Predictive Capacity of Meteorological Data - Will it rain tomorrow

With the availability of high precision digital sensors and cheap storage medium, it is not uncommon to find large amounts of data collected on almost all measurable attributes, both in nature and man-made habitats. Weather in particular has been an area of keen interest for researchers to develop more accurate and reliable prediction models. This paper presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict the day of the week given the weather data for that particular day i.e. temperature, wind, rain etc., and test their reliability across four cities in Australia {Brisbane, Adelaide, Perth, Hobart}. The results provide a comparison of accuracy of these machine learning techniques and their reliability to predict the day of the week by analysing the weather data. We then apply the models to predict weather conditions based on the available data.Comment: 7 pages, 2 Result Set

Lexical Normalisation of Twitter Data

Twitter with over 500 million users globally, generates over 100,000 tweets per minute . The 140 character limit per tweet, perhaps unintentionally, encourages users to use shorthand notations and to strip spellings to their bare minimum "syllables" or elisions e.g. "srsly". The analysis of twitter messages which typically contain misspellings, elisions, and grammatical errors, poses a challenge to established Natural Language Processing (NLP) tools which are generally designed with the assumption that the data conforms to the basic grammatical structure commonly used in English language. In order to make sense of Twitter messages it is necessary to first transform them into a canonical form, consistent with the dictionary or grammar. This process, performed at the level of individual tokens ("words"), is called lexical normalisation. This paper investigates various techniques for lexical normalisation of Twitter data and presents the findings as the techniques are applied to process raw data from Twitter.Comment: Removed typo

DroidNet: An Android Application Security Framework through Crowdsourcing

In the current Android architecture, users have to decide whether an app is safe to use or not by themselves. Savvy users can make correct decisions to avoid unnecessary privacy breaches, however most users are not capable or do not care to make impactful decisions. To assist those users, we propose DroidNet, an Android permission control framework based on crowdsourcing. In this framework, DroidNet runs new apps and their permissions initially, and then collects data based on each individual user’s settings in regards to each permission unique to every installed app. After collecting each user’s data, DroidNet provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we utilize an expertise ranking algorithm using a transitional Bayesian inference model. The recommendation, respective to each application permission, is based on the aggregated expert responses and our generated confidence level, which are collectively stored and sorted in our DroidNet database. The overall culmination of the model resulted in the creation of a real-time Android application which utilizes our Bayesian inference model and aggregate data from each individual user, all of which is connected to our DroidNet database.https://scholarscompass.vcu.edu/capstone/1173/thumbnail.jp

Combined wet milling crystallisation methods for particle engineering

Recent advances in pharmaceutical manufacturing for consistent supply of medicines with the required physical properties has emphasised the need for robust crystallisation processes which is a critical separation and purification technique. Mechanical milling is employed post crystallisation as an offline unit operation usually in a separate dry solids processing facility for adjusting the particle size and shape attributes of crystalline products for downstream processing. An emerging and increasingly applied technology is high shear wet milling in crystalline slurries for inline size and shape modification during particle formation. This potentially avoids the need for multiple crystallisation trials and offline milling saving time, costs and powder handling. Similarly, sonication is a powerful particle engineering tool through immersing ultrasound probes directly in solution. This PhD project is focused on the investigation and process integration of wet milling and indirect ultrasound for enhancing crystallisation processes and engineering particle attributes. The experimental study combined a cooling and isothermal crystallisation (seeded & unseeded) process with wet milling and indirect sonication. Results from the combined method provides the ability to modify and selectively achieve a range of product outcomes including particle sizes with tight spans, equant shapes and low surface energies as well as increased nucleation rates.;High shear from wet milling is also implemented as a seeding protocol configured to a mixed-suspension mixed-product removal continuous crystalliser which proved to be an adequate seed generation strategy.Deploying accurate quantitative analysis of size and shape attributes for solid particles is further explored. A multi-sensor measurement approach was employed using inline sensors, computational tools and offline techniques. The performance of these tools were vigorously tested for strengths and limitations which was proven to be beneficial for characterising the breakage of crystalline materials as well as overall process understanding and opportunities for process control.Recent advances in pharmaceutical manufacturing for consistent supply of medicines with the required physical properties has emphasised the need for robust crystallisation processes which is a critical separation and purification technique. Mechanical milling is employed post crystallisation as an offline unit operation usually in a separate dry solids processing facility for adjusting the particle size and shape attributes of crystalline products for downstream processing. An emerging and increasingly applied technology is high shear wet milling in crystalline slurries for inline size and shape modification during particle formation. This potentially avoids the need for multiple crystallisation trials and offline milling saving time, costs and powder handling. Similarly, sonication is a powerful particle engineering tool through immersing ultrasound probes directly in solution. This PhD project is focused on the investigation and process integration of wet milling and indirect ultrasound for enhancing crystallisation processes and engineering particle attributes. The experimental study combined a cooling and isothermal crystallisation (seeded & unseeded) process with wet milling and indirect sonication. Results from the combined method provides the ability to modify and selectively achieve a range of product outcomes including particle sizes with tight spans, equant shapes and low surface energies as well as increased nucleation rates.;High shear from wet milling is also implemented as a seeding protocol configured to a mixed-suspension mixed-product removal continuous crystalliser which proved to be an adequate seed generation strategy.Deploying accurate quantitative analysis of size and shape attributes for solid particles is further explored. A multi-sensor measurement approach was employed using inline sensors, computational tools and offline techniques. The performance of these tools were vigorously tested for strengths and limitations which was proven to be beneficial for characterising the breakage of crystalline materials as well as overall process understanding and opportunities for process control

Counterfactual conditional analysis using the Centipede Game

The Backward Induction strategy for the Centipede Game leads us to a counterfactual reasoning paradox, The Centipede Game paradox. The counterfactual reasoning proving the backward induction strategy for the game appears to rely on the players in the game not choosing that very same backward induction strategy. The paradox is a general paradox that applies to backward induction reasoning in sequential, perfect information games. Therefore, the paradox is not only problematic for the Centipede Game, but it also affects counterfactual reasoning solutions in games similar to the Centipede Game. The Centipede Game is a prime illustration of this paradox in counterfactual reasoning. As a result, this paper will use a material versus subjunctive/counterfactual conditional analysis to provide a theoretical resolution to the Centipede Game, with the hope that a similar solution can be applied to other areas where this paradox may appear. The solution involves delineating between the epistemic systems of the players and the game theorists

Numerical Modeling of Synthetic Vortical Disturbance Interactions Using OpenFOAM

Nowadays, the number of Advance Air Mobility (AAM) Electic Vertical Take-off and Landing (eVTOL) concepts is rapidly increasing due to their capability of vertical take-off and landing at vertiports located on rooftops of tall urban buildings, which does not require using the ground space in a condensed urban environment. Such flight operations, however, will be greatly affected by the vertiport’s highly unsteady, turbulent flow environment. The goal of this study is to model the turbulent wind interaction with a building to understand the unsteady flow characteristics around vertiports and match the induced unsteady disturbance field to the canonical disturbance forms, which then could be used in the development of the reduced order models (ROMs) for multi-rotor unsteady aerodynamic and acoustics responses. This study employs an open-source Navier-Stokes OpenFOAM solver to model the turbulent wind flow around a building. Based on the previous studies, a momentum source generating an upstream synthetic disturbance field (such as a time-harmonic gust or turbulence with required characteristics) is employed. Validation test studies are conducted to examine and compare with previous results for the benchmark case of 2D time-harmonic gust interaction with NACA0012 airfoil, and then the case of the turbulent wind interaction with a building. Furthermore, the non-uniform mean flow profile characteristic of the urban atmospheric boundary layer (ABL) is implemented in the OpenFOAM simulations to examine and compare the effect of the non-uniform mean flow on the turbulent wind evolution and interaction with the rooftop vertiport

Impact of Brand Recall on Customer Purchase Intention

This study analyzes customers brand Recall and its elements including brand awareness, brand association, and brand recognition. The purpose of this research is to identify the impact of brand recall on customer purchase intention. In this research we also identify the relationship between the dependent and independent variables. This is the primary research and data has been collected through questionnaire and for analysis purpose SPSS software has been used. In this study samples of 400 respondents has been collected and tested the reliability of the model. The result of the study indicates that Brand recognition and association have a positive impact on the customers purchase intention. Customers mostly buy and prefer those products which they recognize and have some positive association or link with that brands

Responsive markets: structures supporting economic activity in postcolonial Mumbai

This project aims to envision a market that engages activity and brings in life from surrounding markets – connecting better into its environment. The project fits into a larger discussion on how architecture left behind by a colonial regime might be addressed formally and conceptually

Investigating the Compressive Strength Plateau of Geopolymer Cement under HPHT

The modern oil and gas industry excessively uses Ordinary Portland Cement (OPC) as their preferred cementing choice. However, the industry is quickly realizing that OPC’s mechanical properties fail to uphold its objectives in deeper wells with higher temperature and pressure. Due to its weak ceramic characteristics, Ordinary Portland cement’s mechanical performance is limited, especially in wells with high temperature and pressure. Comparatively, Geopolymeric materials can better tolerate these work conditions. The scope of study is mainly on designing Geopolymer cement compositions, preparing class G cement composition and testing in accordance to the American Petroleum Institute. The obtained results will be compared in terms of compressive strength with class G cement slurries. The study will comprise standard weight cement slurry