Quetiapine, an atypical antipsychotic drug, is effective in treating the behavioural and the psychological symptoms of dementia (BPSD). The objective of this study was to examine the effects of quetiapine on anxiety-like behaviour in the amyloid precursor protein (APP)/ presenilin 1 (PS1) double transgenic mouse model of Alzheimers disease (AD). The mice were treated with quetiapine (0, 2.5, or 5 mg/kg/day) orally in drinking water for 7 or 10 months starting from 2 months of age. Conditioned anxiety was measured using the elevated T-maze (ETM). To measure memory, the Y-maze and the Morris Water maze were employed. After behavioural testing, â-amyloid (Aâ) plaques in the hippocampus and cortex of transgenic mice were stained using Congo Red. Brain-derived neurotrophic factor (BDNF) in the basolateral amygdala (BLA) and the hippocampus of mice was examined using immunohistochemical methods. The statistics revealed an interaction between quetiapine and APP/PS1 double transgenic mice in the avoidance phase of the ETM. Quetiapine modulates anxiety-like behaviours in the ETM. The anxiety-like behaviours were associated with reductions in BDNF levels in the BLA and hippocampus of the transgenic mice. This was reversed by treatment with quetiapine. Furthermore, chronic administration of quetiapine attenuated the memory impairment and decreased the Aâ plaque load in the brain. This study demonstrates that quetiapine normalizes anxiety-like behaviour and up-regulates cerebral BDNF levels in the APP/PS1 mice, suggesting that quetiapine may function as a neuroprotectant as well as an antipsychotic in treating the BPSD associated with AD.

The launch of Apple iPhone brings the success of App Store application platform, which turns the structure of mobile software industry as well as creating a brand new application business model. It changes not only environment of mobile industry and supply chains, but also the using behaviors of mobile phone users. This research applies wildscale biobligraphy analysis, case study and Delphi method, trying to analyze the market status, business model of application platform. Using 2-phase interviews with smart phone users, developers, and operator, and SWOT and Porter¡¦s Five Force Analysis model to study the business model and strategies for operator¡¦s application platform. The research discovers that telecommunication operator has serveral advantages, such as holding huge user base and payment safety; however, economic scope and service efficiency is the key to the success. Operator should use its strengths and cope with the changing user behavior to create more added value.

Idag används den smarta telefonen i allt större utsträckning för att surfa på Internet. Problemet med att surfa i den smarta telefonen är att den mindre skärmytan ger begränsade möjligheter att rymma alla funktioner hos funktionsrika tjänster. För att lösa detta utvecklas det flera lösningar för en och samma tjänst. Tack vare utökad funktionalitet genom responsiv webbdesign ges utvecklare nya möjligheter att skapa en universell lösning för flera enheter istället för att utveckla en lösning för varje typ av enhet. När responsiv design blir alltmer vanlig blir det intressant att undersöka vad slutanvändarna av dessa tjänster har för åsikter om teknikerna och hur de använder dem. I denna uppsats utformades en enkät baserad på Traderas webbtjänst i syfte att undersöka vad deras användare idag föredrar för lösning i valet mellan app eller responsiv webbsida när de använder Tradera i sina smarta telefoner. Uppsatsen undersöker även de bakomliggande orsakerna till deras val av lösning. Resultatet visade att respondenterna i enkätundersökningen föredrog att använda appen istället för den responsiva webbsidan när de besökte Traderas webbtjänst i sina smarta telefoner. De främsta anledningarna till det var att respondenterna tyckte att appen hade högre grad av användbarhet samt att det var en vana sedan tidigare att använda den lösningen.

As our society gets increasingly mobile, it is becoming commonplace for residences to remain vacant for a significant amount of time every day. Unfortunately, emergencies can occur during those time, which may require immediate mitigatory action. This project proposes an approach that allows the resident to be notified of such emergencies and to perform mitigatory actions, even when she is hundreds of miles away. Our infrastructure includes three components: (1) programmable sensor devices to detect emergency situations; (2) a Web service hosted in the resident's home computer to send a notification to the smartphone of the user; and (3) a smartphone app that communicates with this Web service to notify the user, and provides a interface for the user to perform any mitigatory action. We develop a prototype system for detecting fire and intrusion emergencies. Our prototype system uses two sunSPOTs as sensors, an iRobot Create® as a mitigatory device, an Android app for user notification. / text

Transportation Network Companies (TNC) are companies that use online-enabled platforms to connect passengers with drivers. In recent years, they have sparked controversy with the taxi industry, which accuses TNCs of operating unfairly. In my study, I look at taxi regulation, consumer transportation preferences, and costs and benefits of TNCs. I analyze data comparing three of these companies, Uber, Lyft, and Sidecar, with a traditional taxicab, and evaluate trends in taxi employment from the Bureau of Labor Statistics. I find that Transportation Network Companies generally have shorter wait times, cheaper prices, and increased convenience, aspects that appeal to consumer preferences. I also find that taxi driver employment tends to fluctuate with economic conditions, however cities that are more likely to use TNCs exhibit smaller growth. I predict that at current conditions, TNCs such as Uber and Lyft will overtake taxi services. Thus, the taxi industry must focus on increasing TNC regulation, creating innovative technology, and modifying its service to appeal to consumers.

Low resolution pharyngeal manometry is an invasive diagnostic method that has recently been used as a biofeedback device for swallowing rehabilitation. The University of Canterbury Rose Centre uses pharyngeal manometry to diagnose and rehabilitate subjects who suffer from pharyngeal mis-sequencing. Pharyngeal mis-sequencing occurs when pressure is applied simultaneously throughout the pharynx rather than sequentially. Rehabilitation can only be performed in clinic due to the need for specialized equipment and trained staff, and the invasiveness of the test limits the time that can be spent training. As an alternative method to measure the pharyngeal pressure sequence, bioimpedance has been investigated by a previous University of Canterbury Master’s student. A prototype was developed that measured bioimpedance in two locations as a proxy for pharyngeal pressure sequence. The prototype device named GULPS (Guided Utility for Latency in Pharyngeal Swallowing), measured a change in impedance during swallowing. However, the features of this waveform were inconsistent and were not present during every swallow. The frequency of the current that passes through tissue affects its path through the tissue, therefore impacting the measured impedance. To improve the consistency of the impedance measurement, the effect of current injection frequency was investigated. A modular-hardware system was created from the original design to allow testing of different injection frequencies. The hardware was further developed by replacing the method of generating the constant amplitude current injection signal. The improvement to the design resulted in a differently-shaped waveform to that of the previous prototype, including a new feature. This feature is a single peak that occurred in both channels and was reproduced in every swallow. Experimentation showed that the features were not obviously frequency dependent. The separation between the peaks of the two impedance channels was compared with the separation between the two pressure peaks recorded during simultaneous pharyngeal manometry but there was no significant correlation between the two measures of peak-peak separations. Two alternative hardware/signal conditioning changes were trialled: electrical isolation of each channel and a subtraction method, which aims to remove the effect of the changing impedance between the two electrode channels. Electrical isolation of the two channels had no effect on the impedance waveforms. However, the subtraction method produced a different output and requires further investigation as the output was inconsistent. Bluetooth communication was integrated into the GULPS hardware, and a corresponding Android Application (App) was written. The developed App was successful in displaying the impedance measurement output and adds greater user flexibility, allowing the user to interface with the bioimpedance measurement hardware from their tablet or phone. With no measured significant correlation between GULPS and pharyngeal manometry, further research needs to be performed to better relate the features measured by GULPS to those seen during pharyngeal manometry. Until this can be achieved, the GULPS device cannot replace pharyngeal manometry for biofeedback-based rehabilitation of pharyngeal mis-sequencing.

The mobile application market is becoming increasingly fragmented with the availability of multiple mobile platforms that differ in development procedures. Developers are forced to choose to support only some platforms and specific devices due to limited development resources. To address these challenges, numerous tools have been created to aid developers in building cross-platform applications, however, there is no metric to evaluate the quality of these tools. This thesis introduces a framework for evaluating the features, performance and discuss development experience of existing and future cross-platform development tools. The framework is implemented by benchmarking several tools and the results identify a disparity in the features and performance. This research is carried out in collaboration with industrial partner Desire2Learn, through an NSERC Engage Grant. / NSERC, Desire2Learn

Denna rapport kommer att behandla processen i skapandet av RADA som är en applikation där användare kan dela idéer. RADA är ett uppdrag av Acne Digital som jag har tillägnat 3 månaders praktik. Målet med RADA är att medarbetarna som jobbar på Acne Digital ska på en snabbt och interaktivt sätt kunna dela idéer och bilder. Rapporten beskriver hur arbetet gick till från koncept, design och utveckling till en färdig slutprodukt. Jag kommer att undersöka skillnaderna mellan olika tillvägagångssätt när det gäller utveckling och design för webbaserade applikationer och så kallade native applikationer, som släpps på App Store eller Google play. Jag kommer även att ta upp och undersöka vilka metodval som är bäst lämpade för olika utvecklingssituationer när det gäller utvecklingspaketet Phonegap och dess för- och nackdelar.

行動裝置在現在這個世代相當普遍，而我們需要一個方法來探索App在背後的行為。 本研究提出了一個非監督式的分群方式，目的是在於探討我們是否能使用App中的原始碼當作以行為分群的依據。 在此研究中，我們應用了迭代分群的方式對Apps做分析，並且觀察分群的結果是否恰當。 而在實驗中，我們由App Store下載了數百個App並加以分析，我們發現我們所提出的方式表現相當良好並且能給出正確的分群結果。 / Smart devices are everywhere nowadays. Mobile application (app) development has become one of the main streams in software industry with more than millions of apps that have been developed and published to billions of users. It is essential to have a systematic way to analyze apps, preferably on their executable that are the only public available sources of apps in most cases. In this work, we propose to apply unsupervised clustering to mobile applications on their system call distributions. This is done by first adopting a static binary analysis that reverses engineering on executable of apps to find method call/sequence counts that are embedded in apps. Apps are then clustered iteratively based on this information to reveal implicit relationships among apps based on function call similarity. The GHSOM (Growing Hierarchical Self-Organizing Map), an unsupervised learning tool, is integrated to cluster apps based on the information resolved from their executable directly. We use types of methods and sequences as features. To run the clustering algorithm on apps, however, we immediately confront a problem that we have a large amount of attributes and data that leads to a long/infeasible analysis time with GHSOMs. The new iterative approach is proposed to conquer this problem along with dimension reduction with principle component analysis, cutting attributes with limited information loss. In the preliminary result on analyzing hundreds of apps that are directly downloaded from Apple app store, we can find that the proposed clustering works well and reveals some interesting information. Apps that are developed by the same company are clustered in the same group. Apps that have similar behaviors, e.g., having the same functions on games, painting, socializing, are clustered together.

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