Communication Primitives in Cognitive Radio Networks

Cognitive radio networks are a new type of multi-channel wireless network in which different nodes can have access to different sets of channels. By providing multiple channels, they improve the efficiency and reliability of wireless communication. However, the heterogeneous nature of cognitive radio networks also brings new challenges to the design and analysis of distributed algorithms. In this paper, we focus on two fundamental problems in cognitive radio networks: neighbor discovery, and global broadcast. We consider a network containing $n$ nodes, each of which has access to $c$ channels. We assume the network has diameter $D$, and each pair of neighbors have at least $k\geq 1$, and at most $k_{max}\leq c$, shared channels. We also assume each node has at most $\Delta$ neighbors. For the neighbor discovery problem, we design a randomized algorithm CSeek which has time complexity $\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta)$. CSeek is flexible and robust, which allows us to use it as a generic "filter" to find "well-connected" neighbors with an even shorter running time. We then move on to the global broadcast problem, and propose CGCast, a randomized algorithm which takes $\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta+D\cdot\Delta)$ time. CGCast uses CSeek to achieve communication among neighbors, and uses edge coloring to establish an efficient schedule for fast message dissemination. Towards the end of the paper, we give lower bounds for solving the two problems. These lower bounds demonstrate that in many situations, CSeek and CGCast are near optimal

Well-being increased during the first UK lockdown – but not for everyone

Sam Gilbert, Mark Fabian, and Roberto Foa draw on data from the first UK lockdown to illustrate how well-being levels improved, contrary to what may have been expected. They nevertheless explain that such improvements were not evenly distributed among the population and discuss the policy implications of their findings

Subjective well-being during the 2020-21 global coronavirus pandemic: Evidence from high frequency time series data.

We investigate how subjective well-being varied over the course of the global COVID-19 pandemic, with a special attention to periods of lockdown. We use weekly data from YouGov's Great Britain Mood Tracker Poll, and daily reports from Google Trends, that cover the entire period from six months before until eighteen months after the global spread of COVID-19. Descriptive trends and time-series models suggest that negative mood associated with the imposition of lockdowns returned to baseline within 1-3 weeks of lockdown implementation, whereas pandemic intensity, measured by the rate of fatalities from COVID-19 infection, was persistently associated with depressed affect. The results support the hypothesis that country-specific pandemic severity was the major contributor to increases in negative affect observed during the COVID-19 pandemic, and that lockdowns likely ameliorated rather than exacerbated this effect

Gender Differences Among Academic Staff and Students Offering STEM in National Universities in Uganda: The Case of Kyambogo University

Female University students and academic staff continue to be underrepresented in Science, Technology, Engineering, and Mathematics (STEM) fields. This study examined the status of female academic staff and students offering STEM at Kyambogo University (KyU), Uganda. The status and trend of female to male ratio of academic staff and students were determined. Practical strategies and policies for narrowing the gender gap for students offering STEM were identified. The status and trend of female to male ratio of students was determined by analyzing Student’s Academic Registrar’s   and graduation records   for the academic year 2014-2018. For academic staff, a gender analysis of Human Resources records was conducted based on the number of male or female academic staff teaching at the University in both STEM and non-STEM disciplines. There was an increase over time in student’s graduation from the Non-STEM fields with R2=0.3254 for the undergraduate programmes (P<0.05). The number of students in STEM fields declined gradually overtime, R2= 0.91; P<0.05). Male dominance among students and leadership position among academic staff in STEM and Non-STEM fields was evident. Difference between career pathways are causes for low female students and academic staff enrolment and teaching in STEM fields. The University needs to implement gender responsive programmes that enhance entry, retention, and participation in leadership positions for both female staff and students in STEM fields. Keywords: Status, Female Academic Staff and Students, STEM, Kyambogo University DOI: 10.7176/JEP/12-24-09 Publication date:August 31st 202

Structuring Unreliable Radio Networks

In this paper we study the problem of building a connected dominating set with constant degree (CCDS) in the dual graph radio network model. This model includes two types of links: reliable links, which always deliver messages, and unreliable links, which sometimes fail to deliver messages. Real networks compensate for this differing quality by deploying low-layer detection protocols to filter unreliable from reliable links. With this in mind, we begin by presenting an algorithm that solves the CCDS problem in the dual graph model under the assumption that every process u is provided with a local "link detector set" consisting of every neighbor connected to u by a reliable link. The algorithm solves the CCDS problem in O((Delta log2(n)/b) + log3(n)) rounds, with high probability, where Delta is the maximum degree in the reliable link graph, n is the network size, and b is an upper bound in bits on the message size. The algorithm works by first building a Maximal Independent Set (MIS) in log3(n) time, and then leveraging the local topology knowledge to efficiently connect nearby MIS processes. A natural follow up question is whether the link detector must be perfectly reliable to solve the CCDS problem. To answer this question, we first describe an algorithm that builds a CCDS in O(Delta polylog(n)) time under the assumption of O(1) unreliable links included in each link detector set. We then prove this algorithm to be (almost) tight by showing that the possible inclusion of only a single unreliable link in each process's local link detector set is sufficient to require Omega(Delta) rounds to solve the CCDS problem, regardless of message size. We conclude by discussing how to apply our algorithm in the setting where the topology of reliable and unreliable links can change over time

Artificial Intelligence in Energy Demand Response: A Taxonomy of Input Data Requirements

The ongoing energy transition increases the share of renewable energy sources. To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide flexibility is demand response (DR). Research already reflects several approaches of artificial intelligence (AI) for DR. However, these approaches often lack considerations concerning their applicability, i.e., necessary input data. To help putting these algorithms into practice, the objective of this paper is to analyze, how input data requirements of AI approaches in the field of DR can be systematized from a practice-oriented information systems perspective. Therefore, we develop a taxonomy consisting of eight dimensions encompassing 30 characteristics. Our taxonomy contributes to research by illustrating how future AI approaches in the field of DR should represent their input data requirements. For practitioners, our developed taxonomy adds value as a structuring tool, e.g., to verify applicability with respect to input data requirements

Industrial demand-side flexibility:A key element of a just energy transition and industrial development

In many countries, industry is one of the largest consumers of electricity. Given the special importance of electricity for industry, a reliable electricity supply is a basic prerequisite for further industrial development and associated economic growth. As countries worldwide transition to a low-carbon economy (in particular, by the development of renewable energy sources), the increasing fluctuation in renewable energy production requires new flexibility options within the electricity system in order to guarantee security of supply. It is advanced in this paper that such a flexibility transition with an active participation of industry in general has unique potential: It will not only promote green industrial development, but also become an engine for inclusive industrial development and growth as well as delivering a just transition to a low-carbon economy. Given the high potential of industrial demand-side flexibility, a first monitoring approach for such a flexibility transition is illustrated, which bases on a flexibility index. Our flexibility index allows for an indication of mis-developments and supports an appropriate implementation of countermeasures together with relevant stakeholders. Hence, it holds various insights for both policy-makers and practice with respect to how industrial demand-side flexibility can ensure advances towards an inclusive, just, and sustainable industrial development