We are concerned with an M/M/- queueing model. Particularly, we focus on such models with join the shortest queue policy and N parallel queues. Assuming the steady-state case, a Bayesian paradigm is used in estimating the queueing system's parameters when full data or only queue lengths data are provided. We start with the case N=2 queues and found maximum likelihood estimators and Bayesian estimators for the system's parameters when full data about arrival times, departures times, and jobs' paths is provided. When only queue lengths data is provided, we estimate the traffic intensity of the JSQ-2 model using its asymptotic behaviour studied by Flatto . Also, we provide Bayesian estimation for the traffic intensity using a new approximation to JSQ-2 queue length distribution. Indeed, a generalized bivariate geometric distribution (as proposed by ) is employed with an improper prior and a new proposed generalized bivariate beta distribution. When N is large enough, using the mean filed performance of the JSQ-N studied by Dawson et al. , we estimate the traffic intensity of JSQ-N model considering both maximum likelihood estimators and Bayesian estimators with different prior beliefs of the parameters. Numerical results are provided to show the accuracy of our estimators for JSQ-N for both cases N=2 and large enough N. Furthermore, we introduce a first study of privacy preserving analysis on a sensitive data released by a queueing model M/M/1. In fact, we propose, in the context of Differential Privacy , a Bayesian framework, inspired by , to generate private synthetic data, private service and arrival time rates from M/M/1 queueuing data. Keywords: Bayesian inference, Queueing models, Differential Privacy, Join the Shortest Queue (JSQ), Generalized bivariate beta distribution, Mean filed performance.
In this work, the focus is on price prediction and concurrent strategy building. The modelling approach chosen for this is of the deep reinforcement learning type, and actor-critic class. Specifically, in this work the proximal policy optimization (PPO) architecture is employed individually on each stocks market history in order to try and solve the price prediction problem. A custom RL environment was built to run the proposed experimental sequence and to test which parameter values should be used in regards to learning rate, discount factor, feature space, action space, and look-back length. These values were subsequently used for experiments on different datasets, exploring the portability of the model, effect of transfer learning, as well as portability of the parameter configuration. The results show our experimental sequence can be effectively used for the price prediction problem, and in some instances outperform a practical B&H strategy.
We developed A Questionnaire Toolkit that integrates Google Forms questionnaires into a VR environment. The toolkit's VR environment provides a watch that spawns the Google Forms questionnaires and a task setup for researchers to use in their studies. We conducted a user study to evaluate the self-reported presence, task load, and usability of the Questionnaire Toolkit versus Google Forms on a tablet. The results showed similar presence and usability scores but a higher physical demand and task control for the in-VRQ condition. Participants preferred using the Questionnaire Toolkit in VR experiences over Google Forms on a tablet and highlighted the benefits of using in-VRQs. We also conducted a walkthrough to discover usability issues researchers could face when deploying the Questionnaire Toolkit. Walkthrough participants uncovered several usability issues. Researchers can deploy the toolkit on their devices with the developer's help.
This is a comprehensive study of extreme weather events in southern Ontario from 1950 to 2017, and their impacts on winter wheat and oat yields. Trends in temperature and precipitation were evaluated annually and seasonally. There were significant shifts toward increased warming, growing season length, and the frequency of precipitation events. Warm and precipitation extremes are increasing in intensity, duration, and magnitude. Random Forest regression was used to investigate how different extreme weather indices were related to winter wheat crop yield, across crop pheno-phases and controlling for soil texture. Crop-specific indices were important indicators, explaining 40% of yield variance. Winter Warming Index was the most important index in the RF model, linked to a 72% increase in mean square error when removed. Changing extreme weather distributions in southern Ontario seems to be increasing potential negative impacts on farming winter wheat and milling oats, so adaptive plans should be considered.
With advances in communication technologies, innovative traffic data collection approaches have been developed. A means for this purpose is wireless technology which is sufficiently widespread among road users. Traffic data may be collected using signal scanners detecting wireless signals in the vicinity of them. As the data provided by signal scanners involve no direct information about the position of the signal source, the applications of this technology in traffic studies have been limited to finding some parameters in certain situations. The purpose is developing the applications of wireless signal scanning in traffic studies which require positional data of road users. This is achieved utilizing the potentials of received signal strength indicator (RSSI) of wireless signals transmitted by personal smart devices. Wi-Fi, Bluetooth Classic and Bluetooth Low Energy are three widespread signal modes, transmitted by popular beacons used in daily life. A comparative study of the field performance of these signal modes is conducted, investigating their characteristics important in gathering traffic flow parameters whenever positional data of road users are required. This provides the possibility of selecting the most suitable signal mode for the intended applications of the technology based on the requirements of the methods. A technique for positioning of beacons based on their transmitted signals, applicable in transportation studies is developed. This technique provides the possibility of positioning in intersections and their surrounding areas as well as congested road segments. The technique is based on the strength of signals transmitted by beacons, creating radio maps, and applying an algorithm called k-nearest neighbors. The procedure is optimized, and the accuracy and functionality of the technique is improved via modification of the system arrangement and application of proper filtering algorithms. A method for detection and classification of turning movements applicable in small urban intersections is developed based on wireless signals. The method utilizes the time profiles of the RSSI values of the signals emitted by beacons carried by turning vehicles. The signals are collected by an array of signal scanners carefully located on the intersection approaches. Turning movements are classified comparing signature points of the RSSI-time profiles and their occurrence moments.
This paper examines the role linguists play in the preservation of biocultural diversity by attempting to measure the extent that linguists include ethnobotanical information in language documentation works like dictionaries. This study analyzes various types of dictionaries, on different languages, and compares them to literature on what is recommended for inclusion. The primary method of analysis consists of assessing what types of information are typically included or excluded as well as if factors like year of or type of publication affect inclusion. Relevant entries from each dictionary are listed in the appendices. The global community is seeing increased threats to biological, cultural, and linguistic diversities and there are increasing developments suggesting their interconnectivity. As such, is it vital to assess what documentation measures are being taken, if they are producing desired results, and if not, why. Understanding the results and seeing changes over time will help guide future efforts.
MicroRNAs are short non-coding RNAs that function as sequence-directed post-transcriptional inhibitors of gene expression. The cellular response to ten drugs including actinomycin-D (ACT-D) was examined in a genetically modified HCT116 colon cancer cell line with a deletion in the gene encoding a critical miRNA processing enzyme called DROSHA. We found that the DROSHA-null subline was more susceptible than the parental cells expressing wild-type DROSHA to apoptosis induced by several drugs, most prominently ACT-D. This increase in susceptibility to apoptosis was characterized by increased DNA-fragmentation, increased caspase-3/7 activity, and loss of membrane integrity. The increased susceptibility to apoptosis was not associated with differences in DNA-synthesis, RNA-synthesis, protein-synthesis, metabolic activity, p53 response or the induction of replicative-senescence. Our results suggest that these cell lines are equally sensitive to the direct effects of ACT-D but these DROSHA-null cells are more sensitive to apoptosis induced by a subset of drugs exemplified by ACT-D.
SARS-CoV-2 levels in the wastewater of a Canadian university campus and their residence buildings were monitored to identify changes, peaks, and hotspots of COVID-19 transmission and search for associations with campus events, social gatherings, long weekends, and holidays. Wastewater signals largely correlated with clinically confirmed cases, often increased following long weekends, and decreased after the implementation of lockdowns. Furthermore, the impact of wastewater parameters on SARS-CoV-2 detection was investigated, and the efficiency of ultrafiltration and centrifugation concentration methods were compared. Results indicated more sensitive results with the centrifugation method for wastewater with high solids content and with the ultrafiltration method for low solids content. Wastewater characteristics from the building sewers were more variable than overall campus wastewater. Statistical analysis was performed to manifest the observations. Overall, wastewater surveillance provided actionable information and was able to bring high-risk factors and events to the attention of the decision-makers, enabling timely corrective measures.
As states and populations around the world have become increasingly interconnected and dependent on digitized technologies and cyberspace, threat actors have been aggressively exploiting these changes to their own advantage. Of particular concern to academics, cyber experts and national security practitioners in the West, has been the rapid proliferation of damaging and disruptive cyber attacks carried out by state-actors - particularly authoritarian regimes - and their proxies. Despite global cyber attacks rising year by year, in terms of both frequency and level of technological ability, available data suggests most of the world's major cyber attacks are carried out by a relatively small handful of states. Furthermore, it appears as though these states tend to favour certain types of cyber attacks over others. This observation leads to the central research question of this dissertation: why do certain types of authoritarian regimes tend to favour certain types of cyber attacks over others? Taking a deductive approach, and drawing from existing theories on authoritarian legitimation and cyber conflict, I have developed a needs-based theory of authoritarian behaviour in cyberspace. More specifically, I suggest that different types of authoritarian regimes will use different cyber strategies to fulfil or service the process, strategy, or outcome they need most to maintain domestic support and legitimacy.
This thesis considered two passive safety measures: plug formation and heat pipes. During a reactor core meltdown, the molten corium material can access cooling pipe connections. There is a chance that the passive plugging of melt flow due to solidification can occur, provided there is an adequate heat sink. A numerical model was created to simulate corium flow through an empty vertical pipe. The numerical model was validated through experimental work using gallium and verified using a previously built analytical model. Heat pipes are passive, two-phase heat exchangers with excellent heat transfer capabilities. They can be used in passive reactor core cooling and spent fuel pool cooling. Heat pipes have different operating limits that impact their operating conditions and heat transfer capabilities. A numerical approach was used to determine the operational limits of a liquid metal heat pipe that can be used in nuclear applications.