Thresholds Optimization for One-Bit Feedback Multi-User Scheduling

We propose a new one-bit feedback scheme with scheduling decision based on the maximum expected weighted rate. We show the concavity of the $2$-user case and provide the optimal solution which achieves the maximum weighted rate of the users. For the general asymmetric M-user case, we provide a heuristic method to achieve the maximum expected weighted rate. We show that the sum rate of our proposed scheme is very close to the sum rate of the full channel state information case, which is the upper bound performance

Adaptation to high ethanol reveals complex evolutionary pathways

Tolerance to high levels of ethanol is an ecologically and industrially relevant phenotype of microbes, but the molecular mechanisms underlying this complex trait remain largely unknown. Here, we use long-term experimental evolution of isogenic yeast populations of different initial ploidy to study adaptation to increasing levels of ethanol. Whole-genome sequencing of more than 30 evolved populations and over 100 adapted clones isolated throughout this two-year evolution experiment revealed how a complex interplay of de novo single nucleotide mutations, copy number variation, ploidy changes, mutator phenotypes, and clonal interference led to a significant increase in ethanol tolerance. Although the specific mutations differ between different evolved lineages, application of a novel computational pipeline, PheNetic, revealed that many mutations target functional modules involved in stress response, cell cycle regulation, DNA repair and respiration. Measuring the fitness effects of selected mutations introduced in non-evolved ethanol-sensitive cells revealed several adaptive mutations that had previously not been implicated in ethanol tolerance, including mutations in PRT1, VPS70 and MEX67. Interestingly, variation in VPS70 was recently identified as a QTL for ethanol tolerance in an industrial bio-ethanol strain. Taken together, our results show how, in contrast to adaptation to some other stresses, adaptation to a continuous complex and severe stress involves interplay of different evolutionary mechanisms. In addition, our study reveals functional modules involved in ethanol resistance and identifies several mutations that could help to improve the ethanol tolerance of industrial yeasts

Smart Grid Data Communication and Analytics

With the popularity of smart electrical appliances and home energy management systems, there have been a massive amount of data generated about the electricity consumption. This data can be beneficial for the utility companies as it provides the behaviour patterns of customers, and thus useful decisions can be made to optimize the load on the grid. In this work, we propose and implement a two-way communication system between the transformer agent (TA), attached to a neighbourhood’s electric transformer, and its customer agents (CAs), attached to each house in that neighbourhood. Once data is collected at the TA, it is communicated to the utility which can control and suggest any changes in consumption behaviours. In our system, CAs form a self-healing mesh network with the TA using IP-based Wi-Fi, while TAs communicate with the utility headquarters using the LTE network. Our system is implemented in compliance with the IEEE 2030.5 standard requirements, also known as smart energy profile 2.0. We have performed several tests across the Carleton University campus. We have also tested and implemented this system in real neighbourhoods in Ottawa, including Sandcherry and Viewmount sites to prove the system’s operation and reliability.The data obtained from the communication system is stored in a database hosted by IBM Cloud services. Our aim in this work is not only to communicate the data but ii to further process it and help the utility companies design better demand side management (DSM) programs to ensure efficient transmission and distribution of energy. This solves the problem of balancing electric demand and supply at the grid and also reduces peak demands, which helps lower the electricity bills for the consumers. In this context, we analyze the household electricity consumption data to forecast energy consumption for short-term (hours/days ahead) and long-term (weeks/months ahead). To this end, we use and compare seven different machine learning models predicting the energy consumption: linear regression, polynomial regression, support vector regression (SVR) using linear kernel, SVR using Gaussian kernel(SVR-G), SVR using the polynomial kernel, feed-forward neural networks (FFNN), and recurrent neural networks (RNN) using long-short-term memory (LSTM) neurons. To measure the accuracy of these models, we compute three different error metrics: the normalized mean absolute percentage error (NMAPE), the normalized root mean square error (NRMSE), and R2also known as the coefficient of determination. We then propose a novel approach for short-term load forecasting by combining the power of multiple models and evaluate its performance on a real energy consumption dataset that is publicly available by Massachusetts Institute of Technology (MIT). Results show that our proposed model performs better than existing models for time series energy forecasting

Examining social networking site narratives between government and youth on entrepreneurship : the case of relationship development in Egypt

Analysis of the ways in which SNS (Social Networking Sites) are used by governments, organisations and everyday users has over the past ten years been of significant interest to academic researchers. Part of this analysis of use has included understanding how in the Middle East, SNS were used in the series of anti-government protests known as the Arab Spring. Specifically, in Egypt, during the January 25 Revolution, a large number of youth users went on SNS such as Facebook to disseminate information, create conversations and raise awareness of their perspectives and concerns. Whilst use in protest and demonstration may result in aspects such as a drop in public trust of government agents, SNS could also contribute to significant relational outcomes such as relationship development and trust.This study takes Egypt as its foci in investigating the outcomes of SNS interaction between Government agencies and Youth users. This study aims to understand the role of the topic about which conversations are occurring in communicating with the citizens. Additionally, this study places emphasis on the role of the government agency in changing the perceptions of the Government through SNS interactions.This study contributes to the burgeoning domain of SNS studies by providing a non- traditional approach to its theoretical background. It specifically achieves this by adopting three areas of focus; first, SNS which includes a site and user perspective. Second, the political context which includes Marketing theory and government studies. Third, relationship development and trust which includes a multi theory lens into theorising the outcomes of SNS interactions. Therefore, it is the first study to apply Political Marketing Theory in Egypt in a non-electoral context. Using novel applications of Relationship Marketing and Public Relations theory, this study presents an understanding of the relationship orientation in the interaction between GOFE and Youth on SNS. Furthermore, the analysis regarding trust development in this study is developed through a framework that highlights both the users’ perspective of trust and the organisations' efforts towards achieving trust.This study adopts a social constructivist approach. Therefore, this investigation embraces qualitative inductive methods. Due to the rich culture and high interaction of the context investigated, the research problem at hand was addressed through the application of netnography. The Netnographic package includes; firstly, an online observation of Facebook pages followed by textual analysis. Secondly, it includes two sets of interviews with a sample of the users (i.e. Youth) and the organisations (i.e. GOFE). Using Thematic Analysis ten different themes were extracted from the three sources of data (i.e. Facebook data, GOFE interviews and Youth interviews).The findings from this study suggest that GOFE SNS representation is not yet mature. However, findings demonstrate that GOFE are in the process of becoming a generalisable model of government SNS representation. This could occur with the drop in control over engagement and movement to engagement strategies beyond those targeted primarily at publicity alone. Indeed, this study confirms the significant influence of SNS in fostering positive relational outcomes between the Government and Youth, while confirming the role of the topic and agency. These findings are discussed in light of theoretical contribution and practical implication to the government sector. Whereas previous studies have focused on one aspect of the communication process, this study is the first conducted in the public sector domain in Egypt that focuses on the observed behaviours of GOFE on SNS, perceived behaviours of GOFE by Youth and the strategic intent of GOFE by being present on SNS. This study concludes with limitations incurred and recommendations for practice and future studies. Finally, this study argues that with a further optimised SNS representation, there is indeed hope in developing relationships and achieving trust between Government and citizens in Egypt through SNS interaction

Brain abscess caused by lactococcus lactis in a young male

Lactococcus lactis cremoris is one of the gram positive cocci, not known to be pathogenic in humans. We report a case of brain abscess due to lactococcus lactis in an adolescent. An 18-year male with congenitally corrected transposition of great arteries and dextrocardia was admitted with fever, headache and right-sided numbness. Magnetic resonance imaging revealed a well circumscribed irregular heterogeneous abnormal signal intensity lesion in left temporo-parietal lobe having central area of diffusion restriction and peripheral wall enhancement on post-contrast images. He underwent mini-craniotomy for abscess drainage. Pus culture revealed growth of lactococcus lactis. He was treated with ceftriaxone and remained disability-free on six month follow-up. To our knowledge, this is one of the few reports of brain abscess caused by lactococcus lactis. Key Words: Brain abscess, Lactococcus lactis, Adolescent

COVID-19 presenting with spontaneous pneumothorax

The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease mainly affects respiratory system. Its common clinical findings include fever, cough and shortness of breath. Characteristic radiological features of the disease include peripherally distributed, bilateral ground-glass opacities, predominantly involving the lower lung zones. In this report, we present a case of COVID-19 disease presenting with spontaneous pneumothorax. A 26-year male patient was admitted to the Emergency Department with fever, dry cough, shortness of breath and right-sided chest pain. Radiographic imaging of the patient revealed pneumothorax on the right and peripherally distributed non-homogenous opacification. The patient underwent right lateral tube thoracostomy. COVID-19 was diagnosed on testing of nasopharyngeal swab. In conclusion, spontaneous pneumothorax is one of the rare presentations of COVID-19 pneumonia and should be kept in mind in patients presenting with shortness of breath and chest pain