The role of machine learning in personalised instructional sequencing for language learning

The origins of personalised instructional sequencing can be dated back to the times of the Ancient Greeks to the times of Alexander The Great's tutor, Aristotle. However, over the centuries the demand for education and growth of students has been disproportionately greater than the number of teachers in training. Therefore, there has been a longstanding interest in finding a way to scale education without negatively affecting learning outcomes. This interest was fuelled further with the advent of computers and artificial intelligence, where a plethora of systems and models were built to bring technology driven personalised instructional sequencing to the world. Unfortunately, results were far from groundbreaking and many challenges still remain. In my thesis, I investigate three aspects of personalised instructional sequencing: the personalised instructional sequencing mechanism, the student knowledge representation, and human forgetting. While I do not cover the entirety of personalised instructional sequencing, I cover what I consider the foundational components. I link psychological theory to model selection and design in each of my systems and present experiments to illustrate their impact. I show how reinforcement learning can be used for vocabulary learning. I also present a model that uses neural collaborative filtering to learn student knowledge representations. Lastly, I present a state-of-the-art model to predict the probability of vocabulary word recall for students learning English as a second language. The system's novelty lies in the use of word complexity to adapt the forgetting curve as well as its incorporation of psychological theory to select an appropriate model

Practical Volatility Modeling for Financial Market Risk Management

Being able to choose most suitable volatility model and distribution specification is a more demanding task. This paper introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. We include an illustrative simulation and an empirical application to compare a set of distributions, including symmetric/asymmetric distribution, and a family of GARCH volatility models. We highlight the use of our approach to a daily index, the Kuala Lumpur Composite index (KLCI). Our results shows that the choice of the conditional distribution appear to be a more dominant factor in determining the adequacy of density forecasts than the choice of volatility model. Furthermore, the results support the Skewed for KLCI return distribution.Density forecast; Conditional distribution; Forecast accuracy; KLIC; GARCH models

Treating cancer with heat: Hyperthermia as promising strategy to enhance apoptosis

The fundamental idea and the effects of heat on cancer cells are well known. However, the results obtained in therapy by hyperthermia (HT) alone have been only partially satisfactory. Treatment at temperatures between .40 and 44 degrees C is cytotoxic for cells in an environment with a low oxygen partial pressure and low pH, conditions that are found specifically within tumour tissue, due to insufficient blood perfusion. Under such conditions radiotherapy is less effective, and systemically applied cytotoxic agents will reach such areas in lower concentrations than in well-perfused areas. Therefore, clinically, it is preferred to use hyperthermia in combination with radiation therapy and chemotherapy. Hyperthermia can be applied by several methods: local hyperthermia by external or internal energy sources; regional hyperthermia by perfusion of organs or limbs, or by irrigation of body cavities; and whole-body hyperthermia. Number of studies have reported the combination of thermo-radiotherapy. Consequently, much attention has been focussed on identifying agents among the conventional chemotherapeutic substances that can sensitise tumour cells to hyperthermia-induced damage with minimal effects on normal cells. In this review, we overviewed important mechanisms of hyperthermia-induced apoptosis and the substances which can act as heat sensitisers in cancer therapy

Groundwater pollution risk mapping using modified DRASTIC model in parts of hail region of Saudi Arabia

© 2018 Korean Society of Environmental Engineers. The present study deals with the management of groundwater resources of an important agriculture track of north-western part of Saudi Arabia. Due to strategic importance of the area efforts have been made to estimate aquifer proneness to attenuate contamination. This includes determining hydrodynamic behavior of the groundwater system. The important parameters of any vulnerability model are geological formations in the region, depth to water levels, soil, rainfall, topography, vadose zone, the drainage network and hydraulic conductivity, land use, hydrochemical data, water discharge, etc. All these parameters have greater control and helps determining response of groundwater system to a possible contaminant threat. A widely used DRASTIC model helps integrate these data layers to estimate vulnerability indices using GIS environment. DRASTIC parameters were assigned appropriate ratings depending upon existing data range and a constant weight factor. Further, land-use pattern map of study area was integrated with vulnerability map to produce pollution risk map. A comparison of DRASTIC model was done with GOD and AVI vulnerability models. Model validation was done with NO3, SO4 and Cl concentrations. These maps help to assess the zones of potential risk of contamination to the groundwater resources

Machine Learning Based Approach for Indoor Localization Using Ultra-Wide Bandwidth (UWB) System for Industrial Internet of Things (IIoT)

With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization, its limitation for indoor environments is well known. UltraWideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and NonLine-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environmen

Investigation of driving cycles as tools to assess travel demand management in Edinburgh and Abu Dhabi

Traffic congestion today is a major problem in almost all of the metropolitan areas of the world. An increasing level of congestion results in negative impacts on the urban environment. These include environmental pollution, energy problems and traffic accidents. The analysis of these problems and the predictions of the impacts of any transport policies that could be devised to deal with them are very critical to their success. Traffic problems are almost the same in most modern cities either in developed countries or less economically developed countries.The driving cycle for a vehicle is the representation of a speed–time sequenced profile, which is developed for a specific area or city. It is an important requirement in the evaluation of the driver’s behaviour and the performance of vehicles for a number of applications, mainly in the area of environmental studies. For example, fuel consumption and emissions’ predictions need information input on the characteristics of driving patterns of traffic. The applications of driving cycle analysis can be extended however, to many more other areas. The motivation for this research is to investigate the detailed impacts of travel demand management (TDM) measures, thatare already in application. This is to improve the network performance, using driving cycle analysis. It is important to explicitly assess these measures using a micro-level detailed approach in order to comprehend overall results in terms of emissions and network performance. These understandings will benefit government agencies and policy makers in their planning and appraisals. It will also benefit public transport providers to improve their service in attracting and retaining their customers.The developments of the real world driving cycles in Edinburgh and Abu Dhabi have been presented in this research. The analysis of real world data, which has been obtained from monitoring traffic conditions in both cities using the GPS tracking of traffic, is presented. This data was collected from trips which have been carried out on a number of traffic corridors in both cities. The assessment of various parameters of traffic (i.e. speed, time percentage spent on acceleration, deceleration, idling, cruising and cycle duration) and their statistical validity, produced a real world driving cycle for the buses as well as the private cars. Two TDM measures have been considered; bus lanes and traffic calming measures. At each corridor, a handheld GPS device was used to record speed, acceleration, deceleration and distances driven. This data enabled the analysis of driving cycles for the buses and for the private cars. The driving cycle analysis and investigations have further been investigated using regression analysis techniques. The results suggest that the approach shows potential but further research is needed with more data available. The results suggest that the driving cycle analysis approach would be very useful to have a better understanding of driving behaviour and also the detailed impacts of the transport policies on traffic. In terms of bus lanes and traffic calming measures, the results show some positive impacts of these policies, while there are evidences of some negative impacts as well. These findings would be very valuable for the policy decision makers. It is recommended from this research that the driving cycle analysis could be utilised effectively in the assessment of TDM measures. Further investigations and analysis of driving cycle is urgently recommended in a number of research directions. Combined GIS and GPS data could also enhance the development in this research. Document type: Thesi

ROLE OF EFFECTIVE PROJECT MANAGEMENT IN REDUCING DRUG DEVELOPMENT COST

ABSTRACTPharmaceuticals are passing through the difficult phase due to increasing numbers of patents expiry along with increasing cost of drug development.Protocol design, regulatory cycle time, site selection, patient enrollment and monitoring are some of the cost contributing elements for late phaseclinical trials. This paper applies the principles of project management and suggests means to reduce the cost of late phase drug development. It alsothrows light on the critical role that a project manager can play in overall drug development process.Keywords: Cost of drug development, Effective project management, Reduction in cost of drug development

Warehouse Location Decision in Pakistan:A Real Case Study

The manufacturing industry in Pakistan ispassing through a critical phase of its history. In the changing market placeconsumer are increasingly vigilant and demanding better quality, morecompetitive prices and shorter lead times. Maintaining cost effectivemanufacturing along with it distribution to the different customers across thecountry is becomes challenging day by day. In this paper, we propose anadditional new warehouse location in Pakistan using transportation cost as adecision factor. Initially the proposed warehouse will be run by the third partywarehouse service provider on temporary basis, so that it lower down theinventory level of Lahore warehouse from 0.4 million liters to 0.2 millionliters, saves approximately 1.5 Million Rs. / year with improved customerservice

Data for Thinking Healthy Programme Pakistan trial (THPP-Pakistan)

An anonymised dataset of 570 women (one row per woman) who gave informed consent to participate in a cluster randomised, parallel, superiority, controlled trial in Rawalpindi, Pakistan in 2014-2017. The intervention was an adapted version of the Thinking Healthy Programme, delivered by peers; the control was enhanced usual care. Further details are available in the published protocol and paper. The dataset contains records of variables on stratified randomisation and clusters, socio-demographic information, depression and related outcomes, and therapy adherence. Visits occurred at baseline, and 3 and 6 months post-natal

Frequency of sensorineural hearing loss in chronic suppurative otitis media

Abstract Chronic suppurative otitis media (CSOM) is defined as chronic otorrhea (i.e., lasting \u3e 6-12 weeks) through a perforated tympanic membrane. It is generally associated with some degree of conductive hearing loss. However, recurrent ear infections due to perforated eardrum result in absorption of toxins and macromolecules into the cochlea leading to sensorineural hearing loss (SNHL). We planned to determine the frequency of sensorineural hearing loss in chronic suppurative otitis media. A descriptive cross-sectional study was conducted at Aga Kgan University Hospital, Karachi, from October 2013 to March 2014. Average threshold of speech frequencies was calculated via pure tone audiogram for both diseased and normal contralateral ear. A mean of \u3e25db in diseased ear was labelled as positive case for SNHL. SNHL was reported in 64(52%) patients and the frequency was found to increase with increasing duration. Patients with CSOM should be counselled regarding the risk of developing SNHL if left untreated