7,712 research outputs found
Are Trade Rules Undermining Taxation of the Digital Economy in Africa?
African countries are currently considering provisions in the AfCFTA and at the WTO to
liberalise digital trade. As they face mounting fiscal pressures, it is imperative that they
beware the implications of digital trade provisions for their ability to tax their digital economy.
In this paper, we develop a comprehensive framework for analysing the impact of trade rules
on tax regimes in the digital economy, with a focus on Kenya, Rwanda, and South Africa. We
explore how trade rules ostensibly shape tax policies and their implications for revenue
generation. By examining rules regulating trade in services and the imposition of customs
duties on electronic transmissions, we identify how these rules may directly impact tax
policies and limit revenue generation possibilities. Moreover, digital trade rules, such as
those related to data flows, localisation, and source code sharing, have the capacity to
produce both indirect and administrative effects on tax measures. These rules can alter tax
structures, taxation rights, data collection, and the capacity to monitor and implement tax
measures. Our findings shed light on the complex interplay between trade rules and tax
measures, highlighting potential challenges and opportunities for revenue generation from
the digital economy in African countries
A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution
Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners
Comparing the Performance of Initial Coin Offerings to Crowdfunded Equity Ventures
Uncertainty in markets increases the likelihood of market failure due to volatility and suboptimal functioning. While initial coin offerings (ICOs) and crowdfunded equity (CFE) offerings may improve functioning in growing markets, there is a lack of knowledge and understanding pertaining to the relative efficiency and behavior of ICO markets compared to CFE markets, potentially perpetuating and thwarting the various communities they are intended to serve. The purpose of this correlational study was to compare a group of ICOs with a group of CFE offerings to identify predictive factors of funding outcomes related to both capital offering types. Efficient market hypothesis was the study’s theoretical foundation, and analysis of variance was used to answer the research question, which examined whether capital offering type predicted the amount of funds raised while controlling for access to the offering companies’ secondary control factors: historical financial data, pro forma financial projections, detailed product descriptions, video of product demonstrations, company website, company history, company leadership, and company investors. Relying on a random sample of 115 campaigns (84 ICOs and 31 CFE) from websites ICOdrops.com, localstake.com, fundable.com, and mainvest.com, results showed differences in mean funds raised between CFEs and ICOs (4,756,464, respectively). ANOVA results showed no single secondary control factors and only one two-factor interaction (company leadership and company investors) influenced mean funds raised. This study may contribute to positive social change by informing best practices among market participants including entrepreneurs, regulators, scholars, and investors
Artificial Intelligence and International Conflict in Cyberspace
This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation
Characterising novel genetic causes of growth failure
To identify novel genetic causes of growth failure, I developed a unique, targeted whole gene panel for rapid and accurate genetic testing of patients with short stature and features of Growth Hormone Insensitivity (GHI) or unexplained short stature. This included 64 genes of interest, including those in the GH-IGF1 pathway and genes linked to conditions with overlapping features. In parallel, I also assessed these patients for copy number variants. Using custom bioinformatic pipelines to filter these data sets and a variety of in silico prediction programs, I identified interesting novel genetic defects in both known and candidate growth genes. I then performed functional analysis of these defects to determine if they affected gene structure/function and could explain the patient phenotype. I identified several novel splicing mutations in the Growth Hormone Receptor (GHR) causing a spectrum of GHI. These include a novel mutation deep within intron 6 GHR that leads to mis-splicing and pseudoexon inclusion. Pseudoexon inclusion leads to frameshift of the GHR and thus causes a non-functional Growth Hormone Receptor and severe GHI. I discovered two novel heterozygous GHR mutations in patients with milder GHI phenotypes. These mutations both led to mis-splicing of exon 9 of the GHR and act in a dominant negative effect on the GHR, reducing the efficacy of signalling and explaining their milder phenotypes. I identified a rare novel heterozygous IGF1 variant that I hypothesised would impair IGF-1 cleavage causing functional IGF-1 deficiency. Our patient cohort was enriched for low frequency CNVs, particularly in patients with subtle features of Silver Russell Syndrome. This is the first study to assess CNVs in patients with GHI. From my CNV analysis, I identified CHD1L and HMGA2 as key candidate growth genes and functionally assessed several patient variants identified within our cohort
Constitutions of Value
Gathering an interdisciplinary range of cutting-edge scholars, this book addresses legal constitutions of value.
Global value production and transnational value practices that rely on exploitation and extraction have left us with toxic commons and a damaged planet. Against this situation, the book examines law’s fundamental role in institutions of value production and valuation. Utilising pathbreaking theoretical approaches, it problematizes mainstream efforts to redeem institutions of value production by recoupling them with progressive values. Aiming beyond radical critique, the book opens up the possibility of imagining and enacting new and different value practices.
This wide-ranging and accessible book will appeal to international lawyers, socio-legal scholars, those working at the intersections of law and economy and others, in politics, economics, environmental studies and elsewhere, who are concerned with rethinking our current ideas of what has value, what does not, and whether and how value may be revalued
Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria
Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases
DEFINING A CYBER OPERATIONS PERFORMANCE FRAMEWORK VIA COMPUTATIONAL MODELING
Cyber operations are influenced by a wide range of environmental characteristics, strategic policies, organizational procedures, complex networks, and the individuals who attack and defend these cyber battlegrounds. While no two cyber operations are identical, leveraging the power of computational modeling will enable decision-makers to understand and evaluate the effect of these influences prior to their impact on mission success. Given the complexity of these influences, this research proposes an agent-based modeling framework that will result in an operational performance dashboard for user analysis. To account for cyber team behavioral characteristics, this research includes the development and validation of the Cyber Operations Self-Efficacy Scales (COSES). The underlying statistics, algorithms, research instruments, and equations to support the overall framework are provided. This research represents the most comprehensive cyber operations agent-based performance analysis tools published to date
Guarding the Cloud: An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms
Cloud computing has gained significant popularity due to its reliability and scalability, making it a compelling area of research. However, this technology is not without its challenges, including network connectivity dependencies, downtime, vendor lock-in, limited control, and most importantly, its vulnerability to attacks. Therefore, guarding the cloud is the objective of this paper, which focuses, in a novel approach, on two prevalent cloud attacks: Distributed Denial-of-service (DDoS) attacks and Man-in-the-Cloud (MitC) computing attacks. To tackle the detection of these malicious activities, machine learning algorithms, namely Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN), are utilized. Experimental simulations of DDoS and MitC attacks are conducted within a cloud environment, and the resultant data is compiled into a dataset for training and evaluating the machine learning algorithms. The study reveals the effectiveness of these algorithms in accurately identifying and classifying malicious activities, effectively distinguishing them from legitimate network traffic. The finding highlights Decision Trees algorithm with most promising potential of guarding the cloud and mitigating the impact of various cyber threats
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