multiple trophic levels (plankton to cetaceans) in shallow shelf seas

A combination of scientific, economic, technological and policy drivers is behind a recent upsurge in the use of marine autonomous systems (and accompanying miniaturized sensors) for environmental mapping and monitoring. Increased spatial–temporal resolution and coverage of data, at reduced cost, is particularly vital for effective spatial management of highly dynamic and heterogeneous shelf environments. This proof-of-concept study involves integration of a novel combination of sensors onto buoyancy-driven submarine gliders, in order to assess their suitability for ecosystem monitoring in shelf waters at a variety of trophic levels. Two shallow-water Slocum gliders were equipped with CTD and fluorometer to measure physical properties and chlorophyll, respectively. One glider was also equipped with a single-frequency echosounder to collect information on zooplankton and fish distribution. The other glider carried a Passive Acoustic Monitoring system to detect and record cetacean vocalizations, and a passive sampler to detect chemical contaminants in the water column. The two gliders were deployed together off southwest UK in autumn 2013, and targeted a known tidal-mixing front west of the Isles of Scilly. The gliders’ mission took about 40 days, with each glider travelling distances of >1000 km and undertaking >2500 dives to depths of up to 100 m. Controlling glider flight and alignment of the two glider trajectories proved to be particularly challenging due to strong tidal flows. However, the gliders continued to collect data in poor weather when an accompanying research vessel was unable to operate. In addition, all glider sensors generated useful data, with particularly interesting initial results relating to subsurface chlorophyll maxima and numerous fish/cetacean detections within the water column. The broader implications of this study for marine ecosystem monitoring with submarine gliders are discussed.

Assessing the potential of autonomous submarine gliders for ecosystem monitoring across multiple trophic levels (plankton to cetaceans) in shallow shelf seas

A combination of scientific, economic, technological and policy drivers is behind a recent upsurge in the use of marine autonomous systems (and accompanying miniaturized sensors) for environmental mapping and monitoring. Increased spatial–temporal resolution and coverage of data, at reduced cost, is particularly vital for effective spatial management of highly dynamic and heterogeneous shelf environments. This proof-of-concept study involves integration of a novel combination of sensors onto buoyancy-driven submarine gliders, in order to assess their suitability for ecosystem monitoring in shelf waters at a variety of trophic levels. Two shallow-water Slocum gliders were equipped with CTD and fluorometer to measure physical properties and chlorophyll, respectively. One glider was also equipped with a single-frequency echosounder to collect information on zooplankton and fish distribution. The other glider carried a Passive Acoustic Monitoring system to detect and record cetacean vocalizations, and a passive sampler to detect chemical contaminants in the water column. The two gliders were deployed together off southwest UK in autumn 2013, and targeted a known tidal-mixing front west of the Isles of Scilly. The gliders’ mission took about 40 days, with each glider travelling distances of >1000 km and undertaking >2500 dives to depths of up to 100 m. Controlling glider flight and alignment of the two glider trajectories proved to be particularly challenging due to strong tidal flows. However, the gliders continued to collect data in poor weather when an accompanying research vessel was unable to operate. In addition, all glider sensors generated useful data, with particularly interesting initial results relating to subsurface chlorophyll maxima and numerous fish/cetacean detections within the water column. The broader implications of this study for marine ecosystem monitoring with submarine gliders are discussed.

Monocyte-derived dendritic cells: a potential target for therapy in multiple sclerosis (MS)

Monocytes can differentiate into dendritic cells (DC), cells with a pivotal role in both protective immunity and tolerance. Defects in the maturation or function of DC may be important in the development of autoimmune disease. We sought to establish if there were differences in the cytokine (granulocyte-macrophage colony-stimulating factor and IL-4)-driven maturation of monocytes to DC in patients with MS and whether drugs used to treat MS affected this process in vitro. We have demonstrated that there is no defect in the ability of magnetic activated cell sorting (MACS)-purified monocytes from patients with MS to differentiate to DC, but equally they show no tendency to acquire a DC phenotype without exogenous cytokines. Interferon-beta1a prevents the acquisition of a full DC phenotype as determined by light and electron microscopy and by flow cytometry. Methylprednisolone not only prevents the development of monocyte-derived DC but totally redirects monocyte differentiation towards a macrophage phenotype. Evidence is evolving for a role for DC in central nervous system immunity, either within the brain or in cervical lymph nodes. The demonstrated effect of both drugs on monocyte differentiation may represent an important site for immune therapy in MS.

Laser-driven proton scaling laws and new paths towards energy increase

The past few years have seen remarkable progress in the development of laser-based particle accelerators. The ability to produce ultrabright beams of multi-megaelectronvolt protons routinely has many potential uses from engineering to medicine, but for this potential to be realized substantial improvements in the performances of these devices must be made. Here we show that in the laser-driven accelerator that has been demonstrated experimentally to produce the highest energy protons, scaling laws derived from fluid models and supported by numerical simulations can be used to accurately describe the acceleration of proton beams for a large range of laser and target parameters. This enables us to evaluate the laser parameters needed to produce high-energy and high-quality proton beams of interest for radiography of dense objects or proton therapy of deep-seated tumours.

One-dimensional particle simulation of the filamentation instability: Electrostatic field driven by the magnetic pressure gradient force

Two counterpropagating cool and equally dense electron beams are modeled with particle-in-cell simulations. The electron beam filamentation instability is examined in one spatial dimension, which is an approximation for a quasiplanar filament boundary. It is confirmed that the force on the electrons imposed by the electrostatic field, which develops during the nonlinear stage of the instability, oscillates around a mean value that equals the magnetic pressure gradient force. The forces acting on the electrons due to the electrostatic and the magnetic field have a similar strength. The electrostatic field reduces the confining force close to the stable equilibrium of each filament and increases it farther away, limiting the peak density. The confining time-averaged total potential permits an overlap of current filaments with an opposite flow direction.

Protein kinase B/Akt activity is involved in renal TGF-beta 1-driven epithelial-mesenchymal transition in vitro and in vivo

The molecular pathogenesis of diabetic nephropathy (DN), the leading cause of end-stage renal disease worldwide, is complex and not fully understood. Transforming growth factor-beta (TGF-beta1) plays a critical role in many fibrotic disorders, including DN. In this study, we report protein kinase B (PKB/Akt) activation as a downstream event contributing to the pathophysiology of DN. We investigated the potential of PKB/Akt to mediate the profibrotic bioactions of TGF-beta1 in kidney. Treatment of normal rat kidney epithelial cells (NRK52E) with TGF-beta1 resulted in activation of phosphatidylinositol 3-kinase (PI3K) and PKB/Akt as evidenced by increased Ser473 phosphorylation and GSK-3beta phosphorylation. TGF-beta1 also stimulated increased Smad3 phosphorylation in these cells, a response that was insensitive to inhibition of PI3K or PKB/Akt. NRK52E cells displayed a loss of zona occludins 1 and E-cadherin and a gain in vimentin and alpha-smooth muscle actin expression, consistent with the fibrotic actions of TGF-beta1. These effects were blocked with inhibitors of PI3K and PKB/Akt. Furthermore, overexpression of PTEN, the lipid phosphatase regulator of PKB/Akt activation, inhibited TGF-beta1-induced PKB/Akt activation. Interestingly, in the Goto-Kakizaki rat model of type 2 diabetes, we also detected increased phosphorylation of PKB/Akt and its downstream target, GSK-3beta, in the tubules, relative to that in control Wistar rats. Elevated Smad3 phosphorylation was also detected in kidney extracts from Goto-Kakizaki rats with chronic diabetes. Together, these data suggest that TGF-beta1-mediated PKB/Akt activation may be important in renal fibrosis during diabetic nephropathy.

Observation and characterization of laser-driven Phase Space Electron Holes

The direct observation and full characterization of a phase space electron hole (EH) generated during laser-matter interaction is presented. This structure, propagating in a tenuous, nonmagnetized plasma, has been detected via proton radiography during the irradiation with a ns laser pulse (I?2 ˜ 1014 W/cm2) of a gold hohlraum. This technique has allowed the simultaneous detection of propagation velocity, potential, and electron density spatial profile across the EH with fine spatial and temporal resolution allowing a detailed comparison with theoretical and numerical models.

Freestanding polymer-metal oxide nanocomposite films for light-driven oxygen scavenging

Freestanding films containing nanocrystalline TiO2 and a suitable electron donor embedded in a cellulose matrix deoxygenate a closed environment (see Figure) upon UV illumination as a result of the photocatalytic properties of TiO2. This opens up the potential use of semiconductor photocatalysis in active packaging to achieve light-driven deoxygenation of closed environments.

Laser-Driven Fast Electron Collimation in Targets with Resistivity Boundary

We demonstrate experimentally that the relativistic electron flow in a dense plasma can be efficiently confined and guided in targets exhibiting a high-resistivity-core-low-resistivity-cladding structure analogous to optical waveguides. The relativistic electron beam is shown to be confined to an area of the order of the core diameter (50 mu m), which has the potential to substantially enhance the coupling efficiency of electrons to the compressed fusion fuel in the Fast Ignitor fusion in full-scale fusion experiments.

Scaling of proton acceleration driven by petawatt-laser-plasma interactions

The possibility of using high-power lasers to generate high-quality beams of energetic ions is attracting large global interest. The prospect of using laser-accelerated protons in medicine attracts particular interest, as these schemes may lead to compact and relatively low-cost sources. Among the challenges remaining before these sources can be used in medicine is to increase the numbers and energies of the ions accelerated. Here, we extend the energy and intensity range over which proton scaling is experimentally investigated, up to 400 J and 6 x 10(20) W cm(-2) respectively, and find a slower proton scaling than previously predicted. With the aid of plasma-expansion simulation tools, our results suggest the importance of time-dependent and multidimensional effects in predicting the maximum proton energy in this ultrahigh-intensity regime. The implications of our new understanding of proton scaling for potential medical applications are discussed.